Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues

DNA computing is a novel method of solving a class of intractable computational problem, in which the computing speeds up exponentially with problem size. Up to now, many accomplishments have been made to improve its performance and increase its reliability. The positive integer linear programming is an NP-complete problem, in the paper, we solved the positive integer linear programming problem with fluorescence labeling techniques based on surface chemistry by attempted to apply DNA computing to programming problem. Our method has some significant advantages such as simple encoding, low cost, and short operating time.

DNA computing is being applied to solve problems in combinatorial optimization, logic and Boolean circuits. Breakthrough solutions in combinatorial optimization are the most impressive area of success but, in order to solve combinatorial optimization problems, problems related to the reliability of biological operators, stable DNA expressions, processing speed, expandability and the universality of evaluation criteria must be solved. This study implements a DNA sequence generation system that minimizes errors using DNA coding based on evolutionary models and performs simulation using biological experiment operators. The usefulness of this system is evaluated by applying the Hamiltonian Path Problem (HPP) in the form of a genetic algorithm. The proposed system generates sequences with minimal errors, as compared to existing systems, and identifies optimal solutions for combinatorial optimization problems in significantly reduced processing times.

To achieve quantum error-correction codes with good parameters, the recursive constructions of Hadamard matrices with even length are proposed with special characters. The generators of the stabilizer of the designed codes can be constructed by selecting some rows from these matrices, hence several codes are obtained expediently via the stabilizer quantum code’s constructions. Some of the presented codes are unsurpassed by the previously published codes.

The storage capacity of the conventional neural network is 0.14 times of the number of neurons (P=0.14N). Due to the huge difficulty in recognizing large number of images or patterns,researchers are looking for new methods at all times. Quantum Neural Network (QNN), which is a young and outlying science built upon the combination of classical neural network and quantum computing,is a candidate to solve this problem.This paper presents Quantum Probability Distribution Network (QPDN) whose elements of the storage matrix are distributed in a probabilistic way on the base of quantum linear superposition and applies QPDN on image recognition. Contrasting to the conventional neural network, the storage capacity of the QPDN is increased by a factor of 2

N

,where N is the number of neurons. Besides,the case analysis and simulation tests have been carried out for the recognition of images in this paper, and the result indicates that QPDN can recognize the images or patterns effectively and its working process accords with quantum evolvement process.

A discrete-time version of dynamic portfolio selection model for survival is proposed in fuzzy environments. The investor gains an initial wealth every period and has a given consumption requirement. The investor survives only if his wealth is large enough to meet the requirement every period over a finite time horizon. After consumption the investor allocates the rest between a risky and a risk-free asset. This paper assumes that the gross rate of return on the risky asset is a fuzzy variable, then the functional equation of dynamic programming is established. In order to get the optimal investment policy, a hybrid intelligent algorithm to solve the optimal problem is presented. Finally, an illustrative case is given to demonstrate the effectiveness of the proposed algorithm.

This paper describes how risk-based risk control allocation model works. We begin by discussing the economic rational for allocating risk control in a diversified organization like enterprises. The direct and indirect losses caused by the simulated disasters can be estimated using the engineering and financial analysis model. Basing on the model, we can generate exceeding probability (EP) curve and then calculate how much loss will be ceased or transferred to other entities, if somehow spending budgets on risk control actions. Results from the proposed formulations are compared in case studies. The model attempts to apply risk based budget guidelines to risk reduction measurement with a portfolio-based risk framework.

Different from previous literature on credit term determination mainly applying financial marginal analysis method, this paper proposes a novel idea to model trade credit term determination as an incentive mechanism design problem under supply chain coordination in principal-agent framework. With application of Schwartz’ financing motive theory, a new form of supplier’s net cost function is derived which makes it possible to find an approximation closed-formed solution to term determination. Using approximation and integration techniques, we find the explicit close-formed approximation solutions to the optimal payment time for the retailer and credit term for the supplier.

In environments where node density is massive, placement is heterogeneous and lot of sensory traffic with redundancy is produced; waste of resources such as bandwidth and energy occurs. This waste of resources minimize the network life time. Numerous routing schemes have been proposed to address such problems. They all tend to focus on similar direction, i.e. to find minimum energy path to increase the life time of the network. In this paper, we argue that it is not always wise to use the minimum energy path. Nodes along the optimal path will be used rapidly, burn out energy aggressively and eventually die hastily creating communication holes in network. This brings rapid change in the topology resulting in increased latency, poor connectivity and production of heterogeneous subnets. Therefore, utilizing suboptimal paths is encouraged for load balancing among sensor nodes. We unmitigated our efforts to augment the node life time in sensor network by frequent use of suboptimal paths, and reducing redundant sensory network traffic. Towards this end, we propose an agent-based routing approach that incorporates static and mobile agents. Static agent is responsible for calculating and maintaining the set of optimal paths. Mobile agent accounts for performing data processing and making data aggregation decisions at nodes in the network rather than bring data back to a central processor (sink). To demonstrate the performance evaluation, a prototype of a simulator is implemented.

In this paper, the coalition formation problem is studied in an open environment where agents can arrive dynamically, i.e. the set of agents is not given in advance. In order to maximize the gross income of MAS (Multi-Agent System), task allocator may incline to discard some coalition members, and then introduce some new ones when new agents arrive; we call such problem

coalition restructuring

. To address this problem, we introduce a novel description of the coalition formation problem which takes time into account, and then formally present the

coalition restructuring problem

. What’s more, we study different kinds of measures which agents and task allocator will take because of new agents’ arriving. Finally, we develop an anytime

coalition restructuring

algorithm, which is proved effective and available by the simulation. An example is also designed to make it easy to understand our algorithm.

With the rapid development of WWW, HTML documents become one of the main file formats on the Web. However, blind people find difficulty in accessing the HTML documents for their complex structure and visual reliability. The main methods for the blind to browse the web pages are through screen reader and text web browser with TTS engine. These methods can only read text on the screen without knowing the relationship among the texts. It’s very difficult and time consuming to find out some information from a bunch of texts. In this paper, a special web browser called eGuideDog is designed for the visually impaired people. This web browser can extract the structure and the content of an HTML document and represent it in the form of audio. It helps the blind finding out information they concern more quickly.

This study presents a novel model that integrates agent negotiation into adaptive learning for enhancing interaction efficiency between learner and instructor and promoting learning effectiveness. A constraint-based agent negotiation mechanism is employed to support a one-to-one interaction. Through the negotiation process, the instructor also can gradually perceive the learners’ feedback and then reflect on the appropriateness of the learning sequence, adjust instructional goal, approach and scheme. The instructor can thus provide more adaptive teaching based on learners’ specific needs to enhance learning effectiveness. Experimental results suggested that the proposed methodology was able to improve learning performance and learners also believed that the system enhanced their learning motivation and increased the flexibility of course learning.

The recent rush towards electronic commerce over the Internet raises many challenges, this paper present a simple model of supply chains. First a model of supply chains is discussed, highlighting two characteristic features: hierarchical subtask decomposition, and resource contention. Then a market protocol based on distributed but concurrent auctions is proposed. The protocol allow each of these markets to function separately, while the information exchanged between a sequence of markets along a single supply chain to ensure efficient global behavior across the supply chain. Each market that forms a link in the supply chain operates as a double auction, where the bids on one side of the double auction come from bidders in the corresponding segment of the industry, and the bids on the other side are synthetically generated by the protocol to express the combined information from all other links in the chain.

This paper proposes an enhanced method of Web information extraction by exploiting general phenomena that Web pages in a site tend to have common structures and dynamic Web pages contain multiple data blocks with repeating structural patterns. By considering this kind of regularity in dynamic Web pages, we develop a data block extraction system which basically adopts a supervised learning mechanism with training and extraction phases. In the training phase, the user selects and specifies a data block and the extraction rules for the block are generated. During this phase, the block is defined with the HTML DOM-tree path to the block and the tag sequence of the block. In the extraction phase, the rules are applied to the target pages to extract those blocks that have similar structure as the user-defined block. A series of experiments are performed to evaluate the user-defined data block extraction method for a number of well-known Web sites with dynamic Web pages, and the result of evaluation is satisfactory with high precision and recall measures.

This paper explores the attributes that drive company wealth creation in the Miscellaneous Industrials sector of the Australian Stock Market. It looks at how the company’s wealth creation changes in comparison to the changes in the Miscellaneous Industrial Index. We examine traditional and artificial intelligent (AI) feature selection techniques, to select attributes that drive company wealth and observe if a multiple domain model outperforms a single domain model with regards to predicting company wealth. Using a large number of calculated attributes, our empirical findings suggest that a multiple domain model was most effective. We found that WACC, Funds from Operation / EBITDA and EPS assist in guiding the direction of change in shareholder wealth. Whereas ROA, Capital Turnover and Gross Debt / Cashflow are key attributes in understanding the behaviour of the relative shareholder growth. We observed that ROIC, Ordinary Share Price, EVA, EPS and Trading Revenue / Total Assets are the important attributes that drive relative shareholder wealth in this industry.

The real-time information access of goods plays an increasingly important role for decision-making in operations management of supply chain. Based on mobile agent and radio-frequency identification (RFID) technology, this paper proposes a novel solution to track in transit goods. Firstly, mobile RFID reader integrated in a smart phone retrieves information automatically from RFID tags bound to goods. Then, the front-end system that works on the smart phone send information back to the back-end system via short message system of mobile telecommunication platform. Because the solution is built on mobile agent platform, it enables the back-end system to dispatch mobile agents to the front-end system to process raw RFID data locally, and only the filtered results are fed back. Therefore, the solution promises to reduce time and cost of data transfer between the front-end system and the back-end system. Finally, a prototype system, GTSys named, is implemented and verified on simulation platform.

Model-based diagnosis is one of the active branches of Artificial Intellgence. Conflict recognition, aiming at generating all minimal conflict sets (MCSs), and candidate generation, aiming at generating all minimal hitting sets (MHSs), are of the two important steps towards to the final diagnosis results. Firstly an SE-tree based algorithm (CSSE-tree) for deriving all MCSs is given. Then a concept of inverse SE-tree (ISE-tree) is put forward, and an ISE-tree based algorithm (CSISE-tree) for deriving all MCSs is presented as well. Considering the similarity of generation of all MCSs and all MHSs for the collection of all MCSs, a uniform framework for deriving all MCSs and MHSs is proposed, too. Experimental results show that our algorithms have better efficiency than others in some situations.

This work adopted the fuzzy constraint-directed approach to model opponent’s beliefs in agent negotiation. The fuzzy constraint-directed approach involves the fuzzy probability constraint and the fuzzy instance reasoning. The fuzzy probability constraint is used to cluster the opponent’s regularities and to eliminate the noisy hypotheses or beliefs, so as to increase the efficiency on the convergence of behavior patterns and to improve the effectiveness on beliefs learning. The fuzzy instance reasoning reuses the prior opponent knowledge to speed up problem-solving, and reason the proximate regularities to acquire desirable results on predicting opponent behavior. Besides, the proposed interaction method allows the agent to make a concession dynamically based on desirable objectives. Moreover, experimental results suggest that the proposed framework enabled an agent to achieve a higher reward, a fairer deal, or a less cost of negotiation.

Software agents representing supply chain partners make it possible to automate supply chain management and particularly can address the challenging problem of automating the process of dynamic supply chain formation. This paper puts forward a multi-agent negotiation mechanism for dynamic supply chain formation in semi-monopolized circumstance, i.e., China petroleum supply chain, where the conventional negotiation mechanisms are limited because they are based on the assumption of a pure market. The proposed multi-agent negotiation mechanism is algorithmized and validated, respectively.

Web-learning based on multi-agents is a hotspot in the field of computer software research; this paper aims to present an interactive and collaborative learning environment, a new type of web-learning system, designed for educational purpose and gives a brief summary of agent and creates the structure model of the intelligent web-learning based on multi-agents according to the theory of Constructivism, and it also discusses the function and the structure of every agent. It also discusses that how the functions are carried out.

The issue of coalition formation problem has been investigated from many aspects. However, all of the previous work just take the capability of agent into account, but not consider those factors, such as the time that agent takes to achieve a task, the cost of employing agent, the credit standing of agent, the risk that the task sponsor bears, and the bias of task sponsor and so on. So we originally take these factors into account. The coalition problem in this paper is a constrained problem including a great deal of equality constraints and inequality constraints. So we adopt the

death penalty function

to transform it to an unconstrained one. That is to say, it becomes a single objective function. Being an unconstrained optimization algorithm, the binary particle swarm optimization algorithm is adopted to address this problem. To improve the capability of global searching of our algorithm and convergent rate of the solutions, we divide the process of coalition formation into two stages to deal with respectively. Simulations show that our algorithm is effective and feasible.

Web information system should be considered due to its performance for real-time application with different types of mobile agents serviced by different mobile communication operators. The ubiquitous Web information server accessed by a user-group with various mobile agents should be considered as a unified center for real-time unified-and-ubiquitous Web information services. We studied the performance of Web information access, i.e. registration and retrieval, with intelligent mobile agents for real-time ubiquitous-unified Web information services. Based on our empirical results collected from a test-bed trial service we have determined that real-time ubiquitous-unified Web information services may be applicable to various applications with intelligent mobile agents, e.g. real-time ubiquitous-healthcare service.

There are many schemes to increase energy efficiency in wireless sensor network as energy is precious resource. We focus on improving energy efficiency in sensing module while most of the previous works focus on the energy saving in communication module. When a sensor network continuously senses wide area, energy consumption is needed largely in sensing module. We consider a change rate of sensed data and adjust sensing period to reduce energy consumption while minimizing average delay between change of field and detection. Additionally, cooperation among neighbor nodes is essential to reduce energy consumption and the delay. Our dynamic sensing algorithm reduces the energy consumption and delay between change of field and detection. Our scheme controls sensing cycle based on change of sensing data and sensing cycle of neighbor nodes. It improves energy efficiency up to 90%, and reduces the delay up to 84%, comparing to the previous works.

Most of the sensor networks use a symmetric protocol since sensor networks are comprised of sensor nodes with restricted hardware. Sensor networks with symmetric cryptography contain a global key stored on each sensor node and may be deployed in a hostile environment. When sensors nodes are compromised, an attacker can inject false sensing reports or false Message Authentication Codes into a legitimate report. A probabilistic voting-based filtering scheme has been proposed to combat these threats from compromised nodes. However, this scheme has the problem that it cannot re-establish a session key when some nodes of the source cluster or some intermediate cluster head have been compromised. The scheme cannot also control the position of verification nodes to minimize energy consumption as topology changes. Therefore, we propose a fuzzy-based en-route filtering scheme to deal with these problems. Through performance analysis and simulation, our result shows that the proposed scheme is much more efficient than the probabilistic voting-based scheme in many cases.

A general web-based distance system uses video data and audio data to provide synchronize between teacher and student. This paper presents the design and implementation of an error and an application program sharing agent for collaborative multimedia distance education system which is running on RCSM (Reconfigurable Context Sensitive Middleware) for ubiquitous networks. RCSM provides standardized communication protocols to interoperate an application with others under dynamically changing situations. It describes a hybrid software architecture that is running on situation-aware middleware for a web based distance education system which has an object with an various information for each session. There are two approaches to software architecture on which distributed, collaborative applications are based. Those include CACV (Centralized-Abstraction and Centralized-View) and RARV (Replicated-Abstraction and Replicated-View). And it also supports an application sharing model with fault tolerance for multimedia distance education system based RCSM.

Energy efficiency is a primary problem in wireless sensor networks which employ a large number of intelligent sensor nodes to accomplish complicated tasks. Focused on the energy consumption problem in target tracking applications, this paper proposes a dynamic energy management mechanism with an improved particle filter prediction in wireless sensor networks. The standard particle filter is improved by combining the radial-basis function network to construct the process model and the novel algorithm is adopted to predict the prior position of target. For dynamic awakening, the idle interval of each sensor node is estimated according to its sensing tasks. A cluster head rotating approach is introduced from low-energy adaptive clustering hierarchy for collecting data through the large sensing field. A group of sensor nodes which are located in the vicinity of target will wake up and have the opportunity to report their data. Distributed genetic algorithm is performed on cluster heads to optimize the sensor node selection. In target tracking simulations, we verify that the improved particle filter has more robustness than standard particle filter against the sensing error and dynamic energy management enhances energy efficiency of wireless sensor networks.

The dynamic en-route filtering scheme (DEF) proposed by Yu and Guan was designed to detect and drop false reports in wireless sensor networks. In this scheme the choice of a threshold value that limits the key dissemination is important since it represents a trade-off between detection power and overhead. A large threshold value increases the probability of detecting false reports but it consumes too much energy in the key dissemination phase. Thus, we should choose a threshold value such that it provides sufficient detection power, while consumes energy effectively. In this paper we propose a key dissemination limiting method for DEF. The threshold value to limit the key dissemination is determined by a fuzzy rule-based system with consideration of the energy level of the network, the number of keys in a cluster, and the distance from the base station (BS) to that cluster. The simulation results show that the proposed method can conserve energy, while it provides sufficient detection power.

There are many application areas of wireless sensor networks, such as combat field surveillance, terrorist tracking and highway traffic monitoring. These applications collect sensed data from sensor nodes to monitor events in the territory of interest. One of the important issues in these applications is the existence of the radio-jamming zone between source nodes and the base station. Depending on the routing protocol the transmission of the sensed data may not be delivered to the base station. To solve this problem we propose a genetic algorithm based routing method for reliable transmission while considering the balanced energy depletion of the sensor nodes. The genetic algorithm finds an efficient routing path by considering the radio-jamming zone, transmission distance, average remaining energy and hop count. In simulation, our proposed method is compared with LEACH and Hierarchical PEGASIS. The simulation results show that the proposed method is efficient in both the energy consumption and success ratio of delivery.

With the developments of computer and wireless communication technology, wireless sensor networks have broad application prospects in more and more fields. But sensor nodes are usually powered by a small size and limited battery. In this paper, we propose an pheromone based energy-aware directed diffusion algorithm(PEADD) for sensor networks. The algorithm uses pheromone of ants to improve the energy module in directed diffusion algorithm. The method has been implemented and performed experiments NS2. Our experimental results show the new algorithm extends the network lifetime and characteristics of our method. In the end, the future research directions are discussed.

Dynamic deployment is one of the key topics addressed in wireless sensor networks (WSNs) study, which refers to coverage and detection probability of WSNs. This paper proposes a self-organizing algorithm for enhancing the coverage and detection probability for WSNs which consist of mobile and stationary nodes, which is so-called virtual force-directed particle swarm optimization (VFPSO). The proposed algorithm combines the virtual force (VF) algorithm with particle swarm optimization (PSO), where VF uses a judicious combination of attractive and repulsive forces to determine virtual motion paths and the rate of movement for sensors and PSO is suitable for solving multi-dimension function optimization in continuous space. In VFPSO, the velocity of each particle is updated according to not only the historical local and global optimal solutions but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFPSO has better performance on regional convergence and global searching than PSO algorithm and can implement dynamic deployment of WSNs more efficiently and rapidly.

This paper is concerned with designing of a multirate controller over a network in which a sensor node transmits data to the controller node only if its measurement value changes more than a given specified

δ

value. When the send-on-delta transmission method is used, it is not ensured that the controller node receives data from the sensor nodes regularly at every control updating period. We thus propose a modified Kalman filter in which states of the plant are regularly estimated even if there is no sensor data reception. An optimal LQG controller is then designed in order to minimize the quadratic cost function of state and control effort. By providing the upper and lower bounds of the cost function, we prove that the proposed multirate controller is stable with the given

δ

value, which is a trade-off parameter between control performance and data transmission rate. Through numerical simulations, we demonstrate the feasibility and the usefulness of the proposed control method.

This paper presents a multiagent-based simulation system for the decision-making research of ship collision avoidance. The system has the characteristics of flexible agent, variable topology, isomorphic function structure, distributed knowledge storage, and integrated control method. The architecture is proposed with four kinds of agent models, that is Control_Agent, Union_Agent, Ship_Agent and VTS_Agent. We developed these agent models for modeling the behaviors for human, ship and VTS using a BDI (Beliefs, Desires, and Intentions) agent framework. The agent communication mechanism based on AIS (Automatic Identification System) message is also established and discussed. The proposed multiagent-based simulation system provides a useful platform for studying multi-target encountering problems and different decision-making methods for collision avoidance.

Electrical power consuming is the largest part cost for the operation of Nodding Donkey oil pump. So there is urgent requirement of reducing the power loss in the system, consequently, reducing the cost. After detailed investigation on nodding donkey machines in oil field, it has been known that the unbalance operation of the oil pumps will produce extra energy consuming when inverters are used. Compared with improving structures of prime-mover and oil-pumping units, regulating the output frequency of inverters is much more cost-effective and simple. Based on the research on the operation of beam pumping units and on the fuzzy control theory, to achieve the goal of energy saving, the voltage across the DC-link of inverter is taken as the control object and an adaptive Fuzzy Proportional Derivative controller is put forth to adjust the inverter output frequency. As result, the electrical energy absorbed from the power grid can be saved up to ten percent.

Intelligent pipeline inspection gauges (PIGs) are inspection vehicles that move along within a gas (or oil) pipeline and acquire signals from their surrounding rings of sensors. By analyzing the signals captured by intelligent PIGs, we can detect pipeline defects, such as holes, curvatures and other potential causes of gas explosions. We notice that the size of collected data using a PIG is usually in GB range. Thus, analyzer software must handle such scalable data and provide various kinds of visualization tools so that analysts can easily detect any defects in the pipeline. In this paper, we propose a scalable pipeline data processing framework using database and visualization techniques. Specifically, we analyze requirements for our system, giving its overall architecture of our system. Second, we describe several important subsystems in our system: such as a scalable pipeline data store, integrated multiple visualization, and automatic summary report generator. Third, by performing experiments with GB-range real data, we show that our system is scalable to handle such large pipeline data. Experimental results show that our system outperforms a relational database management system (RDBMS) based repository by up to 31.9 times.

An adaptive sliding mode fuzzy control approach is proposed for a class of underactuated mechanical systems that have one control input and two generalized position variables. The approach combines SMC’s robustness and FLC’s independence of system model. According to the influences on system dynamic performance, both the slope of sliding mode surface and the coordination between the two subsystems are automatically tuned by real time fuzzy inference respectively. A prototype overhead crane is built, the system stability is analyzed and the effectiveness of the proposed control algorithm is demonstrated by experiment results.

For the uncertain chaotic systems, a synchronization design scheme based on a fuzzy observer is proposed. The T-S fuzzy models for uncertain chaotic systems are exactly derived. Based on the fuzzy chaotic models, an observer for synchronization of the uncertain chaotic systems is designed via the adaptive technique. For the unknown parameters of uncertain chaotic systems, the adaptive law is derived to estimate them and the stability is guaranteed by Lyapunov stability theory. The simulation examples are given to demonstrate the validity of the proposed approach.

Air fuel ratio is a key index affecting the emission of gasoline engine, and its accurate control is the foundation of enhancing the three-way catalytic converting efficiency and improving the emission. In order to overcome the existed transmission delay of air fuel ratio signal, which affects the control accuracy of air fuel ratio if using directive air fuel ratio sensor signal., and a multi-step predictive control method of air fuel ratio based on neural network was provided in the paper. A multi-step predictive model of air fuel ratio based on back propagation neural network was set up firstly, and then a fuzzy controller was designed using the error of predictive values and expected values and its derivative. The simulation was accomplished using experiment data of HL495 gasoline engine, and the results show the air fuel ratio error is less than 3% in the faster throttle movement and it is less than 1.5% in the slower throttle movement.

The focus of situation-aware ubiquitous computing has increased lately. An example of situation-aware applications is a multimedia education system. The development of multimedia computers and communication techniques has made it possible for a mind to be transmitted from a teacher to a student in distance environment. This paper proposed a new model of dynamic window binding by analyzing the window and attributes of the attributes of the object, and based on this, a mechanism that offers a seamless view without interfering with concurrency control is also suggested. As the result of this system’s experimental implementation and analysis of the increase of delay factors accordant with the size of collaboration increase, the simulation results clearly showed that the seam increases seriously in that case, and that the dynamic window binding mechanism proposed in this paper is worth implementation and effective when a large scale of collaboration is required.

This study develops an adaptive speed controller from the adaptive neuro-fuzzy inference system (ANFIS) for an indirect field-oriented (IFO) induction motor drive. First, a two-degree-of-freedom controller (2DOFC) is designed quantitatively to meet the prescribed speed command tracking and load regulation responses at the nominal case. When system parameters and operating conditions vary, the prescribed control specifications cannot be satisfied. To improve this, an adaptive mechanism combining on-line system identification and ANFIS is developed for tuning the parameters of the 2DOFC to reduce control performance degradation. With the adaptive mechanism, the desired drive specifications can be achieved under wide operating ranges. Effectiveness of the proposed controller and the performance of the resulting drive system are confirmed by simulation and experimental results.

A new non model-related algorithm that can perform the auto-piloting of the aircraft under all conditions is presented. For improving the precision of the loosely coupled GPS/SINS integrated navigation system, fusing data from a SINS and GPS hardware utilizes wavelet multi-resolution analysis (WMRA) and Radial Basis Function Neural Networks (RBFNN). The WMRA is used to compare the SINS and GPS position outputs at different resolution levels. These differences represent, in general, the SINS errors, which are used to correct for the SINS outputs during GPS outages. The RBFNN model is then trained to predict the SINS position errors in real time and provide accurate positioning of the moving aircraft. The simulations show that good results in SINS/GPS positioning accuracy can be obtained by applying the new method based on WMRA and RBFNN.

Brake energy regeneration is an electrical current management technology that ensures intelligent generation of electric power by restricting production to the engine overrun phases and the application of the brakes. Compressed air energy storage is a technically feasible and economically attractive method for load management. This work proposed a brake energy regeneration system based on electric-controlled compressed air energy storage technology. In the proposed system, we designed a control strategy based on system capacity estimation and driver purpose identifying. The real road driving was adopted to test the system and the control strategy. The experimental results suggest that the brake energy regeneration system enables fuel economy increase of bus more than 10%. About the system initial cost, the return on investment time is about three years.

The paper proposes a method to control the motion orbit of a cartoon object based on chaos theory. We first propose an adaptive delaying feedback controlling method which can control the Lorenz system to generate periodic, quasi-periodic and chaotic orbits. Then we apply our method to the topic of cartoon object motion controlling. It can enrich the motion type of the object thus enhances the visual effect of the cartoon video. The experiments validate the controlling results of the proposed method. Our research leads to a new area in which the chaotic theory is applied in the field of multimedia processing.

In human society, learning is essential to intelligent behavior. However, people do not need to learn everything from scratch by their own discovery. Instead, they exchange information and knowledge with one another and learn from their peers and teachers. When a task is too complex for an individual to handle, one may cooperate with its partners in order to accomplish it. Like human society, cooperation exists in the other species, such as ants that are known to communicate about the locations of food and move it cooperatively. Using the experience and knowledge of other agents, a learning agent may learn faster, make fewer mistakes, and create rules for unstructured situations. In the proposed learning algorithm, an agent adapts to comply with its peers by learning carefully when it obtains a positive reinforcement feedback signal, but should learn more aggressively if a negative reward follows the action just taken. These two properties are applied to develop the proposed cooperative learning method conceptually. The algorithm is implemented in some cooperative tasks and demonstrates that agents can learn to accomplish a task together efficiently through a repetitive trials.

In this paper, the application of fuzzy set theory, genetic algorithms and rough set theory techniques to the control of the High Voltage Direct Current (HVDC) system is studied. A fuzzy adaptive control scheme with the aid of rough set theory via genetic algorithms (GAs) finding the center scaling-factors in place of the classical control is proposed. On the one hand, genetic algorithm gets optimal parameters of an accurate domain model, which tunes the scaling factors of fuzzy adaptive control but is hardly established in a non-liner system. On the other hand, fuzzy adaptive control deals with the dynamics and complexity of responses from a HVDC system in the operation points, by adjusting its control parameters with the aid of rough tuner adaptively. Our study includes a brief introduction to fuzzy sets, fuzzy control and rough set algorithms, both theory and application. We also evaluate the performance of fuzzy adaptive control by simulation in the paper. The focus of our experiments is on the constant current control in HVDC system. The result shows there are many improvements offered by the fuzzy control scheme based on rough set theory in comparison with the conventional HVDC control scheme.

An adaptive fuzzy integral type sliding mode control method is proposed in this paper to compensate nonlinear dynamic friction that exists in single-axle motion control system and to improve the system position tracking performance. A kind of integral type sliding mode function is introduced, and the sliding mode control law that is obtained by using this sliding function does not include the switch controller that exists in conventional variable structure control law, therefore, the chattering phenomenon can be avoided. The parameter adaptive laws are derived in the sense of Lyapunov stability theorem, the parameters in adaptive laws are optimized by genetic algorithm. Simulation results show that adaptive fuzzy integral type sliding mode controller can achieve favorable tracking performance and robust with system nonlinear dynamic friction.

Conventional hysteresis control schemes for direct torque control (DTC) of dual-three-phase induction machine (DTPIM) usually result highly distorted current waveforms. In this paper, fuzzy space voltage modulation technique is presented for DTPIM. Using two fuzzy controllers, amplitude and space angle of desired stator voltage vector, are obtained dynamically. Combined with unified pulse width modulation method, direct torque control is applied to DTPIM. Simulation results show that fast dynamic responses are achieved. The ripple of torque and the harmonics of stator current in steady state can be reduced remarkably compared with conventional DTC method.

In this paper, a suitable particle swarm optimization (PSO) is firstly proposed to obtain the pulse width modulation (PWM) switching time which maintaining a required fundamental voltage and a minimal total harmonic distortion (THD) for very high power medium voltage induction motor drives fed by a double three-level inverter (DTI). The simulation results show that double three-level inverter based variable frequency drive system needs switches with lower voltage rating, and performs lower voltage harmonics than one single three-level inverter. After optimizing the PWM switching time by using particle swarm optimization, the motor drive voltage quality improves more.

This paper studies the application of the adaptive fuzzy sliding mode control (AFSMC) strategies for reducing the dynamic responses of the bridges with sliding bearing isolation hybrid protective system. Hence it is necessary to develop non-linear control methods. AFSMC is a combination of the sliding mode control (SMC) and the Fuzzy control. The performance and robustness of the proposed control methods are all demonstrated by numerical simulation. Simulation results demonstrate that the presented methods are viable and an attractive control strategy for application to seismically excited bridges with sliding bearing isolation.

The are various definitions of instantaneous frequency (IF) but most of them have short comings. The most popular of these definitions, is the one given by Cohen. To estimate IF accuratly, techniques become more and more complex. This paper introduces the idea of aggregate spectrogram (AS). When we are not interested in the entire spectrum (e.g., finding IF), it is better to find out AS which gives an aggregate view of the spectrogram rather than calculating the individual frequencies. Some properties of AS are also defined. The AS lowers the computational complexity. This paper also presents a technique to construct an AS and finding the IF from the AS.

The orthogonal function neural network is utilized to realize the general

H

 ∞ 

synchronization of chaotic systems with different structures. The driving system and the reference system are both disturbed by the external perturbation. Continuous controller is utilized to realize the general

H

 ∞ 

synchronization of chaotic systems according to nonlinear

H

 ∞ 

control theory. Lorenz system and R

ö

ssler system are utilized to simulate to illustrate the performance of the proposed method. The simulation results can show the validity of the proposed method.

In this paper, an image-based robust controller for tracking control of robot manipulators using a single camera is proposed. The proposed controller has robustness to parametric uncertainties of the robot manipulator and compensation for uncertainties included in the image Jacobian. The stability of the closed-loop system is proved by Lyapunov approach. The performance of the proposed method is demonstrated by experiments on a 5-link robot manipulator with two degree of freedom.

In this paper a fuzzy logic controller to minimize the torque ripples associated wit DTC (FL_DTC) of PMSM is presented. In this system, in order to attain the merits of fast torque response, the torque error, the flux error and stator flux position sector are used to select one active vector while the active switching time of this vector is selected as a result of the torque error magnitude and flux error magnitude fuzzification. The output of such fuzzy logic controller is used to adapt the inverter switching to follow the errors magnitude level. Arranging the inverter switching in this manner, results in fast torque response and minimum torque ripples as compared to the traditional HDTC. Additionally it results in less switching losses associated with SVMDTC. The simulated results show considerable torque ripple as well as current ripple reduction. The simulated results are supported with experimental results.

Based on the modified ant colony system (ACS) algorithm, a novel design method for nonlinear PID controller with on-line optimal self-tuning gains is proposed. In this method, first the regulative laws of the three PID controller gains are designed respectively, which are three nonlinear functions of system error and its variation rate, and then the proposed modified ACS algorithm is used to optimize the nine parameters in the three nonlinear functions. The designed controller is called the ACS-NPID controller and was used to control the CIP-I intelligent leg prosthesis. The simulation experiments demonstrated that the ACS-NPID controller has better control performance compared with other three linear PID controllers designed respectively by the differential evolution algorithm, the real-coded genetic algorithm, and the simulated annealing. The simulation results also verified that the modified ACS algorithm has good performance in convergence speed and solution variation characteristic.

A Neural Network based AC-AC voltage restorer is proposed for voltage sags and PWM type active power filter is used for voltage harmonics compensation and EMI reduction. The objective is to apply the neural network switching control technique to the AC-AC voltage restorer to reduce time delays during the switching conditions and switching losses. The aim of the IGBTs used in the AC-AC voltage restorer is to test and to find the best switching frequency-power combination in the steps of the simulation. Thus, the proposed AC-AC voltage restorer has some advantages such as fast switching response, simplicity and more intelligent structure, better output waveform. Neural Network techniques have proven that they were suitable for parameter identification and control of nonlinear systems. The transient condition of the AC-AC voltage restorer is improved via the Neural Network based control technique. On the other side, the proposed strategy for elimination of voltage harmonics using PWM type DC-AC inverter part of the system as an active filter. The last objective of the system is ElectroMagnetic Interference (EMI) reduction with using this filter and voltage restorer.

This paper proposes obstacle collision avoidance algorithm for a mobile robot. To navigate without colliding with obstacles the mobile robot uses both the vision system approach and edge detection approach using ultrasonic sensor. The vision system approach is used when the obstacle is located more than 2 meters away from the mobile robot, and the edge detection approach using ultrasonic sensor is used for detecting obstacle remains within 2 meters range. The collision avoidance algorithm proposed in this paper utilizes both the limit-cycle method and the nearness diagram navigation method. The proposed method is tested using Pioneer 2-DX mobile robot. Through experiment, the effectiveness of proposed methods for navigating a mobile robot in the dynamic environment is demonstrated.

In this paper, identification and adaptive control of networked control systems (NCSs) with uncertain networked-induced delay is considered. A novel and useful scheme for simultaneous estimation of networked-induced delays and dynamics of the plant is presented using linear filter method. Based on the suggested identification algorithm, a quadratically stabilizable adaptive control law with guaranteed cost is derived.

To solve a problem with intelligent planning, an expert has to try his best to write a planning domain. It is hard and time-wasting. Considering software requirement as a problem to be solved by intelligent planning, it’s even more difficult to write the domain, because of software requirement’s feature, for instance, changeability. To reduce the difficulty, we divide the work into two tasks: one is to describe an incomplete domain of software requirement with PDDL(Level 1,Strips) [11]; the other is to complete the domain by learning from plan samples based on business processes. We design a learning tool (Learning Action Model from Plan Samples, LAMPS) to complete the second task. In this way, what an expert needs to do is to do the first task and give some plan samples. In the end, we offer some experiment result analysis and conclusion.

The motion identification for a class of movements in the space by using stereo vision is considered by observing at least three points in this paper. The considered motion equation can cover a wide class of practical movements in the space. The observability of this class of movement is clarified. The estimations of the motion parameters which are all time-varying are developed in the proposed algorithm based on the second method of Lyapunov. The assumptions about the perspective system are reasonable, and the convergence conditions are intuitive and have apparently physical interpretations. The proposed recursive algorithm requires minor a priori knowledge about the system and can alleviate the noises in the image data. Furthermore, the proposed algorithm is modified to deal with the occlusion phenomenon. Simulation results show the proposed algorithm is effective even in the presence of measurement noises.

This paper presents a systematic isotropy analysis of a caster wheeled omnidirectional mobile robot (COMR) with three active caster wheels. Unlike previous analysis, no assumption is made on the relative scale of the steering link offset and the wheel radius. First, with the characteristic length introduced, the kinematic model of a COMR is obtained based on the orthogonal decomposition of the wheel velocities. Second, the necessary and sufficient isotropy conditions are examined to categorize three different groups to be handled in a similar way. Third, the isotropy conditions are further explored to identify four different sets of all possible isotropic configurations. Fourth, the characteristic lengths required for the isotropy of a COMR are obtained in a closed-form. Finally, the local and the global isotropy indices are used to determine the optimal design parameters.

This paper describes a control strategy for mobile robot Auto-seeking the Welded joint. Through this control strategy the mobile robot can find position of the weld joint automatically and adjust welding torch to parallel to it. Many simulation experiments results demonstrate the effectiveness and correctness of the control algorithm.

In the transient stability analysis of power systems it is extremely important to determine the boundary of stability region. Owing to the complexity and multi-time scale natures of electric power systems, it is necessary to correct boundary of stability region of simplified system (approximate reduction order system). In this paper, determining conditions of the stability boundary of multi-time scale systems is presented, O(ε)-correction formula of the stability boundary is obtained for multi-time scale power systems, which decreases the errors caused by employing the approximate reduced model to instead of the exact reduced system. An example and a simulation of one-machine infinite-bus electric power system are given to illustrate the validity of the O(ε)-correction algorithm.

This paper deals with the design of the capacitor insertion method for the three degree-of-freedom (DOF) flexible and deformation mechanisms aimed to eliminate hysteresis effects in piezoelectric actuators. By inserting a capacitor in series with the piezoelectric actuator is applied a 3-DOF nano-precision platform and laser-measuring systems.The Z-tilts(z, pitch, and roll motion) error compensation stage of the nano-measuring machine is accomplished. In addition, a high resolution laser interferometer is used to measure position accurately. Therefore, above the method is effectively applied to a piezoelectric actuator are presented that compensate substantial improvements in positioning control precision and control performance. With the aid of positioning control, this system provides +/-60nm positioning resolution over the total range of 1000nm and +/-0.1 arcsec angle resolution over the total range of 3 arcsec for the stage along the z-direction.

The ATIS (Advanced Traveler Information System) provides travelers with real-time and precise information about the shortest path to the destination, the traffic condition, travel time estimation, and so on. To offer these services, we have to collect the speed data which are necessary to ATIS. However many data are lost due to communication or sensor errors during collecting the data. In order to provide accurate service, the lost data have to be compensated. Thus, a lot of prediction methods have been proposed to compensate the lost speed data. In this paper, we propose new prediction method adopting the back propagation neural network under neighboring links’ speed as well as weekday and time. Experimental results show that our method reduces prediction error up to 41.8 % compared to the previous method.

Based on DCT coefficients, we propose a steganographic technique. Our method embeds a message into the DCT coefficients of an image according to the relative size of a selected DCT coefficient value and the average value of its adjacent coefficients in the block to embed and extract the hiding message bit. The coefficients are chosen middle-to-low frequency coefficients in order to defend against JPEG compression. The method decreases the likelihood of being detected, and the resulted stego-image can be stored in JPEG format. The hidden message can be securely transformed. Our experiments show that our method can extract the data efficiently and blindness.

With the growth of critical worm threats, many researchers have studied worm-related topics and internet anomalies. The researches are mainly concentrated on worm propagation and detection more than the fundamental characteristics of worms. It is very important to know worms’ characteristics because the characteristics provide basic resource for worm prevention. Unfortunately, this kind of research cases are very few until now. Moreover the existing researches only focus on understanding the function structure of the worm propagation or the taxonomy of the worm according to a sequence of worm attacks. Thus, in this paper, we try to confirm the formalized pattern of the worm action from the existing researches and analyze the report of the anti-virus companies. Finally, we define the formalized actions based on temporal behaviors and worm packet flows, and we apply our proposed method for the new worm classification.

System call sequences are useful criteria to judge the behaviors of processes. How to generate an efficient matching algorithm and how to build up an implementable system are two of the most difficult problems. In this paper, we explore the possibility of extending consecutive system call to incorporate temporal signature to the Host-based Intrusion Detection System. In this model, we use the real-time detected system call sequences and their consecutive time interval as the data source, and use temporal signature to filter the real model. During the monitoring procedure, we use data mining methods to analyze the source dynamically and implement incremental learning mechanism. Through studying small size samples and incremental learning, the detecting ability of the system can be still good when the sample’s size is small. This paper also introduces the key technologies to build such a system, and verifies this intrusion detection method in real time environment. Finally, this paper gives the experiments results to verify the availability and efficiency of our system.

Data hiding strategy based on equivalence class is proposed. We transform information hiding problem into finding the representative element in specific equivalence class. Then minimizing the distortion in the equivalence class (MDEC) is proposed, and this is used in the LSB hiding scheme. The theoretic performance of LSB hiding based on MDEC is analyzed in detail. Then a variable LSB method based on MDEC is also proposed. It can solve efficiently the problem of selecting different LSB methods to fit message with different length. Similarly, the performance is also proposed. In fact, there exists a tradeoff between distortion and length of information. However, most spatial hiding scheme based on LSB will reach larger distortion in hiding less information. The proposed hiding strategy can resolve this issue efficiently and can meet such applications where the size of message is very unstable. In addition, proposed strategy not only improves the quality of steg image but also does not sacrifice its security.

In this paper, two new properties of singular value decomposition (SVD) on images are proved. The first property demonstrates the quantitative relationship between singular values and power spectrum. The second one proves that under the condition of losing equal power spectrum, the square-error of the reconstructed image is much smaller when we reduce all singular values proportionally instead of neglect the smaller ones. Based on the two properties, a new data-hiding scheme is proposed. It performs well as for robustness, for it satisfies power-spectrum condition (PSC), and PSC-compliant watermarks are proven to be most robust. Besides, the proposed scheme has a good performance as for capacity and adaptability.

Recent advances in networking technology have increased the potential collaborations for virtual enterprises (VEs) on a global scale. Due to the dynamic nature of collaborations, building and evolving trust is essential to support the formation of VEs. There is a critical need for the new approach suited to this environment. This paper presents a trust-based approach for partner selection problem in the VEs. The proposed model explores the dynamic properties of trustworthy index, which provides a multi-perspective and interactive overview of potential partners to the decision-makers. The model uses the hybrid learning algorithm of fuzzy cognitive map (FCM). As FCM has the excellent ability in modeling complex systems, the proposed model can support historical data mining automatically, and revise the existing model according to the new requirements. Results of three experiments show that this model provides reasonable performance and high adaptability for diverse partner selection problems.

Involvement of decision support systems in the process of selecting optimal decisions in transport logistics requires preliminary estimation of cargo delivery quality. The number of delivery criteria, their ranking according to degree of importance, and correlations among these criteria play an important role in the process of decision making. For the elaboration of effective approaches to the above mentioned tasks in the transport logistics we propose use of an intelligent system that applies methods from formal concept analysis and paraconsistent annotated logic.

The reading comprehension (RC) task- accepting arbitrary text input (a story) and answering questions about it. The RC system needs to draw upon external knowledge sources to achieve deep analysis of passage sentences for answer sentence extraction. This paper proposes an approach towards RC that attempts to utilize semantic information to improve performance beyond the baseline set by the bag-of-words (BOW) approach. Our approach emphasizes matching of linguistic features (i.e. verbs, named entities and base noun phrases) and semantic extending for answer sentence extraction. The approach gave improved RC performance in the Remedia corpus, attaining

HumSent

accuracies of 41.3%. In particular, performance analysis shows that a relative performance of 19.7% is due to the application of linguistic feature matching and a further 10.3% is due to the semantic extending.

Text categorization (TC) has become one the most researched fields in NLP. In this paper, we try to solve the problem of TC through a 2-step feature selection approach. First we cluster the words that appear in the texts according to their distribution in categories. Then we extract concepts from these clusters, which are DEF terms in HowNet. The extraction is according to the word clusters instead of single words. This method maintains the generalization ability of concept extraction based TC and at the same time makes full use of the occurrences of new words that are not found in concept thesaurus. We test the performance of our feature selection method on the Sogou corpus for TC with an SVM classifier. Results of our experiments show that our method can improve the performance of TC in all categories.

Protein-Protein interaction (PPI) information play a vital role in biological research. This work proposes a two-step machine learning based method to extract PPI information from biomedical literature. Both steps use Maximum Entropy (ME) model. The first step is designed to estimate whether a sentence in a literature contains PPI information. The second step is to judge whether each protein pair in a sentence has interaction. Two steps are combined through adding the outputs of the first step to the model of the second step as features. Experiments show the method achieves a total accuracy of 81.9% in BC–PPI corpus and the outputs of the first step can effectively prompt the performance of the PPI information extraction.

The organization and discovery of grid resources are foundational and key subjects in grid research. Many research works in this field have presented the solutions to the problem, but few of them are focused on knowledge resources. This paper aims to explore how to support user’s knowledge requests submitted in form of multi-keywords. We present a decentralized resource discovery method based on keyword combinations and node clusters. In the method, hot keyword combinations are formed based on user’s knowledge requests. Then, grid nodes can be clustered according to these keyword combinations and user’s knowledge requests will be transmitted to those clusters that have high correlations with the requests.

This paper proposes a new method which utilizes noise energy, instead of noise variance, to perform image de-noising based on Principal Component Analysis in Contourlet domain. The Contourlet transform is a new extension of the wavelet transform that provides a multi-resolution and multi-direction analysis for two dimension images. Most of the existing methods for image de-noising rely on accurate estimation of noise variance. However, the estimation of noise variance is difficult in Contourlet domain. The novelty of this method is that it does not rely on the estimation of noise variance, therefore it has great value in solving real-world problems. We compared this method with the wavelet hard-thresholding and soft-thresholding methods which are commonly used in image de-noising. The experimental results show that the proposed approach can obtain better visual results and higher PSNR values, especially for the images that include mostly fine textures and contours.

The block matching algorithm (BMA) is one of the most important processing in the video compression. Since the sub-pixel motion estimation and motion compensation are needed, the computational complexity of the BMA is increased. Recently, the sum of absolute difference (SAD) calculation is widely used for BMA but it accounted for much of the total computation of the video compression. To implement the real-time video compression, the fast algorithm for motion estimation and motion compensation based on SAD computation is needed. The partial distortion elimination (PDE) scheme is one of the most advanced methods to decrease the SAD computational complexity. The basic concept of the PDE is that if the accumulated SAD values are greater than the given accumulated SAD value then the SAD computation is stopped. Where, the given accumulated SAD value is a kind of average value. Therefore, the big problem of the PDE is that the division is needed. And, as initial accumulated SAD value is large, PDE operation becomes efficient. Thus scan order is also important in SAD computation. In this paper, we introduce the new average computation method for PDE operation without division, its mathematical modeling and architecture. The new computational method is named as RAVR (Rough Average). And we propose the advanced scan order for efficient PDE scheme based on ARVR concept. Thus, our proposed algorithm combines above two main concepts and suffers the improving SAD performance and the easy hardware implementation methods.

Face image retrieval has particularity under the situation that the target is unknown. If the image that match some features or fit to the one in memory wants to be find out in the human image database, the request solution must be global optimum, and does not lose the optimal one. In this paper the interactive genetic algorithm ( IGA ) jincorporating with adjust function and support vector machine ( SVM ) jis put forward to keep optimum solution from lose, speed the convergence, alleviate user fatigue, improve and raise retrieval performance.

The accuracy of the existing methods for two - block motifs discovery is usually less than 50%, which is difficult to be increased. Based on position weight matrix (PWM) for two - block motif model, this paper proposed an improved Gibbs sampling algorithm to overcome local converged performance of original Gibbs sampling algorithm and increase the predictive accuracy by introducing motif base. The feasibility and the effectiveness of novel algorithm are verified by the real biological data through computer experiments. The results are analyzed and compared with other algorithms such as RSAT and AlignACE. The accuracy of novel algorithm is larger than 55% for two - block motifs, which is superior to that of existing methods.

Simulating facial aging effects is a challenge task because of the difficulties in understanding and modeling the aging pattern. In this paper, a novel aging model called Aging Increment Distribution Function was proposed to model the age progression in the statistical appearance model space. The trajectory of face samples is learned to build the distribution function with free shape. So it has finer resolution to reveal the underlying aging pattern. Based on modeling the increment of appearance parameter, an analytical framework was formulated to re-render the given face image onto any other age within the maximum age span of training samples. In experiment, the MORPH face database was used to train the aging model, which has been further applied to re-rendering of age effects. Both aging and rejuvenating simulation results presented similar effects comparing to the real images, which verified the effectiveness of proposed method.

In cancer diagnosis and treatment, clustering based on gene expression data has been shown to be a powerful method in cancer class discovery. In this paper, we discuss the use of nonnegative matrix factorization with sparseness constraints (NMFSC), a method which can be used to learn a parts representation of the data, to analysis gene expression data. We illustrate how to choose appropriate sparseness factors in the algorithm and demonstrate the improvement of NMFSC by direct comparison with the nonnegative matrix factorization (NMF). In addition, when using it on the two well-studied datasets, we obtain pretty much the same results with the sparse non-negative matrix factorization (SNMF).

Atrial fibrillation data series show the non-linear and chaos characters in the process of time-space kinetics evolution. In the case of unknowing the fractal dimension of atrial fibrillation chaos, the process of querying the similarity of diagnosis curve figure will be affected to a certain degree. An evaluation formula of varying-time

Hurst

index is established by wavelet and the algorithm of varying-time index is presented, which is applied to extract the characteristics of the atrial fibrillation in this paper. The diagnosis of atrial fibrillation curve figure can be done at some resolution ratio level. The results show that the time-varying fractal dimension rises when atrial fibrillation begins, while it falls when atrial fibrillation ends. The begin and the end characteristics of atrial fibrillation can be successfully detected by means of the change of the time-varying fractal dimension. The results also indicate that the complexity of heart rate variability (HRV) decreases at the beginning of atrial fibrillation. The effectiveness of the method is validated by means of the HRV example in the end.

It is a difficult and important task to classify the types of military vehicles using the acoustic and seismic signals generated by military vehicles. For improving the classification accuracy, we investigate different feature extraction methods and 4 classifiers. Short Time Fourier transform (STFT) is employed for feature extraction from the primary acoustic and seismic signals. Independent component analysis (ICA) and principal component analysis (PCA) are used to extract features further for dimension reduction of feature vector. Four different classifiers including decision tree (C4.5), K-nearest neighbor (KNN), probabilistic neural network (PNN) and support vector machine (SVM) are utilized for classification. The classification results indicate the performance of SVM surpasses those of C4.5, KNN, and PNN. The experiments demonstrate ICA and PCA are effective methods for feature dimension reduction. The results showed the classification accuracies of classifiers with PCA were superior to those of classifiers with ICA. From the perspective of signal source, the classification accuracies of classifiers using acoustic signals are averagely higher 15% than those of classifiers using seismic signals.

The extended Kalman Filter (EKF) is an important method for eliminating stochastic errors of dynamic position in the Global Positioning System (GPS). One of the adaptive methods is called the adaptive fading Kalman filter (AFKF), which employs suboptimal fading factors for solving the divergence problem in an EKF. Incorporation of a scaling factor has been proposed for tuning the fading factors so as to improve the tracking capability. A novel scheme called the fuzzy adaptive fading Kalman filter (FAFKF) is proposed. In the FAFKF, the fuzzy logic reasoning system is incorporated into the AFKF. By monitoring the degree of divergence (DOD) parameters based on the innovation information, the fuzzy logic adaptive system (FLAS) is designed for dynamically adjusting the scaling factor according to the change in vehicle dynamics. GPS navigation processing using the FAFKF will be simulated to validate the effectiveness of the proposed strategy.

The triangle packing problem has yielded many significant theories and applications such as textile cutting and container stuffing. Although the representation method of the popular linear quadtree has many merits, it puts too much emphasis upon the symmetry of image segmentation. Therefore, it is not the optimal representation method. In this paper, inspired by the concept of the triangle packing problem, we present a Triangle Non-symmetry and Anti-packing pattern representation Model (TNAM). Also, we propose a novel algorithm for the TNAM of the gray images. By comparing the algorithm for the TNAM with that for the linear quadtree, the theoretical and experimental results show that the former is much more effective than the latter and is a better method to represent the gray images. The algorithm for the TNAM of the gray images is valuable for the theoretical research and potential business foreground.

PDE (Partial differential equation) is an image interpolation method which interpolates based on local geometry property. It can not preserve texture pattern and can only process natural image. NL (Non Local)-means is an interpolation method that uses global information of image. Entire texture pattern in image can be well preserved because of the high replication property of NL-means, while the problem is that blur is preserved as well. In this paper a novel image interpolation method which combines PDE and NL-means is proposed. Image interpolated by the novel method is clear and smooth, and preserves texture pattern. The new method enhances edges using shock filter PDE which does not strengthen jaggies of block contour in interpolated image; the PDE used in this method to smooth image diffuses along level curve. Divided gray regions caused by PDE are smoothed by NL-means; the broken texture pattern is recovered well. Lastly, it is proved that even noisy image can be directly interpolated to the required size using this method. Both theoretical analysis and experiments have been used to verify the benefits of the novel interpolation method.

It presents an approach to the automatic generation of pencil sketching with the effects of paper texture. First, proposes a texture mapping of strokes in the aspect of skeleton, the filter algorithm and the standard deviation algorithm for rendering image, the near distances recover algorithm for real-time browsing, and finally implement the Pen-and-Ink Style of pencil sketching Rendering System. In the static rendering, it needn’t adjust the threshold for the convenient on rendering and the outlines are more precise and exact. Besides, attaching the graftal and sketching shadow makes the composition of an image more attractive. Through a series of clever image processes, the system finally presents excellent colored pencil style drawings. Because the proposed algorithm is not complicated, the rendering time is quite short compared to other past related studies. Compared with other research works and Photoshop on a set of benchmarks, the system demonstrates its strength in the aspects of full automation, stability of sketching quality and higher visual satisfaction, all achieved in a considerably shorter time.

An antenna array composed of one transmitter and multi-receivers and dedicated to measuring terminal trajectory of the target of interest, which is supposed to be in uniform rectilinear motion, is set up. On the basis of the model, the Vector parameter that can uniquely determine the terminal trajectory of target is introduced, and the measurement equations which describe the respective relationships between the Vector parameter and the instantaneous Doppler frequency and the phase differences extracted from echoes are established. Taking advantage of the measurement equations, we propose an algorithm of estimating the Vector parameter without resolving the phase difference ambiguity; furthermore, the detailed steps in estimating the Vector parameter using numerical optimization techniques are put forward. The Monte Carlo simulation results demonstrate the effectiveness and reliability of our numerical algorithm compared with the traditional method.

An inter-scale adaptive, data-driven threshold for image denoising via wavelet soft-thresholding is proposed. To get the optimal threshold, a Bayesian estimator is applied to the wavelet coefficients. The threshold is based on the accurate modeling of the distribution of wavelet coefficients using generalized Gaussian distribution (GGD), and the near exponential prior of the wavelet coefficients across scales. The new approach outperforms BayesShrink because it captures the statistical inter-scale property of wavelet coefficients, and is more adaptive to the data of each subband. Simulation results show that higher peak-signal-to-noise ratio can be obtained as compared to other thresholding methods for image denoising.

This paper describes our work in implementing a real-time moving object detection system basing on a system on a chip (SoC) system. We have implemented the algorithms necessary for moving object detection while using a SoC IP and have prepared an exclusive image-processing SoC system to implement the algorithms. The implemented IP is the IP for the image, I2C control, edge detection, TFT-LCD, median filter, SRAM control, and moving object detection. Detection of a moving object, as for the input image, requires processing edge detection, image differentiation, application of a median filter, and last, detecting the moving object. The moving object area for a detected movement detects the moving object by the cumulative value of binary conversion density.

In this paper, a strapdown inertial navigation system (SINS) error model is introduced, and the model observability is analyzed. Due to the weak observability of SINS error model, the azimuth error can not be estimated quickly by Kalman filter. To reduce the initial alignment time, a neural network method for the initial alignment of SINS on stationary base is presented. In the method, the neural network is trained based on the data preprocessed by a Kalman filter. To smooth the neural network output data, a filter is implemented when the trained neural network is adopted as a state observer in the initial alignment. Computer simulation results illustrate that the neural network method can reduce the time of initial alignment greatly, and the estimation errors of misalignment angles are within a satisfied range.

Traditional speech recognition systems have relied on power spectral densities, Mel-frequency cepstral, linear prediction coding and formant analysis. This paper introduces two novel input feature sets and their extraction methods for intelligent phoneme identification. These input sets are based on intrinsic phonetic characteristics of Arabic speech comprising of the dimensionally reduced Power Spectral Densities (DPSD) and Location, Trend, Gradient (LTG) values of the captured speech signal spectrum. These characteristics have been subsequently utilized as inputs to four different neural network based recognition classifiers. The classifiers have been tested for twenty-eight Arabic phonemes utterances from over one hundred non-native speakers. The results obtained using the proposed feature sets have been compared and it has been observed that LTG based input feature set provides an average phoneme identification accuracy of 86% as compared to 70% obtained through applying DPSD based inputs for similar classifiers. It is worthwhile to note that the methods proposed in this paper are generic and are equally applicable to other regional languages such as Persian and Urdu.

We describe the architecture and implementation of bilateral background subtraction for real-time stereo vision system. Pre-smoothing a signal and noise removal may help to improve the performance for many signal-processing algorithms such as compression, detection, enhancement, recognition, and more [2]. Bilateral filtering proposed by C. Tomasi and R. Manduchi can be used as an edge-preserving smoother, removing high-frequency components of an image without blurring its edges [1][3]. Recently, [3] showed enhanced real-time stereo through software implementation of bilateral filtering. In this paper, we show hardware implementation of bilateral background subtraction for real-time stereo and present its architecture as well as required hardware resources. Also, we provide experimental results with real data and present our future works.

This paper presents a new description of image edge in the form of asymptote equation utilizing knowledge of differential geometry, and modifies a popular parlance in the traditional image processing. This paper develops the gradient concept and defines a generalized grads operator. The operator is a novel nonlinear transform, which inherits the strongpoint of noise suppression from the classical Gaussian differential filter and has a better edge extraction function. The experimental results show the proposed edge detection algorithm is powerful and effective.

In this paper, we present a new image compression algorithm which combines Wavelet Support Vector Machines (WSVM) learning with the wavelet transform. Based on the characteristic of wavelet transform, Daubechies 9/7 wavelet has been used to transform the image and the wavelet coefficients are trained with WSVM using translation-invariant wavelet kernels. Compression is achieved by using WSVM learning to approximate wavelet coefficients with the predefined level of accuracy. A minimal number of coefficients (support vectors) are then encoded by an effective entropy coder based on run-length and arithmetic coding. Experimental results show that the proposed method gains better performance than that of existing compression algorithm.

A framework based on manifold learning in reconstructed state space is proposed as feature extraction means for nonlinear signal classification. On one hand, manifolds are of importance in characterizing chaotic attractors. On the other hand, there are a large number of toolkits in the context of manifold learning. These motivate us to apply manifold learning in reconstructed state space as feature extraction means for nonlinear signal classification, which bridges the gap between nonlinear science and manifold learning and enables a new viewpoint to study nonlinear signals. In this study, the nonlinear signal analysis is performed as follows. First, we embed the time series of interest into a high-dimensional space via state space reconstruction. Then, we employ locally linear embedding (LLE) to obtain the local manifold characteristics around every point in the reconstructed state space. Finally, we summarize all the local features into a global representation via principal component analysis (PCA). Two case studies of oceanic and EEG signal classification were carried out with the proposed scheme. As confirmed by the experiments, the proposed methodology is effective for such applications. This paper puts forward not only a feature extraction method but also a new direction in which a large number of toolkits are available for nonlinear signal analysis for the sake of signal classification.

In this paper, a frame rate up-conversion (FRC) algorithm using the motion vector frequency of neighboring blocks to reduce the block artifacts caused by failure of conventional motion estimation (ME) based on block matching algorithm (BMA) is proposed. The proposed method is based on the Spiral Full Search with early termination, which is applied to avoid the local minima on pattern-like image. Also, the motion vector correction that replaces a low frequency motion vector by a high frequency motion vector of neighborhood is used in the proposed method to reduce the block artifacts caused by the failure of conventional ME. In addition, bi-directional motion compensated interpolation (MCI) using the blocking index to reduce the block artifacts in occlusion area is used in the proposed method. Experimental results show good performance of the proposed scheme with significant reduction of the erroneous motion vectors and block artifacts.

In design and realization of analog electronic circuit, we usually use preferred value components, the performance of practical circuits often deviate from the ideal design target due to rounding the calculated component values to preferred ones. The best combination of the preferred value components exists in general, but the searching space of all combinations of preferred-value components is very huge. Clonal Selection Algorithms (CSA) is a widely used approach for handling optimization problems. In this paper, CSA is applied into searching optimal components for 4th order Butterworth filter design. Simulation results demonstrate that the proposed method is much superior to the conventional means. This method also can be applied into other types of filter design.

Polygonal models have grown rapidly in complexity over recent years, yet most conventional simplification algorithms were designed to handle modest size datasets of a few tens of thousands of triangles. We present a parallel simplification method for large polygonal models. Our algorithm will partition the original model firstly, send each portion to a slave processor, simplify them concurrently, and merge them together lastly. We give an efficient method to deal with the problem of partition border and portion merging. With parallel implementation, the algorithm can handle extremely large data set, and speed up the execution time. Experiment shows that our algorithm can produce approximations of high quality.

According to Cachin’s steganography security criterion, if the statistical distributions of cover and stego images are identical, the hidden message is assumed undetectable. However, any steganographic method will surely cause some statistical distortions, which gives steganalyst a hint. This paper presents a steganalysis method for JPEG images based on Cachin criterion. It estimates the cover image from the stego one by using a small-scale geometrical transform, and then detects the statistical distortions between the cover and stego images based on some features, which are sensitive to the steganographic modifications. Then a classifier is trained on these features. Three different modern steganographic schemes are tested. Experimental results show that the proposed steganalysis scheme has better performance compared to the current steganalysis methods.

Filtering is a preliminary process in many medical image processing applications. It is aiming at reducing noise in images, and any post-processing tasks may benefit from the reduction of noise. The major two noises in computed radiography (CR) images are Gaussian white noise and Poisson noise. By considering both the characteristics of CR images and the statistical features of wavelet transformed coefficients, an efficient spatial adaptive filtering algorithm, which is based on statistical model of local dependency of CR image wavelet coefficients and the approximate minimum mean squared error (MMSE) estimation, is proposed to decrease the Gaussian white noise in computed images. The process is computational cost saving, and the denoising experiments show the algorithm outperforms other approaches in peak-signal-to-noise ratio (PSNR).

A new image coding method based on discrete directional wavelet transform (S-WT) and quadtree decomposition is proposed here. The S-WT is a kind of transform proposed in [1], which is based on lattice theory, and with the difference with the standard wavelet transform is that the former allows more transform directions. Because the directional property in a small region is more regular than in a big block generally, in order to sufficient make use of the multidirectionality and directional vanishing moment(DVM) of S-WT, the input image is divided into many small regions by means of the popular quadtree segmentation, and the splitting criterion is on the rate-distortion sense. After the optimal quadtree is obtained, a resource bit allocation algorithm is fast implemented utilizing the model proposed in [15]. Experiment results indicate that our algorithms perform better compared to some state-of-the-art image coders.

Emotion is fundamental to human experience influencing cognition, perception and everyday tasks such as learning, communication and even rational decision-making. This aspect must be considered in human-computer interaction. In this paper, we compare four different weighting functions in weighted KNN-based classifiers to recognize five emotions, including anger, happiness, sadness, neutral and boredom, from Mandarin emotional speech. The classifiers studied include weighted KNN, weighted CAP, and weighted D-KNN. To give a baseline performance measure, we also adopt traditional KNN classifier. The experimental results show that the used Fibonacci weighting function outperforms than others in all weighted classifiers. The highest accuracy achieves 81.4% with weighted D-KNN classifier.

This paper introduces the idea that recognition and interpretation of engineering drawings should be two interwoven phases, with each providing feedback to another, and applies this idea to a dynamic-rule-based method. Recognition rules with attributes are obtained by an automatic object feature extraction procedure, and stored in rule database. During the recognition phase, rules are firstly selected according to two attributes, domain and priority. Then the thresholds of the rules are adjusted automatically to obtain better match results and their priorities are modified dynamically to improve recognition efficiency. Especially, the interpretation phase based on the recognition is also valued in validating and rectifying the recognition result automatically and efficiently. This approach was implemented in a system for recognizing and interpreting architectural structure drawings, and has shown to embody good self-adaptability to various drafting conventions.

A novel method is developed for the dichromat’s visual correction. This scheme includes three steps. Firstly, two similarity matrices are established respectively for the normal three-dimensional color space and the color plane in which dichromats can distinguish all the colors. Then a 3D-2D mapping relationship is searched based on these similarity matrices. Finally, the original color image is transformed to a new one which is suitable for dichromats. The experiments on both color test images and real images demonstrate the ability of the scheme for color blindness correction. With the fixed transformation of color space, this scheme may have capability to help training dichromats to recognize most colors.

A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It is a simplex-based method which extracts endmembers sequentially using computations of largest simplex volumes. At each searching step of this extraction algorithm, searching for the simplex with the largest volume is equivalent to searching for a new orthogonal basis which has the largest norm. The new endmember corresponds to the new basis with the largest volume. This algorithm runs very fast and can also avoid the dilemma in traditional simplex-based endmember extraction algorithms such as N-FINDR which generally produces different sets of final endmembers if different initial conditions are used. Moreover, with this set of orthogonal bases, the proposed algorithm can also determine the proper number of endmembers and finish the unmixing of the original images which cannot be done by the traditional simplex-based algorithms. Experimental results of both artificial simulated images and practical remote sensing images demonstrate that the algorithm proposed in this paper is a fast and accurate algorithm for the decomposition of mixed pixels.

Two-dimension road following is one of the crucial tasks of vision navigation. For the reasons of environment complexity and the discrepancy between motion images, the robust outdoor road following for two-dimension image sequence is still a challenging task. This paper proposes a novel road following method, which firstly uses the Mean Shift algorithm with embedded edge confidence to partition the images into homogenous regions with precise boundary. Then, according to the color statistic information of the road/non-road model obtained from previous frames, the Graph Cuts (GC) algorithm is used to achieve the final binary images and update the road/non-road model simultaneously. This algorithm combines the advantages of Graph Cuts algorithm and Mean Shift algorithm, and effectively solves some difficult problems of conventional methods, such as the adaptive selection of road model under complex environments, and the choice of effective criteria for the region merging. Experiment results indicate our method possesses excellent performance under complicated environment, and meets the requirements of fast computing.

This paper presents a digital shot system based on three dimensions integrated accelerometer. The digital shot with almost the same size and weight as the standard shot for open female has been designed, fabricated and tested. The data collecting and processing system, human-machine interface and athlete train guiding system are illuminated in detail. By using wavelet transformation, the characteristics of acceleration signals during the shot-putting period can be extracted. In this manner, the force sensing system serves as a powerful tool for coaches and sports scientists to make scientific researches on shot-put techniques. It also provides an intuitive and reliable guidance for the shot-put athletes to improve their skills.

From the Bayesian Ying-Yang (BYY) harmony learning theory, a harmony function has been developed for finite mixtures with a favorite property that its maximization can make model selection automatically during parameters learning. In this paper, we propose an adaptive gradient learning rule for maximizing the harmony function on Poisson mixtures which are applied more and more extensively in practice, especially for overdispersed data. It is demonstrated by simulation experiments that this adaptive gradient learning rule can determine the number of Poisson distributions during the parameters learning on a sample data set.

In order to recognize the acoustic emission source with different characteristics, the parameter-ratio method was put forward to analyze the characteristic parameters of acoustic emission from different source further. According to the peak amplitude, counts, energy and rise-time, the three ratios of the amplitude to the energy difference, the amplitude to the counts difference and the amplitude to the rise-time difference were used as the parameter-ratios. Based on the matter-element of extension theory, a matter-element model was built to describe the characteristics of the acoustic emission. The dependent function and degree of the characteristics of the acoustic sources were introduced to evaluate the possibility of the acoustic sources. The acoustic sources can be recognized, putting forward the recognition rules of parameter-ratio method. The recognition example was taken to validate the parameter-ratio method. It is shown that the parameter-ratio method can recognize the acoustic emission source well.

In this paper we present the experiments and results obtained in the classification of infant cry using a variety of classifiers, ensembles among them. Three kinds of cry were classified: normal (without detected pathology), hypo acoustic (deaf), and asphyxia. The feature vectors were formed by the extraction of Mel Frequency Cepstral Coefficients (MFCC); these were then processed and reduced through the application of five statistics operations, namely: minimum, maximum, average, standard deviation and variance. For the classification there were used supervised machine learning methods as Support Vector Machines, Neural Networks, J48, Random Forest and Naive Bayes. The ensembles used were combinations of these under different approaches like Majority Vote, Staking, Bagging and Boosting. The 10-fold cross validationtechnique was used to evaluate precision in all classifiers.

The aim of this study is to identify the author of an unauthorized document. Ten different feature vectors are obtained from authorship attributes, n-grams and various combinations of these feature vectors that are extracted from documents, which the authors are intended to be identified. Comparative performance of every feature vector is analyzed by applying Naïve Bayes, SVM, k-NN, RF and MLP classification methods. The most successful classifiers are MLP and SVM. In document classification process, it is observed that n-grams give higher accuracy rates than authorship attributes. Nevertheless, using n-gram and authorship attributes together, gives better results than when each is used alone.

Automatic reconstruction of patient-specific 3D bone model from a limited number of calibrated X-ray images is not a trivial task. Previous published works require either knowledge about anatomical landmarks, which are normally obtained by interactive reconstruction, or a supervised initialization. In this paper, we present an automatic 2D/3D reconstruction scheme and show its applications to reconstruct a surface model of the proximal femur from a limited number of calibrated X-ray images. In our scheme, the geometrical parameters of the proximal femur are obtained by using a Bayesian filter based inference algorithm to fit a parameterized multiple-component geometrical model to the input images. The estimated geometrical parameters are then used to initialize a point distribution model based 2D/3D reconstruction scheme for an accurate reconstruction of a surface model of the proximal femur. Here we report the quantitative and qualitative evaluation results on 10 dry cadaveric bones. Compared to the manual initialization, the automated initialization results in a little bit less accurate reconstruction but has the advantages of elimination of user interactions.

Based on neural network with favorable adaptability to handwritten Chinese character multi-features, in this paper a new method is proposed, using existing multi-features as inputs to structure multi neural network recognition subsystems and these subsystems are integrated with parallel connection mode. The integrated system has the lowest false recognition rate. When using traditional von Neumann architecture computer to implement this system, the system response time is longer as a result of serial computation. This paper introduces a kind of parallel computation method of using pc cluster to implement multi subsystems. It can reduce effectively recognition system’s response time.

This paper proposes an algorithm based on fuzzy support vector machine (FSVM), a new pattern analysis method, for detecting the abnormal trajectory patterns of moving objects from surveillance video. Firstly, feature points are extracted for presenting continuous trajectories. Then fuzzy memberships are introduced to measure contributions of the feature points of trajectory. Finally, the algorithm is applied to detect the abnormal patterns in 2D object trajectories. Experiments on trajectory data set show the validity of the algorithm.

In speaker recognition fields, score normalization is a widely used and effective technique to enhance the recognition performances and is developing further. In this paper, we are focused on the comparison among many kinds of candidates of score normalization methods and a new implementation of the speaker adaptive test normalization (ATnorm) based on a cross similarity measurement is presented which doesn’t need an extra corpus for speaker adaptive impostor cohort selection. The use of ATnorm for the language robustness of the multi-language speaker verification is also investigated. Experiments are conducted on the core task of the 2006 NIST Speaker Recognition Evaluation (SRE) corpus. The experimental results indicate that all the score normalization methods mentioned can improve the recognition performances and ATnorm behaves best. Moreover, ATnorm can further contribute to the performance as a means of language robustness.

In this paper, a novel face recognition method based on binary face edges is presented to deal with the illumination problem. The Binary Face Edge Map (BFEM) is extracted using the Locally Adaptive Threshold (LAT) algorithm. Based on BEFM, a new image similarity metric is proposed. Experimental results show that face recognition rates of 76.32% and 82.67% are achieved respectively on 798 AR images and 150 Yale images with changed lighting conditions and facial expression variations when one sample per subject is used as the target image. The proposed method takes less time for image matching and outperforms some existing face recognition approaches, especially in changed lighting conditions.

This work introduces a new approach to detect fake fingers, based on the analysis of time-series fingerprint images. When a user puts a finger on the scanner surface, a time-series sequence of fingerprint images is captured. Five features are extracted from the image sequence. Two features represent the skin elasticity, and three features represent the physiological process of perspiration. Finally the Support Vector Matching (SVM) is used to discriminate the finger skin from other materials such as gelatin. The experiments carried out on a dataset of real and fake fingers show that the proposed approach and features are effective in fake finger detection.

In this paper, the line segment tracking is considered for an imaging system with translational motions (T.M.), rotational motions (R.M.) and arbitrary motions. Assuming a CCD camera with an arbitrary tilt angle installed on a mobile robot equipped with odometry system, to reduce the search space in the correspondence problem, two constraints are developed. These constraints are location and orientation differences (O.D.) for line segments in consecutive images. The findings of this paper include: 1)The development of the effect of camera tilt on the location constraint for T.M., 2)Illustrating that the upper bound of O.D. for both horizontal and vertical lines with respect to the floor, is a function of tilt in T.M., which can considerably be reduced and thus providing a tight constraint, 3)The development of the location constraint for R.M., 4) The development of the PDF and upper bound of O.D. for R.M., 5) The development of the location and O.D. constraint for arbitrary motions. Furthermore, the efficiency of these developed constraints in a line tracking algorithm was verified.

Content-based image classification has always been a hot research topic. This paper aims to propose an efficient image analysis algorithm using geometric features of skin regions to effectively classify images. First, a nonparametric skin color classifier is used to skin detection. Then, the contours of skin regions are constructed using a curve evolution method based on adaptive grids. Finally, the geometric features are extracted from the contours, and the cosine similarity measure is adopted for image classification. The algorithm is tested on a large database consisting of 6000 images. Experimental results illustrate the proposed method perform well in classifying skin images.

In many cases human identification biometrics systems are motivated by real-life criminal and forensic applications. Some methods, such as fingerprinting and face recognition, proved to be very efficient in computer vision based human recognition systems. In this paper we focus on novel methods of human identification motivated by the forensic and criminal practice. Our goal is to develop computer vision systems that would be used to identify humans on the basis of their lips, palm and ear images.

The quality of character image plays an important role for the performance of character recognition system. However there is no good way to measure the recognition difficulty of a given character image. For the given character image with unknown quality, it is improper that apply the single character database to recognize it by the same feature and the same classifier. This paper proposed a novel approach for multi-resolution character recognition whose feature is extracted directly from gray-scale image and classification is adaptive classification which adaptively selects the appropriate character database and classifiers by evaluating the image quality of the input character. A resolution evaluation algorithm based on gray distribution feature was proposed to decide the adaptive classification weights for the classifiers, which make the classification have the higher probability of being the correct decision. Experiment results demonstrate the proposed approach highly improved the performance of character recognition system.

This paper describes a method to know objects in outdoor environment for autonomous robot navigation. The proposition of the method segments and recognizes the object from an image taken by moving robot in outdoor environment. Features are color, straight line, edge, HCM (Hue Co-occurrence Matrix), PCs (Principal Components), vanishing point and geometrical information. We classify the object natural and artificial. We detect tree of natural object and building of artificial object.Then we define their characteristics individually. In the process, we segment regions objects included by preprocessing. Objects can be recognized when we combine predefined multiple features. The correct object recognition of proposed system is over 92% among our test database which consist about 1200 images. We confirm the result of image segmentation using multiple features and object recognition through experiments.

In this paper, selecting line segments as matching features in stereo vision, the orientation difference (O.D.) of the line segments is more deeply evaluated than the previous studies and two new constraints i.e. ordering and collinearity are proposed. The O.D. was supposed to be used for an indoor application, reducing candidates without elimination of the actual matches, especially if the line is horizontal or vertical with respect to the floor. The findings of this paper are as follows: 1) Applying a threshold on O.D. would result in a missing probability for the actual matches and this probability can be calculated for any given threshold, 2) The upper limit of O.D. for horizontal and vertical lines is a function of geometric parameters of the system, 3) An optimal tilt angle, whose results is the minimum upper limit for O.D., can be computed, 4) for disambiguation process, ordering and collinearity constraints are proposed. These are applied in a matching algorithm and their effectiveness is investigated on real stereo images.

In this paper, we proposed image processing technique for automatic real time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurred in the tunnel, it is necessary to have a system to minimize and to discover the incident as fast as possible. However it is impossible to keep the human observation of Closed-Circuit Television (CCTV) in tunnel for 24 hour. So if the fire and smoke detection system through image processing can warn fire state, it will be very convenient, and it can be possible to minimize damage even when people is not in front of monitor. The fire and smoke detection is different from the forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so that an indigenous algorithm has to be developed. The two algorithms proposed in this paper, are able to detect the exact position, at the earlier stage of incident. In addition, by comparing properties of each algorithm throughout experiment, we have proved the validity and efficiency of proposed algorithm.

Natural and efficient HCI technology has been rapidly developing. However, there is always cognitive ’gap’ between HCI and application space. Taking command post of the future as main researching object, the paper explores how to properly combine information modeling with HCI technology and realize the proper abstraction, description and mapping of command space element, according to an order from ’top’ to ’down’: Cognition level, Operation level, Control level and Device level. Starting with conceptual model, command space is modeled on the cognition level. Next, basic methodology are brought forward, which combine command space cognitive concept and map them to operation and control, based on multi-fingers/two-handed touch interaction. This paper focuses on the modeling method of user multi-fingers touch operation model, and the control method from user operation model to multi-fingers touch HCI, to realize conversation between user and device in command space, to improve efficiency of commanding operation and decision-making. The research above can provide theory and method for constructing command and decision-making space of the future and has forerunner effect on multi-modal HCI system.

New proteomic technologies have brought the hope of discovering novel early cancer-specific biomarkers in complex biological samples. Novel mass spectrometry (MS) based technologies in particular, such as surface-enhanced laser desorption/ionisation time of flight (SELDI-TOF-MS), have shown promising results in recent years. To find new potential biomarkers and establish the patterns for detection of cancers, we proposed a novel method to analysis SELDI-TOF-MS using binary cross section imaging and energy curve technology. The proposed method with advantage of visualization is to mining local information adequately so as to discriminate cancer samples from non-cancer ones. Applying the procedure to MS data of proteomic analysis of serum from ovarian cancer patients and serum from cancer-free individuals in the Food and Drug Administration/National Cancer Institute Clinical Proteomics Database, we find that there are outputs of the cancerous when the threshold is above 90 and M/Z is in the range 9362.3296-9747.2723, while outputs of the non-cancerous will appear when the threshold is 60 80 and M/Z is 243.4940-247.8824.

The problem of face feature points detection is an important research topic in many fields such as face image analysis and human-machine interface. In this paper, we propose a robust method of 2D nose detection and tracking system. This system can be valuable for disabled people or for cases where hands are busy with other tasks. The required information is derived from video data captured with an inexpensive web camera. Position of the nose tip is determined with the use of a Gabor wavelet feature based GentleBoost detector. Once the nose tip is initially located, an improved Lucas-Kanade optical flow method is used to track the nose tip feature point. Experiments show that our system is able to process 18 frames per second at a resolution of 320×240 pixels. This method will in future be used in a non-contact interface for disabled users.

The Minimum Bit Error Rate (MBER) detectors outperform the conventional Minimum Mean Squared Error (MMSE) detector by minimizing the Bit Error Rate (BER) directly. In this paper an MBER multiuser detector for Orthogonal Frequency Division Multiplexing-Space Division Multiple Access (OFDM-SDMA) system is proposed employing Particle Swarm Optimization (PSO) for finding the optimum weight vectors. Simulation results show that the proposed system achieves faster convergence with lower complexity as compared to Genetic Algorithms (GA) with same Bit Error Rate (BER) performance.

We have realized an online gesture recognition platform for hand gestures using 2-channel surface EMG signals acquired from the forearm. Several features, such as AMV, AMV ratio and fourth-order AR model coefficients are extracted from the sEMG signal and the gesture segments are recognized with a Weighted Euclidean Distance Classifier. An above 90% recognition rate has been achieved with only a 400

μ

s recognition time. The methods developed in this study are aimed to be applied in a fast-response sEMG control system and be transplanted into an embedded microprocessor system.

This paper suggests 3D co-occurrence texture features by extending the concept of co-occurrence feature to the 3D world. The suggested 3D features are described as a 3D co-occurrence matrix by using a co-occurrence histogram of digital elevations at two contiguous positions. With the addition of 3D co-occurrence features, we encounter the high dimensionality problem in the classification process. Since these ANN (Artificial Neural Networks) clustering algorithms are known as robust in this situation, FCM (Fuzzy C-mean) and GBFCM (Gradient Based Fuzzy C-mean) clustering algorithms are employed to implement the terrain classifier. Experimental results show that the classification accuracy with the addition of 3D co-occurrence features is significantly improved over the conventional classification method only with 2D features.

In this paper, an efficient approach to search for the global threshold of image using Gaussian mixture model is proposed. Firstly, a gray-level histogram of an image is represented as a function of the frequencies of gray-level. Then to fit the Gaussian mixtures to the histogram of image, the Expectation Maximisation (EM) algorithm is developed to estimate the number of Gaussian mixture of such histograms. Finally, the optimal threshold which is the average of these means is chosen. The paper compares the new method with the classical discriminate analysis method of Otsu’s. And the experimental results show that the new algorithm performs better than that of Otsu’s.

In this paper, Umeyama’s eigen-decomposition approach to weighted graph matching problems is critically examined. We argue that Umeyama’s approach only guarantees to work well for graphs that satisfy three critical conditions: (1) The pair of weighted graphs to be matched must be nearly isomorphic; (2) The eigenvalues of the adjacency matrix of each graph have to be single and isolated enough to each other; (3) The rows of the matrix of the corresponding absolute eigenvetors cannot be very similar to each other. For the purpose of matching general weighted graph pairs without such imposed constraints, we shall propose an approximate formula with a theoretical guarantee of accuracy, from which Umeyama’s formula can be deduced as a special case. Based on this approximate formula, a new algorithm for matching weighted graphs is developed. The experimental results demonstrate great improvements to the accuracy of weighted graph matching.

This paper presents a robust real-time face alignment algorithm based on Active Appearance Models(AAMs). Fitting an AAM to an image is considered to be a problem of minimizing the error between the input image and the closest model instance. If the input image is far from the model space, the fitting process will fail. This can always occur in application because of illumination variation. So, building a good appearance space is very important. We propose a weighted cost function which can incorporate intensity and edgeness of an image into AAMs framework. To achieve high performance, Active Appearance Models proposed by Iain Matthews is employed.

This paper presents an speech information hiding model for transmitting secret speech through subliminal channel covertly for secure communication over PSTN (Public Switched Telephone Network) or VoIP (Voice over Internet Protocol). This model’s main statement is that the embedding operation of a secure communication system should work indeterminate from the attacker’s point of view. This model for secure communication based on the technique of information hiding has more severe requirements on the performances of data embedding than watermarking and fingerprinting in the aspects of real time, hiding capacity, and speech quality. Experiments show that this model meets the requirement of secure communication and suits for practical application of covert communication. The security analysis of this model by means of information theory and actual test proved that it is theoretically and practically secure. This information theory based model can be commonly used to help design a system of speech secure communication with different coding schemes.

This paper presents an approach for speech information hiding based on G.721 scheme. This approach proposes an Analysis-By-Synthesis (ABS)-based speech information hiding and extracting algorithm, is called ABS algorithm, which form the theoretical basis for designing a secure speech communication system. The ABS algorithm adopts a speech synthesizer in a speech coder. Speech embedding and coding are synchronous, i.e. fusing of secret speech information data into speech coding. Dynamic secret speech information data bits can be embedded into original carrier speech data, with high efficiency in steganography and good quality in output speech. This method is superior to available classical algorithms on hiding capacity and robustness. This paper implements the proposed approach based on speech coding scheme G.721 and the experiments show that this approach meets the requirements of information hiding, satisfies the constraints of speech quality for secure communication, and achieves high hiding capacity of 1.6Kbps with an excellent speech quality and complicating speakers’ recognition.

Based on the research of MDS array code of size n(n in distributed storage system, in this paper, we present a novel encoding scheme called the

F-code

and prove that the column distance of the F-code is 3, i.e. F-code is a MDS array code given that odd number n is greater than 3 and does not include factor 3. And we also implement a novel decoding algorithm of the F-code. The algorithm only needs two decoding chains in each linear equation group and is able to recover all unknown symbols on two erasure columns. The analysis of F-code shows that our method extends the range of number n in n×n MDS array code and gets lower/reduction algorithmic complexity. Therefore, the reliability of a distributed storage system that features the F-code can be effectively reinforced.

A new method of motion deadlock resolving by using fuzzy decision in robot soccer games is proposed in this paper. For the reasons of complex competition tasks and limited intelligence, soccer robots fall into motion deadlocks in many conditions, which is very difficult for robots to decide whether it is needed to retreat for finding new opportunities. Based on the analysis of human decision for dealing with these kinds of motion deadlocks, the fuzzy decision method is introduced in this paper. Then, fuzzy rules based deadlock resolving system is designed according to relative positions and orientations among robots and the ball in local regions. Lots of experiments by human experts and the fuzzy controller are implemented for comparison. Experimental results show that the method proposed is reasonable and efficient for motion deadlock resolving in most conditions for real soccer robot games.

In this paper, we propose the novel feature extraction method, called the Polar wavelet, which can improve the search performance for locally distributed time series data. Among various feature extraction methods, the Harr wavelet has been popularly used to extract features from time series data. However, the Harr wavelet does not show the good performance for sequences of similar averages. The proposed method uses polar coordinates which are not affected by averages and can reduce the search space efficiently without false dismissals. The experiments are performed on real temperature dataset to verify the performance of the proposed method.

In this paper medical applications of supervised and unsupervised neural networks image processing algorithms are presented and discussed by means of quantitative experimental results in the field of radiotherapy. The investigated case study concerns the problems and the consequent solutions referred to the two phases of the treatment plan necessary after the quadrantectomy of a cohort of patients affected by breast cancer.

Recognition of road guidance signs is an important issue for developing driving assistance systems. One of major problems in this field is recognition of junction structure from road signs. In the proposed paper both detection and recognition problems are presented. Detection method based on color and shape properties of sign allows detecting signs in various lighting and weather conditions. Using interlaced images for most time-consuming operations and full-resolution images for final result reduces computation time without loss of quality. Recognition method is based on decomposition of guidance signs into principal components and representation of arrow region as a graph. Path extraction uses finite automate methodology which in order to recognize all possible paths to pass the junction. Proposed method showed processing speed about 10 fps and can be used in real-time applications.