Future Information Technology

Traditional clustering methods are unable to describe the generated clusters. Conceptual clustering is an important and active research area that aims to efficiently cluster and explain the data. Previous conceptual clustering approaches provide descriptions that do not use a human comprehensible knowledge. This paper presents an algorithm which uses Wikipedia concepts to process a clustering method. The generated clusters overlap each other and serve as a basis for an information retrieval system. The method has been implemented in order to improve the performance of the system. It reduces the computation cost.

Dealing with Big Data is one of the emerging issues in our society. After a decade or two decades from now, it will become one of operations in our everyday works. What is a new issue at that time? It will be dealing with Big Information. This paper investigates information mining for Big Information as a new challenging issue. The paper also shows that strong relevant logic-based reasoning is one of systematic methods for the information mining, and discusses the automation of information mining with strong relevant logic-based reasoning.

Multiple-input and multiple-output (MIMO) is an important approach in high-rate wireless communications. The Schnorr-Euchner (SE) sphere-decoding algorithm enables fast detection for receivers by recursive tree searching with optimized expanding order. In this paper, we propose a looped-pipeline architecture for an SE MIMO detector that processes multiple groups of data in parallel. The full-pipelined design has a short critical path that provides a maximum working frequency of 229 MHz on FPGA. High throughput is achieved at a relatively low cost.

We propose a data hiding scheme based on Low Density Parity Check(LDPC) codes. Our proposed theoretical point of view, the blind watermarking can be transmitted through a noisy channel. Matrix encoding proposed by Crandall can be used in steganographic data hiding methods. Our proposed “LDPC-DH (Data Hiding)” scheme has a slightly increasing embedding efficiency, and improves highly embedding rate. We therefore propose verifying the embedding rate during the embedding and extracting phase. Experimental results show that the reconstructed secret messages are the same as the original secret messages, and that the proposed scheme exhibits a good embedding rate compared to that of previous schemes.

Traditional Software Engineering focuses its main attention on software reliability. However, today, in this insecure, complex, changing world, the design, development, operation, and maintenance of any information/software system have to consider information security issues carefully and seriously. This position paper discusses intrinsic differences between Software Reliability Engineering and Information Security Engineering, presents some new quality evaluation criteria and related challenges in future advanced Software Engineering, and shows that Ada 2012 will play an important role in future advanced Software Engineering.

In this thesis, the method of input-output linearization for the time-varying nonlinear system is discussed. To this end, the proposed linearization technique of existing research has been extended to nonlinear systems. The suggested technique for an extended input-output linearization method is conversed to time-invariant linear system and formulated as time-varying nonlinear system by having multiple integrators. This suggested method presented the necessary and sufficient condition and proved through examples.

In next-generation wireless networks, mobility management protocols to support global roaming are highly regarded. These technologies bring a broader life using a global roaming account through the connection of multiple devices to mobile users and provide real-time multimedia services. This paper presents a comprehensive performance analysis of the hierarchical mobility management protocols. This result represents that network-based mobility management protocols are more excellent rather than typical host-based mobility management protocols in mobility environment.

We first investigate when it is possible for two nodes in a wireless network to communicate with each other. Based on the result from bond percolation in a two-dimensional lattice, as long as the probability that a sub-square is closed is less than 0.5 and each sub-square contains at least four nodes, percolation occurs. Then, we establish the conditions for full connectivity in a network graph. How two adjacent sub-squares are connected differentiates this work from others. Two adjacent sub-squares are connected if there exists a communicating path between them instead of a direct communication link. The full connectivity occurs almost surely if each sub-square contains at least one node and the probability of having an open sub-edge is no less than 0.3822. Finally, simulations are conducted to validate the proposed conditions for percolation and full connectivity.

For the multinode cooperation system, the Gilbert model behaves the DF protocol at relay nodes. At destination node, the expectation analysis of the total SNR is conducted, including exact result for MRC and proper upper bounds for EGC. Meanwhile, the diversity benefit is compared to the traditional MISO system.

Robots are becoming more sophisticated and they are performing a lot of work instead of humans. For the natural communication between humans and robots, a real-time technology is needed that they can perform a payment service as an individual characteristic. In this paper, we propose security solutions using PUK module not only technical vulnerability but also physical accident such as a robbery and loss case for safe social media services. In addition, we propose NFC payment authentication protocol based on USIM certificate that includes a streamlined payment and authentication step. Lastly, to prove validity and safety of the proposed system, the experimental test of safety was confirmed and provided the results.

The information technology advances are driving the transformation of healthcare. The first characteristic of those modern trends is the large amount of publically available medical information. Harnessing the public medical knowledge for improving the patients’ safety is inevitable. Physicians and patients can, literally, benefit from the vast public information and medical systems should incorporate it. The second characteristic is the greater demand of users to participate in their applications and to fully control them. There are different types of medical system users and building one interface that fits all is very difficult task. We design a healthcare system by leveraging the novel Mashup technology. It can overcome most of the above challenges. Our system uses Mashup to integrate public knowledge and provide customizable workspaces for the various end users. This comprehensive system is built with the aim of using advanced technologies for the patient centric healthcare.

This paper presents a stereoscopic image conversion method using a single frame of a 2D image. To reduce the computational complexity, image sampling is used. The standardization of luminance can separate an object from its background. We assume the general structure of image by analyzing structure of various images for mathematical expression. The efficacy was evaluated using visual test and Absolute Pixel Difference for comparing the stereoscopic image of the proposed method with that of Modified Time Difference.

In real human society, influence on each other is an important factor in a variety of social activities. It is obviously important for recommendation. However, the influence factor is rarely taken into account in traditional recommendation algorithms. In this study, we propose an integrated approach for recommendation by analyzing and mining social data and introducing a set of new measures for user influence and social trust. Our experimental results show that our proposed approach outperforms traditional recommendation in terms of accuracy and stability.

Particle swarm optimization (PSO) has shown its effectiveness to solve many complex optimization problems. However, PSO sometimes may fall into a local optimal solution rather than the global optimal solution of a given problem. For large-scale optimization problems, several parallelized PSOs have been proposed in the literature. In this paper, a query-based learning (QBL) approach is adopted to help PSO jump out of a local optimum. An Oracle is introduced that answers PSO whether there are too many particles in a flat region of the solution space. If yes, the algorithm will redistribute some of the particles in the flat region to somewhere else. A parallelized implementation, referred to as MRPSO-QBL, is developed in the Apache Hadoop MapReduce framework, as the framework provides a simpler and better parallel programming paradigm. The experiment results on several benchmark functions have demonstrated that MRPSO-QBL can find better solutions and converge faster.

In this paper, we suggest a new method to detect female nipple areas, representing harmfulness of input images, by employing a color nipple map and geometrical features. The proposed method first detects human skin color regions from an image. It then defines a color nipple map using the unique characteristics that human nipples have and extracts candidate nipple areas by applying the defined map to the input image. Subsequently, our method selects only real nipple regions after filtering out non-nipple ones by applying geometrical features to candidate nipple areas. Experimental results show that the suggested nipple map and geometrical feature-based algorithm can accurately detect human nipple areas through various experiments.

The problem of automated theorem finding is one of 33 basic research problems in automated reasoning which was originally proposed by Wos. The problem is still an open problem until now. This paper reviews the current state of the research on automated theorem finding and shows some future directions of automated theorem finding. In particular, we propose a systematic procedure for automated theorem finding based on Cheng’s approach, i.e., automated theorem finding by forward deduction based on strong relevant logics.

This paper presents a miniaturized spurline microstrip RF resonator to moisture loaded organic material, soybean seeds using overlay technique. The permittivity has calculated from the frequency response of the moisturized seeds. The RF microstrip spuriine resonator with high Q is so sensitivity to the organic material that helps to sense the moisture content seeds and it is simulated and observed the frequency response at 10 GHz with insertion loss of 40 dB. The designed RF resonator has high quality factory (Q) which is good for using it as a sensor. The resonator can also be characterized using vector network analyzer before and after loading the soybean on the sensitive area of the resonator and we can have frequency difference due to difference in dielectric constant. Random orientations can be done while characterizing the spurline resonator.

Cloud computing emerges with high performance computing, massive data storage and easy access of the Internet. By deploying cloud computing, scientific workflows can be more cost-effective. During workflow execution, large volume data transmission may occur among multiple datacenters, hence incur large cost. Traditional approaches aim at finding data placement strategies for individual workflows only to reduce data transmission cost. However, workflows may share some datasets. Therefore, optimal data placement considering individual workflows in isolation is not necessarily optimal for the situation of multiple workflows as a whole. In this paper, by facilitation of Particle Swarm Optimization (PSO), we build a novel data transmission cost model for developing a new multi-datacenter cost-effective data placement strategy. The experimental results show that our strategy is much better than its traditional counterparts.

As one of market-oriented distributed systems, cloud computing has become popular in recent years. In scientific cloud workflows, scheduling optimization is an important and challenging issue, especially at the task level. Traditional approaches do not consider the time-sharing characteristic of virtual machine (VM), and they cannot reduce the makespan of scheduler within a datacenter effectively. However VM can execute multiple parallel tasks simultaneously. In this paper, with consideration of cost constraint, we present novel efficient task-level Ant Colony Optimization scheduling based on time-sharing VM. The experimental simulation shows that our scheduling can make better use of time-sharing VM to optimize the makespan of scheduler within a datacenter. Therefore, our scheduling can run tasks more efficiently.

In order to tackle the slow response caused by massive patent literatures, a patent literatures translation system based on Hadoop is proposed in this paper. The paper presents a hybrid storage structure and a parallel translation model for massive patent literatures. The hierarchical storage structure is based on HDFS (Hadoop Distributed File System), which stores the patent documents and HBase where directories of such data are stored. This hybrid structure enables faster retrieval through the distributed file system. In translation, The Hadoop MapReduce framework is utilized. The MapReduce computation model not only can translate the patent literatures in highly parallel, but also can process multiple documents simultaneously. The experimental results show that the proposed machine translation system in this paper has better translation performance than the conventional machine translation approach.

Smart device has the advantages of the littleness and lightness. On the other hand, it also has the lack of computing power and specification compared to the fixed computers. Even though these disadvantages are actively resolved through mobile cloud services, are complementary. The services also provide a simple Storage-space, Data-transmission based on network. So, customers who want more functionality and computing capabilities re-purchase the expensive smart devices which are developed and released continuously. In these circumstances, the study of collaborative service model is attracting attention to supply ability to mobile device which has lack with existing devices with mobile cloud computing. In this paper, the collaborative service system model (Smart Collaboration Application Platform: SCAP) is proposed for multiple devices to form a working group for sharing each of functions and data based on mobile cloud service.

Two-hop wireless network is the basic network model for the study of general wireless networks, while cooperative jamming is a promising scheme to achieve the physical layer security. This paper establishes a theoretical framework for the study of eavesdropper-tolerance capability (ETC, i.e., the exact maximum number of eavesdroppers that can be tolerated) in a two-hop wireless network, where the cooperative jamming is adopted to ensure security defined by secrecy outage probability (SOP) and opportunistic relaying is adopted to guarantee reliability defined by transmission outage probability (TOP). For the concerned network, closed form modeling for both SOP and TOP is first conducted, base on which the model for ETC analysis is then developed. Finally, numerical results are provided to illustrate the efficiency of our theoretical framework.

Recent years, LTE-Advanced is a mobile communication standard and high wireless bandwidth technology, formally submitted as a candidate 4G solution in the world. The major difference between 3GPP Release 10 (Rel-10) LTE-Advanced and the original LTE (Rel-8) is a new entity called relay node (RN) introduced in Rel-10. Due to the radio backhaul link in RN, Mobile Relay (MRN) is a hot and important issue in 3GPP LTE-Advanced standard forum. The demand of traffic offloading is increased as the number of UEs under a Mobile Relay increases. It’s suggested to consider the aspect of offloading in determining a suitable architecture for MRN. This paper proposes the supporting of LIPA and SIPTO for MRN and a basic comparison analysis among MRN architecture alternatives is presented.

Because a sensor node has limited resources, such as battery capacity, data aggregation techniques have been proposed for wireless sensor networks (WSNs). On the other hand, because the result of data aggregation is used for making critical decisions, it must be verified before accepting it. For this reason, it is required to design a scheme for WSNs which can ensure the aggregated result has not been polluted (manipulation of data by an adversary) on the way to the query server. There are two data aggregation methods for supporting data integrity, iCPDA and iPDA. But they have high communication cost due to additional integrity checking messages. To resolve this problem, we propose an efficient data aggregation scheme to protect data integrity based on PIR technique for WSNs. To support data integrity, we modify the PIR technique for reducing its communication cost by using Hilbert curve. Through a performance analysis, we show that our scheme outperforms the existing methods in terms of both energy efficiency and integrity checking.

Clos networks is a well-known kind of multistage switching networks, and nonblocking multicast capacity in Clos networks is important for group communication to meet the rapid growing application demand. However, the cost of nonblocking multicast Clos networks is high due to the large number of middle stage switches required. In this paper, we investigate wide-sense nonblocking multicast in three stage Clos networks. The sufficient condition on the number of middle stage switches is provided which is based on the more general case in which both of the minimum fanout

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) as special cases. We also provide a multicast connection request balance strategy with linear time complexity which can evenly distribute all multicast connection requests among input stage switches. Then, we show that the number of middle stage switches required for nonblocking multicast Clos networks can be reduced.

Recommender systems are often used for extracting appropriate items that are to be suggested to a user. A user’s preferences are compared with preferences of previous users. Afterwards the user is assigned to a group with similar preferences, provided such a group exists. New suggestions to that user are made following the recommendations of the group she is assigned to. A lot of research has been done for establishing suitable level of preference similarities. One of the problems that still requires special attention is related to users who do not consistently agree or disagree with any existing preference tendencies. In the scientific literature such users are referred to as ’grey-sheep’. In this article we propose a new approach that can provide a solution to that problem.

Collaborative filtering techniques are often used to predict the unknown preferences of a new user by applying rules derived from the known preferences of a group of users. In the literature users having high correlation with a large number of other users are referred to as ’white sheeps’, while those that express preferences which do not fall into any known to the system group are called ’grey sheeps’. Thus predictions for the latter type users are often inaccurate. To overcome this problem we propose application of residuated lattices.

Spatial interpolation on temperature field has gained increased interest in recent years. In this paper we investigate the interpolation accuracy of three frequently used methods (i.e. Inverse-Distance Weighting, Thin Plate Spline and Ordinary Kriging) on the United States temperature data using cross-validation technique. Our results indicate that Ordinary Kriging method has higher interpolation accuracy than other two methods. Finally, an elevation optimization is added to Ordinary Kriging and we can find that it increases interpolation accuracy even further. Based on the interpolated data, this paper also gives a two-dimensional visualization of temperature data and temperature difference data on United States.

Term recognition in the Japanese language is known as one of the challenging problem in natural language processing and information retrieval. We often use morphological analyzers to process Japanese documents. These tools usually do not recognize compound nouns. These nouns are combinations of single nouns expressing different meaning compared to basic nouns. Morphological analyzers usually separate compound nouns into single nouns. Therefore reconstructing compound nouns is essential to preserve text semantics. There is a tool called TermExtract to do the aforementioned reconstruction. In this study we evaluate its accuracy. To identify terms created by TermExtract, online resources are utilized. They are the ALC online dictionary, Wikipedia and Google phrase search service. Experiments are conducted with abstracts of scientific documents from the NTCIR-1 collection. We found that TermExtract is able to reconstruct 36.23% of all compound nouns in the corpus. Most of these nouns belong to scientific terminology.

Information Security Management Systems (ISMSs) play important roles to manage organization’s information assets securely. However, organizations with ISMSs facing various challenges to develop and maintain such a complex management system effectively. Such organizations demand tools that can provide them with comprehensive support for all tasks in ISMS. This paper presents an engineering environment to support organizations with ISMSs consistently and continuously based on ISO/IEC 27000 series. At first, the paper presents challenges in ISMS and requirements analysis to address the challenges. The paper, then, presents an architecture and some use cases of the environment.

Outsourcing database is becoming a trend for spatial data owners to reduce the cost of managing and maintaining the database. However, the most important challenge in database outsourcing is how to meet privacy requirements and guarantee the integrity of the query result as well. To carry on both privacy and integrity for outsourced spatial data, we propose a spatial transformation scheme that makes use of shearing transformation with rotation shifting. From the performance evaluation, we show that our scheme has outstanding performance against different kinds of attack models and efficiently handles the query integrity of the query result sets.

Formal analysis of cryptographic protocols is necessary to find flaws before using them. However, in traditional approaches based on proving, it is difficult for analysts to enumerate all security goals for proving that a cryptographic protocol is secure because analysts sometimes overlook or do not recognize some security goals. Reasoning approach was proposed as an alternative approach, but its concrete method has not been established yet. In order to establish the concrete method, this paper presents a method of formalization for formal analysis of cryptographic protocols with reasoning approach. The paper presents a method of formalization for key exchange protocols, shows that it can be applied to any key exchange protocols, and shows that it can be extended to be applied to various cryptographic protocols.

Google Android allows developers to easily distribute their applications by the various software marketplaces and end users to readily install them. However, without strict inspection and audit, some serious security concerns are inevitable such as the abuse of permissions. The existing security mechanism in Android cannot differentiate between the actually useful permissions and the unused permissions. In this paper, we present a reinforced Android framework model which is capable of purifying permissions automatically according to actual operations at runtime and supporting users to selectively customize the actual useful permissions of applications by imposing fine-grained constraints on the usage of resources. Experimental results show the automatic process can slim third-party applications efficiently.

"Taiwan Plukers on Education and Technology @Facebook (TPET) is Taiwan’s largest and most popular Facebook based virtual communities with the aim of providing a platform for teachers to exchange, discussion and knowledge sharing. Therefore, it’s essential to investigate ways to facilitate active engagement behavior of members to obtain the most potential benefits. This study identifies IS characteristics as triggers to explore the dynamic and iterative nature of engagement behavior by multi-criteria decision making (MCDM) analysis. Major findings can provide helpful guidance for TPTE managers’ decision making regarding to enhance members’ engagement behavior and promote the sustainable development of the community, and ultimately improve the quality of teaching to create more innovative educational value for students.

Software as a Service (SaaS) foster customer relationship management (CRM). The benefits of CRM of cloud computing for an enterprise are identified by using appropriate qualitative methodologies, including means-end chain (MEC) and interpretive structural modeling (ISM) methods. This study elucidates the organizational benefits of CRM in this emerging field. This study focuses on the following objectives: (1) to understand the feasibility of applying cloud computing-based SaaS technology, (2) to explore the enterprise value of CRM services and introduce cloud computing technology to enterprises.

As one of the emerging SaaS in cloud computing, cloud customer relationship management (Cloud CRM) is even more appealing comparing to the traditional module in the enterprise systems to small and medium-sized businesses which often are short of resources to invest on their own hardware and software. Since the discipline of information systems concerns not only technology aspect, but also organization and people, all related aspects need to be properly aligned with each other to utilize the information technology adopted. This study applies Interactive Qualitative Analysis (IQA) by focus group data to explore the benefits realization model of cloud CRM. The results identify primary and secondary drivers as well as primary and secondary outcomes after the implementation of cloud CRM. These findings are expected to provide helpful guidance on significant benefits realization.

The purpose of this paper was to discuss how various factors intertwine to affect Taiwanese employees’ decisions to behavioral intention to use Facebook to support collaborative learning by applying the expanded technology acceptance model (TAM). The model was assessed by data collected from 385 participants using a survey questionnaire. Results show that social influence has strongest influence on using Facebook supporting collaboration in training adopting intention. Additionally, we found that women are strongly influenced by co-workers and supervisors.

The machine-to-machine (M2M) market is significantly growing, especially for eHealth. In this paper, we analyze the requirements of M2M technologies for device and application domains in the eHealth area, including health centers and patient networks. Our work details some challenges of M2M in providing interoperability and standardization to allow for and promote a homogeneous eHealth ecosystem. We also discuss the necessary investments for implementing M2M (both in the health center and the patient´s home), and for the development of a more detailed M2M standard, enabling interoperability between devices.

Classification is an important task in data mining. Although a number of pattern classification algorithms have been brought forward in the literature, most of them aim to improve classification accuracy. In the time of Big Data, however, besides accuracy, other issues are getting increasingly important, such as scalability and robustness. To that end, in this paper, we revisit two representative classification algorithms from two distinct types: k-nearest-neighbor from the type of parameter-free local learning, and logistic discrimination from the type of parametric global learning. The evaluation is carried out with the well known Pima Diabetes Database. We analyze the two algorithms’ performance in classification accuracy in relation to their underlying assumption, and compare them over complexity and robustness as well.

Due to the limited battery power of each sensor in wireless sensor networks (WSN), energy efficiency has been considered seriously as one of the most important factors in designing WSN protocols. Thus, many WSN protocols often sacrifice performance to minimize energy consumption. To mitigate the energy restriction, this paper proposes a wireless electricity (witricity) charging protocol in a WSN with minimizing energy loss of the wireless electricity charging and maximizing a network lifetime of a WSN. To the best of the author’s knowledge, this is the first attempt to integrate witricity technology to a WSN with minimizing energy leakage and maximizing network lifetime. Among many different witricity charging models for WSNs, this paper focuses on a fully charging protocol with one external stationary energy source for a stationary wireless sensor network. In a stationary wireless sensor network, sensors are not mobile once they are initially deployed.

The coronary cineangiogram (CCA) is an invasive medical image modality which is used to determine the stenosis in Coronary Arteries. The motion artifacts occurring due to the heart pulse makes great disturbance to visualize a stable contrast agent flow within the vessel structure of CCA and it negatively affects to quantify the stenosis based on the functional significance within the arterial flow. This paper describes an application of template matching to reconstruct the CCA by reducing the global motion artifacts. The Normalized Correlation Coefficient (NCC) method has been used for the template matching because, it reports the lowest false matching occurrences. Further, the NCC technique has 99.5% accuracy and demonstrates its ability to maintain the visual correlation of the internal blood flow among the frames. Producing Motion eliminated CCA to maintain the visual correlation of arterial flow is an improvement of angiography technique which can be useful for advanced processing.

In this paper we proposed an authoring system that allows librarians to be able to construct and maintain a graphic-based navigation system by themselves that provides the graphic searching function based on original Online Public Access Catalog (OPAC) systems. On the other hand, patrons can use their mobile devices to display the created graphic map indicating the positions of required books. Through transforming a floor plan into spatial information connected to call numbers, which is equivalent to book addresses, the position accuracy is increased to a coordinate block divided by a shelf.

An embedded system application involves a diverse set of skills that extend across traditional disciplinary boundaries, including computer hardware, software, algorithms, interfacing and application domain. Field Programmable Gate Arrays (FPGAs) which offer flexibility in design like software, but with performance speeds closer to Application Specific Integrated Circuits (ASICs) are used as an embedded-based system in this project. The character recognition project using artificial neural network (ANN) approach is implemented on Altera Cyclone II 2C35 FPGA device and the results shown very promising.

Smokehouse is used to cook the meat with a burning wood smoke aroma. The smoking process is fairly complicated since various parameters are required to be controlled. To ensure the meat is juicy, tendered, cooked, and smooth after being smoked, all the parameters must be precisely controlled. The main purpose is to avoid the meat being over smoked, over cooked and downgrades the taste. Traditional way of smoke cooking is tedious and non-standard process. It requires an exceptionally accurate and efficient cooking system to establish a standard smoke cooking process which is comparable to the traditional method. Thermal property of the whole chicken leg is used to estimate the total smoking time without overcook. Therefore, FPGA-based controller provides simultaneous processing hence reduces time of data reading and provide real time controlling on the smoking process. It capable produces the smoked whole chicken leg in 35 minutes, and maintaining the water loss below 20%. This is able to maintain a juicy smoked of chicken leg.

The etymology of communication is the transmission of data. Data has to be transmitted through different devices, network topologies and geographic locations. The strength of communication has tripled with the advent of cloud technologies providing high scalability and storage on demand. The need for cloud security is increasing in an alarming rate and using biometric techniques over traditional password based alternative has proved to be efficient. A behavioral biometric such as keystroke dynamics can be used to strengthen existing security techniques effectively .Due to the semi-autonomous nature of the typing behavior of an individual it is difficult to validate the identity of the user. This paper proposes a model to validate the identity of the user which acclimatizes to tolerance across multiple devices and provides a robust three dimensional model for classification. As an additional layer of security the model is transformed after every login to prevent professional intruders from predicting the acceptance region.

With the rapid development of Internet, many kinds of personalized academic services have come forward. A key issue to ensure the qualification of these services is how to obtain and represent specific academic background. This paper present Academic Domain Concept Map (ADCM) to provide the current main academic concepts of some research field in an organized form. ADCM is composed of concepts and relations which ensure its abundant semantics. In addition, concept’s relatedness to the domain is evaluated by introducing potential energy from physical field theory, which ensures the effectiveness of ADCM. Experimental results demonstrate the validity of the method presented in this paper.

In the paper, we focus on the H.264’s Bit-Rate control algorithm and propose a new method based on the basic unit layer. The MAD prediction of basic unit in G012 scheme exist larger deviation from the actual MAD value, which may be induce the performance degradation of the rate controlling. In order to reduce the prediction error, we give a self-adaption algorithm based on the spatial and the temporal correlation. Experimental results demonstrate the proposed algorithm can predict MAD more accurately. Compared with the classic G012 algorithm, the PSNR gain of the proposed method can be up to 0.08db at most.

The fundamental challenge confronting micro-blog short texts is to represent, compute and mine their semantics since they are colloquial language and restricted within 140 words. This paper presents an approach to represent micro-blogs semantically based on topic model. First, we get the topic vector of micro-blog through word-themes Mutual Reinforcement of micro-blog texts. Second, we take advantages of improved K-means to cluster micro-blog topic vectors and gain a category set of micro-blogs. Third, we make experiments to prove the above-mentioned methods. Finally, we find these methods can calculate semantics of micro-blogs text accurately and effectively.

Laser range finder sensor can get the distance between sensor and detecting target by measuring flight time of laser pulse. The designed laser pulse measurement sensor use FPGA EP2C20F as the control core of system. In order to improve measurement precision of laser flight time interval, we have designed a time interval measurement module by using a special high precision time to digital converter TDC-GP2. The testing resolution is up to 65ps. This sensor is low power dissipation and high measuring accuracy.

Inspired by the process of human memory and interaction, this paper provides a discovery model of user demands. We can get the keywords network and episodic network by capture and analysis the user’s behavior in the process of interaction, which can help the searching results to meet the user’s actual demands better.

The internet of things for public security has become one of key development aspects in China. Mobile application of the internet of things for public security is also one of major industrial applications of mobile internet. It plays very important role in development of the internet of things for public security. Mobile application, overall architecture, key techniques are introduced. LBS, security technology of terminal and trunkinging communication are triple pivotal technologies in this area. Police digital device, as a typical application system of the internet of things for public security, is been introduced. This paper aims to help for development and promotion to the mobile application of the internet of things for public security.

We talked about the problem of the tradeoff between network connectivity and security for traditional random key pre-distributions in WSNs. An enhanced key management scheme based on random key pre-distribution was proposed to improve the connectivity rate between the adjacent sensor nodes and ensure the security of session key generation. Analysis and performance comparisons showed that the proposed scheme enhanced the probability of establishing secure connection between adjacent node pairs at the less consumption of communications without compromising security.

Association Semantic Link (ASL) can provide theoretical support for many web intelligent activities. However, when we extract the keywords-level Association Semantic Link (k-ASL), a kind of low-density and valuable k-ASL is easy to be discarded because of its sparse distribution. To solve this problem, an extracting approach is proposed to mine this type of low-density and valuable k-ASL. First, the time validity for three types of k-ASL is analyzed to clear and define their semantic characteristic. Second, based on the analysis of the time validity for k-ASL, the existing problems for mining low-density and valuable k-ASL is described. At last, we present the approach for extracting this low-density and valuable k-ASL. Experimental results verify the correctness of the proposed approach.

Data-link is one of key elements and doubler of automatic military commanding system as an information means. It improves the efficiency and accuracy of operational command greatly and plays a vital role in modern warfare. This paper tries to introduce the data link to the police. The conception and function of the data-link is introduced. The necessity of developing data-link is been given. Because of the shortage of police digital trunking, more application technologies of data-link should be explored to push the technology and practical application of public security. This paper aims to help for exploration and development of data-link for public security.

Recently, vehicle classification has become very important in the field of intelligent transportation. The methods based on 3d model can play a better role for it contains more information from any viewing angles than 2d images. This paper presents a method to reconstruct the three-dimension model of vehicles in real-time when they get through our special device which is composed of two parts: a laser scanner and a camera. The main algorithm can be summarized as two parts: data preparation with mixed calibration and 3d model creating with data synthesis. The 3d vehicle model consists of three parts: vertices, texture, and triangle connection structure. The final result is demonstrated that our method can reconstruct the 3d vehicle model fast and accurately.

This paper introduces a video recognition method. To extract frames which contain useful information by detecting image changes in specific region. Then extracting and matching the SURF characters in the frame to recognize vehicle type. The extraction rate of key frames is 98.1%.The recognition rate is higher than 70%. This recognition method is not affected by scaling, rotation and translation of image. So this method has good robustness.

With the development of computer vision, the traditional identity verifying methods have not been satisfied people’s demand. Also, iris and fingerprint detections rely on devices, so these cannot be used in large scope. Face detection is a fundamental and important research theme in the topic of Pattern Recognition and Computer Vision.This paper proposes use improved AdaBoost algorithm,which is much better than normal AdaBoost.

The surveillance video data has grown tremendously in recent years, but traffic violations still cause a lot of accidents and personal injury every year. There are the problems of “cannot find", “difficult to understand” for the mass surveillance video retrieval system. The introduction of ontology in the surveillance video retrieval system can improve the effectiveness and efficiency of the retrieval system. Ontology is a kind of concept model that describes system at the level of semantic knowledge. It aims to access domain knowledge in a general way and provides a common understanding of concepts in the domain so as to realize knowledge sharing and reuse among different application programs and organizations. In this paper, the model of ontology construction is present. The method of constructing concept ontology for traffic violations by identifying the concepts, concept hierarchy is explored. Protégé 4.2 is used for the conduction of traffic violations ontology and exported the ontology using JCreator which displays the Rdf/Xml code generated by the ontology.

Face contour is a kind of intuitional characteristic at the first glance. Due to its simplicity, it is impressive and unforgettable. It can be used for face indexing and retrieval. In this paper, we integrated the techniques of the Active Shape Models (ASM) and the semantic knowledge engine to establish the effective face retrieval framework. Furthermore, we pointed out that other organs on the face also can be described by the ASM and semantic web techniques and the overall face description can be figured out. It is feasible and efficient interactive way in text-based face image search.

In this paper, the deep learning based face detection strategy was proposed. The exploited detection framework has good tolerance to the slight shift of light amplitude and illumination angle. Furthermore, this framework is immune to the slight occlusion. In the detection framework, the CNN was utilized to extract the intrinsic feature and execute the classification on face image and non-face image by recursive convolution and down-sampling process. The convenience of this method lied on that it is not necessary to extract the features explicitly during the detection process. This strategy avoided the information absence due to the inappropriate feature selection. AS a contrast, the LBP-SVM-based feature extraction and classification strategy was utilized to execute the face detection task. The experiment showed the superiority of CNN on detection accuracy and robustness. The comparisons result showed the effectiveness of deep learning mechanism.

This paper proposes a video retargeting method based on spatiotemporal saliency model. Global histograms and regional histograms are first calculated in both spatial domain and temporal domain, respectively, and then the spatiotemporal saliency model is built by evaluating the contrasts between the global histograms and each regional histogram. Based on the spatiotemporal saliency map, a salient object detection method is used to locate salient object regions in the video. Finally, cropping and uniform scaling operations are performed on the basis of salient object regions to generate the retargeted video. Experimental results on a variety of videos demonstrate that the proposed approach is simple and effective, and can preserve important objects well and achieve a better retargeting performance.

We present a new method to locate the objects in a plane using direction of pointing hand and facial orientation. Based on Active Shape Model, facial components such as left eye, right eye, nose tip and mouth corner points can be exactly located. We calibrate color camera and infrared camera of RGB-D camera. The method makes it possible that converts pixel coordinate in color plane image to 3D coordinate of the camera coordinate system. Facial orientation can be figured out with 3D information. Depth segmentation and skin color segmentation are used to attain user’s pointing hand. To estimate effectiveness and robustness of the method, we fix a set of visual targets on the same plane. The point of intersection can be calculated by facial direction line and pointing hand direction line. The experimental results demonstrate that the above method is more robust than the other ones.

In this paper, we focus on the issue how to choose positive and negative examples when updating the adaptive appearance model in Multiple Instance Learning (MIL) tracking algorithm. As a classical discriminative tracking algorithm, MIL tracker poses the tracking problem as a binary classification task. That is, its samples not positive is negative. However, in many image sequences, most negative samples are large differences. The mean and variance of negative samples can’t correctly reflect negative sample distribution, and will occur jump problem leading to tracker drifting. In this paper we propose background classification based MIL tracker to solve these problems. Experimental results showed that our background classification based MIL outperformed the original one.

New types of search engines are emerging to overcome defects of traditional search engines. What the next search engine should be? It is an efficient way to classify and compare current search engines to find possible answers. This paper first defines search pattern as the combination of index structure, user profile, and interaction mechanism, which can model a search engine and help us classify search engines more essentially. Then, current search engines are classified and compared from the view of search pattern to find what the next search engine should be. Finally, a new search pattern for next generation search engine is proposed by synthesizing the advantages of different search patterns, which can help developing new search engines and may be the possible direction of the next search engine.

Recently, the web is becoming an important event information provider and poster due to its real-time, open, and dynamic features. In this paper, social sensors based outbreak power computation algorithm of a web event is developed in order to let the people know a web event clearly and help the social group or government process the events effectively. The “social sensors” are firstly introduced, which is the foundation of using web resources to compute the outbreak power of events on the web. Secondly, five temporal features of web events are developed to provide the basic for computation algorithm. Moreover, the outbreak power presented to integrate the above temporal features of an event. Experiments on real data sets show the proposed algorithm has good performance and high effectiveness in the analysis of web events.

In recent years, with the development of technology, the amount of data is growing in amazing speed, which shows that the era of big data has come. This paper elaborate the definition of big data, and illustrate how big data affecting information technology industry these days. Based on the analysis of the relationship between big data and information technology industry, this paper discusses the challenge, opportunity and bottleneck that the information technology industry will face in the big data era, thus to promote the sound development of science and technology of big data and information technology industry.

In this paper, a novel method for human age estimation is proposed. This research is novel in the following four ways. First, the in-plane rotation of face region is compensated based on the detected positions of two eyes by Adaboost method. The region of interest (ROI) for extracting age features in the detected face region is re-defined based on the distance between two eyes. Second, multi-level local binary pattern (MLBP) method is applied in order to extract the features for age estimation. Third, in order to solve the problem of age estimation by active appearance model (AAM), we extract whole texture information by MLBP which takes low processing time. Fourth, the human age is estimated using support vector regression based on the texture features. The experimental results show that the proposed method can estimate the human age with the mean absolute error (MAE) of 6.58 years.

As pervasive computing is developed, a user’s context information is used in a system to provide intelligent service to the user. Context is defined as information that characterizes the situation of an entity under a certain system environment. In this paper, we classify the context in a system into three types of context: user context, device context, and proximity context, which is context information between user and device. We designed HIML (Human Interaction Markup Language) for the expression of the proposed contexts, and the middleware performed on various platforms including NUI/NUX (Natural User Interface/ Natural User eXperience) platform and interacted between appliances and sensors using HIML. We showed its function by experiments.

representative frame in GoF (Group of frames) of a video is formed by taking spatial and temporal gradients sequentially for image frames and by selecting the pixel of the largest spatial-temporal gradient (STG) for all co-located pixels in the GoF. As a result, the boundary of the moving object in the video is highlighted by the STG operation. Therefore, an optimal bounding box for a moving object can be determined by choosing the maximum spatial density of the STG for various sizes of the bounding box. The bounding box includes the boundary trace of the MOOI (moving object of interest) in the GoF and is used to differentiate the MOOI from the non-MOOI. That is, the pixels outside the bounding box are the non-MOOI and they are the main target for the size reduction of the video frames for a pre-processing of video compression.

With the increased interest in the neurofeedback and brain-computer interface area in recent years, diverse studies are being actively conducted. In this paper, we develop and propose a new BCI system and also implement a brain-wave measurement system to confirm a degree of depression and concentration. Through data digitization and graphs, the system can diagnose the user’s mental state and the degree of concentration in real time, and can be used to develop therapy programs for various nervous and mental disorders.

Natural user interfaces (NUIs) have attracted considerable research attention, and they are increasingly being applied in various fields. NUIs afford more intuitive user inputs than existing UIs. This study proposes an approach to recognize arm motions for intuitive control of an object in a virtual environment. Kinect is used for virtual object control system. For an experimental test, a virtual room is created and the Smart Interior NUI is implemented for arranging furniture.

Multiple datasets can be acquired by detecting the surrounding environment using multiple sensors. In this study, we propose an approach for implementing a third person view for a remote control natural user interface by integrating multiple datasets to reconstruct a three-dimensional (3D) scene. The datasets detected by multiple sensors include 3D point clouds, two-dimensional images, and GPS data. A 3D scene reconstructed in a virtual environment can be viewed from various points based on a third person view so users can execute remote control more easily.

Research and development is actively underway in the Natural User Interface (NUI)-based application program field. This is because existing input devices, including remote controllers, do not satisfy the interface interactivity demanded by users. Consequently, interfaces that are more intuitive and natural are still desired by users. This paper proposes an approach in which a touch interface is implemented by projecting a screen onto a surface using a projector. The technology required to implement the proposed approach is available at a moderate price and is easy to install. In this paper, a Leap Motion device, a ready-made tool, is incorporated to implement the proposed approach. Further, we explain how the proposed approach overcomes the disadvantages present in the Leap Motion device.

Video on Demand (VOD) is so popular that most of Internet traffic is for VOD service. If the user interface of VOD system can recognize user’s gesture then even a disabled person can easily controll the system to enjoy the service. Kinect is an example device that recognizes user’s gesture and voice and is widely used for games. Therefore, this paper proposes a Kinect based VOD user interface, describes our implementation of VOD system with the user interface, and discusses our experimental tests of the VOD system.

With the development of new technology, natural user interface (NUI) is being widely used. NUI provides users with more operational freedom and better user experience. It achieves natural interaction with computers. Foreground and background segmentation technology is an important part of achieving an NUI. This paper presents a Kinect-based human body segmentation method that combines background subtraction and noise removal algorithms. This method works effectively in complex environment.

This paper combines the background subtraction and frame difference methods to detect a moving-hand area. Currently, hand-gesture recognition contains the following parts: hand area detection, hand tracking, and recognition. In this paper, we describe the moving-hand area detection and tracking parts of our work. First, we constructed a background image model that did not contain a moving hand. Then, using a background updating algorithm to obtain the authentic background image, we obtained the moving-hand area by subtracting the current image frame from the background image frame. We utilized a novel dynamic threshold method to enhance detection. We used the Microsoft Kinect to track the hand region because Kinect can capture information about the human body and the position of various body parts. The experiments demonstrated that our methods can be used to detect a moving region from an original image.

With a variety of motion sensing equipments currently appearing, motion sensing games have become fashionable. The development of real-time, fluency, stability, and the users’ sense of immersion is becoming more and more important. In this paper, a new target extraction and 3D point calibration method is presented and applied to a motion sensing shooting game in 4D movie. Compared to a traditional edge and contour detection method, the proposed method is more flexible, accurate, and has a higher real-time performance.

Natural User Interfaces (NUIs) are a novel way to provide Human Computer Interaction (HCI) with natural and intuitive operation interfaces, such as using human gestures and voice. This paper proposes a real-time NUI engine architecture using a web camera as a means of implementing NUI applications with an inexpensive device. The engine integrates the OpenCV library, the CUDA toolkit, and the DirectX SDK. We utilize the OpenCV library to capture video via the web camera, implement real-time image processing using Graphic Processing Unit (GPU) programming, and present the NUI applications using the DirectX SDK; for example, to implant a 3D object in a captured scene and play sounds. To verify the efficacy of our proposed NUI engine, we utilized it in the development and implementation of several mixed reality games and touch-less operation applications. Our results confirm that the methods of the engine are implemented in real time and the interactive operations are intuitive.

Kinetic typography is a technology based on the changes of the color of the character, size, and location that delivers appreciation of the beautiful and creativity. This research’s main purpose is in to use the depth-camera to make natural interaction between users and kinetic typography. We used motion recognition sensor in order to track user’s skeleton informations to synchronize with typography. Kinetic typography does not only deliver simple information, it also allows high dimensional communication such as emotional expression.

Even though many three-dimensional pointing gesture recognition methods have been researched, the problem of self-occlusion has not been considered. When two positions are used to define a pointing vector on a single camera perspective line, one position can occlude the other, which causes detection inaccuracies. In this paper, we propose a pointing gesture recognition method for large display environments based on the Kinect skeleton model. By taking the self-occlusion problem into account, a person’s detected shoulder position is compensated for in the case of a hand occlusion. By using exception handling for self-occlusions, experimental results indicate that the pointing accuracy of a specific reference position is greatly improved. The average root mean square error was approximately 13 pixels in 1920×1080 screen resolution.

In this study, emotion-classification gathered from users was performed, classification-experiments were then conducted using SVM(Support Vector Machine) and K-means algorithm. To extract emotion, watching DVD and IAPS(International Affective Picture System) which is a way to stimulate with photos were applied and SAM(Self-Assessment Manikin) was used in emotion-classification to users’ emotional conditions. The collected users’ Brain-wave signals gathered had been pre-processing using FIR filter and artifacts(eye-blink) were then deleted by ICA(independence component Analysis) using. The data pre-processing were conveyed into frequency analysis for feature extraction through FFT. At last, the experiment was conducted suing classification algorithm; Although, K-means extracted 70% of results, SVM showed better accuracy which extracted 71.85% of results. Then, the results of previous researches adapted SVM were comparatively analyzed.

Up to now, typical motion recognition methods have used markers. The recognition methods were to receive coordinate input values of each marker as relative data and to store each coordinate value into the database. Methods using markers could store and utilize accurate values in the database but as ubiquitous era comes, there was no time enough to handle the preparation process for recognition. To compensate for this problem, we don’t use markers and implement real time motion recognition framework using Kinect camera. Especially the framework of hand mouse and fingers recognition framework is implemented. Also, we implemented for anyone to handle NUI/NUX framework easily and intuitively.

Recently, with the rapid upturn in IT development, smart devices started to appear. Smart devices provide many services with which users interact through a touch screen. For instance, smartphones, which are the most representative devices, are able to perform diverse functions comparable to desktop PCs, thereby providing convenience to many people and the ability to use them wherever and whenever they want. However, many touches and movements are required for character inputs and the touch recognition elements of touch screens that are not high pressure types involve great inconvenience for disabled persons or patients who cannot use their fingers properly, as they cannot input the appropriate characters. Therefore, this paper proposes a virtual keyboard using a gyro sensor (VG), which is an efficient character input keypad, for NUI(Natural User Interface).

Due to the extensive research on information technology (IT), smart multimedia devices using touch screens have been developing rapidly (e.g. multimedia tablet PCs, digital cameras, multimedia smartphones, and multimedia smart TVs). Among the various smart multimedia devices, multimedia smartphones have become the most widespread due to their convenient portability and real-time information sharing, as well as various other built-in features. However, problems such as loss, theft, and information leakage because of convenient portability have also increased proportionally. Pattern lock, personal identification numbers, and passwords are the most used locking features on current smartphones, but these are vulnerable to shoulder surfing and smudge attacks, allowing malicious users to bypass the security feature easily. In particular, the smudge attack technique is a convenient way to unlock multimedia smartphones after they have been stolen. In this paper, we propose a touch time locking system (TTLS) focusing on improved security and convenience for users.

We propose a wearable system that helps visually impaired persons walk to their destination. Once we choose a destination, our system computes a path and guides us using marker data detected by the camera in buildings. It employs positioning data from GPS receiver outdoors. Simultaneously, it exploits multiple ultrasonic sensors to avoid obstacles lying in the path. In addition, we propose a correction algorithm for GPS data that considers speed to reduce the positioning error and deploy a map- matching algorithm when a user breaks way from the correct path. We evaluate complex structure in front of the user with patterns and determine an avoidance direction by analyzing these patterns.

For the sake of providing evidences that contribute to policy making or strategy planning in pharmaceutical industry, we tried to create new R&D indicators to foresight the picture of pharmaceutical industries. We found that R&D pipelines can be good drug-R&D-indicator. We also show new drug-patent-indicators for identifying patents related with pharmaceutical entities’ R&D progress (”Pre-clinical” → ”Phase 1” → ”Phase 2” → ”Phase 3”→ ”Filed ”→ ”Approved” → ”Marketed”). IPC Count, forward citations, and Citations to Non-Patent Literature are found as new drug-patent-indicators. Not only R&D pipelines but also patents extracted by new drug-patent-indicators are considered to foresight pharmaceutical industries’ potential of creating new drugs.

Classification on spatial data is different from classical classification in that spatial context must be taken into account. In particular, the validation criterion functions should incorporate both classification accuracy and spatial accuracy. However, direct combination of the two accuracies is cumbersome, due to their different subjects and scales. To circumvent this difficulty, we develop a new criterion function that indirectly incorporates spatial accuracy into classification accuracy-based functions. Next, we formally introduce a set of ideal properties that an appropriate criterion function should satisfy, giving a more meaningful interpretation for the relative significance coefficient in the weighted scheme. Finally, we compare the proposed new criterion function with existing ones on a large data set for 1980 US presidential election.

The overall objective of this study is to implement the semantic retrieval model, and provide the linked information of retrieval result. The work presented in this paper focuses on the core technique, the method used in our study is to translate the Chinese natural language to SPARQL, and the procedure we followed can be briefly described as the several aspects. First, we get the dependency relationship via language technology platform, and then we find the ‘head’ (or topic) of the sentence. Third, we analyze the relationship between modified words and modifiers of the question with the knowledge base. Finally, we build the RDF triples and compose the SPARQL query, and discover the linked data of retrieval result. We carried out the example in this paper to experiment to test the validity of the method. The studies we have performed showed that the relevent of the model is better than the traditional information retrieval; what’s more, this work also provided the linked information, which supported users to learn more knowledge conveniently.

As the amount of data increases enormously, business analytics such as descriptive, predictive, and prescriptive analytics is one of the most important topics for better decision making especially for CTO or CIO in corporate. Prescriptive analytics shows fundamental difference with descriptive analytics and predictive analytics in that it requires high-value alternative actions or decisions to achieve a given goal. However, only a few studies have been introduced since it is a emerging technology. Thus, this study aims to trigger research on this technical area by implementing a prescriptive analytics system and by verifying it in the point of usability and usefulness. The system, InSciTe Advisory, is focused on improving research performance and is based on 5W1H questions to build actionable strategies to achieve a given goal. The comparison evaluation of the system with Elsevier SciVal showed a rate of 118.8% in usefulness and reliability.

This paper describes a splicing P system for clustering analysis. We use the thought of partitioning around medoids algorithm to design clustering by splicing P systems and implement it in clustering. Then we give an example with 10 points to indicate the feasibility of the provided algorithm. All the processes are conducted in membranes. The process of the proposed algorithm is provided and an instance to prove its feasibility is presented.

A Distributed Power Flow Controller device is designed in this paper. The series and shunt converter capacity are determined by the variation range of the transmission power flow. The maximum amplitude of the fundamental frequency voltage generated by the series converters, the current flowing through the series converters, the current and voltage injected into power system by the shunt converter are all determined, according to its power model and the power conservation law. The DC capacitor voltage and the converter switch parameters are determined in accordance with the relationship between the input and output voltages of the converters. In addition, a simulation model of the Distributed Power Flow Controller installed to a power transmission system is built in PSCAD/EMTDC and the results verified the correctness of the design method presented in the paper.

The law of energy distribution of White Noise (WN) in Haar wavelet decomposition levels (WDLs) of heart sound (HS) and electrocardiogram (ECG) signals is analyzed statistically. The analytical results indicate that for WN signals, energy proportion of the 2

nd

WDL to the 1

st

WDL is fixed. For HS and ECG signals, when sampling frequency is 4 kHz for HS or 400 Hz for ECG, energies of the 1

st

WDL and 2

nd

WDL are almost the same. Based on the wavelet energy distribution laws of HS, ECG and WN, equations are created to estimate WN energy and signal to noise ratio (SNR). Numerical results show that the new WN energy and SNR estimation method has the normalized mean square error of less than 0.1 in all cases considered.

The development of wireless infrastructure and information communication technology provide the accessibility for pervasive healthcare in remote monitoring, telemedicine, emergency management, and health record access applications. However, all these applications require the diagnostic results from medical personnel or health provider, which consequently overloads their burden and induces time delay. Targeting the timely healthcare service available for anyone, anytime and anywhere, a framework architecture for versatile distributed e-home healthcare is introduced in this paper. Hierarchical diagnosis of multi vital signs is integrated into the whole system to fulfill the diversified user demands.

CARTCM, a novel categorization method based on classification and regression tree (CART) is proposed to group 32 hemodynamic parameters (HDPs) and 5 physiological parameters (PPs) for detecting cardiovascular diseases (CVDs) efficiently. The CARTCM has two pivotal procedures: (i) compute importance of each HDP or PP variable automatically using CART and (ii) depends on variable’s importance threshold, categorize HDPs and PPs into 3 different groups (most, less and least import groups) on-line dynamically according to pre-detected type of CVD. In another word, for detecting different CVDs, the number and type of HDP&PP included in each of 3 groups are various. Solving such a refractory bottleneck problem improves greatly the accuracy and efficiency of detecting CVDs.

K-Means clustering algorithm attracts increasing focus in recent years. A pending problem of K-Means clustering algorithm is that the performance is affected by the original cluster centers. In this paper the K-Means algorithm is improved by particle swarm optimization and the initial cluster centers are generated by particle swarm optimization..The experiments and comparisons with the classical K-Means algorithm indicate that the improved k-mean clustering algorithm has obvious advantages on execution time.

In this work a novel approach called peak detection and boundary identification (PD-BI) for period detection of heart sound (HS) signals in noisy environment is proposed. The PD method is inspired by a natural phenomenon that a horizontal soft string hits peaks of uneven ground line when falling from a high position. The BI method is based on the quasi-cyclic feature of HS. The simulation results validate the effectiveness and accuracy of PD-BI.

Many students must learn spatial skills to improve learning achievement in science, mathematics, and engineering. An abundance of literature on the geometric learning theory is available. However, specific guidance on how students can interact with teaching materials through their body is limited. We used a group of undergraduate students as an example and argue that the Kinect sensor-assisted learning interface can provide a “learning-by-doing” framework for learning spatial skills, motivating students, and enhancing students’ effectiveness. The responses to the System Usability Scale (SUS) indicated that our system demonstrated usability and learnability. We conclude that the Kinect sensor-assisted learning system promotes the development of students’ spatial visualization skills and encourages them to become active learners.

Detection of moving objects in video sequences is the first relevant step of information extraction in many computer vision applications. The undesired background image can be filtered out and the complete foreground image can be retained. By doing this, it provides a focus for tracking, recognition, classification, and activity analysis, making these later steps more efficient. In this paper, we build an adaptive background model using a self-organizing neural network; this model can handle scenes containing moving backgrounds, gradual illumination variations, and shadows cast by moving objects; further, this model has no bootstrapping limitation. However, background subtraction leads to a serious camouflage problem. Owing to this phenomenon, we propose a foreground-region-selection algorithm that combines the image space information and initial object mask generated from improved watershed algorithm and background subtraction respectively. The camouflage problem can be effectively solved using the proposed algorithm. The detection results of the proposed algorithm are better than the background subtraction results obtained from the experiments.

In recent years, seniors villages have drawn more attention because of population aging. Moreover, as various deadly and highly infectious diseases have continued to emerge, and the elderly usually have weaker resistance to disease, the outbreak of infection may easily get out of control in seniors villages. This study proposed a visual infection control positioning system for seniors villages using the RFID (Radio Frequency Identification) technology. The system records the indoor movement path of the residents in real-time and visually displays the distance between the patients and other residents. Using the proposed system, the users can identify residents with potentially high risk of infection. In order to effectively calculate the distance, this study combined the KNN (K-Nearest Neighbor) algorithm and the positioning method of fingerprint training database to improve the positioning accuracy and effectively calculate the target position. The positioning errors can thus be reduced, and the number of RFID readers is also fewer, thus lowering the system implementation cost.

With the rapid increasing of wireless request, user mobility is a critical influence on system performance of mobile environments. Unlike conventional distributed systems, the mobility of users is the major characteristic in a mobile computing environment so that the value of data may depend on location, and processing of a query at one site may give different results than that at another. In this paper, we present the broadcast-based location dependent data delivery scheme for location-based queries. In the approach, broadcasted data objects are dynamically adjusted based on their locations, and the server broadcasts the location dependent data along with access popularity. Then, we present a data evaluation scheme, designed to reduce the query response time. Simulation experiments are conducted to compare the performance of our method with other issues. The results show that our schemes outperform the conventional strategies.

Modeling synergy effects to measure the effectiveness of collaboration is a meaningful and important issue. However, there are only few previous works quantifying collaborative effects. Further, previous models consider only positive effects regardless of potential disharmonious factors which are common in real world. In this paper, a mathematical formulation measuring synergy effects which takes both positive and negative effects into consideration is introduced. Implications and comparisons between proposed model and a wildly applied model, Cobb-Douglas function, are also proposed in simulation results.

Malicious attacks have become a major source of risk for service providers. In this paper, a network attack and defense scenario is modeled as a bi-level mathematical formulation where the commanders try to disrupt the services while the defender has to assure the service continuity. Since the high degree of complexity and non-deterministic characteristic, Monte Carlo simulations are applied to evaluate the average network survivability. As for the attack and defense scenario, an emerging type of attack, collaborative attack, is considered. The defender must apply appropriate strategies, which includes deploying various proactive and reactive defense mechanisms, under budget and predefined quality of service constraints to protect the system. The virtualization technology is also considered as the topology platform.

Mobile phone applications (apps) have been widely used in mobile marketing, but nothing is known about app user motives to adopt and use a particular app. This study employs the uses and gratifications (U&G) approach to develop a typology for the motives of app users to adopt an app, and further examines whether user motivations will influence their attitude and addiction to apps. A sample of 441 mobile phone app users was employed to generate information on the structure of user motives to adopt apps. The findings suggest that social benefits, immediate access & mobility, entertainment, self-status seeking, pursuit of happiness, information seeking, and socializing are the primary factors driving app user’s adopting behavior. These motivations explain 53% of the variance in user addiction to apps and 58% of the variance in user attitudes to using apps.

With rapid development of search and web technologies, people are able to surf the web and search information when they need. Online Word-of-Mouth has drawn attention of academic researchers and commercial site managers. People share their experiences in goods and publish their evaluations, opinions and intentions to public. Many study discovered that online Word-of-Mouth is more influential compared to traditional advertisements. This study discusses the night market information and comments posted by consumers in four languages (English, Japanese, Simple Chinese, Traditional Chinese) in social media. These keywords in four languages were analyzed with on-line semantic tools. The correlations between keywords are illustrated with social networking analysis.

In the recent years, along with the emergence of world-wide-web and internet, many studies investigated in particular factors influencing on the repurchase intentions in the on-line group buying environment such as degree of satisfaction, risk evaluation, and personal characteristics. However our study focuses on the relationships between Price perception and Participant roles on group buying behavior through internet. With the findings found in the paper, there is a significant correlation between the roles of group buying and factors as gender, profession, monthly revenue, and if the family members live together. In the price perception, there is a significant discrepancy between price mavenism, sales proneness, as well as price-quality schema and alterations on the roles of group buying.

This paper is focused on the resource allocation of network attack and defense with mathematical programming and to optimize the problem. It adopts a concept, discount coupon, to describe the attack behavior of taking advantage of accumulated experience from his previous attack actions of minimizing future attack cost. The attacker obtains free experience before he launch an attack or from a compromised node which could further reduce the cost of an attack. The attacker’s objective is to minimize the total attack cost, while the core node is compromised and the network could not survive. Here, by transforming with node splitting into a generalized shortest path problem and applying the algorithm to optimally solve it.

How to efficiently evaluate the network survivability is a critical issue in nowadays. Hence, we develop a multi-round network attack-defense scenario with dual-role players, who can attack and defend, and establish a mathematical model to optimize resource allocation and then predict the network survivability by the Average DOD. In each round, the players could allocate their attack resources on the nodes of their own network and on another player’s network after updating related information about another player’s. Furthermore, they could reallocate existing defense resources and repair compromised nodes. To solve the problem, the gradient method and the game theory would be adopted to find the optimal resource allocation strategies for both players.

This study aimed to explore the factors interaction that influence tourist destinations’ attractiveness and the authors used towns with cultural heritage as examples. Eight factors were used to present the conceptualization of tourism destination image (TDI). The DEMATEL method was adopted in this study to investigate the relation and the degree of influences among factors. A questionnaire was designed to collect the data needed for this study. “Beauty of historic-cultural heritage and feelings generated by its perception” and “maintenance/integration of site architecture” are found as the most important of the critical factors respectively. On the other hand, “tourist-cultural management” and “complementary tourist offer or infrastructure” are the factors which dispatches influence most respectively. It is suggested towns with cultural heritage should focus on these factors to increase their attraction.

In modern society, no tourism industry can escape from international competition due to globalization. In this situation, how to increase international competitiveness of the tourism industry has become one of the greatest concerns. The goal of this study is to develop a model for investigation of the tourists’ preference. Ten attractions of tourist destinations were compiled and used as the attributes in this study. It adopts fuzzy set theory as main analysis method to find the tourists’ preference. In this study, 248 data used were retrieved. Besides the evaluations for the factors, the overall evaluations (namely satisfied, neutral and dissatisfied) for every tourism destination were also inquired. After screening, 201 of these data could be used. In these 201 data, 141 were classified into “satisfied” with the tourism destination, accounting for 70.15%, and 49 were “neutral”, accounting for 24.38%, while, 11 were “dissatisfied”, accounting for 5.47%. Through the method of fuzzy preprocess, 8 rules were obtained. Concerning the condition attributes, two of the original ten attributes were found influential, namely level of prices, living costs and tourist safety of the tourism destinations. On the basis of the results of this study, it shows that top management of tourism destinations should put resources in these fields first, in order to allow limited resources to perform to its maximum effectiveness.

In this study, a novel smart edge-preservation filter (SEPF) is proposed for removal of high-density impulse noise in images. Using the sparse matrix transformation, the first stage of SEPF is not only to identify the noisy candidates but also to decide the processing order of them via a rank of noisepixel sparsity in working window. Then the second stage of SEPF utilizes a modified double Laplacian convolution to confirm the truly noisy pixels and yield a directional mean to recover them. This new approach has achieved remarkable success rate of the edge detection than other edge-preservation methods especially in high noise ratio over 0.5. As a result, SEPF has significant improvements in terms of edge preservation and noise suppression exhibited by the peak signal-to-noise ratio (PSNR). Simulation results show that this method is capable of producing better results compared to several median-based filters.

Monitoring electrical power consumption has become an important research issue in order to reduce electricity expense and avoid unnecessary electrical operation. This study is based on the architecture of Non-intrusive load monitoring (NILM) system to monitor the operation status of home appliances. The proposed system automatically identifies the electrical appliance in starting status. This method takes advantage of the combination of the fuzzy theory and neural network theory for system identification. The measurement of the RMS voltage, RMS current, RMS active power and the RMS apparent power are used for feature extraction by using a simple home Smart Meter. In this study, it shows 100% recognition rate in three experiments with different hybrid starting procedure.

With the rapid development of artificial intelligence and natural language processing technologies, several grammar checkers are introduced as writing tools for language learners. Language learners can receive immediate corrective feedback from these grammar checkers. In particularly, the new technologies have greatly increased the opportunities for students to receive input outside of the classroom. Accordingly, technology has become an important role in the field of language education. The purpose of this study is to review the literature of grammar checker technology, to introduce the current available grammar checkers and further to provide suggestions for language learners and teachers.

Graph is suitable for modeling many emerging fields such as configuring a wireless sensor network. The graph databases which representing the underlying real-world structures may therefore possess abounding unknown knowledge waiting to be discovered. Consequently, how to automatically mining the hidden information from these graph databases becomes critical for many new and promising applications. This paper proposes a new algorithm MFG (Mining Frequent subGraph patterns), which employs the global orders of labels in molecular graph patterns, in cooperation with fast pruning mechanisms, to reduce the amount of duplicated candidate enumeration. MFG also utilizes several effective data structures to store the subgraph pattern embedding information. By these proposed techniques, MFG shows its benefits such as it reduces candidate duplication dramatically, eliminates subgraph isomorphism checking completely, and alleviates the cost of graph isomorphism testing. The conducted experimental results show that MFG works with more economical memory consumption and better efficiency compared with a state-of-the-art mining method.

When diagnosing brain tumors, a doctor needs to know the size of the tumor and the location of the tumor in the brain. Positron Emission Tomography (PET) can effectively detect the existence of cancer at early stages based on the heightened glucose metabolism of cancer cells. This research is to extract the regions of brain tumors, brain tissues, and skulls from a serial of PET brain images, and then to reconstruct a 3D model for the images based on the extracted brain tumor, brain tissue, and skull. It is helpful for a doctor to know the relative size and position of the tumor within the skull.

When it becomes much easier to search travel information, more and more people choose self-guided tours rather than package tours. Many social websites have emerged where people can share and discuss their travel plans before departure. After making plans, many backpackers write their plans on small paper notebooks for reference on their journeys. In this paper, a Mobile Travel Support System, referred to as MTSS, for self-guided travel is presented. We’ve prototyped a web-based travel plan editor where a user can make a travel plan, as well as a smart phone app where the user can look up the plan anytime and anywhere. Both subsystems integrate with Google Maps and other 3rd party information systems. Experiment results have successfully demonstrated that the proposed MTSS can significantly help backpackers make self-guide travel easy and convenient.

Smart Living, a brand new service, makes use of technologies to enhance your quality of living. Heterogeneous technological products and ubiquitous computing or pervasive systems are proliferating and populating professional, personal, transit, transport living environment. This paper proposes a MCU-based SNMP agent to efficiently control the smart-living devices. We integrate the Net-SNMP libraries and the MCU functions, without MIB module, to have a small-size design for smart-living devices. This technique will help designer to build a small-size design of SNMP agent for the MCU-based smart-living applications. Such technique extends applicability compares with modern smart-living devices and improves the management convenience as the number of the same module rises.

The advance of technology has brought a revolutionary impact on the development of contemporary exhibition. Using novel technology to plan an exhibition has gradually become the mainstream of exhibition display technology. In this research, we integrate the immersive experience concept and the interactive technology into an immersive interactive exhibition. Besides the static content, the exhibition allows visitors to interact actively with digital contents in an immersive interactive environment through a designed scenario. However, planning an interactive exhibition needs to be customized case by case, which is time-consuming and inefficient. This paper presents an architecture of ImUX Platform (Immersive UX Platform) which integrates the reusable template of interaction in order to save the time costs of the communication between designers and technicians. Through the ImUX Platform, not only can exhibition designers convey their ideas more precisely, but they also can build the immersive interactive environment more quickly and efficiently.

Recently, technology is developing so fast. Now, almost everyone uses high technology products, such as smart phones and Tablet PC. Nowadays, children’s learning tools are not just hard copy books. Children can use smart phones and Tablet PC which can download e-book applications. Children can learn diverse things and enjoy various entertainments. We follow this trend. We designed an application of TTS (text-to-speech) story e-book and it can be put on the platform that can be downloaded and used. In our story content, special festivals from different countries were integrated into the story. By reading this TTS e-book, children can raise their interests in understanding different special festivals and culture from different countries as well as learning English. After finishing our application of audio e-book, we did a survey toward a TTS e-book acknowledgement and the acceptability of our app. Thirty fifth and sixth grade students filled out the surveys. Results of this survey showed that most students thought this TTS e-book APP was a wonderful and good tool for learning. To conclude, we hope this application can motivate more children to read and expose children to interesting festivals in the world. More technical breakthrough in reading need to be put into consideration in order to obtain more optimal outcomes in language learning.

This study uses social cognitive theory as the main structure to explore the interinfluential relations among the personal factors (self-efficacy and outcome expectations), environmental factors (system characteristics) and behavioral factors (psychological attachment). By comparing the influential relationship and degrees of influence among the various criteria, we identify the key factors. The study results show that personal factors affect the environmental factors and behavioral factors while environmental factors affect behavioral factors. These study results demonstrate that personal factors have more influence on the implementation of information systems. In terms of individual factors, self-efficacy is more influential personal in the personal factors aspect; in environmental factors aspect, the perceived ease of use is more influential; in the behavioral factors aspect, the compliance factor is more influential.

This paper used statistical, decision tree and association rule to research the effect of the reduction potential and bulk density and other soil factors to the production of rice. We finding the Non-exchangeable K., changeable K. Organic Matter and bulk density these factors effected the production of rice.

Feature selection is an effective method on the solving of classification problem. Through reduce unnecessary features, feature selection can improve accuracy of classification and reduce the training time of classifiers. Besides, the bio-inspired algorithms are usually used to search the optimal feature subset for a classifier. This study adopted the newly developed bio-inspired algorithm with social behavior of lions to select optimal feature subset for support vector machines. By using the dataset of UCI machine learning database, the proposed method was compared with the genetic algorithms. Experimental results demonstrated that the performance of the proposed method was superior to that of genetic algorithms.

Cloud computing is an emerging technology and gain attention in academic and business area. Resources are pooled and share with users on demand. In order to provide better performance of cloud environment, task scheduling is an important issue. On the other hand, in large scale cloud environment, cloud devices search and query influence the performance also. Therefore, in this paper we propose a AVL-tree based cloud computing environment to organized the cloud devices in

O

(log

n

) regarding the cloud device management operations complexity and task scheduling considering the real-time average system load of cloud computing environment.

In ubiquitous environment, it is important to provide suitable service to user according to user context. Therefore it is necessary to provide services based on the latest information to user through mining by user activity and service history. In this paper we propose a mining method based on spatio-temporal information and service ontology to search frequent service patterns using service history. In this method, we find the frequent service patterns by level-cross approach on service ontology. The proposed method is to be a basic research to find the service patterns to provide high quality service to user according to season, location and age with similar context.

Collaborative workspaces provide virtual workspaces to share resources among users in a systematic manner, managing resources structurally for groups or individuals. Through collaborative workspaces, users can preserve their collaborative work as well as they effectively share their resources among group users. In this paper, we present techniques for supporting collaborative workspaces for chat groups in XMPP messaging services. In addition, based on the collaborative workspaces, we present a new shared board which supports synchronous drawing for chat groups of the messaging services with various drawing tools.

The use of radar techniques to detect, locate, and identify objects is of considerable in recent years. Various types of radars, including Continuous wave (CW) and Pulse radar, have been developed. These two types of radars are very similar with the exception that the pulse radar has a relatively high bandwidth receiver and the CW system has a relatively narrow bandwidth receiver. In this paper, we compare the performance of Continuous Wave Radar and Pulse Radar signals to reduce the noise. This paper analyzes the noise reduction algorithms of Continuous wave (CW) and Pulse radar under the heading of signal to noise ratio (SNR). The simulation results indicate that Pulse radar with Matched filter has strong anti-noise ability while Continuous Wave Radar with Wavelet instead of Matched filter.

C3ware is a middleware which supports group works using collaborative services and workspaces over cloud storage. In this paper, we present a method for operation atomicity to maintain in C3ware both consistency among the meta-data and consistency between the meta-data and the associated resources which can reside in different types of storages. In addition, we present a storage replication algorithm which guarantees that at least one of the back-end cloud storages retains the complete set of resources at any time.

In this paper, we introduced a cloud-based system framework for supporting educational timetabling on smart phones with location support. The framework has been fully considered with robust, modularity and functionality, and can be easily deployed for a new university environment. Evaluations show that the system could handle

reasonable

large amount of requests and its functions could best represents students’ interests and fulfill their needs.

In recent years, RFID tags are widely used in many industries on a global scale. Discovery service (DS) for RFID Network is a pivotal system that serves the following lookup function: Given an RFID identifier of an object, it returns a list of Internet addresses of servers that offer detailed information about this object. The current researches on DS have severe drawbacks in scalability, efficiency and being compatible with different RFID standards. So we propose a novel DS solution based on hybrid architecture to solve these disadvantages and adopt anycast technique to further improve its performance. Compared with the current researches on DS, our DS solution has better scalability, better efficiency and being compatible with different RFID standards.

In the Android platform environment, various techniques to detect personal information leakage are being introduced recently but effective blocking is still long way off. The proposed scheme intends to securely protect personal information on smartphones by monitoring behaviors of various Apps. If an App violates any behavior-based rule, the proposed scheme blocks running the behaviors of the App. For this purpose, I classified the behaviors of smartphone applications and defined the behaviors to monitor. I also proposed the architecture to apply it in the Android framework and applied the proposed scheme in the Android smartphone.

Up to now, typical motion recognition methods have used markers. The recognition methods were to receive coordinate input values of each marker as relative data and to store each coordinate value into the database. Methods using markers could store and utilize accurate values in the database but as ubiquitous era comes, there was no time enough to handle the preparation process for recognition. To compensate for this problem, we don’t use markers and implement real time Multi-modal recognition framework. Especially the framework of hand mouse and facial recognition framework is implemented. Also, we implemented for anyone to handle NUI/NUX framework easily and intuitively.

Vehicular ad hoc networks (VANETs) will play a vital role in reducing the number of road accidents and fatalities as well as vehicular traffic optimization. In this paper, we propose TLV-eCAST, the last vehicle based early broadcasting for disseminating safety-related alert message in VANET. The proposed TLV-eCAST uses the early warning system on the basis of time to collision (TTC) as well as just broadcasting by the last vehicle in the transmission range of sending vehicle. The performance of TLV-eCAST is evaluated through simulation and compared with other message dissemination algorithms.

We present a method by which a user can create his 3D avatar using a photograph. To model the face, we use an Active Appearance Model since it provides a reliable and efficient way to model a face including several facial features within it. The user can change the shape of the face as well as facial features interactively. In addition, this avatar is able to express diverse facial expressions. All software components are implemented as an application program running on a smartphone.

MOST, Media Oriented System Transport, a communication system with a new flexible architecture, used by many different manufacturers, was developed to meet custom’s demands in car. However, for the cost and technical complexity of MOST system, there were limitations people who want to use as the means of in-vehicle networks. Especially, in automotive aftermarket, since MOST technology is confined to a few manufacturers, common automotive companies are hard to develop MOST devices to connect MOST ring networks system. In this paper, through the centralized control proxy method, that is remote control, it is enable to design multi-media system on relatively easy and low level skill. For the cost and energy efficient, proposed system architecture and software contents are useful to develop automotive media system.

Nowadays, Internet becomes more and more popular in our modern life due to the rapid development of information and communication technology. Internet is widely used in a vast of social activities, such as electronic commerce, Internet banking, which are based on a cyber space. To organize these social activities as well as to possess conveniences on the Internet, an electrical media expressed by personal identification certificates (resident cards, driving licenses, passports, etc.) is employed. As a result, it is necessary to generate an exposure of personal information on the web. Therefore, this paper proposes an efficient personal image classification method using support vector machine (SVM) for privacy incident response system(PIRS). In our case, PIRS detects the image included personal information. To extract the optimal features of the image included personal information, the proposed method selects common features from the training set. And then a personal image in the Web is detected using optimal image features and an SVM classification. The experimental results of the proposed method, the classification success rate of personal image is about 82%, and the miss-classification rate is about 13%. The cause of the miss-classification is because the images were digitized by various illuminations. However, the personal images for personal identification showed high classification rate.

Function of smart antennas enhancing the OFDM based WLAN’s immunity to multipath fading environments has been summarized. Time domain implementation of smart antenna algorithm(pre-FFT algorithm) has been explained and a simple modification associated with the weight update of pre-FFT algorithm has been proposed Through computer simulations, the performances of the both algorithms have been shown and it was observed that the proposed algorithm contributed to obtain acceptable performance in multipath fading scenario.

The performance of radio-frequency identification (RFID) must be tested in a fast-moving environment so as to be applied to a high-speed train. A RFID reader antenna design for identification of a high-speed tag is proposed. In order to identify a high-speed tag, the beam pattern of the reader antenna is widened in the direction of the moving tag. The widened beam pattern lengthens the identification range for the fixed backscattering time from the tag. A 2x1 array antenna is used as the reader antenna, and the identification of the RFID tag was measured at different speeds. The identification range was increased 1.4 times by using an angled antenna array. This study tested the detection performance of RFID in a high-speed environment. As a result, we confirmed that a RFID interrogator equipped with our proposed antenna can detect a RFID tag at a speed of 420km/h. In order to verify the applicability of this design to a railroad, running tests were performed in the K-AGT (Korean Automated Guideway Transit) test line. We analyze the RFID response characteristics in a concrete surface environment and present the test results.

This paper presents a performance-tested obstacle detecting algorithm using image processing which is adequate for the level-crossing environment. The proposed algorithm accurately detects objects by analyzing their trajectories in order to minimize the rate of false alarms due to shadows and changes in lighting in the environment of the level crossing. It also outperforms the existing image-sensing process algorithm by complementing the drawbacks appearing the method of background image modeling with small amount of calculation through post-processing, such as object tracking. The performance of the algorithm with the obstacle image sensing device was tested on video during crossing operations.

In this paper, we have discussed the algorithmic approach of rule-based control selector in the automatic vertical farm system. Many researches have been undergone in the automation process to scale down the human labor for the creation of a crop’s ideal environment to reap agriculture benefits for farmers. In an automated environment, analyzing and choosing the right controller takes the main priority. Especially in a mass crop production system where different crops are cultivated at the same time, selecting an appropriate controller has to be handled carefully to avoid any mishaps. In this selection process, factors such as weather and crop’s growth condition are considered and evaluated accordingly. With the help of ontology, Semantic Web Rule Language (SWRL) rules are written for the selection of controller for each sector which in turn has selected crops. The flow of the Control selection involves four basic steps such as identifying, matching, rating and selecting.

The electromagnetic levitation system is a nonlinear system. The force applied by the electromagnet on the levitating magnet can be approximated a nonlinear model. The conventional controller with linearization of nonlinear systems design is presented without highly controlling performance enough. This paper is demonstrated the design of servo system using pole-placement with state observer for the magnetic levitation system from the equilibrium point. In addition, these closed-loop poles correspond to the desired closed-loop poles in the pole-placement approach and state observer estimate immeasurable state variables. Finally, the simulation and experimental results showed effective control objective.

Nowadays biogas is used in agriculture and industrial sectors especially in electric production because its production cost lower is than other fuels such as gasoline and LPG. However, biogas from the waste of swine farm has only 50 – 70 % of methane that decreases the efficiency and power of SI engine. This paper presents a comparative study of gasoline LPG and biogas affecting the dynamic responses of SI engine. The system identification of SI engine uses mean value model with parameter estimation. A mean value model is a mathematical SI engine model which is the intermediate between large cyclic simulation models and simplistic transfer function models. The simulation and experimental results are found useful in the development of control system for biogas engine-generator system and LPG plus biogas engine-generator system.