Pervasive Computing and the Networked World

Duration and dynamic changes of QT and PR intervals as well as QRS complexes of ECG measurements are well established parameters in monitoring and diagnosis of cardiac diseases. Since automated annotations show numerous advantages over manual methods, the aim was to develop an algorithm suitable for online (real time) and offline ECG analysis. In this work we present this algorithm, its verification and the development process.

The algorithm detects R peaks based on the amplitude, the first derivative and local statistic characteristics of the signal. Classification is performed to distinguish premature ventricular contractions from normal heartbeats. To improve the accuracy of the subsequent detection of QRS complexes, P and T waves, templates are built for each class of heartbeats.

Using a continuous integration system, the algorithm was automatically verified against PhysioNet databases and achieved a sensitivity of 98.2% and a positive predictive value of 98.7%, respectively.

Currently, more and more information are spreading on the web. These large amounts of information might influence web users’ emotion quite a lot, for example, make people angry. Thus, it is important to analyze web textual content from the aspect of emotion. Although much former researches have been done, most of them focus on the emotion of authors but not readers. In this paper, we propose a novel method to predict readers’ emotion based on content analysis. We develop an emotion dictionary with a selected weighting coefficient to build text vectors in Vector Space Model, and train Support Vector Machine and Naive Bayesian model for prediction. The experimental results indicate that our approach performs much better on precision, recall and F-value.

The quality of traditional grid-clustering and pure hierarchical clustering methods suffers from some limitations, especially the inability of hierarchical clustering to perform adjustment on once merge or split decision. However, DNA computations can be introduced here to do global search and find the best clusters. Since the grid-clustering can be transformed into HPP (Hamilton Path Problem) and the other one equals to MST (Minimal Spanning Tree) algorithm while using the minimum distance measure, this paper proposes to solve grid-clustering using triple-stranded DNA model and nearest neighbor clustering by 3-armed DNA model based on the above thought. Firstly, it is needed to get the initial data pool containing all the possibilities, then screen those owning all data points to be clustered, and finally get the best one(s). Accordingly, under the special designed biological algorithm, both of the DNA algorithms have the time complexity of

O

(

n

), which

n

represents the number of processed data waiting to be clustered. In fact, the way of using triple-stranded structures to select solutions satisfying the paticularly restricted conditions could be further extended to more DNA algorithms using double-helix. Meanwhile, if other applications are on the basis of binary tree constructions, 3-armed DNA molecules designed here can be made more use.

This paper presents the methodology for synthesis of real-time applications working in the Internet of things environment. We propose the client-server architecture, where smart embedded systems act as clients, while the Internet application is a server of the system. Since centralized systems are prone to contain bottlenecks, caused by accumulation of transmissions or computations, we propose the distributed architecture of the server and the methodology which constructs this architecture using available Internet resources. We assume that the function of the server is specified as a set of distributed algorithms, then our methodology schedules all tasks on existing network infrastructure. It takes into account limited bandwidth of communication channels as well as limited computation power of server nodes. If available network resources are not able to execute all tasks in real-time then the methodology extends the network by adding necessary computation nodes and network components, minimizing the cost of required reconstruction. We also present a sample application for adaptive control of traffic in a smart city, which shows benefits of using our methodology.

We developed an iOS based application called iScope to monitor biosignals online. iScope is able to receive different signal types via a wireless network connection and is able to present them in the time or the frequency domain. Thus it is possible to inspect recorded data immediately during the recording process and detect potential artifacts early without the need to carry around heavy equipment like laptops or complete PC workstations. The iScope app has been tested during various measurements on the iPhone 3GS as well as on the iPad 1 and is fully functional.

In conventional distributed machine learning methods, distributed support vector machines (SVM) algorithms are trained over pre-configured intranet/internet environments to find out an optimal classifier. These methods are very complicated and costly for large datasets. Hence, we propose a method that is referred as the Cloud SVM training mechanism (CloudSVM) in a cloud computing environment with MapReduce technique for distributed machine learning applications. Accordingly, (i) SVM algorithm is trained in distributed cloud storage servers that work concurrently; (ii) merge all support vectors in every trained cloud node; and (iii) iterate these two steps until the SVM converges to the optimal classifier function. Single computer is incapable to train SVM algorithm with large scale data sets. The results of this study are important for training of large scale data sets for machine learning applications. We provided that iterative training of splitted data set in cloud computing environment using SVM will converge to a global optimal classifier in finite iteration size.

Data mining techniques are widely applied and data warehousing is relatively important in this process. Both scalability and efficiency have always been the key issues in data warehousing. Due to the explosive growth of data, data warehousing today is facing tough challenges in these issues and traditional method encounters its bottleneck. In this paper, we present a document-based data warehousing approach. In our approach, the ETL process is carried out through MapReduce framework and the data warehouse is constructed on a distributed, document-oriented database. A case study is given to demonstrate details of the entire process. Comparing with RDBMS based data warehousing, our approach illustrates better scalability, flexibility and efficiency.

Recent advances in integrated electronic devices motivated the use of Body Area Networks in many applications including monitoring, localization, tracking and navigation. In this paper we introduce an indoor navigation approach based on Body Area Network to assist firefighters in finding their way to save human lives and to combat fires. For this we develop a technique based on a real-time graph called Temporal Weighted Graph that provides some special functions such as localization, navigation, communication, and hazard estimation. Then we implement a real time solution aiming to predict firefighters’ isolation time in an indoor space by estimating the horizon of risk deterioration in the graph. And finally, we demonstrate the importance of the presented technique in assisting firefighters during the navigation process. A set of simulation scenarios are conducted to evaluate the performance of the solution.

In this paper we discuss the importance of social network of researchers for personalized recommendation of researchers and papers. We begin by briefly describing collaborative filtering method for personalized recommendation and its cold start problem of the new uses. We present the related studies which have used social network information to provide personalized recommendation. Then, we introduce our original method of extracting implicit social network of researchers from the published papers and paper recommendation algorithm. We test the presented algorithm on real world datasets from Chinese social network site. The result indicates the advantage of recommendation based on implicit social network.

Learning to rank became a hot research topic in recent years and utilizing relational information in list-wise algorithms was discovered to be valuable and was widely adopted in various algorithms. These algorithms’ empirical performances were usually given, but few of them conduct theoretical analysis on the generalization bound. Based on the theory of Rademacher Average, we derive the generalization bound of ranking relational objects algorithms and discuss the effect on the generalization bound of using this method. Especially, an interesting property of ranking relational objects algorithms for Topic Distillation was discovered: the generalization bound does not depend on the size of documents in each query in training set. Experiments are conducted to verify this property.

Planetary gear reducer has a lot of advantages,such as high transmission and efficiency, compact structure, and has a variety of applications in construction machinery and equipment, hoisting and conveying machinery and so on,.The optimization design of the planetary gear train could make the volume at minimum(as well as the weight at minimum) under the conditions of carrying capacity.This paper focus on the optimization of two stage planetary gear train with the differential evolution algorithm, based on Mathmatica. The author established mathematical model and source program is present in this paper. After the optimization, the author verifies the optimal result, including the contact fatigue stress and tooth bending strength fatigue stress. The verification infers that the optimization based on Mathmatica with differential evolution algorithm is effective and correct.

With the development of mobile technology and popularity of pervasive applications, more and more scientific collaborative research works are carried out

in the wild

or

on the move

. How to make an optimal tradeoff between deep collaboration and strict access control in mobile environments is a challenging work. In this paper, we propose

Mobi-CoSWAC

, an access control approach for collaborative scientific workflow in mobile environments. In our approach, ranked access permissions will be provided to users for continuous collaboration in disconnected settings, as well as the owned access permissions can be dynamically assigned according to the role’s evolution in various mobile contexts. The model and access control algorithms of

Mobi-CoSWAC

are elaborated and a prototype is implemented on Android mobile devices in collaborative proteomics research platform

CoPExplorer

to demonstrate its effectiveness.

Recently learning to rank has been widely used in real-time Twitter Search by integrating various of evidence of relevance and recency features into together. In real-time Twitter search, whereby the information need of a user is represented by a query at a specific time, users are interested in fresh messages. In this paper, we introduce a new ranking strategy to rerank the tweets by incorporating multiple features. Besides, an empirical study of learning to rank for real-time Twitter search is conducted by adopting the state-of-the-art learning to rank approaches. Experiments on the standard TREC Tweets11 collection show that both the listwise and pairwise learning to rank methods outperform baselines, namely the content-based retrieval models.

A Curriculum Vitae (CV) or a résumé, in general, consists of personal information, education information, work experience, qualifications and preferences parts. Scanning or making structural transformation of the millions of free-formatted résumés from the databases of companies / institutions with human factor will result in the loss of too much time and human effort. In the literature, a limited number of studies have been done to change the résumés of the free-format to a structural format. The overall objective of the study is to infer required information such as user’s experience, features, business and education from résumés of the potential user of a human resources system. In this article, we proposed an ontology driven information parsing system that is planned to operate on few millions of résumés to convert them structured format for the purpose of expert finding through the Semantic Web approach.

As the development of Service-oriented architecture and service engineering, they have been generally adopted as the architecture and engineering method of software. More and more service applications and systems are constituted by distributed resources and web services which means more challenges in dynamic, varied and complex network environment. At the same time, the dependence and interactivity between the elements of networked service systems result in faults and difficulties in understanding and upgrading system and making the systems much more weakness. In this paper, according to the dynamic characteristics of the networked service system, we propose a network characteristics measure method and metrics for service system (MSS). Service system is defined by the descriptions of six important parameters at system level, which include service complexity, service cooperation relationship factor, service node factor, service cooperation factor, and service composition factor. Then the corresponding simulation is introduced by using the characteristics measure method and the analysis of the simulation results is also given. At last, a dynamic on-demand service composition algorithm based on MSS is designed and its feasibility and effectiveness are verified.

Although many algorithms have been developed to achieve privacy preserving data publishing, few of them can handle incomplete microdata. In this paper, we first show that traditional algorithms based on suppression and generalization cause huge information loss on incomplete microdata. Then, we propose AIM (anatomy for incomplete microdata), a linear-time algorithm based on anatomy, aiming to retain more information in incomplete microdata. Different from previous algorithms, AIM treats missing values as normal value, which greatly reduce the number of records being suppressed. Compared to anatomy, AIM supports more kinds of datasets, by employing a new residue-assignment mechanism, and is applicable to all privacy principles. Results of extensive experiments based on real datasets show that AIM provides highly accurate aggregate information for the incomplete microdata.

Computational CyberPsychology deals with web users’ behaviors, and identifying their psychology characteristics using machine learning. Transfer learning intends to solve learning problems in target domain with different but related data distributions or features compared to the source domain, and usually the source domain has plenty of labeled data and the target domain doesn’t. In Computational CyberPsychology, psychological characteristics of web users can’t be labeled easily and cheaply, so we “borrow” labeled results of related domains by transfer learning to help us improve prediction accuracy. In this paper, we propose transfer learning for Computational CyberPsychology. We introduce Computational CyberPsychology at first, and then transfer learning, including sample selection bias and domain adaptation. We finally give a transfer learning framework for Computational CyberPsychology, and describe how it can be implemented.

According to the decentralization of modeling tasks caused by user who is essentially nondeterministic and highly individual in Ambient Intelligence (AmI) environment, the mental model, plan model and behavior model are introduced to describe both static features and dynamical behavior of user in AmI environment. With the interactive model based on multi-agent, a formal approach to model user is proposed at first. Meanwhile, the relations between agents and AmI system are discussed in detail. The path which maps the natural scenario of an AmI environment into a real system shows that our models can help designers capture user’s static features and dynamic behavior, and these relations between agents and AmI system can help designer to manage and track AmI system from its requirements to implementation through as well.

Multi-robot system provides more advantages over a single robot. In certain situations, robots need to maintain global connectivity while proceeding tasks such as traveling some interested spots in an area. This paper formulates the

Multi-Robot Traveling Problem Constrained by Connectivity

, and proposes a

Connected Nearest Neighbor

solution aiming to minimize the total traveling distance of the robots, which performs nearly twice better than previous work. Additionally, it is load balancing, fast in response, and robust to environmental dynamics and robot failures. Further improvements of the solution are also discussed and developed. Simulations are designed to investigate the cost of maintaining connectivity, the influence of different environments, and the comparison among the algorithms.

The Vehicle Routing Problem of Logistics and Distribution is a hot and difficult issue in current field of combinatorial optimization, therefore this paper presents an improved genetic algorithm. The algorithm which applied the idea of Saving Algorithm to the initialization of groups, and improved algorithm on selection operator and cross operator, In the meantime, it proposes a new way to calculate the adaptive probability in the cross operator. In addition, it also introduces a novel CX crossover operator .By the way of simulating experiments of the Vehicle Routing Problem, it demonstrates that the improved genetic algorithm enhanced the ability of global optimization, moreover it can significantly speed up convergence efficiency.

Due to limited resources, the slow progressive development of wireless sensor networks (Wireless Sensor Network) through the development of hardware and power management technology is currently in progress for the development of the latest IP-based IP-WSN. Those with low-power devices on IPv6 can mount the 6LoWPAN (IPv6 over Low power WPAN) this is getting attention. In these IP-based sensor networks, existing IP-based schemes, which were impossible in wireless sensor networks, become possible. 6LoWPAN is based on the IEEE 802.15.4 sensor network and is a technology developed for IPv6 support. The host-based mobility management scheme in IP-WSN is not suitable due to the additional signaling; the network-based mobility management scheme is suitable. In this paper, we propose an enhanced PMIPv6 route optimization, which considers the multi-6LoWPAN network environment. All SLMA (Sensor Local Mobility Anchor) of the domain 6LoWPAN is connected to the SPIG (Sensor Proxy Internetworking Gateway) and perform cross-domain distributed mobility control. All information of SLMA in the 6LoWPAN domain is maintained by SMAG (Sensor Mobile Access Gateway) and route optimization is performed quickly and the route optimization status information from SPIG is stored to SLMA and is supported without additional signaling.

This system is a virtual assembly one of a type of cylinder cover which based on OSG. The system uses OSG the 3Dgraphics engine to realized model rendering, human-computer interaction, stereoscopic displaying, parts dragging, animation path and other related technologies. At the same time using CEGUI to achieve the interaction of the virtual scene and a graphical interface .Users can directly use the mouse or keyboard to realize some operations such as translation, rotation. The system better achieved virtual simulation of training and teaching of a type of cylinder cover for the user to save costs and improve work efficiency.

Web service technologies have been widely used in diverse applications. However, there are still many security challenges in reliability, confidentiality and data nonrepudiation, which are prominent especially in some Web service systems that have massive resources in diverse forms. An enhanced mechanism for secure accesses of Web resources is presented and implemented based on the combination of modules of identity authentication, authorized access, and secure transmission to improve the security level of these systems. In the identity authentication, the highly safe and recognized authentication method U-Key is used. For the aspect of authorized access, the integration of an improved Spring Security framework and J2EE architecture is applied to ensure authorized access to Web resources, while the security interceptor of Spring Security is extended and a series of security filters are added to keep web attacks away. Moreover, some improvements of the XML encryption and XML decryption algorithm are made to enhance the security and speed of data transmission, by means of mixing RSA and DES algorithm. The above security mechanism has been applied to an online virtual experiment platform based on Web services named

VeePalms

. The experimental results show that most security problems with high severity in the system have been solved and medium-low severe problems degreased dramatically.

Coordinated Multi-Point (CoMP) transmission is a promising technique to improve the coverage of high data rates, the cell-edge throughput in cellular networks, in which centralized CoMP can obtain global optimal cooperation when CU can gather CoMP information (including CSI) and send scheduling decisions without delay. However, due to the imperfect channel characteristic, there is CSI latency during the cooperation, which will decrease the CoMP gain. In this paper, we propose a modified centralized CoMP scheme called DAHCS (Data-Apart Hybrid Centralized Scheduling), in which user data process is set apart from CU and a hybrid structure scheduling is adopted to schedule CoMP UEs intensively and non CoMP UEs locally. Simulation results show that the proposed DAHCS CoMP outperforms the traditional method when a lower capacity/higher latency backhaul link is considered.

Spatiotempoal data model is fundamental to geospatial data representation, organization, analysis and applications. Due to the absence of geospatial semantic modeling and its logical structure, the spatiotemporal data may be interpreted mistakenly by other users or by other systems. For multi-purpose urban geospatial information development and sharing, spatial entities and their semantics should be modeled carefully. This paper presents strategy in geo-ontology-based object-oriented spatiotemporal data modeling to design a geospatial data model which can solve the problems of semantic conflict, and is ease of backtracking query and data sharing. A new approach is proposed to enterprise-level urban geospatial data integration, management and sharing. Modeling experiences show that it is easy to storage, manage and distribute spatiotemporal data for urban enterprise-level GIS applications in the network environment.

Spatio-temporal data model has been recognized as the foundation of the description of spatiotemporal characteristic for geographic entity. In this paper we present a new way to construct seamless spatio-temporal data model and explore the spatiotemporal evolution and the data storage process for a spatial object from both space and time point of view. In addition, this data model has been used to organize and manage city cadastral information for evaluation purpose. It will improve the ability to construct, save and query integrated spatio-temporal data during the dynamic evolution process of geographic entity in our physical world.

As one of the most important parts in the wind turbine gear increaser, the planet carrier plays a role that is irreplaceable in the whole machine equipment. Co-simulation technology is one kind of new method which owes the characteristic of higher accuracy than the traditional safety coefficient method. Because the planet carrier has the features of irregularly shaped and working in alternating dynamic environment, co-simulation technology of ADAMS-ANSYS is applied to the mechanical research of the planet carrier in wind turbine gear increaser. The dynamic simulation of the whole gear increaser system is conducted based on the ADAMS platform in which the compact force、friction force and step driving are taken into consideration, with the maximum forces which are forced on the planet carrier calculated out. In the process of finite element analysis, there are three important steps which are modeling、meshing process、force application and result calculation. Strength check of the planet carrier with all kinds of dynamic elements into consideration is completed by taking the calculated maximum forces as the load inputs in the force application process. The planet carrier designed through the co-simulation method is lighter and more material-saving than it is through the safety factor method.

IEEE 802.16e is a widely used standard for mobile broadband wireless access system and Orthogonal Frequency Division Multiplexing (OFDM) is employed in this system. So, it can perform well in anti-multipath fading and spectrum utilization. But there are also several critical problems, one of major drawbacks of this system is the high Peak-to-Average Power Ratio (PAPR) of the transmit signal. Thus the power amplifier in the transmitter requires a wide dynamic range or to be backed off, to prevent spectrum spreading issued from nonlinear distortion and BER rising issued from in-band distortion. But that will increase the system cost or lead to low power efficiency.

This paper firstly gives a brief of some important PAPR reduction techniques for multicarrier transmission, and then proposes a new PAPR scheme for 802.16e Wireless OFDM physical layer by using clipping and tone reservation (TR), and also considering the amplitude scale factor to reduce the computation cost and the system complexity. This new scheme has a very low out-of-band distortion, requires no side information and gains better PAPR performance by sacrificing fewer sub-carriers. At last, the performance of this new technique on PAPR reduction, BER, and spectrum efficiency has been analyzed through simulation derived from MATLAB. Furthermore, the simulation statistics has been compared with other related techniques.

A class of new cloud computing model DCaaS is proposed. This model combines traditional DNA computing with SaaS model. The main advantage of DCaaS model is to separate biological experiments with DNA computing, and obtain biological operations as a service via DNA programs. As application frame, approximate solution of a class of nonlinear problems is presented.

Mobile cloud computing (MCC) is introduced as a supporting architecture for mitigating the resources and energy limitations of mobile devices. And security is considered as a key factor which significantly affects MCC’s widely deployment. Thus data protection methods such as encryption were applied to ensure the security of data. However, the complexity increased by data protection methods brings down the performance of transaction processing in MCC. In order to reduce the hurts brought to users’ satisfactory by data protection methods, we firstly carried out some experiments based on the Hadoop cloud computing platform for quantitative analysis. We used the two popular encryption algorithms DES and AES(512), simulated the transaction processing, measured the time consumption, and logged the battery voltage change of mobile terminals. Results show that data protection methods have significance effects on the efficiency of transaction processing and the energy consumption of mobile devices. Furthermore, the degree of these effects is closely related to the complexity of protection methods. Based on the analysis, this paper proposed a framework for dynamic data protection, expecting to balance the security and resource consumption in MCC.

Context-oriented Programming (COP) has been proposed as a new promising paradigm for programming context-aware applications in pervasive environments. However, the expressive power of the layer abstraction in current COP extensions is limited, as layers are only suitable for expressing context with boolean or nominal values and lack support for describing temporal property of context. Besides, partial methods of COP are defined only on top of each single layer but not on a combination of layers, and the behavior of an invoked method is unpredictable as it is determined by the layer activation order. In this paper, we enriches COP by replacing layers with well-structured

context entries

. Each

context entry

specifies a piece of context information as well as its temporal property, and a

context

is represented as a set of many

context entries

. Many new

operations

are introduced to manipulate

context

more conveniently. Furthermore, partial methods are now defined on the notion of

situation

which is expressed as a combination of multiple

context entries

, and the evaluation order of multiple active partial methods can be explicitly specified so that the behavior of an invoked method becomes predictable. An implementation on top of

ContextJ*

and experiments for evaluating it in term of time overhead are also presented in this paper.

When solving the electromagnetic issues, the Finite Difference Time Domain method is proposed. It is a simple and effective solution to calculating the dispersion and radiation of the electromagnetic wave in time domain. However, it only could be used in the limited space area as the capacity of computer memory has limitation. Thus, the perfectly matched layer absorbing boundary condition is necessary to simulate the open areas electromagnetic issues. Avoiding the human error accumulation, the numerical stability conditions should also be introduced in the electromagnetic scattering simulation. When the researchers is considering the above mentioned factors, the simulation results must be get faster and also be more accurate.

This study is based on the principle of the finite difference time do-main method (FDTD), which is widely applied in the electromagnetic calculation, on the conditions of reasonable the appropriate baseband Gaussian pulse excitation source choice and also setting a Perfectly matched layer (PML) absorbing boundary conditions. Give full consideration to the numerical stability conditions, it should select the appropriate time step and space step. This paper simulates the TM wave scattering in the two-dimensional space by Matlab, bring out the electromagnetic wave scattering situation of square cylindrical.

With the development of the internet, the number of malicious programs rises observably, which has become one of the main threats to national basic network, information system and so on. Besides, the analysis of malicious programs has a sharp contradiction between accuracy and speed, and how to analyze malicious programs quickly and efficiently has become a popular research direction in safety field. This paper proposes an analysis framework that applies grid technology, the core of the framework is to design an optimized system architecture and an efficient scheduling algorithm to be able to quickly scheduling and analyze risk files in parallel to improve the efficiency of risk files analysis greatly. In order to exhibit the framework better, we develop a set of portlets to present analysis framework into the web portal framework.

To satisfy the need of data processing speed, and increase cache hit rate of traditional hash indexing structure in Main Memory Database (MMDB), a page-based cache-sensitive hash indexing structure is proposed in this article. While maintaining the processing speed, storage efficiency, this structure greatly enhances the processor cache hit rate. This article describes the design and implementation of this cache-sensitive hash indexing structure in detail. After that, theoretical analysis and simulation experiments are performed. We come to a conclusion that for real MMDB, this new hash structure improves cache hit rate greatly and the whole database becomes more effective than traditional ones.

Modern Critical infrastructures have command and control systems. These command and control systems are commonly called supervisory control and data acquisition (SCADA). In the past, SCADA system has a closed operational environment, so these systems were designed without security functionality. Nowadays, as a demand for connecting the SCADA system to the open network growths, the study of SCADA system security is an issue. A key-management scheme is critical for securing SCADA communications. Numerous key-management structures for SCADA also have been suggested. 11770-2 Mechanism 9 Key establishment Protocol has been used in SCADA communication however a security proof for the 11770-2 Mechanism 9 protocol is needed. The purpose of this paper is to provide a general overview about SCADA system, and its related security issues. Furthermore, we try to investigate the importance of key management protocol and the need of formal security poof.

As an import trend of cyberspace in the future, cloud computing has attracted much attention from the IT industry. Many research institutions and companies have launched their own cloud platforms, which have virtual machine schedulers to manage the infrastructure resource pool. The virtual machine scheduling modules in these platforms are built in the platform and it is hard for developers to re-program. Since developers cannot design and implement special policies in the platform, the flexibility of the virtual machine scheduler is poor. Furthermore, the schedule architecture which has a firm and unchangeable interface is designed and customized for one kind of cloud platform. It leads to poor portability. To target the problems above, this paper presents a dynamic and reusable scheduling system for cloud infrastructure service, called Cranduler, which introduces the advantages of cluster schedulers to the virtual machine scheduling in cloud infrastructure. The scheduling policies of Cranduler could be dynamically configured by developers. Developers can easily insert the custom policy. In addition, Cranduler provides a set of unified interfaces to the cloud platform, which make the system easily access resources from different cloud platforms and be reused in different cloud platforms.

With the increasing deployment of large-scale virtualized datacenters, using virtual machine (VM) migration technology to consolidate VMs is becoming very important for improving the efficiency of data center. The primary prerequisite for VM consolidation is to determine the best candidate VM for migration, and the most previous work targets only on optimizing single objective in VM selection. In this paper, we first propose a multi-objective optimization model based on detailed analysis of the impact of CPU temperature, resource usage and power consumption in VM selection. We then develop a VM selection algorithm to optimize the synthesized effect of VM migration, which will ultimately improve the system performance of physical machines (PMs). We further evaluate our algorithm by comprehensive experiments based on VM monitor Xen, and the results show that it can achieve the best tradeoffs among the resource usage, CPU temperature and power consumption of data center.

This thesis uses the virtual simulation of the new screw pile hammers as an example, and puts forward a virtual simulation method based on a combination of multi-software platforms. It simply introduces how to design the size of the machine, and check the strength of each part with the help of ANSYS. Then focuses on the introduction to the working process of this virtual simulation method, achieves the goal of seamless connection of different software, the build of 3d model of the pile hammer, and the production of the virtual pile hammer. Meanwhile, the virtual simulation of four processes demonstrates the functions of the new screw pile machine in different stages and the principle of piling. Compared with the traditional method which stimulates and analyzes with a single software, the practical applications demonstrate that, the virtual stimulation method based on a combination of multi-software platforms is easier to operate, more flexible to modify and more authentic.

In order to find useful information efficiently among vast amounts of information, Pirolli built the basic social information foraging (SIF) model (basic SIF model) and proposed related theories. Traditional information foraging theory proposed that information foraging process is divided into within-patch and between-patch. This paper focused on the interference effect between patches in the social information foraging environment, which has not been covered by the SIF model. Then this paper proposed that group diversity play an important role in this process and built the interference cost model of this process to improve the social information foraging model. Experimental results show the correctness and practical value of this model.

Membrane computing has a characteristic of great parallelism, so it has been applied in broad fields such as Biological modeling, NPC problems and combinatorial problems by reducing the computational time complexity greatly. In this paper we approach the problem of hierarchical clustering with a new method of membrane computing. An improved P system with external output is designed for finite set individuals with nonnegative integer variables. In the process of hierarchical clustering, the clustering is obtained depending on the dissimilarity between individuals or groups, so the less dissimilar two individuals are, the more similar they are. For an arbitrary matrix

P

Nk

representing the values of N individuals, one possible hierarchy with clusters can be obtained by this improved P system in a non-deterministic way. The time complexity is polynomial in the number of individuals, the number of variables and the certain maximum value

A

*

without increasing the complexity of the classical clustering algorithms. At the end of this paper, we cluster an example of dataset to obtain the final results. Through example test, we verify the feasibility and effectiveness of this improved P system to solve hierarchical clustering problems. A greater range of hierarchical clustering problems will be solved with this improved P system.

In face of an increasingly competitive mobile communications market, how to keep the customers is the most important factor for operators and enterprises. The development of enterprise cannot be separated from customers. In general, clients always require more stable and better quality of network services, and more rapid response to their demand. The operators keep of making customers’ more satisfactory by a deal of services. In this paper, we study how to position the mobile terminal by analyzing user communication data which includes the base stations, the communication signal data, user information, and so on. The text shows the reasons that affect the positioning accuracy of mobile network. At last we propose a fast locating algorithm about the mobile terminals based on multiple base stations. The experimental results of current mobile network data show the efficiency of our method.

In this paper, we provide capacity and outage probability analysis with different sensing results for three different transmission scenarios in the cognitive radio networks, i.e., overlay, underlay, and cooperative transmission of secondary users (SUs) relaying for primary users(PUs) (CToSP) according to [1]. Numerical results show that CToSP model achieves better performance under given situations, and different sensing results impact much.

Since the population distribution on the earth surface is highly non-uniform, the traffic requirements are unbalanced in LEO (Low Earth Orbit) satellite networks, some ISLs(Inter-Satellite Link) of satellite networks are congested while others are underutilized.This work proposes a QoS routing protocol for LEO satellite networks called BQR (Balanced QoS Routing) on the Iridium Constellation that implements a balanced mechanism based on the population density. The occupancy factor has a classification by location of the satellite and changes according the countries where the satellite is flying above. The countries with high population will receive high value of occupancy factor that will help to calculate the ISL cost based on a combination of propagation and queuing delay. The routing table maintains many entrieswith different ISL cost for the same destination, a path with high ISL cost has less probability to be selected that will balance the satellite network traffic.The integration between OPNET and STK is used to simulate the proposed BQR routing protocol. The simulation results show that our protocol can achieve a better traffic load balancing that leads to minimize the end to end delay and optimize the throughput.

Aiming to extend the network lifespan through balancing the energy consumption, an EDRAND (Enhanced DRAND) algorithm based on the node’s residual energy is presented in this paper. The EDRAND can be considered as an improved DRAND in which slot sizes are determined by the residual energy of sensor nodes. By taking into account of the nodes’ residual energy and the network traffic, an E-ZMAC (Energy-ZMAC) protocol which employs energy control and traffic adaptive mechanism in the Z-MAC protocol, is introduced. In the presented protocol, a node in the network will switch to sleep mode for a while within its slot to reduce power consumption and network traffic. The time span in the sleep mode of a node will be shortened with the growth of the network traffic. A node will switch to TDMA mode when the network traffic reaches a certain upper bound. By doing so, the channel access mode is enabled to transit smoothly from competition-based to scheduling-based. In the presented methodology, the contention window size will be increased with the growth of the network traffic, and the backoff span will be negatively related to the amount of residual energy. To validate the presented approach, a group of simulations are conducted in NS2 under different scenarios. The simulation results have demonstrated that the proposed E-ZMAC protocol outperforms Z-MAC protocol in terms of network lifespan, throughput and energy consumption.

Data centers generally consume an enormous amount of energy, which not only increases the running cost but also simultaneously enhances their greenhouse gas emissions. Given the rising costs of power, many companies are looking for the solutions of best usage of the available power. However, most of the previous works only address this problem in the homogeneous environments. Considering the increasing popularity of heterogeneous data centers, this paper investigates how to distribute limited power among multiple heterogeneous servers in a data center so as to maximize performance. Specifically, we optimize the power allocation in two case: single-class service case and multiple-class service case. In each case, we develop an algorithm to find the optimal solution and demonstrate numerical data of the analytical method respectively. The simulation results show that our proposed approach is efficient and accurate for the performance optimization problem at the data center level.

This paper enumerates and analyzes problems existing in the process of printing and filing engineering and architectural drawings/records used in railway design. An archiving and processing system is proposed that can plot and file drawings centrally and intelligently based on the requirements of integrating information systems in railway survey and railway design hereinafter. Technical difficulties of designing and implementing such a system are discussed. These include auto detection and extraction of PLT drafting zone size, plotter load balancing strategy based on entropy, consistency of paper documents and the respective electronic documents, and etc. The initial and preliminary evaluation of the proposed approach is promising and showed the feasibility in real-life settings.

Full virtualization is vastly applied in desktop virtualization. Although hardware-assisted virtualization greatly improves the performance, the interactive performance is still a bottleneck for full virtualization. Interactive performance is mainly influenced by I/O devices. In one hand, I/O devices are slow device. In another hand, they are often shared by multiple virtual machines through simulation. Our study focuses on the interactive performance optimization, and can mainly be classified into three categories: (1) Targeting multi-core system, we investigate virtual machine deployment to ensure stable performance of the whole system and individual virtual machine. (2) Dynamically adjust the resource among virtual machines based on the individual machine’s interactive behavior. (3) Optimize the I/O request scheduler in term of the virtualization implementation.

With the development of high performance computing (HPC), clusters play a more and more important role in a lot of scientific fields such as graphics, biology, physics, and climatology etc. Therefore, monitoring becomes more critical for sufficient utilization of clusters considering the limited availability of HPC resources. However, the traditional clusters monitoring systems have a common disadvantage of poor mobility which limits the efficiency of cluster management. For this reason, the mobile clusters monitoring system designed on Android platform is presented in this paper that will make it possible to monitor the whole cluster anywhere and anytime to allow administrators to manage, diagnose, and troubleshoot cluster issues more accurately and promptly. The monitoring system is developed on the popular monitoring tools, Ganglia and Nagios, to collect required information from server and display on client – mobile phone. The remote monitoring system also provides the communication module and the other applications such as instant messaging, global positioning and shift changing notice. This system makes clusters monitoring more flexible, efficient, and convenient. Most importantly, the method described in this paper can provide a possibility to customize and extend functions based on user requirements.

Load balance is critical for large-scale storage systems to produce high I/O performance. Decentralized solutions are especially preferred for no single point of bottleneck. We implement four typical hypercube-based decentralized load balancing algorithms in a prototype storage system, and conduct extensive experiments with the system running on a testbed comprising 32 nodes. We compare the efficiency and scalability of the four algorithms through the experiments. The comparison results lead to the following new observations contrary to the conclusions obtained in previous simulation studies. Firstly, algorithms with no redundant load migration do not actually achieve savings of migration costs. Secondly, algorithms tolerating a certain degree of redundancy in load migration may achieve improvements in scalability. The two observations provide new insights into the design of load balancing algorithms in distributed storage systems.

Opportunistic networks is aimed to provide a solution for communication in disrupt tolerate networks, which has attracted lots of researches. However, for reasons such as finite buffer size and energy of nodes in opportunistic networks, the further improvement of network performance is limited. Therefore, we set powerful cluster nodes in the network to form hierarchical opportunistic networks. In this paper, a publish/subscribe scheme in hierarchical opportunistic networks is put forward. The object of publish/subscribe scheme is to realize the sharing of resources between nodes. In our scheme, nodes disseminate their data requests with a TTL value to limit their flooding and after receiving the subscribed data items, an ACK will be flooded to delete the useless data packets in the network. To choose appropriate relays for data packets, a utility function to estimate the relay ability of nodes is designed, what’s more, cluster nodes are brought in as relay nodes too. Simulations are given to demonstrate the performance improvement of our scheme.

Alliance is the trend of third party logistics’s development, the management of third party logistics can be improved by the advantage of e-commerce information technology. This paper discusses the development status of third party logistics enterprise, analyzes the characteristic of the third party logistics alliance. A third party logistics alliances system based on e-commerce is established , the logistics alliance composition, structure and operation processes of the system are determined, then a system platform of a third party logistics alliance based on e-commerce is built. The result provides a theory reference for the development of the third-party logistics enterprise and e-commerce logistics.

Internet users’ access pattern for objects has been observed to follow Zipf’s law. The preference for network resource is showing strong influence on real-time lookup performance in large-scale distributed systems. In order to guarantee search response rate with limited memory space, we develop a new object indexing and locating algorithm called Bloom filter Arrays based on Zipf’s-distributed user Preference (ZPBA). The algorithm uses a compact data structure to achieve high accuracy in item lookup. We give the theoretical analysis of ZPBA and then conduct experiments with one million item corpus and 100,000 queries to validate our design. Comparison shows that our solution can be 77% more space efficient than traditional bloom filter based index approaches for applications of concentrated user access preference. The algorithm demonstrates practical application potential in fault tolerant large-scale distributed indexing and item lookup.

In this paper, a spectrum sensing module of cognitive radio for a high speed WLAN is proposed, which aims at a G-bit data transmission rate. Energy detection for spectrum sensing is selected due to its needless of knowledge of PU signal. Also, a new method of adaptive threshold generation mechanism is introduced and accomplished. Both simulation and implementation are done and the performance is analyzed. With all the above work completed, this potential application may be used for the future WLAN to achieve higher performance. In addition, the aspects of potential methods to improve the performance of this module are provided for further study.

Cloud Computing is promising as a new style of collaborative environment. Efficient Workflow Scheduling is crucial for achieving high performance in Cloud Computing environment. In spite of workflow scheduling has been widely studied. And various algorithms have been proposed to optimize execution time and cost. However the existing cloud services are owned and operated by third-party organizations or enterprises in a closed network. The uncertainty and unreliability existed in the network has caused great threat to the applications. Therefore trust services-oriented strategies must also be considered in workflow scheduling. This paper proposes a

Trust services-oriented multi-objectives Workflow Scheduling (TMOWS)

model. And a case study has been given to explain the proposed model.

Recently the flexibility of RDF data model makes increasing number of organizations and communities keep their data available in the RDF format. There is a growing need for querying these data in scalable and efficient way. MapReduce is a parallel data processing solution for processing large data-intensive workloads, which is not supported directly for join-intensive workloads. In this paper, we present a schema based hybrid partitioning technique for RDF triples placement according to the relationships between them, and reduce the necessary number of MR cycles in each SAPRQL query job. Then we propose a lightweight sideways information passing techniques which pass the join information across MR jobs to decrease the intermediate results involved in join operations. The experimental results show that our approaches achieve a substantial performance improvement, and outperform the previous system by a factor of 2-20 using LUBM benchmark.

In this paper, an iterative receiver using joint channel estimation and channel decoding is proposed for LTE downlink. In each iterative process, a modified LMMSE estimator is adopted to estimate the channel gain based on pilots as well as signals fed back from Turbo decoder. In contrast to a traditional LMMSE estimator, the modified estimator can make more precise channel estimation because a smaller spacing interpolation is performed with the help of feedback signals. The estimator and the decoder provide each other more accurate information in an iterative way, thus the performance of the receiver is improved. Three distribution patterns of the signals fed back from Turbo decoder with different density is compared, and the one with medium density is demonstrated to be the best by simulation results. To reduce the computational complexity of the receiver, cyclic redundancy check (CRC) is used to early stop the iteration. Simulation is done according to LTE physical layer specifications and the results show that the iterative receiver can provide about 50% accuracy improvement compared with a non-iterative receiver when SNR is 10dB. At the same time, the iterative receiver’s computational complexity is in the same order of magnitude as the non-iterative receiver.

An applied electronic lottery system is designed and constructed in this paper. In the system, electronic lottery has all characteristics which lottery based on paper has. It is a simple, facilitate, shortcut lottery sales system. Lottery buyer can buy lottery by Internet; it is impossible to counterfeit a lottery; the Personal information who win a prize in the lottery system should be protected privacy; if a person lost his lottery, he can find it back in the lottery number database which is convenience to inquire about; it can reduce the cost of lottery. It will help the players develop electronic lottery system with safety.

In the system of primary users and cognitive users sharing spectrum resource, dynamic spectrum allocation becomes a key issue to face with, especially when the services are various. A cognitive channel allocation scheme is put forward to maximize the throughput and to reduce the blocking rate of multiple services in the premise of not increasing the interruption probability. In the scheme, cognitive users do not need the capability of spectrum handover, and just by adjusting the index of channels timely, the allocation becomes orderly and simple. Markov chain is used to analyze the performance of the proposed allocation scheme. The numerical results show that the proposed scheme can achieve better performances.

The enormous increase in Internet traffic usage has been leading to problems such as increased complexity of routing topology, explosion in routing table entries, provider-dependent addressing, which reduce the speed of network service. The emerging new techniques such as CDN, P2P, VPN, etc. speed up the network from different perspectives. A new speed up system called CANR, content aware and name based routing, is proposed in this paper, which integrates benefits of several existing mehanisms. CANR consists of a cluster of proxy peers deployed in different network domains, which can work as collaborative routers, forwarding requests to each other to speed up the cross-domain visits. CANR can automatically aware the changes of network and re-construct name-based routing table based on a new multi objectie k shortest algoritm by itself, finding a set of cheapest and most fast k routing paths, which is different from current static preconfigured systems.

The directed Hamiltonian path (DHP) problem is one of the hard computational problems for which there is no practical algorithm on conventional computer available. Many problems, including the traveling sales person problem and the longest path problem, can be translated into DHP problems. Inspired by the biological neurons, priority of rules in membrane computing, we introduce spiking neural P systems with priority and multiple output neurons into the application of DHP problems. In this paper, a new SN P System based algorithm is presented. We use neurons to stand for all the possible path and filter out the DHP we want automatically, all the processes will implement in the new SN P system. Instances indicate that the proposed SN P system based algorithm reduces the time complexity efficiently by huge parallelism.

The main goal of informatization top-level design is to reduce appearance of new isolated information islands. To find a kind of methods which restrains isolated information islands in informatization areas is our common goal recent years. TDMI as a sort of methods is a new type of methods for informatization top-level design. It aims at solving the isolated information islands problems from multi-level, such as informatization architecture framework, system architecture framework, data architecture framework. In this paper we discuss informatization top-level design for government areas using TDMI. The method firstly requires to describe the status and goals in government areas. Then we build the information architecture framework, systems architecture framework and data architecture framework. After that ,it starts into developing phrase. Lastly, they are integrated testing, testing run, run and maintenance phrase. The core of TDMI is to plan and restrain information systems design under the center for data.

Jack-up wind-power installation vessel is the most important tool in construction of wind farm. And the control system for hydraulic lifting platform is the key point of jack-up wind-power installation vessel. Therefore the design of the control system for hydraulic lifting platform with jack-up wind-power installation vessel is a basic and formidable problem. This article provides an integrated solution to the design of control system. Firstly, on the base of actual working condition, we introduce the control principle and basic requirements of the hydraulic lifting platform, such as the structure of leg, vessel gradient, alarm, calibration. Secondly we introduce the design solution of PLC (Programmable Logic Controller) control system, which includes the design solution of hardware as well as software for the hydraulic control system. In this part, we introduce the basic components and main function of hydraulic control system, hardware scheme, software design project. Then we introduce a good human-machine interface. Finally we do the work of program design according to the process of lifting unit. By using wincc-flexible and PLCSIM, we can layout the control module well and realize the man-machine interaction. At last, we can obtain a viable hydraulic control system and verify feasibility of the hydraulic control system by simulation software. At the same time, we obtain the working parameters of the lifting unit from the simulation.

This article mainly focused on interactive virtual assembly platform with interactive technology and motion control technology. Basing on the physical properties of the object, the authors put forward a method that combines dynamics and particle spring principle to realize the interactive motion control of flexible object on the virtual assembly platform. It adopts the node matrix operation method to realize the real-time assembly and disassembly. A 3D graphics engine tool Open Scene Graph (OSG) is used to complete the development of interactive virtual assembly and virtual disassembly platform of a crane. It realize the select, drag, assembly and disassembly of interactive parts. The physical properties of the object is displayed by physics engine It realizes the motion control of flexible object. The automatic assembly and roam of parts in the scene can be realized by using the function of event callback and update callback.

With the development of wireless sensor networks, more and more applications require the high data rate and real time decision making based on sensing data. In this paper, to achieve energy efficiency and quick reaction to real time event monitoring, a novel algorithms for collecting WSN data based on clustering is proposed. The algorithm depends on the similarity to cluster sensor nodes. In every cluster, only one representative node needs to report its data. Hence, the time slots of other nodes can be saved. When there is an event monitoring query offered by users, the query evaluation can return a query answer as soon as possible. Significant reaction time then can be saved. Furthermore, with the reduced reaction time, less sensor nodes are involved in the data communication, the energy efficiency can also be achieved in terms of transmission because of longer sleeping time can be guaranteed.

In this paper, we study the information dissemination in mobile ad-hoc network, where mobile nodes are randomly and independently distributed with a given density on a square. Nodes in network move following a random direction mobility (RDM) model. One piece of information is disseminated from source to all other nodes in the network, utilizing the basic epidemic routing protocol. We develop an analytical model based on Ordinary Differential Equation (ODE) approach, in which we take the transmission range as the critical and intuitive system parameter instead of pair-wise meeting rate. Typically, we proceed to study the impacts of overlap among the moving informed nodes on the percolation ratio and the delivery delay. The analytical mode is verified by simulations. This research captures the characteristics of information disseminated in mobile ad-hoc network.

Delay Tolerant Networks

(DTN) is one of the mobile wireless networks that topology logic may change frequently. Variable topology logic characteristics lead to several low efficiency routing problems, and in which how to increase the routing efficiency in low node density is an important issue that must be solved. The mainstream DTN routing strategy is multi-copy based routing and in order to solve the problem of flooding scale, the routing strategy always makes the messages flooding scale change under specific conditions called

Waveform Decreasing Multi-copy

(WDM) routing strategy: the flooding scale could increase or decrease. In this paper, we propose an improved routing strategy named WDM routing strategy aimed to improve the routing efficiency under low node density in DTN environment. Using several special mechanisms, the improved WDM routing strategy can obtain good performance in the case of low node density: transfer new messages first, special message transfer list, and the way of flooding scale changed. The simulation results show the WDM routing significantly improve the performance than the two mainstream routing: the Spray and Wait routing and the MaxProp routing.

Collaborative scheduling problem of container terminal is one of multi-objective, multi-restraint, multi-resources, dynamic NP-combination optimization problems. This problem is made more difficult by some uncertain factors, such as equipment failure, changeable climate and so on. It is difficult for General analytical method to solve it, so the mobile Agent system can intelligently percept sudden changes of environment in real time and then make intelligent decisions during the rapid development of wireless communication network.

In this paper, to reduce interfering of uncertain factors and coordinate the conflict of optimization purposes, based on analyzing and coupling uncertain factors, a hybrid distributed model of cooperative scheduling system is established through adopting computer architecture-based Agent by the bottom-up modeling approach combined with the central processor and bus technology.

The selection of hyper-parameters is a crucial challenge in Support Vector Machine modeling. Differed from using basic statistics of residuals in previous method, the new approach selects hyper-parameters by checking whether or not there is information redundancy in residual sequence. Furthermore, Omni-Directional Correlation Function (ODCF) is applied to test redundancy in residual, and the proof of the accuracy of the methodology is given in terms of numerical demonstration. Experiments conducted on benchmark time series, annual sunspot number and Mackey-Glass time series; indicate that the proposed method has better performance than the recorded in previous literatures.

To improve the quality of the recommendation of the recommendation system, a distance-interest affective model is proposed to combine user location context on the preferences of user interests. Based on the model and user-based collaborative filtering algorithm, the location context aware collective filtering algorithm is designed. Firstly, measure the location-similarity between users through the user’s location context information. Second, calculate the origin user-similarity from the user-item rating matrix. Then, gain the location-similarity as a weight of final user similarity, calculate the final similarity. Finally, recommendation is supplied by top-N recommendation. The simulation results were compared with the traditional algorithm to prove the precision and recall rate of the proposed algorithm is superior to traditional algorithms.

This article proposes a fuzzy C-means clustering analysis method, which is based on DNA genetic algorithm. DNA encoding is used to analyze the center of the cluster and the quality of clustering is judged by eigenvectors and the sum of Euclidean distance of the corresponding cluster center. Through selection, crossover, mutation and inversion operation the encoding of cluster centers can be optimized, thus to get the best cluster center of cluster division. According to the simulation results the effect of this method is superior to the genetic algorithm of fuzzy C-means clustering analysis.

With the development of cloud technologies, more and more electronic records will be stored and processed in the cloud. In order to manage massive electronic records, a kind of storage system named CloudDA is proposed in cloud environment in our paper. In the CloudDA, the HUABASE database and the THCFS file system are designed. HUABASE is used to store all kinds of structured data such as the metadata and index information. And the THCFS is used to store massive archives files. Finally, we design a kind of massive electronic records processing prototype system for digital archives in the cloud.

Ranking results in micro-blog search as user’s interests is challenging because of the special form of micro-blog search results. To attempt to solve the problem, in this paper, we summarize the characteristics of micro-blog search results, propose a method using a sort of decision model- binary logistic model, test the confidence level of the model and estimate the weight of the variable in the model collecting the real samples. The result shows the relation between user’s decision and the factors from each individual micro-blog search result as well as the feasibility of ranking using the model. We also analyze the model.

This study displays a method to recognize and segment the time-sequential postures of golf swing. It’s crucial to develop a system that can effectively recognize the steps of golf swing and facilitate self-learning of correct golf swing. First, a game controller, Kinect, is used to capture the 3D skeleton coordination of a golfer while performing swing. Second, a Hidden Markov Model (HMM) is applied onto the symbol sequence to recognize and segment the postures of golf swing. Results indicate that the proposed methods can effectively identify and categorize golf swing into 5 stages In conclusions, this developed golf swing training system is cost-effective as compared to traditional camera based golf swing trainer.

Recently, depression detection is mainly completed by some rating scales. This procedure requires attendance of physicians and the results may be more subjective. To meet emergent needs of objective and pervasive depression detection, we propose an EEG based approach for females. In the experiment, EEG of 13 depressed females and 12 age matched controls were collected in a resting state with eyes closed. Linear and nonlinear features extracted from artifact-free EEG epochs were subjected to statistical analysis to examine the significance of differences. Results showed that differences were significant for some EEG features between two groups (

p

<0.05) and the classification rates reached up to 92.9% and 94.2% with KNN and BPNN respectively. Our methods suggest that the discrimination of depressed females from controls is possible. We expect that our EEG based approach could be a pervasive assistant diagnosis tool for psychiatrists and health care specialists.

To promote the performance of Opportunistic Networks (ONs), novel network architecture with Fixed Infrastructure Nodes (FINs) is proposed. FINs have large storage capacity and short-range wireless communication ability. Two location strategies of FINs are compared in this paper including location at Points of Interest (POIs) and location at hot spots with high traffic. Routing protocol named PROPHET-F is proposed to employ the FINs’ ability. The main difference between PROPHET-F and original PROPHET is that FINs own the highest message forwarding priority. FINs can collect all messages carried by the nodes passing by the FINs and transfer to other proper mobile nodes. The performance of this novel architecture is evaluated in ONE simulation platform. The simulation results indicate that Message Delivery Probability (MDP) in ONs with FINs is improved. MDP is higher while FINs are located at hot spots than at POIs. Furthermore, MDP of PROPHET-F protocol is superior to that of PROPHET.

In the paper, an algorithm based on DNA computing which can solve the elevator scheduling problem is improved. Considering the inefficiency of the existing algorithm caused by the large scale of the initial solution space, the author introduces a new conception –"connecting strand" to help produce the initial solution space in the new algorithm. “Connecting strand” can connect those rational DNA strands encoding different elevators’ running routes into one and the strand obtained just stands for the “sum-route” of the elevator system. With the help of “connecting strand”, the size of initial solution space is largely reduced and the performance of the algorithm is thus improved. In the end, the author proves the effectiveness of the algorithm by a simulation.

This paper describes the current status and requirements of Campus Network operation &maintenance management, introduces ITIL to the campus network management as a new common framework, and points out the need for building a unified operation & maintenance management system. Through the establishment of the network operation & maintenance management and service based on ITIL Service Desk, it is possible to improve business processes and gradually standardize the campus network operation & maintenance management and service.

In this paper a clustering algorithm based on a P System with active membranes is proposed which provides new ideas and methods for cluster analysis. The membrane system has great parallelism. It could reduce the computational time complexity. Firstly a clustering problem is transformed into a graph theory problem by transforming the objects into graph nodes and dissimilarities into edges with weights of complete undirected graph, and then a P system with all the rules to solve the problem is constructed. The specific P system with external output is designed for the dissimilarity matrix associated with n objects. First all combinations of all nodes are listed to show all possibilities of the paths (the solution space) by using division rules of P system. Then a shortest path with the minimum sum of weights is selected. At last the path is divided into k parts from the edges with the k-1 biggest weights according to the preset number of clusters k. That is to say, all nodes are divided into k clusters. The calculation of the P system can get all the clustering results. Through example test, the proposed algorithm is appropriate for cluster analysis. This is a new attempt in applications of membrane system.

Location is a key context in the location-based services (LBS) which have been well studied in the domain of pervasive computing. The radio frequency (RF) based positioning plays important role in the LBS applications due to the good resolution, clear identification and low cost. However, the signal collision is a very critical issue which determines the system accuracy and throughput, especially in high-accuracy positioning such as UWB (Ultra Wide Band) and CSS (Chirp Spread Spectrum). CSS is an emerging technology which can offer highly-accurate positioning with TOA (Time-Of-Arrival) based manner similar with UWB, but is cheaper than UWB. This paper proposes a novel ALOHA-based algorithm for the signal anti-collision in CSS based positioning, which can guarantee the high throughput. Experimental results show the algorithm can maintain competitive throughput with the guaranteed accuracy in comparison with the known algorithms.

Research on Internet of Things (IoT) and relevant applications has attracted more and more attention from both academic and industrial communities. In this paper, we focus on a particular use case of IoT, bridge health monitoring that ensures bridge health security and detects hidden defects of the infrastructure. Using total station technology, acceleration sensor technology and fiber grating technology, this application monitors a wide range of parameters, e.g. deformation of bridge structure, relative displacement of arch foot level, skewback uneven settlement, main beam distortion, suspender force, rid temperature and strain of main beam, in real time and online, so as to collect, transmit, store, statistical analyze bridge health information, and perform remote monitoring and warning timely and accurately.

Our system improves the comprehensive monitoring efficiency, assists decision-making for bridge monitoring, management and maintenance and ensures the safety of the bridge infrastructure. The paper based on real-life scenarios, offering IoT technology in the health monitoring application case study at the Yan-Cheng Century Avenue Tong-Yu River in the Jiangsu province. Our paper demonstrates how the theory is combined with practice in real-life project, that will provides useful insights and learnt lessons to projects aiming at similar applications.