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The goal of Hyper-Spectral Unmixing (HSU) is to decompose HSC imagery into a group of pure material spectral signatures also known as endmember signatures and fractional proportions weights that characterize the contribution of each endmember in forming a sample. In 1999, Winter’s proposed an idea to address HSU that considers vertices of a simplex whose volume is maximum as pure pixel vectors. In hyperspectral remote sensing, this belief has much influence on HSU, especially on endmember extraction methods. Moreover, this belief has inspired much attention, resulting in various endmember extraction frameworks such as Simplex Growing Algorithm (SGA), N-point FINDeR (NFINDR), Alternating Volume Maximization (AVMAX), Successive Volume Maximization (SVMAX). In this paper, we propose an ensemble of these frameworks intending to utilize the best part of the result of each framework. The proposed ensemble framework uses a majority voting approach. Our experiments, applied on four hyperspectral datasets (Cuprite, Urban, Samson and Jasper), expose that the ensemble framework by majority voting can provide efficient and competitive performance compared to individual winter’s belief-based endmember extraction frameworks.

Down syndrome is a genetic disorder in which disrupts infants, physical and cognitive development. Down syndrome is characterized by the absence of nasal bone during the late first trimester of pregnancy. Presently Down syndrome is identified by visually examining the ultra sonogram image of foetus of 11 to 13 weeks of gestation for the presence of nasal bone. The visually identification by the change in the contrast of nasal bone region of ultra sonogram is a very difficult task. So the image processing based features extraction by considering various parameters have been extremely important. This paper provides comprehensive survey on various medical imaging techniques that can be effectively used for detecting the syndrome in the early stage of pregnancy. Our proposed survey consider different methods based on the various parameters extracted using a series of operations such as Region Of Interest (ROI), Nasal Bone (NB) segmentation using morphological, Otsu thresholding and logical operations from the ultra sonogram images, both in spatial domain as well as transform domain using Discrete Cosine Transform (DCT) and wavelet transforms. The extracted data is normalized and used to train classifiers like Back Propagation Neural Network (BPNN). This paper illustrates overview of various states of methods available in the Down syndrome detection and comparison analysis of each method is discussed.

Efficient and safe distribution network is crucial to the economic efficiency and competitiveness of enterprises. Based on current distribution status of the Measurement Center of State Grid Tianjin Electric Power Company, this paper aims to build a multi-product distribution route optimization model with the minimum total cost and maximum vehicle loading rate for different distribution demand characteristics. The model is solved and applied by CPLEX software and verified with actual data. The model can not only meet the existing requirements for distribution services, but also provide a solution on scheduling program for distribution vehicles in case of urgent demands. Our conclusions will help firms to save costs, improve distribution efficiency and meet the customer satisfaction’s requirements.

Internet of Things (IoT) is a scientific advancement that is likely to transfigure our traditional way of life by automating everything connected to the internet. Smart physical devices/objects have become indispensable part of our lives thereby making our being simplified and way more organized. Internet of Things encompass and empower several heterogeneous physical devices/objects for sensing and gathering information from surroundings, consequently exchanging it in a seamless mode to realize smart application goals without human intervention, for instance, home automation, smart cities, smart agriculture, and smart industries etc. The growth in the number of such wired/wireless physical devices/objects and the speed of that growth raises concern over security. To be able to implement a smart world with such billions of physical devices/objects, appropriate mechanisms are required to provide secure end-to-end communications amongst them. In addition to it, a variety of attacks may occur in various layers of Internet of Things. Without a well-founded and congruous Internet of Things environment, budding Internet of Things applications cannot be exploited to their full potential. Hence, reliability, security and recovery from attacks are amongst the most unsparing sides of the Internet of Things. In this paper, we have presented a comprehensive review of Internet of Things security issues followed by security measures in Internet of Things. The focus in this paper is also on research challenges and application domains of Internet of Things.

In this paper, we present the mobile autonomous underwater vehicle (AUV)-aided efficient localization scheme for underwater wireless sensor networks (UWSNs). Localization is one of the major issues in UWSNs as it is important in some large scale applications to know the accurate position of sensor nodes. It is more difficult to localize a node in underwater environment compared to terrestrial. The global positioning system (GPS) signals can not travel underwater, so in UWSNs the use of GPS service for localization is not feasible. The sensor nodes deployed in underwater network are greatly affected by water currents. Due to water currents the sensor nodes move freely. In order to find the accurate position of sensor nodes, we introduce an effective localization solution. An AUV-aided localization technique which helps to localize ordinary nodes with less localization error is introduced in this paper. Three mobile AUVs are introduced in proposed scheme, that act as a reference nodes. These mobile AUVs are deployed in underwater network at predefined depth. The mobile AUVs accelerate towards the surface to find their three dimensional coordinates with the help of GPS satellite and dive back to underwater network. These mobile nodes act as reference nodes and are responsible for the localization of ordinary unlocalized nodes. By exploiting spatial correlation, the ordinary nodes predict their mobility pattern. Mobile AUVs provide enough coverage to the underwater network, which results in efficient localization.

IEEE 802.11 is the most important standard for wireless local area networks (WLANs). In IEEE 802.11, the fundamental medium access control (MAC) scheme is distributed coordination function (DCF), whose performance has been studied analytically in the literature. However, to the best of the authors' knowledge, there is no accurate model that takes into account both the incoming traffic loads and the effect of bit transmission errors, which, in addition to collision, can also result in unsuccessful packet delivery. In this paper, we address this issue and provide a new analytical model to evaluate the performance of DCF in binary symmetric channels (BSCs). In our study, we consider the impact of different factors together, including the binary exponential backoff mechanism in DCF, various incoming traffic loads, distribution of incoming packet size, queueing system at the MAC layer, and the packet transmission errors, which has never been done before. Extensive simulation and analysis results show that our analytical model can accurately predict the delay and throughput performance of IEEE 802.11 DCF under different traffic and transmission error conditions

The handover procedures in Mobile IPv6 (MIPv6) often affect the quality of particular services such as delay-sensitive applications and loss-sensitive applications. Fast Handovers for MIPv6 (FMIPv6) protocol, where operation can be classified into the predictive mode and the reactive mode, is proposed to alleviate the unacceptable effect. In this paper, we implement FMIPv6 in a real test-bed environment and study its performance in terms of handover latency and packet loss. Especially, effects of the anticipation time are carefully considered. Predictive fast handover generally outperforms reactive fast handover, but it requires a sufficient anticipation time to complete necessary operations before the current network is broken. In our test-bed, cross-layer interactions between the link layer and the network layer are introduced to achieve successful predictive fast handover with an optimal anticipation time. We also tackle the implementation of link layer triggers which are critical to the performance of fast handovers.

Batch-learning SOM (BL-SOM) is known as one type of SOM, where all input vectors are processed in one batch and updated collectively. The advantage of BL-SOM is that the resulting map is obtained independently with the order of input vectors. However, when applied to a large set of input vectors, the amount of computation becomes enormous. In this paper, we investigate a parallel architecture for accelerating Batch-learning Self-organizing map by using dedicated hardware. In the evaluation, we present the estimation results of the relationship between the execution time and chip area for various degrees of parallelism.

A web service (WS) is called compound or composite when its execution involves interactions with other WS to use their features. The composition of WS specifies which services need to be invoked, in what order and how to handle exception conditions. This paper gives an overview of research efforts of WS composition. The approaches proposed in the literature are diverse, interesting and have opened important research areas. Based on many studies, we extracted the most important role of WS composition use in order to facilitate its introduction in WS concept.

Channel coding for IMT-ADVANCED system needs to support higher data throughput and transmission rate. Due to the inherent encoder/decoder parallel feature, low density parity check (LDPC) codes is more suitable for hardware implementation than turbo codes when the data throughput is higher than 100 Mbps. As one of the contributors to Wimax LDPC and 3GPP2 LDPC, ZTE has funded some experience in the design of LDPC codes for international standards. In this paper, we provide an complete channel coding scheme based on structured LDPC codes, which can not only provide flexible code rate and code size, but also support IR-HARQ. Our scheme includes matrix generation and encoding, rate matching (RM) and bit priority mapping (BPM) solutions. Simulation result shows the perfect performance for this coding scheme.

Communication plays a crucial role in the design and performance of multi-core systems-on-chip (SoCs). Networks-on-chip (NoCs) have been proposed as a promising solution to simplify and optimize SoC design. However, it is expected that improving traditional communication technologies and interconnect organizationswill not be sufficient to satisfy the demand for energy-efficient and high-performance interconnect fabrics, which continues to grow with each new process generation. Multiple options have been envisioned as compelling alternatives to the existing planar metal/dielectric communication structures. In this paper we outline the opportunities and challenges associated with three emerging interconnect paradigms: three-dimensional (3-D) integration, nanophotonic communication, and wireless interconnects.

With the development of network technology, there exists large number of identity management systems with different architectures and different application areas in cyberspace. How to manage various network identities is becoming a challenging problem. In this paper, we begin by reviewing the characteristics of traditional identity management system and propose a new unified trust service scheme based on blockchain to integrate the isolated identity management system. We then present the theory verification that we show the analysis and experiment under our new scheme. Interestingly, our new constructions are more feasible than traditional centralized solutions. Furthermore, traditional solutions for heterogeneous identity alliance only consider the central authority to integrate different identity management system. We consider the natural extension where different identity management system can join alliance autonomously rather than compulsively. We formally define a unified trust service scheme for Heterogeneous identity alliance, and present an efficient construction.

In this paper, energy-shaping theory-based passivity controller is designed for boost converter feeding constant power load. To regulate the average output voltage indirectly, Passivity based controllers namely Euler-Lagrange and Port-controlled Hamiltonian (PCH) controllers are implemented. Design of these controllers is carried out following an ‘energy shaping and damping injection’ scheme. A mathematical modeling of proposed system along with controllers is carried out under steady state condition. A Simulink model of boost converter rated for 20 W, 48V is developed and aforesaid passivity based controllers are implemented. Based on simulation study, performances of these controllers are investigated under various operating conditions. The robustness of the proposed controller is investigated against source and load variations.

This work develops techniques for modeling and distributed simulation of complex situated multi-agent systems, which are highly dynamic and possess an intrinsic spatial character. Each agent owns its spatial coordinates and acts and interacts with its peers in a hosting territory. In a distributed/parallel scenario of a typical situated agent model, the territory represents a huge shared variable. Frequent access by agents to territory information easily becomes a bottleneck degrading system performance and scalability. This paper proposes an original approach and supporting API to distributing situated multi-agent models using actors. The approach exploits time management for resolving conflicts and to achieve data consistency. Approach contribution is twofold: (a) it simplifies M&S tasks by making the modeler unaware of distribution concerns, (b) it is capable of ensuring good model scalability and simulation performance. Practical aspects of the approach are demonstrated by a Tile-World model and its distributed simulation experiments.

The topic of webpage content annotation has been an important research area in the field of human computer interaction. Annotation tools enable "value-adding" info to digital resources, by adding additional data in the form of provenances, understandings, references, comments and other types of remarks. They facilitate learners' and reviewers' understanding and remembering of information accessible over the Web, and make the collaborative learning easier. This paper describes an efficient annotation tool RCBA (Role Control Based Annotator), that we have developed which uses RBAC (Role Based Access Control) theory to identify and authenticate users and restrict their access to annotations stored on the database server, and enhance the knowledge sharing and reuse in communities' E-learning.

This paper proposes and analyses a novel multipurpose power electronic interface (MPEI) designed for the new generation plug-in electric vehicles (PEVs), and plug-in hybrid electric vehicles (PHEVs). The proposed topology allows vehicle to grid (V2G) and grid to vehicle (G2V) operation to support the grid in times of high load or stress. The operation principles of the MPEI in its different modes are explained and practical results obtained using a real-time controller are discussed and validated in this paper.

Machine learning techniques are attracting a huge amount of interest from both industry and academia. For instance, Convolutional deep Neural Networks (CNNs) have recently enjoyed a notable success in image understanding. The automotive industry is already using image classifiers for Advanced Driver-Assistance Systems and in the development of the upcoming autonomous cars, which will have to guarantee high levels of reliability. The certification of systems based on machine learning is an open issue but it is clear that any improvement in the performance of image classifiers is to be welcomed. CNNs need to be trained to act as image classifiers. This training leads to slightly different classification capacity depending on some training parameters. In this paper we present a first exploration on the use of schemes based on voting on the results of several CNNs trained differently, as a means to increase the final classification performance, and thus the reliability, of this type of systems.

The Internet of things technology provides people with new experiences through the interaction between devices, people and networks. Examples include smart grid, smart health, smart home, smart workplace, e-commerce, smart industrial management, and e-governance. More and more devices are connected every day, resulting in greater security threats and problems. An extensive IoT security model is needed in order to aid resource-based IoT devices and end security. In this paper, we focus on the IoT devices applications and networks, in addition to the attack vectors and security requirements for IoT systems, as well as the organizational approach towards IoT security. We also propose a security architecture to provide security enabled IoT services, and provide a baseline for security deployment.

To solve the problems of low accuracy in long time series prediction and low generality of network parameter model in the existing data-driven Lagrangian fluid simulation, a light neural network prediction model which is physics-based multi-layer shared perceptron was proposed. Each fluid particle is standardized by searching neighbor particles through the optimized parallel processing module. The neural network is used to predict the effect of each neighbor particle on the central particle. The solid-liquid two-state differentiated aggregation operation is used to predict the acceleration of each fluid particle. The experimental results show that, compared with the existing methods, the method proposed in this paper greatly improves the prediction accuracy with less time overhead, and at the same time maintains more fluid motion details. In addition, we can enables more accurate long-term fluid motion prediction. Compared with PointRNN, PointNet++ and other single-channel data-driven methods, we can better deal with the fluid-solid coupling problem, and has wider network versatility.

In this paper, we present a series of methods to authenticate a user with a graphical password. To that end, we employ the user?s personal handheld device as the password decoder and the second factor of authentication. In our methods, a service provider challenges the user with an image password. To determine the appropriate click points and their order, the user needs some hint information transmitted only to her handheld device. We show that our method can overcome threats such as key-loggers, weak password, and shoulder surfing. With the increasing popularity of handheld devices such as cell phones, our approach can be leveraged by many organizations without forcing the user to memorize different passwords or carrying around different tokens.

Domain-specific reconfigurable arrays have shown to provide an efficient trade-off between flexibility of FPGA and performance of ASIC circuit. Nonetheless, the design of these heterogeneous arrays is a labour intensive process. Furthermore, the manual creation of the array architecture could not have been fully optimised, hence limiting their performance. This paper presents a placement technique for mapping logic elements into heterogeneous reconfigurable arrays. At its core, it implements a Genetic Algorithm, which was used to reduce the span of all the interconnections as well as critical delay. It therefore minimises the amount of routing resource required in the architecture. The algorithm was tested on two arrays implementing DCT and Speech Coding. The resulting architecture achieves optimal close to that of an expert designer in a fraction of the time.

In this paper, a multi-feature encoder for gesture recognition based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system is proposed to extract the gesture characteristics, i.e., range, Doppler, azimuth and elevation, from the low-level raw data. The radar system updates the hand information for every measurement-cycle on all the scattering centers in its field of view, and our proposed encoder is devised to only focus on those essential scattering centers. After observing the hand over several measurement-cycles, we encode the gesture characteristics sequentially into a 2-D feature matrix, which is successively fed into a shallow convolutional neural network (CNN) for classification. For the purpose of distinguishing relevant gestures, the proposed multi-feature encoder is able to efficiently extract adequate information from a multi-dimensional feature space. Thus, the proposed approach is practical for industrial applications where the available dataset is mostly small-scale. The experimental results show that the proposed multi-feature encoder could guarantee a promising performance for a gesture dataset with 12 gestures.

The bistatic system contains a transmitting station and a receiving station. In this paper, it is assumed that the measurements of T station are the slant range and the azimuth referred to T station and the ones of R station are the range sum (target to T and R) and the azimuth to the R station. The method of 2-D target-position location is firstly described with the measurements of T station. With the inaccurate assumption of the target height, the accuracy of the target-position location is discussed. Each measurement defines a surface of position (SOP) on which the target must lie. The SOPs derived from the noisy measurements will not usually intersect in a single point, a statistical analysis of the target-position location accuracy is presented. The improved algorithm and the similarity analysis is presented respectively. In the environment of electronic countermeasures, the data of the T station may not be obtained. The algorithm of 2-D target-position location and its accuracy analysis with the measurements of the R station is given. The resulting location ambiguity is discussed. Finally, the constant-accuracy contours are drawn to show the location characteristics by means of computer simulation and the conclusions are drawn through analyzing the results.

Rapid and easy transaction through credit card system has increased fallacious cases everywhere. Machine Learning algorithms has been applied for identifying fraudulent transactions. Fraud detection problem have two major issues, firstly, the legitimate and fraudsters consistently change behaviour and secondly, datasets are severely skewed. Implementation of the system littered with machine learning algorithms, excerpting variables and sampling accession on datasets. This paper investigates the performance of Logistic Regression, Random Forest, Decision Tree and SVM (Support Vector Machine). The proposed system shows results according to accuracy, sensitivity, specificity, precision of above techniques. Transactions in the dataset are heavily right skewed. Undersampling and oversampling is conducted on the data. The work is implemented in Python.

When a person learns, they observe and interact with their surroundings, and monitor the outcome of these interactions. During this process, the brain only examines single snapshots of information. It does not need to continuously revisit past instances of time to retain learned information. Supervised neural networks, as much as they resemble the human brain, do not learn well incrementally. The standard multilayer perceptron, radial basis function network and others, have a common problem of losing learned information when additional data points are presented. In this paper, we develop a supervised learning neural network that converges on new information without overriding previous knowledge. The proposed Neural Field can operate in real time environments where a data point is seen once and never revisited, or chase a function that changes over time without continuously relearning on a full dataset. A dataset can be presented in portions and the network will attain knowledge incrementally. With a static ordered grid of radial basis function neurons, the Neural Field both learns incrementally and generalizes effectively.

IEEE802.16 OFDM system can support wide band wireless multi-service access. Because of its high data transmission rate and strong ability of anti multi-path, it has the good application prospect. The adaptive allocation algorithm can be adopted to raise the frequency spectrum efficiency and satisfy the users' quality of service (QoS). An adaptive resource allocation algorithm based on the IEEE802.16 OFDM system is proposed in this paper. Firstly, each sub-carrier chooses one user who has the biggest intermediate value. Then the bit load algorithm of the single user can be introduced to determine the number of bit and power of each sub-carrier. Finally the resources adjustment can be adopted according to the service priority. The simulation results demonstrate that the transmission rate is higher than comparison algorithm.

Alzheimer's disease (AD) accounts for 60%-70% of all dementia cases, and clinical diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify disease progression or alleviate symptoms are being developed, to assess their efficacy, novel robust biomarkers of brain function are urgently required. This paper aims to explore a routine to gain such biomarkers using the quantitative analysis of electroencephalography (QEEG). This paper proposes a supervised classification framework that uses EEG signals to classify healthy controls (HC) and AD participants. The framework consists of data augmentation, feature extraction, K-nearest neighbor (KNN) classification, quantitative evaluation, and topographic visualization. Considering the human brain either as a stationary or a dynamical system, both the frequency-based and time-frequency-based features were tested in 40 participants. The results show that: 1) the proposed method can achieve up to a 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared with alternative machine learning approaches 2) the features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT and 3) in the spatial domain, the temporal and parietal areas offer the best distinction between healthy controls and AD. The proposed framework can effectively classify HC and AD participants with high accuracy, meanwhile offering identification and the localization of significant QEEG features. These important findings and the proposed classification framework could be used for the development of a biomarker for the diagnosis and monitoring of disease progression in AD. 27 5 826 835

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multi-class crop classification using Multispectral satellite image is proposed. the purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. the capabilities of GEP as a classifier is also studied. the proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. from the results we can conclude that the proposed method is effective for classification.

This paper presents a dq flux-linkage modeling approach and the virtual testing of a wound rotor synchronous machine that assures the propulsion of a battery electrical vehicle. The motor model, organised using Energetic Macroscopic Representation, is constructed based on a series of electromagnetic finite-element analysis defined by various armature and rotor excitation currents. The performances of the dq-model, together with its control and electronic units, are evaluated through virtual tests. Furthermore, the model accuracy is validated by comparing the simulation results with the experimental data obtained from different tests performed on a Renault ZOE Q90 car. Two test scenarios, an urban test and a highway one, are chosen in order to evaluate the precision and the reliability of the developed model under normal and maximum electrical vehicle speed conditions. The combination of dq-modeling and virtual testing represents a way to rapidly develop and validate high performance system models and accelerate time to market.

This paper studies optimal decoding for a special case of the deletion channel, referred by the t-deletion channel, which deletes exactly $t$ symbols of the transmitted word uniformly at random. The goal of the paper is to understand how such an optimal decoder operates in order to minimize the expected normalized distance. A full characterization of a decoder for this setup is given for a channel that deletes one or two symbols. For $t$ = 1 it is shown that when the code is the entire space, the decoder is the lazy decoder which simply returns the channel output. Similarly, for $t$ = 2 it is shown that the decoder acts as the lazy decoder in almost all cases and when the longest run is significantly long, it prolongs the longest run by one symbol.

In recent years, different high performance techniques/platforms have been researched intensively in CT imaging, especially in CT image reconstruction. Muller and his group achieved lots of significant progress on using NVIDIA's GPU/GPGPU to accelerating CT reconstructions [13]. Kachelriess and his colleagues use Cell Broadband Engine (CBE) to accelerate the reconstruction and got a hyper fast result [4~5]. Recently, Xing et al proved AMD's GPGPU also can been used for accelerating CT reconstruction[6]. The introduction of these high-performance techniques/platforms also brings different concepts and coding environments, which leads to difficulties in software development for practical applications because these environments do not talk to each other and codes are not compatible. For example, codes for acceleration programmed with Nvidia CUDA will not fit on AMD GPU, AMD stream codes can't work on Nvidia GPU too, though they have plenty of common things. Open Computing Language (OpenCL), as an open standard and framework for writing programs that execute across those heterogeneous parallel platforms, makes it possible to solve these problems. In this study, our objective is to implement and test the NVIDIA's and AMD's early OpenCL compatibility implementation release on a back-projection step, the most time consuming part of an FBP reconstruction, and the Haralick's texture feature[7] extraction algorithm for CT images. The paper is arranged as follows. In section II, we will briefly introduce the model of OpenCL. Our implementation for BP and CT image recognition algorithm will be presented in section III. Finally, in section IV, the result and discussion will be given.

New attack surfaces are emerging with the rise of Industrial Control System (ICS) devices exposed on the Internet. ICS devices must be protected in a holistic and efficient manner; especially when these are supporting critical infrastructure. Taking this issue into account, cyber-security research is recently being focused on providing early detection and warning mechanisms for ICSs. In this paper we present a novel honeypot capable of detecting multi-stage attacks targeting ICS networks. Upon detecting a multi-stage attack, our honeypot can generate signatures so that misuse Intrusion Detection Systems (IDSs) can subsequently thwart attacks of the same type. Our experimental results indicate that our honeypot and the signatures it generates provide good detection accuracy and that the Bro IDS can successfully use the signatures to prevent future attacks.

Sparse coding is a very powerful method to learn high-level features from raw data input. It is able to learn an over complete basis that has the potential to capture robust and discriminative patterns within the data. However, like many other feature learning algorithms, it is unable to detect very similar features or stimuli on different input channels. In this paper, we propose a novel method to build general features that can be applicable to different sets of channels. This succinct representational model will express the stimuli independent of the locality in which they appeared. As a result, it prepares the groundwork for transferring the learned features from a set of input channels to other possible sets of input channels.

The source codec parameters determined by modern speech, audio, or video encoders are, especially at high compression rates, highly sensitive to transmission errors and they usually exhibit natural residual redundancy due to delay and complexity limitations in the encoding process. Error concealment techniques like softbit source decoding (SBSD) utilize the residual redundancy as a priori knowledge for parameter estimation. Therewith, the annoying artifacts of transmission errors can considerably be concealed. But, the optimal utilization of all terms of residual redundancy requires large computational complexity. In this paper, we derive a new efficient, nearly optimal version to SBSD. Significant complexity savings become possible by exploiting the intra-frame and inter-frame redundancy separately using two nested forward-backward algorithms. Compared to the optimal joint evaluation, the performance loss is negligibly small.

In this paper, the problem of locating a target in a distributed multiple-input multiple-output radar system using bistatic range measurements is addressed. An algebraic closed-form two-stage weighted least squares solution for the considered problem is developed and analyzed. In the first stage, we establish a set of linear equations by eliminating the nuisance parameters first and then we apply a weighted least squares estimator to determine the target position estimate. In the second stage, in order to improve the localization performance and refine the solution of the first stage, an estimate of the target position estimation error is obtained. The final solution is obtained by subtracting the solution of the second stage from the solution of the first stage. The Cramer-Rao lower bound (CRLB) for target localization accuracy is developed in the case of Gaussian distribution. The proposed method is shown to be an approximately unbiased estimator, which is able to attain the CRLB accuracy under small noise conditions. Numerical simulations are included to examine the algorithm's performance and corroborate the theoretical developments.

With the wide-spread use of high-speed network connections and high performance computing resources available over the Internet, live video broadcasting services have become available on the Internet. In this service, anyone who has a laptop, desktop, or mobile terminal with a camera can broadcast his own video to any user who is interested in watching it in real time. Such a system must be able to accommodate a large number of simultaneous video broadcasts and allow the user to locate the desired content quickly. Furthermore, it should minimize the end-to-end streaming delay between the video broadcaster and the viewer to support the real-time video broadcasting. In order to satisfy these requirements, the advantages of peer-to-peer network technology must be leveraged to build a completely distributed live video broadcasting system. Peer-to-peer technology makes it possible for the system to efficiently match video broadcasting to the interested viewers, and to select the appropriate resources that can distribute the streamed content to all viewers in real time. This paper considers the performance of the proposed system using a realistic computer simulation to show that the system has scalability for a large-scale distributed system that attracts a very large number of users.

This paper conduct research on Dynamic multi-project human resource allocation methods from the four aspects: Firstly, it established project evaluation system by using hierarchical fuzzy evaluation method based on fuzzy theory, Secondly, it quantified staff capacity and estimated staff task efficiency based on multidimensional model and Fleischmann analysis system, Thirdly, it developed multi-project Human Resource & Time Allocation formula based on knapsack problem principle, Finally, it made Dynamic Multi-project Human Resource Allocation optimization. The simulation example showed that method research for dynamic multi-project human resource allocation made the human resource allocation more rational, and reduced project cost which was a scientific and efficiency method to scheduling human resources.

Flexible instrument insertion is one of the most commonly performed procedures in percutaneous surgery. Target movement caused by tissue deformation results in targeting inaccuracy no matter how well the initial planning is. Various efforts have been put into modeling the tissue deformation to predict the target movement. This paper investigates two approaches in reducing the target movement physically. One is high frequency translational oscillation of the needle. The other is rotational drilling of the needle. Experiments have been carried out accordingly to test these hypotheses. From the results, we can see that both methods can reduce the target movement effectively

The paper discusses various concepts of process alarm analysis and disturbance analysis system, presents the essential features of a few computer based alarm systems in different control environments and reviews the emerging possibilities of artificial intelligence in constructing an optimal man-machine interface for supervision of complex automatically controlled processes.

With continued scaling down of the NAND flash memory, the multi-level cell (MLC) technique emerges to dramatically increase the storage density. Meanwhile, this technique also leads to worse error performance owing to the cell-to-cell interference (CCI). Recently, low-density parity-check (LDPC) code has appeared to be a promising solution to combat the performance degradation of NAND flash memory. Although LDPC codes can provide stronger error performance, the conventional belief propagation (BP) decoding algorithm requires calculating soft-decision log-likelihood (LLR) information and hence possesses a massive computational complexity. In this paper, by exploiting the characteristics of MLC NAND flash memory channels, we present a modified soft reliability-based iterative majority-logic decoding (SRBI-MLGD) algorithm for the LDPC codes over MLC NAND flash memory channels. The modified decoding algorithm has less computational complexity as compared with the sum-product decoding algorithm (SPA). Moreover, the simulation results show that the decoding algorithm offers an effective trade-off between complexity and performance over high-column-weight LDPC coded MLC NAND flash memory channels.

This paper investigates packet acquisition in a random multiple access (RMA) environment. An analytical approach to performance evaluation is provided, which enables the waveform designer to set design parameters quickly such that the required performance is achieved without over-design. RMA is a medium access methodology used for time-frequency hopped signaling and has been widely considered in the literature for mobile ad-hoc networking. It has a number of desirable characteristics, including low latency, frequency diversity and robustness against undesirable interferences. However, a major difficulty exists: in order to demodulate a packet, the receiver must Arst determine its arrival time. The difficulty is exacerbated with multiple access interference, a situation for which the receiver performance has not been well quantified. To illustrate the accuracy of the analytical results, simulations are performed over a wide range of design parameters, e.g., the pulse length, pulse duty factor and the number of synchronization pulses, and various operation-time variables such as the number of users and the relative power levels of the signal, interference, and noise.

In this paper, a novel dual iterative Q-learning algorithm is developed to solve the optimal battery management and control problems in smart residential environments. The main idea is to use adaptive dynamic programming (ADP) technique to obtain the optimal battery management and control scheme iteratively for residential energy systems. In the developed dual iterative Q-learning algorithm, two iterations, including external and internal iterations, are introduced, where internal iteration minimizes the total cost of power loads in each period and the external iteration makes the iterative Q function converge to the optimum. For the first time, the convergence property of iterative Q-learning method is proven to guarantee the convergence property of the iterative Q function. Finally, numerical results are given to illustrate the performance of the developed algorithm.

This paper aims to stroke recognition, where the strokes are connected forms of cursive handwritten scripts, and in particular, we concern on recognition of Farsi handwriting strokes. In Farsi and some other writing systems, connected letters have special shapes that are often unrecognizable from their separated shapes. Despite that quite efficient algorithms have been developed for recognition of handwritten digits and disjoint letters, adapting these algorithms to stroke recognition is so arduous that development of a holistic approach is preferable. In this paper, we develop a method for Farsi handwriting recognition based on profile-HMM and study aspects of modeling the spatiotemporal features of handwriting strokes. The modular architecture of profile-HMMs provides a flexible framework for stroke modeling. Stroke shrinking and elongation are naturally modeled by the recurrent states and the silent states of profile-HMMs and make the model insensitive to writing speed and subtle slides. Our experimental results show that the profile-HMM is quite robust with respect to downsampling of the curve points, also is robust with respect to various settings in the training procedure. Our method correctly recognizes the main stroke of 90.8%, 98.5%, and 99.2% of handwriting samples, respectively in the top first, top five, and top ten hits.

A parallel manipulator of hexaglide structure has been proposed to develop a knee surgical robot. The surgical robot provides enhanced safety by its mechanical and software constraint. The working envelope of a parallel manipulator is mechanically limited by its mechanical parameters, such as link lengths, sliders' strokes etc; and is easily to be designed only to moving and orientating within the small local area of operation. This feature protects both the patients and medical staffs from unexpected harms when the manipulator malfunctions. The software constraint provides a surgical boundary for bone cutting so that a surgeon can easily assign a desired cutting shape of bone and rely on it for bone cutting. Software constraint provides different impedances which is determined by the position and direction of the cutting movement and implemented by impedance control strategy. Therefore the manipulator with software constraint possesses both the skill of the robot in accurate cutting and the skill of reflecting the designated impedance to the surgeon in being able to judge kinesthetically. The software constraint can be used to substitute a sequence of aligning and positioning templates in conventional surgical procedures; and to achieve better outcomes. The advantage of using the hexaglide structure was also demonstrated in this paper for its simple inverse kinematics and high rigidity.

A new method for automatic data acquisition from colored topographic maps is presented. Cartographic paper-based maps describe the real landscape in a symbolic and reduced form. The data acquisition from maps provides a spatial reference and a data basis for geographic information systems (GISs). GISs are data processing systems that are used in geographic disciplines, e.g. for environmental planning tasks. In comparison to known algorithms, the development of the interpretation strategy described has included both the modelling and the evaluation of recursive structures, conflicts and identities, and the enormous data amount caused by the high information density of topographic maps.

Interference alignment (IA) is a technique shown to be able to achieve a significant overall throughput in a μ — user multiple-input multiple-output (MIMO) interference channel (IC). In this paper, we try to modify the conventional IA designs to achieve enhanced sum-rate performance. We jointly design transmit and receive IA filters (precoding and suppression filters) at a central unit (CU) to reduce the channel state information (CSI) feedback/sharing overhead. At the CU, in a one-way iterative strategy (left ‘L’ to right ‘R’, see Fig. 1), the precoding filters are optimized (on side ‘L’) in the direction of the gradient of the sum-rate, and the suppression filters are chosen (on side ‘R’) to minimize the leakage interferences. Then, in a two-way iterative strategy, the proposed scheme alternates between ‘L’ and ‘R’ sides to design the IA filters through the joint sum-rate maximization and interference leakage minimization on each side. Finally, a modified version of the two-way iterative strategy is proposed to design the IA filters on the basis of signal-to-interference-plus-noise ratio (SINR) (instead of the interference minimization) and the sum-rate maximization techniques. Comparing to other conventional IA designs, the proposed designs with provable convergence show a significant sum-rate performance improvement and often outperform other widely used algorithms, as shown in the simulation examples.

Document classification has received extensive attention in the past few decades due to its wide applications in many fields. To efficiently deal with this problem, a novel document classification algorithm based on information bottleneck (IB) and least square version of SVM(LS-SVM) is proposed in this paper. Extensive experimental results on the real-word document corpus show that the proposed algorithm achieves much better performance than SVM algorithm

SOCK is a process calculus for the modeling of Service Oriented systems recently extended with primitives for dynamic fault and compensation handling. In this paper we investigate the relationships between the Sagas calculi for compensable flow composition and SOCK. First, we present an encoding of Parallel Sagas (with interruption and centralized compensation) into SOCK. Then, we dis- cuss a new semantics for Parallel Sagas that we consider more adequate to the dynamic approach to fault and compensation handling.

Electrolytic capacitors with a DC-side inductor, and the slim DC-link capacitor are two typical filtering configurations in Adjustable Speed Drives (ASDs). The reliability performance of these capacitive DC-link solutions is an essential aspect to be considered, which depends on both component inherent capability and the operational conditions (e.g., electro-thermal stresses) in the field operation. This paper studies the reliability performance of the LC filter and slim capacitor based filter in a standard ASD system. Considering the variations of the capacitor parameters, environment stresses, and lifetime model, the lifetime of the two configurations are compared. Moreover, scalability analysis is presented in terms of power rating, amplitude and phase angle unbalance levels at the grid side. The results serve as a guideline for reliability aspect performance benchmarking of different DC-link solutions in ASD applications.

In home automation (HA) industry, various roles are involved such as device manufactures, system integrators, and installers. Engineering tools are required not only to improve installation efficiency and reduce the engineering cost, but also to simplify information exchange among different stakeholders. In this paper, a RESTful information exchange method among installer-friendly engineering tools is proposed and demonstrated. It can simplify the engineering workload and optimizes device interoperability among different wireless technologies. Finally, a prototype is implemented to verify the technical feasibility of the proposed RESTful based method.