On the QoS Assessment of Video Sessions in Heterogeneous 3G-WLAN Networks with Seamless and Secure Mobility Support

Real-time video data transmission is currently emerging as a popular application among mobile users but it is very sensitive to QoS degradation due to packet losses in wireless networks.In order to achieve service continuity and integrity upon handoffs among heterogeneous networks,provisioning of seamless and secure mobility is required.However,in order to reduce the delay and packet losses during vertical handovers we need to employ supportive protocols like context transfer.In this paper we evaluate the QoS of video transmission over a heterogeneous 3G-WLAN network.The aggregate video data traffic is represented by a dynamic two-dimensional Markov chain model,which has been evaluated against real video data measurement.Upon the vertical handover, appropriate AAA handshaking and enhanced mobility management using context transfer have been considered.Perceived QoS evaluation of video streams was performed based on peak signal-noise ratio(PSNR) measurements,while we analyticallyestimated the number of packet losses during handovers.The results show that both packet loss within the converged network and loss occurrence affecting the perceived video quality is reduced. Moreover,the proposed context transfer scheme minimizes handover delay and the number of lost packet up to 3 times compared to standard AAA handshaking.

Optimal design of coordination control strategy for distributed generation system

This paper presents a novel design procedure for optimizing the power distribution strategy in distributed generation system. A coordinating controller, responsible to distribute the total load power request among multiple DG units, is suggested based on the conception of hierarchical control structure in the dynamic system. The optimal control problem was formulated as a nonlinear optimization problem subject to set of constraints. The resulting problem was solved using the Kuhn-Tucker method. Computer simulation results demonstrate that the proposed method can provide better efficiency in terms of reducing total costs compared to existing methods. In addition, the proposed optimal load distribution strategy can be easily implemented in real-time thanks to the simplicity of closed-form solutions.

Rough set and radial basis function neural network based insulation data mining fault diagnosis for power transformer

Rough set (RS) and radial basis function neural network (RBFNN) based insulation data mining fault diagnosis for power transformer is proposed. On the one hand rough set is used as front of RBFNN to simplify the input of RBFNN and mine the rules. The mined rules whose “confidence” and “support” is higher than requirement are used to offer fault diagnosis service for power transformer directly. On the other hand the mining samples corresponding to the mined rule, whose “confidence and support” is lower than requirement, are used to be training samples set of RBFNN and these samples are clustered by rough set. The center of each clustering set is used to be center of radial basis function, i.e., as the hidden layer neuron. The RBFNN is structured with above base, which is used to diagnose the case that can not be diagnosed by mined simplified valuable rules based on rough set. The advantages and effectiveness of this method are verified by testing.

Robust fault detection and diagnosis for uncertain nonlinear systems

This paper considers robust fault detection and diagnosis for input uncertain nonlinear systems. It proposes a multi-objective fault detection criterion so that the fault residual is sensitive to the fault but insensitive to the uncertainty as much as possible. Then the paper solves the proposed criterion by maximizing the smallest singular value of the transformation from faults to fault detection residuals while minimizing the largest singular value of the transformation from input uncertainty to the fault detection residuals. This method is applied to an aircraft which has a fault in the left elevator or rudder. The simulation results show the proposed method can detect the control surface failures rapidly and efficiently.

Multimodality image registration and fusion using neural network

Multimodality image registration and fusion are essential steps in building 3-D models from remotesensing data. We present in this paper a neural network technique for the registration and fusion of multimodali-ty remote sensing data for the reconstruction of 3-D models of terrain regions. A FeedForward neural network isused to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data arepresented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusionresults.

Multi-slots Wideband Cooperative Spectrum Sensing Based on Weighed Data Fusion in Cognitive Radio

In order to avoid the interference to the primary user(PU), in this paper Cognitive Radio (CR) periodically senses the presence of PU, and during one period, CR can sense all the sub-channels based on weighed data fusion and then use all the idle channels decided by the coordinator. The local sensing time of CR is divided into multi-slots in which CR can sense any sub-channel. Through reasonably allocating the sensing slots and users by mathematic optimization, the proposed algorithm can improve the total throughput of CR. The optimization problem of the proposed scheme which seeks to maximize the throughput subject to the constraint of the detected performance of each sub-channel is proposed in order to choose the optimum local sense time and the number of the cooperative CRs. The simulation results indicate that the proposed scheme can obtain higher throughput than the conventional single-channel sense, and there are the optimum local sense time and the number of cooperative CRs to make the throughput reach maximum.

Natural Scene Classification Inspired by Visual Perception and Cognition Mechanisms

The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural scenes.Inspired by this fact,we propose a biologically plausible approach for natural scene image classification.This approach consists of one visual perception model and two visual cognition models.The visual perception model,composed of two steps,is used to extract discriminative features from natural scene images.In the first step,we mimic the oriented and bandpass properties of human primary visual cortex by a special complex wavelets transform,which can decompose a natural scene image into a series of 2D spatial structure signals.In the second step,a hybrid statistical feature extraction method is used to generate gist features from those 2D spatial structure signals.Then we design a cognitive feedback model to realize adaptive optimization for the visual perception model.At last,we build a multiple semantics based cognition model to imitate human cognitive mode in rapid natural scene categorization.Experiments on natural scene datasets show that the proposed method achieves high efficiency and accuracy for natural scene classification.

无线开关传感网络研究

设计了利用无线模块进行电源开关的遥控电路,以及多点信号采集后经USB接口模块送入客户机与服务器进行远程传输电路。发现各遥控开关实际上构成了一个无线节点,对此进行了利用这些节点和无线模块空余端口构成的自组织无线传输网络研究,对信号的发送和接收路径进行了讨论。其网络的显著优点是克服了有线传输的布线问题,系统寄生在无线电源开关上,降低了网络成本,并能与局域网连接实现报警信号的远程传送。

Secure Packet Video Transmission over Wireless Multimedia Sensor Networks

This paper proposes a packet scheduling scheme thatoptimizing the coded video transmission overmultipath wireless multimedia sensor networks interms of received video distortion and power efficiencyenhances the security aspects of the underlyingsystem.When the aggregate transmission rateavailable at the network cannot support the requiredtransmission rate,the scheduling algorithm can selectivelydrop combinations of video packets prior totransmission to adapt the rate of the sender to thelimitations of the wireless channel capacity.Twoscheduling algorithms are proposed.The Baselinescheme utilizes a novel distortion prediction modeland decides upon which packet can be dropped priorto transmission based on the packet’s impact on thevideo distortion.In addition to the bandwidthlimitations,the Power aware packet scheduling is an extension of the Baseline capable of estimating thepower that will be consumed by each node during thetransmission;hence it can control the power consumptionby selectively drop packets of low importanceto the decoded video.Simulation results indicatethe efficiency of the proposed scheduling schemein terms of received video distortion(PSNR)andpower consumption.

New coordination scheme for multi-robot systems based on state space models

A new coordination scheme for multi-robot systems is proposed. A state space model of the multi- robot system is defined and constructed in which the system＇s initial and goal states are included along with the task definition and the system＇s internal and external constraints. Task accomplishment is considered a transition of the system state in its state space （SS） under the system＇s constraints. Therefore, if there exists a connectable path within reachable area of the SS from the initial state to the goal state, the task is realizable. The optimal strategy for the task realization under constraints is investigated and reached by searching for the optimal state transition trajectory of the robot system in the SS. Moreover, if there is no connectable path, which means the task cannot be performed Successfully, the task could be transformed to be realizable by making the initial state and the goal state connectable and finding a path connecting them in the system＇s SS. This might be done via adjusting the system＇s configuration and/or task constraints. Experiments of multi-robot formation control with obstacles in the environment are conducted and simulation results show the validity of the proposed method.

RFID-based 3D human pose tracking: A subject generalization approach

Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.

Analysis on topology control boundary conditions in delay tolerant wireless sensor networks

In wireless sensor networks(WSNs),nodes are often scheduled to alternate between working mode and sleeping mode from energy efficiency point of view.When delay is tolerable,it is not necessary to preserve network connectivity during activity(working or sleeping) scheduling,enabling more sensors to be switched to sleeping mode and thus more energy savings.In this paper,the nodal behavior in such delay-tolerant WSNs(DT-WSNs) is modeled and analyzed.The maximum hop count with a routing path is derived in order not to violate a given sensor-to-sink delay constraint,along with extensive simulation results.

Advanced in power control for distributed generation system using cooperative algorithms

To develop efficient power control strategies for a distributed generation system in order to improve the overall system efficiency,we propose a cooperative algorithm to analyze and design the controller,in which elements of conventional mathematical optimization algorithms are combined with adaptive dynamic elements drawn from intelligent control theory.In our design,the sequential quadratic programming algorithm was first utilized to obtain an optimal solution for power distribution among multiple units.Fuzzy system was then developed to implement the optimal strategies on the basis of optimal solution.In addition,parameters of the fuzzy system were adapted via a genetic algorithm.The simulation results illustrate that the methodology described is useful for a range of control system designs.

Pupil center detection with a single webcam for gaze tracking

This paper presents a user friendly approach to localize the pupil center with a single web camera.Several methods have been proposed to determine the coordinates of the pupil center in an image,but with practical limitations.The proposed method can track the user’s eye movements in real time under normal image resolution and lighting conditions using a regular webcam,without special equipment such as infrared illuminators.After the pre-processing steps used to deal with illumination variations,the pupil center is detected using iterative thresholding by applying geometric constraints.Experimental results show that robustness and speed in determining the pupil’s location in real time for users of various ethnicities,under various lighting conditions,at different distances from the webcam and with standard resolution images.

ZTE Communications Special Issue on Recent Advances in Smart Grid

The smart grid is the next generatiou electric： grid that enables effi- cient, intelligent, and economical power generation, transmission, and distribution. It has attracted significant attentions and become a global trend due to the immense potential benefits including en- hanced reliability and resilience, higher operational efficiency, more efficient energy consumption, and better power quality. This special issue expects to address smart grid issues related to data sensing, data communications and data networking, including high-level ideology/methodology, concrete smart grid inspired data communications and networking technolngies, smart grid system ar- chitecture, QoS, energy-efficiency, and fault tolerance in smart grid systems, management of smart grid systems, and real-world deploy- ment experiences.

Design of satisfaction output feedback controls for stochastic nonlinear systems under quadratic tracking risk-sensitive index

In this paper, the design problem of satisfaction output feedback controls for stochastic nonlinear systems in strict feedback form under long-term tracking risk-sensitive index is investigated. The index function adopted here is of quadratic form usually encountered in practice, rather than of quartic one used to beg the essential difficulty on controller design and performance analysis of the closed-loop systems. For any given risk-sensitive parameter and desired index value, by using the integrator backstepping method, an output feedback control is constructively designed so that the closed-loop system is bounded in probability and the risk-sensitive index is upper bounded by the desired value.

IDENTIFICATION ERROR BOUNDS AND ASYMPTOTIC DISTRIBUTIONS FOR SYSTEMS WITH STRUCTURAL UNCERTAINTIES

这个工作涉及服从于象 unmodeled 动力学那样的不仅测量噪音，而且结构的不确定性的系统的鉴定，传感器非线性的失配，和观察偏爱。鉴定错误在这些结构的不确定性上为他们的依赖被分析。评价错误的放大序列的 Asymptotic 分布被导出。