International Journal of Information Acquisition

Wireless network of wearable biomedical sensors by human body shows great potential to enhance the biometrics performance significantly. Meanwhile, it poses prominent characteristics and challenges to physicians and engineers for its particular medical application as compared to other application of wireless sensor networks (WSN). Mobility pattern plays an important role in designing the wireless body sensor networks (WBSN) and will also affect the accuracy of modeling WBSN in health care application. Much of the mobility scenarios generated in current work of wireless body sensor networks has used fairly simple models to generate the mobile topological graph, which bear little resemblance to the actual mobility patterns. This paper is the first attempt to investigate the mobility model in WBSN based on the existing mobility models in wireless data networks and ad hoc networks. We first briefly review the existing mobility models in related research areas such as wireless ad hoc network and cellular networks. Further on, we propose a dedicated and more realistic mobility model named BAMM (Body Area Mobility Model) for wireless body sensor networks by concentrating on the unique characteristics of WBSN and finally study the effects of mobility on the performance of WBSN by simulation experiments.

This paper considers the problem of detecting ground moving targets in a wireless sensor network for military surveillance application. The sensor nodes are severely constrained in both power and computational performance, and the battle field environments are commonly very complicated. A modified order statistic constant false alarm rate (OS-CFAR) detection algorithm and its distributed detection algorithm were chosen to be implemented on sensor nodes. The algorithm includes background estimate and adaptive threshold. The experimental results with real acoustic and seismic data indicate that the OS-CFAR is robust and flexible and distributed algorithm can improve the detection performance somewhat.

Wireless sensor networks (WSN) are a kind of networks made of tiny sensing embedded devices with processors, memories and short-range wireless communication modules. They are planted for sensing the area which people mostly care about and to send data of light, pressure, sound, and so on, back to people to make further analyses. Differing from normal linear networks, WSN are usually restricted by energy store, plethoric information flow, and short communication distance. So, there are many research hotpots in WSN such as routing, economization of energy, adaptive self-configuration, etc. Among these aspects, the area of quality of service (QoS) remains largely open. This is due to that the optimal alive sensor nodes number is difficult to determine in a certain focused area which satisfies the requirement of QoS. In order to make an optimal number of nodes to power up in a focused area, the paper proposes the density domination strategy with location information and localized information based on the modified Gur Game which is embedded with gradient mechanism. In the strategies, the base station receives the QoS feedback and gives the dynamic density domination information. The simulation results about the two kinds of density domination strategies are given. The results show that both of the strategies can effectively control the density of power-up nodes and meet the requirement of QoS.

This paper points out the disadvantages of a formerly proposed data transfer mode used in the monitoring system of WTP. A data transmit solution which uses ZigBee wireless communication standard is proposed, and the technical characteristic of ZigBee technology is introduced. The hardware structure and software design are presented. The paper emphasizes on the realization of ZigBee protocol and data communication flow. The experimental results show that ZigBee is suitable for the wireless data transmission in the WTP. The reliable transmission of data to the control center of WTP has been realized in some in situ applications.

We have built up a system to apperceive unknown environment based on multi-sensors' data fusion, which enabled the self-determinative mobile robot recognize timely the type of obstacles around it in the course of travel, and the mobile robot would be provided with more intelligent operations. Firstly, the paper introduced the theory of environmental perception for mobile robot and the configuration of the multi-sensors' data fusion technology. Secondly, combining with the characteristics of neural network (NN) and aiming at the shortages of the former solving projects, it was brought forward that the MLP (Multi-layer perceptron) based on the improved back propagation (BP) arithmetic and four mature items of fusion rules, which were applied to apperceive the real surroundings. Thirdly, the paper presents a kind of structure of multi-sensor information data fusion. Two sub-networks for barrier recognition are separately built and incorporated with each other to form a high-powered recognition system by parallel connection.

The particle filter can deal with nonlinear/non-Gaussian problems and it has been introduced to the algorithm of the interacting multiple model (IMM) for higher precision. The general IMM based on Kalman filter or extended Kalman filter (IMMEKF) cannot deal with non-Gaussian problems and also does not work as well as the IMM based on the particle filter (IMMPF) for the nonlinear problems. However the problem of the particle filter is its expensive computation, because a particle filter usually has a lot of particles, which will increase the computation load greatly. Here an algorithm of IMM combining the Kalman filter and the particle filter (IMMK-PF) for maneuvering target tracking is proposed to improve the computation efficiency as compared to the IMMPF. For nonlinear/Gaussian problems the new algorithm is expected to have a good performance as the IMMPF, while for linear problems it will perform similarly to the IMMEKF and will work better than the IMMPF.

In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. In this paper, we present an extension of KFD method based on the data-dependent kernel, called the adaptive kernel discriminant analysis (AKDA), for feature extraction and pattern classification. AKDA is more adaptive to the input data than KDA owing to the optimization of projection from input space to feature space with the data-dependent kernel, which enhances the performance of KDA. Experimental results on ORL, Yale and MNIST database show that the proposed AKDA gives the higher performance than KDA.

A measurement system based on crack-opening method has been developed to measure the fracture toughness of silicon direct bonding wafers. The theory of crack-opening method was introduced and amended according to the shape of the specimen. The parameters and function required in the measurement of bond energy were mentioned, and the selection principle of thickness of the razor was given. A new experimental device based on IR vision and image processing in the measurement was developed. Finally, a contrast experiment was carried out successfully and the error of the method was analyzed which validated the feasibility and the localization of the method.

Symbolic sequence analysis method was applied to analyze two-phase flow measurement fluctuating signals and some issues about key parameters which were effect on the symbolic statistical quantities were discussed. Based on the above study, eighty groups differential pressure fluctuating signals of gas/liquid two-phase flow were gathered in vertical upward pipes and we extracted the time irreversibility and chi-square statistics. When the gas superficial velocity is less than 0.02 m/s, the random variable motion feature and the dynamic character of bubble flow become gradually complex with the gas superficial velocity increasing. When gas superficial velocity exceeds 0.02 m/s, the dynamic character of flow pattern evolution becomes gradually simple from bubble flow to slug flow and the dynamic character becomes more and more complex from slug flow to churn flow with increasing gas superficial velocity. This study shows that time irreversibility and chi-square statistics are sensitive feature quantities of characterizing gas/liquid two-phase flow patterns and this can better help for understanding dynamic characteristics of gas/liquid two-phase flow patterns by observing the two statistical quantities changing with the two-phase flow parameters.

This paper presents the design of a relative low-cost and more compatible autonomous helicopter system using HIROBO 50 scale as an experimental platform. Because of the limit of helicopter payload, we choose the MP2128 Autopilot and a number of sensors to build the system and the weight of instrumentation is about 500 g, much less than the payload capability of model helicopter. Thus it is feasible to design the binocular stereo-camera system to achieve full autonomous flight and the whole weight (include power) of instrumentation is about 1500 g. After getting the model of the helicopter using the subspace model identification (SMI) algorithms, we present the structure of fuzzy PID controller.