Stochastic Bounded Consensus Tracking of Second-Order Multi-Agent Systems with Measurement Noises and Sampled-Data

This paper investigates the stochastic bounded consensus tracking problems of second-order multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbors or the virtual leader with a time-varying reference state, and the measurements are corrupted by random noises. The probability limit theory, the algebra graph theory, and some other techniques are employed to derive the necessary and sufficient condition guaranteeing the mean square bounded consensus tracking. It turns out that the maximum allowable sampling period depends on not only the network topology but also the constant feedback gains. Furthermore, the effects of the sampling period on tracking performance, including the tracking speed and the static tracking error, are also analyzed. The results show that reducing the sampling period can accelerate the tracking speed and decrease the static tracking error. Simulations are provided to demonstrate the effectiveness of the theoretical results.     

Research on development of embedded uninterruptable power supply system for IOT-based mobile service

Under the banner of Internet of Things (IOT), mobile service is an important hot research topic. As a part of IOT, embedded systems can offer access to the devices on Internet. For supporting mobile service, one design and implementation solution of embedded Uninterruptable Power Supply (UPS) system is brought forward for long-distance monitoring and controlling UPS based on IOT. The key insight of our development is that we adopt new idea to realize new application based on new technology of IOT. The target is for mobile service to support pervasive computing paradigm. Our new idea is involved in the developed system which is based on new Arm chips, a set of new ATmega products (such as ATmega 161, ATmega 162), new Network Interface Controller, TCP/IP protocol suite and other software components. In our designed UPS system, an enhanced embedded file system is designed and implemented which saves the data and index information on the chip (such as a serial EEPROM) in structured way and communicates with MCU through I²C bus. The new file system is embedded in the UPS system. Mobile users can access and manipulate local or remote data on the client side based on IOT. Embedded file system on the chips plays a major role in the growth of IP networking. Based on our experiments, mobile users can easily monitor and control UPS in different places of long-distance. By our experiments and applications, the performance of this kind of new embedded UPS system is satisfied the requirements of all kinds of IOT-based mobile services.     

Vanishing point detection using cascaded 1D Hough Transform from single images

Vanishing point detection algorithms based on 2D histogramming techniques have been employed in a variety of computer vision systems. Previous algorithms achieved some good results but still failed to maintain a balanced performance in both accuracy and time. Recent research (Li et al., 2010) shows that, vanishing point detection could be converted to a 1D histogram search problem, which largely accelerates the procedure. In this paper, we further improve this idea and propose a complete scheme for vanishing point detection from images of the so called “Manhattan world”. We test our algorithm and some commonly used vanishing point detection methods on public database YorkUrbanDB and our own implemented database PKUCampusDB. Our algorithm shows significant performance improvements.     

Pinning a stochastic neural network to the synchronous state

In this paper, the asymptotic stability of the pinning synchronous solution of stochastic neural networks with and without time-delays is analyzed. The delays are time-varying, and the uncertainties are norm-bounded that enter into all the parameters of network and control. The aim of this paper is not only to establish easily verifiable conditions under which the pinning synchronous solution of stochastic neural network is globally asymptotically stable but also to give a feasible way to offset the limitation of network itself in order to reach synchronization. In addition, a specific neurobiological network is also introduced, and some numerical examples are provided to illustrate the applicability of the proposed criteria.     

Privacy enabled video surveillance using a two state Markov tracking algorithm

Nowadays video surveillance systems are widely deployed in many public places. However, the widespread use of video surveillance violates the privacy rights of the people. Many authors have addressed the privacy issues from various points of view. In this paper we propose a novel, on-demand selectively revocable, privacy preserving mechanism. The surveillance video can be tuned to view with complete privacy or by revoking the privacy of any subset of pedestrians while ensuring complete privacy to the remaining pedestrians. We achieve this by tracking the pedestrians using a novel Markov chain algorithm with two hidden states, detecting the head contour of the tracked pedestrians and obscuring their faces using an encryption mechanism. The detected pedestrian face/head is obscured by encrypting with a unique key derived from a master key for the privacy preservation purpose. The performance evaluations on many challenging surveillance scenarios show that the proposed mechanism can effectively and robustly track as well as identify multiple pedestrians and obscure/unobscure their faces/head in real time.     

Supervised polarimetric SAR classification method based on Fisher linear discriminant

A supervised polarimetric SAR land cover classification method was proposed based on the Fisher linear discriminant.The feature parameters used in this classification method could be selected flexibly according to land covers to be classified.Polarimetric and texture feature parameters extracted from co-registered multifrequency and multi-temporal polarimetric SAR data could be combined together for classification use,without consideration of the dimension difference of each feature parameter and the joint probability density function of those parameters.Experimental result with AGRSAR L/C-band full polarimetric SAR data showed that a total classification accuracy of 94.33% was achieved by combining the polarimetric with texture feature parameters extracted from L/C dual band SAR data,demonstrating the effectiveness of this method.     

Heat balance of solar-soil source heat pump compound system

Based on the state-of-the-art studies of solar-soil source heat pump compound system, operation patterns of solar-soil compound system were analyzed, particularly the advantages of parallel operation pattern. It is found that parallel operation pattern is better for solar-soil compound system. Furthermore, the heat balance issue of solar-soil compound system was emphatically analyzed from four aspects, which were annual analysis of heating and cooling load, the heat exchange of ground heat exchanger, capacity determination of solar-assisted heat source and heat balance calculation of solar-soil compound system. Moreover, annual rate of heat balance in a solar-soil source heat pump compound system was calculated with a case study. It is shown that the annual heat unbalance ratio is 19%, which is less than 20%. As a result, the practical solar-soil compound system can basically maintain the heat balance of soil.