Mansour Moniri

Efficient Object Localization Using Sparsely Distributed Passive RFID Tags

Radio-frequency identification (RFID) technology has been widely used in passive RFID localization application due to its flexible deployment and low cost. However, current passive RFID localization systems cannot achieve both highly accurate and precise moving object localization task owing to tag collisions and variation of the behavior of tags. Most researchers increase the density of tag distribution to improve localization accuracy and then consider using either anti-collision process embedded in the hardware of the RFID reader or advanced localization algorithms to enhance localization precision. However, advanced anti-collision processes for RFID devices are challenged by the physical constraint characteristics of radio frequency; and improved localization algorithm cannot fundamentally reduce the impacts of tag collision on localization precision. This research work attempts to improve localization precision of a passive RFID localization system by using sparsely distributed RFID tags. This paper first defines a measure for accuracy and precision in a passive RFID localization system with regard to RFID tag distribution. An exponential-based function is then derived from experimental measurements, which reflects the relationship between RFID tag distribution and localization precision. This function shows that localization precision is mainly determined by tag density of RFID tag distribution. Based on the experimental findings, a sparse RFID tag distribution approach is proposed. The results show that in comparison with the conventional RFID tag distribution, passive RFID localization system with sparse RFID tag distribution can deliver a higher localization precision for the required accuracy.

Classification of smart video surveillance systems for commercial applications

Video surveillance has a large market as the number of installed cameras around us can show. There are immediate commercial needs for smart video surveillance systems that can make use of the existing camera network (e.g. CCTV) for more intelligent security systems and to contribute in more applications (beside or) rather than security applications. This work introduces a new classification for smart video surveillance systems depending on their commercial applications. This paper highlights different links between the research and the commercial applications. The work reported here has both research and commercial motivations. Our goals are first to define a generic model of smart video surveillance systems that can meet requirements of strong commercial applications. Our second goal is to categorize different smart video surveillance applications and to relate capabilities of computer vision algorithms to the requirement of commercial application.

Power harvesting for smart sensor networks in monitoring water distribution system

Recently, there has been a growing interest in using wireless sensor networks for monitoring water distribution infrastructure to help drinking water utilities to have better understanding of hydraulic and water quality statement of their underground assets. One of the challenges is limited power resources for operating the smart sensors and sensor networks. Current common used power supplies for sensor node are batteries. Batteries have many drawbacks such as short life time and need to be replaced on regular basis which is uneconomical and unmanageable in hard access environment such as buried underground water pipelines. Energy harvesting of ambient energy in the water pipeline and powering wireless sensor node including sensing, processing, and communications would be particularly attractive option because the life time of the node will be potentially infinite for supporting wireless sensor networks. The paper will review and discuss the potential of using power harvesting techniques for monitoring water distribution networks and the work done in the area of monitoring water distribution systems using smart sensor networks.

Effect of Directional Antenna on the Doppler Spectrum in 3-D Mobile Radio Propagation Environment

In this paper, an analytical model is proposed for a macrocellular communication system with directional antennas at elevated base station (BS) to quantify the effect of directivity of the radiated waves from antennas on the Doppler spectrum in a 3-D radio propagation environment. The impacts of antenna beamwidth and motion of mobile station (MS) are thoroughly investigated on the statistical distribution of the power Doppler spectrum. Closed-form expressions for trivariate probability density functions (pdfs) of propagation path distance, power, and Doppler shift are derived. Furthermore, general expressions for joint and marginal pdfs of elevation angle of arrival, power, and Doppler shift are established. Finally, the obtained theoretical results, along with the observations that illustrate the effect of directivity of the antenna beamwidth and the direction of the MSs motion on the distribution characteristics of the power Doppler spectrum, are presented. It is established that for motion of the MS in all directions, the spread in distribution of the Doppler shift observed is significantly reduced due to the use of a directional antenna at the BS with a narrow beam directed toward the desired user. It is also observed that, for a sharp azimuthal beam of directional antenna, the multipath components corresponding to the scatterers in the elevation plane result in the reduction of Doppler shift with an increase in their vertical distance from the MS.

SLAM Algorithm for 2D Object Trajectory Tracking based on RFID Passive Tags

Tracking the physical location of nodes in a 2D environment is critical in many applications such as camera tracking in virtual studio, indoor mobile objects tracking. RFID technique poses an interesting solution to localizing the nodes because the passive RFID tags could store the position unit information according to unique tag ID. Based on tags pattern, algebraic approach could solve the 2D trajectory tracking problem. However, the tracking accuracy of this approach is highly related to the tags position distribution and position unit. It would be inaccurate for some erratic trajectory tracking. Thus, we would try to apply and evaluate the probabilistic approaches, such as SLAM (Simultaneous Localization and Mapping), into RFID tag based trajectory tracking. In this paper, we propose an RFID tag based SLAM algorithm for 2D trajectory tracking. Also a technique called Map adjustment is proposed to increase the efficiency of the algorithm. The simulation results show that the approach could improve the accuracy for some parts of trajectory tracking compared to RFID algebraic approach. The limitation and future work are given in the conclusion.

Fragile and semi-fragile image authentication based on image self-similarity

We propose a new approach for fragile and semi-fragile image authentication based on image self-similarity. The authentication signature is extracted from image representation using generalized finite automata which represents the image in terms of its perceptual self-similarity. This self-similarity is more likely to be damaged due to illegitimate manipulations rather than compression algorithms such as JPEG. The proposed technique is also capable of changing the degree of fragility by setting the value of the image self-similarity parameter /spl alpha/. Furthermore, our approach features an extremely compact signature length whilst maintaining a rather simple implementation. Experimental results are presented for well known images to support these claims.

Systematic generation of current mode dual ouput OTA filters using a building block approach

A synthesis method is described for generating current mode dual-output OTA filter configurations. The procedure is based on a building-block approach and provides a systematic and structual method for generating filter circuits. Using this method, the capabilities of dual-output OTA devices in active filter design can be fully investigated. While the procedure is general, it is shown how filter circuits (LP, HP, BP, and BR) based on one, two and three dual-output OTAs are generated.

Evolutionary multi-objective design optimisation of energy harvesting MEMS: The case of a Piezoelectric

The design and optimisation of Energy Harvesting (EH) Micro-Electromechanical-Systems (MEMS) is of particular interest in this research. The application of such devices is becoming an attractive alternative to the traditional use of batteries in wireless and body sensor networks. An evolutionary Multi-Objective Design Optimisation (DO) Framework is developed to experiment with one class of EH-MEMS, namely, Piezoelectric, using a reconstructed analytical model of the system. The application of such a Framework in this application domain is unprecedented and has already shown very promising results and in some cases it outperformed the human engineer. A thorough analysis of the results has been undertaken, which reveals interesting conclusions about the behaviour and physics of such devices. Besides, the main features of the Framework are explored enabling the enhancement of the MEMS-DO.

Current-mode elliptic filter design based on symmetrical current switching

This paper describes a current-mode elliptic filter structure based on dual-output OTAs and grounded capacitors. The filter is capable of producing both lowpass and highpass notch responses without changing the filter structure. This is achieved using a symmetrical current switching technique based on two switches controlled with a 2-bit digital word. The proposed filter structure forms a basic second-order building block in the design of high-order elliptic filters with tuneable frequency response. To confirm the theoretical analysis, simulated and measured results of fourth-order elliptic lowpass and highpass filters with tuneable bandwidth in the range of 0.65MHz to 1.3MHz are included. Finally, detailed analysis of the OTAs non-ideal parameters on the filter performance is presented and an example is given.

Adaptive compressive sensing for target tracking within wireless visual sensor networks-based surveillance applications

Wireless Visual Sensor Networks (WVSNs) have gained significant importance in the last few years and have emerged in several distinctive applications. The main aim is to design low power WVSN surveillance application using adaptive Compressive Sensing (CS) which is expected to overcome the WVSN resource constraints such as memory limitation, communication bandwidth and battery constraints. In this paper, an adaptive block CS technique is proposed and implemented to represent the high volume of captured images in a way for energy efficient wireless transmission and minimum storage. Furthermore, to achieve energy-efficient target detection and tracking with high detection reliability and robust tracking, to maximize the lifetime of sensor nodes as they can be left for months without any human interactions. Adaptive CS is expected to dynamically achieve higher compression rates depending on the sparsity nature of different datasets, while only compressing relative blocks in the image that contain the target to be tracked instead of compressing the whole image. Hence, saving power and increasing compression rates. Least mean square adaptive filter is used to predicts target’s next location to investigate the effect of CS on the tracking performance. The tracking is achieved in both indoor and outdoor environments for single/multi targets. Results have shown that with adaptive block CS up to 20 % measurements of data are required to be transmitted while preserving the required performance for target detection and tracking.