Shenqiu Zhang

An Analytical Approach to the Design of Energy Harvesting Wireless Sensor Nodes

Energy harvesting is one of the promising solutions to the problem of limited battery capacity in many wireless devices. This paper addresses the problem of system design of energy harvesting capable wireless devices in terms of the required sizes for energy and data buffers, as well as the size of the harvester, for given delay and loss requirements. We analyze the performance of an energy harvesting node, considering a stochastic model that takes into account energy harvesting and event arrival processes. We derive closed-form expressions for the probability of event loss and the average queueing delay. Our event-driven continuous time simulations validate our analytical results. Employing these results, we provide a near-optimal approach to the design of the system in terms of sizing the energy harvesting device, the energy storage, and the event queue capacity.

Analysis and Design of Energy Harvesting Wireless Sensor Networks with Linear Topology

An energy harvesting sensor network with a linear topology is investigated. The probability of packet loss in the network due to lack of energy in the nodes is derived. Based on this result, a near-optimal design for sizing the storage and the harvesting components of sensors is obtained. A more practical design guideline assuming identical sensors is also provided. Simulations validate the analysis and demonstrate the near optimal performance of the proposed theoretical designs.

The nonredundant contourlet transform (NRCT): A multiresolution and multidirection image representation

As a directional multiresolution image representation, the contourlet transform can efficiently capture curved and oriented geometrical structures in images. However, it has the drawback of a 4/3 redundancy in its oversampling ratio. This paper proposes an innovative framework of filter banks to achieve the same frequency partition as the contourlet transform, while eliminating its redundancy. The new image representation is called the nonredundant contourlet transform (NRCT). The proposed NRCT possesses two important properties: critical sampling and perfect reconstruction. Moreover, the NRCT is an extension of the wavelet transform. Numerical experiments are presented to demonstrate the potential of the NRCT for image compression.

Statistical models for harvested power from human motion

This paper investigates the statistical properties of human motion-based harvested power, and provides models for the distribution, auto-correlation and cross-correlation of harvested power at different body locations, namely left wrist, right wrist, left ankle and waist. The models are developed based on empirical acceleration measurements while the subjects perform unscripted daily tasks. The measured accelerations are converted to harvestable power by assuming a velocity-damped resonant harvesting generator. The provided models enable realistic analysis and simulation of wearable communication systems with motion-based energy harvesting.

The 2-directional wavelet transform: Theory and implementation

The 2-directional wavelet transform is an image representation which separates the spectrum of an image into a lowpass subband, a horizontal highpass subband and a vertical high-pass subband. Each of the decomposed highpass subbands, corresponding to pairwise trapezoidal regions of the spectrum of an original image, is critically sampled to a rectangular shaped coefficient image in the spatial domain. This paper illustrates how an innovative method can be generated by the creative combination of filter banks, integer resampling, and frequency shifting. The proposed 2-directional wavelet transform achieves critical sampling and perfect reconstruction properties. The new transform is an extension of the traditional wavelet transform, and is also the fundamental stage of a new method called the nonredundant contourlet transform (NRCT) developed in a companion paper. Experimental results are presented to demonstrate the potential of the 2- directional wavelet transform.

Statistical Models for Harvested Power From Human Motion

This paper investigates the statistical properties of human motion-based harvested power, and provides models for the distribution, auto-correlation and cross-correlation of harvested power at different body locations, namely left wrist, right wrist, left ankle and waist. The models are developed based on empirical acceleration measurements while the subjects perform unscripted daily tasks. The measured accelerations are converted to harvestable power by assuming a velocity-damped resonant harvesting generator. The provided models enable realistic analysis and simulation of wearable communication systems with motion-based energy harvesting.

Performances of IEEE 802.15.4 and its high rate extensions for body sensor networks

The 2360–2400 MHz frequency band has been proposed for the medical use of body sensor networks. This paper investigates the performance of IEEE 802.15.4 and its high rate extensions (to 500 kbps and 2 Mbps) in this new band. Two receiver structures, namely correlation and hard decision receivers are evaluated based on a number of performance metrics including BER, spectrum efficiency, throughput, and interference. The results show that the IEEE 802.15.4, with lowered transmit power, is suitable for most medical body sensor network applications, though higher rate extensions are necessary for some particular applications.

Gray-Scale Fingerprint Image Compression Based on the Hybrid-NRCT

As a directional multiresolution image representation, the contourlet transform can efficiently capture curved and oriented geometrical structures in images. However, the contourlet transform has the drawback of a 4/3 redundancy in its oversampling ratio. Recently, Zhang and Moloney have developed a nonredundant version of the contourlet transform, called the nonredundant contourlet transform (NRCT) and have demonstrated that this 4/3 redundancy can be eliminated by the NRCT. With the advantages of critical sampling and perfect reconstruction, the NRCT is suitable for tracking and efficiently coding oriented structure in images, such as the texture of ridges in fingerprint images. Moreover, as an extension of the wavelet transform, the NRCT is easily compatible with the wavelet transform. A new transform which combines the NRCT with the wavelet transform is called the hybrid-NRCT. This paper proposes a compression scheme for fingerprint images using the hybrid-NRCT, and compares its performance with other transform-based fingerprint image compression schemes.