Parallel energy-efficient coverage optimization using WSN with Image Compression

Energy constraint is an important issue in wireless sensor networks. This paper proposes a distributed energy optimization method for target tracking applications. Sensor nodes are clustered by maximum entropy clustering. Then, the sensing field is divided for parallel sensor deployment optimization. For each cluster, the coverage and energy metrices are calculated by grid exclusion algorithm and Dijkstra’s algorithm, respectively. Cluster heads perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric. Particle filter is improved by combing the radial basis function network, which constructs the process model. Thus, the target position is predicted by the improved particle filter. Dynamic awakening and optimal sensing scheme are then discussed in dynamic energy management mechanism. A group of sensor nodes which are located in the vicinity of the target will be awakened up and have the opportunity to report their data. The selection of sensor node is optimized considering sensing accuracy and energy consumption. Experimental results verify that energy efficiency of wireless sensor network is enhanced by parallel particle swarm optimization, dynamic awakening approach, and sensor node selection

An Intelligent Method Based Medical Image Compression

Compression methods are important in many medical applications to ensure fast interactivity through large sets of images (e.g. volumetric data sets, image databases), for searching context dependant images and for quantitative analysis of measured data. Medical data are increasingly represented in digital form. The limitations in transmission bandwidth and storage space on one side and the growing size of image datasets on the other side has necessitated the need for efficient methods and tools for implementation. Many techniques for achieving data compression have been introduced. Wavelet transform techniques currently provide the most promising approach to high-quality image compression, which is essential for Teleradiology. This paper presents an approach of intelligent method to design a vector quantizer for image compression. The image is compressed without any loss of information. It also provides a comparative study in the view of simplicity, storage space, robustness and transfer time of various vector quantization methods. The proposed approach presents an efficient method of vector quantization for image compression and application of SOFM

Intelligent Image compression in Multi-agent system

When using wireless sensor networks for real-time data transmission, some critical points should be considered. Restricted computational power, memory limitations, narrow bandwidth and energy supplied present strong limits in sensor nodes. Therefore, maximizing network lifetime and minimizing energy consumption are always optimization goals. To reduce the energy consumption of the sensor network during image transmission, an energy efficient image compression scheme is proposed. The image compression scheme reduces the required memory. To address the above mentioned concerns, in this paper we describe an approach of image transmission in WSNs , taking advantage of JPEG2000 still image compression standard and using MATLAB . These features were achieved using techniques: the Discrete Wavelet Transform (DWT), and Embedded Block Coding with Optimized Truncation (EBCOT). Performance of the proposed image compression scheme is investigated with respect to image quality and energy consumption. Simulation results are presented and show that the proposed scheme optimizes network lifetime and reduces significantly the amount of required memory by analyzing the functional influence of each parameter of this distributed image compression algorithm

Peritubular cells may modulate Leydig cell–mediated testosterone production through a nonclassic pathway

Objective: To evaluate whether paracrine signals are responsible for hormone-independent Leydig cell (Lc) steroidogenesis in the testis. Design: Testicular peritubular cells (PTc), Sertoli cells (Sc), and Lc were isolated and cultured, and their effect on each other was evaluated in terms of lactate production by Sc and testosterone (T) production by Lc. Setting: Research institution. Animal(S): Wistar rats. Intervention(S): Testes were surgically removed, and a new, easily adoptable procedure for PTc was developed; culture media from Sc, PTc, and Lc cultures were used for treating pure populations of these cells. Cells were also cocultured together. Main Outcome Measure(s): To assess culture or coculture supernatants for presence of metabolites and Lc messenger RNA analysis. Result(s): Although PTc secreted factor(s) did not augment production of Sc lactate, essential for germ cell survival, they significantly augmented T secretion by Lc, independent of StAR gene expression. Coculture studies showed that T production by Lc was significantly stimulated when Lc were cocultured with PTc, even in the absence of hormones. Conclusion(s): Testicular peritubular cell-derived factor(s) can potentially augment T production by Lc in a nonclassic manner even in a gonadotropin-deficient environment