Efficient JPEG 2000 Image Compression Scheme for Multihop Wireless Networks

 When using wireless sensor networks for real-time data transmission, some critical points should be considered. Restricted computational power, reduced memory, narrow bandwidth and energy supplied present strong limits in sensor nodes. Therefore, maximizing network lifetime and minimizing energy consumption are always optimization goals. To overcome the computation and energy limitation of individual sensor nodes during image transmission, an energy efficient image transport scheme is proposed, taking advantage of JPEG2000 still image compression standard using MATLAB and C from Jasper. JPEG2000 provides a practical set of features, not necessarily available in the previous standards. These features were achieved using techniques: the discrete wavelet transform (DWT), and embedded block coding with optimized truncation (EBCOT). Performance of the proposed image transport 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.

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput

DC and RF characteristics optimization of AlGaN/GaN/BGaN/GaN/Si HEMT for microwave-power and high temperature application

AlGaN/GaN/Si high electron mobility transistors (HEMTs) developed by molecular beam epitaxy (MBE) are studied with several methods for characterization, the most utilized are direct-current and radio-frequency measurements, to see power and microwave performance of components. The increase in these parameters is not based just with on improvement technological for example, decrease of length gate (Lg) and passivation. For sure, another very important point is to reduce the thickness of barrier while keeping the reduction in the length of gate, in order to reduce the transit time (τ), and consequently access to highest cut-off frequency (FT). For this situation, it’s appears a harmful phenomenon of type “punch-through”, because of the weak confinement of electrons in the channel. In this paper, the main objective is to show how to reduce this effect. Keywords: HEMT GaN, Current-voltage characteristics, RF characteristics, Alloys BGaN, Radio-frequenc

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput

Evaluation of endothelial cell adhesion onto different protein/gold electrodes by EIS.

International audienceTo study cell attachment to biomaterials, several proteins such as fibronectin, collagen IV, heparin, immunoglobulin G, and albumin have been deposited onto polystyrene adsorbed on a self-assembled monolayer (silane or thiol) on glass or gold, respectively. The different steps of this multilayer assembly have been characterized by electrochemical impedance spectroscopy (EIS). These data are compared to those of adhesion rate, viability percentage, and cytoskeleton labeling for a better understanding of the cell adhesion process to each protein. All the proteins are endothelial cell adhering biomolecules but not with the same features. A linear relationship has been established between adhesion rate and resistance of the endothelial cell/protein interface for all negatively charged proteins

Immobilization of native membrane-bound rhodopsin on biosensor surfaces

International audienc