Connectivity properties of large-scale sensor networks

In wireless sensor networks, both nodes and links are prone to failures. In this paper we study connectivity properties of large-scale wireless sensor networks and discuss their implicit effect on routing algorithms and network reliability. We assume a network model of n sensors which are distributed randomly over a field based on a given distribution function. The sensors may be unreliable with a probability distribution, which possibly depends on n and the location of sensors. Two active sensor nodes are connected with probability p _e (n) if they are within communication range of each other. We prove a general result relating unreliable sensor networks to reliable networks. We investigate different graph theoretic properties of sensor networks such as k-connectivity and the existence of the giant component. While connectivity (i.e. k = 1) insures that all nodes can communicate with each other, k-connectivity for k > 1 is required for multi-path routing. We analyze the average shortest path of the k paths from a node in the sensing field back to a base station. It is found that the lengths of these multiple paths in a k-connected network are all close to the shortest path. These results are shown through graph theoretical derivations and are also verified through simulations.     

CEDAR: Modeling impact of component error derating and read frequency on system-level vulnerability in high-performance processors

Reliability of the current microprocessor technology is seriously challenged by radiation-induced soft errors. Accurate Vulnerability Factor (VF) modeling of system components is crucial in designing cost-effective protection schemes in high-performance processors. Although Statistical Fault Injection (SFI) techniques can be used to provide relatively accurate VF estimations, they are often very time-consuming. Unlike SFI techniques, recently proposed analytical models can be used to compute VF in a timely fashion. However, VFs computed by such models are inaccurate as the system-level impact of soft errors is overlooked.     

RTDGPS Accuracy Improvement Using PSO-LSWSVM and Low-Cost GPS Receivers

Wireless Personal Communications - The position of mobile devices is determined by Real Time Differential Global Positioning System (RTDGPS). This system is composed of fixed and mobile station....     

Content‐Based Image Retrieval Based on Relevance Feedback and Reinforcement Learning for Medical Images

To enable a relevance feedback paradigm to evolve itself by users’ feedback, a reinforcement learning method is proposed. The feature space of the medical images is partitioned into positive and negative hypercubes by the system. Each hypercube constitutes an individual in a genetic algorithm infrastructure. The rules take recombination and mutation operators to make new rules for better exploring the feature space. The effectiveness of the rules is checked by a scoring method by which the ineffective rules will be omitted gradually and the effective ones survive. Our experiments on a set of 10,004 images from the IRMA database show that the proposed approach can better describe the semantic content of images for image retrieval with respect to other existing approaches in the literature.     

Gain improvement and microwave operation of 4H–SiC MESFET with a new recessed metal ring structure

A new multi-recessed 4H-SiC MESFET with recessed metal ring for RF embedded circuits is proposed (MR2-MESFET). The key idea in the proposed structure is based on the elimination of the spaces adjacent to gate and stopped the depletion region extending towards drain and source and the reduction of the channel thickness between gate and drain to increase breakdown voltage (V_BR); meanwhile the elimination of the gate depletion layer extension to source/drain to decrease gate-source capacitance (C_gs). The influence of multi-recessed drift region and recessed metal ring structures on the characteristics of the MR2-MESFET is studied by numerical simulation. The optimized results show that the V_BR of the MR2-MESFET is 119% larger than that of the conventional 4H–SiC MESFET (C-MESFET); meanwhile maintain 85% higher saturation drain current. Therefore, the maximum output power density of the MR2-MESFET is 23.1 W/mm compared to 5.5 W/mm of the C-MESFET. Also, the cut-off frequency (f_T) and the maximum oscillation frequency (f_max) of 24.9 and 91.7 GHz are obtained for the MR2-MESFET compared to 11 and 40 GHz of the C-MESFET structure, respectively. The proposed MR2-MESFET shows a maximum stable gain (MSG) exceeding 23.6 dB at 3.1 GHz which is the highest gain yet reported for SiC MESFETs, showing the potential of this device for high power RF applications.     

High-Performance Mixed-Mode Universal Min-Max Circuits for Nanotechnology

In this paper a low-power, high-speed and high-resolution voltage-mode Min-Max circuit, as well as a new efficient universal structure for determining the minimum and maximum values of the input digital signals, is proposed for nanotechnology. In addition, the proposed designs provide rail-to-rail input and output signals which enhance the performance and the robustness of the circuits. The advantage of the proposed Min-Max circuit is that it is extendable for any arbitrary n-digit and radix-r input numbers. Comprehensive simulation results at CMOS and CNFET technologies demonstrate the low-power and high-performance operation as well as insusceptibility to PVT variations of the proposed structure.     

Advanced Bioresponsive Multitasking Hydrogels in the New Era of Biomedicine

Advanced forms of hydrogels have many inherently desirable properties and can be designed with different structures and functions. In particular, bioresponsive multifunctional hydrogels can carry out sophisticated biological functions. These include in situ single-cell approaches, capturing, analysis, and release of living cells, biomimetics of cell, tissue, and tumor-specific niches. They can allow in vivo cell manipulation and act as novel drug delivery systems, allowing diagnostic, therapeutic, vaccination, and immunotherapy methods. In the present review of multitasking hydrogels, new approaches and devices classified into point-of-care testing (POCT), microarrays, single-cell/rare cell approaches, artificial membranes, biomimetic modeling systems, nanodoctors, and microneedle patches are summarized. The potentials and application of each format are critically discussed, and some limitations are highlighted. Finally, how hydrogels can enable an “all-in-one platform” to play a key role in cancer therapy, regenerative medicine, and the treatment of inflammatory, degenerative, genetic, and metabolic diseases is being looked forward to.     

SVM-based validation of motor unit potential trains extracted by EMG signal decomposition

Motor unit potential trains (MUPTs) extracted via electromyographic (EMG) signal decomposition can aid in the diagnosis of neuromuscular disorders and the study of the neural control of movement, but only if they are valid. In this paper, support vector machine (SVM)-based supervised classifiers are proposed to estimate the validity of extracted MUPTs. The classifiers use either the MU firing pattern or the MUP shape consistency of an MUPT, or both, to estimate its validity. The developed classifiers estimate the class label of an MUPT (i.e., valid/invalid) and a degree of support for the decision being made. A single SVM that estimates the validity of a given MUPT using extracted MU firing pattern and MUP shape features was investigated. In addition, the effectiveness of multiclassifier techniques which estimate the overall validity of a train by fusing the MU firing pattern and MUP shape validity of a given MUPT, determined separately by two distinct SVMs, was also investigated. Training based only on simulated data showed robust classification performance of the several multiclassifier methods when tested using both simulated and real test data. Of the methods studied, the multiclassifier constructed using trainable logistic regression to aggregate base classifier outputs had the best performance. Assuming 12.7% of extracted MUPTs are on average invalid, the estimated accuracy for this method in correctly categorizing MUPTs extracted during decomposition was 99.4% and 98.8% for simulated and real data, respectively.     

Energy efficiency optimization of one-way and two-way DF relaying considering circuit power

In this paper, the energy efficiency (EE) of a decode and forward (DF) relay system is studied, where two sources communicate through a half-duplex relay node in one-way and two-way relaying strategies. Both the circuitry power and the transmission power of all nodes are taken into consideration. In addition, three different coding schemes for two-way DF relaying strategy with two phases and two-way DF relaying with three phases are considered. The aim is to maximize the EE of the system for a constant spectral efficiency (SE). For this purpose, the transmission time and the transmission power of each node are optimized. Simulations are used to compare the EE–SE curve of different DF strategies with one-way and two-way amplify and forward (AF) strategies and direct transmission (DT), to find the best energy efficient strategy in different SE conditions. Analytical and simulation results demonstrate that in low SE conditions, DF relaying strategies are more energy efficient compared to that of AF strategies and DT. However, in high SE conditions, the EE of two-way AF relaying and DT strategy outperform some of the DF relaying strategies. In simulations, the impact of different circuitry power and different channel conditions on the EE–SE curves are also investigated.     

Low-voltage and low-power Ku-band CMOS LNA using capacitive feedback

Analog Integrated Circuits and Signal Processing - An ultra-wideband (12–18 GHz) low-noise amplifier (LNA) using a 65 nm CMOS technology is proposed, in which a...