An efficient anonymous communication protocol for wireless sensor networks

Anonymous communication is very important for many wireless sensor networks, because it can be used to hide the identity of important nodes, such as the base station and a source node. In sensor networks, anonymous communication includes several important aspects, such as source anonymity, communication relationship anonymity, and base station anonymity. Existing sensor network anonymous schemes either cannot achieve all the anonymities or have large computation, storage, and communication overheads. In this paper, we propose an efficient anonymous communication protocol for sensor networks that can achieve all the anonymities while having small overheads on computation, storage, and communication. We compare our anonymous communication protocol with several existing schemes, and the results show that our protocol provides strong anonymity protection and has low overheads. Copyright © 2011 John Wiley & Sons, Ltd.     

基于伪随机的微处理器验证方法及改进

处理器验证是验证工作中最重要也是时间开销最大的部分.文中提出了一种基于仿真的微处理器验证方法,以伪随机算法为主自动生成测试向量,实现了验证的自动化和验证环境的可重用性.并在此基础上引入神经网络算法,提高了整个验证过程的自动化程度和效率.通过一款DSP处理器的验证结果表明,该方法确实提高了微处理器功能验证的有效性和完备性.     

High throughput route selection in multi-rate wireless mesh networks

Most existing Ad-hoc routing protocols use the shortest path algorithm with a hop count metric to select paths. It is appropriate in single-rate wireless networks, but has a tendency to select paths containing long-distance links that have low data rates and reduced reliability in multi-rate networks. This article introduces a high throughput routing algorithm utilizing the multi-rate capability and some mesh characteristics in wireless fidelity （WiFi） mesh networks. It uses the medium access control （MAC） transmission time as the routing metric, which is estimated by the information passed up from the physical layer. When the proposed algorithm is adopted, the Ad-hoc on-demand distance vector （AODV） routing can be improved as high throughput AODV （HT-AODV）. Simulation results show that HT-AODV is capable of establishing a route that has high data-rate, short end-to-end delay and great network throughput.     

基于ICNN和IGAN的SAR目标识别方法

近年来,卷积神经网络(CNN)已广泛应用于合成孔径雷达(SAR)目标识别。由于SAR目标的训练数据集通常较小,基于CNN的SAR图像目标识别容易产生过拟合问题。生成对抗网络(GAN)是一种无监督训练网络,通过生成器和鉴别器两者之间的博弈,使生成的图像难以被鉴别器鉴别出真假。本文提出一种基于改进的卷积神经网络(ICNN)和改进的生成对抗网络(IGAN)的SAR目标识别方法,即先用训练样本对IGAN进行无监督预训练,再用训练好的IGAN鉴别器参数初始化ICNN,然后用训练样本对ICNN微调,最后用训练好的ICNN对测试样本进行分类。MSTAR实验结果表明,提出的方法不仅能够在训练样本数降至原样本数30%的情况下获得高达96.37%的识别率,而且该方法比直接采用ICNN的方法具有更强的抗噪声能力。     

GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs

A wireless sensor network (WSN) is a prominent technology that could assist in the fourth industrial revolution. Sensor nodes present in the WSNs are functioned by a battery. It is impossible to recharge or replace the battery, hence energy is the most important resource of WSNs. Many techniques have been devised and used over the years to conserve this scarce resource of WSNs. Clustering has turned out to be one of the most efficient methods for this purpose. This paper intends to propose an efficient technique for election of cluster heads in WSNs to increase the network lifespan. For the achievement of this task, grey wolf optimizer (GWO) has been employed. In this paper, the general GWO has been modified to cater to the specific purpose of cluster head selection in WSNs. The objective function for the proposed formulation considers average intra‐cluster distance, sink distance, residual energy, and CH balancing factor. The simulations are carried out in diverse conditions. On comparison of the proposed protocol, ie, GWO‐C protocol with some well‐known clustering protocols, the obtained results prove that the proposed protocol outperforms with respect to the consumption of the energy, throughput, and the lifespan of the network. The proposed protocol forms energy‐efficient and scalable clusters.     

An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.     

Queue length‐based load balancing in data center networks

Traffic load balancing in data centers is an important requirement. Traffic dynamics and possibilities of changes in the topology (e.g., failures and asymmetries) make load balancing a challenging task. Existing end‐host–based schemes either employ the predominantly used ECN or combine it with RTT to get congestion information of paths. Both congestion signals, ECN and RTT, have limitations; ECN only tells whether the queue length is above or below a threshold value but does not inform about the extent of congestion; similarly, RTT in data center networks is on the scale of up to few hundreds of microseconds, and current data center operating systems lack fine‐grained microsecond‐level timers. Therefore, there is a need of a new congestion signal which should give accurate information of congestion along the path. Furthermore, in end‐host–based schemes, detecting asymmetries in the topology is challenging due to the inability to accurately measure RTT on the scale of microseconds. This paper presents QLLB, an end‐host–based, queue length–based load balancing scheme. QLLB employs a new queue length–based congestion signal that gives an exact measure of congestion along the paths. Furthermore, QLLB uses relative‐RTT to detect asymmetries in the topology. QLLB is implemented in ns‐3 and compared with ECMP, CONGA, and Hermes. The results show that QLLB significantly improves performance of short flows over the other schemes and performs within acceptable level, of CONGA and Hermes, for long flows. In addition, QLLB effectively detects asymmetric paths and performs better than Hermes under high loads.     

Analog circuit optimization using adjoint network based sensitivity analysis

Performance optimization as per the desired specifications is a major requirement of analog and mixed signal circuit design process. Rapid scaling of the semiconductor technology demands efficient optimization techniques with minimal manual efforts. In this paper, a gradient based method for analog circuit optimization using adjoint network based sensitivity analysis is presented. The sensitivity of circuit response with respect to the different parameters is computed by using analog circuit and its adjoint transformation. The proposed method is applied to optimize performance of a two stage operational amplifier (OpAmp). Subsequently, the OpAmp circuit is simulated using Cadence Virtuoso for optimized parameters and the results are validated with post fabrication measurement results.     

基于Kohonen神经网络的深度图像分割方法

提出一种基于Ｋｏｈｏｎｅｎ神经网络无监督的深度图像分割方法。首先计算了深度图像各点的导数,进而得到各点的法向,以法向和深度值作为每一点的特征矢量,引入自组织神经网络进行初始的聚类;为消除初始聚类产生的过分割现象,采取相邻表面片法向分析的方法进行再分割,得到最终的分割结果。本方法避免了通常的区域分割方法初始种子不易选取的弱点,聚类所用样本少,速度快。实验结果表现了算法良好的性能。     

短波组网拓扑结构研究

从拓扑结构的角度研究短波通信,根据短波通信的特点,给出了几种有针对性的拓扑结构,并进行了分析。