Current Issue : July - September Volume : 2018 Issue Number : 3 Articles : 5 Articles
In this paper, a novel consensus-based adaptive algorithm for distributed target tracking in large scale camera\nnetworks is presented, aimed at situations characterized by limited sensing range, high-level clutter, and possibly\nocculted targets. The concept of Integrated Probabilistic Data Association (IPDA) is introduced in the distributed\nadaptive tracker design so that the proposed algorithm, named IPDA Adaptive Consensus Filter (IPDA-ACF),\nincorporates probabilities of acquiring target-originated measurements, conditioned on either target perceivability or\ntarget existence. A distributed adaptation scheme represents the core element of the algorithm, allowing fast\nconvergence under a large variety of operating conditions, emphasizing the influence of the nodes with the highest\nprobability of obtaining target-originated measurements. A theoretical analysis of stability and reduction of noise\ninfluence allows getting an insight into the relationship between the local trackers and the global consensus scheme.\nA comparison with analogous existing methods done by extensive simulations shows that the proposed method\nachieves the best performance, in spite of lower communication and computation requirements....
The vibration signals analysis is a very effective and reliable method for detecting the gear failures. Because the vibration signals\nacquired from the gear in the variable speed condition often containmore useful fault information, the analysis of the gear vibration\nsignals during the variable speed condition has been a hot research topic. In this paper, a method based on the multiscale chirplet\npath pursuit (MSCPP) and the linear canonical transform (LCT) has been applied to diagnose the gear fault in the variable speed\ncondition for the first time. First, by using the MSCPP method to estimate the instantaneous meshing frequency, the suitable\nsignal segment approximation to the acceleration or deceleration process can be selected. Then, because the LCT is a novel and\nefficient non stationary signals analysis tool, the optimal LCT spectrum of the selected signal has been at tainted to diagnose the\ngear faults based on the properties of the LCT. In addition, the simulations and the experimental evaluation are provided to verify\nthe effectiveness of the proposed method....
We revisit the multiple importance sampling (MIS) estimator and investigate the bound on the efficiency\nimprovement over balance heuristic estimator with equal count of samples established in Veach�s thesis. We revise\nthe proof for this and come to the conclusion that there is no such bound and henceforth it makes sense to look for\nnew estimators that improve on balance heuristic estimator with equal count of samples. Next, we examine a recently\nintroduced non-balance heuristic MIS estimator that is provably better than balance heuristic with equal count of\nsamples, and we improve it both in variance and efficiency. We then obtain an equally provably better one-sample\nbalance heuristic estimator, and finally, we introduce a heuristic for the count of samples that can be used when the\nindividual techniques are biased. All in all, we present three new sampling strategies to improve on both variance and\nefficiency on the balance heuristic using non-equal count of samples.\nOur scheme requires the previous knowledge of several quantities, but those can be obtained in an adaptive way. The\nresults also show that by a careful examination of the variance and properties of the estimators, even better\nestimators could be discovered in the future. We present examples that support our theoretical findings....
Multiple-Input Multiple-Output (MIMO) relay communication systems are used as an efficient system in spectral\nefficiency and power allocation view point. In these systems, some of the facilities need channel state information\n(CSI). Besides, new estimation methods based on compressed sensing (CS) are well known for their spectral efficiency\nand accuracy. In this paper, we have used a Distributed CS-based channel estimation method to improve the accuracy\nand spectral efficiency of channel estimation for MIMO-Orthogonal Frequency Division Multiplexing relay network.\nSpecifically, using Least Squares (LS) estimation increases the accuracy of well-known Compressive Sampling Matching\nPursuit (CoSaMP) algorithm and proposes Block-verified CoSaMP (B-vCoSaMP). To improve the accuracy of estimation,\nwe are encountered with a combinatorial optimization which is dealt with probability-based approaches in this paper.\nMore particularly, three probability-based optimization methods have been proposed to optimize the mutual\ncoherence of measurement matrix called Sequential Cross-Entropy (SCE), Extended Estimating of Distribution\nAlgorithm (EEDA), and Parallel Cross-Entropy (PCE). All these methods are based on sampling from a Probability\nDensity Function (PDF) which is updated in each iteration using elite samples of the population. The simulation results\nrepresent the accuracy and speed of the proposed methods, and the comparison is expressed as well....
Neurons can detect weak target signals from complex background signals through stochastic resonance (SR) and vibrational\nresonance (VR) mechanisms. However, random phase variation of rapidly fluctuating background signals is generally ignored\nin classical VR or SR studies. Here, the rapidly fluctuating background signals are modeled by bounded noise with random rapidly\nfluctuating phase derived fromWiener process. Then, the influences of bounded noise on the weak signal detection are discussed in\nthe FitzHughââ?¬â??Nagumo (FHN) neuron. Numerical results reveal the occurrence of bounded noise-induced single- and biresonance\nas well as a transition between them. Randomness in phase can enhance the adaptability of neurons, but at the cost of signal\ndetection performance so that neurons can work in more complex environments with a wider frequency range.More interestingly,\nbounded noise with appropriate parameters can not only optimize information transmission but also simultaneously reduce energy\nconsumption. Finally, the potential mechanism of bounded noise is explained....
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