Current Issue : October - December Volume : 2020 Issue Number : 4 Articles : 5 Articles
In this paper, a second-order asynchronous delta-sigma modulator (ADSM) is proposed\nbased on the active-RCintegrators. The ADSM is implemented in the 0.18....................
Variational mode decomposition (VMD) method has been widely used in the field of signal processing with significant advantages\nover other decomposition methods in eliminating modal aliasing and noise robustness. The number (usually denoted by K) of\nintrinsic mode function (IMF) has a great influence on decomposition results. When dealing with signals including complex\ncomponents, it is usually impossible for the existing methods to obtain correct results and also effective methods for determining\nK value are lacking. A method called center frequency statistical analysis (CFSA) is proposed in this paper to determine K value.\nCFSA method can obtain K value accurately based on center frequency histogram. To shed further light on its performance, we\nanalyze the behavior of CFSA method with simulation signal in the presence of variable components amplitude, components\nfrequency, and components number as well as noise amplitude. The normal and fault vibration signals obtained from a bearing\nexperimental setup are used to verify the method. Compared with maximum center frequency observation (MCFO), correlation\ncoefficient (CC), and normalized mutual information (NMI) methods, CFSA is more robust and accurate, and the center\nfrequencies results are consistent with the main frequencies in FFT spectrum....
Optimization for power is one of the most important design objectives in modern digital signal processing (DSP) applications. The\ndigital finite duration impulse response (FIR) filter is considered to be one of the most essential components of DSP, and\nconsequently a number of extensive works had been carried out by researchers on the power optimization of the filters. Datadriven\nclock gating (DDCG) and multibit flip-flops (MBFFs) are two low-power design methods that are used and often treated\nseparately. The combination of these methods into a single algorithm enables further power saving of the FIR filter. The experimental\nresults show that the proposed FIR filter achieves 25% and 22% power consumption reduction compared to that using\nthe conventional design....
In target estimating sea clutter or actual mechanical fault diagnosis, useful signal is often submerged in strong chaotic noise, and\nthe targeted signal data are difficult to recover. Traditional schemes, such as Elman neural network (ENN), backpropagation\nneural network (BPNN), support vector machine (SVM), and multilayer perceptron- (MLP-) based model, are insufficient to\nextract the weak signal embedded in a chaotic background. To improve the estimating accuracy, a novel estimating method for\naiming at extracting problem of weak pulse signal buried in a strong chaotic background is presented. Firstly, the proposed\nmethod obtains the vector sequence signal by reconstructing higher-dimensional phase space data matrix according to the Takens\ntheorem. Then, a Jordan neural network- (JNN-) based model is designed, which can minimize the error squared sum by mixing\nthe single-point jump model for targeting signal. Finally, based on short-term predictability of chaotic background, estimation of\nweak pulse signal from the chaotic background is achieved by a profile least square method for optimizing the proposed model\nparameters. The data generated by the Lorenz system are used as chaotic background noise for the simulation experiment. The\nsimulation results show that Jordan neural network and profile least square algorithm are effective in estimating weak pulse signal\nfrom chaotic background. Compared with the traditional method, (1) the presented method can estimate the weak pulse signal in\nstrong chaotic noise under lower error than ENN-based, BPNN-based, SVM-based, and -ased models and (2) the proposed\nmethod can extract the weak pulse signal under a higher output SNR than BPNN-based model....
Aiming at the problem of intersection signal control, a method of traffic phase combination and signal timing optimization based\non the improved K-medoids algorithm is proposed. Firstly, the improvement of the traditional K-medoids algorithm embodies in\ntwo aspects, namely, the selection of the initial medoids and the parameter k, which will be applied to the cluster analysis of\nhistorical saturation data. The algorithm determines the initial medoids based on a set of probabilities calculated from the\ndistance and determines the number of clusters k based on an exponential function, weight adjustment, and elbow ideas.\nSecondly, a phase combination model is established based on the saturation and green split data, and the signal timing is\noptimized through a bilevel programming model. Finally, the algorithm is evaluated over a certain intersection in Hangzhou,\nand results show that this algorithm can reduce the average vehicle delay and queue length and improve the traffic capacity of\nthe intersection in the peak hour....
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