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In this study, classification of 11 different Power Quality (PQ) disturbances with Artificial Neural Networks (ANN) has been done by using the attributes obtained with S-Transform. It was aimed to achieve accurate and high classification performance in noisy environment by using the least number of attributes representing PQ disturbances. The most suitable ones from the attributes were selected by...
User localization information is an important data source for ubiquitous assistance in smart environments. This paper proposes a device-free passive user localization approach based on room-equipped passive RFID instead of battery powered hardware. Based on this approach recent work tried to formulate physical model based localization algorithms. These approaches suffer from their inability of integrating...
In this paper, the signal processing by using polynomial neural network (NN) and its equivalent polynomial function is studied and simulated. To demonstrate the superiority of the equivalent polynomial function proposed, the signal recognition in a two-dimension (NC2) non-convex system and system identification were simulated and discussed. All simulations were performed by using the conventional...
Nuclear magnetic resonance (NMR) relaxation spectrum is often used as fingerprints of molecular species, structure and dynamics in the study of complex multiphase system. Inversion algorithms such as singular value decomposition (SVD), Non-negative least square (NNLS), Solid iteration rebuild technique (SIRT) have been widely used in analyzing NMR data to obtain a T1 or T2 spectrum. However, due to...
The fire signal detection is a non-structural problem and difficult to be precise described by mathematical model, which increase the difficulty of fire detection. According to the special type of signal detection technique such as fire signal detection, a fire detection model based on fuzzy-neural network is presented. This paper described the design method of the model, as well as its learning algorithm...
Random Matrix Theory has generated tremendous interest in recent years, partly from powerful results developed for multi-user detection theory but also for growing applications in statistics, signal processing and econometrics. However the current theory has emphasized white noise data. In this paper we present for the first time some results applicable to temporally stationary processes.
Many problems in signal processing involve finding sparse solutions to linear systems of equations. The usual way of achieving this involves minimizing a mixed penalty function composed of a quadratic l2 term and a sparse inducing l1 term. Some existing algorithms for minimization include cyclic descent, gradient projection and iterative fixed point methods. Cojugate gradient is well known as a fast...
In this paper we propose the analogy measurement of two random variables, and discuss the principle of maximizing nongaussianity of observed data to estimate independent components sequentially based on unsupervised learning neural network. We also prove the non-polynomial moment theorem in a generalized scheme, and reveal the feasibility that replaces the analogy measurement by the expectation of...
RBF (Radial Basis Function) neural network is used to predict the magnitude of earthquake in this article. The self-adaptive and nonlinear approach abilities of RBF neural network are suitable to process the complexity of the production mechanism of earthquake. Firstly, RBF neural network is used to learn the data which include the information of earthquake. Then, use the trained RBF neural network...
Notice of Violation of IEEE Publication Principles"Evaluation of GP Model for Software Reliability,"by S. Paramasivam, and M. Kumaran,in the 2009 International Conference on Signal Processing Systems, May 2009After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication...
Aiming at some problems such as ldquoover-fitrdquo with ANN modeling, a new type of combined neural network and corresponding optimal modeling method are proposed in this paper. By this method, expectation error (namely, possible minimum error) is firstly estimated according to data quality; then, optimize network structure and choose optimal training result according to the difference between actual...
In this paper, we propose to use artificial neural networks (ANN) for voice conversion. We have exploited the mapping abilities of ANN to perform mapping of spectral features of a source speaker to that of a target speaker. A comparative study of voice conversion using ANN and the state-of-the-art Gaussian mixture model (GMM) is conducted. The results of voice conversion evaluated using subjective...
FECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during pregnancy and labour. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of this paper is to model the developed...
This paper suggests a novel method named DOSCWTRBFN based on radial basis function neural network (RBFN) with direct orthogonal signal correction (DOSC) and wavelet transform (WT) as a pre-processing tool for the simultaneous spectrophotometric determination of Mn(II), Zn(II), Co(II) and Cd(II). In this case, by optimization, the number of DOSC components, tolerance factor, wavelet function, decomposition...
Dirichlet process mixture (DPM) model, which is the state-of-the-art Bayesian nonparametric model, was introduced here to signal processing research field. In present Bayesian statistics it is used to model and inference random nongaussian distributions. We explored its ability to model and estimate nongaussian unknown stationary noise and our work will help dealing with problems in many fields of...
This paper discusses the researching process of simulating dynamic weighting signal in the aspect of signal on MATLAB, based on the conception and researching method of virtual instrument. The signal simulation includes parts of the production line, the mechanism of balance (a Second-Order Underdamped System), response of object, response of the mechanism and power frequency noise. This paper analyzes...
This paper discusses two pitch detection algorithms (PDA) for simple audio signals which are based on zero-cross rate (ZCR) and autocorrelation function (ACF). As it is well known, pitch detection methods based on ZCR and ACF are widely used in signal processing. This work shows some features and problems in using these methods, as well as some improvements developed to increase their performance.
This paper proposed a two layer authorization mechanism, including traditional password system and rhythm recognition. The whole system includes two phases: preprocessing and usual operation for users. In preprocessing phase, users type password in a specific rhythm in order to record and analyse the characteristics of behaviour of users. In the second phase, how to verify a user in usual operation...
Non-negative sparse coding (NSC) is a powerful technique for low-rank data approximation, and has found several successful applications in signal processing. However, the temporal dependency, which is a vital clue for many realistic signals, has not been taken into account in its conventional model. In this paper, we propose a general framework, i.e., convolutive non-negative sparse coding (CNSC),...
A kind of signal processing method of the autocorrelation function envelope detection was presented for the radio ranging system, combined pseudo-random code binary-phase modulation with linear frequency modulation. The ranging equation was deduced. The virtual simulation model of ranging system was established and the ranging property was analyzed. The simulation results of ranging precision were...
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