REV Journal on Electronics and Communications
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Simultaneous Compression of Multiple Pulse Streams for All-Optical Serial/Parallel Data Exchanges
Get PDFWe present our recent development of a simultaneous compression of multiple pulse streams utilizing the distributed Raman amplifier (DRA) for applications in serial/parallel high-speed data exchange. The optical clock pulse compression is operated based on adiabatic soliton compressor using DRA with pulsewidth tunable and multiple wavelength operations. We show an experimental demonstration of a simultaneous compression up to four 10 GHz clock pulse trains to picosecond pulsewidth range. The compressed multiple pulse streams are then used for applications in serial-to-parallel and parallel-to-serial data conversions which are one of key functionalities to realize all-optical wavelength/time-division trans-multiplexing in heterogeneous optical networks
An Extended Occlusion Detection Approach for Video Processing
Get PDFOcclusions become conspicuous as failure regions in video processing when unified over time because the contraventions of the restriction of brightness have accumulated and evolved in occluded regions. The accuracy at the boundaries of the moving objects is one of the challenging areas that required further exploration and research. This paper presents the work in process approach that can detect occlusion regions by using pixel-wise coherence, segment-wise confidence and interpolation technique. Our method can get the same result as usual methods by solving only one Partial Differential Equations (PDE) problem; it is superior to existing methods because it is faster and provides better coverage rates for occlusion regions than variation techniques when tested against a varied number of benchmark datasets. With these improved results, we can apply and extend our approach to a wider range of applications in computer vision, such as background subtraction, tracking, 3D reconstruction, video surveillance, video compression
Performance Analysis of Hybrid ALOHA/CDMA RFID Systems with Quasi-decorrelating Detector in Noisy Channels
Get PDFIn this paper we investigate the performance of a hybrid Aloha/CDMA radio frequency identification (RFID) system with quasi-decorrelating detector (QDD). Motivated by the fact that the QDD outperforms the conventional decorrelating detector (DD) in noisy network scenarios, we study and propose using QDD as one of the most promising candidates for the structure of RFID readers. Performance analysis in terms of bit error rate and the RFID system efficiency is considered considering CDMA code collision and detection error. Computer simulations are also performed, and the obtained results of QDD-based structure are compared with those of DD-based one to confirm the correctness of the design suggestion in different practical applications of tag identification and missing-tag detection
Precoding Designs for Full-Duplex Multi-User MIMO Cognitive Networks with Imperfect CSI
Get PDFThis paper studies a cognitive radio (CR) network which consists of a full-duplex (FD) multi-user (MU) multipleinput multiple-output (MIMO) secondary user (SU) networks operating within the coverage of multiple primary users (PUs). It is assumed that the channel state information (CSI) matrices associated with SU systems are perfectly known whereas the CSI ones from SUs to PUs are imperfectly estimated. The problem of interest is to design robust precoding matrices at the SUs to maximize the CR sum rate subject to the SU transmit power constraints and harmful interference restrictions at PUs. Due to non-concavity of the objective function and intractability of robust PU interference constraints, the design problem is non-convex and challenging to directly solve. We exploit the difference of two concave functions to recast the sum rate objective function as a lower bounded concave one. In addition, a linear matrix inequality (LMI) transformation is used to handle the semi-infinite robust interference constraints. Then, the sequential convex programming method is carried out to iteratively solve a convex optimization problem in each iteration. The simulation results are provided to investigate the CR sum-rate (spectral efficiency) performance and the robustness against the CSI uncertainty
Improved Three-Component Decomposition Technique for Forest Parameters Estimation from PolInSAR Image
Get PDFPolarimetric SAR interferometry (PolInSAR) is an efficient remote sensing technique that allows to extract forest heights by means of model-based inversion. Recently, there have been plenty of researches on the retrieval of vegetation parameters by single frequency single baseline PolInSAR, such as the ESPRIT method and three-stage inversion method. However, these methods have several shortcomings which tend to underestimate the forest height due to attenuations of the electromagnetic waves in the ground medium. In order to overcome these shortcomings, an improved three-component decomposition technique using PolInSAR image is proposed in this paper. By means of coherence set and a Newton-Raphson method, the proposed method improves the accuracy of forest height estimation. The proposed algorithm performance is evaluated with simulated data from PolSARProSim software and L-band PolInSAR image pair of Tien-Shan test site which is acquired by the SIR-C/X-SAR system
Three-Way Tensor Decompositions: A Generalized Minimum Noise Subspace Based Approach
Get PDFTensor decomposition has recently become a popular method of multi-dimensional data analysis in various applications. The main interest in tensor decomposition is for dimensionality reduction, approximation or subspace purposes. However, the emergence of “big data” now gives rise to increased computational complexity for performing tensor decomposition. In this paper, motivated by the advantages of the generalized minimum noise subspace (GMNS) method, recently proposed for array processing, we proposed two algorithms for principal subspace analysis (PSA) and two algorithms for tensor decomposition using parallel factor analysis (PARAFAC) and higher-order singular value decomposition (HOSVD). The proposed decomposition algorithms can preserve several desired properties of PARAFAC and HOSVD while substantially reducing the computational complexity. Performance comparisons of PSA and tensor decomposition of our proposed algorithms against the state-of-the-art ones were studied via numerical experiments. Experimental results indicated that the proposed algorithms are of practical values
A Multistage System for Automatic Detection of Epileptic Spikes
Get PDFA multistage automatic detection system for epileptic spikes is introduced as an assistant tool for epileptic analysis and diagnosis based on electroencephalogram (EEG). The system consists of four stages: preprocessing, feature extraction, classifier and expert system. Multiple state-of-the-art signal processing and machine learning techniques including wavelet transform, spectral filtering, artificial neural network are utilized in order to improve the ability of the overall system stage by stage. Compared to other works, our contributions are three-fold: peaks in the EEG recording are categorized into two groups of non-epileptic spikes and possible epileptic spikes by a committee of three perceptrons; appropriate mother wavelet and wavelet scales are selected for the best system performance; and, based on the neurological fact that an epileptic spike is usually followed by a slow wave, a simple expert system is presented to eliminate pseudo-spikes which are closely analogous to true epileptic spikes. Experimental results show that the proposed system is capable of detecting epileptic spikes efficiently