Current Issue : January - March Volume : 2020 Issue Number : 1 Articles : 5 Articles
Image compression techniques aim to reduce redundant information in order\nto allow data storage and transmission in an efficient way. In this work, we\npropose and analyze a lossy image compression method based on the singular\nvalue decomposition using an optimal choice of eigenvalues and an adaptive\nmechanism for block partitioning. Experiments are conducted on several images\nto demonstrate the effectiveness of the proposed compression method in\ncomparison with the direct application of the singular value decomposition....
Signal-to-noise ratio (SNR) is a priori information necessary for many signal processing\nalgorithms or techniques. However, there are many problems exsisting in conventional SNR estimation\ntechniques, such as limited application range of modulation types, narrow effective estimation range\nof signal-to-noise ratio, and poor ability to accommodate non-zero timing offsets and frequency offsets.\nIn this paper, an SNR estimation technique based on deep learning (DL) is proposed, which is a\nnon-data-aid (NDA) technique. Second and forth moment (M2M4) estimator is used as a benchmark,\nand experimental results show that the performance and robustness of the proposed method are\nbetter, and the applied ranges of modulation types is wider. At the same time, the proposed method\nis not only applicable to the baseband signal and the incoherent signal, but can also estimate the SNR\nof the intermediate frequency signal....
Alzheimerâ??s Disease (AD) is a neurological disorder characterized by a progressive\ndeterioration of brain functions that affects, above all, older adults. It can be difficult to make an early\ndiagnosis because its first symptoms are often associated with normal aging. Electroencephalography\n(EEG) can be used for evaluating the loss of brain functional connectivity in AD patients. The purpose\nof this paper is to study the brain network parameters through the estimation of Lagged Linear\nConnectivity (LLC), computed by eLORETA software, applied to High-Density EEG (HD-EEG) for\n84 regions of interest (ROIs). The analysis involved three groups of subjects: 10 controls (CNT),\n21 Mild Cognitive Impairment patients (MCI) and 9 AD patients. In particular, the purpose is to\ncompare the results obtained using a 256-channel EEG, the corresponding 10-10 system 64-channel\nEEG and the corresponding 10-20 system 18-channel EEG, both of which are extracted from the\n256-electrode configuration. The computation of the Characteristic Path Length, the Clustering\nCoefficient, and the Connection Density from HD-EEG configuration reveals a weakening of smallworld\nproperties of MCI and AD patients in comparison to healthy subjects. On the contrary, the\nvariation of the network parameters was not detected correctly when we employed the standard\n10-20 configuration. Only the results from HD-EEG are consistent with the expected behavior of the\nAD brain network....
Impulsive noise is commonly present in many applications of actual communication\nnetworks, leading to algorithms based on the Gaussian model no longer being applicable. A robust\nparameter estimator of frequency-hopping (FH) signals suitable for various impulsive noise\nenvironments, referred........................
In this study, to solve the hidden limitation of conventional weighted fractional Fourier transform (WFRFT), a random\nmodulation order parameter pool is established by applying chaos technology. Further, a WFRFTsecure communication method\nbased on the chaotic parameter pool (CPP) is proposed. Based on the effective characteristics of tent mapping and the sequence\noutput range, the parameter pool constructor is established by parameter transformation. Furthermore, for each parameter\nselection period, the information can be processed by WFRFT using different modulation orders. The modeling and simulation\ndemonstrate that this method can significantly increase the bit error rate and processing time of unauthorized receivers. This\nindicates that it can greatly increase the scanning difficulty of unauthorized users and improve the concealment and security of the\noriginal information transmission....
Loading....