A comparison between the performances of CDMA multiuser detectors for binary image transmission

This article presents the results obtained by using CDMA multiuser detectors structures when binary images are transmitted. The performances are compared in terms of bit error rate as well as peak signal-to-noise ratio as functions of the channel signal-to-noise ratio and for different transmission scenarios. In the final section, several interesting conclusions are highlighted.     

An optimal multiedge detector for SAR image segmentation

Edge detection is a fundamental issue in image analysis. Due to the presence of speckle, which can be modeled as a strong, multiplicative noise, edge detection in synthetic aperture radar (SAR) images is extremely difficult, and edge detectors developed for optical images are inefficient. Several robust operators have been developed for the detection of isolated step edges in speckled images. The authors propose a new step-edge detector for SAR images, which is optimal in the minimum mean square error (MSSE) sense under a stochastic multiedge model. It computes a normalized ratio of exponentially weighted averages (ROEWA) on opposite sides of the central pixel. This is done in the horizontal and vertical direction, and the magnitude of the two components yields an edge strength map. Thresholding of the edge strength map by a modified version of the watershed algorithm and region merging to eliminate false edges complete an efficient segmentation scheme. Experimental results obtained from simulated SAR images as well as ERS-1 data are presented     

On the performance of digital detectors with dependent samples

The asymptotic performances of three digital detectors operating with dependent samples are examined. Analytic expressions for the performances when the number of dependent samples M is large are obtained. Examples are also given which indicate how the performances vary with M. It is shown that the performances of hard-limiting detectors may be improved by processing more dependent samples. The amount of the hard-limiting loss that is recoverable depends upon the detector prefilter. A simple expression is derived which gives the irretrievable hard-limiting loss as a function of the noise autocorrelation,     

Predictive ordering technique and feedback transform coding fordata compression of still pictures

A picture data compression method consisting of a hybrid cascade having four processing stages is presented. The processing stages are: predictive ordering technique (POT), feedback transform coding (FTC), vertical subtraction of quantized coefficients (VSQC), and predictive coding refinements in the signal space consisting of either overshoot suppression (OS) as a first variant or hybrid block truncation coding (HBTC) as a second one. Each of these stages is described, and reconstructed pictures are presented with their coding fidelity performances (mean square quantization error, mean absolute error and signal-to-noise ratio), using as test pictures a portrait and a LANDSAT image. It is shown that good quality images at low bit rates (0.55 to 1.1 bits/pixel) have been obtained     

Edge detection of intestinal parasites in stool microscopic images using multi-scale wavelet transform

Biomedical image processing is experiencing a significant progress with many applications. However, automatic recognition of microscopic pathogens from their images remains a challenge that will allow clinical laboratories to increase both the speed of tests and the reliability of diagnoses. We present an algorithm for edge detection of parasites in microscopic images of stools, using the multi-scale wavelet transform. This method is an evolution of the Canny–Mallat detector which gives the possibility to vary the frequency of the analysis in order to find the outlines of the most significant edges. The various contours obtained are chained across the scales from the coarsest to the finest. Using this algorithm, we were able to correctly represent the contours of the features of parasites found in microscopic images. The results obtained were compared with those produced by classical edge detectors on the same images. It comes out from both subjective and objective quantitative performances evaluation that our detector, better than all others, can clearly mark the outlines of the structures of the pathogen on an image of stools.     

基于特征检测器的能量采集认知无线电系统优化研究

将特征检测器频谱感知方案引入能量采集的认知无线电系统中, 分析比较了不同检测器背景下感知阈值和感知时间对系统性能的影响,且在能量约束和碰撞限制条件下,以最大化系统次级用户的吞吐量为目标计算出最优的感知时间。计算机仿真结果表明,在合适的感知阈值条件下,三种检测器的感知时间最优解有所差异,能量检测器同时依赖于能量约束和碰撞限制,最大特征值检测器仅受限于能量,而最大最小特征值检测器则无法找到最优的感知时间。     

Digital Dynamic Speech Detectors

This paper proposes two dynamic-type speech detectors; their performances are described also by means of in-field experimental results. The two detectors are based on the same operational principle: the presence of the speech signal is detected by analyzing the dynamic variations of the short-time-power of the channel signal.     

MultiTempGAN: Multitemporal multispectral image compression framework using generative adversarial networks

Multispectral satellites that measure the reflected energy from the different regions on the Earth generate the multispectral (MS) images continuously. The following MS image for the same region can be acquired with respect to the satellite revisit period. The images captured at different times over the same region are called multitemporal images. Traditional compression methods generally benefit from spectral and spatial correlation within the MS image. However, there is also a temporal correlation between multitemporal images. To this end, we propose a novel generative adversarial network (GAN) based prediction method called MultiTempGAN for compression of multitemporal MS images. The proposed method defines a lightweight GAN-based model that learns to transform the reference image to the target image. Here, the generator parameters of MultiTempGAN are saved for the reconstruction purpose in the receiver system. Due to MultiTempGAN has a low number of parameters, it provides efficiency in multitemporal MS image compression. Experiments were carried out on three Sentinel-2 MS image pairs belonging to different geographical regions. We compared the proposed method with JPEG2000-based conventional compression methods and three deep learning methods in terms of signal-to-noise ratio, mean spectral angle, mean spectral correlation, and laplacian mean square error metrics. Additionally, we have also evaluated the change detection performances and visual maps of the methods. Experimental results demonstrate that MultiTempGAN not only achieves the best metric values among the other methods at high compression ratios but also presents convincing performances in change detection applications.     

Propagation of measurement noise through backprojection reconstruction in electrical impedance tomography

A framework to analyze the propagation of measurement noise through backprojection reconstruction algorithms in electrical impedance tomography (EIT) is presented. Two measurement noise sources were considered: noise in the current drivers and in the voltage detectors. The influence of the acquisition system architecture (serial/semi-parallel) is also discussed. Three variants of backprojection reconstruction are studied: basic (unweighted), weighted and exponential backprojection. The results of error propagation theory have been compared with those obtained from simulated and experimental data. This comparison shows that the approach provides a good estimate of the reconstruction error variance. It is argued that the reconstruction error in EIT images obtained via backprojection can be approximately modeled as a spatially nonstationary Gaussian distribution. This methodology allows us to develop a spatial characterization of the reconstruction error in EIT images.     

自然地表红外图像的模拟

自然界的地表是非常复杂的,影响其温度分布和红外辐射特性的因素很多,如地表的起伏、土壤类型、植被类型、土壤湿度和气象条件等.要精确计算自然地表的温度和红外辐射特性,生成红外图像,必须建立考虑各种影响因素的三维传热传质模型.这种方法要花费大量的计算时间,是不实用的.文中利用一维模型的计算结果并结合随机模拟方法,针对具体自然地表(不同植被、裸露地表等)的实际红外辐射强度的相关长度、均方根误差以及相邻像素点的空间距离,模拟生成了自然地表的红外图像.探测器的视场和视角对观察到的图像影响是很大的,对同一地面区域,使用不同视场的探测器观察,得到的图像不同,即使使用同一探测器,如果用不同的视角观察同一区域,得到的图像也是不同的.因此文中根据不同的红外探测器视场以及同一探测器的不同的视角,将该图像转换成了红外探测器视场内的图像,分析了对应不同视场和视角的自然地表红外图像的特征.     

Analysis of the Generalized Order Statistics Constant False Alarm Rate Detector

In this paper, we present an architecture of the constant false alarm rate (CFAR) detector called the generalized order statistics (GOS) CFAR detector, which covers various order statistics (OS) and cell-averaging (CA) CFAR detectors as special cases. For the proposed GOS CFAR detector, we obtain unified formulas for the false alarm and detection probabilities. By properly choosing coefficients of the GOS CFAR detector, one can utilize any combination of ordered samples to estimate the background noise level. Thus, if we use a reference window of size N, we can realize (2^N - 1) kinds of CFAR processors and obtain their performances from the unified formulas. Some examples are the CA, the OS, the censored mean level, and the trimmed mean CFAR detectors. As an application of the GOS CFAR detector to multiple target detection, we propose an algorithm called the adaptive mean level detector, which censors adaptively the interfering target returns in a reference window.     

Reduced Rank Technique for Joint Channel Estimation and Joint Data Detection in TD-SCDMA Systems

In time division-synchronous code division multiple access systems, the channel estimation for multiple subscribers requires the computation of very complicated algorithms through short training sequences. This situation causes mismodeling of the actual channels and introduces significant errors in the detected data of multiple users. This paper presents a novel channel estimation method with low complexity, which relies on reducing the rank order of the total channel matrix H. We exploit the rank deficient of H to reduce the number of parameters that characterizes this matrix. The adopted reduced rank technique is based on singular value decomposition algorithm. Equations for reduced rank-joint channel estimation (JCE) are derived and compared against traditional full rank-joint channel estimators: least square (LS) or Steiner, enhanced LS, and minimum mean square error algorithms. Simulation results of the normalized mean square error for the above mentioned estimators showed the superiority of reduced rank estimators. Multi-user joint data detectors based linear equalizers are used to suppress inter-symbol interference and mitigate intra-cell multiple access interference. The detectors: zero forcing block linear equalizer and minimum mean square error block linear equalizer algorithms are considered in this paper to recover the data. The results of bit error rate simulation have shown that reduced rank-JCE based detectors have an improvement by 5 dB lower than other traditional full rank-JCE based detectors.     

An Adaptive Multiuser Detection Algorithm for CDMA Systems Using Antenna Diversity

Based on the minimum mean squared error (MMSE) between the data stream and the linear combiner output, a new multiuser detection (MUD) algorithm that combines space–time (ST) processing and antenna array on direct-sequence CDMA signals is proposed. The proposed ST-MUD algorithm is proved to be equivalent to two existing MMSE-based ST-MUD algorithms, and the theoretical BER performances for all the three algorithms are the same. The most attractive feature of the new ST-MUD algorithm is based on the fact that the new method does not require explicit estimation of channel and signaling information. This avoids any channel estimation error, and the method is thus more robust and more accurate than the other two ST-MUD algorithms in practical implementation. Adaptation of the proposed ST-MUD algorithm is implemented by using training sequences. Performance of this new multiuser detector is compared with that of two existing MMSE multiuser detectors and the conventional single-user space–time rake receiver through simulations. The proposed ST-MUD algorithm provides a performance better than existing algorithms and is especially suitable for practical CDMA systems.     

The distribution of an electric field in p-n junctions of silicon edgeless detectors

Development of silicon edgeless detectors started in 2004 and was motivated by preparations for the total elastic and diffractive cross-section measurement (TOTEM) experiment at the Large Hadron Collider (LHC) at CERN. In the context of this experiment, it would be necessary to detect protons scattered at ultimately small angles with respect to the LHC proton beam, which brings about a limitation imposed on the maximum distance between the beam and the sensitive region of the detector. In order to solve this problem, a new type of silicon detector (edgeless detectors) was developed; these detectors have the structure, which controls the distribution of the current near the edge of the p-n junction. In this paper we report the results of studying the distribution of the potential and an electric field in the region of the cut edge in the silicon edgeless detectors; the models, which account for the obtained results, as well as their consistency with current-voltage characteristics of silicon edgeless detectors developed for the TOTEM experiment, are discussed.     

Optimization of a parallel distributed detection system using distance measures

In this paper, we use both J-divergence and Bhattacharyya coefficient as distance measures to optimize a parallel distributed detection system consisting of N local detectors and a global decision maker. The optimization is obtained by allowing the thresholds of the local detectors to be functions of the global probability of false alarm. Numerical results for the case of four and six local detectors are obtained. These results indicate the superiority of our approach over those reported in the literature in which the local thresholds were specified without taking the level of global probability of false alarm into account.     

AN ERROR-RESILIENT H.263＋ CODING SCHEME FOR VIDEO TRANSMISSION OVER WIRELESS NETWORKS

Video transmission over wireless networks has received much attention recently for its restricted bandwidth and high bit-error rate, Based on H.263＋, by reversing part stream sequences of each Group Of Block （GOB）, an error resilient scheme is presented to improve video robustness without additional bandwidth burden. Error patterns are employed to simulate Widcband Code Division Multiple Acccss,（WCDMA） channels to check out error resilience performances. Simulation results show that both subjective and objective qualities of the reconstructed images are improved remarkably. The mean Peak Signal to Noise Ratio （PSNR） is increased by 0.5dB, and the highest increment is 2dB.     

GaN基紫外光探测器研究进展

介绍了不同类型的GaN基半导体光电探测器的结构和性能,回顾了其发展历史,并且综述了GaN基紫外光电探测器的研究新进展.     

Nonlinear processing and analysis of angular signals

Physical quantities referring to angles, like vector direction, color hue, etc., exhibit an inherently periodic nature. Due to this periodicity, digital filters and edge operators proposed for data on the line cannot be applied on such data. We introduce filters for angular signals (circular mean, circular median, circular a-trimmed mean, circular modified trimmed mean). Particular emphasis is given to the circular median filter, for which some interesting properties are derived. We also use estimators of circular dispersion to introduce edge detectors for angular signals. Three variations for the extension of quasirange to circular data are proposed, and expressions for their output PDF are derived. These “circular” quasiranges have good and user-controlled properties as edge detectors in noisy angular signals. The performance of the proposed edge operators is evaluated on angular edges, using certain quantitative criteria. Finally, a series of experiments featuring one-dimensional (1-D) angular signals and hue images is used to illustrate the operation of the new filters and edge detectors     

Constrained Detection for Spatial-Multiplexing Multiple-Input–Multiple-Output Systems

A family of detectors that exploit signal constraints is developed for maximum-likelihood detection for multiple-input-multiple-output (MIMO) systems. Real constrained detectors and decision-feedback detectors are proposed for real constellations by forcing the relaxed solution to be real. A generalized minimum mean square error (GMMSE) and constrained least squares MIMO detectors are also developed for unitary and nonunitary signal constellations. Using these constrained detectors, we propose a new ordering scheme to achieve a tradeoff between interference suppression and noise enhancement. Moreover, to mitigate the inherent error propagation, the decision-feedback MIMO detectors are integrated with signal constraints. The simulation results show that our combined detector achieves a significant performance gain over vertical Bell Laboratories layered space-time (V-BLAST) detection.     

Channel knowledge and optimal performance for two-wave Rayleighfading channels

The optimal detector structures and error probability performances for the two-wave Rayleigh fading channel with known delay are compared for different levels of channel knowledge. A number of different detectors are examined, for equal energy signals having equal complex autocorrelation magnitudes. Envelope orthogonal frequency-shift keying and variants of chirp signals are considered, so that the complex cross correlation of the signals is unconstrained. The performance of the optimal detector, when the fading in neither wave is tracked, is obtained. Two other, suboptimal, quadratic detectors are also considered for this case. Optimal detection, when the fading in only one of the waves is tracked, while a statistical knowledge of the other wave is available, is examined. The optimal performance that can be achieved by a time-varying matched-filter detector that makes use of complete knowledge of the channel fading in both waves is also determined. These detectors, for all the different levels of channel information considered, are studied in a unified framework, the probability of error being expressed as the probability that a quadratic form in Gaussian random variables is less than zero. It is found that the power gain that can be derived from partial or complete tracking is small. All these detectors exhibit a diversity-like effect for all nonzero values of the delay and for most values of the signal parameters. Signals with larger dispersion factors, such as chirp signals and variants, perform well on the channel, enhancing the diversity effect, even at small delays