Multiple frequencies and AR parameters estimation from one bitquantized signal via the EM algorithm

The authors present a novel algorithm which is used to estimate the coefficients of q AR processes from a coarsely quantized signal. The input signal to the quantizer is the superposition of q AR processes and noise. In a related problem a modified version of the above algorithm is used to estimate the frequencies of coarsely quantized data obtained from q sinusoids embedded in noise. The proposed algorithm can accommodate a nonuniform m-level quantizer, as well as the special case of a one bit quantizer. The proposed estimator is based on the maximum likelihood (ML) criterion, and is realized by judiciously combining the expectation-maximization (EM) algorithm of Dempster, Laird and Rubin (1977), and the “Gaussian fit” scheme of Curry (1970). Simulations reveal that they can accurately estimate the coefficients of several AR processes, or the frequencies of several sinusoids, from one bit quantized data at low signal to noise ratios and moderate number of observations     

基于单路滑窗宽变窄的超宽谱线性调频侦收技术

采用信号自相关技术解线调,将超宽谱线性调频信号转化为窄带信号,然后量化为数字信号,做短时傅立叶变换识别信号,采用自适应滤波器组分离多信号。仿真证明,该方法可在-12dB的低信噪比情况下识别信号;在多信号下,能有效抑制交叉项的干扰;并且系统运算量较低,适合于实时处理。     

On the Expected Rate of Slowly Fading Channels With Quantized Side Information

We study a multiple-layer variable-rate system employing quantized feedback to maximize the expected rate over a single-input single-output slowly fading Gaussian channel. The transmitter uses partial channel-state information, which is obtained via an optimized resolution-constrained feedback link, to adapt the power and to assign code layer rates, subject to different power constraints. To systematically design the system parameters, we develop a simple iterative algorithm that successfully exploits results in the study of parallel broadcast channels. We present the necessary and sufficient conditions for single-layer coding to be optimal, irrespective of the number of code layers that the system can afford. Unlike in the ergodic case, even coarsely quantized feedback is shown to improve the expected rate considerably. Our results also indicate that with as little as one bit of feedback information, the role of multilayer coding reduces significantly     

Review and advances in delta-sigma DAC error estimation based on additive noise modelling

High-resolution and very high resolution data conversion is dominated by the use of delta-sigma modulating converters. Oversampling and noise-shaping is employed to enable a coarsely quantized conversion with high effective resolution. The time-domain output waveform from a delta-sigma modulator is often impossible to predict analytically, therefore modulator design is largely based on high level digital simulations and rule-of-thumb estimation. However, the output waveform also largely determines the distortion caused by analog error sources in the converter. Therefore optimization of the modulator with regards to digital quantization noise might not yield an optimal design when analog errors are included. This paper extends common estimation methods to include analog error sources, with the objective of enabling more global rule-of-thumb optimization.     

高速光学模数转换器的研究现状及进展

高速高精度模数转换器（Analog-to-digital Converters,ADC）是现代数字信息处理系统中的关键组成部分,现有的电子模数转换器存在运行速率严重受限的难题,因此引入光学方法实现高速高精度模数转换成为研究的热点。本文重点针对高速光学模数转换器的研究现状及进展进行了探讨,并对光学辅助、光采样电量化、电采样光量化以及全光采样量化目前4种最主要的光学模数转换器的原理、结构和最新的研究进展进行了详细的介绍。     

Resilience properties of redundant expansions under additive noise and quantization

Representing signals using coarsely quantized coefficients of redundant expansions is an interesting source coding paradigm, the most important practical case of which is oversampled analog-to-digital (A/D) conversion. Signal reconstruction from quantized redundant expansions and the accuracy of such representations are problems which are not well understood and we study them in this paper for uniform scalar quantization in finite-dimensional spaces. To give a more global perspective, we first present an analysis of the resilience of redundant expansions to degradation by additive noise in general, and then focus on the effects of uniform scalar quantization. The accuracy of signal representations obtained by applying uniform scalar quantization to coefficients of redundant expansions, measured as the mean-squared Euclidean norm of the reconstruction error, has been previously shown to be lower-bounded by an 1/r/sup 2/ expression. We establish some general conditions under which the 1/r/sup 2/ accuracy can actually be attained, and under those conditions prove a 1/r/sup 2/ upper error bound. For a particular kind of structured expansions, which includes many popular frame classes, we propose reconstruction algorithms which attain the 1/r/sup 2/ accuracy at low numerical complexity. These structured expansions, moreover, facilitate efficient encoding of quantized coefficients in a manner which requires only a logarithmic bit-rate increase in redundancy, resulting in an exponential error decay in the bit rate. Results presented in this paper are immediately applicable to oversampled A/D conversion of periodic bandlimited signals.     

Curve smoothing for improved feature extraction from digitized pictures

Curves can be expressed by parametrized functions. If a curve is represented by a sampled picture, its parametrized functions appear quantized. The noise induced by quantization can drastically be reduced by an iterative curvilinear filtering of the parametrized functions. At each iteration, this filtering processes the curve coordinates and a better arc length is estimated.     

图像分块压缩感知观测值的DPCM非均匀量化方法

在实际的信号处理中,有必要对采集到的信号进行量化处理。量化是信号数字化、实现数字信号高效传输的必要步骤。图像分块压缩感知(Block Compressed Sensing, BCS)观测模型中,测量域上图像相邻块的观测值之间存在较强的相关性。根据这一特点,本文应用差分脉冲编码调制(Differential pulse-code modulation, DPCM)系统减小相邻块之间的冗余,并结合非均匀标量量化,对分块压缩感知图像的观测值进行量化处理。文中分析了DPCM系统的预测误差概率分布,发现在统计意义上这一分布与非均匀量化特性的变化趋势具有一致性,并以此作为所提出的量化方法的理论基础。仿真实验表明,本文提出的量化方案有效地提高了压缩感知观测值的量化信噪比(quantized signal to noise ratio, quantized SNR),同时图像的重构质量得到了提升。      

Low bit rate video coding using robust motion vector regenerationin the decoder

In this paper, we present a novel coding technique that makes use of the nonstationary characteristics of an image sequence displacement field to estimate and encode motion information. We utilize an MPEG style codec in which the anchor frames in a sequence are encoded with a hybrid approach using quadtree, DCT, and wavelet-based coding techniques. A quadtree structured approach is also utilized for the interframe information. The main objective of the overall design is to demonstrate the coding potential of a newly developed motion estimator called the coupled linearized MAP (CLMAP) estimator. This estimator can be used as a means for producing motion vectors that may be regenerated at the decoder with a coarsely quantized error term created in the encoder. The motion estimator generates highly accurate motion estimates from this coarsely quantized data. This permits the elimination of a separately coded displaced frame difference (DFD) and coded motion vectors. For low bit rate applications, this is especially important because the overhead associated with the transmission of motion vectors may become prohibitive. We exploit both the advantages of the nonstationary motion estimator and the effective compression of the anchor frame coder to improve the visual quality of reconstructed QCIF format color image sequences at low bit rates. Comparisons are made with other video coding methods, including the H.261 and MPEG standards and a pel-recursive-based codec.     

On quantization of noisy signals

Quantized noise distributions derived from continuous signals with additive noise are studied. Two noise sources are considered, quantized image noise derived from the continuous input noise source and noise due to quantization roundoff error. These are treated as statistically independent sources. An analytic solution for the quantized noise probability density is obtained. The analytic solution is estimated by two expressions valid for normally distributed noise over different ranges of variance. The estimates have excellent agreement in the region of overlapping validity. Quantized noise variance is related to the continuous noise variance from normally distributed noise using these expressions. A table and plots of useful values are included. These results are helpful in choosing a quantization interval for a particular application. They can also be used to determine quantizer output noise level and signal-to-noise ratio in digital applications     

利用DRFM实现基于Zoom FFT的频率测量

提出了一种采用数字射频储频器的测频方法。该方法首先利用FPGA的FFT快扫阵列技术对瞬时带宽为500 MHz的信号粗测频,再利用Zoom FFT电路进行精测频。此方案能将ESM与ECM集成一体。     

相遇时间对双布里渊放大池放大控制脉冲波形的影响

在传统独立双池结构的受激布里渊散射(SBS)相位共轭镜(PCM)系统中,对布里渊放大池进行改进。引入修整放大池,采用主放大池与修整放大池相结合的双布里渊放大池结构控制脉冲波形,主放大池对脉冲波形进行粗调,修整放大池对脉冲波形,特别是脉冲前沿进行微调。理论和实验研究了相遇时间对双布里渊放大池放大控制脉冲波形的影响,得到了不同相遇时间下,脉冲波形随双池间距的变化规律。研究表明,相遇时间是决定放大光脉冲波形整体形状的重要参数,相遇时间越长,脉冲可控范围越大,但是系统的效率越低。     

基于DVB-S的视频实时监测处理方法

针对视频节日监测采集到的活动与静止的视频场景变化,采用GoP数据量波动率对场景变换进行粗判,并利用I帧和直方图帧差方法进行细判的联合检测算法,能较好检测场景变换,实现视频数据自动监控记录,大大节省了存储空间和工作量.     

Bit allocation for discrete signal detection

This paper considers a system in which observations at a remote sensor are sampled and quantized, and then transmitted to a processor for signal detection. A constraint on the transmission rate is assumed. The problem of bit allocation among different samples is studied and illustrated by means of an example     

Optimal sensor and path selection for target tracking in wireless sensor networks

This paper addresses target tracking in wireless sensor networks where the nonlinear observed system is assumed to progress according to a probabilistic state space model. Thus, we propose to improve the use of the quantized variational filtering by jointly selecting the optimal candidate sensor that participates in target localization and its best communication path to the cluster head. In the current work, firstly, we select the optimal sensor in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor networks. This selection is also based on the local cluster node density and their transmission power. Secondly, we select the best communication path that achieves the highest signal‐to‐noise ratio at the cluster head; then, we estimate the target position using quantized variational filtering algorithm. The best communication path is designed to reduce the communication cost, which leads to a significant reduction of energy consumption and an accurate target tracking. The optimal sensor selection is based on mutual information maximization under energy constraints, which is computed by using the target position predictive distribution provided by the quantized variational filtering algorithm. The simulation results show that the proposed method outperforms the quantized variational filtering under sensing range constraint, binary variational filtering, and the centralized quantized particle filtering. Copyright © 2012 John Wiley & Sons, Ltd.     

密钥生成系统中的双门限量化算法研究

针对密钥生成系统中传统量化算法导致合法双方初始不一致率较高从而降低最终的密钥生成长度的问题,提出了双门限量化的基本模型,合法双方同时设置一个上门限和下门限,将高于上门限的采样值量化为1,低于下门限的采样值量化为0,同时将上、下门限之间的采样值舍弃。通过分析可知,这种量化方法有较高的可靠性,并且不会泄露有关密钥的任何信息。本文分析了其在密钥生成中的具体应用,并以合法双方最终生成的密钥的长度为目标函数,推导得到了双门限量化时最优的量化因子。进一步的仿真表明,相比较传统的等概量化,通过选择合适的量化因子,合法双方能够生成更长的密钥。在信噪比为15 dB-25 dB时,密钥长度的提升在0.1比特以上。      

基于有限反馈的机会波束系统

针对目前机会波束系统中存在反馈量过多的问题,提出一种基于有限反馈的机会波束系统.设置反馈门限,当用户的接收信干噪比大于反馈门限时,对信干噪比进行量化,再将量化电平反馈给基站,否则无需进行量化和反馈.以吞吐量最大化为原则设定最佳反馈门限和量化电平,在瑞利块衰落信道中对系统进行仿真,并与传统机会波束系统比较,结果表明,该系统大大降低了反馈量.     

Previsualized image vector quantization with optimized pre- andpostprocessors

The optimal previsualized image vector quantization method for compressing digital images to a bit rate of 0.75 bpp or below with moderately low to very low subjective distortion is presented. The encoding method incorporates a visual model as part of the distortion measure. By modeling the quantization noise as an additive signal-dependent noise process, an optimum pre- and postprocessing system, which minimizes the mean-squared error measured inside the visual model, is derived. The analysis of the system performance and a coordinate descent design algorithm are discussed. A set of experiments was conducted using the optimum system, and the results were compared to those obtained by other methods. The study shows that the images quantized by the method presented exhibit much less sawtooth, blocking, and contouring effects and higher subjective quality. Images of surprising quality have been produced by this method at a bit rate of about 0.1 bpp with a compression ratio of 80:1 relative to a normal 8 bpp original     

Sampled baseband correlators for in-air ultrasonic rangefinders

Conventional in-air ultrasonic rangefinders for robotic applications employ inexpensive analog signal processing techniques, based on threshold methods, to perform range measurements. Digital signal processing techniques using correlation methods can provide more accurate measurements, at the expense of greater system complexity and cost. The authors propose using a modified second-order bandwidth sampling technique for making data acquisition concomitant with frequency shift of the sampled narrow-band signals from in-air ultrasonic transducers to their equivalent baseband representations. The reduction of the frequency-analyzing interval allows one to conceive sampled baseband correlators for multiple receiving transducers with a complexity comparable to that of analog receivers. They develop a procedure of sensor calibration that is based on a stochastic model of time-of-flight noise; the effect of the receiver noise on the ranging accuracy is, thus, evaluated separately from the effect, due to environmental changes of the speed of sound. The simulation study and the experimental results obtained with their demonstration system offer promise for multichannel sampled baseband correlators, the time-of-flight estimation accuracy of which is close to the theoretical Cramer-Rao (CR) lower bound for signals in additive white Gaussian noise