A General Rate-Distortion Optimization Method for Block Compressed Sensing of Images

Block compressed sensing (BCS) is a promising technology for image sampling and compression for resource-constrained applications, but it needs to balance the sampling rate and quantization bit-depth for a bit-rate constraint. In this paper, we summarize the commonly used CS quantization frameworks into a unified framework, and a new bit-rate model and a model of the optimal bit-depth are proposed for the unified CS framework. The proposed bit-rate model reveals the relationship between the bit-rate, sampling rate, and bit-depth based on the information entropy of generalized Gaussian distribution. The optimal bit-depth model can predict the optimal bit-depth of CS measurements at a given bit-rate. Then, we propose a general algorithm for choosing sampling rate and bit-depth based on the proposed models. Experimental results show that the proposed algorithm achieves near-optimal rate-distortion performance for the uniform quantization framework and predictive quantization framework in BCS.     

基于网格编码量化的超光谱图像压缩

提出了一个基于小波网格编码量化的超光谱图像压缩方法。谱间和空间冗余处理构成了超光谱图像压缩算法的主要内容,该算法使用一个谱间差分预测步骤来去除谱间冗余,而后对预测残差图像进行小波变换并利用均匀阈值网格编码量化(trellis-coded quantization)方法来量化各小波子带,最后使用自适应算术编码对量化码字进行熵编码。为使编码器能为所有子带获取率-失真意义上最优的量化阈值,设计了一个基于子带统计特性和网格编码量化器率-失真特性的比特分配算法。在实验中,该算法表现出优良的压缩性能,对于实验的超光谱图像,该方法在压缩比为32时可得到37．1dB的峰值信噪比,这表明本算法能有效压缩超光谱图像,适于超光谱图像压缩应用。     

Deep Task-Based Quantization

Quantizers play a critical role in digital signal processing systems. Recent works have shown that the performance of acquiring multiple analog signals using scalar analog-to-digital converters (ADCs) can be significantly improved by processing the signals prior to quantization. However, the design of such hybrid quantizers is quite complex, and their implementation requires complete knowledge of the statistical model of the analog signal. In this work we design data-driven task-oriented quantization systems with scalar ADCs, which determine their analog-to-digital mapping using deep learning tools. These mappings are designed to facilitate the task of recovering underlying information from the quantized signals. By using deep learning, we circumvent the need to explicitly recover the system model and to find the proper quantization rule for it. Our main target application is multiple-input multiple-output (MIMO) communication receivers, which simultaneously acquire a set of analog signals, and are commonly subject to constraints on the number of bits. Our results indicate that, in a MIMO channel estimation setup, the proposed deep task-bask quantizer is capable of approaching the optimal performance limits dictated by indirect rate-distortion theory, achievable using vector quantizers and requiring complete knowledge of the underlying statistical model. Furthermore, for a symbol detection scenario, it is demonstrated that the proposed approach can realize reliable bit-efficient hybrid MIMO receivers capable of setting their quantization rule in light of the task.     

An Adaptive Rate Blocked Compressive Sensing Method for Video

An adaptive rate Compressive Sensing (CS) method for video signals is proposed. The Blocked Compressive Sensing (BCS) scheme is adopted in this method. Firstly, each video frame is blocked and measured by the BCS scheme, and then the mean and variance of each image block are estimated by observing the CS measurement results. Using the mean and variance of each image block, the sparsity of the block is estimated and then the block can be classified. Adaptive rate sampling is realized by assigning different sampling rates to different classes. At the same time, in order to make better use of the correlation between video frames, a reference block subtraction method is also designed in this paper, which uses the estimates of the sparsity of image blocks as the basis for the reference block update. All operations of the proposed method only depend on the CS measurement results of image blocks and all calculations are simple. Thus, the proposed method is suitable for implementation in CS sampling devices with limited computational performance. Experiment results show that, compared with the actual values, the sparsity estimates and block classification results of the proposed method are accurate. Compared with the latest adaptive Compressive Video Sensing methods, the reconstructed image quality of the proposed method is better.     

一种无线传感器网络中的混沌信号重构算法

将无线传感器网络节点观测区域中的一个混沌信号发送到融合中心,进行信号重构.由于节点的通信带宽受限,信号传输之前需要进行量化,给信号带来量化噪声,使得信号重构工作变得更为棘手.本文提出用平方根容积卡尔曼滤波器对融合中心收集的信号进行重构.首先估计观测信号的概率密度函数,使用最优量化器量化观测信号,在有限的量化比特数下,取得最优的信号量化性能.平方根容积卡尔曼滤波器相对无先导卡尔曼算法具有较少的求容积分点,因此具有计算量小的优点,同时迭代过程采用传递误差矩阵的平方根矩阵,保证迭代过程的稳定性和提高数据估计精度.仿真结果表明,该算法能够有效和快速地重构观测信号,并且比基于无先导卡尔曼滤波的算法更快.     

Compressive sensing for multi-static scattering analysis

Compressive sensing (CS) is a framework in which one attempts to measure a signal in a compressive mode, implying that fewer total measurements are required vis à vis direct sampling methods. Compressive sensing exploits the fact that the signal of interest is compressible in some basis, and the CS measurements correspond to projections (typically random projections) performed on the basis function coefficients. In this paper, we demonstrate that ideas from compressive sensing may be exploited in the context of electromagnetic modeling, here multi-static scattering from an arbitrary target. In this context, the computational analysis may be viewed as a numerical experiment, and ideas from compressive sensing may be used to reduce the number of computations required for target characterization. It is demonstrated that the compressive sensing framework may be applied with relatively minor modifications to many existing numerical models, with examples presented here for a fast-multipole computational engine.     

An improved partial SPIHT with classified weighted rate-distortion optimization for interferential multispectral image compression

Based on the property analysis of interferential multispectral images, a novel compression algorithm of partial set partitioning in hierarchical trees (SPIHT) with classified weighted rate-distortion optimization is presented.After wavelet decomposition, partial SPIHT is applied to each zero tree independently by adaptively selecting one of three coding modes according to the probability of the significant coefficients in each bitplane.Meanwhile the interferential multispectral image is partitioned into two kinds of regions in terms of luminous intensity, and the rate-distortion slopes of zero trees are then lifted with classified weights according to their distortion contribution to the constructed spectrum.Finally a global ratedistortion optimization truncation is performed.Compared with the conventional methods, the proposed algorithm not only improves the performance in spatial domain but also reduces the distortion in spectral domain.     

混沌信号在无线传感器网络中的盲分离

混沌信号在本质上属于非线性非高斯信号,它在无线传感器网络下的应用还涉及到信号量化问题,这使得混沌信号在此应用环境下的信号盲分离更为棘手.针对此问题,本文在容积卡尔曼粒子滤波的框架下提出一种解决方法.文中首先推导出观测信号的概率密度函数,在量化比特有限的情况下,采用最优量化器,获得最优的量化结果.在此基础上,使用容积卡尔曼滤波器产生粒子滤波中的重要性概率密度函数,融入最新的观测值,提高粒子对系统状态后验概率的逼近,提高信号盲分离的精度.仿真结果表明算法能够有效地分离混合混沌信号,参数估计的精度及其运算量均优于已有的无先导卡尔曼粒子滤波算法,其运行时间为无先导卡尔曼粒子滤波算法的88.77%.     

A constant modulus algorithm for blind equalization in α-stable noise

Channel noise is often assumed to be Gaussian in most of the existing channel equalization algorithms. The performance of these algorithms will degrade seriously when the noise is non-Gaussian. This paper deals with the problem of blind channel equalization in impulsive noise environment that is modeled as α-stable process. A modified adaptive error-constrained constant modulus algorithm (MAECCMA) is proposed by soft-limiting the amplitude of the equalizer input and transforming the error signal of the original adaptive error-constrained constant modulus algorithm (AECCMA) nonlinearly to suppress the influence of α-stable noise. Computer simulation results of two underwater acoustic channels show that, MAECCMA has almost the same performance as AECCMA and they both have faster convergence rate than constant modulus algorithm (CMA) and normalized least mean absolute deviation (NLMAD) algorithm in Gaussian noise, while MAECCMA provides the best performance of those four algorithms in α-stable noise.     

Quantization and other nonlinear distortions of the hologram transmittance

A quantization process is a special nonlinear process, whereby the input signal I at point x is converted into an associated output signal Î at the same point x. The output signal can assume only a few discrete values. We develop a suitable theory and apply it to the quantization of the hologram transmittance. Such a process might be employed in remoted holography or in mass production copying of holograms. The theory applies also to other nonlinearities. The nonlinear hologram can always be interpreted as the superposition of bleached holograms.     

分段匹配追踪式Karhunen-Loeve非相干字典语音压缩感知

压缩感知(Compressed Sensing,CS)理论突破了经典采样定理的理论边界,为信号压缩提供了另一种途径。基于CS理论框架,做了两方面工作:为提高语音字典对信号的匹配性,设计了一种基于K-L展开的非相干语音字典;针对现有匹配追踪(MP,OMP)算法的不足,提出分段匹配追踪(Segment MP,SegMP)算法。首先对语音自相关函数进行建模并估计模型参数,构造语音自适应非相干字典,然后采用SegMP对语音稀疏向量分段观测,获得多个低维矢量,最后结合模型参数重建字典并重构信号,实现了语音压缩感知。语音测试结果表明:相比现有方案,本文方案对信号的稀疏表示更为精准,具有更好的重构质量,且降低了计算复杂度。      

Secure collaborative cognitive radio based on chaotic modulation and compressive sensing

Cognitive radio (CR) is a wireless technology that is used to overcome the spectrum scarcity problem. CR includes several stages, spectrum sensing is the first stage in the CR cycle. Traditional spectrum sensing (SS) techniques have many challenges in the wideband spectrum. CR security is an important problem, since when an attacker from outside the network access the sensing information this produces an increase in sensing time and reduces the opportunities for exploiting vacant band. Compressive sensing (CS) is proposed to capture all the wideband spectrum at the same time to solve the challenges and improve the performance in the traditional techniques and then one of the traditional SS techniques are applied to the reconstructed signal for detection purpose. The sensing matrix is the core of CS must be designed in a way that produces a low reconstruction error with high compression. There are many types of sensing matrices, the chaotic matrix is the best type in terms of security, memory storage, and system performance. Few works in the literature use the chaotic matrix in CS based CR and these works have many challenges: they used sample distance in the chaotic map to generate a chaotic sequence which consumes high resources, they did not take into consideration the security in reporting channel, and they did not measure their works using real primary user (PU) signal of a practical application under fading channel and low SNR values. In this paper, we propose a chaotic CS based collaborative scenario to solve all challenges that have been presented. We proposed a chaotic matrix based on the Henon map and use the differential chaotic shift keying (DCSK) modulation to transmit the measurement vector through the reporting channel to increase the security and improve the performance under fading channel. The simulation results are tested based on a recorded real-TV signal as PU and Compressive Sampling Matching Pursuit (CoSaMP) recovery algorithm under AWGN and TDL-C fading channels in collaborative and non-collaborative scenarios. The performance of the proposed system has been measured using recovery error, mean square error (MSE), derived probability of detection (Pdrec), and sensitivity to initial values. To measure the improvement introduced by the proposed system, it is evaluated in comparison with selected chaotic and random matrices. The results show that the proposed system provides low recovery error, MSE, with high Pdrec, security, and compression under SNR equal to −30 dB in AWGN and TDL-C fading channels as compared to other matrices in the literature.     

Enhancement of noise reduction efficiency based on compensation in the ANC headset using fixed-point DSP

In this paper, we have proposed a new algorithm considering commutation error and feedback effect to enhance the convergence rate and noise reduction efficiency of ANC controller. In order to improve noise reduction performance of the ANC headset with fixed-point DSP, we have proposed a new FxLMS algorithm, FxLMS CF, which considers the commutation error and feedback. Also, using a non-real-time simulation, we have decided the phase and amplitude compensation factors of anti-noise signal considering round-off and quantization error, nonlinear distortion and delay of analog device. We estimated the phase and amplitude compensation factors by simulation without using any special measuring devices or analysis devices, and reduced the broadband noise by 24 dB.     

卫星干涉多光谱图像压缩新算法

根据卫星干涉多光谱图像的成像特性,提出一种基于分类权值率失真优化截取和自适应编码深度控制的部分SPIHT光谱图像压缩算法.首先根据干涉区域类型和编码平面的重要性,对各棵零树各个编码过程赋予不同的重要性权值,然后采用部分SPIHT算法对每棵零树独立编码,编码时根据比特平面层中重要系数的统计概率自适应地进行3种编码模式的选择,同时依据重要性权值和深度控制因子自适应地控制每棵零树的编码深度,最后在编码深度内,根据不同干涉区域的零树对恢复光谱的失真贡献,采用分类权值率失真方法对码流进行优化截取,使码流分配与失真达成最优.实验结果表明,本算法比传统算法更好地保护了光谱信息.     

基于三维集合分裂嵌入式零块编码算法的超光谱图像压缩

基于超光谱图像的特点,提出了一种三维集合分裂嵌入式零块编码(3D SPEZBC)的超光谱图像压缩算法。该算法首先采用三维小波包变换有效地去除超光谱图像的空间和谱间相关性,然后对于所生成的每个二维子带利用基于集合分裂的方法进行零块编码,最后再采用基于上下文的自适应算术编码来进一步提高编码性能。实验结果表明,3D SPEZBC算法具有与三维嵌入式零块编码(3D EZBC)算法相同的压缩编码性能,在各比特率下编码性能均明显优于三维集合分裂嵌入式块编码(3D SPECK)、三维等级树集合分裂(3D SPIHT)和非对称三维等级树集合分裂(AT-3D SPIHT)算法,并且略好于多分量JPEG2000编码(JPEG2000-MC)算法。此外,3D SPEZBC编码算法不但可以提供较好的率失真性能,而且相对于3D EZBC编码算法可以节省大量的存储空间。     

A weak signal detection method based on adaptive parameter-induced tri-stable stochastic resonance

Aiming at detecting the weak signal in a strong noise background, an enhanced weak signal detection method based on adaptive parameter-induced tri-stable stochastic resonance is proposed. Firstly, because the system can switch among the monostable, bistable and tri-stable state, the potential function characteristic of tri-stable systems is studied by analyzing the potential function curves with different system parameters. And the dynamic characteristics of system parameters on the depth of the potential well is analyzed. The ranges of R and the system parameters are determined, which is essential for ensuring the system is tri-stable state. Secondly, the range of R is used as the constraint condition and the average output signal-to-noise ratio is used as the fitness function of the adaptive algorithm. The system parameters a, b, c are optimized by the differential evolution particle swarm optimization (DEPSO) method to obtain the best output effect. Finally, the proposed adaptive parameter-induced tri-stable stochastic resonance method is adopted to detect the mixed multiple high-frequency weak signal. The detection results are compared with that of adaptive bistable stochastic resonance. At the meanwhile, the method is also applied to detect the fault signal of single crystal furnace. Both the simulation analysis and experiment results show that the proposed method can effectively improve the output signal-to-noise ratio and detect multi-frequency weak signal in the strong noise background.     

时域压缩合成孔径超分辨水声成像算法

提出一种针对水下稀疏目标的时域压缩合成孔径声呐成像方法(TC-SAS),实现了水声目标高分辨实时成像。通过多子阵的孔径合成,在时域上构造出成像网格格点到有效孔径内逐帧阵列的格林函数,并给出成像区域散射强度到数据域的映射矩阵;然后利用该区域空域稀疏的先验知识,通过正交匹配追踪的稀疏重构方式,解算出成像区域散射系数矩阵,实现了稀疏目标高分辨成像.同时,针对线性调频信号提出数据缩减的方法,通过对观测数据和字典矩阵同时脉压后截取,减小了数据规模;进一步结合二维矩阵数表查表的方法,以空间换时间,实现了区块实时成像。数值仿真以及湖试试验表明,所提算法能分辨出传统的时延求和算法难以分辨的目标,并且在图像清晰度指标上平均提升4.9 dB.改善了合成孔径声呐的成像质量.      

基于自适应量化的超光谱图像压缩

提出了一种基于谱间预测和小波量化编码的超光谱图像压缩方法。在充分考虑子带图像非平稳特性的基础上,首先实现了子带分类步骤,通过分类设计自适应预测器,提高了谱间去相关效率。由于子带中不同子类表现出了不同的统计特性,所以使用空变均匀阈值量化器完成了量化工作。研究了均匀量化器对不同分布训练样本的率 失真表现,并为相关特性建立了模型描述。基于率失真模型和系数序列的统计特性,提出了一个率分配算法,以便能为不同的子类系数序列设计率失真意义上的最优量化器。实验证明,这种方法能高效地压缩超光谱图像,表现出了优异的压缩性能。     

基于三元伪随机序列集的多声源房间脉冲响应测量方法*

在多输入多输出系统中,诸多情况下需并行测量任意输入输出之间的脉冲响应。虽然利用以Kasami序列集为代表的二元伪随机序列集良好的相关特性可以实现多声源并行测量,但是囿于其编解码信号相同且为二元信号的特点,使得其相关特性仍存在较大的可优化空间。该文提供了一种基于三元伪随机序列集的多声源房间脉冲响应测量方法,具有更好的相关特性,提高了测量准确度。仿真和实验验证了该测量方法的可行性和准确性。在封闭空间几何模型构建等领域中,可使用该方法提高测量效率并降低由于各通道不同步导致的误差。