2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)最新文献

英文中文

Parametric spectral signal restoration via maximum entropy constraint and its application 基于最大熵约束的参数谱信号复原及其应用

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

Pub Date : 2015-08-01 DOI: 10.1109/DSP-SPE.2015.7369579

Hai Liu, Zhaoli Zhang, Sanya Liu, Jiangbo Shu, Tingting Liu

In this paper, we will propose a new framework which can estimate the desired signal and the instrument response function (IRF) simultaneously from the degraded spectral signal. Firstly, the spectral signal is considered as a distribution, thus, new entropy (called differential-entropy, DE) is defined to measure the distribution with a uniform distribution, which allows negative value existing. Moreover, the IRF is parametrically modeled as a Lorentzian function. Comparative results manifest that the proposed method outperforms the conventional methods on peak narrowing and noise suppression. The deconvolution IR spectrum is more convenient for extracting the spectral feature and interpreting the unknown chemical mixtures.

在本文中，我们将提出一个新的框架，可以同时估计期望信号和仪器响应函数(IRF)从退化的频谱信号。首先，将谱信号视为一个分布，定义新熵(称为微分熵，DE)来度量均匀分布的分布，允许负值存在。此外，IRF被参数化建模为洛伦兹函数。对比结果表明，该方法在峰窄和噪声抑制方面优于传统方法。反褶积红外光谱更便于提取光谱特征和解释未知化学混合物。

引用次数: 3

Using the IPython notebook as the computing platform for signals and systems courses 使用IPython笔记本作为信号与系统课程的计算平台

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

Pub Date : 2015-08-01 DOI: 10.1109/DSP-SPE.2015.7369568

McKenna R. Lovejoy, M. Wickert

The use of open-source Python as opposed to traditional computing platforms such MATLAB, Mathematica, and C/C++, is becoming more and more noticeable as all forms of opensource software develop. The Python user community itself is very vibrant, but what really stands out for those of us in signals and systems, is what is happening in the numerical computing side of Python. This paper will describe how in particular, the IPython notebook can be used as an analysis and simulation tool for teaching signals and systems courses. Specific code modules have been developed to augment existing Python code contained in the scipy.signal module. Case studies will be used to demonstrate the capabilities of the IPython notebook to augment lecture material with live calculations and simulations. Additionally, examples of how the IPython notebook has been successfully used by students for homework problems, computer projects and lab reports will be illustrated. Both student and industry team members in subcontract work, have responded favorably to the use of Python as an engineering problem solving platform.

与MATLAB、Mathematica和C/ c++等传统计算平台相反，随着各种形式的开源软件的发展，开源Python的使用正变得越来越引人注目。Python用户社区本身是非常活跃的，但真正让我们这些信号和系统领域的人脱颖而出的，是Python在数值计算方面所发生的事情。本文将特别描述如何将IPython笔记本用作信号和系统课程教学的分析和仿真工具。已经开发了特定的代码模块来增强scipy中包含的现有Python代码。信号模块。案例研究将用于演示IPython笔记本通过实时计算和模拟来增强讲座材料的功能。此外，还将举例说明IPython笔记本如何被学生成功地用于家庭作业问题、计算机项目和实验报告。学生和分包工作的行业团队成员都对使用Python作为工程问题解决平台做出了积极的反应。

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引用次数: 8

Evaluating the performance of max current AC-DCT based colored digital image fusion for Visual Sensor Network's 基于最大电流AC-DCT的视觉传感器网络彩色数字图像融合性能评价

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

Pub Date : 2015-08-01 DOI: 10.1109/DSP-SPE.2015.7369587

Arun Begill, Shruti Puniani, Kamaljot Singh, Navjot Kaur

This paper presents an efficient digital image fusion strategy that is created for Visual Sensor Networks(VSN's) to work in resource restricted, hazardous environments like battlefields. We aimed to use multiple partially unfocused colored images to develop a single multi-focus image using Discrete Cosine Transformation(DCT) depending on maximum value Alternating Current(AC) coefficients. This technique is beneficial in computation restricted environments of reduced computational powered devices to achieve image quality of higher degree. Our experiments shown that, the evaluated technique had produced better quality images as compared to other available methods of fusion in DCT domain.

提出了一种有效的数字图像融合策略，使视觉传感器网络(VSN)能够在战场等资源受限的危险环境中工作。我们的目标是使用多个部分未聚焦的彩色图像来开发一个单一的多聚焦图像，使用离散余弦变换(DCT)依赖于最大值交流电流(AC)系数。这种技术有利于在计算能力较弱的设备的计算受限环境中获得更高程度的图像质量。我们的实验表明，与其他可用的DCT域融合方法相比，评估的技术产生了更好的图像质量。

引用次数: 0

Subspace smearing and interference mitigation with array radio telescopes 阵列射电望远镜的子空间涂抹和干扰缓解

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

Pub Date : 2015-08-01 DOI: 10.1109/DSP-SPE.2015.7369566

G. Hellbourg

Array radio telescopes are suitable for the implementation of spatial filters. These filters present the advantage of canceling potential radio frequency interference (RFI) while recovering uncorrupted Time-Frequency data, of interest to astronomers. Although information regarding the sources of RFI can be a priori known or reliably inferred, the complexity of radio telescope systems randomizes the formulation of the subspace spanned by the RFI due to a lack of calibration or characterization. This knowledge is however necessary for building an efficient spatial filter, and needs therefore to be estimated.

阵列射电望远镜适合于空间滤波的实现。这些滤波器的优点是可以消除潜在的射频干扰(RFI)，同时恢复未损坏的时间-频率数据，这是天文学家感兴趣的。虽然关于RFI来源的信息可以先验地知道或可靠地推断，但由于缺乏校准或表征，射电望远镜系统的复杂性使RFI所跨越的子空间的公式随机化。然而，这些知识对于构建有效的空间滤波器是必要的，因此需要进行估计。

引用次数: 10

Hands-on software defined radio experiments with the low-cost RTL-SDR dongle 动手软件定义无线电实验与低成本的RTL-SDR加密狗

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

Pub Date : 2015-08-01 DOI: 10.1109/DSP-SPE.2015.7369529

M. Wickert, McKenna R. Lovejoy

Software defined radio (SDR) is an exciting merger of digital signal processing and wideband radio hardware. The term SDR came into more common usage in 1992 by Dr. Joe Mitola, but actually had its beginnings back in 1984 at E-Systems. The ideal SDR receiver consists of an antenna connected to an analog-to-digital converter (ADC) followed by a digital signal processing system (DSPS) to extract the signal of interest. Low-cost, as in $20, SDR receivers originally designed for digital video broadcasting, have been available for several years. Giving undergraduate students hands-on experience in this area is needed. In this paper we describe the details of an SDR laboratory experiment for students in a first semester communications theory course. Complete open-source SDR receiver software is used to get started, then coding of DSP algorithms is explored to process captured radio signals generated using test equipment and then actual over-the-air broadcasts. Being able to write code to process live signals and then see and hear the results really connects with the students. Both Matlab and Python support code libraries are available.

软件定义无线电(SDR)是数字信号处理和宽带无线电硬件的令人兴奋的结合。SDR一词在1992年被Joe Mitola博士广泛使用，但实际上早在1984年E-Systems就已经开始使用了。理想的SDR接收机由连接到模数转换器(ADC)的天线和数字信号处理系统(dsp)组成，用于提取感兴趣的信号。最初为数字视频广播设计的低成本SDR接收机，售价20美元，已经有好几年了。让本科生在这一领域获得实践经验是必要的。本文以通讯理论课第一学期的学生为对象，描述了一个特别提款权实验的细节。使用完整的开源SDR接收机软件开始，然后探索DSP算法的编码，以处理使用测试设备产生的捕获的无线电信号，然后进行实际的空中广播。能够编写代码来处理实时信号，然后看到和听到结果，这真的与学生们联系在一起。Matlab和Python都支持代码库。

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引用次数: 15

Memory efficient spectral estimation on parallel computing architectures 基于并行计算架构的内存高效频谱估计

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

Pub Date : 2015-08-01 DOI: 10.1109/DSP-SPE.2015.7369576

M. Barjenbruch, Franz Gritschneder, K. Dietmayer, J. Klappstein, J. Dickmann

A method for spectral estimation is proposed. It is based on the multidimensional extensions of the RELAX algorithm. The fast Fourier transform is replaced by multiple Chirp-Z transforms. Each transform has a much shorter length than the transform in the original algorithm. This reduces the memory requirements significantly. At the same time a high degree of parallelism is preserved. A detailed analysis of the computational requirements is given. Finally, the proposed method is applied to automotive radar measurements. It is shown, that the multidimensional spectral estimation resolves multiple scattering centers on an extended object.

提出了一种光谱估计方法。它基于RELAX算法的多维扩展。快速傅里叶变换被多个Chirp-Z变换所取代。每个变换的长度都比原算法中的变换短得多。这大大降低了内存需求。同时保持了高度的并行性。对计算要求进行了详细的分析。最后，将该方法应用于汽车雷达测量。结果表明，多维光谱估计可以解决扩展目标上的多个散射中心。

引用次数: 1

The performance limit for distributed Bayesian estimation with identical one-bit quantizers 具有相同位量化器的分布式贝叶斯估计的性能限制

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

Pub Date : 2015-08-01 DOI: 10.1109/DSP-SPE.2015.7369521

Xia Li, Jun-hai Guo, Hao Chen, U. Rogers

In this paper, a performance limit is derived for a distributed Bayesian parameter estimation problem in sensor networks where the prior probability density function of the parameter is known. The sensor observations are assumed conditionally independent and identically distributed given the parameter to be estimated, and the sensors employ independent and identical quantizers. The performance limit is established in terms of the best possible asymptotic performance that a distributed estimation scheme can achieve for all possible sensor observation models. This performance limit is obtained by deriving the optimal probabilistic quantizer under the ideal setting, where the sensors observe the parameter directly without any noise or distortion. With a uniform prior, the derived Bayesian performance limit and the associated quantizer are the same as the previous developed performance limit and quantizers under the minimax framework, where the parameter is assumed to be fixed but unknown. This proposed performance limit under distributed Bayesian setting is compared against a widely used performance bound that is based on full-precision sensor observations. This comparison shows that the performance limit derived in this paper is comparatively much tighter in most meaningful signalto- noise ratio (SNR) regions. Moreover, unlike the unquantized observations performance limit which can never be achieved, this performance limit can be achieved under certain noise observation models.

本文推导了已知参数先验概率密度函数的传感器网络分布贝叶斯参数估计问题的性能极限。在给定待估计参数的情况下，假设传感器的观测值是条件独立且同分布的，并且传感器采用独立且相同的量化器。性能极限是根据分布式估计方案对所有可能的传感器观测模型所能达到的最佳渐近性能来确定的。该性能限制是通过在理想设置下推导最优概率量化器获得的，在理想设置下，传感器直接观察参数，没有任何噪声或失真。在均匀先验条件下，导出的贝叶斯性能极限和相关量化器与先前在极小极大框架下开发的性能极限和量化器相同，其中假设参数是固定但未知的。在分布式贝叶斯设置下提出的性能限制与广泛使用的基于全精度传感器观测的性能界限进行了比较。比较表明，在大多数有意义的信噪比(SNR)区域，本文推导的性能极限相对要严格得多。而且，与非量化观测的性能极限不同，这种性能极限在一定的噪声观测模型下是可以达到的。

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引用次数: 4

Relay misbehavior detection for robust diversity combining in cooperative communications 协作通信中鲁棒分集组合中继错误行为检测

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

Pub Date : 2015-08-01 DOI: 10.1109/DSP-SPE.2015.7369550

Tsang-Yi Wang, Po-Heng Chou, Wan-Jen Huang

Most previous studies on cooperative communications assume that the relays operate normally and are trustworthy. However, this assumption may not always be true in practice. Accordingly, this study proposes a robust cooperative communication scheme in the physical layer for combating relay misbehavior. Two models for cooperative communications are considered, namely with direct path (WDP) and without direct path (WODP). For each model, a signal-correlation detection scheme is proposed in which the destination identifies the misbehaving relays within the network and then excludes their messages when performing diversity combining to infer the symbols of interest sent by the source. The proposed signal-correlation-detection rules are designed in such a way as to minimize the probability of error in identifying the misbehaving relays. The simulation results show that the proposed schemes yield an excellent detection performance in cooperative communication networks with relay misbehavior.

以往的合作通信研究大多假设中继运行正常且可信。然而，这种假设在实践中可能并不总是正确的。因此，本研究提出了一种在物理层对抗中继错误行为的鲁棒合作通信方案。考虑了有直接路径(WDP)和无直接路径(WODP)两种协作通信模型。针对每个模型，提出了一种信号相关检测方案，目的端识别网络中行为不端的中继，并在进行分集组合推断源端发送的感兴趣符号时排除其消息。所提出的信号相关检测规则的设计使识别异常继电器的错误概率最小化。仿真结果表明，该方案在具有中继异常行为的协作通信网络中具有良好的检测性能。

引用次数: 0

ON THE BLOCK-SPARSITY OF MULTIPLE-MEASUREMENT VECTORS. 多测量向量的块稀疏性。

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

Pub Date : 2015-08-01 DOI: 10.1109/DSP-SPE.2015.7369556

Mohammad Shekaramiz, Todd K Moon, Jacob H Gunther

Based on the compressive sensing (CS) theory, it is possible to recover signals, which are either compressible or sparse under some suitable basis, via a small number of non-adaptive linear measurements. In this paper, we investigate recovering of block-sparse signals via multiple measurement vectors (MMVs) in the presence of noise. In this case, we consider one of the existing algorithms which provides a satisfactory estimate in terms of minimum mean-squared error but a non-sparse solution. Here, the algorithm is first modified to result in sparse solutions. Then, further modification is performed to account for the unknown block sparsity structure in the solution, as well. The performance of the proposed algorithm is demonstrated by experimental simulations and comparisons with some other algorithms for the sparse recovery problem.

基于压缩感知(CS)理论，可以通过少量的非自适应线性测量来恢复在适当基下可压缩或稀疏的信号。在本文中，我们研究了在存在噪声的情况下，用多测量向量(mmv)恢复块稀疏信号。在这种情况下，我们考虑一种现有的算法，该算法在最小均方误差方面提供了令人满意的估计，但是非稀疏解。在这里，首先修改算法以得到稀疏解。然后，进一步进行修改，以解释解决方案中未知的块稀疏性结构。通过实验仿真和与其他稀疏恢复算法的比较，验证了该算法的性能。

引用次数: 7

Adaptive likelihood codebook reordering vector quantization for 1-D data sources 一维数据源的自适应似然码本重排序矢量量化

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

Pub Date : 2015-08-01 DOI: 10.1109/DSP-SPE.2015.7369536

Chu Meh Chu, Nathan V. Parrish, David V. Anderson

This paper outlines an adaptive extension of likelihood codebook reordering (LCR) vector quantization. By providing a method for allowing the vector quantization to adapt in a predetermined way, the codebook may be adaptively reordered to allow more efficient encoding by giving preference to encountered vectors in the dictionary. In particular, adaptation allows the trained dictionaries to be more efficient in representing specific data. The difference in the training and testing sets produces different transition matrices which are used to encode testing vectors. The adaptive likelihood codebook reordering vector quantization adapts the a priori transition matrix obtained from training data set to the testing data set on an online instantaneous basis. This method yields improvements in coding rate when entropy coding is applied to the reordered indices obtained from the adaptive version of the LCR algorithm.

提出了一种自适应扩展的似然码本重排序矢量量化方法。通过提供一种允许矢量量化以预先确定的方式进行调整的方法，码本可以自适应地重新排序，以便通过优先考虑字典中遇到的矢量来实现更有效的编码。特别是，自适应允许经过训练的字典在表示特定数据时更有效。训练集和测试集的差异产生了不同的转换矩阵，用于编码测试向量。自适应似然码本重排序矢量量化将训练数据集获得的先验转移矩阵在线瞬时地适应于测试数据集。该方法对自适应LCR算法得到的重排序索引进行熵编码，提高了编码率。

引用次数: 1

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