Forensic applications of signal processing

The IEEE Code of Ethics [1] states, ... its members agree ... to be honest and realistic in stating claims or estimates based on available data; ... to improve the understanding of technology, its appropriate application, and potential consequences; ... to maintain and improve our technical competence and to undertake technological tasks for others only if qualified by training or experience, or after full disclosure of pertinent limitations. A professional engineer is a practicing scientist. In all our professional endeavors, we are bound by basic scientific principles and must use scientific methods.     

Listen to your data [signal processing applications]

This paper gives emphasis on the importance of testing the performance of signal processing algorithms with real data and learning from the data. The discussion is made primarily within the context of a patient monitoring research. The author suggests six steps on how to test with and learn from real data. These steps are: (1) minimize measurement uncertainty so your physiological phenomenon is consistently observable, (2) understand your data, (3) acquire a significant number of appropriate training and testing sets, (4) construct high-performance validation criteria, (5) design algorithms that can generalize in the clinical environment, and (6) test and learn from your data. Results show that this process is also applicable to other fields as well.     

Digital signal processing for video applications

Digital video signal processing is one result of the fast progress in NMOS-VLSI techniques. The attractions of using digital data processing methods in an analog application field are the availability of CAD tools for the design of digital ICs and the integration of digital filter functions. Besides the key components such as microcomputers, A/D, and D/A converters, the digital filter techniques are the most important functions in this application field. It is demonstrated that digital signal processing is not only restricted to amplitude modulated video signals, but also that frequency modulated signals can be processed and methods for FM modulation and demodulation have been developed.     

Surface-acoustic-wave devices for signal processing applications

A survey of SAW devices is presented including delay lines, frequency filters, oscillators, matched filters, and Fourier transformers. Application of these devices to signal processing is discussed.     

Comparative study between traditional and modified probabilistic neural networks

Probabilistic neural network (PNN) is a kind of supervised neural network, proposed by Specht as an alternative to back-propagation neural network. The key advantages of PNN are that, training requires only a single pass, and decision surfaces are guaranteed to approach the Bayes-optimal decision boundaries, as the number of training samples grows. Furthermore, shape of the decision surface can be made as complex as necessary, or as simple as desired, by choosing an appropriate value of the smoothing parameter; erroneous samples can be tolerated, and sparse samples are adequate for network performance. This paper reviews the PNN, modified PNN, various learning approaches employed to train the PNN and some comparisons of various types of PNN. Experimental results have been carried out to verify the ability of modified PNN in achieving good classification rate over traditional PNN, BPNN and KNN.     

The application of bionic wavelet transform to speech signal processing in cochlear implants using neural network simulations

Cochlear implants (CIs) restore partial hearing to people with severe to profound sensorineural deafness; but there is still a marked performance gap in speech recognition between those who have received cochlear implant and people with a normal hearing capability. One of the factors that may lead to this performance gap is the inadequate signal processing method used in CIs. This paper investigates the application of an improved signal-processing method called bionic wavelet transform (BWT). This method is based upon the auditory model and allows for signal processing. Comparing the neural network simulations on the same experimental materials processed by wavelet transform (WT) and BWT, the application of BWT to speech signal processing in CI has a number of advantages, including: improvement in recognition rates for both consonants and vowels, reduction of the number of required channels, reduction of the average stimulation duration for words, and high noise tolerance. Consonant recognition results in 15 normal hearing subjects show that the BWT produces significantly better performance than the WT (t = -4.36276, p = 0.00065). The BWT has great potential to reduce the performance gap between CI listeners and people with a normal hearing capability in the future.     

Perfect sampling: a review and applications to signal processing

Markov chain Monte Carlo (MCMC) sampling methods have gained much popularity among researchers in signal processing. The Gibbs and the Metropolis-Hastings (1954, 1970) algorithms, which are the two most popular MCMC methods, have already been employed in resolving a wide variety of signal processing problems. A drawback of these algorithms is that in general, they cannot guarantee that the samples are drawn exactly from a target distribution. New Markov chain-based methods have been proposed, and they produce samples that are guaranteed to come from the desired distribution. They are referred to as perfect samplers. We review some of them, with the emphasis being given to the algorithm coupling from the past (CFTP). We also provide two signal processing examples where we apply perfect sampling. In the first, we use perfect sampling for restoration of binary images and, in the second, for multiuser detection of CDMA signals     

小波变换在心电信号处理中的应用

在心脏病诊断过程中,心电信号的检测是重要的环节,然而心电信号的噪声很强,为了能够较好地滤除信号中的噪声,对信号的特点进行准确标定,利用基于小波变换的阈值去噪算法和基于小波的模极大值-极小值的算法进行心电信号的处理.采用MIT/BIH中的数据进行仿真调试验证,实验结果表明,被引入的几种噪声能被很好地去除,而且心电信号能较完整地保留下来,特征点能被准确地检测到,从而提高了诊断心脏等疾病的诊断效率.     

小波去噪在超声多普勒信号处理中的应用

在超声多普勒流量计的研制过程中,由于多普勒信号属于微弱信号,超声波回波信号的去噪处理这一步是十分必要的。通常采用小波阈值去噪的方法对此信号进行预处理,本文所采用的方法是在小波阈值去噪方法的基础上,利用一种改进的阈值函数,此阈值函数克服了硬阈值函数不连续的不足,继承了软阈值函数的连续性特点,并且解决了软阈值函数中存在的小波系数与分解小波系数之间存在着恒定偏差的缺陷,与此同时,它具有软硬阈值函数不可比拟的灵活性。对实验所获得的超声波回波信号进行去噪,仿真结果表明,采用新的阈值函数去噪能有效抑制在信号奇异点附近产生的Pseudo-Gibbs现象,并且从信噪比增益和均方误差意义上分析看出改进型阈值函数对于超声回波信号的去噪效果均优于传统的软、硬阈值方法。     

Synchronous SRAM for digital signal processing applications

A synchronous compact RAM that is designed for digital signal processing applications is presented. The small area of the RAM is achieved by using a dual global word line architecture. Circuit techniques which minimise the RAM clock cycle time and improve the performance of the sense amplifiers are also described. The circuits were implemented successfully in 0.5 μm CMOS technology     

利用结构化ASIC实现信号处理应用

FPGA所具有的设计灵活性和大吞吐量特性使其成为传统数字信号处理(DSP)器件可靠的芯片解决方案,例如无线基站、医学成像和图像记录等高性能DSP应用.在很多情况下,FPGA和高密度ASIC、DSP一起布置在同一块电路板上.通常由ASIC和FPGA分担的硬件功能现在主要由FPGA来实现,这是因为FPGA能够为DSP提供具有成本效益的方案,广泛应用于各种领域.     

Radar signal categorization using a neural network

Neural networks are used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy-minimizing network. The limiting process contains the state vector within a set of limits, and this model is called the brain state in a box, or BSB model, which forms energy minima (attractors in the network dynamical system) based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to tentatively identify them based on their observed parameters. As a test of this idea, a neural network was used to form a small database that could potentially make emitter identification. There were three errors of classification. The number of iterations are required to reach an attractor state was very long, and some of the final states were not fully limited. These factors indicate the uncertainty of the neural network