Spline function approach to digital signal processing

Models of digital signal processing are built on the signal spaces composed of spline functions. It is revealed that the models include and generalize the ideal model on the band-limited signal space, and the conventional model used in practice, with a zero-order or a first-order hold circuit. The models can be implemented accurately using real circuits so far as they are not identical with the ideal model because a spline function can be generated as a piecewise polynomial. Thus the models make it possible to treat signal processing systems which exist between the conventional ideal model and the conventional model used in practice, in design through implementation.     

FAD-flexibility in digital signal processing

A digital filter and detection chip (FAD) has been developed by British Telecom in the UK for application as a tone detector in digital telephone exchanges. The chip has a flexible structure and has since found application in many other communication systems. This paper describes the chip and indicates how it can be used to perform a variety of different algorithms for these purposes.     

Adaptation of group algebras to signal and image processing

Filtering techniques in digital signal and image processing typically use linear and/or cyclic convolutions, both of which are based on polynomial arithmetic. When the input data and the filter weights are treated as coefficients of polynomials, linear convolution is computed directly as a product of these polynomials. Cyclic convolution is used as an efficient means of computing the linear convolution. In this case, the input is organized into sections, each section treated as the coefficients of a polynomial. The output data are generated by selecting the last coefficients of the products of the section polynomials with the filter polynomial. We develop a more general methodology for filtering signals and images on the basis of the ideas of: (1) group algebras, a generalization of polynomials and polynomial arithmetic, and (2) value functions, a generalization of the coefficient selection used in generating the output data. Applications of these ideas to signal and image processing are given. Finally, the concepts are incorporated into the framework of image algebras.     

A state variable approach to digital image processing

Digital filtering of images is considered. A recursive filter design for two-dimensional separable filters is discussed which is based on state variable methods. Advantages of this design are guaranteed stability, and shorter word length than filters designed by bilinear mapping techniques, greater computational efficiency and easier design as compared with nonrecursive filters. Results are shown for filtering high frequency additive noise from a low spatial frequency image and for filtering a low frequency multiplicative noise from a high spatial frequency image.     

数字图像技术在陶瓷显微图像处理中的应用

以陶瓷显微组织图像为例,运用数字图像技术,结合教学形态学和面向对象思想,对陶瓷显微组织图像进行滤波处理,减少图像分析中的干扰因素和误差以确保获得组元精确的测量结果,再对图像进行区域分割及边缘获取,将具有相近特征的组元分离提取,获得陶瓷各组元的分割图和轮廓图.该方法能快速准确提取陶瓷显微组织图像中的组元,有效地协助研究人员进行陶瓷显微结构定量分析,最终实现陶瓷组元体视学参数的测量.     

Convolution wavelet packet transform and its applications to signal processing

The length of decomposition results of traditional wavelet packet transform (WPT) will decrease by half in the next level for downsampling, then the length of sequences in the last level will become very short, and this is very inconvenient for further analysis of these sequences. One kind of WPT based on convolution definition is put forward, its fast decomposition and reconstruction algorithms are given, and the outstanding characteristic of this convolution WPT is that no matter how many levels a signal is decomposed, the length of sequences got in every level will never decrease and can always keep the same as that of the original signal, so the defect of traditional WPT is overcome. For traditional WPT, to achieve the same effect of direct decomposition of convolution WPT, reconstruction operation must be done and the calculation will greatly increase. Based on the length invariance property of convolution WPT, a noise reduction algorithm is proposed, and signal processing example shows that its denoising performance is better than that of traditional WPT, and also much better than that of wavelet transform.     

Specific applications of image processing to surface flaw detection

An approach to detect surface flaws automatically through digital image processing techniques is investigated in this work. In addition, a novel pre-processing transducer applicable to this area is presented.The digitized image of the surface of a part to be inspected is obtained by means of a vidicon camera. The image is then properly segmented to separate the information-bearing objects from the background by means of a thresholding scheme. An analysis of the resulting binary image is then made by following the borders present.Objects on interest in the image can be located by means of template matching. The use of a hardware Number Theoretic Transform (NTT) convulutional filter is shown to provide processing times commensurate with industrial requirements.Results arer shown for 3 practical cases in which the system was used to detect surface flaws.To assist in the measurement of surface flaws a transducer is introduced which converts true surface-depth information into a gray level image for processing by the system.     

基于MATLAB下的数字信号处理与仿真

对IIR滤波器模型进行了详细的理论分析,利用MATLAB工具箱重要函数及图形用户界面工具SPTool设计IIR数字滤波器,并以此为基础,在Simulink下实现对IIR数字滤波器可视化建模与动态仿真。     

A fast digital Walsh-Hadamard signal processing system

A fast digital hardware system for signal analysis and synthesis is presented. This system, combined with data compression or information rate reduction techniques, which are also discussed in the paper, provides a powerful tool for a variety of control, communication and signal processing applications. An illustrative example is given.     

Low power architectures for digital signal processing

Low power architectures for digital signal processing algorithms requiring inner product computation are presented. In the first step a power efficient memory organization exploiting data reuse is determined. In the second step an order of evaluation of the partial products that reduces the switching activity at the inputs of the computational units is derived. Information related to both coefficients which are static and data which are dynamic, is used to drive the reordering of computation. Experimental results for several signal processing algorithms prove that the proposed techniques lead to significant savings in net switching activity and thus in power consumption.     

Experiment automation using digital signal processing methods

Theoretical methods and algorithms of digital signal processing (DSP), as well as certain approaches to practical implementation of the experiments in industrial and laboratory measurements and tests are considered, based on utilization of ZETLab hardware-software complex. An example of real-time evaluating the spatial coordinates for the source of low-intensity vibration (noise disturbance) is provided. The concept of virtual device (VD) serves as the foundation in planning and implementing the experiment; note that the actual hardware components, i.e., seismometers and analog-to-digital converters (ADC), are involved in the process, having PC interface as a certain link among the measurement object, mathematical framework and operator. The suggested method of coordinate evaluation operates according to the logic based on spectrum and correlation analysis of the signals, with subsequent data visualization via reciprocal projection technique.     

Improvements in microphotometry by digital signal processing

From noise measurements with a microscope photometer it is deduced that the square roots of the measured intensities are linear with respect to photon noise. It is demonstrated by using a cytological specimen with a large dynamic range of intensities, that an IIR filter uniformly smoothes the square roots of the intensities, whereas the effect of that filter on the measured intensities is larger in dark parts of the image than in bright areas.     

数字信号处理的FPGA实现

介绍了三种常见的数字信号处理过程的FPGA实现,包括频率合成、AM调制和FM调制,并进行仿真和硬件下载验证。     

Time domain analysis of digital signal processing

Digital signal processing methods, in the broad sense, involve the extraction of useful information from received signals using digital computers and/or digital filters. Digital filters refer to equipment which use digital components such as gates, multipliers, and delay circuits. The analysis of digital signal processing methods, given in most of engineering literature, is done in frequency rather than time domain. This paper presents methods for the analysis of digital systems entirely in the time domain. It discusses such topics as discrete Fourier transform, quadrature components, mechanization of digital filters, and quantization errors.     

Tiny GAs for image processing applications

The expedience of today's image-processing applications is no longer based on the performance of a single algorithm alone. These systems appear to be complex frameworks with a lot of sub-tasks that are solved by specific algorithms, adaptation procedures, data handling, scheduling, and parameter choices. The venture of using computational intelligence (CI) in such a context, thus, is not a matter of a single approach. Among the great choice of techniques to inject CI in an image-processing framework, the primary focus of this presentation will be on the usage of so-called tiny-GAs. This stands for an evolutionary procedure with low efforts, i.e. small population size (like 10 individuals), little number of generations, and a simple fitness. Obviously, this is not suitable for solving highly complex optimization tasks, but the primary interest here is not the best individual's fitness, but the fortune of the algorithm and its population, which has just escaped the Monte-Carlo domain after random initialization. That this approach can work in practice will be demonstrated by means of selected image-processing applications, especially in the context of linear regression and line fitting; evolutionary post processing of various clustering results, in order to select a most suitable one by similarity; and classification by the fitness values obtained after a few generations.     

Greyscale image processing for industrial applications

The processing of images of industrial components viewed in relatively good and relatively cluttered environments is discussed and illustrated. It is shown that a hypothesis generation and verification procedure can be used to improve feature representation. Finally, the requirements for robust modelling schemes are discussed.     

Several digital realizations of two-dimensional E-filters applied to image processing

This paper presents several digital implementations for two-dimensional E-filters. The E-filter is based on a non-linear transformation into the spatial domain. Its response is essentially independent of the frequency of the incoming signal under certain conditions, but depends largely on the digital amplitude. Several digital realizations and attendant problems are presented for E-filters applied to biomedical images obtained from an Anger scintillation camera. The associated sensitivity of the digital E-filter to changes of its parameters is examined. A two-dimensional linear filter is realized by the use of a bi-cubic spline interpolation scheme. The boundary detection of unprocessed and filtered images is also investigated using the hexagonal Golay transform. Several examples of abnormal heart and brain-scans are presented including the filtered images and bounded areas of infarcted and abnormal tissue.     

Realtime digital signal processing system using a parallel processing architecture

A low cost, high-speed, general-purpose ditigal signal processing system was constructed using the TMS32010 digital signal processor. The system was designed with simplicity, compactness, flexibility and expandibility in mind. A parallel processing architecture was adopted to achieve realtime performance. Four processors were used in the prototype system, but this can be expanded easily. Interprocessor data transfer and communications with the host computer are facilitated via a single common bus and a bank of shared memory. A one-dimensional digital FIR filter and a realtime FFT program were used to evaluate the performance of the system. In addition, a realtime spectrogram was implemented as an application example.