Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

Wavelet-based multiresolution data representations for scalable distributed GIS services

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.Includes bibliographical references (p. 155-160).Demand for providing scalable distributed GIS services has been growing greatly as the Internet continues to boom. However, currently available data representations for these services are limited by a deficiency of scalability in data formats. In this research, four types of multiresolution data representations based on wavelet theories have been put forward. The designed Wavelet Image (WImg) data format helps us to achieve dynamic zooming and panning of compressed image maps in a prototype GIS viewer. The Wavelet Digital Elevation Model (WDEM) format is developed to deal with cell-based surface data. A WDEM is better than a raster pyramid in that a WDEM provides a non-redundant multiresolution representation. The Wavelet Arc (WArc) format is developed for decomposing curves into a multiresolution format through the lifting scheme. The Wavelet Triangulated Irregular Network (WTIN) format is developed to process general terrain surfaces based on the second generation wavelet theory. By designing a strategy to resample a terrain surface at subdivision points through the modified Butterfly scheme, we achieve the result: only one wavelet coefficient needs to be stored for each point in the final representation. In contrast to this result, three wavelet coefficients need to be stored for each point in a general 3D object wavelet-based representation. Our scheme is an interpolation scheme and has much better performance than the Hat wavelet filter on a surface. Boundary filters are designed to make the representation consistent with the rectangular boundary constraint.(cont.) We use a multi-linked list and a quadtree array as the data structures for computing. A method to convert a high resolution DEM to a WTIN is also provided. These four wavelet-based representations provide consistent and efficient multiresolution formats for online GIS. This makes scalable distributed GIS services more efficient and implementable.by Jingsong Wu.Ph.D

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Satellite Image Compression Using Wavelet

Image data is a combination of information and redundancies, the information is part of the data be protected because it contains the meaning and designation data. Meanwhile, the redundancies are part of data that can be reduced, compressed, or eliminated. Problems that arise are related to the nature of image data that spends a lot of memory. In this paper will compare 31 wavelet function by looking at its impact on PSNR, compression ratio, and bits per pixel (bpp) and the influence of decomposition level of PSNR and compression ratio. Based on testing performed, Haar wavelet has the advantage that is obtained PSNR is relatively higher compared with other wavelets. Compression ratio is relatively better than other types of wavelets. Bits per pixel is relatively better than other types of wavelet

Enhancing the performance of spread spectrum techniques in different applications

Spread spectrum, Automotive Radar, Indoor Positioning Systems, Ultrasonic and Microwave Imaging, super resolution technique and wavelet transformMagdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2006von Omar Abdel-Gaber Mohamed Al

River bed sediment surface characterisation using wavelet transform-based methods.

The primary purpose of this work was to study the morphological change of river-bedsediment surfaces over time using wavelet transform analysis techniques. The wavelettransform is a rapidly developing area of applied mathematics in both science andengineering. As it allows for interrogation of the spectral made up of local signalfeatures, it has superior performance compared to the traditionally used Fouriertransform which provides only signal averaged spectral information. The main study ofthis thesis includes the analysis of both synthetically generated sediment surfaces andlaboratory experimental sediment bed-surface data. This was undertaken usingtwo-dimensional wavelet transform techniques based on both the discrete and thestationary wavelet transforms.A comprehensive data-base of surface scans from experimental river-bed sedimentsurfaces topographies were included in the study. A novel wavelet-basedcharacterisation measure - the form size distribution ifsd) - was developed to quantifythe global characteristics of the sediment data. The fsd is based on the distribution ofwavelet-based scale-dependent energies. It is argued that this measure will potentiallybe more useful than the traditionally used particle size distribution (psd), as it is themorphology of the surface rather than the individual particle sizes that affects the nearbed flow regime and hence bed friction characteristics.Amplitude and scale dependent thresholding techniques were then studied. It was foundthat these thresholding techniques could be used to: (1) extract the overall surfacestructure, and (2) enhance dominant grains and formations of dominant grains withinthe surfaces. It is shown that assessment of the surface data-sets post-thresholding mayallow for the detection of structural changes over time

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity