Academic literature on the topic 'Wiener-hope technique'

Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation scales, dimension reduction techniques, and appropriate matching strategies. This study investigates how different filtering approaches might be combined to improve images. The palmprint identification system uses a 3W filter, which combines wavelet, Wiener, and weighted filters. Optimizing results entails coordinating the 3W filter with Gabor orientation scales, matching processes, and dimension reduction methods. The research shows that accuracy may be considerably increased using a 3W filter with a Gabor orientation scale of [8×7], the kernel principal component analysis (KPCA) dimension reduction methodology, and a cosine matching method. Specifically, a value of 99.722% can be achieved. These results highlight the importance of selecting appropriate settings and techniques for palmprint recognition systems.

In 1951 A. S. Besicovitch, who was my research supervisor, suggested that I look at the problem of determining the dimension of the range of a Brownian motion path. This problem had been communicated to him by C. Loewner, but it was a natural question which had already attracted the attention of Paul Lévy. It was a good problem to give to an ignorant Ph.D. student because it forced him to learn the potential theory of Frostman [33] and Riesz[75] as well as the Wiener [98] definition of mathematical Brownian motion. In fact the solution of that first problem in [81] used only ideas which were already twenty-five years old, though at the time they seemed both new and original to me. My purpose in this paper is to try to trace the development of these techniques as they have been exploited by many authors and used in diverse situations since 1953. As we do this in the limited space available it will be impossible to even outline all aspects of the development, so I make no apology for giving a biased account concentrating on those areas of most interest to me. At the same time I will make conjectures and suggest some problems which are natural and accessible in the hope of stimulating further research.

This issue contains selected papers from the International IEEE Conference on Computational Cybernetics that took place in August 2003 in Hungary at the site of lake Balaton. Computational Cybernetics is the synergetic integration of Cybernetics and Computational Intelligence techniques. Cybernetics was defined by Wiener as "the science of control and communication, in the animal and the machine". The word "cybernetics" itself stems from the Greek "kybernetes" that means pilot or governor. Thus, the science of computational Cybernetics is especially concerned with the comparative study of automatic control systems. Furthermore, Computational Cybernetics covers not only mechanical, but biological (living), social and economical systems and for this uses computational intelligence based results of communication theory, signal processing, information technology, control theory, the theory of adaptive systems, the theory of complex systems (game theory, operational research), and computer science. We have selected 14 papers from the conference covering the fields of system design and modeling, neural networks, and fuzzy control, which resemble the great variety of computational cybernetics. While it is sometimes difficult to integrate over these differing fields, we expect the evolution of future intelligent systems at the service of mankind by the synergetic integration of these different areas. It is our hope that the papers in this issue will inspire and help our readers in the development of advanced intelligent systems.

This issue contains selected papers from the International IEEE Conference on Computational Cybernetics that took place in Vienna 2004 in Austria at the Vienna University of Technology. Computational Cybernetics is the synergetic integration of Cybernetics and Computational Intelligence techniques. Cybernetics was defined by Wiener as "the science of control and communication, in the animal and the machine". The word "cybernetics" itself stems from the Greek "kybernetes" that means pilot or governor. While the roots of cybernetics go back to the time when James Watt equipped his steam engine with a Governor, that is a simple feedback mechanism for regulation of steam flow, the computational component was a child of the 20th century with the rise of information processing machines. The science of cybernetics and the science of computer science have in common, that both infiltrated many fields of application such as mathematics, telecommunication, regulated engines, living systems/medicine, social systems, and economical systems. Thus, on the one hand, the science of computational cybernetics encompasses a wide field, like the comparative study of automatic control systems, mechanical, biological (living), social and economical systems, communication theory, signal processing, information technology, control theory, the theory of adaptive systems, and the theory of complex systems (game theory, operational research). On the other hand, this research allows for finding common roots and common behavior among this broad field. This dichotomy between a broad overarching topic and the focus on computational cybernetics establishes the basis for interesting talks and discussions between scientists of different disciplines. We have selected 11 papers from the conference covering the fields of system design and modeling, neural networks, control theory, robotics and pattern recognition, which resemble the great variety of computational cybernetics. After the conference, each of these papers has undergone another peer review cycle in which the papers had been improved in order to fit this journal's topic and quality. It is our hope that the papers in this issue will inspire and help our readers in the development of advanced intelligent systems at the service of mankind.