ScaleSpaceViz: α-Scale spaces in practice

Kernels of the so-called α-scale space have the undesirable property of having no closed-form representation in the spatial domain, despite their simple closed-form expression in the Fourier domain. This obstructs spatial convolution or recursive implementation. For this reason an approximation of the 2D α-kernel in the spatial domain is presented using the well-known Gaussian kernel and the Poisson kernel. Experiments show good results, with maximum relative errors of less than 2.4%. The approximation has been successfully implemented in a program for visualizing α-scale spaces. Some examples of practical applications with scale space feature points using the proposed approximation are given. The text was submitted by the authors in English. Frans Kanters received his MSc degree in Electrical Engineering in 2002 from the Eindhoven University of Technology in the Netherlands. Currently he is working on his PhD at the Biomedical Imaging and Informatics group at the Eindhoven University of Technology. His PhD work is part of the “Deep Structure, Singularities, and Computer Vision (DSSCV)” project sponsored by the European Union. His research interests include scale space theory, image reconstruction, image processing algorithms, and hardware implementations thereof. Luc Florack received his MSc degree in theoretical physics in 1989 and his PhD degree cum laude in 1993 with a thesis on image structure, both from Utrecht University, the Netherlands. During the period from 1994 to 1995, he was an ERCIM/HCM research fellow at INRIA Sophia-Antipolis, France, and IN-ESC Aveiro, Portugal. In 1996 he was an assistant research professor at DIKU, Copenhagen, Denmark, on a grant from the Danish Research Council. From 1997 to June 2001, he was an assistant research professor at Utrecht University in the Department of Mathematics and Computer Science. Since June 1, 2001, he has been working as an assistant professor and, then, as an associate professor at Eindhoven University of Technology, Department of Biomedical Engineering. His interest includes all multiscale structural aspects of signals, images, and movies and their applications to imaging and vision. Remco Duits received his MSc degree (cum laude) in Mathematics in 2001 from the Eindhoven University of Technology, the Netherlands. Today he is a PhD student at the Department of Biomedical Engineering at the Eindhoven University of Technology on the subject of multiscale perceptual organization. His interest subtends functional analysis, group theory, partial differential equations, multiscale representations and their applications to biomedical imaging and vision, perceptual grouping. Currently, he is finishing his thesis “Perceptual Organization in Image Analysis (A Mathematical Approach Based on Scale, Orientation and Curvature).” During his PhD work, several of his submissions at conferences were chosen as selected or best papers—in particular, at the PRIA 2004 conference on pattern recognition and image analysis in St. Petersburg, where he received a best paper award (second place) for his work on invertible orientation scores. Bram Platel received his Masters Degree cum laude in biomedical engineering from the Eindhoven University of Technology in 2002. His research interests include image matching, scale space theory, catastrophe theory, and image-describing graph constructions. Currently he is working on his PhD in the Biomedical Imaging and Informatics group at the Eindhoven University of Technology. Bart M. ter Haar Romany is full professor in Biomedical Image Analysis at the Department of Biomedical Engineering at Eindhoven University of Technology. He has been in this position since 2001. He received a MSc in Applied Physics from Delft University of Technology in 1978, and a PhD on neuromuscular nonlinearities from Utrecht University in 1983. After being the principal physicist of the Utrecht University Hospital Radiology Department, in 1989 he joined the department of Medical Imaging at Utrecht University as an associate professor. His interests are mathematical aspects of visual perception, in particular linear and non-linear scale-space theory, computer vision applications, and all aspects of medical imaging. He is author of numerous papers and book chapters on these issues; he edited a book on non-linear diffusion theory and is author of an interactive tutorial book on scale-space theory in computer vision. He has initiated a number of international collaborations on these subjects. He is an active teacher in international courses, a senior member of IEEE, and IEEE Chapter Tutorial Speaker. He is chairman of the Dutch Biophysical Society.     

A multimodal virtual reality interface for 3D interaction with VTK

The object-oriented visualization Toolkit (VTK) is widely used for scientific visualization. VTK is a visualization library that provides a large number of functions for presenting three-dimensional data. Interaction with the visualized data is controlled with two-dimensional input devices, such as mouse and keyboard. Support for real three-dimensional and multimodal input is non-existent. This paper describes VR-VTK: a multimodal interface to VTK on a virtual environment. Six degree of freedom input devices are used for spatial 3D interaction. They control the 3D widgets that are used to interact with the visualized data. Head tracking is used for camera control. Pedals are used for clutching. Speech input is used for application commands and system control. To address several problems specific for spatial 3D interaction, a number of additional features, such as more complex interaction methods and enhanced depth perception, are discussed. Furthermore, the need for multimodal input to support interaction with the visualization is shown. Two existing VTK applications are ported using VR-VTK to run in a desktop virtual reality system. Informal user experiences are presented. Arjan J. F. Kok is an assistant professor at the Department of Computer Science at the Open University of the Netherlands. He studied Computer Science at the Delft University of Technology, The Netherlands. He received his Ph.D. from the same university. He worked as a Scientist for TNO (Netherlands Organization for Applied Scientific Research) and as assistant professor at the Eindhoven University of Technology before he joined the Open University. His research interests are visualization, virtual reality, and computer graphics. Robert van Liere studied Computer Science at the Delft University of Technology, the Netherlands. He received his Ph.D. with the thesis “Studies in Interactive Scientific Visualization” at the University of Amsterdam. Since 1985, he has worked at CWI, the Center for Mathematics and Computer Science in Amsterdam in which he is the head of CWI’s visualization research group. Since 2004, he holds a part-time position as full professor at the Eindhoven University of Technology. His research interests are in interactive data visualization and virtual reality. He is a member of IEEE.     

Geometric Analysis of Particle Motion in a Vector Image Field

This paper proposes a geometrical model for the Particle Motion in a Vector Image Field (PMVIF) method. The model introduces a c-evolute to approximate the edge curve in the gray-level image. The c-evolute concept has three major novelties: (1) The locus of Particle Motion in a Vector Image Field (PMVIF) is a c-evolute of image edge curve; (2) A geometrical interpretation is given to the setting of the parameters for the method based on the PMVIF; (3) The gap between the image edge’s critical property and the particle motion equations appeared in PMVIF is padded. Our experimental simulation based on the image gradient field is simple in computing and robust, and can perform well even in situations where high curvature exists. Chenggang Lu received his Bachelor of Science and PhD degrees from Zhejiang University in 1996 and 2003, respectively. Since 2003, he has been with VIA Software (Hang Zhou), Inc. and Huawei Technology, Inc. His research interests include image processing, acoustic signaling processing, and communication engineering. Zheru Chi received his BEng and MEng degrees from Zhejiang University in 1982 and 1985 respectively, and his PhD degree from the University of Sydney in March 1994, all in electrical engineering. Between 1985 and 1989, he was on the Faculty of the Department of Scientific Instruments at Zhejiang University. He worked as a Senior Research Assistant/Research Fellow in the Laboratory for Imaging Science and Engineering at the University of Sydney from April 1993 to January 1995. Since February 1995, he has been with the Hong Kong Polytechnic University, where he is now an Associate Professor in the Department of Electronic and Information Engineering. Since 1997, he has served on the organization or program committees for a number of international conferences. His research interests include image processing, pattern recognition, and computational intelligence. Dr. Chi has authored/co-authored one book and nine book chapters, and published more than 140 technical papers. Gang Chen received his Bachelor of Science degree from Anqing Teachers College in 1983 and his PhD degree in the Department of Applied Mathematics at Zhejiang University in 1994. Between 1994 and 1996, he was a postdoctoral researcher in electrical engineering at Zhejiang University. From 1997 to 1999, he was a visiting researcher in the Institute of Mathematics at the Chinese University of Hong Kong and the Department of Electronic and Information Engineering at The Hong Kong Polytechnic University. Since 2001, he has been a Professor at Zhejiang University. He has been the Director of the Institute of DSP and Software Techniques at Ningbo University since 2002. His research interests include applied mathematics, image processing, fractal geometry, wavelet analysis and computer graphics. Prof. Chen has co-authored one book, co-edited five technical proceedings and published more than 80 technical papers. (David) Dagan Feng received his ME in Electrical Engineering & Computing Science (EECS) from Shanghai JiaoTong University in 1982, MSc in Biocybernetics and Ph.D in Computer Science from the University of California, Los Angeles (UCLA) in 1985 and 1988 respectively. After briefly working as Assistant Professor at the University of California, Riverside, he joined the University of Sydney at the end of 1988, as Lecturer, Senior Lecturer, Reader, Professor and Head of Department of Computer Science/School of Information Technologies, and Associate Dean of Faculty of Science. He is Chair-Professor of Information Technology, Hong Kong Polytechnic University; Honorary Research Consultant, Royal Prince Alfred Hospital, the largest hospital in Australia; Advisory Professor, Shanghai JiaoTong University; Guest Professor, Northwestern Polytechnic University, Northeastern University and Tsinghua University. His research area is Biomedical & Multimedia Information Technology (BMIT). He is the Founder and Director of the BMIT Research Group. He has published over 400 scholarly research papers, pioneered several new research directions, made a number of landmark contributions in his field with significant scientific impact and social benefit, and received the Crump Prize for Excellence in Medical Engineering from USA. More importantly, however, is that many of his research results have been translated into solutions to real-life problems and have made tremendous improvements to the quality of life worldwide. He is a Fellow of ACS, HKIE, IEE, IEEE, and ATSE, Special Area Editor of IEEE Transactions on Information Technology in Biomedicine, and is the current Chairman of IFAC-TC-BIOMED.     

Estimating the number of clusters using a windowing technique

Clustering is the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups (clusters). A fundamental and unresolved issue in cluster analysis is to determine how many clusters are present in a given set of patterns. In this paper, we present the z-windows clustering algorithm, which aims to address this problem using a windowing technique. Extensive empirical tests that illustrate the efficiency and the accuracy of the propsoed method are presented. The text was submitted by the authors in English. Basilis Boutsinas. Received his diploma in Computer Engineering and Informatics in 1991 from the University of Patras, Greece. He also conducted studies in Electronics Engineering at the Technical Education Institute of Piraeus, Greece, and Pedagogics at the Pedagogical Academy of Lamia, Greece. He received his PhD on Knowledge Representation from the University of Patras in 1997. He has been an assistant professor in the Department of Business Administration at the University of Patras since 2001. His primary research interests include data mining, business intelligence, knowledge representation techniques, nonmonotonic reasoning, and parallel AI. Dimitris K. Tasoulis received his diploma in Mathematics from the University of Patras, Greece, in 2000. He attained his MSc degree in 2004 from the postgraduate course “Mathematics of Computers and Decision Making” from which he was awarded a postgraduate fellowship. Currently, he is a PhD candidate in the same course. His research activities focus on data mining, clustering, neural networks, parallel algorithms, and evolutionary computation. He is coauthor of more than ten publications. Michael N. Vrahatis is with the Department of Mathematics at the University of Patras, Greece. He received the diploma and PhD degree in Mathematics from the University of Patras in 1978 and 1982, respectively. He was a visiting research fellow at the Department of Mathematics, Cornell University (1987–1988) and a visiting professor to the INFN (Istituto Nazionale di Fisica Nucleare), Bologna, Italy (1992, 1994, and 1998); the Department of Computer Science, Katholieke Universiteit Leuven, Belgium (1999); the Department of Ocean Engineering, Design Laboratory, MIT, Cambridge, MA, USA (2000); and the Collaborative Research Center “Computational Intelligence” (SFB 531) at the Department of Computer Science, University of Dortmund, Germany (2001). He was a visiting researcher at CERN (European Organization of Nuclear Research), Geneva, Switzerland (1992) and at INRIA (Institut National de Recherche en Informatique et en Automatique), France (1998, 2003, and 2004). He is the author of more than 250 publications (more than 110 of which are published in international journals) in his research areas, including computational mathematics, optimization, neural networks, evolutionary algorithms, and artificial intelligence. His research publications have received more than 600 citations. He has been a principal investigator of several research grants from the European Union, the Hellenic Ministry of Education and Religious Affairs, and the Hellenic Ministry of Industry, Energy, and Technology. He is among the founders of the “University of Patras Artificial Intelligence Research Center” (UPAIRC), established in 1997, where currently he serves as director. He is the founder of the Computational Intelligence Laboratory (CI Lab), established in 2004 at the Department of Mathematics of University of Patras, where currently he serves as director.     

Interactive online learning

This paper describes a system for visual object recognition based on mobile augmented reality gear. The user can train the system to the recognition of objects online using advanced methods of interaction with mobile systems: Hand gestures and speech input control “virtual menus,” which are displayed as overlays within the camera image. Here we focus on the underlying neural recognition system, which implements the key requirement of an online trainable system—fast adaptation to novel object data. The neural three-stage architecture can be adapted in two modes: In a fast training mode (FT), only the last stage is adapted, whereas complete training (CT) rebuilds the system from scratch. Using FT, online acquired views can be added at once to the classifier, the system being operational after a delay of less than a second, though still with reduced classification performance. In parallel, a new classifier is trained (CT) and loaded to the system when ready. The text was submitted by the authors in English. Gunther Heidemann was born in 1966. He studied physics at the Universities of Karlsruhe and Münster and received his PhD (Eng.) from Bielefeld University in 1998. He is currently working within the collaborative research project “Hybrid Knowledge Representation” of the SFB 360 at Bielefeld University. His fields of research are mainly computer vision, robotics, neural networks, data mining, bonification, and hybrid systems. Holger Bekel was born in 1970. He received his BS degree from the University of Bielefeld, Germany, in 1997. In 2002 he received a diploma in Computer Science from the University of Bielefeld. He is currently pursuing a PhD program in Computer Science at the University of Bielefeld, working within the Neuroinformatics Group (AG Neuroinformatik) in the project VAMPIRE (Visual Active Memory Processes and Interactive Retrieval). His fields of research are active vision and data mining. Ingo Bax was born in 1976. He received a diploma in Computer Science from the University of Bielefeld in 2002. He is currently pursuing a PhD program in Computer Science at the Neuroinformatics Group of the University of Bielefeld, working within the VAMPIRE project. His fields of interest are cognitive computer vision and pattern recognition. Helge J. Ritter was born 1958. He studied physics and mathematics at the Universities of Bayreuth, Heidelberg and Munich. After a PhD in physics at Technical University of Munich in 1988, he visited the Laboratory of Computer Science at Helsinki University of Technology and the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. Since 1990 he has headed the Neuroinformatics Group at the Faculty of Technology, Bielefeld University. His main interests are principles of neural computation and their application to building intelligent systems. In 1999, she was awarded the SEL Alcatel Research Prize, and in 2001, the Leibniz Prize of the German Research Foundation DFG.     

Development of a biomimetic miniature robotic crawler

The paper presents the development of segmented artificial crawlers endowed with passive hook-shaped frictional microstructures. The goal is to find design rules for fabricating biomimetic, adaptable and mobile machines mimicking segmented animals with hydrostatic skeleton, and intended to move effectively along unstructured substrates. The paper describes the mechanical model, the design and the fabrication of a SMA-actuated segmented microrobot, whose locomotion is inspired by the peristaltic motion of Annelids, and in particular of earthworms (Lumbricus Terrestris). Experimental locomotion performance are compared with theoretical performance predicted by a purposely developed friction model -taking into account design parameters such as number of segments, body mass, special friction enhancement appendixes—and with locomotion performance of real earthworms as presented in literature. Experiments indicate that the maximum speed of the crawler prototype is 2.5 mm/s, and that 3-segment crawlers have almost the same velocity as earthworms having the same weight (and about 330% their length), whereas 4-segment crawlers have the same velocity, expressed as body lengths/s, as earthworms with the same mass (and about 270% their length). Arianna Menciassi (MS, 1995; PhD, 1999) joined the CRIM Lab of the Scuola Superiore Sant’Anna (Pisa, Italy) as a Ph.D. student in Bioengineering with a research program on the micromanipulation of mechanical and biological micro-objects. The main results of the activity on micromanipulation were presented at the IEEE International Conference on Robotics & Automation (May 2001, Seoul) in a paper titled “Force Feedback-based Microinstrument for Measuring Tissue Properties and Pulse in Microsurgery”, which won the “ICRA2001 Best Manipulation Paper Award”. In the year 2000, she was offered a position of Assistant Professor in Biomedical Robotics at the Scuola Superiore Sant’Anna and in June 2006 she obtained a promotion to Associate Professor. Her main research interests are in the field of biomedical microrobotics, biomimetics, microfabrication technologies, micromechatronics and microsystem technologies. She is working on several European projects and international projects for the development of minimally invasive instrumentation for medical applications and for the exploitation of micro- and nano-technologies in the medical field. Samuele Gorini received his Laurea Degree in Mechanical Engineering (with honors) from the University of Pisa, Italy, in 2001. In 2005 he obtained the Ph.D. in Microsystem Engineering with a thesis on locomotion methods and systems for miniaturised endoscopic devices. Since 2000, he has been working at the CRIM Lab of the Scuola Superiore Sant’Anna in Pisa, Italy. His research interests are in the field of biomedical robotics with a special focus on actuation technologies. Starting from the year 2004 he has been president of Era Endoscopy S.r.l., a start-up company of Scuola Superiore Sant’Anna developing novel devices for endoscopy. Dino Accoto (MS 1998, PhD 2002) is Assistant Professor of Biomedical Engineering at Scuola Sant’Anna (Pisa, Italy). He received the Laurea degree in Mechanical Engineering from the University of Pisa (cum laude) in 1998, the diploma in Engineering from the Scuola Sant’Anna (cum laude) in 1999 and the PhD degree from the Scuola Sant’Anna in 2002. From October 2001 to September 2002 he has been visiting scholar at the RPL-Lab, Stanford University (Ca, USA). Since 2004 he cooperates with the Biomedical Robotics & EMC Lab at Campus Bio-Medico University in Rome. His main research field is the modelling and development of small electromechanical systems, with a special attention to multi-physics and multi-domain approaches. The research, often inspired by the analysis of natural mechanisms, has been mainly applied to hybridizing microtechnologies, including microfluidics, and robotics. He has co-authored more than 30 papers, appeared in international journals and conference proceedings. Paolo Dario received his Dr. Eng. Degree in Mechanical Engineering from the University of Pisa, Italy, in 1977. He is currently a Professor of Biomedical Robotics at the Scuola Superiore Sant’Anna in Pisa.. He also teaches courses at the School of Engineering of the University of Pisa and at the Campus Biomedico University in Rome. He has been Visiting Professor at Brown University, Providence, RI, USA, at the Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland, at Waseda University, Tokyo, Japan, at the College de France, Paris, and at the Ecole Normale Superieure de Cachan, France. He was the founder of the ARTS (Advanced Robotics Technologies and Systems) Laboratory and is currently the Co-ordinator of the CRIM (Center for the Research in Microengineering) Laboratory of the Scuola Superiore Sant’Anna, where he supervises a team of about 70 researchers and Ph.D. students. His main research interests are in the fields of medical robotics, bio-robotics, mechatronics and micro/nanoengineering, and specifically in sensors and actuators for the above applications, and in robotics for rehabilitation. He is the coordinator of many national and European projects, the editor of two books on the subject of robotics, and the author of more than 200 scientific papers (75 on ISI journals). He is Editor-in-Chief, Associate Editor and member of the Editorial Board of many international journals. Prof. Dario has served as President of the IEEE Robotics and Automation Society in the years 2002–2003. He has been the General Chair of the IEEE RAS-EMBS BioRob’06 Conference and he is the General Co-Chair of ICRA 2007 Conference. Prof. Dario is an IEEE Fellow, a Fellow of the European Society on Medical and Biological Engineering, and a recipient of many honors and awards, such as the Joseph Engelberger Award. He is also a member of the Board of the International Foundation of Robotics Research (IFRR).     

Object extraction from an image of wear particles on a complex background

The object extraction of a debris image is an important basic task in identifying wear particles in ferrographic analysis. However, there is some difficulty in object extraction because of noise jamming in the original debris image. In the present study, two methods of image enhancement—weighted mean filtering and adaptive median filtering—were applied in order to improve the image quality. Then, the adaptive thresholding selection method was used, which is based on an improved debris image. Finally, the effective segmentation of the debris image and the automatic extraction of debris objects were realized. At the same time, targetting the characteristics of low proportion of an object in the total image, a novel method of adaptive thresholding selection was put forward, which is based on the Ostu thresholding method. The segmentation results along with the debris image prove that the current method can give more precise and accurate segmentation of objects than the classical methods. The results also showed that methods in the present paper were concise and effective, which provides an important basis for the further study of debris recognition, fault diagnosis, and condition monitoring of machines. The text was submitted by the authors in English. Xianguo Hu (born 1963), PhD, is a professor at the School of Mechanical and Automotive Engineering at the Hefei University of Technology, China. He received his BS and MS in Powder Metallurgy Material and Mechanics (Tribology) from the Hefei University of Technology in 1985 and 1988, respectively. His PhD degree was awarded at Szent Istvan University, Hungary, in 2002. As a visiting scientist, he conducted research at the Technical University of Budapest, Hungary, and the Technical University of Berlin, Germany, from 1994 to 1997. His research areas include wear debris analysis, optimal tribological design, friction and wear mechanisms, etc. He is the author or coauthor of more than 100 published technical papers. Peng Huang (born 1981) is an MS student at the School of Mechanical and Automotive Engineering of Hefei University of Technology, China. His main focus is on wear debris analysis. Shousen Zheng (born 1963) is an associate professor at the School of Engineering, SunYat-Sen University, China. He received his BS, MS, and PhD in Mechanical Engineering from Hefei University of Technology in 1985, 1988, and 2001, respectively. From 1988 to 2004, he was employed at the Department of Mechanical Engineering at the Hefei University of Technology. In 2005, he moved to the current university. His research interests include computer language, auto CAD/CAM, wear debris analysis, etc. He is the author or coauthor of more than 40 published technical papers.     

A new texture representation approach based on local feature saliency

Although it has been studied in some depth, texture characterization is still a challenging issue for real-life applications. In this study, we propose a multiresolution salient-point-based approach in the wavelet domain. This incorporates a two-phase feature extraction scheme. In the first phase, each wavelet subband (LH, HL, or HH) is used to compute local features by using multidisciplined (statistical, geometrical, or fractal) existing texture measures. These features are converted into binary images, called salient point images (SPIs), via threshold operation. This operation is the key step in our approach because it provides an opportunity for better segmentation and combination of multiple features. In the final phase, we propose a set of new texture features, namely, salient-point density (SPD), non-salient-point density (NSPD), salient-point residual (SPR), saliency and non-saliency product (SNP), and salient-point distribution non-uniformity (SPDN). These features characterize various aspects of image texture such as fineness/coarseness, primitive distribution, internal structures, etc. These features are then applied to the well-known K-means algorithm for unsupervised segmentation of texture images. Experimental results with the standard texture (Brodatz) and natural images demonstrate the robustness and potential of the proposed features compared to the wavelet energy (WE) and local extrema density feature (LED). The text was submitted by the authors in English. Md. Khayrul Bashar was born in Chittagong, Bangladesh in 1969. He received his B.E. (1993), M.Tech. (1998), and PhD (2004) degrees from Bangladesh University of Engineering and Technology (BUET), Indian Institute of Technology (IIT) Bombay, and Nagoya University, respectively. He was a research engineer from 1995 to 1999 at Bangladesh Space Research and Remote Sensing Organization (SPARRSO) and assistant professor from 1999 to 2000 at the department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology (CUET), Bangladesh. Since 2004, he has been a research fellow in the department of Information Engineering, Nagoya University, Japan. Dr. Bashar is a member of IEEE, IEICE, BCS, and IEB. His research interest includes developing algorithms for image understanding, content-based image retrieval and web-application design, analysis and testing. Noboru Ohnishi was born in Aichi, Japan in 1951. He received his B.E., M.E., and PhD degrees in Electrical Engineering from Nagoya University in 1973, 1975, and 1984, respectively. From 1975–1986, he worked as a researcher in the Rehabilitation Engineering centre under the Ministry of Labor, Japan. In 1986, he joined as an Assistant Professor in the dept. of Electrical Engineering of Nagoya University. Currently, he is a professor of the dept. of Information Engineering at the same university. During his long professional life, he has also served as a visiting researcher (1992–1993) in the laboratory of artificial intelligence at Michigan University, and team leader (1993–2001) at the Bio-mimetic Control Research Center, RIKEN, Nagoya, Japan. He also holds many respectable positions at various professional bodies in Japan and he has published many research papers (more than 140) in various international journals. For his technical creativity and ingenuity, he was awarded SICE society prizes in 1996 and 1999. His research interest includes brain analysis, modeling, and brain support, computer vision, and audition. He is a member of IEEE, IPSJ, IEICE, IEEJ, IIITE, JNNS, SICE and RSJ. Kiyoshi Agusa received his PhD degree in computer science from Kyoto University in 1982. Currently, he is a professor of the department of Information Systems, Graduate School of Information Science, Nagoya University. His research area includes software engineering, program repository, and software reuse. Since 2003, he has been working as a team leader of a university-industry collaboration project entitled “e-Society,” which is a part of the “e-Japan” project, and doing research on reliability issues for web-based applications. He is a member of IPSJ, ISSST, IEICE, ACM and IEEE.     

Real-time arithmetic unit

In this paper we discuss the paradigm of real-time processing on the lower level of computing systems. An arithmetical unit based on this principle containing addition, multiplication, division and square root operations is described. The development of the computation operators model is based on the imprecise computation paradigm and defines the concept of the adjustable calculation of a function that manages delay and the precision of the results as an inherent and parameterized characteristic. The arithmetic function design is based on well-known algorithms and offers progressive improvement in the results. Advantages in the predictability of calculations are obtained by means of processing groups of k-bits atomically and by using look-up tables. We report an evaluation of the operations in path time, delay and computation error. Finally, we present an example of our real-time architecture working in a realistic context. Higinio Mora-Mora received the BS degree in computer science engineering and the BS degree in business studies in University of Alicante, Spain, in 1996 and 1997, respectively. He received the PhD degree in computer science from the University of Alicante in 2003. Since 2002, he is a member of the faculty of the Computer Technology and Computation Department at the same university where he is currently an associate professor and researcher of Specialized Processors Architecture Laboratory. His areas of research interest include computer arithmetic and the design of floating points units and approximation algorithms related to VLSI design. Jerónimo Mora-Pascual received the BS degree in computer science engineering from University of Valencia (Spain), in 1994. Since 1994, he has been a member of the faculty of the Computer Technology and Computation department at the University of Alicante, where he is currently an associate professor. He completed his PhD in computer science at University of Alicante in 2001. He has worked on neural networks and its VLSI implementation. His current areas of research interest include the design of floating points units and its application for real-time systems and processors for geometric calculus. Juan Manuel García-Chamizo received his BS in physics at the University of Granada (Spain) in 1980, and the PhD degree in Computer Science at the University of Alicante (Spain) in 1994. He is currently a full professor and director of the Computer Technology and Computation department at the University of Alicante. His current research interests are computer vision, reconfigurable hardware, biomedical applications, computer networks and architectures and artificial neural networks. He has directed several research projects related to the above-mentioned interest areas. He is a member of a Spanish Consulting Commission on Electronics, Computer Science and Communications. He is also member and editor of some program committee conferences. Antonio Jimeno-Morenilla is associate professor in the Computer Technology and Computation department at the University of Alicante (Spain). He received his PhD from the University of Alicante in 2003. He concluded his bachelor studies at the EPFL (Ecole Polytechnique Fe’de’rale de Lausanne, Switzerland) and received his BS degree in computer science from the Polytechnical University of Valencia (Spain) in 1994. His research interests include sculptured surface manufacturing, CAD/CAM, computational geometry for design and manufacturing, rapid and virtual prototyping, 3D surface flattening, and high performance computer architectures. He has considerable experience in the development of 3D CAD systems for shoes. In particular, he has been involved in many government and industrial funded projects, most of them in collaboration with the Spanish Footwear Research Institute (INESCOP).     

Forms and theories of communication: from multimedia to Kansei Mediation

In this paper we describe a form of communication that could be used for lifelong learning as contribution to cultural computing. We call it Kansei Mediation. It is a multimedia communication concept that can cope with non-verbal, emotional and Kansei information. We introduce the distinction between the concepts of Kansei Communication and Kansei Media. We then develop a theory of communication (i.e. Kansei Mediation) as a combination of both. Based on recent results from brain research the proposed concept of Kansei Mediation is developed and discussed. The biased preference towards consciousness in established communication theories is critically reviewed and the relationship to pre- and unconscious brain processes explored. There are two tenets of the Kansei Mediation communication theory: (1) communication based on connected unconciousness, and (2) Satori as the ultimate form of experience. Ryohei Nakatsu received the B.S. (1969), M.S. (1971) and Ph.D. (1982) degrees in electronic engineering from Kyoto University. After joining NTT in 1971, he mainly worked on speech recognition technology. He joined ATR (Advanced Telecommunications Research Institute) as the president of ATR Media Integration & Communications Research Laboratories (1994–2002). From the spring of 2002 he is full professor at School of Science and Technology, Kwansei Gakuin University in Sanda (Japan). At the same time he established a venture company, Nirvana Technology Inc., and became the president of the company. In 1978, he received Young Engineer Award from the Institute of Electronics, Information and Communication Engineers Japan (IEICE-J). In 1996, he received the best paper award from the IEEE International Conference on Multimedia. In 1999, 2000 and 2001, he was awarded Telecom System Award from Telecommunication System Foundation and the best paper award from Virtual Reality Society of Japan. In 2000, he got the best paper award from Artificial Intelligence Society of Japan. He is a fellow of the IEEE and the Institute of Electronics, Information and Communication Engineers Japan (IEICE-J), a member of the Acoustical Society of Japan, Information Processing Society of Japan, and Japanese Society for Artificial Intelligence. Matthias Rauterberg received the B.S. in psychology (1978) at the University of Marburg (Germany), the B.S. in philosophy (1981) and computer science (1983), the M.S. in psychology (1981) and computer science (1985) at the University of Hamburg (Germany), and the Ph.D. in computer science (1995) at the University of Zurich (Switzerland). He was a senior lecturer for ‘usability engineering’ in computer science and industrial engineering at the Swiss Federal Institute of Technology (ETH) in Zurich. He was the head of the Man–Machine Interaction research group (MMI) of the Institute for Hygiene and Applied Physiology (IHA) from the Department of Industrial Engineering at the ETH, Zurich. Since 1998, he is a fulltime professor for ‘human communication technology’ at the Department of Industrial Design at the Technical University Eindhoven (The Netherlands), and also since 2004, he is appointed as a visiting professor at the Kwansei Gakuin University (Japan). He received the German GI-HCI award for the best Ph.D. in 1997 and the Swiss Technology Award together with Martin Bichsel for the BUILD-IT system in 1998. Since 2005, he is elected as a member of the Cream of Science in The Netherlands. Ben Salem received the Dip.Arch. (1987) at the Ecole Polytechnique d'Architecture et d'Urbanisme EPAU (Algiers), the M.Arch. (1993) at the School of Architectural Studies of the University of Sheffield (UK), and the Ph.D. in electronics (2003) at the Department of Electronic and Electrical Engineering, University of Sheffield (UK). Since 2001, he is director of Polywork Ltd. (UK). Since 2003. he has a PostDoc position at the Department of Industrial Design of the Technical University Eindhoven (The Netherlands).     

Progressive viewing and sharing of sensitive images

The paper proposes a progressive viewing method useful in sharing a sensitive image. As in visual cryptography, this method characterizes its ability to recover the image by stacking transparencies without any computation. However, the method balances the sensitivity and the daily-processing convenience of the image. The text was submitted by the authors in English. Wen-Pinn Fang was born in 1971 in Taiwan, Republic of China. He received his BS degree in mechanical engineering from National Sun-Yet-Sans University in 1994, and MS degree in mechanical engineering from National Chiao Tung University in 1998. In 2006 he received his PhD in Computer Science Department of National Chiao Tung University. His recent research interests include image sharing and image processing. Ja-Chen Lin was born in 1955 in Taiwan, Republic of China. He received his BS degree in computer science in 1977 and MS degree in applied mathematics in 1979, both from National Chiao Tung University, Taiwan. In 1988 he received his PhD in mathematics from Purdue University, United States. In 1981–1982, he was an instructor at National Chiao Tung University. From 1984 to 1988, he was a graduate instructor at Purdue University. He joined the Department of Computer and Information Science at National Chiao Tung University in August 1988 and is currently a professor there. His recent research interests include pattern recognition and image processing. Dr. Lin is a member of the Phi-Tau-Phi Scholastic Honor Society.     

Rank order filters and priority queues

Summary A derivation of a parallel algorithm for rank order filtering is presented. Both derivation and result differ from earlier designs: the derivations are less complicated and the result allows a number of different implementations. The same derivation is used to design a collection of priority queues. Both filters and priority queues are highly efficient: they have constant response time and small latency. Anne Kaldewaij received an M.Sc. degree in Mathematics from the University of Utrecht (The Netherlands) and a Ph.D. degree in Computing Science from the Eindhoven University of Technology. Currently, he is associate professor in Computing Science at Eindhoven University. His research includes parallel programming and the design of algorithms and data structures. He enjoys teaching and he has written a number of textbooks on mathematics and programming. Jan Tijmen Udding received an M.Sc. degree in Mathematics in 1980 and a Ph.D. degree in Computing Science in 1984 from Eindhoven University of Technology. Currently, he is associate professor at Groningen University. His main research interests are mathematical aspects of VLSI, program derivation and correctness, and functional programming.     

Combining Seminorms in Adaptive Lifting Schemes and Applications to Image Analysis and Compression

In this paper, we present some adaptive wavelet decompositions that can capture the directional nature of images. Our method exploits the properties of seminorms to build lifting structures able to choose between different update filters, the choice being triggered by the local gradient-type features of the input. In order to deal with the variety and wealth of images, one has to be able to use multiple criteria, giving rise to multiple choice of update filters. We establish the conditions under these decisions can be recovered at synthesis, without the need for transmitting overhead information. Thus, we are able to design invertible and non-redundant schemes that discriminate between different geometrical information to efficiently represent images for lossless compression methods. The work of Piella is supported by a Marie-Curie Intra-European Fellowships within the 6th European Community Framework Programme. Gemma Piella received the M.S. degree in electrical engineering from the Polytechnical University of Catalonia (UPC), Barcelona, Spain, and the Ph.D. degree from the University of Amsterdam, The Netherlands, in 2003. From 2003 to 2004, she was at UPC as a visiting professor. She then stayed at the Ecole Nationale des Telecommunications, Paris, as a Post-doctoral Fellow. Since September 2005 she is at the Technology Department in the Pompeu Fabra University. Her main research interests include wavelets, geometrical image processing, image fusion and various other aspects of digital image and video processing. Beatrice Pesquet-Popescu received the engineering degree in telecommunications from the “Politehnica” Institute in Bucharest in 1995 and the Ph.D. thesis from the Ecole Normale Supérieure de Cachan in 1998. In 1998 she was a Research and Teaching Assistant at Université Paris XI and in 1999 she joined Philips Research France, where she worked for two years as a research scientist, then project leader, in scalable video coding. Since Oct. 2000 she is an Associate Professor in multimedia at the Ecole Nationale Supérieure des Télécommunications (ENST). Her current research interests are in scalable and robust video coding, adaptive wavelets and multimedia applications. EURASIP gave her a “Best Student Paper Award” in the IEEE Signal Processing Workshop on Higher-Order Statistics in 1997, and in 1998 she received a “Young Investigator Award” granted by the French Physical Society. She is a member of IEEE SPS Multimedia Signal Processing (MMSP) Technical Committee and a Senior Member IEEE. She holds 20 patents in wavelet-based video coding and has authored more than 80 book chapters, journal and conference papers in the field. Henk Heijmans received his masters degree in mathematics from the Technical University in Eindhoven and his PhD degree from the University of Amsterdam in 1985. Since then he has been in the Centre for Mathematics and Computer Science, Amsterdam, where he had been directing the “signals and images” research theme. His research interest are focused towards mathematical techniques for image and signal processing, with an emphasis on mathematical morphology and wavelet analysis. Grégoire Pau was born in Toulouse, France in 1977 and received the M.S. degree in Signal Processing in 2000 from Ecole Centrale de Nantes. From 2000 to 2002, he worked as a Research Engineer at Expway where he actively contributed to the standardization of the MPEG-7 binary format. He is currently a PhD candidate in the Signal and Image Processing Departement of ENST-Telecom Paris. His research interests include subband video coding, motion compensated temporal filtering and adaptive non-linear wavelet transforms.     

On some operations on strings suggested by gene assembly in ciliates

We define three operations on strings and languages suggested by the process of gene assembly in hypotrichous ciliates. This process is considered to be a prine example of DNA computing in vivo. This paper is devoted to some computational aspects of these operations from a formal language point of view. The closure of the classes of regular and context-free languages under these operations is settled. Then, we consider theld-macronuclear language of a given languageL, which consists of allld-macronuclear strings obtained from the strings ofL by iteratively applying the loop-direct repeat-excision. Finally, we discuss some open problems and further directions of research. Rudolf Freund: He received his master and doctor degree in computer science from the Vienna University of Technology, Austria, in 1980 and 1982, respectively. In 1986, he received his master degree in mathematics and physics from the University Vienna, Austria. In 1988 he joined the Vienna University of Technology in Austria, where he became an Associate Professor in September 1995. He has given various lectures in theoretical computer science, especially on formal languages and automata. His research interests include array and graph grammars, regulated rewritung, infinite words, syntactic pattern recognition, neural networks, and especially models and systems for biological computing. In these fields he is author of more than sixty scientific papers. Carlos Martín-Vide: He is Professor and Head of the Research Group on Mathematical Linguistics at Rovira i Virgili University, Tarragona, Spain. His specialities are formal language theory and mathematical linguistics. His last volume edited is Where Mathematics, Computer Science, Linguistics and Biology Meet (Kluwer, 2001, with V. Mitrana). He published 150 papers in conference proceedings and journals such as: Acta Informatica, BioSystems. Computational Linguistics, Computers and Artificial Intelligence, Information Processing Letters, Information Sciences, International Journal of Computer Mathematics, New Generation Computing, Publicationes Mathematicae Debrecen, and Theoretical Computer Science. He is the editor-in-chief of the journal Grammars (Kluwer), and the chairman of the 1st International PhD School in Formal Languages and Applications (2001–2003). Victor Mitrana, Ph.D.: He is Professor of Computer Science at the Faculty of Mathematics, University of Bucharest. He received his MSc and PhD from the University of Bucharest in 1986 and 1993, respectively. In 1999 he was awarded with the “Gheorghe Lazar” Prize for Mathematics of the Romanian Academy. His research interests include: formal language theory and applications, combinatorics on words, computational models inspired from biology, mathematical linguistics. In these areas, he published three books, more than 100 papers, and edited two books. He is an associate editor of “The Korean Journal of Computational and Applied Mathematics” and an editor of “Journal of Universal Computer Science”.     

Singular value decomposition based data distortion strategy for privacy protection

Privacy-preserving is a major concern in the application of data mining techniques to datasets containing personal, sensitive, or confidential information. Data distortion is a critical component to preserve privacy in security-related data mining applications, such as in data mining-based terrorist analysis systems. We propose a sparsified Singular Value Decomposition (SVD) method for data distortion. We also put forth a few metrics to measure the difference between the distorted dataset and the original dataset and the degree of the privacy protection. Our experimental results using synthetic and real world datasets show that the sparsified SVD method works well in preserving privacy as well as maintaining utility of the datasets. Shuting Xu received her PhD in Computer Science from the University of Kentucky in 2005. Dr. Xu is presently an Assistant Professor in the Department of Computer Information Systems at the Virginia State University. Her research interests include data mining and information retrieval, database systems, parallel, and distributed computing. Jun Zhang received a PhD from The George Washington University in 1997. He is an Associate Professor of Computer Science and Director of the Laboratory for High Performance Scientific Computing & Computer Simulation and Laboratory for Computational Medical Imaging & Data Analysis at the University of Kentucky. His research interests include computational neuroinformatics, data miningand information retrieval, large scale parallel and scientific computing, numerical simulation, iterative and preconditioning techniques for large scale matrix computation. Dr. Zhang is associate editor and on the editorial boards of four international journals in computer simulation andcomputational mathematics, and is on the program committees of a few international conferences. His research work has been funded by the U.S. National Science Foundation and the Department of Energy. He is recipient of the U.S. National Science Foundation CAREER Award and several other awards. Dianwei Han received an M.E. degree from Beijing Institute of Technology, Beijing, China, in 1995. From 1995to 1998, he worked in a Hitachi company(BHH) in Beijing, China. He received an MS degree from Lamar University, USA, in 2003. He is currently a PhD student in the Department of Computer Science, University of Kentucky, USA. His research interests include data mining and information retrieval, computational medical imaging analysis, and artificial intelligence. Jie Wang received the masters degree in Industrial Automation from Beijing University of Chemical Technology in 1996. She is currently a PhD student and a member of the Laboratory for High Performance Computing and Computer Simulation in the Department of Computer Science at the University of Kentucky, USA. Her research interests include data mining and knowledge discovery, information filtering and retrieval, inter-organizational collaboration mechanism, and intelligent e-Technology.     

Speech Detection in Non-Stationary Noise Based on the 1/f Process

In this paper,an effective and robust active speech detection method is proposed based on the 1/f process technique for signals under non-stationary noisy environments.The Gaussian 1/f process ,a mathematical model for statistically self-similar radom processes based on fractals,is selected to model the speech and the background noise.An optimal Bayesian two-class classifier is developed to discriminate them by their 1/f wavelet coefficients with Karhunen-Loeve-type properties.Multiple templates are trained for the speech signal,and the parameters of the background noise can be dynamically adapted in runtime to model the variation of both the speech and the noise.In our experiments,a 10-minute long speech with different types of noises ranging from 20dB to 5dB is tested using this new detection method.A high performance with over 90% detection accuracy is achieved when average SNR is about 10dB.     

Improving the compression and encryption of images using FPGA-based cryptosystems

Compression and encryption technologies are important to the efficient solving of network bandwidth and security issues. A novel scheme, called the Image Compression Encryption Scheme (ICES), is presented. It combines the Haar Discrete Wavelet Transform (DWT), Significance-Linked Connected Component Analysis (SLCCA), and the Advance Encryption Standard (AES). Because of above reason the ICES efficiently reduce the overall processing time. This study develops a novel hardware system to compress and encrypt an image in real-time using an image compression encryption scheme. The proposed system exploits parallel processing to increase the throughout of the cryptosystem for Internet multimedia applications to implement the ICES. Using hardware acceleration for encryption and decryption, the FPGA implementation of DWT, SLCCA and the AES algorithm can be used. Using a pipeline structure, a very high data throughput of 330 Mbit/s at a clock frequency of 40 MHz was obtained. Therefore, the ICES is secure, fast and suited to high speed network protocols such as ATM (Asynchronous Transfer Mode), FDDI (Fiber Distributed Data Interface) or Internet multimedia applications. Shih-Ching Ou is working with the Department of Electrical Engineering, National Central University as a senior professor. His research interests include computer aided design, e-learning system, and virtual reality, etc. In August 2004, he serves as Leader University Professor and Director of Research and Development, now he act as Leader University Professor and Institute of Applied Information (Chairman). He has published a number of international journal and conferences papers related to these areas. Currently, he is the chief of Bioinformatics & CAD Laboratory. Hung-Yuan Chung joined the Department of Electrical Engineering at the National Central University, Chung-li, Taiwan as an associate professor in August 1987. Since August 1992, he was promoted as professor. In addition, he is a registered professional Engineer in R. O. C. He is a life member of the CIEE and the CIE. He received the outstanding Electrical Engineer award of the Chinese Institute of Electrical Engineering in October 2003. His research and teaching interests include System Theory and Control, Adaptive Control, Fuzzy Control, Neural Network Applications, and Microcomputer-Based Control Applications. Wen-Tsai Sung is a PhD candidate at Department of Electrical Engineering, National Central University in Taiwan. His research interests include computer aided design, web-based learning system, bioinformatics and virtual reality. He has published a number of international journal and conferences papers related to these areas. He received a BS degree from the Department of Industrial Education, National Taiwan Normal University, Taiwan in 1993 and received a MS degree from the Department of Electrical Engineering, National Central University, Taiwan in 2000. He has win the dragon thesis award; master degree thesis be recognized the most outstanding academic research. The thesis entitle is: “Integrated computer graphics system in a virtual environment.” Sponsor is Acer Foundation (Acer Universal Computer Co.). Currently, he is studying PhD at the Department of Electrical Engineering, National Central University as a researcher of Bioinformatics & CAD Laboratory.     

A grid-oriented genetic algorithm framework for bioinformatics

In this paper, we propose a framework for enabling for researchers of genetic algorithms (GAs) to easily develop GAs running on the Grid, named “Grid-Oriented Genetic algorithms (GOGAs)”, and actually “Gridify” a GA for estimating genetic networks, which is being developed by our group, in order to examine the usability of the proposed GOGA framework. We also evaluate the scalability of the “Gridified” GA by applying it to a five-gene genetic network estimation problem on a grid testbed constructed in our laboratory. Hiroaki Imade: He received his B.S. degree in the department of engineering from The University of Tokushima, Tokushima, Japan, in 2001. He received the M.S. degree in information systems from the Graduate School of Engineering, The University of Tokushima in 2003. He is now in Doctoral Course of Graduate School of Engineering, The University of Tokushima. His research interests include evolutionary computation. He currently researches a framework to easily develop the GOGA models which efficiently work on the grid. Ryohei Morishita: He received his B.S. degree in the department of engineering from The University of Tokushima, Tokushima, Japan, in 2002. He is now in Master Course of Graduate School of Engineering, The University of Tokushima, Tokushima. His research interest is evolutionary computation. He currently researches GA for estimating genetic networks. Isao Ono, Ph.D.: He received his B.S. degree from the Department of Control Engineering, Tokyo Institute of Technology, Tokyo, Japan, in 1994. He received Ph.D. of Engineering at Tokyo Institute of Technology, Yokohama, in 1997. He worked as a Research Fellow from 1997 to 1998 at Tokyo Institute of Technology, and at University of Tokushima, Tokushima, Japan, in 1998. He worked as a Lecturer from 1998 to 2001 at University of Tokushima. He is now Associate Professor at University of Tokushima. His research interests include evolutionary computation, scheduling, function optimization, optical design and bioinformatics. He is a member of JSAI, SCI, IPSJ and OSJ. Norihiko Ono, Ph.D.: He received his B.S. M.S. and Ph.D. of Engineering in 1979, 1981 and 1986, respectively, from Tokyo Institute of Technology. From 1986 to 1989, he was Research Associate at Faculty of Engineering, Hiroshima University. From 1989 to 1997, he was an associate professor at Faculty of Engineering, University of Tokushima. He was promoted to Professor in the Department of Information Science and Intelligent Systems in 1997. His current research interests include learning in multi-agent systems, autonomous agents, reinforcement learning and evolutionary algorithms. Masahiro Okamoto, Ph.D.: He is currently Professor of Graduate School of Systems Life Sciences, Kyushu University, Japan. He received his Ph.D. degree in Biochemistry from Kyushu University in 1981. His major research field is nonlinear numerical optimization and systems biology. His current research interests cover system identification of nonlinear complex systems by using evolutional computer algorithm of optimization, development of integrated simulator for analyzing nonlinear dynamics and design of fault-tolerant routing network by mimicking metabolic control system. He has more than 90 peer reviewed publications.     

Robust classification by a nearest mean-median rule for generalized Gaussian pattern distributions

To provide stability of classification, a robust supervised minimum distance classifier based on the minimax (in the Huber sense) estimate of location is designed for the class of generalized Gaussian pattern distributions with a bounded variance. This classifier has the following low-complexity form: with relatively small variances, it is the nearest mean rule (NMean), and with relatively large variances, it is the nearest median rule (NMed). The proposed classifier exhibits good performance both under heavy-and short-tailed pattern distributions. The text was submitted by the authors in English. Maya Shevlyakova received an MS in Applied Mathematics from St. Petersburg State Polytechnic University, St. Petersburg, Russia in 2006. Her work was devoted to statistical analysis of medical data. At present, she is a M.S. student in applied statistics at école Polytéchnique Fédérale de Lausanne, Lausanne, Switzerland, working on the application of statistical methods to genetics analysis. Vladimir Klavdiev received an MS in Fluid Mechanics from the Leningrad Polytechnic Institute, Leningrad, Russia in 1971 and a PhD in Engineering Cybernetics from the CVUT, Prague, Czechoslovakia in 1981. Since 1981 he has been with the Department of Applied Mathematics at St. Petersburg State Polytechnic University as an Associate Professor. His research interests include statistics, data analysis, information theory, and mathematical logic. He has published more than 40 papers. Georgy Shevlyakov received an MS in Control and System Theory (summa cum laude) and a PhD in Signal Processing and Information Theory from the Leningrad Polytechnic Institute, Leningrad, Russia in 1973 and 1976, respectively. In 1991, he received a Dr. Sci. in Mathematical and Applied Statistics from the St. Petersburg Technical University, St. Petersburg, Russia. From 1976 to 1979, he was with the Biometrics Group at the Vavilov Research Institute in Leningrad as a Research Associate. From 1979 to 1986, he was with the Department of Mechanics and Control Processes at the Leningrad Polytechnic Institute as a Senior Researcher working in the field of robust statistics and signal processing. From 1986 to 1992, he was with the Department of Mathematics of the St. Petersburg Technical University as an Associate Professor, and from 1992 as a Professor. He is currently a Visiting IT Professor at the Department of Information and Communications, Gwangju Institute of Science and Technology (GIST), Korea. His research interests include robust and nonparametric statistics, data analysis, and queuing and information theory along with their applications to signal processing. He has published a monograph on robust statistics (2002), a textbook on probability and mathematical statistics (2001), and more than 70 papers. He is a member of the IEEE and Bernoulli societies.