Divide and conquer approach to improve performance on ATR systems

In this paper different methods applied to the Automatic Target Recognition problem are studied. A database of High Range Resolution radar profiles of six kinds of aircrafts is used to study the performance of four classification methods: k-Nearest Neighbor method, Multilayer Perceptrons, Radial Basis Function Networks, and Support Vector Machines. Results obtained with these classifiers show a high correlation between two of the classes of targets that cause the majority of errors. We propose to split the task into two subtasks. A first one in which the classes of correlated targets are grouped in a single class, and a second one to distinguish between them. Different classifiers are studied to be applied to each subtask. Results demonstrate that Radial Basis Function Networks are very good classifiers for the main subtask, while Support Vector Machines are the best classification method, among the studied, to distinguish between the correlated targets. The text was submitted by the authors in English. Roberto Gil Pita, born in 1978, obtained the degree of Telecomunication Engineer at Alcala University, 2001. Roberto Gil Pita is currently the Lecturer in Signal Theory and Communications Department, Polytechnical School, Alcala University (Spain). His research interests include signal processing and radar applications. Roberto Gil Pita has published ten journal papers, five book chapters, and nine conference contributions. He is an IEEE student member. Manuel Utrilla Manso, born in 1972, obtained the degree of Telecomunication Engineer at Polythecnic University of Madrid, 1999. Manuel Utrilla Manso is currently the Lecturer in Signal Theory and Communications Department, Polytechnical School, Alcala University (Spain). His research interests include signal processing, and digital filters and applications. Manuel Utrilla Manso has published 3 journals papers, 5 book chapters, 23 conference contributions, and 1 book. He is an IEEE student member. Manuel Rosa Zurera, born in 1968, obtained the degree of Telecomunication Engineer at Polythecnic University of Madrid, 1995. Manuel Rosa Zurera is currently the Associated Professor and Head of the Department in Signal Theory and Communications Department, Polytechnical School, Alcala University (Spain). His fields of research are signal processing, signal detection, and radar systems. Manuel Rosa Zurera has published 18 journal papers, 10 book chapters, 41 conference contributions, and 2 books. He is an IEEE member. Raúl Vicen Bueno, born in 1977, obtained the degree of Telecomunication Engineer at Alcala University, 2002. Raul Vicen Bueno is currently the Lecturer in Signal Theory and Communications Department, Polytechnic School, Alcala University (Spain). His research interests include signal processing and radar applications. Raul Vicen Bueno has published six journal papers, three book chapters, four conference contributions, and one book. Awards and prizes for achievements in research or applications Second place in the “Liberalizacion de las Telecomunicaciones” awards, given by the “Colegio Oficial de Ingenieros Tecnicos de Telecomunicacion” (COITT) of Spain. Maria Pilar Jarabo Amores, born in 1971, obtained the degree of Telecomunication Engineer at Polythecnical University of Madrid, 1997. Maria Pilar Jarabo Amores is currently the Lecturer in Signal Theory and Communications Department, Polytechnical School, Alcala University (Spain). Her research interests include signal processing, signal detection, and radar systems. Maria Pilar Jarabo Amores has published 11 journals papers, 8 book chapters, 21 conference contributions, and 2 books. She is an IEEE student member. Francisco López Ferreras, born in 1948, obtained the degree of Telecommunication Engineer at Polythecnic University of Madrid, 1970. Francisco Lopez Ferreras is currently Associated Professor and Dean of the Signal Theory and Communications Department, Polytechnic School, Alcala University (Spain). His field of research is signal processing. Francisco Lopez Ferreras has published 23 journal papers, 16 book chapters, 88 conference contributions, and 10 books. He is an IEEE member.     

Automated extraction of road network from medium-and high-resolution images

This paper presents an automatic methodology for road network extraction from medium-and high-resolution aerial images. It is based on two steps. In the first step, the road seeds (i.e., road segments) are extracted using a set of four road objects and another set of connection rules among road objects. Each road object is a local representation of an approximately straight road fragment and its construction is based on a combination of polygons describing all relevant image edges, according to some rules embodying road knowledge. Each road seed is composed by a sequence of connected road objects in which each sequence of this type can be geometrically structured as a chain of contiguous quadrilaterals. In the second step, two strategies for road completion are applied in order to generate the complete road network. The first strategy is based on two basic perceptual grouping rules, i.e., proximity and collinearity rules, which allow the sequential reconstruction of gaps between every pair of disconnected road segments. This strategy does not allow the reconstruction of road crossings, but it allows the extraction of road centerlines from the contiguous quadrilaterals representing connected road segments. The second strategy for road completion aims at reconstructing road crossings. Firstly, the road centerlines are used to find reference points for road crossings, which are their approximate positions. Then these points are used to extract polygons representing the contours of road crossings. This paper presents the proposed methodology and experimental results. The text was submitted by the authors in English. Aluir Porfirio Dal Poz. Year of birth: 1960. Year of graduation/Name of the institution: 1987 (Cartographic Engineering)/Sao Paulo State University. Year in which an academic degree was awarded: M.Sc. degree in Geodetic Science at Parana Federal University: 1991. Ph.D. degree in Engineering at Sao Paulo University: 1996. Affiliation: Sao Paulo State University. Position: Associate Professor. Area of research: Digital Photogrammetry and Image Analysis. Number of publications: 5 Book Chapters, 25 in Journals, and 75 in Proceedings. Membership to academies: Scientific societies: Brazilian Society of Cartography, Brazilian Society of Applied and Computational Mathematics, and Canadian Institute of Geomatics. Editorial boards and journals: Associate Editor of the Series in Geodetic Science and member of the editorial board of Brazilian Journal of Cartography. Awards and prizes for achievements in research or applications: Scientific Beginner in Cartography (1995) and Cartographic Merit (1999), both awarded by Brazilian Society of Cartography. Rodrigo Bruno Zanin. Year of birth: 1976. Year of graduation/Name of the institution: 2000 (Mathematics)/Sao Paulo State University. Year in which an academic degree was awarded: M.Sc. degree in Cartographic Sciences at Sao Paulo State University: 2004. Affiliation: Sao Paulo State University. Position: Ph.D. Candidate. Area of research: Digital Photogrammetry and Image Analysis. Number of publications: 2 in Journals and 5 in Proceedings. Giovane Maia do Vale. Year of birth: 1969. Year of graduation/Name of the institution: 1998 (Mathematics)/Sao Paulo State University. Year in which an academic degree was awarded: M.Sc. degree in Cartographic Sciences at Sao Paulo State University: 2003. Affiliation: Sao Paulo State University. Position: Ph.D. Candidate. Area of research: Digital Photogrammetry and Image Analysis. Number of publications: 3 in Journals and 10 in Proceedings. Membership to academies: Scientific societies: Brazilian Society of Applied and Computational Mathematics.     

Wiener estimation method in estimating of spectral reflectance from RGB images

Color is one of the most important features in digital images. The representation of color in digital form with a three-component image (RGB) is not very accurate, hence the use of a multiple-component spectral image is justified. At the moment, acquiring a spectral image is not as easy and as fast as acquiring a conventional three-component image. One answer to this problem is to use a regular digital RGB camera and estimate its RGB image into a spectral image by the Wiener estimation method, which is based on the use of a priori knowledge. In this paper, the Wiener estimation method is used to estimate the spectra of icons. The experimental results of the spectral estimation are presented. The text was submitted by the authors in English. Pekka Tapani Stigell. Year of birth 1976. Year of graduation and name of institution: Last year undergraduate student in the Department of Computer Science in the University of Joensuu, Finland. Affiliation: InFotoics Center, Department of Computer Science, University of Joensuu. Position: Trainee. Area of research: Color research. Number of publications: 1. Membership to scientific societies: Pattern Recognition Society of Finland, member-society of IAPR (International Association for Pattern Recognition). Prizes for achievements in research or applications: The best young scientist award in PRIA-7-2004 (shared with two other scientists). Kimiyoshi Miyata. Year of birth: 1966. Year of graduation and name of institution: 2000. Graduate School of Science and Technology, Chiba University, Japan. Year of graduation: 1990, BE degree (Chiba University), 1992, ME degree (Chiba University), 2000, Ph.D degree (Chiba University). Affiliation: Museum Science Division, Research Department, National Museum of Japanese History. Position: Assistant Professor. Area of research: Improvement of image quality, color management, application of imaging science and technology to museum activities. Number of publications: 11. Membership to scientific societies: Society of Photographic Science and Technology of Japan, Optical Society of Japan, Institute of Image Electronics Engineers of Japan, Society for Imaging Science and Technology. Prizes for achievements in research or applications: Progressing Award from Society of Photographic Science and Technology of Japan in 2000, Itek Award from Society for Imaging Science and Technology in 2000. Markku Hauta-Kasari. Year of birth: 1970. Graduation and name of the institution: University of Technology, Lappeenranta, Finland. Year of graduation: 1999, Ph.D. degree (University of Technology, Lappeenranta). Affiliation: InFotonics Center, Department of Computer Science, University of Joensuu. Position: Director. Area of research: Color research, neural computation, pattern recognition, optical pattern recognition, computer vision, image processing. Number of publications: Articles in refereed international scientific journals: 5, Articles in refereed international scientific conferences: 9, Other Scientific Publications: 40. Membership to academies: Chairman of the Pattern Recognition Society of Finland May 2003. Membership to scientific societies: Pattern Recognition Society of Finland, member-society of IAPR (International Association for Pattern Recognition), Finnish Information Processing Association, Finnish Union of University Researchers and Teachers, Optical Society of Japan, Optical Society of America. Prizes for achievements in research or applications: The best Ph.D.-thesis award in the field of pattern recognition in 1998–1999 in Finland. Award was issued by the Pattern Recognition Society of Finland on April 25, 2000.     

Nonlinear Control for the Dual Smart Drive Using Backstepping and a Time-Optimal Reference

The Dual Smart Drive is a specially designed nonlinear actuator intended for use in climbing and walking legged robots. It features a continuously changing transmission ratio and dual properties and is very suitable for situations where the same drive is required to perform two different types of start-stop motions of a mobile link. Then, the associated control problem to this nonlinear actuator is established and a backstepping design strategy adopted to develop Lyapunov-based nonlinear controllers that ensure asymptotic tracking of the desired laws of motion, which have been properly selected using time-optimal control. The approach is extended for bounded control inputs. Both simulation and experimental results are presented to show the effectiveness and feasibility of the proposed nonlinear control methods for the Dual Smart Drive. Supported by the Spanish Ministry of Education under Grant F.P.U. Supported by the National Science Foundation under Grant No. ECS-0242798 Supported by the Spanish Ministry of Science and Technology under Grant Ramón y Cajal, Project “Theory of optimal Dual Drives for Automation and Robotics” Roemi E. Fernández was born in Madrid, Spain, in 1977. She received the B.S. degree in Electronic Engineering from Santa Maria La Antigua University, Panamá, in 2000. She is currentlya PhD candidate at the Polytechnic University of Madrid, Spain and at the Industrial Automation Institute, which belongs to the Spanish Council for Scientific Research. Her research interests include nonlinear control theory, walking and climbing robots, resonance and quasi-resonance drives, and mechatronics. Joáo P. Hespanha was born in Coimbra, Portugal, in 1968. He received the Licenciatura and the M.S. degree in electrical and computer engineering from Instituto Superior Técnico, Lisbon, Portugal, in 1991 and 1993, respectively, and the M.S. and Ph.D. degrees in electrical engineering and applied science from Yale University, New Haven, Connecticut, in 1994 and 1998, respectively. For his PhD work, Dr. Hespanha received Yale University's Henry Prentiss Becton Graduate Prize for exceptional achievement in research in Engineering and Applied Science. Dr. Hespanha currently holds an Associate Professor position with the Department of Electrical and Computer Engineer at the University of California, Santa Barbara. From 1999 to 2001 he was an Assistant Professor at the University of Southern California, Los Angeles. His research interests include switching and hybrid systems; nonlinear control, both robust and adaptive; control of communication networks; the use of vision in feedback control; and stochastic games. Dr. Hespanha is the recipient of an NSF CAREER Award (2001) and the 2002–2004 Automatica Theory/Methodology best paper prize. Since 2003, he has been an Associate Editor of the IEEE Transactions on Automatic Control. Teodor Akinfiev received his M.S. degree from the Moscow State University and PhD degree from Mechanical Engineering Research Institute of the Academy of Sciences of Russia. From Year 1976 he was Researcher, Principal Researcher and Head of the Research Laboratory at the Mechanical Engineering Research Institute of the Academy of Sciences of Russia. From Year 1995 he holds Position at the Industrial Automation Institute, which belongs to the Spanish Council for Scientific Research. Teodor Akinfiev is the author over 200 publications (including more than 70 patents). His research interests include oscillation theory, mechanical engineering, control systems, robotics, intelligent drives, and mechatronics. In Year 2002 he was elected a Member of the Academy of Natural Sciences of Russia for his research cycle on resonance and quasi-resonance drives. Manuel A. Armada received his PhD in Physics from the University of Valladolid (Spain) in 1979. Since 1976 he has been involved in research activities related to Automatic Control and Robotics. He has been working in more than forty RTD projects (European ones: EUREKA, ESPRIT, BRITE/EURAM, GROWTH, with Latin America: CYTED). He is member of the Russian Academy of Natural Sciences. Dr. Armada owns several patents and has published over 200 papers. He is currently the Head of the Automatic Control Department at the Instituto de Automatica Industrial (IAI-CSIC), being his main research in walking and climbing robots.     

Implementation of algebraic controllers for non-conventional sampled-data systems

Nowadays, in industrial control applications, is rather usual to sample and update different variables at different rates, although it is common to consider all these activities equally and regularly spaced on time. These applications are implemented on real-time operating systems by decomposing them into several tasks in such a way that pre-emption and blocking may appear due to task priorities and resource sharing. This could imply the presence of delays, leading to a non-regular periodic behaviour and, as a result, the control performance can be degraded. In order to undertake this problem, a solution based on a modelling methodology for non-conventional sampled-data systems is proposed. This technique permits the consideration of any cyclic sampling pattern. Thus, these delays can be considered in the modelling step, and later on, a non-conventional controller based on this model can be designed. In this way, if the considered non-conventional control system is implemented assuming a real-time operating system (Tornado-VxWorks, in this case), a clear performance improvement can be observed. ángel Cuenca was born in Valencia (Spain) in 1974. He received his M.Sc. degree in Computer Science in 1998 and his Ph.D. in Control Engineering in 2004, from the Polytechnic University of Valencia. He is with the department of Systems Engineering and Control at the Polytechnic University of Valencia. He has been teaching courses on systems theory, programmable logic controllers and multi-rate sampled-data systems. His research interests include non-conventionally sampled-data systems and networked based control systems. He has taken part in several national and European research projects. Julian Salt was born in Valencia, Spain in 1960. He received his M.Sc. degree in industrial engineering in 1986 and his Ph.D. in Control Engineering in 1992, from Valencia Polytechnic University. His current position is as Professor of Automatic Control (2000-), Valencia Polytechnic University (UPV), teaching a wide range of subjects in the area from continuous and discrete simulation to automation and programmable logic controllers applications. His research interests include non-conventionally sampled control systems and networked based control systems. He has taken part in research projects funded by local industries, government and the European Science Foundation. He has also been involved in educational projects and currently is Head of the Systems Engineering and Control Department at UPV. He has been director of 8 PhD thesis and coauthor of about 60 technical papers in journals and technical meetings. Pedro Albertos, full Professor since 1975, currently at the Dept of Systems Engineering and Control at the Polytechnic University of Valencia, Spain. He has been Director from 1979 to 1995 and in 1998. He has been teaching courses on Advanced Control Systems, Intelligent Control Systems and Systems Theory. He is Honorary Profesor at the Northwestern University, Senhyang, China and Doctor Honoris Causa at the Universities of Oulu (Finland) and Polytechnic of Bucarest (Rumania). Invited Professor in more than 20 Universities, all around the world, he has delivered seminars in more than 30 universities and research centres. Authored more than 300 papers, book chapters and congress communications, he is co-editor of 7 books and co-author of Multivariable Control Systems (Springer 2004). He has directed 16 PhD thesis, and he is the coordinator of the PhD Program on Automatica and Industrial Informatics, which has been implemented in Spain, Mexico, Columbia and Venezuela. He has participated in many national and international research projects. Currently is involved in the ARTIST2 Node of Excellence on Embedded Control Systems. He is associated editor of Control Engineering Practice and Automatica and editor in chief of the journal Revista RIAI (Revista Iberoamericana de Automática e Informática Industrial). In the period 1999–2002 he was the IFAC President.     

Structural image analysis methods to classify dendritic structures

There are many possible approaches for direct image classification of dendritic crystals. Dendritic crystallization can be observed by iron in special conditions; common examples are fern leafs or dendritic crystallized snowflakes. This contribution is especially focused on the new structural classification methods we used (Hough transform, fractal dimension estimation, and fuzzy assisted filtering and classification) and relates to our previous contribution. It contains the discriminatory analysis results of all methods we have used so far as well. The text was submitted by the authors in English. Jirí Vařenka. Year of birth: 1971. Year of graduation: 1995, Faculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic. Academic degrees: 1995 Ing. (MSc. EE). Area of Research: Computer aided pattern recognition. Number of publications: 22 articles. Roman Kubínek. Year of birth: 1957. Year of graduation: 1981, Faculty of Science, Palacky University, Olomouc, Czech Republic. Academic degrees: 1981 RNDr, 1989 CSc. 1998—Associated Professor of Applied Physics. Area of Research: Experimental methods of applied physics used in nanotechnology. Number of publications: 2 monographs and 85 articles. Scientific societies: Czech Microscopy Society.     

Solving the multi object occlusion problem in a multiple camera tracking system

An efficient method to overcome adverse effects of occlusion upon object tracking is presented. The method is based on matching paths of objects in time and solves a complex occlusion-caused problem of merging separate segments of the same path. Mikhail Mozerov. Graduated from Moscow State University in 1982 and received Candidate degree (Eng.) from Institute of Information Transmission Problems, Russian Academy of Sciences, in 1995. Currently a Project Director with the Computer Vision Center of Universitat Autónoma de Barcelona (UAB), Spain. Scientific interests: signal and image processing, pattern recognition, and digital holography. Ariel Amato. Received Electronic Engineer degree from Universidad Tecnológica Nacional (UTN-FRC), Argentina, and MS degree in Computer Vision from Universitat Autónoma de Barcelona (UAB), Spain, in 2007. Currently a PhD student at the Computer Vision Center of UAB. Scientific interests: active camera control, segmentation, tracking, and human motion understanding. Jordi González. Received PhD degree from Universitat Autónoma de Barcelona (UAB), Spain, in 2004. Currently holds the position of a Juan de la Cierva posdoctoral researcher at Institut de Robótica i Informática Industrial (UPC-CSIC). Scientific interests: cognitive evaluation of human behaviors in image sequences. Author of more than 70 papers on active camera control, segmentation, tracking, human motion understanding (interpretation and reasoning), natural language text generation, and automatic behavioral animation. Participated as Workpackage Leader in the European projects HERMES and VIDI-Video. He has co-founded the Image Sequence Evaluation research group with Computer Vision Center in Barcelona. Xavier Roca. Graduated from Universitat Autónoma de Barcelona (UAB) in 1990 and was awarded PhD degree in Computer Sciences by the same university in 1998. Currently a Director of Computer Sciences Department at UAB. Author of 30 papers published in proceeding of national and international conferences and in international journals.     

Design and implementation of contractual based real-time scheduler for multimedia streaming proxy server

In nowadays World Wide Web topology, it is not difficult to find the presence of proxy servers. They reduce network traffic through the cut down of repetitive information. However, traditional proxy server does not support multimedia streaming. One of the reasons is that general scheduling strategy adopted by most of the traditional proxy servers does not provide real-time support to multimedia services. Based on the concept of contractual scheduling, we have developed a proxy server that supports real-time multimedia applications. Moreover, we developed the group scheduling mechanism to enable processing power transfer between tasks that can hardly be achieved by traditional schedulers. They result in a substantially improved performance particularly when both time-constrained and non-time-constrained processes coexist within the proxy server. In this paper, the design and implementation of this proxy server and the proposed scheduler are detailed. Wai-Kong Cheuk received the B.Eng. (Hons.) and M. Phil. degrees in 1996 and 2001, respectively, from the Hong Kong Polytechnic University, where he is currently pursuing the Ph.D. degree. His main research interests include distributed operating systems and video streaming. Tai-Chiu Hsung (M'93) received the B.Eng. (Hons.) and Ph.D. degrees in electronic and information engineering in 1993 and 1998, respectively, from the Hong Kong Polytechnic University, Hong Kong. In 1999, he joined the Hong Kong Polytechnic University as a Research Fellow. His research interests include wavelet theory and applications, tomography, and fast algorithms. Dr. Hsung is also a member of IEE. Daniel Pak-Kong Lun (M'91) received his B.Sc. (Hons.) degree from the University of Essex, Essex, U.K., and the Ph.D. degree from the Hong Kong Polytechnic University, Hung Hom, Hong Kong, in 1988 and 1991, respectively. He is currently an Associate Professor and the Associate Head of the Department of Electronic and Information Engineering, the Hong Kong Polytechnic University. His research interests include digital signal processing, wavelets, multimedia technology, and Internet technology. Dr. Lun was the Secretary, Treasurer, Vice-Chairman, and Chairman of the IEEE Hong Kong Chapter of Signal Processing in 1994, 1995–1996, 1997–1998, 1999–2000, respectively. He was the Finance Chair of 2003 IEEE International Conference on Acoustics, Speech and Signal Processing, held in Hong Kong, in April 2003. He is a Chartered Engineer and a Corporate member of the IEE.     

Nonparametric pattern recognition systems based on learning a sample decomposition by its dimension

The technique of multilevel nonparametric pattern recognition systems synthesis on the basis of learning sample decomposition principles and parallel computing technology is proposed. This basis provides effective processing of highly dimensional information. Aleksandr Vasil’evich Lapko was born in 1949 and graduated from Frunze Polytechnic Institute in 1971. He has been a doctor of technical sciences since 1990 and a leading researcher at the Institute of Computational Modeling of the Siberian Branch of the Russian Academy of Sciences. His scientific interests include the following: nonparametric statistics, pattern recognition systems, and the design and optimization of indefinite systems. He is the author of 223 publications, including 13 monographs. He is chairman of the Krasnoyarsk regional department of the Pattern Recognition and Image Analysis Association and an Honored Science Worker of the Russian Federation. Vasilii Aleksandrovich Lapko was born in 1974 and graduated from Krasnoyarsk State Technical University in 1996. He has been a doctor of technical sciences since 2004 in systems analysis, management, and information processing. He is a senior researcher at the Institute of Computational Modeling of the Siberian Branch of the Russian Academy of Sciences. His scientific interests include the following: nonparametric statistics, pattern recognition systems, the design of indefinite systems, and collective evaluation methods. He is the author of 105 publications, including 4 monographs. He was awarded by the Russia Academy of Sciences a medal for the best scientific publication in 2005 for young scientists in Informatics, Computer Engineering, and Automation.     

A hybrid ASM approach for sparse volumetric data segmentation

Three-Dimensional (3D) Active Shape Modeling (ASM) is a straightforward extension of 2D ASM. 3D ASM is robust when true volumetric data is considered. However, when the information in one dimension is sparse, pure 3D ASM tends to be less robust. We present a hybrid 2D + 3D methodology which can deal with sparse 3D data. 2D and 3D ASMs are combined to obtain a “global optimal” segmentation of the 3D object embedded in the data set, rather than the “locally optimal” segmentation on separate slices. Experimental results indicate that the developed approach shows equivalent precision on separate slices but higher consistency for whole volumes when compared to 2D ASM, while the results for whole volumes are improved when compared to the pure 3D ASM approach. The text was submitted by the authors in English. Stuart Michael Williams, born in 1967, graduated with BAHons in 1989, BMBCh in 1992 from Oxford University, UK; MRCP (1995), FRCR(1999); Stuart Michael Williams is currently the Consultant Radiologist of Norfolk and Norwich University Hospital, Norwich, UK. His research areas include oncological radiology with an interest in image analysis and medical education. Stuart Michael Williams has 24 publications (monographs and articles). He is a member of the Royal College of Radiologists; member of the European Congress of Radiology; and a member of the European Society of Magnetic Resonance in Medicine and Biology. Yanong Zhu, born in 1975, graduated with B. Sci. in 1997 and M. Sci. in 2002 from Northwest University, China and PhD in 2006 from the University of East Anglia, Norwich, UK. His research areas include computer vision, medical image understanding, and analysis. Yanong Zhu has eight publications (monographs and articles). Reyer Zwiggelaar, born in 1963, graduated with B. Sci. from State University Groningen, the Netherlands in 1989. He was awarded his PhD in 1993 by University College London, UK. Reyer Zwiggelaar is currently the Senior Lecturer at the University of Wales Aberystwyth, UK. Dr. Zwiggelaar has more than 80 publications (monographs and articles). His research areas include medical image understanding, especially concentrating on mammographic data, pattern recognition, statistical methods, and feature detection techniques.     

The situation categorization for support decision making

The estimation to situations in task of computer support of problems decision making by means of the categorizations mechanism of observed variable measurements or estimation collections is offered in article. The article is published in the original. Vyacheslav Leonidovich Tokarev. Born in 1942. Graduated from the Tula Polytechnic Institute (the city of Tula) in 1965 on specialty “Automatics and Telemechanics.” Defended a Ph.D. thesis in 1981 and doctoral thesis in 2000. Works at the Tula State University as a professor at the department of electronic computers. Circle of scientific interests: intelligent decision-making support systems, the development and application of the methods of the pattern recognition and image analysis theory for solving complex practical tasks. Two monographs and more than 20 articles on the results of the scientific activity were published. Member of the RAROAI (the Russian Association of Pattern Recognition and Image Analysis).     

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.     

A purely reactive navigation scheme for dynamic environments using Case-Based Reasoning

This paper presents a new sonar based purely reactive navigation technique for mobile platforms. The method relies on Case-Based Reasoning to adapt itself to any robot and environment through learning, both by observation and self experience. Thus, unlike in other reactive techniques, kinematics or dynamics do not need to be explicitly taken into account. Also, learning from different sources allows combination of their advantages into a safe and smooth path to the goal. The method has been succesfully implemented on a Pioneer robot wielding 8 Polaroid sonar sensors. Cristina Urdiales is a Lecturer at the Department of Tecnología Electrónica (DTE) of the University of Málaga (UMA). She received a MSc degree in Telecommunication Engineering at the Universidad Politécnica de Madrid (UPM) and her Ph.D. degree at University of Málaga (UMA). Her research is focused on robotics and computer vision. E.J. Pérez was born in Barcelona, Spain, in 1974. He received his title of Telecommunication Engineering from the University of Málaga, Spain, in 1999. During 1999 he worked in a research project under a grant by the Spanish CYCIT. From 2000 to the present day he has worked as Assistant Professor in the Department of Tecnología Electrónica of the University of Málaga. His research is focused on robotics and artificial vision. Javier Vázquez-Salceda is an Associate Researcher of the Artificial Intelligence Section of the Software Department (LSI), at the Technical University of Catalonia (UPC). Javier obtained an MSc degree in Computer Science at UPC. After his master studies he became research assistant in the KEMLg Group at UPC. In 2003 he presented his Ph.D. dissertation (with honours), which has been awarded with the 2003 ECCAI Artificial Intelligence Dissertation Award. The dissertation has been also recently published as a book by Birkhauser-Verlag. From 2003 to 2005 he was researcher in the Intelligent Systems Group at Utrecht University. Currently he is again member of the KEMLg Group at UPC. His research is focused on theoretical and applied issues of Normative Systems, software and physical agents' autonomy and social control, especially in distributed applications for complex domains such as eCommerce or Medicine. Miquel Sànchez-Marrè (Barcelona, 1964) received a Ph.D. in Computer Science in 1996 from the Technical University of Catalonia (UPC). He is Associate Professor in the Computer Software Department (LSI) of the UPC since 1990 (tenure 1996). He was the head of the Artificial Intelligence section of LSI (1997–2000). He is a pioneer member of International Environmental Modelling and Software Society (IEMSS) and a board member of IEMSS also, since 2000. He is a member of the Editorial Board of International Journal of Applied Intelligence, since October 2001. Since October 2004 he is Associate Editor of Environmental Modelling and Software journal. His main research topics are case-based reasoning, machine learning, knowledge acquisition and data mining, knowledge engineering, intelligent decision-support systems, and integrated AI architectures. He has an special interest on the application of AI techniques to Environmental Decision Support Systems. Francisco Sandoval was born in Spain in 1947. He received the title of Telecommunication Engineering and Ph.D. degree from the Technical University of Madrid, Spain, in 1972 and 1980, respectively. From 1972 to 1989 he was engaged in teaching and research in the fields of opto-electronics and integrated circuits in the Universidad Politécnica de Madrid (UPM) as an Assistant Professor and a Lecturer successively. In 1990 he joined the University of Málaga as Full Professor in the Department of Tecnología Electrónica. He is currently involved in autonomous systems and foveal vision, application of Artificial Neural Networks to Energy Management Systems, and in Broad Band and Multimedia Communication.     

Social network(ing) sites…revisiting the story so far: A response to danah boyd & Nicole Ellison

David Beer is Senior Lecturer and Head of Programme for Communication in the Faculty of Business & Communication at York St John University. He has written a number of chapters and articles on digital culture, and particularly digital music culture. His book New Media: The Key Concepts, co-authored with Nick Gane, is due to be published by Berg in 2008.     

Chance discovery and scenario analysis

Scenario analysis is often used to identify possible chance events. However, no formal, computational theory yet exists for scenario analysis. In this paper, we commence development of such a theory by defining a scenario in an argumentation context, and by considering the question of when two scenarios are the same. Peter McBurney, Ph.D.: He is a lecturer in the Department of Computer Science at the University of Liverpool, UK. He has a first degree in Pure Mathematics and Statistics from the Australian National University, Canberra, and a Ph.D in Artificial Intelligence from the University of Liverpool. His Ph.D research concerned the design of protocols for rational interaction between autonomous software agents, and he has several publications in this area. Prior to completing his Ph.D he worked as a consultant to major telecommunications network operating companies, primarily in mobile and satellite communications, where his work involved strategic marketing programming. Simon Parsons, Ph.D.: He is currently visiting the Sloan School of Management at Massachusetts Institute of Technology (MIT) and is a Visiting Professor at the University of Liverpool, UK. He holds a first degree in Engineering from Cambridge University, and an MSc and Ph.D in Artificial Intelligence from the University of London. In 1998, he was awarded the Young Engineer Achievement Medal of the British Institution of Electrical Engineers (IEE), the largest professional engineering society in Europe. He has published 4 books and over 100 articles on autonomous agents and multi-agent systems, uncertainty formalisms, risk and decision-making.     

A systematic literature review of software requirements prioritization research

ContextDuring requirements engineering, prioritization is performed to grade or rank requirements in their order of importance and subsequent implementation releases. It is a major step taken in making crucial decisions so as to increase the economic value of a system.ObjectiveThe purpose of this study is to identify and analyze existing prioritization techniques in the context of the formulated research questions.MethodSearch terms with relevant keywords were used to identify primary studies that relate requirements prioritization classified under journal articles, conference papers, workshops, symposiums, book chapters and IEEE bulletins.Results73 Primary studies were selected from the search processes. Out of these studies; 13 were journal articles, 35 were conference papers and 8 were workshop papers. Furthermore, contributions from symposiums as well as IEEE bulletins were 2 each while the total number of book chapters amounted to 13.ConclusionPrioritization has been significantly discussed in the requirements engineering domain. However, it was generally discovered that, existing prioritization techniques suffer from a number of limitations which includes: lack of scalability, methods of dealing with rank updates during requirements evolution, coordination among stakeholders and requirements dependency issues. Also, the applicability of existing techniques in complex and real setting has not been reported yet.     

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.     

The Shadow Function for Rough Surfaces

For many remote sensing applications it is beneficial to know how the amount of shadows on a surface depends on illumination. Many natural surfaces (planetary surfaces being an example) may be successfully described by a fractal model. While the fractal shadowing function can be easily calculated experimentally, to date no rigorous analytical model of self-shadowing on a fractal surface exists. In this paper we offer an integral form of the shadowing function for fractal surfaces with different fractal and roughness parameters. The analysis is based on working out the covariance matrix for an arbitrarily long sequence of heights in a fractal profile.Svetlana Barsky received her BSc degree in Mathematics and Applied Mathematics from Novosibirsk State University, Russia, in 1992, and her MSc and PhD degrees from the University of Surrey, UK, in 1999 and 2003 respectively. Since then she has been working as a research fellow at the Centre for Vision, Speech and Signal Processing of the School of Electronics and Physical Sciecnes of Surrey University.Maria Petrou studied Physics at the Aristotle University of Thessaloniki, Greece, Applied Mathematics in Cambridge and she did her PhD in the Institute of Astronomy in Cambridge, UK. She has been working on image processing and computer vision since 1986. She has been the Professor of Image Analysis since 1998 and leads a group of 20 researchers on this topic in Surrey University. She has published more than 250 scientific papers, on Astronomy, Remote Sensing, Computer Vision, Machine Learning, Colour analysis, Industrial Inspection, Medical Signal and Image Processing. She has co-authored a book “Image Processing: the fundamentals” published by John Wiley in 1999 and reprinted in 2000 and 2003, and numerous book chapters. She is a Fellow of the Royal Academy of Engineering, Fellow of IEE and Fellow of IAPR. She has served as the Chairman of the Technical Committee for Remote Sensing of IAPR, the Chairman of the British Machine Vision Association (BMVA), as an Associate Editor of IEEE Transactions on Image Processing, as the Newsletter Editor of IAPR and is currently the treasurer of IAPR and an Honorary Editor of IEE Electronics Letters. A full list of publications and other details can be found in     

Ontology of the data mining subject domain

We consider the subject domain related to discovering of regularities in arrays of statistical or experimental data and applications of these regularities to solving problems of recognition, prediction, clustering, etc. Ontology describes methods for pre-and post-processing, which are specific for each kind of initial information, and processing methods universal for all kinds of information. Nikolai Grigor’evich Zagoruiko finished a secondary school in Novosibirsk oblast. Graduated from the Electroengineering Department of the Leningrad Institute of Motion-picture Engineers in 1953. Since 1960 has been working at the Institute of Mathematics of the Siberian Branch of the Russian Academy of Sciences. Received candidate’s degree in 1962 and doctoral degree in Pattern Recognition in 1969. Since 1973 to 1982 had been working as a vice-rector of the Novosibirsk State University. In 1988–1990 had been heading a project in the International Laboratory of Artificial Intelligence in Bratislava (Slovakia). Scientific interests: pattern recognition and prediction. Author of 219 publications, including 13 monographs. Sergei Evgen’evich Gulyaevskii born 1980 in Novosibirsk. Graduated from the Department of Mechanics and Mathematics of the Novosibirsk State University in 2003 and from the post-graduate school of the Institute of Mathematics of Siberian Branch of the Russian Academy of Sciences in 2005. Author of 12 publications. Scientific interests: data mining, image mining, text mining, high performance, data mining algorithms. Boris Yakovlevich Kovalerchuk finished a secondary school in Tashkent. Graduated from the Department of Mechanics and Mathematics of the Novosibirsk State University in 1971. Received candidate’s degree in Pattern Recognition in 1977. Author of two monographs, five chapters in collective monographs, and 65 papers in journals. Works as a Professor of Department of Computer Science, Central Washington University, USA. Scientific interests: data mining, artificial intelligence, pattern recognition, machine learning, fuzzy systems, and control.