Segmentation of stick text based on sub connected area analysis

A new stick text segmentation method based on the sub connected area analysis is introduced in this paper.The foundation of this method is the sub connected area representation of text image that can represent all connected areas in an image efficiently.This method consists mainly of four steps:sub connected area classification,finding initial boundary following point,finding optimal segmentation point by boundary tracing,and text segmentaton.This method is similar to boundary analysis method but is more efficient than boundary analysis.     

Improving retrieval performance by region constraints and relevance feedback

In this paper, region features and relevance feedback are used to improve the performance of CBIR. Unlike existing region-based approaches where either individual regions are used or only simple spatial layout is modeled, the proposed approach simultaneously models both region properties and their spatial relationships in a probabilistic framework. Furthermore, the retrieval performance is improved by an adaptive filter based relevance feedback. To illustrate the performance of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images, which render promising results on a wide variety of queries.     

An Optimum Placement Search Algorithm Based on Extended Corner Block List

A non-slicing approach,Corner Block List(CBL),has been presented recently.Since CBL only can represent floorplans without empty rooms,the algorithm based on CBL cannot get the optimum placement.In this paper,an extended corner block list,ECBLλ,is proposed.It can represent non-slicing floorplan including empty rooms.Based on the optimum solution theorem of BSG(bounded-sliceline grid),it is proved that the solution space of ECBLn,where n is the number of blocks,contains the optimum block placement with the minimum area.A placement algorithm based on ECBLλ,whose solution space can be controlled by setting λ,the extending ratio,is completed.Whenλ is set as n,the algorithm based on ECBLn is the optimum placement search algorithm.Experiments show that λ has a reasonable constant range for building block layout problem,so the algorithm can translate an ECBLλ representation to its corresponding placement in O(n) time,Experimental results on MCNC benchmarks show promising performance with 7% improvement in wire length and 2% decrease in dead space over algorthms based on CBL.Meanwhile,compared with other algorithms,the proposed algorithm can get better results with less runtime.     

Fast evaluation of Bounded Slice-line Grid

Bounded Slice-line Grid (BSG) is an elegant representation of block placement, because it is very intuitionistic and has the advantage of handling various placement constraints. However, BSG has attracted little attention because its evaluation is very time-consuming. This paper proposes a simple algorithm independent of the BSG size to evaluate the BSG representation in O(nloglogn) time, where n is the number of blocks. In the algorithm, the BSG-rooms are assigned with integral coordinates firstly, and then a linear sorting algorithm is applied on the BSG-rooms where blocks are assigned to compute two block sequences, from which the block placement can be obtained in O(n log logn) time. As a consequence, the evaluation of the BSG is completed in O(nloglogn) time, where n is the number of blocks. The proposed algorithm is much faster than the previous graph-based O(n^2) algorithm. The experimental results demonstrate the efficiency of the algorithm.     

HarkMan—A Vocabulary-Independent Keyword Spotter for Spontaneons Chinese Speech

In this paper,a noverl technique adopted in HarkMan is introduced.HarkMan is a keywore-spotter designed to automatically spot the given words of a vocabulary-independent task in unconstrained Chinese telephone speech.The speaking manner and the number of keywords are not limited.This paper focuses on the novel technique which addresses acoustic modeling,keyword spotting network,search strategies,robustness,and rejection.The underlying technologies used in HarkMan given in this paper are useful not only for keyword spotting but also for continuous speech recognition.The system has achieved a figure-of-merit value over 90%.     

Adaptive filtering under minimum information divergence criterion

Traditional filtering theory is always based on optimization of the expected value of a suitably chosen function of error, such as the minimum mean-square error (MMSE) criterion, the minimum error entropy (MEE) criterion, and so on. None of those criteria could capture all the probabilistic information about the error distribution. In this work, we propose a novel approach to shape the probability density function (PDF) of the errors in adaptive filtering. As the PDF contains all the probabilistic information, the proposed approach can be used to obtain the desired variance or entropy, and is expected to be useful in the complex signal processing and learning systems. In our method, the information divergence between the actual errors and the desired errors is chosen as the cost function, which is estimated by kernel approach. Some important properties of the estimated divergence are presented. Also, for the finite impulse response (FIR) filter, a stochastic gradient algorithm is derived. Finally, simulation examples illustrate the effectiveness of this algorithm in adaptive system training. Recommended by Editorial Board member Naira Hovakimyan under the direction of Editor Jae Weon Choi. This work was supported in part by the National Natural Science Foundation of China under grants 50577037 and 60604010. Badong Chen received the B.S. and M.S. degrees in Control Theory and Engineering from Chongqing University, Chongqing, China, in 1997 and 2003, respectively, and the Ph.D. degree in Computer Science and Technology from Tsinghua University, Beijing China, in 2008. He is currently a Postdoctor of the Institute of Manufacturing Engineering, Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, China. His research interests are in signal processing, adaptive control, and information theoretic aspects of control systems. Yu Zhu received the B.S. of Radio Electronics in 1983 at Beijing Normal University, and the M.S. of Computer Applications in 1993, and the Ph.D. of Mechanical Design and Theory in 2001 at China University of Mining & Technology. He is now a Professor of the Institute of Manufacturing Engineering of Department of Precision and Mechanology of Tsinghua University. His current research interests are parallel machanism and theory, two photon micro-fabrication, ultra-precision motion system and motion control. Jinchun Hu received the Ph.D. in Control Science and Engineering from Nanjing University of Science and Technology, Nanjing, China, in 1998. Since then, he has been a postdoctoral researcher in Nanjing University of Aeronautics and Astronautics in 1999 and Tsinghua University in 2002 respectively. His research interests are in flight control, aerial Robot and intelligent control. Dr. Hu is currently an Associate Professor of the Department of Computer Science and Technology of Tsinghua University, Beijing, China. Zengqi Sun received the B.S. degree from the Department of Automatic Control, Tsinghua University, Beijing, China, in 1966 and the Ph.D. degree in Control Engineering from the Chalmas University of Technology, Sweden, in 1981. He is currently a Professor of the Department of Computer Science and Technology, Tsinghua University, Beijing, China. He is the author or coauthor of more than 100 paper and eight books on control and robotics. His research interests include robotics, intelligent control, fuzzy system, neural networks, and evolutionary computation.     

A Stereovision Method for Obstacle Detection and Tracking in Non-Flat Urban Environments

Obstacle detection is an essential capability for the safe guidance of autonomous vehicles, especially in urban environments. This paper presents an efficient method to integrate spatial and temporal constraints for detecting and tracking obstacles in urban environments. In order to enhance the reliability of the obstacle detection task, we do not consider the urban roads as rigid planes, but as quasi-planes, whose normal vectors have orientation constraints. Under this flexible road model, we propose a fast, robust stereovision based obstacle detection method. A watershed transformation is employed for obstacle segmentation in dense traffic conditions, even with partial occlusions, in urban environments. Finally a UKF (Unscented Kalman filter) is applied to estimate the obstacles parameters under a nonlinear observation model. To avoid the difficulty of the computation in metric space, the whole detection process is performed in the disparity image. Various experimental results are presented, showing the advantages of this method.Qian Yu received the B.E. degree in Computer Science from Tsinghua University, Beijing, China, in 2001, and the Master degree in Computer Science also from Tsinghua University in 2004, working at the Artificial Intelligence Laboratory. From October 2002 to April 2003, he was a visiting student at the Institute of System and Robotics (ISR), University of Coimbra, Portugal. His current research interests are in computer vision and robotics.Helder Araujo is currently Associate Professor in the Department of Electrical and Computer Engineering, University of Coimbra, Portugal. He is co-founder of the Portuguese Institute for Systems and Robotics (ISR), where he is now a Researcher and Vice-Director of the Coimbra pole. His primary research interests are in computer vision and mobile robotics.Hong Wang received his Ph.D. degree from the Department of Computer Science and Technology, Tsinghua University in 1993. He is currently an associate professor at Department of Computer Science and Technology, Tsinghua University. He worked as a visiting researcher at the Department of Intelligent Assistant Driving, Daimler-Benz Research, Stuttgart, Germany, from August 1996 to August 1997. His main research interests include Artificial Intelligence, Mobile Robotics, Vision Navigation, Multi-sensor Data Fusion. He has published over 40 papers in international conference and journals. He is a member of Special Committee of Machine Perception and Virtual Reality of the Chinese Association of Artificial Intelligence and a member of Scientific Committee of the Olympiad in Informatics of the Chinese Computer Association. He has served as an Associated Director of the Central Laboratory of the State Key Laboratory of Intelligent Technology and Systems, Tsinghua University.     

Leakage current estimation of CMOS circuit with stack effect

Leakage current of CMOS circuit increases dramatically with the technology scaling down and has become a critical issue of high performance system. Subthreshold, gate and reverse biased junction band-to-band tunneling (BTBT) leakages are considered three main determinants of total leakage current. Up to now, how to accurately estimate leakage current of large-scale circuits within endurable time remains unsolved, even though accurate leakage models have been widely discussed. In this paper, the authors first dip into the stack effect of CMOS technology and propose a new simple gate-level leakage current model. Then, a table-lookup based total leakage current simulator is built up according to the model. To validate the simulator, accurate leakage current is simulated at circuit level using popular simulator HSPICE for comparison. Some further studies such as maximum leakage current estimation, minimum leakage current generation and a high-level average leakage current macromodel are introduced in detail. Experiments on ISCAS85 and ISCAS89 benchmarks demonstrate that the two proposed leakage current estimation methods are very accurate and efficient.     

S-Club: an overlay-based efficient service discovery mechanism in CROWN Grid

Information service plays a key role in grid system, handles resource discovery and management process. Employing existing information service architectures suffers from poor scalability, long search response time, and large traffic overhead. In this paper, we propose a service club mechanism, called S-Club, for efficient service discovery. In S-Club, an overlay based on existing Grid Information Service (GIS) mesh network of CROWN is built, so that GISs are organized as service clubs. Each club serves for a certain type of service while each GIS may join one or more clubs. S-Club is adopted in our CROWN Grid and the performance of S-Club is evaluated by comprehensive simulations. The results show that S-Club scheme significantly improves search performance and outperforms existing approaches. Chunming Hu is a research staff in the Institute of Advanced Computing Technology at the School of Computer Science and Engineering, Beihang University, Beijing, China. He received his B.E. and M.E. in Department of Computer Science and Engineering in Beihang University. He received the Ph.D. degree in School of Computer Science and Engineering of Beihang University, Beijing, China, 2005. His research interests include peer-to-peer and grid computing; distributed systems and software architectures. Yanmin Zhu is a Ph.D. candidate in the Department of Computer Science, Hong Kong University of Science and Technology. He received his B.S. degree in computer science from Xi’an Jiaotong University, Xi’an, China, in 2002. His research interests include grid computing, peer-to-peer networking, pervasive computing and sensor networks. He is a member of the IEEE and the IEEE Computer Society. Jinpeng Huai is a Professor and Vice President of Beihang University. He serves on the Steering Committee for Advanced Computing Technology Subject, the National High-Tech Program (863) as Chief Scientist. He is a member of the Consulting Committee of the Central Government’s Information Office, and Chairman of the Expert Committee in both the National e-Government Engineering Taskforce and the National e-Government Standard office. Dr. Huai and his colleagues are leading the key projects in e-Science of the National Science Foundation of China (NSFC) and Sino-UK. He has authored over 100 papers. His research interests include middleware, peer-to-peer (P2P), grid computing, trustworthiness and security. Yunhao Liu received his B.S. degree in Automation Department from Tsinghua University, China, in 1995, and an M.A. degree in Beijing Foreign Studies University, China, in 1997, and an M.S. and a Ph.D. degree in computer science and engineering at Michigan State University in 2003 and 2004, respectively. He is now an assistant professor in the Department of Computer Science and Engineering at Hong Kong University of Science and Technology. His research interests include peer-to-peer computing, pervasive computing, distributed systems, network security, grid computing, and high-speed networking. He is a senior member of the IEEE Computer Society. Lionel M. Ni is chair professor and head of the Computer Science and Engineering Department at Hong Kong University of Science and Technology. Lionel M. Ni received the Ph.D. degree in electrical and computer engineering from Purdue University, West Lafayette, Indiana, in 1980. He was a professor of computer science and engineering at Michigan State University from 1981 to 2003, where he received the Distinguished Faculty Award in 1994. His research interests include parallel architectures, distributed systems, high-speed networks, and pervasive computing. A fellow of the IEEE and the IEEE Computer Society, he has chaired many professional conferences and has received a number of awards for authoring outstanding papers.     

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.     

A new algebraic modelling approach to distributed problem-solving in MAS

This paper is devtoed to a new algebraic modelling approach to distributed problem-solving in multi-agent systems(MAS),which is featured by a unified framework for describing and treating social behaviors,social dynamics and social intelligence.A coneptual architecture of algebraic modelling is presented.The algebraic modelling of typical social be-haviors,social situation and social dynamics is discussed in the context of distributed problem-solving in MAS .The comparison and simulation on distributed task allocations and resource assignments in MAS show more advantages of the algebraic approach than other conventional methods.     

Spontaneous Speech Parsing in Travel Information Inquiring and Booking Systems

Grammar-based parsing is a prevalent method for natural language understanding(NLU)and has been introduced into dialogue systems for spoken language processing (SLP).A robust parsing scheme is proposed in this paper to overcome the notorious phenomena,such as garbage,ellipsis,word disordering,fragment ,and ill-form,which frequently occur in splien utterances,Keyword categories are used as terminal symbols,and the definition of grammar is extended by introducing three new rule types,by-passing,up-messing and overcrossing,in addition to the general rules called up-tying in this paper,and the use of semantic items simplifies the semantics extraction.The corresponding parser marionette,which is essentially a partial chart parser,is enhanced to parse the semantic grammar.The robust parsing scheme integrating the above methods has been adopted in an air traveling information service system called EasyFlight,and has achieved a high performance when used for parsing spontaneous speeches.     

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.     

A new approach to fuzzy modeling and control for nonlinear dynamic systems: Neuro-fuzzy dynamic characteristic modeling and adaptive control mechanism

The study on nonlinear control system has received great interest from the international research field of automatic engineering. There are currently some alternative and complementary methods used to predict the behavior of nonlinear systems and design nonlinear control systems. Among them, characteristic modeling (CM) and fuzzy dynamic modeling are two effective methods. However, there are also some deficiencies in dealing with complex nonlinear system. In order to overcome the deficiencies, a novel intelligent modeling method is proposed by combining fuzzy dynamic modeling and characteristic modeling methods. Meanwhile, the proposed method also introduces the low-level learning power of neural network into the fuzzy logic system to implement parameters identification. This novel method is called neuro-fuzzy dynamic characteristic modeling (NFDCM). The neuro-fuzzy dynamic characteristic model based overall fuzzy control law is also discussed. Meanwhile the local adaptive controller is designed through the golden section adaptive control law and feedforward control law. In addition, the stability condition for the proposed closed-loop control system is briefly analyzed. The proposed approach has been shown to be effective via an example. Recommended by Editor Young-Hoon Joo. This work was jointly supported by National Natural Science Foundation of China under Grant 60604010, 90716021, and 90405017 and Foundation of National Laboratory of Space Intelligent Control of China under Grant SIC07010202. Xiong Luo received the Ph.D. degree from Central South University, Changsha, China, in 2004. From 2005 to 2006, he was a Postdoctoral Fellow in the Department of Computer Science and Technology at Tsinghua University. He currently works as an Associate Professor in the Department of Computer Science and Technology, University of Science and Technology Beijing. His research interests include intelligent control for spacecraft, intelligent optimization algorithms, and intelligent robot system. Zengqi Sun received the bachelor degree from Tsinghua University, Beijing, China, in 1966, and the Ph.D. degree from Chalmers University of the Technology, Gothenburg, Sweden, in 1981. He currently works as a Professor in the Department of Computer Science and Technology, Tsinghua University. His research interests include intelligent control of robotics, fuzzy neural networks, and intelligent flight control. Fuchun Sun received the Ph.D. degree from Tsinghua University, Beijing, China, in 1998. From 1998 to 2000, he was a Postdoctoral Fellow in the Department of Automation at Tsinghua University, where he is currently a Professor in the Department of Computer Science and Technology. His research interests include neural-fuzzy systems, variable structure control, networked control systems, and robotics.     

An efficient method for geo-referenced video mosaicing for environmental monitoring

Environmental monitoring applications require seamless registration of optical data into large area mosaics that are geographically referenced to the world frame. Using frame-by-frame image registration alone, we can obtain seamless mosaics, but it will not exhibit geographical accuracy due to frame-to-frame error accumulation. On the other hand, the 3D geo-data from GPS, a laser profiler, an INS system provides a globally correct track of the motion without error propagation. However, the inherent (absolute) errors in the instrumentation are large for seamless mosaicing. The paper describes an effective two-track method for combining two different sources of data to achieve a seamless and geo-referenced mosaic, without 3D reconstruction or complex global registration. Experiments with real airborne video images show that the proposed algorithms are practical in important environmental applications. Zhigang Zhu received his B.E., M.E. and Ph.D. degrees, all in computer science from Tsinghua University, Beijing, in 1988, 1991 and 1997, respectively. He is currently an associate professor in the Department of Computer Science, the City College of the City University of New York. Previously, he was an associate professor at Tsinghua University, and a senior research fellow at the University of Massachusetts, Amherst. His research interests include 3D computer vision, HCI, virtual/augmented reality, video representation, and various applications in education, environment, robotics, surveillance and transportation. He has published over 90 technical papers in the related fields. He is a member of IEEE and ACM. Edward M. Riseman received his B.S. degree from Clarkson College of Technology in 1964 and his M.S. and Ph.D. degrees in electrical engineering from Cornell University in 1966 and 1969, respectively. He joined the Computer Science Department at UMass-Amherst as assistant professor in 1969, has been a professor since 1978, and served as chairman of the department from 1981 to 1985. Professor Riseman has conducted research in computer vision, artificial intelligence, learning, and pattern recognition, and has more than 200 publications. He has co-directed the Computer Vision Laboratory since its inception in 1975. Professor Riseman has been on the editorial boards of Computer Vision and Image Understanding (CVIU) from 1992 to 1997 and of the International Journal of Computer Vision (IJCV) from 1987 to the present. He is a senior member of IEEE, and a fellow of AAAI. Allen R. Hanson received his B.S. degree from Clarkson College of Technology in 1964 and his M.S. and Ph.D. degrees in electrical engineering from Cornell University in 1966 and 1969, respectively. He joined the Computer Science Department at UMass-Amherst as an associate professor in 1981, and has been a professor there since 1989. Professor Hanson has conducted research in computer vision, artificial intelligence, learning, and pattern recognition, and has more than 150 publications. He is co-director of the Computer Vision Laboratory at UMass-Amherst, and has been on the editorial boards of the following journals: Computer Vision, Graphics and Image Processing 1983–1990, Computer Vision, Graphics, and Image Processing—Image Understanding 1991–1994, and Computer Vision and Image Understanding 1995–present. Howard Schultz received a M.S. degree in physics from UCLA in 1974 and a Ph.D. in physical oceanography from the University of Michigan in 1982. Currently, he is a senior research fellow with the Computer Science Department at the University of Massachusetts, Amherst. His research interests include quantitative methods for image understanding and remote sensing. The current focus of his research activities are on developing automatic techniques for generating complex, 3D models from sequences of images. This research has found application in a variety of programs including real-time terrain modeling and video aided navigation. He is a member of the IEEE, the American Geophysical Union, and the American Society of Photogrammetry and Remote Sensing.     

Using discriminant analysis for multi-class classification: an experimental investigation

Many supervised machine learning tasks can be cast as multi-class classification problems. Support vector machines (SVMs) excel at binary classification problems, but the elegant theory behind large-margin hyperplane cannot be easily extended to their multi-class counterparts. On the other hand, it was shown that the decision hyperplanes for binary classification obtained by SVMs are equivalent to the solutions obtained by Fisher's linear discriminant on the set of support vectors. Discriminant analysis approaches are well known to learn discriminative feature transformations in the statistical pattern recognition literature and can be easily extend to multi-class cases. The use of discriminant analysis, however, has not been fully experimented in the data mining literature. In this paper, we explore the use of discriminant analysis for multi-class classification problems. We evaluate the performance of discriminant analysis on a large collection of benchmark datasets and investigate its usage in text categorization. Our experiments suggest that discriminant analysis provides a fast, efficient yet accurate alternative for general multi-class classification problems. Tao Li is currently an assistant professor in the School of Computer Science at Florida International University. He received his Ph.D. degree in Computer Science from University of Rochester in 2004. His primary research interests are: data mining, machine learning, bioinformatics, and music information retrieval. Shenghuo Zhu is currently a researcher in NEC Laboratories America, Inc. He received his B.E. from Zhejiang University in 1994, B.E. from Tsinghua University in 1997, and Ph.D degree in Computer Science from University of Rochester in 2003. His primary research interests include information retrieval, machine learning, and data mining. Mitsunori Ogihara received a Ph.D. in Information Sciences at Tokyo Institute of Technology in 1993. He is currently Professor and Chair of the Department of Computer Science at the University of Rochester. His primary research interests are data mining, computational complexity, and molecular computation.     

On the suitability of Biological structure determination by electron microscopy to Grid Computing

The present contribution describes a potential application of Grid Computing in Bioinformatics. High resolution structure determination of biological specimens is critical in BioSciences to understanding the biological function. The problem is computational intensive. Distributed and Grid Computing are thus becoming essential. This contribution analyzes the use of Grid Computing and its potential benefits in the field of electron microscope tomography of biological specimens. Jose-Jesus Fernandez, Ph.D.: He received his M.Sc. and Ph.D. degrees in Computer Science from the University of Granada, Spain, in 1992 and 1997, respectively. He was a Ph.D. student at the Bio-Computing unit of the National Center for BioTechnology (CNB) from the Spanish National Council of Scientific Research (CSIC), Madrid, Spain. He became an Assistant Professor in 1997 and, subsequently, Associate Professor in 2000 in Computer Architecture at the University of Almeria, Spain. He is a member of the supercomputing-algorithms research group. His research interests include high performance computing (HPC), image processing and tomography. Jose-Roman Bilbao-Castro: He received his M.Sc. degree in Computer Science from the University of Almeria in 2001. He is currently a Ph.D. student at the BioComputing unit of the CNB (CSIC) through a Ph.D. CSIC-grant in conjuction with Dept. Computer Architecture at the University of Malaga (Spain). His current research interestsinclude tomography, HPC and distributed and grid computing. Roberto Marabini, Ph.D.: He received the M.Sc. (1989) and Ph.D. (1995) degrees in Physics from the University Autonoma de Madrid (UAM) and University of Santiago de Compostela, respectively. He was a Ph.D. student at the BioComputing Unit at the CNB (CSIC). He worked at the University of Pennsylvania and the City University of New York from 1998 to 2002. At present he is an Associate Professor at the UAM. His current research interests include inverse problems, image processing and HPC. Jose-Maria Carazo, Ph.D.: He received the M.Sc. degree from the Granada University, Spain, in 1981, and got his Ph.D. in Molecular Biology at the UAM in 1984. He left for Albany, NY, in 1986, coming back to Madrid in 1989 to set up the BioComputing Unit of the CNB (CSIC). He was involved in the Spanish Ministry of Science and Technology as Deputy General Director for Research Planning. Currently, he keeps engaged in his activities at the CNB, the Scientific Park of Madrid and Integromics S.L. Immaculada Garcia, Ph.D.: She received her B.Sc. (1977) and Ph.D. (1986) degrees in Physics from the Complutense University of Madrid and University of Santiago de Compostela, respectively. From 1977 to 1987 she was an Assistant professor at the University of Granada, from 1987 to 1996 Associate professor at the University of Almeria and since 1997 she is a Full Professor and head of Dept. Computer Architecture. She is head of the supercomputing-algorithms research group. Her research interest lies in HPC for irregular problems related to image processing, global optimization and matrix computation.