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Communications in computer and Information Science 2013年 414卷 24-34页

作者： Ali, Hazrat Ahmad, Nasir Zhou, Xianwei Ali, Muhammad Manjotho, Ali Asghar Department of Communication Engineering School of Computer and Communication Engineering University of Science and Technology Beijing Beijing10083 China Department of Computer Systems Engineering University of Engineering and Technology Peshawar Peshawar25120 Pakistan Department of Electrical and Computer Engineering North Dakota State University FargoND58108-6050 United States Department of Computer Systems Engineering Mehran University of Engineering and Technology Jamshoro Pakistan Machine Learning Group School of Informatics City University London LondonEC1V 0HB United Kingdom

Urdu is amongst the five largest languages of the world and enjoys extreme importance by sharing its vocabulary with several other languages of the Indo-Pak. However, there has not been any significant research in the area of Automatic Speech Recognition of Urdu. This paper presents the statistical based classification technique to achieve the task of Automatic Speech Recognition of isolated words in Urdu. For each isolated word, 52 Mel Frequency Cepstral Coefficients have been extracted and based upon these coefficients;the classification has been achieved using Linear Discriminant Analysis. As a prototype, the system has been trained with audio samples of seven speakers including male/female, native/non-native and speakers with different ages while the testing has been done using audio samples of three speakers. It was determined that majority of words exhibit a percentage error of less than 33%. Words with 100% error were declared to be bad words. The work reported in this paper may serve as a strong baseline for future research work on Urdu ASR, especially for continuous speech recognition of Urdu. © Springer International Publishing Switzerland 2014.

关键词： Speech recognition

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Brain tumor cell density estimation from multi-modal MR images based on a synthetic tumor growth model

Brain tumor cell density estimation from multi-modal MR imag...

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15th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2012

作者： Geremia, Ezequiel Menze, Bjoern H. Prastawa, Marcel Weber, M.-A. Criminisi, Antonio Ayache, Nicholas Asclepios Research Project INRIA Sophia-Antipolis France Computer Science and Artificial Intelligence Laboratory MIT United States Computer Vision Laboratory ETH Zurich Switzerland Scientific Computing and Imaging Institute University of Utah United States Diagnostic and Interventional Radiology Heidelberg University Hospital Germany Machine Learning and Perception Group Microsoft Research Cambridge United Kingdom

ISBN: (纸本)9783642366192

This paper proposes to employ a detailed tumor growth model to synthesize labelled images which can then be used to train an efficient data-driven machine learning tumor predictor. Our MR image synthesis step generates images with both healthy tissues as well as various tumoral tissue types. Subsequently, a discriminative algorithm based on random regression forests is trained on the simulated ground truth to predict the continuous latent tumor cell density, and the discrete tissue class associated with each voxel. The presented method makes use of a large synthetic dataset of 740 simulated cases for training and evaluation. A quantitative evaluation on 14 real clinical cases diagnosed with low-grade gliomas demonstrates tissue class accuracy comparable with state of the art, with added benefit in terms of computational efficiency and the ability to estimate tumor cell density as a latent variable underlying the multimodal image observations. The idea of synthesizing training data to train data-driven learning algorithms can be extended to other applications where expert annotation is lacking or expensive. © 2013 Springer-Verlag.

关键词： Tumors

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Caging Complex Objects with Geodesic Balls

Caging Complex Objects with Geodesic Balls

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Dmitry Zarubin Florian T. Pokorny Marc Toussaint Danica Kragic Machine Learning and Robotics Lab Universitat Stuttgart Stuttgart Germany Centre for Autonomous Systems Computer Vision and Active Perception Lab School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

This paper proposes a novel approach for the synthesis of grasps of objects whose geometry can be observed only in the presence of noise. We focus in particular on the problem of generating caging grasps with a realistic robot hand simulation and show that our method can generate such grasps even on complex objects. We introduce the idea of using geodesic balls on the object's surface in order to approximate the maximal contact surface between a robotic hand and an object. We define two types of heuristics which extract information from approximate geodesic balls in order to identify areas on an object that can likely be used to generate a caging grasp. Our heuristics are based on two scoring functions. The first uses winding angles measuring how much a geodesic ball on the surface winds around a dominant axis, while the second explores using the total discrete Gaussian curvature of a geodesic ball to rank potential caging postures. We evaluate our approach with respect to variations in hand kinematics, for a selection of complex real-world objects and with respect to its robustness to noise.

关键词： Caging Complex Objects Geodesic Balls Gaussian curvature

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Big Data: Prospects and Challenges

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Vikalpa 2015年第1期40卷 74 - 96页

作者： Janakiraman Moorthy Rangin Lahiri Neelanjan Biswas Dipyaman Sanyal Jayanthi Ranjan Krishnadas Nanath Pulak Ghosh (Coordinator) Director and Professor of Marketing at the Institute of Management Technology Dubai. Earlier he was Professor of Marketing at the IIM Calcutta and IIM Lucknow. He received his PhD from IIM Ahmedabad. His recent research papers were published in the leading scholarly ournals such as Marketing Science British Food Journal Journal of Information Technology Case and Application Research Journal of Database Marketing & Customer Strategy Management. He has wide experience in the banking and investment industry. He was earlier the Global Research and Project Director of the Institute for Customer Relationship Management Atlanta USA. He was the Convener of the prestigious CAT Exam 2011. e-mail: Practice Director leading Atos India's CRM practice while supporting Strategic Business Development for North American Market. With an experience of more than 15 years Rangin has worked extensively as a Business Consultant in Information Technology (Sales Automation Marketing & Service Management area) Customer Data Management and CRM Analytics. e-mail: Business Consultant at Atos with extensive experience in Business Analysis Risk Management Analytics Business Development Presales Solution Ideation on Enterprise Data Management Enterprise Reference Data and Master Data Management area. e-mail: founder and CEO of dono consulting a boutique quantitative analytics and investment research firm. He has worked for leading financial firms in New York and India including Dow Jones Blackstone Sorin Capital (VP Quantitative Modeling) and Thomson Reuters (Head of Real Estate Analytics). A CFA charter holder and Commonwealth Scholar Deep has an MS (Applied Economics) from University of Texas Dallas and an MA (Economics) from Jadavpur University e-mail: Professor in the Information Systems Group of the Institute of Management Technology Ghaziabad. Her PhD is in the field of data mining from Jamia Millia Islamia Central University India. She has published five edited books. She is serving on the editorial b

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A vision based geometrical method to find fingers positions in real time hand gesture recognition

Journal of Software

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Journal of Software 2012年第4期7卷 861-869页

作者： Chaudhary, Ankit Raheja, Jagdish L. Raheja, Shekhar Computer Vision Research Group BITS Pilani Rajasthan India Machine Vision Lab CEERI CSIR Pilani Rajasthan India Digital Systems Group CEERI CSIR Pilani Rajasthan India

A novel method to calculate the bended finger's angle has presented here which could be used to control the electro-mechanical robotic hand. It is assumed that the robotic hand has the human hand like joints and same number of degree of freedom as human hand. In many applications, equipment like human hand is needed to do the same kind of operation like human do. These days it is easy to make the electro-mechanical robotic hand which has five fingers and same joints but it is not easily controllable as the human hand for accurate job. In our method the hand gesture will be interpreted for controlling the robotichand. The angles for all the fingers will be calculated and that could be further passed to the robotic hand for controlling its finger. User would perform gesture according to the action as he wants to be done by robotic hand. Here finger positions are detected using geometric analysis of hand in the extracted image, while region of interest cropping from the image make the algorithm faster. © 2012 ACADEMY PUBLISHER.

关键词： Real time systems

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AI@NICTA

AI@NICTA

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作者： Barnes, Nick Baumgartner, Peter Caetano, Tiberio Durrant-Whyte, Hugh Klein, Gerwin Sanderson, Penelope Sattar, Abdul Stuckey, Peter Thiebaux, Sylvie Van Hentenryck, Pascal Walsh, Toby NICTA Australia Software Systems Research Group NICTA Australia Machine Learning Research Group NICTA Australia Trustworthy Systems Project NICTA Australia University of Queensland Australia Institute for Integrated and Intelligent Systems Griffith University Australia Department of Computing and Information Systems University of Melbourne Australia School of Computer Science Australian National University Australia Melbourne School of Engineering Australia

National Information and Communications Technology Australia (NICTA) is the largest ICT research center in Australia, having been established 10 years ago in 2002. It has five laboratories in four Australian capital cities: Sydney, Canberra, Melbourne, and Brisbane. There are currently around 700 staff and Ph.D. students working at NICTA. In June 2009, the 100th Ph.D. student to study at NICTA graduated. At present and averaged over the year, one new Ph.D. student studying at NICTA graduates every 10 days. NICTA has close links with its university members (Australian National University, the University of New South Wales, and the University of Melbourne) as well as with its partner universities (University of Sydney, Griffith University, Queensland University of Technology, University of Queensland, and most recently Monash University). Many of the researchers at NICTA are seconded from these universities. In addition, most of the other researchers at NICTA hold adjunct positions at one of these universities, enabling them to teach courses and supervise Ph.D. students. NICTA also has close links with a number of other research organizations (including Australia's CSIRO, France's INRIA, Japan's NII, and Germany's Fraunhofer Institute) and major companies (including Microsoft, Google, SAP, and Ericsson). Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Students

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Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation

The Journal of Machine Learning Research

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The Journal of machine learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Daniel Hernández-Lobato José Miguel Hernández-Lobato Pierre Dupont Google MPI for Intelligent Systems Computer Science Department Universidad Autónoma de Madrid Madrid Spain Department of Engineering University of Cambridge Cambridge United Kingdom Machine Learning Group ICTEAM Université Catholique de Louvain Louvain-la-Neuve Belgium

We describe a Bayesian method for group feature selection in linear regression problems. The method is based on a generalized version of the standard spike-and-slab prior distribution which is often used for individual feature selection. Exact Bayesian inference under the prior considered is infeasible for typical regression problems. However, approximate inference can be carried out efficiently using Expectation Propagation (EP). A detailed analysis of the generalized spike-and-slab prior shows that it is well suited for regression problems that are sparse at the group level. Furthermore, this prior can be used to introduce prior knowledge about specific groups of features that are a priori believed to be more relevant. An experimental evaluation compares the performance of the proposed method with those of group LASSO, Bayesian group LASSO, automatic relevance determination and additional variants used for group feature selection. The results of these experiments show that a model based on the generalized spike-and-slab prior and the EP algorithm has state-of-the-art prediction performance in the problems analyzed. Furthermore, this model is also very useful to carry out sequential experimental design (also known as active learning), where the data instances that are most informative are iteratively included in the training set, reducing the number of instances needed to obtain a particular level of prediction accuracy.

关键词： approximate inference expectation propagation generalized spike-and-slab priors group feature selection sequential experimental design signal reconstruction sparse linear model

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Subspace Clustering

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IEEE SIGNAL PROCESSING MAGAZINE 2011年第2期28卷 52-68页

作者： Vidal, Rene He was coeditor of the book Dynamical Vision and has coauthored more than 100 articles in biomedical image analysis computer vision machine learning hybrid systems and robotics.

The past few years have witnessed an explosion in the availability of data from multiple sources and modalities. For example, millions of cameras have been installed in buildings, streets, airports, and cities around the world. This has generated extraordinary advances on how to acquire, compress, store, transmit, and process massive amounts of complex high-dimensional data.

关键词： Principal component analysis Polynomials Clustering algorithms Signal processing algorithms Noise Data models Subspace constraints

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Trace chemical and major constituents measurements of the international space station atmosphere by the vehicle cabin atmosphere monitor

Trace chemical and major constituents measurements of the in...

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42nd International Conference on Environmental systems 2012, ICES 2012

作者： Darrach, M.R. Chutjian, A. Bornstein, B.J. Croonquist, A.P. Garkanian, V. Hofman, J. Karmon, D. Kenny, J. Kidd, R.D. Lee, S. Macaskill, J.A. Madzunkov, S.M. Mandrake, L. Schaefer, R.T. Toomarian, N. Jet Propulsion Laboratory California Institute of Technology Pasadena CA 91109 United States Atomic and Molecular Physics Group JPL/Caltech Pasadena CA 91109 United States Instrument Autonomy Group JPL/Caltech Pasadena CA 91109 United States MicroDevices Group JPL/Caltech Pasadena CA 91109 United States Optical Communications Group JPL/Caltech Pasadena CA 91109 United States Instruments and Science Data Systems JPL/Caltech Pasadena CA 91109 United States Instrument Integration and Test JPL/Caltech Pasadena CA 91109 United States Planetary Chemistry and Astrobiology JPL/Caltech Pasadena CA 91109 United States High Capacity Computing and Modeling JPL/Caltech Pasadena CA 91109 United States Machine Learning and Instrument Autonomy Group JPL/Caltech Pasadena CA 91109 United States Advanced Computer Systems and Technology JPL/Caltech Pasadena CA 91109 United States Advanced Instrument Concepts JPL/Caltech Pasadena CA 91109 United States

ISBN: (纸本)9781600869341

We report on trace gas and major atmospheric constituents results obtained by the Vehicle Cabin Atmosphere Monitor (VCAM) following almost two years of operation aboard the International Space Station (ISS). VCAM isan autonomous environmental monitor based on a highly compact gas chromatograph/quadrupole ion trap mass spectrometer. It was flown to the International Space Station (ISS) on shuttle mission STS-131 and commenced operations on June 2010. VCAM is capable of providing measurements of both parts-per-billion (ppb) levels of volatile trace-gas constituents, and of the atmospheric major constituents (nitrogen, oxygen, argon, and carbon dioxide) in a space vehicle or station. It is designed to operate autonomously and maintenance-free, approximately once per day, with a self-contained gas supply sufficient for a one-year lifetime. VCAM's performance is sufficient to detect and identify 90% of the target compounds at their 180-day Spacecraft Maximum Allowable Concentration levels. © 2012 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Gases

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Tracking of fingertips and centers of palm using KINECT

Tracking of fingertips and centers of palm using KINECT

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2nd International Conference on Computational Intelligence, Modelling and Simulation 2011, CIMSim 2011

作者： Raheja, Jagdish L. Chaudhary, Ankit Singal, Kunal Machine Vision Lab. Digital Systems Group CEERI/CSIR Pilani Rajasthan India Computer Vision Research Group BITS Pilani Rajasthan India Punjab Engineering College Chandigarh India

ISBN: (纸本)9780769545622

Hand Gesture is a popular way to interact or control machines and it has been implemented in many applications. The geometry of hand is such that it is hard to construct in virtual environment and control the joints but the functionality and DOF encourage researchers to make a hand like instrument. This paper presents a novel method for fingertips detection and centres of palms detection distinctly for both hands using MS KINECT in 3D from the input image. KINECT facilitates us by providing the depth information of foreground objects. The hands were segmented using the depth vector and centres of palms were detected using distance transformation on inverse image. This result would be used to feed the inputs to the robotic hands to emulate human hands operation. © 2011 IEEE.

关键词： Image segmentation

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