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International Symposium on Artificial Intelligence and signal processing (AISP)

作者： Omid Haji-Maghsoudi Alireza Talebpour Hamid Soltanian-Zadeh Navid Haji-maghsoodi Department of Radiation Medicine Shahid Beheshti University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Hospital Detroit MI USA Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Mechatronics Sharif University of Tehran Tehran Iran

Wireless capsule endoscopy (WCE) views the entire gastrointestinal (GI) tract. A main problem associated with this novel device is that too many frames must be reviewed by physicians. Thus it is essential to find an automatic and intelligent method to help physicians. One of the problems in WCE is its difficulty to distinguish among different organ's tissues. So, we introduce two novel algorithms which are able to classify main organs (among esophagus, stomach, small bowel and colon) in WCE's frames. In order to obtain our aim, we use statistic features (Haralick features) and non-statistic features (different diagrams and Gabor filter banks), colored features and non-colored features. Our experimental studies indicate good results that are shown in conclusion.

关键词： Level set Medical services Gabor filters Active contours Wireless communication Optical wavelength conversion Esophagus

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012年 7191 LNCS卷 V-VII页

作者： Cichocki, Andrzej Theis, Fabian Yeredor, Arie Zibulevsky, Michael Helmholtz Center Munich Institute of Bioinformatics and Systems Biology Ingolstädter Landstr. 1 85764 Neuherberg Germany Riken Brain Science Institute Laboratory for Advanced Brain Signal Processing 2-1 Hirosawa Wako-shi 351-0198 Saitama Japan Tel-Aviv University School of Electrical Engineering Tel-Aviv 69978 Israel Technion - Israel Institute of Technology Department of Computer Science Haifa 32000 Israel

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COLLABORATIVE SYSTEM FOR signal processing EDUCATION

COLLABORATIVE SYSTEM FOR SIGNAL PROCESSING EDUCATION

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Gregory A. Krudysz James H. McClellan Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology

ISBN: (纸本)9781457705380

In this paper we present a collaborative system designed to develop problem solving skills in learners through problem-centric exercises. This system is part of a data collection study consisting of hundreds of students which has been incorporated into the curriculum of a signal processing course. The purpose of this study is to examine a large population of students learning about DSP as they work through concept-specific problems. By tracking student problem-solving sessions, we aim to data mine the results to model student comprehension and characterize learning preferences.

关键词： Intelligent Tutoring System Web-based Learning Tools Collaborative Filtering

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Web-based platform for problem-centered learning in DSP

Web-based platform for problem-centered learning in DSP

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IEEE Digital signal processing Workshop

作者： Gregory A. Krudysz James H. McClellan Center of Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology USA

In this paper we present a web-based tutoring system designed to enhance a student's conceptual understanding by providing many concept-centered exercises. The system relies on a course textbook from an introductory signal processing course for knowledge representation, and also on a large database of relevant problems. We construct concept maps from the textbook to create meta-data for various textbook derived resources, as well as for tagging all the exercise questions. The system tracks each student's comprehension of the course material by evaluating the student's problem solving proficiency in terms of the concepts from the textbook..

关键词： Mathematical model Equations Problem-solving Frequency domain analysis Books Indexes

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Effective supervised classification of fMRI activation maps between populations by spatial descriptors

Effective supervised classification of fMRI activation maps ...

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2011 7th Iranian Conference on Machine Vision and image processing, MVIP 2011

作者： Eshaghian, Shaghayegh Hossein-Zadeh, Gholam-Ali Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit MI 48202 United States

ISBN: (纸本)9781457715358

The major obstacle in discrimination between different groups of subjects in a common cognitive state, by functional Magnetic Resonance Imaging (fMRI), has been the high intersubject functional and anatomical variability in the spatial patterns of brain activity. To overcome this, we have used two types of spatial descriptors that characterize the brain regions of interest (ROIs) involved in the cognitive tasks. They include, firstly three-dimensional invariant moment descriptors (3-DMIs), and secondly k-dimensional feature vectors based on concentric spheres. Both types of descriptors are applied to analyze the spatial patterns of cognitive activity of a challenging task and then to classify them across two different subject groups. SVM classifiers along with sequential floating forward feature selection technique are applied to the extracted descriptors of each ROI across the subjects. Our method is applied to experimental fMRI data with the aim of discriminating mental status of heroin IV (Intravenous) abusers and from of those in control subjects in a visual cue task which can induce drug craving. Our results demonstrate that 3-D texture of activation maps provide a good discrimination (with high accuracy) between healthy and addict group. © 2011 IEEE.

关键词： Brain

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Universal image steganalysis against spatial-domain steganography based on energy distribution of singular values

Universal image steganalysis against spatial-domain steganog...

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7th International Conference on Information Technology and Application, ICITA 2011

作者： Shojaei-Hashemi, Anahita Soltanian-Zadeh, Hamid Ghaemmagham, Shahrokh Kamarei, Mahmoud School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI United States Electronics Research Institute Sharif University of Technology Tehran Iran

ISBN: (纸本)9780980326741

A passive image steganalysis method is proposed to universally detect spatial-domain steganography schemes. It is shown to have better performance than universal steganalyzers known to be powerful in spatial domain, including the WFLogSv and the WAM methods. This level of accuracy is the result of improving the WFLogSv steganalyzer by considering a more comprehensive relationship between the singular values of each image block and the linear correlation of the rows and the columns. That is, instead of the closeness of the lower singular values to zero, the energy distribution of the singular values is investigated. An innovative measure is proposed for this investigation, which is inspired from arithmetic mean-geometric mean inequality. Experimental results confirm the supremacy of the proposed steganalysis scheme over its counterparts.

关键词： Singular value decomposition

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Universal steganalysis based on local prediction error in wavelet domain

Universal steganalysis based on local prediction error in wa...

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7th International Conference on Intelligent Information Hiding and Multimedia signal processing, IIHMSP 2011

作者： Shojaei-Hashemi, Anahita Mehdipour-Ghazi, Mostafa Ghaemmaghami, Shahrokh Soltanian-Zadeh, Hamid School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Electronics Research Institute Sharif University of Technology Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI United States

ISBN: (纸本)9780769545172

A passive universal image steganalysis method is proposed that is shown to be of higher detection accuracy than existing truly blind steganalysis methods including Farid's and the WAM. This is achieved by improving some weaknesses of Farid's steganalysis scheme in feature extraction, that is, instead of deriving an over-determined equation system for each sub band of the wavelet decomposition, the sub bands are divided into overlapping blocks and an over-determined equation system is constructed for each block. To guarantee the existence of finite answers, the over-determined equation systems are solved in a way different from Farid's by using Moore-Penrose pseudo-inverse concept. Further improvement to the performance is achieved by adding diagonal directions and increasing the number of moments. The comparative evaluations confirm the superiority of the proposed method over prevalent blind steganalysis schemes. © 2011 IEEE.

关键词： Wavelet decomposition

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Appropriate twinkling frequency and inter-sources distance selection in SSVEP-based HCI systems

Appropriate twinkling frequency and inter-sources distance s...

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IEEE International Conference on signal and image processing Applications (ICSIPA)

作者： Seyed Navid Resalat Seyed Kamaledin Setarehdan Fardin Afdideh Ali Heidarnejad Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering College of Engineering Sharif University of Technology Tehran Iran

Steady-State Visual Evoked Potentials (SSVEPs) are one of the most important EEG signals used in Human computer Interface (HCI) systems. These signals are generated by Looking at flickering external light sources stimulating the central part of the retina. By increasing the number of external light sources, detection of the corresponding SSVEPs from the recorded EEG signal becomes more complicated. On the other hand, the ratio of the sensitivity to specificity in high-speed classifiers becomes more significant. This study presents the effect of the twinkling frequencies and the inter-sources distance of two Light Emitting Diodes (LEDs) on the ratio of the sensitivity to specificity of the two classes. The features used for signal classification is the amplitude of the main frequencies in the spectrum of each frequency pairs that is simply classified by the Max classifier. The purpose of this is to find the best twinkling frequencies and the best inter-sources distance among a set of predefined values when there are only two light sources in order to nearly equalize the sensitivity and the specificity. For this aim, seven different frequency pairs of LEDs in five distinct inter-sources distances are examined and it is shown that the best frequency pair is 10 and 15 Hz with inter-sources distances of 24 or 44 cm.

关键词： Light sources Sensitivity Light emitting diodes Electroencephalography Educational institutions Steady-state Visualization

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Superiority of the 3rd order approximation model to the 2nd order in Diffusion Kurtosis Imaging

Superiority of the 3rd order approximation model to the 2nd ...

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作者： Iraji, Armin Davoodi-Bojd, Esmaeil Soltanian-Zadeh, Hamid Hossein-Zadeh, Gholam-Ali Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit MI 48202 United States

ISBN: (纸本)9789644634284

For estimating the kurtosis parameter in Diffusion Kurtosis Imaging (DKI), usually second order expansion of the diffusion signal is used in conventional acquisition data. However, in this work, we show that this is not sufficient and should be reconsidered. Instead of using the second order expansion, we propose to use the third order expansion. At first, we show that the third order term of the Taylor's expansion has considerable values compared to second and first terms for the conventional b-values used in DKI. Then, for proving our claim empirically, we use real data to compare the proposed model with the previous one. To this end, five regions of the brain are extracted and for each region the fitting errors of both models are computed. The results confirm that the fitting error of the third order model is always (for all regions) less than the second order model. Although the improvement of the fitting error seems to be small, but considering the sensitivity analysis, this improvement can enhance the estimation of kurtosis. The resulting maps of the kurtosis parameter show this enhancement. The white matter in the map of third order model is more distinguishable than in the other model. © 2011 AmirKabir Univ of Tech.

关键词： Diffusion

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Guest Editorial: Vehicular communications and networks - Part I

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IEEE Journal on Selected Areas in Communications 2011年第1期29卷 1-6页

作者： Wang, Cheng-Xiang Vasilakos, Athanasios V. Murch, Ross D. Stüber, Gordon Shen, Xuemin Sherman Chen, Wai Kosch, Timo Joint Research Institute for Signal and Image Processing School of Engineering and Physical Sciences Heriot-Watt University Edinburgh EH14 4AS United Kingdom Dept. of Computer and Telecommunications Engineering University of Western Macedonia Kozani 50100 Greece Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada Applied Research Telcordia Technologies Inc. Piscataway NJ 07054 United States BMW Group Research and Technology Hanauer Strasse 46 D-80992 Munich Germany

Part I of this special issue is devoted to the physical layer, MAC layer, and physical-link cross-layer design technologies of vehicular communications and networks. There are 21 papers in this issue.

关键词： Communication Research iodine VEHICULAR COMMUNICATIONS

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