This curriculum vitae summarizes the career and accomplishments of Dr. Yuan Yan Tang. Dr. Tang holds a Ph.D. in Computer Science from Concordia University and has held prestigious positions including Chair Professor at Hong Kong Baptist University and Dean of the College of Computer Science at Chongqing University. He has organized numerous international conferences and published over 300 papers. Dr. Tang is a fellow of both the IEEE and IAPR, and has received many honors and awards for his scholarly contributions and leadership in his field.
The document is a call for papers for the International Conference on Machine Learning and Data Analysis (ICMLDA 2008) to be held October 22-24, 2008 in San Francisco, USA. It provides information on submission guidelines and important dates, as well as an overview of topics that will be covered at the conference related to machine learning and data analysis techniques and applications. Accepted papers will be published in the conference proceedings and considered for publication in relevant journals.
Fayin Li is seeking a full-time research position in machine learning, computer vision, and image processing. He has over 10 years of experience in these fields, including 6 years of software development experience and expertise in machine learning algorithms, pattern recognition, and computer vision. He received his Ph.D from George Mason University where he conducted research on topics such as object recognition, face recognition, and motion estimation.
TOP 10 CITED PAPERS FOR CRYPTOGRAPHY AND INFORMATION SECURITYijcisjournal
International Journal on Cryptography and Information Security (IJCIS)
ISSN : 1839-8626
https://wireilla.com/ijcis/index.html
TOP 10 CITED PAPERS FOR CRYPTOGRAPHY AND INFORMATION SECURITY
https://www.academia.edu/41468947/TOP_10_CITED_PAPERS_FOR_CRYPTOGRAPHY_AND_INFORMATION_SECURITY
Top Cited Articles in Advanced Computational Intelligence : October 2020aciijournal
Text mining is the process of extracting interesting and non-trivial knowledge or information from unstructured text data. Text mining is the multidisciplinary field which draws on data mining, machine learning, information retrieval, omputational linguistics and statistics. Important text mining processes are information extraction, information retrieval, natural language processing, text classification, content analysis and text clustering. All these processes are required to complete the preprocessing step before doing their intended task. Pre-processing significantly reduces the size of the input text documents and the actions involved in this step are sentence boundary determination, natural language specific stop-word elimination, tokenization and stemming. Among this, the most essential and important action is the tokenization. Tokenization helps to divide the textual information into individual words. For performing tokenization process, there are many open source tools are available. The main objective of this work is to analyze the performance of the seven open source tokenization tools. For this comparative analysis, we have taken Nlpdotnet Tokenizer, Mila Tokenizer, NLTK Word Tokenize, TextBlob Word Tokenize, MBSP Word Tokenize, Pattern Word Tokenize and Word Tokenization with Python NLTK. Based on the results, we observed that the Nlpdotnet Tokenizer tool performance is better than other tools.
The document provides the schedule for a machine learning conference. It includes the times for registration, invited talks on topics like machine learning in space and applying machine learning to real-world problems, contributed talks on research topics, coffee breaks, lunch, and a poster session. The day concludes with a panel discussion and concluding remarks.
Jason K. Johnson is a researcher interested in probabilistic graphical models, signal and image processing, statistical physics, and related topics. He received his Ph.D. in Electrical Engineering and Computer Science from MIT in 2008, working with Prof. Alan Willsky. He has since held positions at Los Alamos National Laboratory and continues to publish widely in top journals and conferences on inference in graphical models.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Industrial neuroscience in aviation evaluation of mental states in aviation ...BatDeegii
This document provides an overview of a book that evaluates mental states in aviation personnel using neuroscience methods. It discusses three main topics: 1) important mental states like workload, cognitive control, and training. 2) Experimental environments and methods used to objectively assess states like workload. 3) Results initially from controlled studies with students and finally with professional personnel in realistic environments. The goal is to use neuroscience to help maintain safety and improve human-machine interaction in aviation by providing objective data on users' cognitive states.
The document is a call for papers for the International Conference on Machine Learning and Data Analysis (ICMLDA 2008) to be held October 22-24, 2008 in San Francisco, USA. It provides information on submission guidelines and important dates, as well as an overview of topics that will be covered at the conference related to machine learning and data analysis techniques and applications. Accepted papers will be published in the conference proceedings and considered for publication in relevant journals.
Fayin Li is seeking a full-time research position in machine learning, computer vision, and image processing. He has over 10 years of experience in these fields, including 6 years of software development experience and expertise in machine learning algorithms, pattern recognition, and computer vision. He received his Ph.D from George Mason University where he conducted research on topics such as object recognition, face recognition, and motion estimation.
TOP 10 CITED PAPERS FOR CRYPTOGRAPHY AND INFORMATION SECURITYijcisjournal
International Journal on Cryptography and Information Security (IJCIS)
ISSN : 1839-8626
https://wireilla.com/ijcis/index.html
TOP 10 CITED PAPERS FOR CRYPTOGRAPHY AND INFORMATION SECURITY
https://www.academia.edu/41468947/TOP_10_CITED_PAPERS_FOR_CRYPTOGRAPHY_AND_INFORMATION_SECURITY
Top Cited Articles in Advanced Computational Intelligence : October 2020aciijournal
Text mining is the process of extracting interesting and non-trivial knowledge or information from unstructured text data. Text mining is the multidisciplinary field which draws on data mining, machine learning, information retrieval, omputational linguistics and statistics. Important text mining processes are information extraction, information retrieval, natural language processing, text classification, content analysis and text clustering. All these processes are required to complete the preprocessing step before doing their intended task. Pre-processing significantly reduces the size of the input text documents and the actions involved in this step are sentence boundary determination, natural language specific stop-word elimination, tokenization and stemming. Among this, the most essential and important action is the tokenization. Tokenization helps to divide the textual information into individual words. For performing tokenization process, there are many open source tools are available. The main objective of this work is to analyze the performance of the seven open source tokenization tools. For this comparative analysis, we have taken Nlpdotnet Tokenizer, Mila Tokenizer, NLTK Word Tokenize, TextBlob Word Tokenize, MBSP Word Tokenize, Pattern Word Tokenize and Word Tokenization with Python NLTK. Based on the results, we observed that the Nlpdotnet Tokenizer tool performance is better than other tools.
The document provides the schedule for a machine learning conference. It includes the times for registration, invited talks on topics like machine learning in space and applying machine learning to real-world problems, contributed talks on research topics, coffee breaks, lunch, and a poster session. The day concludes with a panel discussion and concluding remarks.
Jason K. Johnson is a researcher interested in probabilistic graphical models, signal and image processing, statistical physics, and related topics. He received his Ph.D. in Electrical Engineering and Computer Science from MIT in 2008, working with Prof. Alan Willsky. He has since held positions at Los Alamos National Laboratory and continues to publish widely in top journals and conferences on inference in graphical models.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Industrial neuroscience in aviation evaluation of mental states in aviation ...BatDeegii
This document provides an overview of a book that evaluates mental states in aviation personnel using neuroscience methods. It discusses three main topics: 1) important mental states like workload, cognitive control, and training. 2) Experimental environments and methods used to objectively assess states like workload. 3) Results initially from controlled studies with students and finally with professional personnel in realistic environments. The goal is to use neuroscience to help maintain safety and improve human-machine interaction in aviation by providing objective data on users' cognitive states.
The document is a curriculum vitae for Colin Fyfe. It summarizes that he is currently a Personal Professor at the University of the West of Scotland, with educational qualifications including a BSc in Mathematics, MSc in Information Technology, and a PhD in neural networks. It also outlines his extensive employment history in education and research, as well as his significant research contributions and roles in academic administration and conference organization.
DiaMe: IoMT deep predictive model based on threshold aware region growing tec...IJECEIAES
Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.
Most Cited Articles in Academia --Signal & Image Processing : An Internationa...sipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
resume Jose Perez-Macias Biomedical Engineer Machine Audition Health ScientistJose Maria Perez-Macias
José Maria Pérez-Macías is looking for a permanent position in Helsinki, Finland. He has a PhD in biomedical signal processing and data science from Tampere University of Technology. His research focuses on sleep studies using various sensors and developing algorithms to detect sleep disorders. He has extensive experience in research organizations and currently works as an industrial PhD intern at Huawei Technologies in Helsinki evaluating wearable sensor performance.
11.development of a feature extraction technique for online character recogni...Alexander Decker
The document describes a study that developed a hybrid feature extraction technique for online character recognition. The technique combines geometrical and statistical features. Geometrical features included stroke information (number, pressure, junctions, horizontal projection count) and contour pixels. Statistical features included zoning, which divides the character image into zones and calculates the percentage of black pixels in each zone. A hybrid algorithm was created that integrated geometrical and statistical features to take advantage of their complementarity and gain new insights into character properties. The goal was to improve recognition performance over existing single-feature techniques.
Development of a feature extraction technique for online character recognitio...Alexander Decker
The document describes a study that developed a hybrid feature extraction technique for online character recognition. The technique combines geometrical and statistical features. Geometrical features included stroke information (number, pressure, junctions, horizontal projection count) and contour pixels. Statistical features included zoning, which divides the character image into zones and calculates the percentage of black pixels in each zone. A hybrid algorithm was created that integrated geometrical and statistical features to take advantage of their complementarity and gain new insights into character properties. The goal was to improve recognition performance over existing single-feature techniques.
Machine Learning in Material Characterizationijtsrd
Machine learning has shown great potential applications in material science. It is widely used in material design, corrosion detection, material screening, new material discovery, and other fields of materials science. The majority of ML approaches in materials science is based on artificial neural networks ANNs . The use of ML and related techniques for materials design, development, and characterization has matured to a main stream field. This paper focuses on the applications of machine learning strategies for material characterization. Matthew N. O. Sadiku | Guddi K. Suman | Sarhan M. Musa "Machine Learning in Material Characterization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46392.pdf Paper URL : https://www.ijtsrd.com/engineering/electrical-engineering/46392/machine-learning-in-material-characterization/matthew-n-o-sadiku
This document provides biographical information about Taewoo Kim in 3 sections: education, experience, and honors & awards. It summarizes that Kim received his PhD in electrical engineering from the University of Illinois in 2015. He has held postdoctoral positions at Caltech and Washington University studying quantitative phase imaging. His research focuses on optical imaging techniques for biomedical applications. He has received several honors and awards for his graduate work and published over 20 papers.
This document outlines the research axes and focus areas of the 3IA Côte d'Azur institute. It discusses four main research axes: 1) core elements of AI, 2) AI for integrative computational medicine, 3) AI for computational biology and bio-inspired AI, and 4) AI for smart and secure territories. Each axis has several chairs and focus areas such as interpretability, healthcare applications, computational neuroscience, urban modeling and more. The institute aims to advance foundational AI research while applying it to real-world problems in health, biology and smart communities.
Mahesh Joshi has studied computer science and engineering, obtaining degrees from universities in India and the US. He is currently pursuing a Masters in Language Technologies at Carnegie Mellon University. His research focuses on natural language processing techniques such as word sense disambiguation and abbreviation expansion, especially in medical texts. He has published papers in conferences and released several related software tools.
Georgios Chalkiadakis is a PhD candidate at the University of Toronto specializing in artificial intelligence. He received a Master's degree from the University of Crete in 1999 and a Diploma in computer science from the University of Crete in 1997. His research interests include multi-agent reinforcement learning, coalition formation, and distributed systems. He has published papers in these areas and attended several conferences, including AAMAS.
This CV summarizes the professional experience and qualifications of Marc J Sobel. It lists his educational background including a B.A. in Mathematics and Philosophy from University of Minnesota in 1977 and a Ph.D. in Statistics from University of California, Berkeley in 1983. It also outlines his current position as an Associate Professor of Statistics at Temple University since 1992, and provides details on his teaching experience, research interests, and publications.
CONTENT RECOVERY AND IMAGE RETRIVAL IN IMAGE DATABASE CONTENT RETRIVING IN TE...Editor IJMTER
Digital Images are used in magazines, blogs, website, television and more. Digital image processing
techniques are used for feature selection, pattern extraction classification and retrieval requirements. Color, texture
and shape features are used in the image processing. Digital images processing also supports computer graphics
and computer vision domains. Scene text recognition is performed with two schemes. They are character
recognizer and binary character classifier models. A character recognizer is trained to predict the category of a
character in an image patch. A binary character classifier is trained for each character class to predict the existence
of this category in an image patch. Scene text recognition is performed on detected text regions. Pixel-based layout
analysis method is adopted to extract text regions and segment text characters in images. Text character
segmentation is carried out with color uniformity and horizontal alignment of text characters. Discriminative
character descriptor is designed by combining several feature detectors and descriptors. Histogram of Oriented
Gradients (HOG) is used to identify the character descriptors. Character structure is modeled at each character
class by designing stroke configuration maps. The scene text extraction scheme is also supports for smart mobile
devices. Text recognition methods are used with text understanding and text retrieval applications. The text
recognition scheme is enhanced with content based image retrieval process. The system is integrated with
additional representative and discriminative features for text structure modeling process. The system is enhanced to
perform text and word level recognition using lexicon analysis. The training process is included with word
database update task.
This document provides information about the Department of Computer Engineering and Informatics at the University of Patras in Greece. It details the department's history, structure, faculty, research areas, facilities, and undergraduate and postgraduate degree programs. The department was founded in 1980 and has over 1380 undergraduate students and 350 postgraduate students. It offers undergraduate degrees in computer engineering and postgraduate degrees including masters and PhD programs.
This document outlines the syllabus for the course EES6004-Biomedical Signal Processing. The course will cover topics related to biomedical signals including anatomy, physiology, signal acquisition, analysis in time and frequency domains, modeling, classification, and applications. Assessment will include mid and end semester exams worth 30% and 50% respectively, along with a 15% project and 5% quizzes. Lectures will be led by Dr. M. Sabarimalai Manikandan and Dr. Debi Prosad Dogra and will cover various biomedical signals and processing techniques. MATLAB will be used for programming assignments.
This curriculum vitae summarizes the academic and professional background of Yuan Yan Tang. Tang has a Ph.D. in Computer Science from Concordia University and has held professorships at Hong Kong Baptist University and Chongqing University. He has extensive experience organizing international conferences and has published over 300 papers. He is a fellow of the IEEE and IAPR, and serves on the editorial boards of several journals.
This curriculum vitae summarizes the career and accomplishments of Dr. Yuan Yan Tang. Dr. Tang holds a Ph.D. in Computer Science and has held prestigious positions including Chair Professor and Dean. He has organized numerous international conferences and published over 300 papers. Dr. Tang's research focuses on machine learning, pattern recognition, and document analysis using techniques such as wavelet analysis.
Top Cited Article in Informatics Engineering Research: October 2020ieijjournal
Informatics is rapidly developing field. The study of informatics involves human-computer interaction and how an interface can be built to maximize user-efficiency. Due to the growth in IT, individuals and organizations increasingly process information digitally. This has led to the study of informatics to solve privacy, security, healthcare, education, poverty, and challenges in our environment. The Informatics Engineering, an International Journal (IEIJ) is a open access peer-reviewed journal that publishes articles which contribute new results in all areas of Informatics. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on the human use of computing fields such as communication, mathematics, multimedia, and human-computer interaction design and establishing new collaborations in these areas.
This document lists the publications of Chien-Cheng Lee and Cheng-Yuan Shih from 2004-2012. It includes 13 journal papers published in peer-reviewed journals, 10 conference papers presented at international conferences, 2 magazine articles, and 2 patents. The publications focus on topics related to neural networks, time series prediction, image processing, video analysis, and biomedical applications.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This document is a resume for Amit Sethi summarizing his professional experience and qualifications. It outlines his objective to obtain a research and development position in industry. It then details his education at the University of Illinois at Urbana-Champaign where he is pursuing a PhD in Electrical and Computer Engineering, as well as a previous degree from Indian Institute of Technology. His experience includes research in machine learning, computer vision, and video processing. He has several publications and awards and is skilled in programming languages such as C++.
This document is a resume for Amit Sethi summarizing his professional experience and qualifications. It outlines his objective to obtain a research and development position in industry. It then details his education at the University of Illinois at Urbana-Champaign where he is pursuing a PhD in Electrical and Computer Engineering, as well as a previous degree from Indian Institute of Technology. His experience includes research in machine learning, computer vision, and video processing. He has several publications and awards and is proficient in programming languages and computer skills relevant to his field.
The document is a curriculum vitae for Colin Fyfe. It summarizes that he is currently a Personal Professor at the University of the West of Scotland, with educational qualifications including a BSc in Mathematics, MSc in Information Technology, and a PhD in neural networks. It also outlines his extensive employment history in education and research, as well as his significant research contributions and roles in academic administration and conference organization.
DiaMe: IoMT deep predictive model based on threshold aware region growing tec...IJECEIAES
Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.
Most Cited Articles in Academia --Signal & Image Processing : An Internationa...sipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
resume Jose Perez-Macias Biomedical Engineer Machine Audition Health ScientistJose Maria Perez-Macias
José Maria Pérez-Macías is looking for a permanent position in Helsinki, Finland. He has a PhD in biomedical signal processing and data science from Tampere University of Technology. His research focuses on sleep studies using various sensors and developing algorithms to detect sleep disorders. He has extensive experience in research organizations and currently works as an industrial PhD intern at Huawei Technologies in Helsinki evaluating wearable sensor performance.
11.development of a feature extraction technique for online character recogni...Alexander Decker
The document describes a study that developed a hybrid feature extraction technique for online character recognition. The technique combines geometrical and statistical features. Geometrical features included stroke information (number, pressure, junctions, horizontal projection count) and contour pixels. Statistical features included zoning, which divides the character image into zones and calculates the percentage of black pixels in each zone. A hybrid algorithm was created that integrated geometrical and statistical features to take advantage of their complementarity and gain new insights into character properties. The goal was to improve recognition performance over existing single-feature techniques.
Development of a feature extraction technique for online character recognitio...Alexander Decker
The document describes a study that developed a hybrid feature extraction technique for online character recognition. The technique combines geometrical and statistical features. Geometrical features included stroke information (number, pressure, junctions, horizontal projection count) and contour pixels. Statistical features included zoning, which divides the character image into zones and calculates the percentage of black pixels in each zone. A hybrid algorithm was created that integrated geometrical and statistical features to take advantage of their complementarity and gain new insights into character properties. The goal was to improve recognition performance over existing single-feature techniques.
Machine Learning in Material Characterizationijtsrd
Machine learning has shown great potential applications in material science. It is widely used in material design, corrosion detection, material screening, new material discovery, and other fields of materials science. The majority of ML approaches in materials science is based on artificial neural networks ANNs . The use of ML and related techniques for materials design, development, and characterization has matured to a main stream field. This paper focuses on the applications of machine learning strategies for material characterization. Matthew N. O. Sadiku | Guddi K. Suman | Sarhan M. Musa "Machine Learning in Material Characterization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46392.pdf Paper URL : https://www.ijtsrd.com/engineering/electrical-engineering/46392/machine-learning-in-material-characterization/matthew-n-o-sadiku
This document provides biographical information about Taewoo Kim in 3 sections: education, experience, and honors & awards. It summarizes that Kim received his PhD in electrical engineering from the University of Illinois in 2015. He has held postdoctoral positions at Caltech and Washington University studying quantitative phase imaging. His research focuses on optical imaging techniques for biomedical applications. He has received several honors and awards for his graduate work and published over 20 papers.
This document outlines the research axes and focus areas of the 3IA Côte d'Azur institute. It discusses four main research axes: 1) core elements of AI, 2) AI for integrative computational medicine, 3) AI for computational biology and bio-inspired AI, and 4) AI for smart and secure territories. Each axis has several chairs and focus areas such as interpretability, healthcare applications, computational neuroscience, urban modeling and more. The institute aims to advance foundational AI research while applying it to real-world problems in health, biology and smart communities.
Mahesh Joshi has studied computer science and engineering, obtaining degrees from universities in India and the US. He is currently pursuing a Masters in Language Technologies at Carnegie Mellon University. His research focuses on natural language processing techniques such as word sense disambiguation and abbreviation expansion, especially in medical texts. He has published papers in conferences and released several related software tools.
Georgios Chalkiadakis is a PhD candidate at the University of Toronto specializing in artificial intelligence. He received a Master's degree from the University of Crete in 1999 and a Diploma in computer science from the University of Crete in 1997. His research interests include multi-agent reinforcement learning, coalition formation, and distributed systems. He has published papers in these areas and attended several conferences, including AAMAS.
This CV summarizes the professional experience and qualifications of Marc J Sobel. It lists his educational background including a B.A. in Mathematics and Philosophy from University of Minnesota in 1977 and a Ph.D. in Statistics from University of California, Berkeley in 1983. It also outlines his current position as an Associate Professor of Statistics at Temple University since 1992, and provides details on his teaching experience, research interests, and publications.
CONTENT RECOVERY AND IMAGE RETRIVAL IN IMAGE DATABASE CONTENT RETRIVING IN TE...Editor IJMTER
Digital Images are used in magazines, blogs, website, television and more. Digital image processing
techniques are used for feature selection, pattern extraction classification and retrieval requirements. Color, texture
and shape features are used in the image processing. Digital images processing also supports computer graphics
and computer vision domains. Scene text recognition is performed with two schemes. They are character
recognizer and binary character classifier models. A character recognizer is trained to predict the category of a
character in an image patch. A binary character classifier is trained for each character class to predict the existence
of this category in an image patch. Scene text recognition is performed on detected text regions. Pixel-based layout
analysis method is adopted to extract text regions and segment text characters in images. Text character
segmentation is carried out with color uniformity and horizontal alignment of text characters. Discriminative
character descriptor is designed by combining several feature detectors and descriptors. Histogram of Oriented
Gradients (HOG) is used to identify the character descriptors. Character structure is modeled at each character
class by designing stroke configuration maps. The scene text extraction scheme is also supports for smart mobile
devices. Text recognition methods are used with text understanding and text retrieval applications. The text
recognition scheme is enhanced with content based image retrieval process. The system is integrated with
additional representative and discriminative features for text structure modeling process. The system is enhanced to
perform text and word level recognition using lexicon analysis. The training process is included with word
database update task.
This document provides information about the Department of Computer Engineering and Informatics at the University of Patras in Greece. It details the department's history, structure, faculty, research areas, facilities, and undergraduate and postgraduate degree programs. The department was founded in 1980 and has over 1380 undergraduate students and 350 postgraduate students. It offers undergraduate degrees in computer engineering and postgraduate degrees including masters and PhD programs.
This document outlines the syllabus for the course EES6004-Biomedical Signal Processing. The course will cover topics related to biomedical signals including anatomy, physiology, signal acquisition, analysis in time and frequency domains, modeling, classification, and applications. Assessment will include mid and end semester exams worth 30% and 50% respectively, along with a 15% project and 5% quizzes. Lectures will be led by Dr. M. Sabarimalai Manikandan and Dr. Debi Prosad Dogra and will cover various biomedical signals and processing techniques. MATLAB will be used for programming assignments.
This curriculum vitae summarizes the academic and professional background of Yuan Yan Tang. Tang has a Ph.D. in Computer Science from Concordia University and has held professorships at Hong Kong Baptist University and Chongqing University. He has extensive experience organizing international conferences and has published over 300 papers. He is a fellow of the IEEE and IAPR, and serves on the editorial boards of several journals.
This curriculum vitae summarizes the career and accomplishments of Dr. Yuan Yan Tang. Dr. Tang holds a Ph.D. in Computer Science and has held prestigious positions including Chair Professor and Dean. He has organized numerous international conferences and published over 300 papers. Dr. Tang's research focuses on machine learning, pattern recognition, and document analysis using techniques such as wavelet analysis.
Top Cited Article in Informatics Engineering Research: October 2020ieijjournal
Informatics is rapidly developing field. The study of informatics involves human-computer interaction and how an interface can be built to maximize user-efficiency. Due to the growth in IT, individuals and organizations increasingly process information digitally. This has led to the study of informatics to solve privacy, security, healthcare, education, poverty, and challenges in our environment. The Informatics Engineering, an International Journal (IEIJ) is a open access peer-reviewed journal that publishes articles which contribute new results in all areas of Informatics. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on the human use of computing fields such as communication, mathematics, multimedia, and human-computer interaction design and establishing new collaborations in these areas.
This document lists the publications of Chien-Cheng Lee and Cheng-Yuan Shih from 2004-2012. It includes 13 journal papers published in peer-reviewed journals, 10 conference papers presented at international conferences, 2 magazine articles, and 2 patents. The publications focus on topics related to neural networks, time series prediction, image processing, video analysis, and biomedical applications.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This document is a resume for Amit Sethi summarizing his professional experience and qualifications. It outlines his objective to obtain a research and development position in industry. It then details his education at the University of Illinois at Urbana-Champaign where he is pursuing a PhD in Electrical and Computer Engineering, as well as a previous degree from Indian Institute of Technology. His experience includes research in machine learning, computer vision, and video processing. He has several publications and awards and is skilled in programming languages such as C++.
This document is a resume for Amit Sethi summarizing his professional experience and qualifications. It outlines his objective to obtain a research and development position in industry. It then details his education at the University of Illinois at Urbana-Champaign where he is pursuing a PhD in Electrical and Computer Engineering, as well as a previous degree from Indian Institute of Technology. His experience includes research in machine learning, computer vision, and video processing. He has several publications and awards and is proficient in programming languages and computer skills relevant to his field.
This document provides a summary of an experienced educator seeking a challenging position utilizing over 32 years of experience in education administration and management. The educator holds a bachelor's degree in electronics and telecommunication engineering and has worked in various roles including principal, vice principal, and head of departments. Areas of expertise include leadership, management, teaching, and coordinating accreditation. The educator has international journal publications, guided PhD scholars, and is currently supervising research scholars. The objective is to find a dynamic position to apply skills and experience.
June 2020: Most Downloaded Article in Soft Computing ijsc
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
International conference on mechanical engineering and applied mechanics[icme...Mellah Hacene
The document summarizes the 2021 International Conference on Mechanical Engineering and Applied Mechanics (ICMEAM 2021) that was organized by Yaseen Academy and originally planned to be held in Sanya, China but was changed to an online format due to COVID-19 restrictions. It provides details on the conference organization, topics, presenters and keynote speakers. The conference included presentations on mechanical engineering, structural mechanics and materials science. It featured two keynote speeches from researchers at Innowledgement GmbH on developing the world's smallest and fastest mechanical nano-tools.
This document provides a summary of Se-Young Yun's background and qualifications. It outlines her education, awards, professional experience, research interests, and publications. She received her B.S. and Ph.D. in Electrical Engineering from KAIST and has held several prestigious postdoctoral research positions. Her research focuses on topics like community detection, low-rank matrix approximation, and ranking aggregation. She has over 20 peer-reviewed publications in top conferences and journals.
Here is my updated CV using the ModernCV template (http://www.latextemplates.com/template/moderncv-cv-and-cover-letter).
You can find the Tex source file in (https://dl.dropbox.com/u/2810224/Homepage/resume/modern%20style.rar)
Fayin Li is seeking a full-time research position in machine learning, computer vision, and image processing. He has over 10 years of experience in these fields, including expertise in mathematics, algorithms, software development, machine learning techniques, and programming languages. He received his Ph.D from George Mason University where he conducted research on topics such as object recognition, face recognition, and motion estimation.
October 2021: Top Read Articles in Soft Computingijsc
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This document is a resume for Chenhui Hu, a PhD candidate in Electrical Engineering at Harvard University. It summarizes his education, research experience, publications, awards, and other qualifications. He has conducted research in machine learning, signal processing, and wireless networks. Some of his accomplishments include improving Alzheimer's disease classification accuracy by over 24% using graph-signal processing and deep learning, and reducing brain atrophy progression prediction error by 60% using decomposed sparse vector-autoregression models.
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The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
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1. CURRICULUM VITAE
Yuan Yan Tang, Ph. D.
IEEE Fellow, IAPR Fellow
Chair of Technical Committee on Machine Learning
in IEEE Systems, Man, and Cybernetics Society
(IEEE SMC)
IEEE SMC Fellow Committee Member
Chair Professor, Department of Computer Science,
Hong Kong Baptist University
Dean, College of Computer Science,
Chongqing University, China
EDUCATION
Ph.D. degree Computer Science at Concordia University, Montreal, Canada.
Master degree Electrical Engineering at Beijing University of Posts and Telecommunications,
China.
Bachelor degree Electronic & Computer Engineering at Chongqing University, China.
SCHOLARLY AND PROFESSIONAL ACTIVITIES
Organization of International Conferences:
General Chair: The IEEE International Conferences on Wavelet Analysis and Pattern
Recognition (ICWAPR’07, ICWAPR’08).
General Chair: The 18th International Conferences on Pattern Recognition, (ICPR’06), Hong
Kong, August 2006
General Chair: The 8th International Conferences on Document Analysis and Recognition
(ICDAR’05), Seoul, Korea, 2005
General Chair: The International Computer Congress 2004 on Wavelet Analysis and Its
Applications, and Active Media Technology
General Chair: The 3rd International Conference on Wavelet Analysis and Its Applications
(ICWAA’03), Chongqing, May 2003
Program Chair: The International Conference on Machine Learning and Cybernetics 2002
(ICMLC-2002), Beijing, November 4-5, 2002
Program Chair: The 2nd International Conference on Wavelet Analysis and Its Application
(ICWAA’01), Hong Kong, December 17-19, 2001
General Chair: The 1st International Conference on Information Technology and
Application (ICITA 2001), Sanya, China, January 16-17, 2001
Program Chair: The 2nd International Conference on Multimodel Interface (ICMI’99), Hong
Kong, January 5-7, 1999
General Chair: The 17th International Conference on Computer Processing of Oriental
Languages (ICCPOL’97), Hong Kong, April 2-4, 1997
Co-Chair, Local Arrangements of the 3rd International Conferences on Document Analysis
and Recognition (ICDAR'95), Montreal, Canada, August 14-16, 1995
Session Chairs or Program Committee Members of many conferences
Scholarship/Awards/Recognition:
2. Fellow of IEEE
Fellow of Pattern Recognition Society (IAPR)
Chair of Technical Committee on Machine Learning - IEEE Systems, Man, and
Cybernetics Society (Cybernetics)
IEEE SMC Fellow Committee Member
Life Member of Chinese Language Computer Society (CLCS)
Marquis Who’s Who in the World, 1999, 2000
The Hong Kong Baptist University Staff Research Fellowship, 1997
Visiting Fellowship of KC Wong Education Foundation, 1996
Co-founder of a new international research center (International Center for Wavelet
Analysis and Its Applications) in China with researches in Mainland, USA and Taiwan,
2000
Founder of a new journal: International Journal on Wavelets, Multiresolution, and
Information Processing (IJWMIP) 2001
Editors of International Journals:
Founder & Editor-in-Chief: International Journal on Wavelets, Multiresolution, and
Information Processing (IJWMIP)
Associate Editor: International Journal of Pattern Recognition & Artificial Intelligence
(IJPRAI)
Member of Editorial Board: International Journal of Document Analysis and Recognition
(IJDAR)
Associate Editor: Frontiers of Computer Science in China
Reviewer for the Journals and International Conferences:
IEEE Transactions (Pattern Anal. and Machine Intell, System, Man, and Cybernetics, Image
Processing, Signal Processing, Circuits and Systems)
IEEE Computer
Journal of Information Processing Letters
Computer Vision and Applications
Journal of Pattern Recognition and Artificial Intelligence
Signal Processing
Pattern Recognition Letters
Information Sciences: An International Journal
Computer Processing of Chinese and Oriental Languages
Many international conferences
Invited Lectures / Seminars :
Utah State University, USA
University of Michigan-Dearborm, USA
City University of New York, USA
Apple Computer offices in Cupertino, USA
Rennes University, France
Indian Statistical Institute, Kolkata, Inndia
Chungbuk National University, Korea
Korea University, Korea
Macao University of Science and Technology
Institute of Information Science, Academia Sinica, Taipei, Taiwan
Many Universities in China, such as
Tsinghua University, Pekin University, Beijing University of Posts & Telecommunications,
Chengdu University of Science and Technology, Chinese Academy of Medical Sciences,
Chongqing University, Southwest China Normal University, Cardiovascular Institute &
Fuwai Hospital, Beijing, Institute of Automation, Academy of Science, Nankai University,
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3. Northeast University, Northern Jiaotong University, Shengyang Institute of Aeronautical
Engineering, Southwestern Jiaotong University, Sichua University, Nanjing University,
Shanghai University, Xidian University, Huazhong University of Science and Technology,
etc.
The General Office of the Central Committee of the Chinese Communist Party, China
Supervision of Postgraduate Students (Ph. D. and M. Sc.):
Hong Kong Baptist University
The University of Hong Kong
Canada Concordia University
Sichuan University
Chongqing University
Huazhong University of Science and Technology
Research Projects:
26 research projects.
PUBLICATIONS
More than 300 international publications, including 22 books/chapters
The number of citations is over 1500
Selected Publications
1. Taiping Zhang, Bin Fang, Yuan Yan Tang, Guanghui He and Jing Wen, “Topology Preserving
Nonnegative Matrix Factorization for Face Recognition,” IEEE Trans. on Image Processing, Vol. 17,
No. 4, pp. 574-584, 2008.
2. Yuan Yan Tang, “Status of Pattern Recognition with Wavelet Analysis,” Frontiers of Computer
Science, Vol. 2, No. 3, pp. 268-294, 2008
3. Yuan Yan Tang, “Remarks on Different Reviews of Chinese Character Recognition,” Frontiers of
Computer Science, Vol. 1, No. 2, pp. 123-125, 2007.
4. Xiaofan Yang and Yuan Yan Tang, “Efficient Fault Identification of Diagnosable Systems Under the
Comparison Model,” IEEE Trans. on Computers, 2007.
5. Xinge You and Yuan Yan Tang, “Wavelet-based Approach to Character Skeleton,” IEEE Trans. on
Image Processing, Vol. 16, No. 3, 2007.
6. Taiping Zhang, Bin Fang, Yuan Yan Tang, Guanghui He and Jing Wen, “Topology Preserving
Nonnegative Matrix Factorization for Face Recognition,” IEEE Trans. on Image Processing, 2007 (in
press).
7. B. Fang and Yuan Yan Tang, “Improved Class Statistics Estimation for Sparse Data Problems in Off-
line Signature Verification,” IEEE Trans. on Systems, Man, and Cybernetics (C), Vol. 35, No. 3, pp.
276-286,2005.
8. Xiaoyuan Jing, David Zhang and Yuan Yan Tang, “Improved LDA Approach,” IEEE Trans.
on Systems, Man, and Cybernetics (B), Vol. 34, No. 10, pp. 1942-1951, 2004.
9. Yuan Yan Tang and X. G. You, “Skeletonization of Ribbon-like Shapes Based on A New
Wavelet Function,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 25, No. 9,
pp. 1118-1133, 2003.
10. Penglang Shui, Zheng Bao and Yuan Yan Tang, “Three-Band Biorthogonal Interpolating
Complex Wavelets with Stopband Suppression via Lifting Scheme,” IEEE Trans. on Signal
Processing, Vol. 51, No. 5, pp. 1293-1305, 2003.
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4. 11. L. H. Yang, C. Y. Suen and Yuan Yan Tang, “A Width-Invariant Property of Curves Based on
Wavelet Transform with a Novel Wavelet Function,” IEEE Trans. on Systems, Man, and
Cybernetics (B), Vol. 33, No. 3, pp. 541-548, 2003.
12. Yuan Yan Tang, F. Yang and Jiming Liu, “Basic Processes of Chinese Character Based on
Cubic B-Spline Wavelet Transform,” IEEE Trans. on Pattern Analysis and Machine
Intelligence, Vol. 23, No. 12, pp. 1443-1448, 2001.
13. Yuan Yan Tang, L. H. Yang and Jiming Liu, “Characterization of Dirac-Structure Edges with
Wavelet Transform,” IEEE Trans. on Systems, Man, and Cybernetics (B), Vol. 30, No. 1, pp.
93-109, 2000.
14. Jiming Liu and Yuan Yan Tang, “Adaptive Image Segmentation with Distributed Behavior-
based Agents”, IEEE Trans. on Pattern Analysis and Machine Intelligence Vol. 21, No. 6, pp.
544-551, 1999.
15. Yuan Yan Tang, Bing-Fa Li, Hong Ma and Jiming Liu, “Ring-Projection-Wavelet-Fractal
Signatures: a Novel Approach to Feature Extraction,” IEEE Trans. on Circuits and Systems II,
Vol. 45, No. 8, pp. 1130-1134, 1998.
16. Yuan Yan Tang, Lo-Ting Tu, Jiming Liu, Seong-Whan Lee, Win-Win Lin and Ing-Shyh
Shyu, “Off-line Recognition of Chinese Handwriting by Multi-feature and Multi-level
Classification,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 20, No. 5,
pp.556-561, 1998.
17. Yuan Yan Tang, Hong Ma, Dihua Xi, Xiaogang Mao, and Ching Y. Suen, “Modified Fractal
Signature (MFS): A New Approach to Document Analysis for Automatic Knowledge
Acquisition,” IEEE Trans. on Knowledge and Data Engineering, Vol. 9, No. 5, pp. 747-762,
1997.
18. Yuan Yan Tang, Hong Ma, Jiming Liu, Bing Fa Li, and Dihua Xi, “Multiresolution Analysis
in Extraction of Reference Lines from Documents with Grey-level Background,” IEEE Trans.
on Pattern Analysis and Machine Intelligence, Vol. 19, No. 8, pp.921-926, 1997.
19. Jiming Liu, Yuan Yan Tang and Y. C. Cao, “An Evolutionary Autonomous Agents Approach
to Image Feature Extraction,” IEEE Trans. on Evolutionary Computing, Vol. 1, No. 2, pp.
141-158, 1997.
20. Yuan Yan Tang, Ching Y. Suen, C.D. Yan and M. Cheriet, “Financial Document Processing
Based on Staff Line and Description Language,” IEEE Trans. on Systems, Man, and
Cybernetics, Vol. 25, No. 5, pp. 738-754, 1995.
21. Yuan Yan Tang, C.D. Yan and Ching Y. Suen, “Document Processing for Automatic
Knowledge Acquisition,” IEEE Trans. on Knowledge and Data Engineering, Vol. 6, No. 1, pp.
3-21, 1994.
22. Yuan Yan Tang and Ching Y. Suen, “New Algorithms for Fixed and Elastic Geometric
Transformation Models” IEEE Trans. on Image Processing, Vol. 3, No. 4, pp. 355-366, 1994.
23. Yuan Yan Tang and Ching Y. Suen, “RPDT Algorithm and its VLSI Implementation,” IEEE
Trans. on Systems, Man, and Cybernetics, Vol. 24, No. 1, pp. 87-99, 1994.
24. Yuan Yan Tang and Ching Y. Suen, “Image Transformations Approach to Nonlinear Shape
Restoration,” IEEE Trans. on Systems, Man, and Cybernetics, Vol. 23, No. 1, pp. 155-172,
1993.
25. Z.C. Li, Ching Y. Suen, T.D. Bui, Yuan Yan Tang and Q.L. Gu, “Splitting-Integrating
Method for Nonlinear Images by Inverse Transformations,” IEEE Trans. on Pattern Analysis
and Machine Intelligence, Vol. 14, No. 6, pp. 678-686, 1992.
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5. 26. H.D. Cheng, Yuan Yan Tang and Ching Y. Suen, “VLSI Architectures for Image
Transformation,” IEEE Trans. on Systems, Man, and Cybernetics, Vol. 21, NO. 2, pp. 409-413,
1991.
27. Z.C. Li, T.D. Bui, Ching Y. Suen and Yuan Yan Tang, “Splitting-Shooting Method for
Nonlinear Transformations of Digitized Pattern,” IEEE Trans. on Pattern Analysis and Machine
Intelligence, Vol. 12, No. 7, pp. 671-682, 1990.
28. Yuan Yan Tang, “Document Analysis and Understanding,” in Handbook of Pattern
Recognition and Computer Vision, Third Edition, The World Scientific Publishing Co. Pte,
Ltd., Singapore, 2005.
29. 唐远炎、王玲《小波分析与文本文字识别》, 科学出版社,2004.
30. Yuan Yan Tang, X. C. Feng Lu Sun L. Wang, “Geometric Transformation by Moment
Method with Wavelet Matrix,” in Soft Computing Approach to Pattern Recognition and Image
Processing, The World Scientific Publishing Co. Pte, Ltd., Singapore, 2002.
31. Yuan Yan Tang, V. Wickerhauser, P. C. Yuen and C. H. Li (Eds.), Wavelet Theory and its
Applications, Springer, London, 2001.
32. Yuan Yan Tang, Lihua Yang, Jiming Liu and Hong Ma, Wavelet Theory and its Application
to Pattern Recognition, The World Scientific Publishing Co. Pte, Ltd., Singapore, 2000.
33. Jiming Liu, N. Zhong, Yuan Yan Tang and Pareick S. P. Wang Agent Engineering, The
World Scientific Publishing Co. Pte, Ltd., Singapore, 2001.
34. S.W Lee, Yuan Yan Tang and Patrick S.P. Wang, Advances in Oriental Document Analysis
and Recognition Techniques, The World Scientific Publishing Co. Pte, Ltd., Singapore, 1999.
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