IEICE Transactions on Information and Systems
Institute of Electronics, Information and Communication Engineers
About: IEICE Transactions on Information and Systems is an academic journal published by Institute of Electronics, Information and Communication Engineers. The journal publishes majorly in the area(s): Computer science & Artificial neural network. It has an ISSN identifier of 0916-8532. It is also open access. Over the lifetime, 7106 publications have been published receiving 45866 citations. The journal is also known as: IEICE transactions & Institute of Electronics, Information and Communication Engineers transactions on information and systems.
Topics: Computer science, Artificial neural network, Artificial intelligence, Image processing, Cluster analysis
Papers published on a yearly basis
TL;DR: Paul Milgram's research interests include display and control issues in telerobotics and virtual environments, stereoscopic video and computer graphics, cognitive engineering, and human factors issues in medicine.
Abstract: Paul Milgram received the BASc degree from the University of Toronto in 1970, the MSEE degree from the Technion (Israel) in 1973 and the PhD degree from the University of Toronto in 1980 From 1980 to 1982 he was a ZWO Visiting Scientist and a NATO Postdoctoral in the Netherlands, researching automobile driving behaviour From 1982 to 1984 he was a Senior Research Engineer in Human Engineering at the National Aerospace Laboratory (NLR) in Amsterdam, where his work involved the modelling of aircraft flight crew activity, advanced display concepts and control loops with human operators in space teleoperation Since 1986 he has worked at the Industrial Engineering Department of the University of Toronto, where he is currently an Associate Professor and Coordinator of the Human Factors Engineering group He is also cross appointed to the Department of Psychology In 1993-94 he was an invited researcher at the ATR Communication Systems Research Laboratories, in Kyoto, Japan His research interests include display and control issues in telerobotics and virtual environments, stereoscopic video and computer graphics, cognitive engineering, and human factors issues in medicine He is also President of Translucent Technologies, a company which produces "Plato" liquid crystal visual occlusion spectacles (of which he is the inventor), for visual and psychomotor research
TL;DR: A vocoder-based speech synthesis system, named WORLD, was developed in an effort to improve the sound quality of realtime applications using speech and showed that it was superior to the other systems in terms of both sound quality and processing speed.
Abstract: A vocoder-based speech synthesis system, named WORLD, was developed in an effort to improve the sound quality of realtime applications using speech. Speech analysis, manipulation, and synthesis on the basis of vocoders are used in various kinds of speech research. Although several high-quality speech synthesis systems have been developed, real-time processing has been difficult with them because of their high computational costs. This new speech synthesis system has not only sound quality but also quick processing. It consists of three analysis algorithms and one synthesis algorithm proposed in our previous research. The effectiveness of the system was evaluated by comparing its output with against natural speech including consonants. Its processing speed was also compared with those of conventional systems. The results showed that WORLD was superior to the other systems in terms of both sound quality and processing speed. In particular, it was over ten times faster than the conventional systems, and the real time factor (RTF) indicated that it was fast enough for real-time processing. key words: speech analysis, speech synthesis, vocoder, sound quality, realtime processing
TL;DR: Petrify as discussed by the authors is a tool for manipulating concurrent specifications and synthesis and optimization of asynchronous control circuits given a Petri Net (PN), a Signal Transition Graph (STG), or a Transition System (TS) it generates another PN or STG which is simpler than the original description and produces an optimized net-list of an asynchronous controller in the target gate library.
Abstract: Petrify is a tool for (1) manipulating concurrent specifications and (2) synthesis and optimization of asynchronous control circuits. Given a Petri Net (PN), a Signal Transition Graph (STG), or a Transition System (TS) it (1) generates another PN or STG which is simpler than the original description and (2) produces an optimized net-list of an asynchronous controller in the target gate library while preserving the specified input-output behavior. An ability of back-annotating to the specification level helps the designer to control the design process. For transforming a specification petrify performs a token flow analysis of the initial PN and produces a transition system (TS). In the initial TS, all transitions with the same label are considered as one event. The TS is then transformed and transitions relabeled to fulfill the conditions required to obtain a safe irredundant PN. For synthesis of an asynchronous circuit petrify performs state assignment by solving the Complete State Coding problem. State assignment is coupled with logic minimization and speed-independent technology mapping to a target library. The final net-list is guaranteed to be speed-independent, i.e., hazard-free under any distribution of gate delays and multiple input changes satisfying the initial specification. The tool has been used for synthesis of PNs and PNs composition, synthesis and re-synthesis of asynchronous controllers and can be also applied in areas related with the analysis of concurrent programs. This paper provides an overview of petrify and the theory behind its main functions.
TL;DR: Several learning to rank methods using SVM techniques are described in details and the fundamental problems, existing approaches, and future work of learning toRank are explained.
Abstract: Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in Information Retrieval, Natural Language Processing, and Data Mining. Intensive studies have been conducted on the problem and significant progress has been made,. This short paper gives an introduction to learning to rank, and it specifically explains the fundamental problems, existing approaches, and future work of learning to rank. Several learning to rank methods using SVM techniques are described in details.