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The fifth IEEE Global Conference on Signal and Information Processing (GlobalSIP) will be held in Montreal, Quebec, Canada on November 14-16, 2017. GlobalSIP is a flagship IEEE Signal Processing Society conference. It focuses on signal and information processing with an emphasis on up-and-coming signal processing themes. The conference features world-class plenary speeches, distinguished Symposium talks, tutorials, exhibits, oral and poster sessions, and panels. Visit website.
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- Read more about HAMMERSLEY-CHAPMAN-ROBBINS LOWER BOUNDS ON POLE AND RESIDUE ESTIMATES FROM IMPULSE RESPONSE DATA
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- Read more about COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET
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- Read more about A Mobile EEG System for Practical Applications
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In this study, we present a new 64-channel mobile EEG system (NeusenW, Neuracle Inc.), and compare it to a state-of-the-art wired laboratory EEG system and evaluate the EEG signal quality. Previous studies were only performed on seated participants in laboratory environments, and only a very limited number focus on motion conditions. In this study, we instead implemented experiments in standing, walking and running conditions.
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- Read more about Defense against Stealthy Jammer Attacks in Wide-band Radios- A Physical Layer Approach
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- Read more about Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs
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- Read more about A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury
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Assistive technologies such as wheelchairs, canes, and walkers have significantly improved the mobility, function, and quality of life for individuals with spinal cord injury (SCI). In this article, we propose a framework which combines machine learning algorithms with wearable sensors to capture and track mobility in individuals with SCI. Pilot testing in two individuals without SCI indicated that four to seven features obtained from sensors worn on the body or placed on the assistive technology could successfully detect mobility and mobility modes.
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- Read more about GLOBALSIP presentation
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Target re-identification across non-overlapping camera views is a challenging task due to variations in target appearance, illumination, viewpoint and intrinsic parameters of cameras. Brightness transfer function (BTF) was introduced for inter-camera color calibration, and to improve the performance of target re-identification methods. There have been several works based on BTFs, more specifically using weighted BTFs (WBTF), cumulative BTF (CBTF) and mean BTF (MBTF). In this paper, we present a novel method to model the ap-pearance variation across different camera views.
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- Read more about Automatic Question-answering Using a Deep Similarity Neural Network
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Automatic question-answering is a classical problem in natural language processing, which aims at designing systems that can automatically answer a question, in the same way as human does. In this work, we propose a deep learning based model for automatic question-answering. First the questions and answers are embedded using neural probabilistic modeling. Then a deep similarity neural network is trained to find the similarity score of a pair of answer and question. Then for each question, the best answer is found as the one with the highest similarity score.
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