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Bo-Wei Chen
Researcher at Princeton University
Publications - 95
Citations - 1089
Bo-Wei Chen is an academic researcher from Princeton University. The author has contributed to research in topics: Support vector machine & Speaker recognition. The author has an hindex of 16, co-authored 91 publications receiving 966 citations. Previous affiliations of Bo-Wei Chen include Monash University & National Cheng Kung University.
Papers
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Real-Time Big Data Analytical Architecture for Remote Sensing Application
TL;DR: The proposed architecture results in efficiently analyzing real-time remote sensing Big Data using earth observatory system using Hadoop, and has the capability of storing incoming raw data to perform offline analysis on largely stored dumps, when required.
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A Novel Video Summarization Based on Mining the Story-Structure and Semantic Relations Among Concept Entities
TL;DR: A structural video content browsing system and a novel summarization method by utilizing the four kinds of entities: who, what, where, and when to establish the framework of the video contents and demonstrates that the system can achieve better performance and information coverage.
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Smart Homecare Surveillance System: Behavior Identification Based on State-Transition Support Vector Machines and Sound Directivity Pattern Analysis
TL;DR: This study presents a smart homecare surveillance system, which utilizes sound-steered cameras to identify behavior of interest and a new direction-of-arrival (DOA) algorithm is proposed by introducing cascaded frequency filters, which can quickly calculate directions without creating much complexity.
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Mixed Sound Event Verification on Wireless Sensor Network for Home Automation
TL;DR: This study proposes a framework, which consists of sound separation and sound verification techniques based on a wireless sensor network (WSN), to realize sound-triggered automation and demonstrates the robustness and feasibility of the proposed system for mixed sound verification in WSN-based home environments.
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Gabor-Based Nonuniform Scale-Frequency Map for Environmental Sound Classification in Home Automation
TL;DR: This work presents a novel feature extraction approach called nonuniform scale-frequency map for environmental sound classification in home automation and reveals that the proposed approach is superior to the other time-frequency methods.