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New Findings on Machine Learning Described by Investigators at National Kaohsiung University of Applied Sciences

May 12, 2014

By a News Reporter-Staff News Editor at Robotics & Machine Learning -- Data detailed on Machine Learning have been presented. According to news reporting from Kaohsiung, Taiwan, by VerticalNews journalists, research stated, "In intelligent transportation systems, human detection is an important issue and has been widely used in many applications. Histograms of oriented gradients (HOG) are proven to be able to significantly outperform existing feature sets for human detection."

The news correspondents obtained a quote from the research from the National Kaohsiung University of Applied Sciences, "In this paper, we present a low-cost high-speed hardware implementation for HOG feature extraction. The simulation shows that the proposed circuit can achieve 167 MHz with 153-K gate counts by using Taiwan Semiconductor Manufacturing Company 0.13-mu m technology."

According to the news reporters, the research concluded: "Compared with the previous hardware architectures for HOG feature extraction, our circuit requires fewer hardware costs and achieves faster working speed."

For more information on this research see: An Efficient Hardware Implementation of HOG Feature Extraction for Human Detection. IEEE Transactions on Intelligent Transportation Systems, 2014;15(2):656-662. IEEE Transactions on Intelligent Transportation Systems can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers -; IEEE Transactions on Intelligent Transportation Systems -

Our news journalists report that additional information may be obtained by contacting P.Y. Chen, Natl Kaohsiung Univ Appl Sci, Dept. of Elect Engn, Kaohsiung 80778, Taiwan. Additional authors for this research include C.C. Huang, C.Y. Lien and Y.H. Tsai.

Keywords for this news article include: Asia, Taiwan, Kaohsiung, Machine Learning

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Source: Robotics & Machine Learning

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