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A neuromorphic system for video object recognition
Deepak Khosla,Kyungnam Kim
Frontiers in Computational Neuroscience , 2014, DOI: 10.3389/fncom.2014.00147
Abstract: Automated video object recognition is a topic of emerging importance in both defense and civilian applications. This work describes an accurate and low-power neuromorphic architecture and system for real-time automated video object recognition. Our system, Neuormorphic Visual Understanding of Scenes (NEOVUS), is inspired by computational neuroscience models of feed-forward object detection and classification pipelines for processing visual data. The NEOVUS architecture is inspired by the ventral (what) and dorsal (where) streams of the mammalian visual pathway and integrates retinal processing, object detection based on form and motion modeling, and object classification based on convolutional neural networks. The object recognition performance and energy use of the NEOVUS was evaluated by the Defense Advanced Research Projects Agency (DARPA) under the Neovision2 program using three urban area video datasets collected from a mix of stationary and moving platforms. These datasets are challenging and include a large number of objects of different types in cluttered scenes, with varying illumination and occlusion conditions. In a systematic evaluation of five different teams by DARPA on these datasets, the NEOVUS demonstrated the best performance with high object recognition accuracy and the lowest energy consumption. Its energy use was three orders of magnitude lower than two independent state of the art baseline computer vision systems. The dynamic power requirement for the complete system mapped to commercial off-the-shelf (COTS) hardware that includes a 5.6 Megapixel color camera processed by object detection and classification algorithms at 30 frames per second was measured at 21.7 Watts (W), for an effective energy consumption of 5.45 nanoJoules (nJ) per bit of incoming video. These unprecedented results show that the NEOVUS has the potential to revolutionize automated video object recognition toward enabling practical low-power and mobile video processing applications.
Microstructural Evolution and Thermal Stability of Ultra-fine Grained Al-4Mg Alloy by Equal Channel Angular Pressing
Hongbin GENG,Subbong KANG,Shiyu HE,
Hongbin GENG
,Subbong KANG,Shiyu HE School of Materials Science and Engineering,Harbin Institute of Technology Harbin,China Korea Institute of Machinery and Materials,Sangnam Dong,Changwon,Kyungnam -,South Korea

材料科学技术学报 , 2004,
Abstract: Experiments were conducted to evaluate the grain refinement and thermal stability of ultra-fine grained Al-4Mg alloy introduced by equal-channel angular pressing (ECAP) at 473 K. The results show that the intensities of X-ray (111/222) and (200/400) peaks for the alloy processed by ECAP decrease significantly and the peak widths of half height become broadening compared with the corresponding value in the annealed alloy. The microstructure of 2 passes ECAPed alloy consists of both elongated and equiaxed subgrains. The residual strain in the alloy increases with increasing passes numbers, that appears as increasing dislocation density and lattice constant of matrix. An equiaxed ultra-fine grained structure of ~0.2μm is obtained in the present alloy after 8 passes. The ultra-fine grains are stable below 523 K, because the alloy retains extremely fine grain size of ~1μm after static annealing at 523 K for1 h.
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