Maximum length sequence encoded Hadamard measurement paradigm for compressed sensing
文献类型：会议论文
| 作者 | Qin SJ(秦书嘉) ; Bi S(毕盛)
|
| 出版日期 | 2014 |
| 会议日期 | July 8-11, 2014 |
| 会议地点 | Besancon, France |
| 关键词 | Artificial Intelligence Computer Programming Hadamard Matrices Hadamard Transforms Intelligent Mechatronics Medical Imaging Spectroscopy |
| 页码 | 1151-1156 |
| 英文摘要 | The development of compressed sensing technology has greatly facilitated its applications in many fields, such as medical imaging, multi-sensor and distributed sensing, coding theory, hyper-spectral imaging, and machine learning. Among these applications, the permuted Walsh-Hadamard matrices are frequently chosen for modeling real-world measurements that are limited to binary entry states by special structure (one typical example is the digital mirror device in singlepixel cameras) because the fast Walsh-Hadamard transform can efficiently calculate its multiplications; however, for a large-scale problem, the Walsh-Hadamard matrix would become unacceptably large to be stored in advance. To eliminate this defect, this paper proposes a maximum length sequence encoded Hadamard measurement paradigm that can be simply realized on chip without any usage of external memory, and proves this method can degenerate to a special permutation of the sequence ordered Walsh-Hadamard matrix so that the fast Walsh-Hadamard transform keeps feasible. Simulations show that compared with the conventional permuted Walsh-Hadamard matrix, the proposed one can emerge from the limit of external memory without losing much randomness performance in the measurement basis required by compressed sensing. © 2014 IEEE. |
| 产权排序 | 1 |
| 会议录 | IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
 |
| 会议录出版者 | Institute of Electrical and Electronics Engineers Inc. |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-4799-5736-1 |
| WOS记录号 | WOS:000346499600192 |
| 源URL | [http://ir.sia.cn/handle/173321/15155]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 作者单位 | 1.University of Chinese Academy of Sciences, East Lansing, MI 48824, United States 2.Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, United States 3.School of Computer Science and Engineering, South China University of Technology, East Lansing, MI 48824, United States 4.Department of Mechanical and Biomedical Engineering, City University of Hong Kong, East Lansing, MI 48824, United States 5.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, East Lansing, MI 48824, United States |
| 推荐引用方式 GB/T 7714 | Qin SJ,Bi S. Maximum length sequence encoded Hadamard measurement paradigm for compressed sensing[C]. 见:. Besancon, France. July 8-11, 2014. |
入库方式： OAI收割
来源：沈阳自动化研究所
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