2019 IEEE International Workshop on Signal Processing Systems (SiPS)最新文献_第2页

Pipelined Implementations for Belief Propagation Polar Decoder: From Formula to Hardware 信念传播极性解码器的流水线实现:从公式到硬件

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020515

Chao Ji, Zaichen Zhang, X. You, Chuan Zhang

引用次数: 2

Generation of Efficient Self-adaptive Hardware Polar Decoders Using High-Level Synthesis 基于高级合成的高效自适应硬件极性解码器的生成

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020441

Yann Delomier, B. Gal, J. Crenne, C. Jégo

引用次数: 1

RNN Models for Rain Detection 用于雨检测的RNN模型

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020603

H. Habi, H. Messer

引用次数: 4

Co-Design of Sparse Coding and Dictionary Learning for Real-Time Physiological Signals Monitoring 基于稀疏编码和字典学习的生理信号实时监测协同设计

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020428

Kuan-Chun Chen, Ching-Yao Chou, A. Wu

{"title":"Co-Design of Sparse Coding and Dictionary Learning for Real-Time Physiological Signals Monitoring","authors":"Kuan-Chun Chen, Ching-Yao Chou, A. Wu","doi":"10.1109/SiPS47522.2019.9020428","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020428","url":null,"abstract":"Compressive sensing (CS) is a novel technique to reduce overall transmission power in wireless sensors. For physiological signals telemonitoring of wearable devices, chip area and power efficiency need to be considered simultaneously. There are many prior studies aim to develop algorithms that applied to CS reconstruction chips with reconfigurable architecture. However, representative dictionaries are also important when these CS reconstruction chips are verified in real-time physiological signals monitoring tasks. That is, a more representative dictionary can not only enhance the reconstruction performance of these chips but also alleviate memory overhead. In this paper, we apply the concept of co-design between sparse coding algorithms and learned dictionaries. We also explore the representativeness and compatibility of each learned dictionary. In addition, the computational complexity of each reconstruction algorithm is provided through simulations. Our results show that the dictionaries trained by fast iterative shrinkage-thresholding algorithm (FISTA) are more representative according to the quality of reconstruction for physiological signals monitoring. Besides, FISTA reduces more than 90% of the computational time compared with other hardware-friendly reconstruction algorithms.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129957040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Hybrid Preconditioned CG Detection with Sequential Update for Massive MIMO Systems 大规模MIMO系统的序列更新混合预处理CG检测

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020319

Jing Zeng, Jun Lin, Zhongfeng Wang, Yun Chen

{"title":"Hybrid Preconditioned CG Detection with Sequential Update for Massive MIMO Systems","authors":"Jing Zeng, Jun Lin, Zhongfeng Wang, Yun Chen","doi":"10.1109/SiPS47522.2019.9020319","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020319","url":null,"abstract":"Massive Multi-Input Multi-Output (MIMO) is one of the key technologies for the fifth generation communication systems. Conjugate Gradient (CG) algorithm approximates the minimum mean-square error (MMSE) in an iterative manner, which avoids full matrix inversion. Pre-conditioned CG (PCG) was presented to improve the robustness of CG method. However, for the PCG, a sparse matrix inversion is still required in preprocessing and the performance is only comparable to MMSE. In this paper, a hybrid PCG algorithm (HPCG) with sequential update is proposed with superior performance and low complexity. The preconditioned matrix is replaced by a diagonal matrix by exploring its characteristics, which avoids matrix inversion and incomplete Cholesky factorization. Besides, to improve the bit error performance, a sequential update strategy is employed for estimated signals after PCG detection. For a MIMO system with 128 receive antennas, simulation results show the proposed HPCG algorithm outperforms MMSE by 0.25 dB to 1.5 dB under different numbers of users. Based on the channel hardening theories, several signal vectors can be transmitted in the same channel condition. When 10 signal vectors are considered, compared to the other CG based algorithms, the overall complexity of HPCG can be reduced by 3.9% to 56%.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126381982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Joint Image Deblur and Poisson Denoising based on Adaptive Dictionary Learning 基于自适应字典学习的联合图像去模糊和泊松去噪

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020314

Xiangyang Zhang, Hongqing Liu, Zhen Luo, Yi Zhou

引用次数: 0

Memory Reduction through Experience Classification f or Deep Reinforcement Learning with Prioritized Experience Replay 基于经验分类的深度强化学习记忆减少与优先经验回放

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020610

Kai-Huan Shen, P. Tsai

引用次数: 1

SiPS 2019 Author Index SiPS 2019作者索引

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/sips47522.2019.9020593

引用次数: 0

Dynamic Reconfigurable PUFs Based on FPGA 基于FPGA的动态可重构puf

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020444

Yijun Cui, Chenghua Wang, Yunpeng Chen, Ziwei Wei, Mengxian Chen, Weiqiang Liu

引用次数: 2

A Novel Approach to Angle-of-Arrival Estimation Based on Layered Ensemble Learning 一种基于分层集成学习的到达角估计方法

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020316

Rui Li, Tingqiang Deng, Yongming Huang, Chuan Zhang, Luxi Yang

引用次数: 0