Pub Date : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020515
Chao Ji, Zaichen Zhang, X. You, Chuan Zhang
A general design method for pipelined belief propagation (BP) polar decoder is proposed in this paper. By associating data flow graph (DFG) of polar encoder with factor graph (FG) of BP polar decoder, regular structure of FG helps to determine the generation formula representing pipelined BP polar decoder. Using Python as a compiler, the generation formula is translated into a series of synthesizable Verilog HDL files for various code lengths and parallelisms. Considering the balance between performance and cost, this formula-to-hardware design can be extended to explore the design space, where we are able to make tradeoffs according to specific application requirements. With the evaluation of auto-generation system, implementation results have shown that our design is reliable and practicable.
{"title":"Pipelined Implementations for Belief Propagation Polar Decoder: From Formula to Hardware","authors":"Chao Ji, Zaichen Zhang, X. You, Chuan Zhang","doi":"10.1109/SiPS47522.2019.9020515","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020515","url":null,"abstract":"A general design method for pipelined belief propagation (BP) polar decoder is proposed in this paper. By associating data flow graph (DFG) of polar encoder with factor graph (FG) of BP polar decoder, regular structure of FG helps to determine the generation formula representing pipelined BP polar decoder. Using Python as a compiler, the generation formula is translated into a series of synthesizable Verilog HDL files for various code lengths and parallelisms. Considering the balance between performance and cost, this formula-to-hardware design can be extended to explore the design space, where we are able to make tradeoffs according to specific application requirements. With the evaluation of auto-generation system, implementation results have shown that our design is reliable and practicable.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"91 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":"115413222","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}
Pub Date : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020441
Yann Delomier, B. Gal, J. Crenne, C. Jégo
Recent advances in 5G digital communication standard implementations advocate for the use of polar codes for the Enhanced Mobile Broad Band (EMBB) control channels. However, in many cases, implementing efficient hardware decoder over a short duration is very challenging. Specialized knowledge is required to facilitate testing, rapid design iterations and fast prototyping. In this paper, we present a model-based design methodology to generate efficient hardware SC polar decoders from high-level synthesis tools. The abstraction level flexibility is evaluated and generated decoders architectures are compared to competing approaches. It is shown that the fine-tuning of computation parallelism, bit width, pruning level and working frequency enable high throughput decoder designs with moderate hardware complexities. Decoding throughput between 10 to 310 Mbit/s and hardware complexity between 1,000 and 21,000 LUTs are reported for the generated architectures.
{"title":"Generation of Efficient Self-adaptive Hardware Polar Decoders Using High-Level Synthesis","authors":"Yann Delomier, B. Gal, J. Crenne, C. Jégo","doi":"10.1109/SiPS47522.2019.9020441","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020441","url":null,"abstract":"Recent advances in 5G digital communication standard implementations advocate for the use of polar codes for the Enhanced Mobile Broad Band (EMBB) control channels. However, in many cases, implementing efficient hardware decoder over a short duration is very challenging. Specialized knowledge is required to facilitate testing, rapid design iterations and fast prototyping. In this paper, we present a model-based design methodology to generate efficient hardware SC polar decoders from high-level synthesis tools. The abstraction level flexibility is evaluated and generated decoders architectures are compared to competing approaches. It is shown that the fine-tuning of computation parallelism, bit width, pruning level and working frequency enable high throughput decoder designs with moderate hardware complexities. Decoding throughput between 10 to 310 Mbit/s and hardware complexity between 1,000 and 21,000 LUTs are reported for the generated architectures.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"4 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":"124529070","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}
Pub Date : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020603
H. Habi, H. Messer
The task of rain detection, also known as wet-dry classification, using recurrent neural networks (RNNs) utilizing data from commercial microwave links (CMLs) has recently gained attention. Whereas previous studies used long short-term memory (LSTM) units, here we used gated recurrent units (GRUs). We compare the wet-dry classification performance of LSTM and GRU based network architectures using data from operational cellular backhaul networks and meteorological measurements in Israel and Sweden, and draw conclusions based on datasets consisting of actual measurements over two years in two different geological and climatic regions
{"title":"RNN Models for Rain Detection","authors":"H. Habi, H. Messer","doi":"10.1109/SiPS47522.2019.9020603","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020603","url":null,"abstract":"The task of rain detection, also known as wet-dry classification, using recurrent neural networks (RNNs) utilizing data from commercial microwave links (CMLs) has recently gained attention. Whereas previous studies used long short-term memory (LSTM) units, here we used gated recurrent units (GRUs). We compare the wet-dry classification performance of LSTM and GRU based network architectures using data from operational cellular backhaul networks and meteorological measurements in Israel and Sweden, and draw conclusions based on datasets consisting of actual measurements over two years in two different geological and climatic regions","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"4 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":"128469995","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}
Pub Date : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020428
Kuan-Chun Chen, Ching-Yao Chou, A. Wu
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.
{"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}
Pub Date : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020319
Jing Zeng, Jun Lin, Zhongfeng Wang, Yun Chen
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%.
{"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}
Pub Date : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020314
Xiangyang Zhang, Hongqing Liu, Zhen Luo, Yi Zhou
This paper describes a blind image reconstruction algorithm for blurred image under Poisson noise. To that aim, in this work, the group sparse domain is explored to sparsely represent the image and blur kernel, and then $ell_{1} -$norm is utilized to enforce the sparse solutions. In doing so, a joint optimization framework is developed to estimate the blur kernel matrix while removing Poisson noise. To effectively solve the developed optimization, a two-step iteration scheme involving two sub-problems is proposed. For each subproblem, the alternating direction method of multipliers (ADMM) algorithm is devised to estimate the blur or denoise. The experimental simulations demonstrate that the proposed algorithm is superior to other approaches in terms of restoration quality and performance metrics.
{"title":"Joint Image Deblur and Poisson Denoising based on Adaptive Dictionary Learning","authors":"Xiangyang Zhang, Hongqing Liu, Zhen Luo, Yi Zhou","doi":"10.1109/SiPS47522.2019.9020314","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020314","url":null,"abstract":"This paper describes a blind image reconstruction algorithm for blurred image under Poisson noise. To that aim, in this work, the group sparse domain is explored to sparsely represent the image and blur kernel, and then $ell_{1} -$norm is utilized to enforce the sparse solutions. In doing so, a joint optimization framework is developed to estimate the blur kernel matrix while removing Poisson noise. To effectively solve the developed optimization, a two-step iteration scheme involving two sub-problems is proposed. For each subproblem, the alternating direction method of multipliers (ADMM) algorithm is devised to estimate the blur or denoise. The experimental simulations demonstrate that the proposed algorithm is superior to other approaches in terms of restoration quality and performance metrics.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"1 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":"126303495","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}
Pub Date : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020610
Kai-Huan Shen, P. Tsai
Prioritized experience replay has been widely used in many online reinforcement learning algorithms, providing high efficiency in exploiting past experiences. However, a large replay buffer consumes system storage significantly. Thus, in this paper, a segmentation and classification scheme is proposed. The distribution of temporal-difference errors (TD errors) is first segmented. The experience for network training is classified according to its updated TD error. Then, a swap mechanism for similar experiences is implemented to change the lifetimes of experiences in the replay buffer. The proposed scheme is incorporated in the Deep Deterministic Policy Gradient (DDPG) algorithm, and the Inverted Pendulum and Inverted Double Pendulum tasks are used for verification. From the experiments, our proposed mechanism can effectively remove the buffer redundancy and further reduce the correlation of experiences in the replay buffer. Thus, better learning performance with reduced memory size is achieved at the cost of additional computations of updated TD errors.
{"title":"Memory Reduction through Experience Classification f or Deep Reinforcement Learning with Prioritized Experience Replay","authors":"Kai-Huan Shen, P. Tsai","doi":"10.1109/SiPS47522.2019.9020610","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020610","url":null,"abstract":"Prioritized experience replay has been widely used in many online reinforcement learning algorithms, providing high efficiency in exploiting past experiences. However, a large replay buffer consumes system storage significantly. Thus, in this paper, a segmentation and classification scheme is proposed. The distribution of temporal-difference errors (TD errors) is first segmented. The experience for network training is classified according to its updated TD error. Then, a swap mechanism for similar experiences is implemented to change the lifetimes of experiences in the replay buffer. The proposed scheme is incorporated in the Deep Deterministic Policy Gradient (DDPG) algorithm, and the Inverted Pendulum and Inverted Double Pendulum tasks are used for verification. From the experiments, our proposed mechanism can effectively remove the buffer redundancy and further reduce the correlation of experiences in the replay buffer. Thus, better learning performance with reduced memory size is achieved at the cost of additional computations of updated TD errors.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"23 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114095066","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}
Pub Date : 2019-10-01DOI: 10.1109/sips47522.2019.9020593
{"title":"SiPS 2019 Author Index","authors":"","doi":"10.1109/sips47522.2019.9020593","DOIUrl":"https://doi.org/10.1109/sips47522.2019.9020593","url":null,"abstract":"","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"91 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":"127554218","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}
Wind power generation has gradually developed into an important approach of energy supply. Meanwhile, due to the difficulty of electricity storage, wind power is greatly affected by the real-time wind speed in wind fields. Generally, wind speed has the characteristics of nonlinear, irregular, and non-stationary, which make accurate wind speed forecasting a difficult problem. Recent studies have shown that ensemble forecasting approaches combining different sub-models is an efficient way to solve the problem. Therefore, in this article, two single models are ensembled for wind speed forecasting. Meanwhile, four data pre-processing hybrid models are combined with the reliability weights. The proposed ensemble approaches are simulated on the real wind speed data in the Longdong area of Loess Plateau in China from 2007 to 2015, the experimental results indicate that the ensemble approaches outperform individual models and other hybrid models with different pre-processing methods.
{"title":"Ensemble Neural Network Method for Wind Speed Forecasting","authors":"Binbin Yong, Fei Qiao, Chen Wang, Jun Shen, Yongqiang Wei, Qingguo Zhou","doi":"10.1109/SiPS47522.2019.9020410","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020410","url":null,"abstract":"Wind power generation has gradually developed into an important approach of energy supply. Meanwhile, due to the difficulty of electricity storage, wind power is greatly affected by the real-time wind speed in wind fields. Generally, wind speed has the characteristics of nonlinear, irregular, and non-stationary, which make accurate wind speed forecasting a difficult problem. Recent studies have shown that ensemble forecasting approaches combining different sub-models is an efficient way to solve the problem. Therefore, in this article, two single models are ensembled for wind speed forecasting. Meanwhile, four data pre-processing hybrid models are combined with the reliability weights. The proposed ensemble approaches are simulated on the real wind speed data in the Longdong area of Loess Plateau in China from 2007 to 2015, the experimental results indicate that the ensemble approaches outperform individual models and other hybrid models with different pre-processing methods.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"4 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":"122137657","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}
Pub Date : 2019-10-01DOI: 10.1109/SiPS47522.2019.9020534
Xun Liu, M. Dohler
The emerging Internet of Skills that exchanges tactile and other sensorial data, significantly augments traditional multimedia. The increase of data scale and modalities demands for codecs dedicated to these sensorial data. In this paper, we propose a codec for compression of vibrotactile data in the spirit of Weber’s law. To be specific, a companding function is applied to the vibrotactile data, so that the quantisation step of high amplitude is larger than that of low amplitude. The curve of the companding function is optimised through a data-driven approach. To evaluate the performance of the vibrotactile codec in terms of human perceived quality, rigorous subjective tests are conducted. The results demonstrate that 75% compression of vibrotactile data is achieved without perceivable degradation. More importantly, the computational complexity is much lower and the latency performance is superior, compared with other vibrotactile codecs. The computational complexity of the proposed codec is about 1/20 of that of previous codecs, while the time delay is approximately 1/30 of that of previous codec.
{"title":"A Data-Driven Approach to Vibrotactile Data Compression","authors":"Xun Liu, M. Dohler","doi":"10.1109/SiPS47522.2019.9020534","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020534","url":null,"abstract":"The emerging Internet of Skills that exchanges tactile and other sensorial data, significantly augments traditional multimedia. The increase of data scale and modalities demands for codecs dedicated to these sensorial data. In this paper, we propose a codec for compression of vibrotactile data in the spirit of Weber’s law. To be specific, a companding function is applied to the vibrotactile data, so that the quantisation step of high amplitude is larger than that of low amplitude. The curve of the companding function is optimised through a data-driven approach. To evaluate the performance of the vibrotactile codec in terms of human perceived quality, rigorous subjective tests are conducted. The results demonstrate that 75% compression of vibrotactile data is achieved without perceivable degradation. More importantly, the computational complexity is much lower and the latency performance is superior, compared with other vibrotactile codecs. The computational complexity of the proposed codec is about 1/20 of that of previous codecs, while the time delay is approximately 1/30 of that of previous codec.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"1 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":"129747547","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}
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