Pub Date : 2018-11-01DOI: 10.1109/ICDSP.2018.8631860
Xiaofeng Shu, Yi Zhou, Yin Cao
In this paper, a speech enhancement method based on the framework of progressive deep neural networks (PDNNs) is proposed for low signal-to-noise ratio (SNR) and highly reverberant environments. It aims at assisting the complicated regression task of mapping noisy and reverberant speech to clean speech by utilizing two independent tasks, which suppress reverberation and noises respectively. Furthermore, a progressive learning approach is used for each task, which brings intermediate learning targets to enhance system performances. Experimental results reveal that the proposed method can achieve improvements in both objective and subjective evaluations in low SNR and high reverberation time 60 (RT60) environments when compared with the conventional deep neural network-based method.
{"title":"A Progressive Enhancement Method for Noisy and Reverberant Speech","authors":"Xiaofeng Shu, Yi Zhou, Yin Cao","doi":"10.1109/ICDSP.2018.8631860","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631860","url":null,"abstract":"In this paper, a speech enhancement method based on the framework of progressive deep neural networks (PDNNs) is proposed for low signal-to-noise ratio (SNR) and highly reverberant environments. It aims at assisting the complicated regression task of mapping noisy and reverberant speech to clean speech by utilizing two independent tasks, which suppress reverberation and noises respectively. Furthermore, a progressive learning approach is used for each task, which brings intermediate learning targets to enhance system performances. Experimental results reveal that the proposed method can achieve improvements in both objective and subjective evaluations in low SNR and high reverberation time 60 (RT60) environments when compared with the conventional deep neural network-based method.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131509297","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631592
Xu Cheng, D. Ciuonzo, P. Rossi
We consider decentralized detection (DD) of an unknown signal corrupted by zero-mean unimodal noise via wireless sensor networks (WSNs). To cope with energy and/or bandwidth constraints, we assume that sensors adopt multilevel quantization. The data are then transmitted through binary symmetric channels to a fusion center (FC), where a Rao test is proposed as a simpler alternative to the generalized likelihood ratio test (GLRT). The asymptotic performance analysis of the multi-bit Rao test is provided and exploited to propose a (signal-independent) quantizer design. Numerical results show the effectiveness of Rao test in comparison to GLRT and the performance gain obtained by threshold optimization.
{"title":"Multi-bit Decentralized Detection of a Weak Signal in Wireless Sensor Networks with a Rao test","authors":"Xu Cheng, D. Ciuonzo, P. Rossi","doi":"10.1109/ICDSP.2018.8631592","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631592","url":null,"abstract":"We consider decentralized detection (DD) of an unknown signal corrupted by zero-mean unimodal noise via wireless sensor networks (WSNs). To cope with energy and/or bandwidth constraints, we assume that sensors adopt multilevel quantization. The data are then transmitted through binary symmetric channels to a fusion center (FC), where a Rao test is proposed as a simpler alternative to the generalized likelihood ratio test (GLRT). The asymptotic performance analysis of the multi-bit Rao test is provided and exploited to propose a (signal-independent) quantizer design. Numerical results show the effectiveness of Rao test in comparison to GLRT and the performance gain obtained by threshold optimization.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131557056","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631813
Bingyi Zhang, Xin Li, Jun Han, Xiaoyang Zeng
Visual object tracking (VOT) is a computer vision application and has a wide range of use. However, related state of the art algorithms using deep learning methods, are computationally intensive and storage explosive. Whats more, despite many deep learning accelerators have been proposed, many of them are general structure. So, in this paper, we propose a lightweight CNN-based system–-MiniTracker, integration of algorithm and hardware–-particularly efficient for VOT. Because of the fully-convolutional Siamese network we used, the parameters of network do not need online training, which reduces computation consumptions dramatically. We adapt the original Siamese network (SN) into effective hardware implementation by parameter pruning and quantization. Then a lightweight CNN with the 8-bit parameters is produced, which is only 1.939MB. The real tracking rate is 18.6 frames per second at the cost of 1.284W on ZedBoard. Moreover, Compared with other hardware implementations, our system is robust to challenging scenarios, such as occlusions, changing appearance, illumination variations and etc.
{"title":"MiniTracker: A Lightweight CNN-based System for Visual Object Tracking on Embedded Device","authors":"Bingyi Zhang, Xin Li, Jun Han, Xiaoyang Zeng","doi":"10.1109/ICDSP.2018.8631813","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631813","url":null,"abstract":"Visual object tracking (VOT) is a computer vision application and has a wide range of use. However, related state of the art algorithms using deep learning methods, are computationally intensive and storage explosive. Whats more, despite many deep learning accelerators have been proposed, many of them are general structure. So, in this paper, we propose a lightweight CNN-based system–-MiniTracker, integration of algorithm and hardware–-particularly efficient for VOT. Because of the fully-convolutional Siamese network we used, the parameters of network do not need online training, which reduces computation consumptions dramatically. We adapt the original Siamese network (SN) into effective hardware implementation by parameter pruning and quantization. Then a lightweight CNN with the 8-bit parameters is produced, which is only 1.939MB. The real tracking rate is 18.6 frames per second at the cost of 1.284W on ZedBoard. Moreover, Compared with other hardware implementations, our system is robust to challenging scenarios, such as occlusions, changing appearance, illumination variations and etc.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133919642","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631644
Himanshu Kumar, Sumana Gupta, K. Venkatesh
Focal Plane Ambiguity (FPA) is a fundamental limitation of the Depth from Defocus (DFD) technique and refers to ambiguity of two possible distances corresponding to a single defocus blur value. Since, mix-sided scenes exist frequently in images and image-sequences, the assumption of a one sided focused scene often does not hold true. This leads to errors in the estimated defocus map. However, the inherent ordering of defocus blurs at the edges due to chromatic aberration in the R, G and B color planes can be used to correct this ambiguity. But, in highly defocused regions the ordering of defocus blurs becomes unreliable as the detection of edges becomes erroneous. In this paper, we propose a novel region based method using Color Uniformity Principle (CUP) for detecting the ordering of defocus blurs in R, G and B color planes to resolve the FPA.
焦平面模糊(Focal Plane Ambiguity, FPA)是离焦深度(Depth from Defocus, DFD)技术的一个基本缺陷，它是指一个离焦模糊值对应的两个可能距离的模糊度。由于混合面场景经常存在于图像和图像序列中，因此单面聚焦场景的假设往往不成立。这将导致估计散焦图中的错误。然而，由于R、G和B色平面的色差导致的边缘离焦模糊的固有顺序可以用来纠正这种模糊性。但是，在高度散焦区域，由于边缘检测错误，散焦模糊的排序变得不可靠。本文提出了一种新的基于区域的方法，利用颜色均匀性原理(CUP)来检测R、G和B颜色平面上离焦模糊的顺序，以解决FPA问题。
{"title":"Resolving Focal Plane Ambiguity using Chromatic Aberration and Color Uniformity Principle","authors":"Himanshu Kumar, Sumana Gupta, K. Venkatesh","doi":"10.1109/ICDSP.2018.8631644","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631644","url":null,"abstract":"Focal Plane Ambiguity (FPA) is a fundamental limitation of the Depth from Defocus (DFD) technique and refers to ambiguity of two possible distances corresponding to a single defocus blur value. Since, mix-sided scenes exist frequently in images and image-sequences, the assumption of a one sided focused scene often does not hold true. This leads to errors in the estimated defocus map. However, the inherent ordering of defocus blurs at the edges due to chromatic aberration in the R, G and B color planes can be used to correct this ambiguity. But, in highly defocused regions the ordering of defocus blurs becomes unreliable as the detection of edges becomes erroneous. In this paper, we propose a novel region based method using Color Uniformity Principle (CUP) for detecting the ordering of defocus blurs in R, G and B color planes to resolve the FPA.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134176536","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631649
S. Chan, H. C. Wu, Jianqiang Lin, Z. G. Zhang
Conventional procedures for preliminary diagnosis of Alzheimer's disease (AD) are invasive and painful. It is important to devise noninvasive biomarker which can provide conclusive diagnosis of early onset of AD and mild cognitive impairment (MCI). Recent attention has been drawn recently to gene microarray analysis for understanding disease onset and progression. In this paper, we extend our previous work to develop a new large-scale partial least squares based multivariate regression approach for the identification of putative interacting partners of gene markers for high-throughput gene microarray and other related data. Preliminary analysis of the interacting gene partners of a marker gene of frontotemporal dementia show that the identified genes are significantly enriched in innate immune and inflammatory response processes, which align well with the nature of the disease. These suggest that the proposed approach may serve as a valuable tool for inferring putative gene interacting partners in biological studies involving gene microarray data and other related datasets.
{"title":"A Partial least squares-based regression approach for analysis of frontotemporal dementia gene markers in human brain gene microarray data","authors":"S. Chan, H. C. Wu, Jianqiang Lin, Z. G. Zhang","doi":"10.1109/ICDSP.2018.8631649","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631649","url":null,"abstract":"Conventional procedures for preliminary diagnosis of Alzheimer's disease (AD) are invasive and painful. It is important to devise noninvasive biomarker which can provide conclusive diagnosis of early onset of AD and mild cognitive impairment (MCI). Recent attention has been drawn recently to gene microarray analysis for understanding disease onset and progression. In this paper, we extend our previous work to develop a new large-scale partial least squares based multivariate regression approach for the identification of putative interacting partners of gene markers for high-throughput gene microarray and other related data. Preliminary analysis of the interacting gene partners of a marker gene of frontotemporal dementia show that the identified genes are significantly enriched in innate immune and inflammatory response processes, which align well with the nature of the disease. These suggest that the proposed approach may serve as a valuable tool for inferring putative gene interacting partners in biological studies involving gene microarray data and other related datasets.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130320293","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631610
Huafei Wang, Xianpeng Wang, Mengxing Huang, Chunjie Cao, G. Bi
The most of existing off-grid direction of arrival (DOA) estimation methods are based on the perfect array-manifold. However, in practice, it is often hard to obtain a perfect array-manifold. In this paper, to achieve the DOA estimation under mutual coupling condition with low computational complexity, we propose a robust root Sparse Bayesian Learning (SBL) method. In the proposed method, firstly, we adopt the banded complex symmetric Toeplitz structure of the mutual coupling matrix to remove the negative influence of mutual coupling on DOA estimation. Then the DOA with off-grid is estimated by formulating the root-SBL strategy. Compared with the existing SBL-based algorithms, our method can not only maintain superior DOA estimation performance under the condition of mutual coupling, especially with strong mutual coupling, but also have lower computational complexity. Simulation results demonstrate that the proposed method can still accurately estimate DOAs under strong mutual coupling conditions, while other SBL-based methods fail to work.
{"title":"Off-Grid DOA Estimation in Mutual Coupling via Robust Sparse Bayesian Learning","authors":"Huafei Wang, Xianpeng Wang, Mengxing Huang, Chunjie Cao, G. Bi","doi":"10.1109/ICDSP.2018.8631610","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631610","url":null,"abstract":"The most of existing off-grid direction of arrival (DOA) estimation methods are based on the perfect array-manifold. However, in practice, it is often hard to obtain a perfect array-manifold. In this paper, to achieve the DOA estimation under mutual coupling condition with low computational complexity, we propose a robust root Sparse Bayesian Learning (SBL) method. In the proposed method, firstly, we adopt the banded complex symmetric Toeplitz structure of the mutual coupling matrix to remove the negative influence of mutual coupling on DOA estimation. Then the DOA with off-grid is estimated by formulating the root-SBL strategy. Compared with the existing SBL-based algorithms, our method can not only maintain superior DOA estimation performance under the condition of mutual coupling, especially with strong mutual coupling, but also have lower computational complexity. Simulation results demonstrate that the proposed method can still accurately estimate DOAs under strong mutual coupling conditions, while other SBL-based methods fail to work.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115867149","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631851
Jianqiang Lin, S. Chan
The estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm is a widely used subspace-based method for direction-of-arrival (DOA) estimation in array signal processing and spectral analysis. It requires the estimation of the signal subspaces of rotational invariance sub-arrays of a sensor array, from which the DOAs can be estimated by solving an eigenvalue problem. This paper proposes a projection approximation subspace tracking (PAST)-based adaptive ESPRIT algorithm with variable forgetting factor (VFF) and variable regularization (VR). The VFF and VR PAST algorithm is based on a recently proposed Locally Optimal FF (LOFF) scheme with improved convergence speed and steady state error performance. Moreover, variable regularization is incorporated to reduce the estimation variance during ill-conditioning or low input signal level. The proposed LOFF-VR adaptive ESPRIT method is also utilized for tracking the eigenvalues and hence the DOAs. Experimental simulations show that the proposed LOFF-VR-ESPRIT algorithm outperforms the conventional approaches in stationary and nonstationary environments, especially in the presence of signal fading.
{"title":"A New PAST-Based Adaptive ESPIRT Algorithm with Variable Forgetting Factor and Regularization","authors":"Jianqiang Lin, S. Chan","doi":"10.1109/ICDSP.2018.8631851","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631851","url":null,"abstract":"The estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm is a widely used subspace-based method for direction-of-arrival (DOA) estimation in array signal processing and spectral analysis. It requires the estimation of the signal subspaces of rotational invariance sub-arrays of a sensor array, from which the DOAs can be estimated by solving an eigenvalue problem. This paper proposes a projection approximation subspace tracking (PAST)-based adaptive ESPRIT algorithm with variable forgetting factor (VFF) and variable regularization (VR). The VFF and VR PAST algorithm is based on a recently proposed Locally Optimal FF (LOFF) scheme with improved convergence speed and steady state error performance. Moreover, variable regularization is incorporated to reduce the estimation variance during ill-conditioning or low input signal level. The proposed LOFF-VR adaptive ESPRIT method is also utilized for tracking the eigenvalues and hence the DOAs. Experimental simulations show that the proposed LOFF-VR-ESPRIT algorithm outperforms the conventional approaches in stationary and nonstationary environments, especially in the presence of signal fading.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131507419","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631857
Fuxi Wen, W. Liu
Data-driven false data injection attack is one of the emerging techniques in smart grids, provided that the adversary can monitor the meter readings. The basic idea is constructing attack vectors from the estimated signal subspace, without knowing system measurement matrix. However, its stealthy performance is significantly influenced by the accuracy of the estimated subspace. Furthermore, it is computationally demanding, because full-size singular value decomposition (SVD) is required for model order selection. In this paper, we propose a truncated SVD based computationally efficient attacking scheme using only the first dominant eigenvector. Both experiment and simulation results are provided to evaluate the performance of the proposed scheme. Compared with the standard false data injection techniques with known measurement matrix, similar stealthy performance is achieved with a reasonable computational complexity.
{"title":"An Efficient Data-Driven False Data Injection Attack in Smart Grids","authors":"Fuxi Wen, W. Liu","doi":"10.1109/ICDSP.2018.8631857","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631857","url":null,"abstract":"Data-driven false data injection attack is one of the emerging techniques in smart grids, provided that the adversary can monitor the meter readings. The basic idea is constructing attack vectors from the estimated signal subspace, without knowing system measurement matrix. However, its stealthy performance is significantly influenced by the accuracy of the estimated subspace. Furthermore, it is computationally demanding, because full-size singular value decomposition (SVD) is required for model order selection. In this paper, we propose a truncated SVD based computationally efficient attacking scheme using only the first dominant eigenvector. Both experiment and simulation results are provided to evaluate the performance of the proposed scheme. Compared with the standard false data injection techniques with known measurement matrix, similar stealthy performance is achieved with a reasonable computational complexity.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132248425","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631684
Yawei Lu, Wei Chen, H. Poor
Proactive caching holds the promise of improving network throughput and lowering transmission costs in content-centric networks. However, how to encode data in cache-aided networks is still an open problem. In this paper, a two-phase caching system is investigated, in which users are equipped with buffers and user requests are characterized by a probability measure. Based on content division, a unified framework for caching in arbitrary networks is presented. From the perspective of linear space, optimization problems are formulated to obtain the optimal linear coded joint placement and delivery (LCJPD) policy. A low-complexity algorithm is also proposed to give a sub-optimal LCJPD policy. Simulation results demonstrate that caching in networks can significantly reduce the transmission cost.
{"title":"A Unified Framework for Caching in Arbitrary Networks","authors":"Yawei Lu, Wei Chen, H. Poor","doi":"10.1109/ICDSP.2018.8631684","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631684","url":null,"abstract":"Proactive caching holds the promise of improving network throughput and lowering transmission costs in content-centric networks. However, how to encode data in cache-aided networks is still an open problem. In this paper, a two-phase caching system is investigated, in which users are equipped with buffers and user requests are characterized by a probability measure. Based on content division, a unified framework for caching in arbitrary networks is presented. From the perspective of linear space, optimization problems are formulated to obtain the optimal linear coded joint placement and delivery (LCJPD) policy. A low-complexity algorithm is also proposed to give a sub-optimal LCJPD policy. Simulation results demonstrate that caching in networks can significantly reduce the transmission cost.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131716697","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631667
Zeguo Yang, Ma Shang, Shuai Hu, Kun Zheng, Xuyuan Cao, Jienan Chen
Finite impulse response (FIR) digital filter is one of the key computational units in modern digital signal processing (DSP) systems. This paper presents a design method based on algebraic integer quantization for FIR digital filter with constant coefficients. To quantify the filter coefficients by appropriate algebraic integer, the multipliers can be replaced by the few adders, thereby reducing the implementation complexity. Compared with the traditional architectures, this method is faster and saves about 10%-20% in area. At the same time, this method has a better interconnection structure, which makes the area increment smaller when the timing constraints are strengthened.
{"title":"Design of FIR Filter Based on Algebraic Integer Quantization","authors":"Zeguo Yang, Ma Shang, Shuai Hu, Kun Zheng, Xuyuan Cao, Jienan Chen","doi":"10.1109/ICDSP.2018.8631667","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631667","url":null,"abstract":"Finite impulse response (FIR) digital filter is one of the key computational units in modern digital signal processing (DSP) systems. This paper presents a design method based on algebraic integer quantization for FIR digital filter with constant coefficients. To quantify the filter coefficients by appropriate algebraic integer, the multipliers can be replaced by the few adders, thereby reducing the implementation complexity. Compared with the traditional architectures, this method is faster and saves about 10%-20% in area. At the same time, this method has a better interconnection structure, which makes the area increment smaller when the timing constraints are strengthened.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131835701","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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