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2014 19th International Conference on Digital Signal Processing最新文献

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Online estimation of secondary path in active noise control systems using Generalized Levinson Durbin algorithm 基于广义Levinson - Durbin算法的主动噪声控制系统二次路径在线估计
2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900726
Shashank Tyagi, Vibhav Katre, N. George
{"title":"Online estimation of secondary path in active noise control systems using Generalized Levinson Durbin algorithm","authors":"Shashank Tyagi, Vibhav Katre, N. George","doi":"10.1109/ICDSP.2014.6900726","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900726","url":null,"abstract":"A novel online secondary path modeling scheme for active noise control (ANC) systems based on the Generalized Levinson Durbin (GLD) algorithm is proposed in this paper. A short duration zero mean white Gaussian noise is injected into the system using an active loudspeaker and the GLD algorithm is employed to recursively estimate the secondary path. The performance of the proposed scheme is compared with that obtained by filtered-x least mean square (FxLMS) algorithm based ANC system and Eriksson's method in terms of ensemble mean square error and convergence rate. The new scheme is shown to effectively model the secondary path even without prior knowledge of its order.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129344075","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: 5
Quantized nonnegative matrix factorization 量化非负矩阵分解
2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900690
R. Fréin
{"title":"Quantized nonnegative matrix factorization","authors":"R. Fréin","doi":"10.1109/ICDSP.2014.6900690","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900690","url":null,"abstract":"Even though Nonnegative Matrix Factorization (NMF) in its original form performs rank reduction and signal compaction implicitly, it does not explicitly consider storage or transmission constraints. We propose a Frobenius-norm Quantized Nonnegative Matrix Factorization algorithm that is 1) almost as precise as traditional NMF for decomposition ranks of interest (with in 1-4dB), 2) admits to practical encoding techniques by learning a factorization which is simpler than NMF's (by a factor of 20-70) and 3) exhibits a complexity which is comparable with state-of-the-art NMF methods. These properties are achieved by considering the quantization residual via an outer quantization optimization step, in an extended NMF iteration, namely QNMF. This approach comes in two forms: QNMF with 1) quasi-fixed and 2) adaptive quantization levels. Quantized NMF considers element-wise quantization constraints in the learning algorithm to eliminate defects due to post factorization quantization. We demonstrate significant reduction in the cardinality of the factor signal values set for comparable Signal-to-Noise-Ratios in a matrix decomposition task.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116177704","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: 6
A dictionary-learning algorithm for the analysis sparse model with a determinant-type of sparsity measure 具有决定型稀疏度测度的分析稀疏模型的字典学习算法
2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900819
Yujie Li, Shuxue Ding, Zhenni Li
{"title":"A dictionary-learning algorithm for the analysis sparse model with a determinant-type of sparsity measure","authors":"Yujie Li, Shuxue Ding, Zhenni Li","doi":"10.1109/ICDSP.2014.6900819","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900819","url":null,"abstract":"Dictionary learning for sparse representation of signals has been successfully applied in signal processing. Most the existing methods are based on the synthesis model, in which the dictionary is overcomplete. This paper addresses the dictionary learning and sparse representation with the so-called analysis model. In this new model, the analysis dictionary multiplying the signal can lead to a sparse outcome. Though it has been studied in the literature, there is still not an investigation in the context of nonnegative signal representation, which should not be a trivial problem. In this paper, moreover, we propose to learn an analysis dictionary from signals using a determinant-type of sparsity measure. In the formulation, we adopt the Euclidean distance as the error measure. Based on these, we present a new algorithm for the dictionary learning and sparse representation. Numerical experiments on recovery of analysis dictionary show the effectiveness of the proposed method.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124969695","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: 16
Efficient method for solving globally optimal solutions of weighted LP norm and L2 norm optimization problems 加权LP范数和L2范数优化问题全局最优解的有效求解方法
2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900700
Langxiong Xie, B. Ling, Zhijing Yang, Qingyun Dai
{"title":"Efficient method for solving globally optimal solutions of weighted LP norm and L2 norm optimization problems","authors":"Langxiong Xie, B. Ling, Zhijing Yang, Qingyun Dai","doi":"10.1109/ICDSP.2014.6900700","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900700","url":null,"abstract":"This paper extends the existing L1 norm separable surrogate functional (SSF) iterative shrinkage algorithm to approximate the objective function of a weighted Lp norm and L2 norm optimization problem by N one dimensional independent objective functions. However, as the weighted Lp norm and L2 norm optimization problem is nonconvex, there may be more than one locally optimal solution. Hence, it is difficult to find the globally optimal solution. To address this difficulty, this paper further characterizes the regions that the signs of the convexity of the objective function within the regions remain unchanged. Then, the optimal solution within each region and eventually the globally optimal solution of the original optimization problem are found.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125068115","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: 0
New partial update robust kernel least mean square adaptive filtering algorithm 新的部分更新鲁棒核最小均方自适应滤波算法
2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900788
Yi Zhou, Hongqing Liu, S. Chan
{"title":"New partial update robust kernel least mean square adaptive filtering algorithm","authors":"Yi Zhou, Hongqing Liu, S. Chan","doi":"10.1109/ICDSP.2014.6900788","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900788","url":null,"abstract":"This paper studies a partial update (PU) robust kernel least mean square (KLMS) adaptive filtering algorithm which is particularly suitable for nonlinear acoustic echo cancellation (NLAEC) application. By exploring the data mapping property from the linear space to the high-dimensional feature space using polynomial kernel, the sequential PU scheme for conventional linear adaptive filters can be applied to the KLMS algorithm. This results in reduced computational complexity with moderate convergence rate loss. Moreover, in order to enhance the robustness of the KLMS algorithm to impulsive interference, the robust M-estimate scheme is incorporated into the kernel trick used in KLMS to develop a robust kernel least mean M-estimate (KLMM) algorithm. Finally, computer simulations are conducted to verify the advantages of the proposed work.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127785779","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: 3
Deep learning for posture analysis in fall detection 深度学习在跌倒检测中的姿态分析
2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900806
P. Feng, Miao Yu, S. M. Naqvi, J. Chambers
{"title":"Deep learning for posture analysis in fall detection","authors":"P. Feng, Miao Yu, S. M. Naqvi, J. Chambers","doi":"10.1109/ICDSP.2014.6900806","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900806","url":null,"abstract":"We propose a novel computer vision based fall detection system using deep learning methods to analyse the postures in a smart home environment for detecting fall activities. Firstly, background subtraction is employed to extract the foreground human body. Then the binary human body images form the input to the classifier. Two deep learning approaches based on a Boltzmann machine and deep belief network are compared with a support vector machine approach. The final decision on the occurrence of a fall is made on the basis of combining the classifier output with certain contextual rules. Evaluations are performed on recordings from a real home care environment, in which 15 people create 2904 postures.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121350468","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: 42
Hybrid DCT-Wiener-Based interpolation using dual MMSE estimator scheme 基于双MMSE估计方案的混合dct - wiener插值
2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900764
Jun-Jie Huang, Kwok-Wai Hung, W. Siu
{"title":"Hybrid DCT-Wiener-Based interpolation using dual MMSE estimator scheme","authors":"Jun-Jie Huang, Kwok-Wai Hung, W. Siu","doi":"10.1109/ICDSP.2014.6900764","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900764","url":null,"abstract":"Hybrid DCT-Wiener-Based interpolation scheme using the learnt Wiener filter can significantly improve both objective and subjective performance by learning a suitable Wiener filter to fit the hybrid scheme with a good mix of spatial and transform domain process. Using the adaptive k-NN MMSE estimation for each block achieves extraordinary up-sampling results. However, it needs a large database and relatively long processing time. In this paper, we investigate using multiple learnt Wiener filters and combine the information from both the external training images and the original low-resolution image. The proposed dual MMSE estimators adaptively resolve the problem of one general learnt Wiener filter and use less computation time compared with that of the k-NN MMSE estimation. Experimental results show that the proposed dual MMSE estimators achieve around 1dB PSNR improvement compared to the original hybrid DCT-Wiener-Based scheme and provide more natural visual quality.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123897137","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: 0
Low-complexity MIMO detection: A mixture of ZF, ML and SIC 低复杂度MIMO检测:ZF, ML和SIC的混合物
2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900841
Yinman Lee, Hou-Cheng Shih, Chongda Huang, Jyong-Yi Li
{"title":"Low-complexity MIMO detection: A mixture of ZF, ML and SIC","authors":"Yinman Lee, Hou-Cheng Shih, Chongda Huang, Jyong-Yi Li","doi":"10.1109/ICDSP.2014.6900841","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900841","url":null,"abstract":"For multiple-input multiple-output (MIMO) spatial multiplexing systems, it is known that the maximum likelihood (ML) detector can achieve the optimal error-rate performance at the cost of high computational complexity, while the zero-forcing (ZF) detector and its variation with successive interference cancelation (SIC) attain low complexity with degraded performance. In this paper, we propose a new low-complexity MIMO detector which smartly includes ZF, ML and SIC for processing. The required computational complexity of this special mixture can be lower than that of the ordered SIC-ZF method in many cases. Importantly, simulation results show that this proposed low-complexity detector can significantly outperform the ordered SIC-ZF method in terms of the bit-error rate (BER), and own a diversity gain quite similar to that of the ML detector.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115798311","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: 7
Face detection for low power event detection in intelligent surveillance system 人脸检测在智能监控系统中的低功耗事件检测
2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900728
Hyungil Kim, Seung-ho Lee, J. I. Moon, Hyun-Sang Park, Yong Man Ro
{"title":"Face detection for low power event detection in intelligent surveillance system","authors":"Hyungil Kim, Seung-ho Lee, J. I. Moon, Hyun-Sang Park, Yong Man Ro","doi":"10.1109/ICDSP.2014.6900728","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900728","url":null,"abstract":"Recently, the development of intelligent surveillance system increasingly requires low power consumption. For the power saving, this paper presents an event detection function based on automatically detected human faces, which adaptively convert from low power camera mode to high performance camera mode. We propose an efficient face detection (FD) method for operating under the low power camera mode. By employing two-stage structure (i.e., region-of-interest (ROI) selection and false positive (FP) reduction), the proposed FD method requires a very low computational complexity and memory requirements without sacrificing the face detection robustness. Experimental results demonstrated that the proposed FD could be implemented in low power video cameras with promising performance.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132670874","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: 3
A moving objects based real-time defogging method for traffic monitoring videos 一种基于运动物体的交通监控视频实时去雾方法
2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900715
Xiaocheng Hu, L. Zhuo, Xiaoguang Li
{"title":"A moving objects based real-time defogging method for traffic monitoring videos","authors":"Xiaocheng Hu, L. Zhuo, Xiaoguang Li","doi":"10.1109/ICDSP.2014.6900715","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900715","url":null,"abstract":"In this paper, a moving objects based real-time defogging method for traffic monitoring videos is proposed. Firstly, dark channel prior based image defogging method has been improved. Then, the proposed image defogging method is used for traffic monitoring video defogging. To improve the processing speed, the correlation between the adjacent frames of videos is exploited. The moving objects are detected using adjacent frame difference method. The frame content is divided into moving foreground and background. Afterwards, the foreground and background are processed with different defogging manners to reduce the computational complexity of defogging processing. Experimental results show that the proposed method can generate a good defogging effect which will facilitate the subsequent intelligent traffic analysis. Furthermore, the proposed method is fast enough to process the standard-definition videos at the speed of 26 frames per second on average.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126456730","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: 10
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