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2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)最新文献

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Learning Approach with Random Forests on Vehicle Detection 随机森林在车辆检测中的学习方法
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631871
Li-Wen Wang, Xuefei Yang, W. Siu
{"title":"Learning Approach with Random Forests on Vehicle Detection","authors":"Li-Wen Wang, Xuefei Yang, W. Siu","doi":"10.1109/ICDSP.2018.8631871","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631871","url":null,"abstract":"A Close-up Monitoring System (CMS) has been designed in our research laboratory, which aims at avoiding any potential collision risk by detecting the frontal train’s distance from the captured video. Histogram of orientated gradient (HOG) has been used as a feature descriptor, because it gives robust performance in various illumination conditions. Random forest algorithm is a conventional machine learning tool, but it is new in the driving assistant application. Besides, the predicting process of our classifier is very fast because it only depends on a limited number of simple tests in each randomly-trained decision tree. Based on the HOG features and random forest algorithm, a close-range train detector has been designed. This proposed detector works as one detection module in CMS, and the correct detection rate of the close-range train was nearly 100%, which means there was no miss-detection in our control experiment. Compared with the traditional non-learning method, our learning-based approach achieves much stronger recognition ability with less false alarms.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"59 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":"128756371","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: 4
Vision-Based Rain Gauge for Dynamic Scenes 基于视觉的动态场景雨量计
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631542
Cheen-Hau Tan, Jie Chen, Yun Ni, Lap-Pui Chau, L. M. Soh
{"title":"Vision-Based Rain Gauge for Dynamic Scenes","authors":"Cheen-Hau Tan, Jie Chen, Yun Ni, Lap-Pui Chau, L. M. Soh","doi":"10.1109/ICDSP.2018.8631542","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631542","url":null,"abstract":"In this paper we develop a vision-based rain intensity measurement method for dynamic scenes. The method first measures the area density of rain by analyzing temporal changes in pixel values in the video input. The area density, represented as a binary rain map, is then mapped to a rain intensity value using linear regression. To ensure temporal consistency of scene content across frames in dynamic scenes, we applied superpixel-based content alignment. Potential false detections in the binary rain map are removed using directional morphological opening. Experiments show that both superpixel-based content alignment and morphological opening are important for good rain map generation and rain intensity estimation","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"3 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":"124817643","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
Long-range speech acquirement and enhancement with dual-point laser Doppler vibrometers 双点激光多普勒振动仪的远程语音采集与增强
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631671
Renhua Peng, Binbin Xu, Guoteng Li, C. Zheng, Xiaodong Li
{"title":"Long-range speech acquirement and enhancement with dual-point laser Doppler vibrometers","authors":"Renhua Peng, Binbin Xu, Guoteng Li, C. Zheng, Xiaodong Li","doi":"10.1109/ICDSP.2018.8631671","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631671","url":null,"abstract":"This paper presents a long-range speech acquirement and speech enhancement system by utilizing two laser Doppler vibrometers (LDV) to measure two separate points on only one vibration object or two different vibration objects. This proposed LDV system can provide dual-channel synchronous signals, and thus the coherent-to-diffused ratio-based and the multi-channel linear prediction-based algorithms can be introduced to reduce the reverberation and the noise of the acquired speech signals. The two algorithms are combined together to further improve the speech enhancement performance. Experimental results show that the proposed algorithm can significantly improve the PESQ scores.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"19 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":"126460415","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: 4
Boosting the Performance of Scene Recognition via Offline Feature-Shifts and Search Window Weights 通过离线特征转移和搜索窗口权重提高场景识别性能
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631883
Chu-Tak Li, W. Siu, D. Lun
{"title":"Boosting the Performance of Scene Recognition via Offline Feature-Shifts and Search Window Weights","authors":"Chu-Tak Li, W. Siu, D. Lun","doi":"10.1109/ICDSP.2018.8631883","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631883","url":null,"abstract":"This paper presents a key frame recognition algorithm, using novel offline feature-shifts approach and search window weights. We extract effective feature patches from key frames with an offline feature-shifts approach for real-time key frame recognition. We focus on practical situations in which blurring and shifts in viewpoints occur in our dataset. We compare our method with some conventional keypoint-based matching methods and the newest CNN features for scene recognition. The experimental results illustrate that our method can reasonably preserve the performance in key frame recognition when comparing with methods using online feature-shifts approach. Our proposed method provides larger tolerance of unmatched pairs which is useful for decision making in real-time systems. Moreover, our method is robust to illumination and blurring. We achieve 90% accuracy in a nighttime sequence while CNN approach only attains 60% accuracy. Our method only requires 33.8 ms to match a frame on average using a regular desktop, which is 4 times faster than CNN approach with only CPU mode.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"57 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":"125895462","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: 2
A Modified Frequency Weighted MUSIC Algorithm for Multiple Sound Sources Localization 一种改进的频率加权MUSIC多声源定位算法
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631636
Shan Gao, Yankun Huang, Zhang Tao, Xihong Wu, T. Qu
{"title":"A Modified Frequency Weighted MUSIC Algorithm for Multiple Sound Sources Localization","authors":"Shan Gao, Yankun Huang, Zhang Tao, Xihong Wu, T. Qu","doi":"10.1109/ICDSP.2018.8631636","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631636","url":null,"abstract":"The traditional weighted MUSIC algorithm is usually implemented based on a sparsity assumption named W-Disjoint Orthogonality (WDO) when the number of sound sources is unknown, which may not be suitable in many scenarios. In this paper, a modified weighted MUSIC algorithm is proposed to improve the localization performance in multiple sound sources. Instead of using the maximum eigenvalue as the weight of each frequency band, we use the signal-to-noise ratio (SNR) as the weight coefficient of each frequency band, which can reduce the disturbance cases by the multiple sources bands. The simulation experiments are conducted to evaluate the performance of our proposed method and compare with the traditional weighted MUSIC algorithm. The results show that the proposed algorithms have a better localization accuracy in multiple-source environment.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"147 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":"127260268","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: 4
Link Adaption aided Enhanced Spatial Modulation for MIMO Transmissions 链路自适应辅助MIMO传输的增强空间调制
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631619
Jing Zhu, Ping Yang, Yue Xiao, Y. Guan, Shaoqian Li
{"title":"Link Adaption aided Enhanced Spatial Modulation for MIMO Transmissions","authors":"Jing Zhu, Ping Yang, Yue Xiao, Y. Guan, Shaoqian Li","doi":"10.1109/ICDSP.2018.8631619","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631619","url":null,"abstract":"In this paper, we investigate the benefits of the link adaptation (LA) techniques for enhanced spatial modulation (ESM) based multiple-input multiple-output (MIMO) systems. To be specific, we first apply the power allocation (PA) technique to ESM and propose a novel PA algorithm, namely approximated maximum minimum distance (AMMD)-based PA, in order to improve the bit error rate (BER) performance. Then, we combine transmit antenna selection (TAS) technique and ESM scheme to overcome the constraint that the number of transmit antennas in ESM has to be a power of two as well as to enhance its BER performance by using the space resource. Finally, to seek higher BER performance gain, we consider the joint application of PA and TAS in ESM-MIMO systems. Our simulation results show that the proposed PA-ESM, TAS-ESM and joint PA and TAS aided ESM schemes provide beneficial system performance improvements compared to the conventional ESM scheme.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"3 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":"121955472","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: 2
A Parameter Estimation Method of Frequency Hopping Signal Based On Sparse Time-frequency Method 基于稀疏时频法的跳频信号参数估计方法
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631661
Yongzhi Wang, Yun Lin, Xiuwei Chi
{"title":"A Parameter Estimation Method of Frequency Hopping Signal Based On Sparse Time-frequency Method","authors":"Yongzhi Wang, Yun Lin, Xiuwei Chi","doi":"10.1109/ICDSP.2018.8631661","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631661","url":null,"abstract":"Frequency hopping communication is a common communication method in the field of modern wireless communication countermeasures. Due to the progress of the signal processing in frequency hopping signals, the demand for the estimation of its parameters is also increasing. This paper research on the estimating parameter of frequency hopping signal based on the sparse liner regression of compressed sensing. In addition to the basic sparse analysis, we propose an improved method which combining the algorithm of approximating LO norm and morphological filtering. The simulation of parameter estimation shows that it has a great reduction in estimation error in low SNR to use two improved methods at the same time. And it can reduce about 0.3 in estimation error at-6dB. Also, the estimation error which using the improved method with approximating LO norm and morphological filtering can reach less than 0.003 at-6dB. The experimental results show that the method of processing frequency hopping signals used in this paper can effectively estimate its parameters.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"83 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120824834","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
Exploring Resource-Aware Deep Neural Network Accelerator and Architecture Design 探索资源感知深度神经网络加速器与体系结构设计
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631853
Baoting Li, Longjun Liu, Jiahua Liang, Hongbin Sun, Li Geng, Nanning Zheng
{"title":"Exploring Resource-Aware Deep Neural Network Accelerator and Architecture Design","authors":"Baoting Li, Longjun Liu, Jiahua Liang, Hongbin Sun, Li Geng, Nanning Zheng","doi":"10.1109/ICDSP.2018.8631853","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631853","url":null,"abstract":"Due to the ever-increasing number of neural networks(NNs) connections and parameters, computation on neural networks is becoming both power hankering and memory intensive. In this paper, we propose a sparse neural networks accelerator to improve memory resource utilization and improve power efficiency. In contrast to prior works, we introduce a highly integrated software and hardware co-design technique that combines resource-aware software compression algorithms and specialized hardware inference engine in the accelerator. Compared with other designs, our design can compress parameters by 90× and substantially improve storage resource utilization, performance (6.9×) and power (1.2×) for NN accelerators.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"62 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":"129533341","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
Robust Constrained Recursive Least P-Power Algorithm for Adaptive Filtering 鲁棒约束递归最小p幂自适应滤波算法
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631663
Jiajun Sun, Siyuan Peng, Qinglai Liu, Ruijie Zhao, Zhiping Lin
{"title":"Robust Constrained Recursive Least P-Power Algorithm for Adaptive Filtering","authors":"Jiajun Sun, Siyuan Peng, Qinglai Liu, Ruijie Zhao, Zhiping Lin","doi":"10.1109/ICDSP.2018.8631663","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631663","url":null,"abstract":"In this paper, we develop a novel constrained adaptive filtering algorithm called constrained recursive least p-power (CRLP) algorithm, which incorporates a set of linear constraints into the least mean p-power error (LMP) criterion to solve a constrained optimization problem directly. Compared with the conventional constrained adaptive filtering algorithms including constrained least mean square (CLMS), constrained recursive least square (CRLS) and constrained least mean p-power (CLMP), CRLP can achieve better performance under non- Gaussian noises. Simulation results are presented to confirm the superior performance of the new algorithm.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"17 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":"127997971","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
FPGA-based Baseband Solution for High Performance Industrial Wireless Communication 基于fpga的高性能工业无线通信基带解决方案
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631662
Mingjie Hao, Ludwig Karsthof, Jochen Rust, Johannes Demel, C. Bockelmann, A. Dekorsy, Ahmad Al Houry, Fabian Mackenthun, S. Paul
{"title":"FPGA-based Baseband Solution for High Performance Industrial Wireless Communication","authors":"Mingjie Hao, Ludwig Karsthof, Jochen Rust, Johannes Demel, C. Bockelmann, A. Dekorsy, Ahmad Al Houry, Fabian Mackenthun, S. Paul","doi":"10.1109/ICDSP.2018.8631662","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631662","url":null,"abstract":"Recently, the wireless commuication system development driven by the ongoing Industry 4.0 (I40) and Indusrial Internet of Thing (IIoT) has been growing enormously. In industrial automation and manufacturing, closed-loop control applications are commonly used, which require completely new solutions to achieve latencies of 1ms with extremely high reliability and stringent requirements on message timing and repetitive packet losses. A hardware-based solution for baseband signal processing aiming at such challenges is introduced in this work. With a novel design and integration of the baseband signal processing blocks, the key highlights of this solution are a) multi-user support, b) simple and flexible integration in the industrial application, c) low hardware overhead, and d) high data throughput with low latency.","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":"128695939","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
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