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2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)最新文献

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Constant false alarm energy detection based on Markov transfer characteristics in cognitive radio 基于认知无线电马尔可夫转移特性的恒虚警能量检测
2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2017-11-01 DOI: 10.1109/ISPACS.2017.8266458
X. Qin, Shengliang Peng, Renyang Gao, Weibin Zheng
{"title":"Constant false alarm energy detection based on Markov transfer characteristics in cognitive radio","authors":"X. Qin, Shengliang Peng, Renyang Gao, Weibin Zheng","doi":"10.1109/ISPACS.2017.8266458","DOIUrl":"https://doi.org/10.1109/ISPACS.2017.8266458","url":null,"abstract":"Cognitive Radio is an emerging technology to improve the utilization of licensed spectrum. Spectrum sensing is one of the key tasks for cognitive radio. Previous research on spectrum sensing has not fully investigated the characteristics of the primary user. This paper analyzes the Markov transfer characteristics of the primary user, based on which the current state of the primary user is predicted to adjust the decision threshold and improve detection accuracy. Firstly, we illustrate the Markov transfer characteristics of the primary user. Secondly, we illustrate benefits of the characteristics and derive the upper bound of the detection probability we can achieve. Finally, we introduce a new algorithm to exploit the Markov transfer characteristics. Simulation results are given to verify the performance of the proposed algorithm in this paper.","PeriodicalId":166414,"journal":{"name":"2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133397652","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
Invariant feature extraction for image classification via multi-channel convolutional neural network 基于多通道卷积神经网络的图像分类不变性特征提取
2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2017-11-01 DOI: 10.1109/ISPACS.2017.8266528
Shaohui Mei, Ruoqiao Jiang, Jingyu Ji, Jun Sun, Yang Peng, Yifan Zhang
{"title":"Invariant feature extraction for image classification via multi-channel convolutional neural network","authors":"Shaohui Mei, Ruoqiao Jiang, Jingyu Ji, Jun Sun, Yang Peng, Yifan Zhang","doi":"10.1109/ISPACS.2017.8266528","DOIUrl":"https://doi.org/10.1109/ISPACS.2017.8266528","url":null,"abstract":"The invariance for feature extraction, such as invariance for specificity of homogeneous sample and rotation invariance, is crucial for object detection and classification applications. Current researches mainly focus on a specific invariance of features, such as rotation invariance. In this paper, a novel multi-channel convolutional neural network (mCNN) is proposed to extract invariant features for object classification. Multi-channel convolutions sharing identical weights are used to alleviate the feature variance of sample pairs with different rotations in the same category. As a result, the invariance for specificity of homogeneous object and rotation invariance are simultaneously encountered to improve the invariance of features. More importantly, the proposed mCNN is especially effective for small training samples. Experimental results on two benchmark datasets for handwriting recognition demonstrate that the proposed mCNN is very effective to extract invariant feature with small amount of training samples.","PeriodicalId":166414,"journal":{"name":"2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133795984","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: 14
Novel DLA-based digital pre-distortion technique for power amplifier 一种新的基于fpga的功率放大器数字预失真技术
2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2017-11-01 DOI: 10.1109/ISPACS.2017.8266453
Wei Xue, Jingqi Wang, Yurou Tian, Jing Tan, Wen Wu
{"title":"Novel DLA-based digital pre-distortion technique for power amplifier","authors":"Wei Xue, Jingqi Wang, Yurou Tian, Jing Tan, Wen Wu","doi":"10.1109/ISPACS.2017.8266453","DOIUrl":"https://doi.org/10.1109/ISPACS.2017.8266453","url":null,"abstract":"As a coefficients extraction method of power amplifier linearization technique based on the direct learning architecture (DLA), the traditional iterative method has the merits of less iteration times and high convergence rate and is commonly used. Its computation, however, because of the two order partial derivatives of the cost function is quite complex. In order to overcome the shortcomings of the traditional iterative method, a new DLA-based pre-distortion technique, which combines steepest descent with instantaneous gain, is proposed in this paper. By employing steepest descent method, the coefficient update method for digital pre-distorter avoids complicated two order partial derivatives of traditional iterative methods. Furthermore, the concept of instantaneous gain of power amplifier is introduced to avoid solving power amplifier model during the iteration. Simulation results show that the proposed technique can improve the convergence speed and performance of the algorithm.","PeriodicalId":166414,"journal":{"name":"2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128414922","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
High-performance SAR image registration algorithm using guided filter & ROEWA-based SIFT framework 基于引导滤波和基于roewa的SIFT框架的高性能SAR图像配准算法
2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2017-11-01 DOI: 10.1109/ISPACS.2017.8266507
Qiuze Yu, Shan Zhou, Peng Wu, Yan Zhang
{"title":"High-performance SAR image registration algorithm using guided filter & ROEWA-based SIFT framework","authors":"Qiuze Yu, Shan Zhou, Peng Wu, Yan Zhang","doi":"10.1109/ISPACS.2017.8266507","DOIUrl":"https://doi.org/10.1109/ISPACS.2017.8266507","url":null,"abstract":"To address the performance degradation of SIFT-based SAR image registration algorithm caused by speckle noise and local deformation of SAR images, this paper presents a novel SIFT-framework algorithm for SAR image registration based on improved multi-scale space construction strategy and a novel local feature detection and descriptors. In our proposed algorithm, the multi-scale space construction is generated by Guided Filter because of its real-time and edge preserving. The feature detection section adopts Harris-Laplace combined with ROEWA, which is effective to suppress the false alarm on high-contrast areas of SAR image. Moreover, the feature description adopts the GLOH by ROEWA method, since the phase method of GLOH descriptor is robust to rotation. At last, we suggest using K-Nearest Neighbors (KNN) to speed up the search for quick rough match, and then using the random sample consensus algorithm (RANSAC) to remove false match points. Experimental results indicate that our proposed algorithm is real-time and produces better performance than SIFT-based methods on SAR image registration.","PeriodicalId":166414,"journal":{"name":"2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128465572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A neural network method for risk assessment and real-time early warning of mountain flood geological disaster 山洪地质灾害风险评估与实时预警的神经网络方法
2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2017-11-01 DOI: 10.1109/ISPACS.2017.8266537
Jia Xichun, Wang Ruilan, Dai Hao, Zhang Wei, Liu Zhiwei, Cong Peitong
{"title":"A neural network method for risk assessment and real-time early warning of mountain flood geological disaster","authors":"Jia Xichun, Wang Ruilan, Dai Hao, Zhang Wei, Liu Zhiwei, Cong Peitong","doi":"10.1109/ISPACS.2017.8266537","DOIUrl":"https://doi.org/10.1109/ISPACS.2017.8266537","url":null,"abstract":"Zhongshan County of Guangxi Zhuang Autonomous Region was selected as the study area to investigate the intelligent assessment and early warning system of mountain flood geological disaster. Remote sensing images, spectral data and DEM data were processed on ENVI and ArcGIS platforms and the quantized data including slope, NDVI, soil looseness coefficient, valley and ridge classification and rainfall were obtained. And then a generalized regression neural network model for risk assessment of mountain flood geological disaster in Zhongshan County was established with the above quantized data as the input factors and the risk degree of the mountain flood geological disaster as the output factor. The trained model by using historical data has an excellent self-learning function and provide a good prediction on the risk degree of the mountain flood geological disaster in Zhongshan County.","PeriodicalId":166414,"journal":{"name":"2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116106230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Parallel two-class 3D-CNN classifiers for video classification 并行两类3D-CNN视频分类器
2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2017-11-01 DOI: 10.1109/ISPACS.2017.8265636
Jing Li
{"title":"Parallel two-class 3D-CNN classifiers for video classification","authors":"Jing Li","doi":"10.1109/ISPACS.2017.8265636","DOIUrl":"https://doi.org/10.1109/ISPACS.2017.8265636","url":null,"abstract":"The required amount of computation and training data for training 3D-CNN, especially for complex classification tasks with videos, hinders the wide application of 3D-CNN. In this paper, inspired by the exclusion method in human's judgement, a parallel 3D-CNN architecture is proposed to decompose the multi-class classification task using one 3D-CNN into the combination of multiple two-class classification tasks. 3D-CNN is used for each of the two-class classification tasks, and the difficulty and the data requirement on training such a 3D-CNN is reduced greatly comparing with the 3D-CNN for multi-class classification. In addition, the combination of two-class classifiers provides the ability of recognizing unknown class to the proposed 3D-CNN model. The feasibility of this proposed 3D-CNN model is verified via its application on video copy detection on the CC_WEB_VIDEO dataset, which shows the potentiality of the proposed parallel two-class 3D-CNN model in video classification.","PeriodicalId":166414,"journal":{"name":"2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117311776","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: 11
Tag propagation by using multi-view NMF consistent matrix for image annotation 使用多视图NMF一致矩阵进行图像标注的标签传播
2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2017-11-01 DOI: 10.1109/ISPACS.2017.8266533
S. Cai, Lihong Ma, Fuping Zhong, Renlong Pan
{"title":"Tag propagation by using multi-view NMF consistent matrix for image annotation","authors":"S. Cai, Lihong Ma, Fuping Zhong, Renlong Pan","doi":"10.1109/ISPACS.2017.8266533","DOIUrl":"https://doi.org/10.1109/ISPACS.2017.8266533","url":null,"abstract":"An image tag propagation method could assign tags to a new query image from labeled training samples by paired comparison of visual distances. One of its challenges is the inconsistency between visual similarities and tag similarities. In this paper, to benefit from the structure information commonly described by visual features and image tags, we propose a novel propagation based on multi-view Negative-Matrix-Factorization(NMF) clustering and sparse tag sensing. A ranked consistent matrix is created from multiview NMF observations to estimate the clustering structures, while sparse tags of a query image are approximately reconstructed with consistent matrix sensing. Compared to the best performing 2PKNN algorithm, our proposed method gains 3.2%, 0.8% in term of average recall and F1-score.","PeriodicalId":166414,"journal":{"name":"2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127338739","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
Power allocation for two-way communication underlay full duplex cognitive radio systems with QOS constraint of primary user 基于主用户QOS约束的全双工认知无线电系统双向通信功率分配
2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2017-11-01 DOI: 10.1109/ISPACS.2017.8266459
Peng Lan, Guowei Zhang, Lun Liu, Bin Wang, Fenggang Sun
{"title":"Power allocation for two-way communication underlay full duplex cognitive radio systems with QOS constraint of primary user","authors":"Peng Lan, Guowei Zhang, Lun Liu, Bin Wang, Fenggang Sun","doi":"10.1109/ISPACS.2017.8266459","DOIUrl":"https://doi.org/10.1109/ISPACS.2017.8266459","url":null,"abstract":"To enhance the spectrum utilization efficiency, cognitive radio and full duplex techniques are considered as the two main effective emthods. For the two-way communication underlay full duplex cognitive radio networks, this paper studied the power control problem to enhance the secondary transmission while satisfy the primary transmission requirement. In practical scenarios, only the primary user (PU) statistical channel state information (CSI) is retrievable for the secondary users (SUs), and the joint power allocation between SUs are studied utilize the statistical CSI. Simulation results show that the proposed approach can provide an improved performance, as compared to the existing equal resource allocation (ERA) scheme.","PeriodicalId":166414,"journal":{"name":"2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126637395","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
An efficient parallel approach using OpenCL for pupil detection and pupil size measurement 一种使用OpenCL进行瞳孔检测和瞳孔大小测量的高效并行方法
2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2017-11-01 DOI: 10.1109/ISPACS.2017.8266494
Gang He, Wenxin Yu, Yuan-Wen Zou, Jin-Chuan Li
{"title":"An efficient parallel approach using OpenCL for pupil detection and pupil size measurement","authors":"Gang He, Wenxin Yu, Yuan-Wen Zou, Jin-Chuan Li","doi":"10.1109/ISPACS.2017.8266494","DOIUrl":"https://doi.org/10.1109/ISPACS.2017.8266494","url":null,"abstract":"Pupil detection techniques based on video camera are useful for human monitoring and machine-human interface devices. For example, PD can be used in the field of noninvasive intracranial pressure monitor, fatigue detection etc. During the pupil detection process, image pixels can dramatically influence the detection speed, especially when two cameras are used for acquiring images from both left and right eyes. The low detection speed would impact the application for real-time detections. In this paper, in order to improve the detection speed, a parallel pupil detection and pupil size measurement method using the OpenCL (Open Computing Language) framework for the parallel computing was proposed. The experimental results indicated that the proposed method present excellent efficiency than that of CPU-only platforms without compromising the accuracy of pupil detection and pupil size measurement.","PeriodicalId":166414,"journal":{"name":"2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126697986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Multiplicative noise removal using deep CNN denoiser prior 基于先验深度CNN去噪的乘法去噪
2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2017-11-01 DOI: 10.1109/ISPACS.2017.8265635
Guodong Wang, Guotao Wang, Zhenkuan Pan, Zhimei Zhang
{"title":"Multiplicative noise removal using deep CNN denoiser prior","authors":"Guodong Wang, Guotao Wang, Zhenkuan Pan, Zhimei Zhang","doi":"10.1109/ISPACS.2017.8265635","DOIUrl":"https://doi.org/10.1109/ISPACS.2017.8265635","url":null,"abstract":"Multiplicative noise removal is always a hard problem in fundamental image processing task. Many methods are proposed for the multiplicative noise removal by using different denoiser prior in variational framework. Among the image prior, total variation (TV) are first proposed and then many other regularization such as PM, TGV, nonlocal and many other priors are also proposed for enhance the denoising ability. Although using the priors can get good performance, the models are hard to be resolved with sophisticated priors. A new model based on the deep CNN denoiser prior for removing multiplicative noise is proposed in this paper. The proposed energy function is effectively calculated via several sub-optimal questions by split bregman method and alternative minimization is used for the solution. The proposed method needn't deduce the sophisticated formula and can achieve good performance. From the experiments, we can see that our method achieved good results.","PeriodicalId":166414,"journal":{"name":"2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122752066","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
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