Proceedings of the 2nd International Conference on Vision, Image and Signal Processing最新文献_第4页

Low Complex Simple Measurement Matrix for Sparse Recovery of Speech Signal 语音信号稀疏恢复的低复简单测量矩阵

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271597

M. A. Sankar, S. P. Savithri

{"title":"Low Complex Simple Measurement Matrix for Sparse Recovery of Speech Signal","authors":"M. A. Sankar, S. P. Savithri","doi":"10.1145/3271553.3271597","DOIUrl":"https://doi.org/10.1145/3271553.3271597","url":null,"abstract":"Compressed Sensing (CS), the methodology of signal capturing, allows sampling at flexible rates below Nyquist, with the constraint that the sparsifying basis and the level of sparsity are known in advance for the signal of interest. Many speech codecs based on CS frame work are developed using Linear Predictive Coding (LPC), Discrete Cosine Transform (DCT) and Code Excited Linear Prediction (CELP). In most of them, Gaussian random matrix is used for deriving the observation vector which is computationally complex and has large memory requirements. In this paper, a modified binary sensing matrix, specifically for speech signal is proposed, which has low coherence with the sparsifying bases used for reconstruction. The Signal-to-Noise Ratio (SNR) improvement goes beyond 3-4 dB and it is more significant at very high compression ratios. The application of the proposed sensing matrix to CS based codecs using CELP and dynamic DCT&LPC bases shows significant improvement in the perceptual quality of the reconstructed speech. This enables the functioning of these codecs at lower bit rates without compromising the quality.","PeriodicalId":414782,"journal":{"name":"Proceedings of the 2nd International Conference on Vision, Image and Signal Processing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122366180","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

Study of Gearbox Fault Diagnosis Based on Wavelet Decompostion Adaptive ELMD 基于小波分解自适应ELMD的齿轮箱故障诊断研究

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271613

Hongxia Pan, Mingzhi Pan, Jun Huang

引用次数: 0

Research and Implementation on Multi-beacon Aided AUV Integrated Navigation Algorithm Based on UKF 基于UKF的多信标辅助水下航行器组合导航算法研究与实现

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271606

Dajun Sun, C. Zheng, Miao Yu, Yixian Shuai

引用次数: 0

Monitoring Network Traffic of Different OSes in Different IP Protocols 监控不同IP协议下不同操作系统的网络流量

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271599

Chenhuan Liu, Chen Su, Xing Li

引用次数: 0

Deep Active Learning for Text Classification 文本分类的深度主动学习

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271578

Bang An, Wenjun Wu, Huimin Han

{"title":"Deep Active Learning for Text Classification","authors":"Bang An, Wenjun Wu, Huimin Han","doi":"10.1145/3271553.3271578","DOIUrl":"https://doi.org/10.1145/3271553.3271578","url":null,"abstract":"In recent years, Active Learning (AL) has been applied in the domain of text classification successfully. However, traditional methods need researchers to pay attention to feature extraction of datasets and different features will influence the final accuracy seriously. In this paper, we propose a new method that uses Recurrent Neutral Network (RNN) as the acquisition function in Active Learning called Deep Active Learning (DAL). For DAL, there is no need to consider how to extract features because RNN can use its internal state to process sequences of inputs. We have proved that DAL can achieve the accuracy that cannot be reached by traditional Active Learning methods when dealing with text classification. What's more, DAL can decrease the need of the great number of labeled instances for Deep Learning (DL). At the same time, we design a strategy to distribute label work to different workers. We have proved by using a proper batch size of instance, we can save much time but not decrease the model's accuracy. Based on this, we provide batch of instances for different workers and the size of batch is determined by worker's ability and scale of dataset, meanwhile, it can be updated with the performance of the workers.","PeriodicalId":414782,"journal":{"name":"Proceedings of the 2nd International Conference on Vision, Image and Signal Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126814078","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: 18

Text Mining of English Picture Books 英语绘本的文本挖掘

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271616

Hiromi Ban, T. Oyabu

引用次数: 0

Technology of Radar Terminal on Researching Software Display 雷达终端软件显示技术研究

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271615

Zhenkui Miao, Wei Xu, Hong Chen

引用次数: 0

Facial Expression Recognition Using Convolutional Neural Network 基于卷积神经网络的面部表情识别

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271584

Yijun Gan

引用次数: 35

Cryptographic Key Generation Using Burning Ship Fractal 利用燃烧船分形生成密码密钥

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271577

Shafali Agarwal

{"title":"Cryptographic Key Generation Using Burning Ship Fractal","authors":"Shafali Agarwal","doi":"10.1145/3271553.3271577","DOIUrl":"https://doi.org/10.1145/3271553.3271577","url":null,"abstract":"The study introduces a key generation scheme using a burning ship fractal function, Hilbert transformation and an external key. The burning ship function is a modified version of a well-known Mandelbrot set function in which absolute value of a complex variable is considered. The process starts with the scrambling of the fractal image pixels by applying a Hilbert curve scanning. To enhance the randomness and complexity, an external key is obtained using a pseudo random number generator (PRNG), whose length depends on the size of the used fractal image. Further, a covering module is applied in which eight different types of operations are performed recursively to cover the scrambled fractal image pixels using eight different keys. At each iteration, a modified external key was used to perform the respective operation to the remaining image pixels. Moreover, to ensure the robustness of the proposed scheme, each block of tempkey (created in previous step) permuted using the sorting indexes of the modified tempkey blocks. The performance analysis of the given method is carried out in terms of the key space, key sensitivity, key generation time, histogram, and correlation coefficient. The results indicate that the proposed method is reliable and secure with great potential to be further use in the image encryption applications.","PeriodicalId":414782,"journal":{"name":"Proceedings of the 2nd International Conference on Vision, Image and Signal Processing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125150356","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

Gaussian Reciprocal Sequences from the Viewpoint of Conditionally Markov Sequences 从条件马尔可夫序列的观点看高斯倒易序列

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing Pub Date : 2018-08-27 DOI: 10.1145/3271553.3271587

R. Rezaie, X. Rong Li

{"title":"Gaussian Reciprocal Sequences from the Viewpoint of Conditionally Markov Sequences","authors":"R. Rezaie, X. Rong Li","doi":"10.1145/3271553.3271587","DOIUrl":"https://doi.org/10.1145/3271553.3271587","url":null,"abstract":"The conditionally Markov (CM) sequence contains several classes, including the reciprocal sequence. Reciprocal sequences have been widely used in many areas of engineering, including image processing, acausal systems, intelligent systems, and intent inference. In this paper, the reciprocal sequence is studied from the CM sequence point of view, which is different from the viewpoint of the literature and leads to more insight into the reciprocal sequence. Based on this viewpoint, new results, properties, and easily applicable tools are obtained for the reciprocal sequence. The nonsingular Gaussian (NG) reciprocal sequence is modeled and characterized from the CM viewpoint. It is shown that a NG sequence is reciprocal if and only if it is both CML and CMF (two special classes of CM sequences). New dynamic models are presented for the NG reciprocal sequence. These models (unlike the existing one, which is driven by colored noise) are driven by white noise and are easily applicable. As a special reciprocal sequence, the Markov sequence is also discussed. Finally, it can be seen how all CM sequences, including Markov and reciprocal, are unified.","PeriodicalId":414782,"journal":{"name":"Proceedings of the 2nd International Conference on Vision, Image and Signal Processing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125496626","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: 19