International Conference on Signal Processing Systems最新文献

A preliminary acoustic study of Mandarin speech in drug addicts 吸毒人员普通话语音的初步声学研究

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631443

Puyang Geng, Hong Guo, Qimeng Lu, Shaopei Shi

引用次数: 1

Concentration prediction of binary mixed gases based on random forest algorithm in the electronic nose system 电子鼻系统中基于随机森林算法的二元混合气体浓度预测

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631552

Tao Wang, Yu Wu, Yongwei Zhang, Wen Lv, Xinwei Chen, Zhi-Wen Yang

{"title":"Concentration prediction of binary mixed gases based on random forest algorithm in the electronic nose system","authors":"Tao Wang, Yu Wu, Yongwei Zhang, Wen Lv, Xinwei Chen, Zhi-Wen Yang","doi":"10.1117/12.2631552","DOIUrl":"https://doi.org/10.1117/12.2631552","url":null,"abstract":"The concentration prediction of mixed gases is crucial to the pattern recognition research of electronic nose (E-nose) systems. The response experiments of the E-nose towards the ethanol and propanol mixture with different concentrations are carried out. Five kinds of machine learning algorithms, including linear regression, support vector machine, K-nearest neighbor, random forest, and decision tree, are used for training the multiple output regressors to predict the content of each component simultaneously. The R2 score, root mean squared error, and mean absolute error are used to evaluate the performance of these models. The relationship between prediction accuracy and concentration distribution has also been studied. The results show that the model based on the random forest has superior performance for forecasting the concentration of ethanol and propanol, with the R2 score more than 0.98 in the 5-fold cross-validation. This study provides a significant inspiration for designing a multi-output regression model to realize the quantitative prediction of mixed gases by the E-nose.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114816993","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

Research on different classifiers of automatic target recognition and classification for low-resolution ground radar 低分辨率地面雷达目标自动识别与分类的不同分类器研究

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631543

Yu-Li You, Renhong Xie, Jinwei Gu, Teng Wang, Peng Li, Yibin Rui

{"title":"Research on different classifiers of automatic target recognition and classification for low-resolution ground radar","authors":"Yu-Li You, Renhong Xie, Jinwei Gu, Teng Wang, Peng Li, Yibin Rui","doi":"10.1117/12.2631543","DOIUrl":"https://doi.org/10.1117/12.2631543","url":null,"abstract":"As low-resolution radar is still the main radar in service in China, the ground target classification and recognition technology of low-resolution radar has a wide application prospect in modern military and civil fields. This paper mainly studies and compares two main types of automatic target recognition and classification method for low-resolution ground radar: conventional recognition based on feature extraction and neural networks, and the conclusion is that the latter has better performance and needs less time to train. The former model in this paper fuses the time domain and frequency domain features of ground target echo, then simulates, compares and analyzes the performance of different classifiers. The classifiers studied include: naive bayes classifier (NBC), decision tree classifier (DT), linear discriminant analysis (LDA) classifier, k nearest neighbors (KNN) classifier and support vector machine (SVM) classifier. Five-fold cross validation is adopted in the experiment to effectively avoid the impact of arbitrariness on the results caused by the random division of the sample set into training sample set and test sample set. Besides, based on conventional convolutional neural networks, a new neural network structure named multi-scale residual neural network (Multi-scale ResNet) is proposed for one-dimensional feature target recognition, which effectively reduces the data dimension through auto-encoder and solves the problem of performance degradation caused by the difficulty in training too many levels of traditional convolutional neural network. The bayesian hyper-parameter optimization method is utilized to optimize the hyper-parameters of different classifiersl. Finally, compared the accuracy of the two types of target recognition, the best performance of the pattern recognition is the support vector machine, which recognition rate is 91.2%, while multi-scale residual neural network recognition rate is up to 99.6%.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129870077","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

Expression-assisted facial action unit detection through an attention mechanism and smooth class-weighted Loss 基于注意机制和平滑类加权损失的表情辅助面部动作单元检测

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631478

Zhongling Liu, Ziqiang Shi, Rujie Liu, Liu Liu, Xiaoyue Mi, Kentaro Murase

引用次数: 0

Detection of voice disability and its severity in children 儿童语音残疾的检测及其严重程度

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631479

Mohina Ahmadi, Pavani Mullapudi, Shreyashri Athani, Shikha Tripathi

引用次数: 0

A method of multi-target bearings-only association and location with two platforms 一种双平台多目标纯方位关联定位方法

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631444

Tao Jiang, Ganggang Tu, Jiutao Wu

引用次数: 0

Image inpainting based on directional Gaussian graph model using multi-head reference 基于多头参考的方向高斯图模型的图像绘制

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631560

Zhonghao Zhang, Lihong Ma

引用次数: 0

Referential genome sequence compression with low memory consumption 参考基因组序列压缩与低内存消耗

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631583

Zhiwen Lu, Jianhua Chen, Rongshu Wang

引用次数: 0

A multi-scale adaptive feature enhancement network for image denoising 图像去噪的多尺度自适应特征增强网络

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631573

Qing Zhao, Z. Miao, Wanru Xu, Shaoyue Song

引用次数: 1

A modulation index estimation algorithm for multi-h CPM signals 一种多h CPM信号调制指数估计算法

International Conference on Signal Processing Systems Pub Date : 2022-05-01 DOI: 10.1117/12.2631576

Hao Chen, Xiaolin Zhang, Xiaopeng Zhang

{"title":"A modulation index estimation algorithm for multi-h CPM signals","authors":"Hao Chen, Xiaolin Zhang, Xiaopeng Zhang","doi":"10.1117/12.2631576","DOIUrl":"https://doi.org/10.1117/12.2631576","url":null,"abstract":"Continuous Phase Modulation (CPM) signal has been widely used in modern satellite, mobile communication and military communication system due to its good spectrum and power utilization and constant envelope characteristics. The premise of demodulation or jamming of CPM signal intercepted in military communication confrontation is the accurate estimation of signal parameters. Aiming at the difficulty and complexity of multi-h CPM signal modulation index estimation, this paper puts forward a kind of blind estimation algorithm of the modulation index based on first order cyclic moment and the second-order cyclic cumulants, using the first and second order cyclic properties in frequency domain on the spectral properties to signal parameter estimation. The estimation algorithm is suitable for both single h CPM signals and multi-h CPM signals, where the multi-h CPM requires the synchronization of the guidance sequence. Simulation results show that the algorithm has better performance under low signal-to-noise ratio with less symbols required. When the number of symbols is 128 and the signal-to-noise ratio is 15 dB, the accurate recognition rate of the multi-index CPM signal can reach 98% and the recognition rate can reach 99% with the allowable error of 1/32.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124948256","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