2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)最新文献_第2页

A Hardware-Software Codesign Case Study: The SHA3-512 algorithm Implementation on the LEON3 Processor 一个软硬件协同设计案例研究:SHA3-512算法在LEON3处理器上的实现

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231959

Mouna Karmani, N. Benhadjyoussef, B. Hamdi, M. Machhout

引用次数: 0

Monaural speech separation based on linear regression optimized using gradient descent 基于梯度下降优化线性回归的单耳语音分离

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231542

Belhedi Wiem, M. B. Messaoud, A. Bouzid

引用次数: 0

Data and information quality assessment in a possibilistic framework based on the Choquet Integral 基于Choquet积分的可能性框架中的数据和信息质量评估

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231627

Sonda Ammar Bouhamed, Hatem Dardouri, I. Kallel, É. Bossé, B. Solaiman

引用次数: 2

Detection of Epileptic High Frequency Oscillations Using Support Vector Machines 支持向量机检测癫痫高频振荡

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231905

S. Chaibi, Fatma Krikid, C. Mahjoub, Tarek Lajnef, R. Bouquin-Jeannès, A. Kachouri

{"title":"Detection of Epileptic High Frequency Oscillations Using Support Vector Machines","authors":"S. Chaibi, Fatma Krikid, C. Mahjoub, Tarek Lajnef, R. Bouquin-Jeannès, A. Kachouri","doi":"10.1109/ATSIP49331.2020.9231905","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231905","url":null,"abstract":"Recently, several studies have proved that High Frequency Oscillations (HFOs) of [80500] Hz are reliable biomarkers for delineating the epileptogenic zone. The total duration of HFOs is extremely short compared to the entire duration of EEG dataset to be analyzed. Therefore, visual marking of HFOs is timeconsuming and laborious process. In order to promote the clinical use of HFOs oscillations as reliable biomarkers of epileptogenic tissue and to conduct large-scale investigations on cerebral HFOs activities, several automatic detection techniques have been proposed over the past few years. In the present framework, we propose a novel approach for detecting HFOs based on Support Vector Machines (SVM). Our method is subsequently compared with six other methods. HFOs detection performance is evaluated in terms of sensitivity, false discovery rate, area under the ROC curve and execution time. Our results demonstrate that SVM approach yields low false detection (FDR = 6.36%) but, in its current implementation, is moderately sensitive to detect HFOs with a sensitivity of 71.06%.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134054199","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 Multi-view Deep Convolutional Neural Network for Reduction of False Positive Findings in Breast Cancer Screening 用于减少乳腺癌筛查假阳性结果的多视图深度卷积神经网络

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231738

N. Derbel, Hedi Tmar, A. Mahfoudhi

{"title":"A Multi-view Deep Convolutional Neural Network for Reduction of False Positive Findings in Breast Cancer Screening","authors":"N. Derbel, Hedi Tmar, A. Mahfoudhi","doi":"10.1109/ATSIP49331.2020.9231738","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231738","url":null,"abstract":"Screening mammography is commonly the only imaging exam allowing early-stage detection of breast cancer. The early detection is, in fact, associated with a decreased breast cancer mortality rate amongst women. However, false positive recall is one of the main limitations of screening practices and it is often associated with unnecessary workups and biopsies. To tackle this issue and improve the medical image classification performance in order to carry out a screening/diagnosis task, we propose to use a multi-view deep convolutional neural network - the proposed network can extract discriminative features from Cranial Caudal (CC) and Medio-Lateral Oblique (MLO) views for each breast of a patient (a set of four images). We experiment it on an augmented-data based subset selected from the open Digital Database for Screening Mammography (DDSM) using 5400 images. We show how the proposed method can lead to a better performance than the state-of-the-art ones, especially in terms of prediction accuracy and false positive rate reduction. In fact, The results show statistically significant reduction in false findings without increasing false negative cases. Our method achieves a specificity rate of 98% and an accuracy rate of 98.88%. Index Terms–Mammography, Breast cancer diagnosis, False positive findings, Deep learning, Multi-view deep convolutional neural network.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116672141","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

Enhanced data-driven Damage Detection for Structural Health Monitoring Systems 结构健康监测系统的增强数据驱动损伤检测

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231646

Marwa Chaabane, A. Hamida, M. Mansouri, H. Nounou, M. Nounou

引用次数: 0

A Comparative Study of Fingerprint Enhancement Algorithms 指纹增强算法的比较研究

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231817

Sarra Hajri, F. Kallel, A. Hamida, A. Naït-Ali

引用次数: 2

Conventional Machine Learning Techniques with Features Engineering for Preventive Larynx Cancer Detection 基于特征工程的传统机器学习技术用于预防性喉癌检测

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231797

A. B. Aicha

引用次数: 0

Demographic Face Profiling Based on Age, Gender and Race 基于年龄、性别和种族的人口面部特征分析

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231835

Asma El Kissi Ghalleb, Safa Boumaiza, N. Amara

引用次数: 2

Combining Speech Features for Aggression Detection Using Deep Neural Networks 结合语音特征的深度神经网络攻击检测

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231791

Noussaiba Jaafar, Z. Lachiri

{"title":"Combining Speech Features for Aggression Detection Using Deep Neural Networks","authors":"Noussaiba Jaafar, Z. Lachiri","doi":"10.1109/ATSIP49331.2020.9231791","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231791","url":null,"abstract":"Predicting the intensity level of aggression is a challenging problem in surveillance applications. Since there are no trivial fusion rules or classifiers, we developed a fusion framework to accomplish this complex task using Deep Neural Networks. This framework used a low level that contains the audio-visual features, an intermediate level composed of a set of concepts (meta-features) and a high level which is a final evaluation of the multimodal aggression detection. In this paper, we study the prediction of multimodal level for aggression detection and both Context and Semantics meta-features. This prediction is based on the audio modality using sensor and semantic information. Using meta-features for the semantic part of speech, we show the added value of such extra-information on the fusion process when the situations are more complicated. We also propose to use different text-based features such as linguistic and word affect features that will provide significant results for predicting the two meta-features and the multimodal aggression level using Deep Neural Networks when they are fused with the acoustic features although the nature of spontaneous speech.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129175982","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