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

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Deep learning for internet of things in fog computing: Survey and Open Issues 雾计算中物联网的深度学习:调查和开放问题
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231685
Jihene Tmamna, Emna Ben Ayed, Mounir Ben Ayed
{"title":"Deep learning for internet of things in fog computing: Survey and Open Issues","authors":"Jihene Tmamna, Emna Ben Ayed, Mounir Ben Ayed","doi":"10.1109/ATSIP49331.2020.9231685","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231685","url":null,"abstract":"In recent years, the internet of things is getting very popular where it arose in several areas such as education, and healthcare to enhance our live. This popularity has led to an increase number of IoT devices and thus generates massive volume of data. However, this data requires efficient methods of analysis to provide intelligent services. Recently, the deep learning can meet the requirements of IoT data analysis by providing techniques for large scale data analysis and meaningful feature extraction. The deep learning implementation is traditionally delivered to cloud computing due to its high compute resources provisioning. However, given the sheer volume of IoT data, the cloud computing fall to meet the IoT requirements, it presents many issues in term of time response, large data transmission, energy consumption, etc. To address this challenges the fog computing, new layer between cloud computing and internet of things devices, appears. So, moving the implementation of deep learning to fog computing can achieve the requirements of internet of things systems and enhance their performances. In this paper, we introduce deep learning for internet of things, next the application of deep learning in internet of things. We address fog computing for the internet of things. Finally, we present the deep learning in fog computing.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"11 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132870272","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
Speckle Denoising of the Multipolarization Images by Hybrid Filters 基于混合滤波器的多极化图像散斑去噪
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231773
M. Yahia, Tarig Ali, M. Mortula
{"title":"Speckle Denoising of the Multipolarization Images by Hybrid Filters","authors":"M. Yahia, Tarig Ali, M. Mortula","doi":"10.1109/ATSIP49331.2020.9231773","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231773","url":null,"abstract":"Speckle reduction in synthetic aperture radar (SAR) images and SAR polarimetry (PolSAR) is important for the extraction of important information for homogeneous extended areas. Finding good balance between spatial detail preservation and high equivalent number of looks is a challenge. In this paper, we validate the use of the infinite number of looks filter (INLP) method for the denoising of the multipolarization channels (i. e. hh, hv and vv) by representing their analogy with the eingenvalues of the coherency matrix. The denoised pixels obtained by a traditional filtering method served as inputs for the INLP method. Results demonstrated that the proposed method outperformed traditional filters i. e. boxcar and the refined sigma filter in terms of spatial detail preserving and speckle reduction.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"18 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":"114388842","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
Multidatabase ECG signal processing 多数据库心电信号处理
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231880
Taissir Fekih Romdhane, R. Ouni, Mohamed Atri
{"title":"Multidatabase ECG signal processing","authors":"Taissir Fekih Romdhane, R. Ouni, Mohamed Atri","doi":"10.1109/ATSIP49331.2020.9231880","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231880","url":null,"abstract":"An Electrocardiogram (ECG) records the electrical activity of the heart to locate the abnormalities. ECG signal processing is an emerging tool for the cardiologists in medical diagnosis for effective treatments. Many researches focus on how to improve preprocessing and processing algorithms in order to classify ECG signals with low cost and high accuracy. These algorithms consist of removing all types of noise that contaminate the ECG recording as well as extracting the most important features. In this paper, we present a useful Matlab GUI to analyze and classify ECG signal using efficient preprocessing and processing techniques. These techniques allow acquiring ECG recorders from various universal cardiac databases, filtering them using Butterworth low pass filter and IIR notch filter and extracting the most important cardiac features based on discrete wavelet transform db6.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"45 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":"125866621","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
Feature Optimization for Gifted Children Diagnosis 天才儿童诊断的特征优化
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231719
Kawther Benharrath, B. Khaddoumi, M. Sayadi, H. Rix, Olivier Meste, J. Lebrun, S. Guetat, M. Magnié-Mauro
{"title":"Feature Optimization for Gifted Children Diagnosis","authors":"Kawther Benharrath, B. Khaddoumi, M. Sayadi, H. Rix, Olivier Meste, J. Lebrun, S. Guetat, M. Magnié-Mauro","doi":"10.1109/ATSIP49331.2020.9231719","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231719","url":null,"abstract":"This paper deals with the diagnosis of intellectual precocity in gifted children (GC) cases. The P300 component is usually used for giftedness identification. By the use of empirical mode decomposition (EMD), a significant P300 detection is obtained through electroencephalogram signals (EEG). The novelty of the proposed work is to speed up the intellectual ability characterization based on statistical features extraction from P300 response. In order to get an optimized number of estimated information, a selection technique based on the characterization degree criterion (CD-J) is then introduced. This allows a considerably computing time decreasing and an excessive performance of the achieved results. Besides that, the proposed analysis method is applied on (GC) dataset, covering a parental relationship. Compared to the previous works, the proposed approach seems to be promising and useful for the characterization children and their diagnostic improvement.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"17 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":"122227745","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 Implementation of Face Detection System Using CUDA 基于CUDA的人脸检测系统的高效并行实现
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231723
Hana Ben Fredj, Souhir Sghaier, C. Souani
{"title":"An Efficient Parallel Implementation of Face Detection System Using CUDA","authors":"Hana Ben Fredj, Souhir Sghaier, C. Souani","doi":"10.1109/ATSIP49331.2020.9231723","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231723","url":null,"abstract":"Face detection is a highly efficient component in diverse domains such as security surveillance. Especially, the Viola-Jones algorithm has achieved significant performances in the field of detection face. In the last years, graphics processors have fast become the mainstay to solve the problem of detection face applications and to accelerate data parallel computing. This is due to their flexibility, and in particular, to the single-instruction, multiple-data execution model exploited for streaming processors by a Graphics Processing Unit (GPU). Therefore, in this paper, the researchers develop a robust face detection implementation based on the GPU component. The implementation has been optimized by following up a strategy to use the different memory resources in GPU and the warp scheduler technique, so as to accelerate the access to the memory, with better exploitation of resources proved by GPU. The results display that the suggested method is very important and consumes less execution time compared with the standard implementation and sequential implementation.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"104 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":"130155942","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
An effective approach for the diagnosis of melanoma using the sparse auto-encoder for features detection and the SVM for classification 提出了一种基于稀疏自编码器特征检测和支持向量机分类的黑色素瘤诊断方法
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231611
Nadia Smaoui Zghal, I. Kallel
{"title":"An effective approach for the diagnosis of melanoma using the sparse auto-encoder for features detection and the SVM for classification","authors":"Nadia Smaoui Zghal, I. Kallel","doi":"10.1109/ATSIP49331.2020.9231611","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231611","url":null,"abstract":"Malignant melanoma is considered one of the terrible disorders causing death. The goal of the modern dermatology is the early screening of skin cancer, aiming at reducing the mortality rate with less extensive treatment. In this context, this work focuses on the problem of an automatic melanoma diagnosis. The proposed approach uses unsupervised robustness of deep learning to extract significant characteristics from pixels of the images. A preprocessing step is used to remove unwanted artifacts and to improve the contrast of the images. Then, features are extracted by a deep Sparse Auto-encoder. Finally, the classifier Support Vector Machine (SVM) is used to distinguish respectively between three populations which are Melanoma, suspicious cases, and non-melanoma. For evaluation, we test the proposed approach using images from the PH2 dataset. The results show remarkable performance in terms of specificity, sensitivity, and accuracy.","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":"129429590","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
Why To Model Remote Sensing Measurements In 3d? Recent Advances In Dart: Atmosphere, Topography, Large Landscape, Chlorophyll Fluorescence And Satellite Image Inversion 为什么要建立三维遥感测量模型?大气、地形、大景观、叶绿素荧光和卫星影像反演的最新进展
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231884
J. Gastellu-Etchegorry, Y. Wang, O. Regaieg, T. Yin, Z. Malenovský, Z. Zhen, X. Yang, Z. Tao, L. Landier, A. Al Bitar, Deschamps, N. Lauret, J. Guilleux, E. Chavanon, B. Cao, J. Qi, A. Kallel, Z. Mitraka, N. Chrysoulakis, B. Cook, D. Morton
{"title":"Why To Model Remote Sensing Measurements In 3d? Recent Advances In Dart: Atmosphere, Topography, Large Landscape, Chlorophyll Fluorescence And Satellite Image Inversion","authors":"J. Gastellu-Etchegorry, Y. Wang, O. Regaieg, T. Yin, Z. Malenovský, Z. Zhen, X. Yang, Z. Tao, L. Landier, A. Al Bitar, Deschamps, N. Lauret, J. Guilleux, E. Chavanon, B. Cao, J. Qi, A. Kallel, Z. Mitraka, N. Chrysoulakis, B. Cook, D. Morton","doi":"10.1109/ATSIP49331.2020.9231884","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231884","url":null,"abstract":"Remote sensing (RS) dedicated to the study of land surfaces benefits from more and more advanced sensors. However, the interpretation of RS data is often is often inaccurate due to the complexity of the observed land surfaces. Therefore, RS models, in particular physical models, that simulate RS observations of the three-dimensional (3D) landscapes are critical to correctly interpret RS data. DART is one of the most comprehensive 3D models of Earthatmosphere optical radiative transfer (RT), from ultraviolet (UV) to thermal infrared (TIR). It simulates the optical signal of proximal, aerial and satellite imaging spectrometers and laser scanners, the 3D RB and solar-induced chlorophyll fluorescence (SIF) signal, for any urban or natural landscape and any experimental or instrument configuration. It is freely available for research and teaching activities (dart.omp.eu). After illustrating three significant sources of inaccuracy in RS interpretation, five recent DART advances are presented: RT in the atmosphere and topography, fast RS image simulation of large landscapes, SIF modelling, and satellite image inversion.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"37 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":"131885198","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
Case-Based Reasoning: Problems And Importance Of Similarity Measure 基于案例的推理:相似性度量的问题和重要性
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231755
I. Chourib, G. Guillard, M. Mestiri, B. Solaiman, I. Farah
{"title":"Case-Based Reasoning: Problems And Importance Of Similarity Measure","authors":"I. Chourib, G. Guillard, M. Mestiri, B. Solaiman, I. Farah","doi":"10.1109/ATSIP49331.2020.9231755","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231755","url":null,"abstract":"This article attempts to provide an overview of the basic concepts, structure and terms related to case-based reasoning, because we aim to work on an evolution of the CBR process which allows us to integrate the knowledge base, ensure the consistency of information, integrate the modes of reasoning: similarity, classification and hybrid. In addition, this article discusses the concept and issues related to similarity measure, which is very important in medical system and using it allows to reduce the risks of health error for patients. However, it seems very complex because the measure of similarity must take into account the diversity of the types of information (qualitative, textual, quantitative, etc.) for each patient. And at the end, we represent an evaluation of similarity measure which was applied in (GS.52).","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"11 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":"131047620","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
Subjective evaluation of approximate Discrete Sine Transform for the Versatile Video Coding standard 通用视频编码标准中近似离散正弦变换的主观评价
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231720
S. B. Jdidia, M. Amor, Fatma Belghith, N. Masmoudi
{"title":"Subjective evaluation of approximate Discrete Sine Transform for the Versatile Video Coding standard","authors":"S. B. Jdidia, M. Amor, Fatma Belghith, N. Masmoudi","doi":"10.1109/ATSIP49331.2020.9231720","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231720","url":null,"abstract":"For the past few years, a Joint Video Exploration Team (JVET) has been working to establish a new compression standard known as Versatile Video Coding (VVC). Several coding tools have been introduced. The Adaptive Multiple Transform (AMT), used as a residual transform coding, involves five discrete transforms that belong to cosine (DCT) and Sine (DST) families. However, transform computation using direct matrix multiplication requires much hardware resources and yields high run-time complexity. Particularly the DST-VII, frequently used by the transform module, relies on complex matrix multiplication. This results in a significant issue while implementing it in hardware. Therefore, DST-VII approximation was addressed as an efficient solution to reduce the operation count and the execution time, meanwhile preserve almost a similar coding performance to the exact transform. This paper deals with objective and perceptual assessment of the proposed transform using the Peak Signal to Noise Ratio (PSNR) and the Global Score Reduced reference Video quality assessment based on Human visual system $(GS_{RVH})$ metrics respectively.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"34 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":"115002249","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 Clinical support system for Prediction of Heart Disease using Machine Learning Techniques 使用机器学习技术预测心脏病的临床支持系统
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231760
Halima El Hamdaoui, S. Boujraf, N. Chaoui, M. Maaroufi
{"title":"A Clinical support system for Prediction of Heart Disease using Machine Learning Techniques","authors":"Halima El Hamdaoui, S. Boujraf, N. Chaoui, M. Maaroufi","doi":"10.1109/ATSIP49331.2020.9231760","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231760","url":null,"abstract":"Heart disease is a leading cause of death worldwide. However, it remains difficult for clinicians to predict heart disease as it is a complex and costly task. Hence, we proposed a clinical support system for predicting heart disease to help clinicians with diagnostic and make better decisions. Machine learning algorithms such as Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Decision Tree are applied in this study for predicting Heart Disease using risk factors data retrieved from medical files. Several experiments have been conducted to predict HD using the UCI data set, and the outcome reveals that Naïve Bayes outperforms using both cross-validation and train-test split techniques with an accuracy of 82.17%, 84.28%, respectively. The second conclusion is that the accuracy of all algorithm decrease after applying the cross-validation technique. Finally, we suggested multi validation techniques in prospectively collected data towards the approval of the proposed approach.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"122 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":"128159410","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
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