2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)最新文献_第9页

ATSIP 2020 Cover Page ATSIP 2020封面

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/atsip49331.2020.9231647

引用次数: 0

Medication Code Recognition using Convolutional Neural Network 使用卷积神经网络的药物代码识别

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231801

A. Zaafouri, M. Sayadi

引用次数: 0

Face Emotion Recognition From Static Image Based on Convolution Neural Networks 基于卷积神经网络的静态图像人脸情绪识别

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231537

M. Nasri, Mohamed Amine Hmani, Aymen Mtibaa, D. Petrovska-Delacrétaz, M. Slima, A. Hamida

引用次数: 5

Classification of DNA Microarrays Using Deep Learning to identify Cell Cycle Regulated Genes DNA微阵列分类使用深度学习识别细胞周期调控基因

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231888

Hiba Lahmer, A. Oueslati, Z. Lachiri

{"title":"Classification of DNA Microarrays Using Deep Learning to identify Cell Cycle Regulated Genes","authors":"Hiba Lahmer, A. Oueslati, Z. Lachiri","doi":"10.1109/ATSIP49331.2020.9231888","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231888","url":null,"abstract":"The aim of this work is to take advantage of the power and growth of machine learning methods and deep learning algorithms in the biomedical field, and how to use it to predict and recognize repetitive patterns. The ultimate goal is to analyze the large amount set of data produced from the DNA (Deoxyribonucleic acid) microarrays technology. We can use this data to extract facts, information, and skills, such as gene expression level. Our target here is to classify two genes’ types. The first represents cell cycle regulated genes and the second represents the non-cell cycle ones. For the classification purpose, we preprocess the data, and we implement deep learning models. Then we evaluate our approach and compare its precision with Liu and al results. In the literature, the latest approaches are depending on processing the numerical data related to the DNA microarrays genes progression. In our work, we adopt a novel approach using directly the Microarrays image data. We use the Convolutional Neural Network and the fully connected neural network algorithms, to classify our processed image data. The experiments demonstrate that our approach outperforms the state of art by a margin of 20 per cent. Our model accomplishes real time test accuracy of ~ 92.39 % at classifying.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"28 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":"131648160","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

e-ASPECTS for early detection and diagnosis of ischemic stroke e-ASPECTS在缺血性脑卒中早期发现和诊断中的应用

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231788

Haifa Touati, Boughariou Jihene, Sellemi Lamia, Ben Hamida Ahmed

引用次数: 1

Blurred Image Detection In Drone Embedded System 无人机嵌入式系统中的模糊图像检测

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231665

Ratiba Gueraichi, A. Serir

引用次数: 3

Fast pore matching method based on core point alignment and orientation 基于核心点对准和定向的快速孔隙匹配方法

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231829

Houda Khmila, I. Kallel, Sami Barhoumi, N. Smaoui, H. Derbel

{"title":"Fast pore matching method based on core point alignment and orientation","authors":"Houda Khmila, I. Kallel, Sami Barhoumi, N. Smaoui, H. Derbel","doi":"10.1109/ATSIP49331.2020.9231829","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231829","url":null,"abstract":"Nowadays, high-resolution fingerprint images are more and more used in the fingerprint recognition systems thanks to the recognition accuracy that they provide. Indeed, they offer more sufficient details such as sweat pores, ridges, contours, and other details. Pores have been adopted to be one of the brilliant nominees in improving the efficiency of automated fingerprint identification systems to maintain a high level of security. However, the geometric transformations, that occur during the acquisition phase, can cause several defects on the result of the matching process, hence they decline the accuracy of the recognition. To overcome this problem, alignment is often needed. This image pretreatment is classically based on complex geometric operations that are time-consuming. Otherwise, for pore matching, the majority of approaches are based only on pore coordinates. In this paper, we propose a novel pore matching method based, firstly, on only one of the singular points, namely the core points for the alignment phase, and also the valuable features used for the score calculation namely position and the orientation of pores. We assess our proposed approach using the PolyU-HRF database and we compare it to some well-known approaches of level 3 fingerprint recognition. The experimental results demonstrate that the proposed method can achieve significant performance recognition accuracy across various qualities of fingerprint images.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"26 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":"125154389","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

Low Cost and Low Power Stacked Sparse Autoencoder Hardware Acceleration for Deep Learning Edge Computing Applications 面向深度学习边缘计算应用的低成本低功耗堆叠稀疏自编码器硬件加速

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231748

T. Belabed, M. G. Coutinho, Marcelo A. C. Fernandes, C. Valderrama, C. Souani

{"title":"Low Cost and Low Power Stacked Sparse Autoencoder Hardware Acceleration for Deep Learning Edge Computing Applications","authors":"T. Belabed, M. G. Coutinho, Marcelo A. C. Fernandes, C. Valderrama, C. Souani","doi":"10.1109/ATSIP49331.2020.9231748","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231748","url":null,"abstract":"Nowadays, Deep Learning DL becoming more and more interesting in many areas, such as genomics, security, data analysis, image, and video processing. However, DL requires more and more powerful and parallel computing. The calculation performed by super-machines equipped with powerful processors, such as the latest GPUs. Despite their power, these computing units consume a lot of energy, which makes their use very difficult in small embedded systems and edge computing. To overcome the problem for which we must keep the maximum performance and satisfy the power constraint, it is necessary to use a heterogeneous strategy. Some solutions are promising when using less energyconsuming electronic circuits, such as FPGAs associated with less expensive topologies such as Stacked Sparse Autoencoders. Our target architecture is the Xilinx ZYNQ 7020 SoC, which combines a dual-core ARM processor and an FPGA in the same chip. In the interest of flexibility, we decided to leverage the performance of Xilinx’s high-level synthesis tools, evaluate and choose the best solution in terms of size and performance of the data exchange, synchronization and pipeline processing. The results show that our implementation gives high performance at very low energy consumption. Indeed, the evaluation of our accelerator shows that it can classify 1160 MNIST images per second, consuming only 0.443 W; 2.4 W for the entire system. More than the low energy consumption and the high performance, the platform used only costs $ 125.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"19 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":"134421327","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

Binary hierarchical multiclass classifier for uncertain numerical features 不确定数值特征的二元层次多类分类器

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231804

Marwa Chakroun, Amal Charfi, Sonda Ammar Bouhamed, I. Kallel, B. Solaiman, H. Derbel

引用次数: 0

Identification of the user by using a hardware device 通过使用硬件设备来标识用户

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231602

H. Hamam

引用次数: 0