2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)最新文献_第7页

Nuclei Segmentation Approach for Digestive Neuroendocrine Tumors Analysis Using optimized Color Space Conversion 基于优化颜色空间转换的消化神经内分泌肿瘤核分割分析方法

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231536

Hana Rmili, B. Solaiman, A. Mouelhi, R. Doghri, S. Labidi

{"title":"Nuclei Segmentation Approach for Digestive Neuroendocrine Tumors Analysis Using optimized Color Space Conversion","authors":"Hana Rmili, B. Solaiman, A. Mouelhi, R. Doghri, S. Labidi","doi":"10.1109/ATSIP49331.2020.9231536","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231536","url":null,"abstract":"Microscopic examination plays a significant role in the decision making for a reliable diagnosis of digestive neuroendocrine tumors (NETs), an immunohistochemical (IHC) analysis should be conducted by pathologists in order to identify cell morphology, tissue structure, and various histological disorders. The visual and manual assessment task, performed by experts, is tedious, time-consuming, and prone to inter-observer variability. Hence, there is an urgent need for developing an automatic nuclei segmentation approach which can provide an accurate number of cancerous histological tissues and overcome the issue of overlapping cells. In the proposed study, a morphological method for microscopic image segmentation is presented, this approach is mainly based on the choice of the appropriate color space, which highlights stained cells nuclei caused by stain variability and insufficient lighting conditions. Stromal cells, that differ from tumor cells in their particular form and small size, should be removed using shape criterion. Then marker-controlled watershed technique is applied in order to reduce the over-segmentation and to detach the connected cells in the resulting images. The proposed method is compared to ground truth segmentation, the results gave a Dice score of 0.959.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"364 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":"126703725","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

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

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

Fault Detection in Waste Water Treatment Plants using Improved Particle Filter-based Optimized EWMA 基于改进粒子过滤器的优化EWMA污水处理厂故障检测

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231954

I. Baklouti, M. Mansouri, H. Nounou, M. Nounou, A. Hamida

{"title":"Fault Detection in Waste Water Treatment Plants using Improved Particle Filter-based Optimized EWMA","authors":"I. Baklouti, M. Mansouri, H. Nounou, M. Nounou, A. Hamida","doi":"10.1109/ATSIP49331.2020.9231954","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231954","url":null,"abstract":"Environmental, health, and safety concerns are of major importance world-wide. These concerns are closely tied to the availability and quality of water that can be used in various domestic and industrial applications. Therefore, the objective of this paper is to develop a general framework for modeling and monitoring technique that aims at enhancing the operation of wastewater treatment plants. In this work, an improved PF (IPF) method will be developed to better handle the nonlinear and high dimensional state estimation problem involved in modeling wastewater treatment plants. Then, an improved detection control chart to enhance the monitoring of WWTP will be developed. The contributions of this work are the foorfold: 1) to estimate a nonlinear state variables of WWTPs using improved particle filter in three types of weathers (dry, storm and rain). 2) to develop an new optimized EWMA (OEWMA) based on the best selection of smoothing parameter ($lambda$) and control width L. 3) to combine the advantages of state estimation technique, with OEWMA chart to improve the fault detection of WWTP. 4) to investigate the effect of fault types (change in variance and mean in shift) and sizes on the monitoring performances.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"6 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":"124231976","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

The Design, by Physical Topology Optimizing, of a Passive UHF RFID Identification System: Suitable for Applications with Various Constraints 基于物理拓扑优化的无源超高频RFID识别系统设计:适用于各种约束的应用

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231580

Rahma Zayoud, H. Hamam

{"title":"The Design, by Physical Topology Optimizing, of a Passive UHF RFID Identification System: Suitable for Applications with Various Constraints","authors":"Rahma Zayoud, H. Hamam","doi":"10.1109/ATSIP49331.2020.9231580","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231580","url":null,"abstract":"RFID technology is booming. It catches the attention of several researchers. This technology uses the radio wave to identify objects on which an RFID tag is placed. RFID has a lot of advantages, but it also has limitations. Its limits are its sensitivity to liquids, metals and speed.We design an objects identification system by radio frequency that works in different RFID technology application environments, based on passive UHF RFID technology and through the physical topology optimization in real time too. This system is also adapted to various constraints, in order to solve overcome difficulties, related to various applications, at once, and operates, without any problem, in different fields.We use the simulated annealing algorithm to find the optimal physical topology that has the highest average reading rate. RFID antennas are installed on brackets, attached to a tripod system by servo motors. These servo motors are controlled by the middleware, where the simulated annealing optimization algorithm is already implemented, to vary the angles of the detection connectors in an automated way, in order to find the optimal topology. So the optimization process will be guided by an optimization algorithm and not by trial and error processes. Tripod mechanical systems are movable media for antennas to play on XYZ dimensions too.The design of the mechanical system and the choice of materials were made. The first results, such as the identity of each detected object and the identity of each antenna that will detect an object, are here. Following reached results will be shared in several next publications.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"190 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":"128401293","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

Denoising techniques for multi-parametric prostate MRI: A Comparative Study 多参数前列腺MRI去噪技术的比较研究

2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2020-09-01 DOI: 10.1109/ATSIP49331.2020.9231751

A. Latrach, Rania Trigui, Lamia Sellemi

引用次数: 0