Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875449
Raghda Alilouch, F. Slaoui-Hasnaoui
The detection of faults on transmission lines is an essential and important part of power system monitoring and control. Providing high-quality electric power requires an efficient, reliable, and intelligent protection, a system that can handle transmission line outages that result from a variety of random reasons. This system will allow a fast detection and gives an accurate fault location, thus isolating the faulted section and avoiding catastrophic damage to material and human assets.In this paper, the use of artificial neural network algorithm ANN is proposed, which can be implemented in a numerical relay, this approach has been noticed by many researchers in the field of power system protection. ANN is trained using the measurements of the three-phase currents and voltages. The feedforward neural network was used together with the backpropagation algorithm to detect, classify, and localize the fault. To validate the choice of the neural network, a detailed analysis was performed with a different number of hidden layers. Simulation results show that the present artificial neural network-based method performs satisfactorily in detecting, classifying, and locating faults on transmission lines. To test the proposed method, different fault scenarios were simulated
{"title":"Intelligent Relay Based on Artificial Neural Networks ANN for Transmission Line","authors":"Raghda Alilouch, F. Slaoui-Hasnaoui","doi":"10.1109/SETIT54465.2022.9875449","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875449","url":null,"abstract":"The detection of faults on transmission lines is an essential and important part of power system monitoring and control. Providing high-quality electric power requires an efficient, reliable, and intelligent protection, a system that can handle transmission line outages that result from a variety of random reasons. This system will allow a fast detection and gives an accurate fault location, thus isolating the faulted section and avoiding catastrophic damage to material and human assets.In this paper, the use of artificial neural network algorithm ANN is proposed, which can be implemented in a numerical relay, this approach has been noticed by many researchers in the field of power system protection. ANN is trained using the measurements of the three-phase currents and voltages. The feedforward neural network was used together with the backpropagation algorithm to detect, classify, and localize the fault. To validate the choice of the neural network, a detailed analysis was performed with a different number of hidden layers. Simulation results show that the present artificial neural network-based method performs satisfactorily in detecting, classifying, and locating faults on transmission lines. To test the proposed method, different fault scenarios were simulated","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129366578","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}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875494
Mohsen Othmani, T. Ezzedine
Wireless Sensor Network (WSN) localization is still an open topic for improvement and continues to prove its necessity in our daily life in parallel with the technological evolution and our needs in various fields. The issue of energy efficiency and network lifetime extension, localization, secure data communication, latency reduction, efficient quality of service, data communication assurance, high scalability also remain topics for improvement.In a context of indoor localization based on learning a database of collected RSSI values, we detail how to proceed with a refinement approach in order to gain in memory, execution time, and localization accuracy. We took advantage of our previous studies in which dynamic cell structure neural networks (DCSNN) have proven superior performance to apply our approach.The results obtained show a memory gain of 82% relative to the data to be extracted from the training database and to be learned, and consequently a gain in execution time and energy consumed. The precision of the coordinates is about 0.031m.Note that the training database is presented as a matrix of RSSI (Received Signal Strength Indicator) values collected by the anchor nodes for each reference point.
随着技术的发展和各个领域的需求，无线传感器网络(WSN)的定位仍然是一个有待改进的开放话题，并不断证明其在我们日常生活中的必要性。能源效率和网络寿命延长、本地化、安全数据通信、降低延迟、高效服务质量、数据通信保证、高可扩展性等问题也仍有待改进。在基于学习收集的RSSI值数据库进行室内定位的背景下，我们详细介绍了如何使用改进方法来获得内存、执行时间和定位精度。我们利用我们之前的研究，其中动态细胞结构神经网络(DCSNN)已经证明了优越的性能来应用我们的方法。获得的结果显示，相对于从训练数据库中提取和学习的数据，内存增加了82%，因此执行时间和消耗的能量增加了。坐标精度约为0.031m。请注意，训练数据库以锚节点为每个参考点收集的RSSI (Received Signal Strength Indicator，接收信号强度指标)值矩阵的形式呈现。
{"title":"Refinement Approach for Node Localization Using Dynamic Cell Structures Neural Networks","authors":"Mohsen Othmani, T. Ezzedine","doi":"10.1109/SETIT54465.2022.9875494","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875494","url":null,"abstract":"Wireless Sensor Network (WSN) localization is still an open topic for improvement and continues to prove its necessity in our daily life in parallel with the technological evolution and our needs in various fields. The issue of energy efficiency and network lifetime extension, localization, secure data communication, latency reduction, efficient quality of service, data communication assurance, high scalability also remain topics for improvement.In a context of indoor localization based on learning a database of collected RSSI values, we detail how to proceed with a refinement approach in order to gain in memory, execution time, and localization accuracy. We took advantage of our previous studies in which dynamic cell structure neural networks (DCSNN) have proven superior performance to apply our approach.The results obtained show a memory gain of 82% relative to the data to be extracted from the training database and to be learned, and consequently a gain in execution time and energy consumed. The precision of the coordinates is about 0.031m.Note that the training database is presented as a matrix of RSSI (Received Signal Strength Indicator) values collected by the anchor nodes for each reference point.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124935653","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}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875536
Oussama Attia, Makram Khalil, Chokri Ben Salah
nowadays, Climate change is one of the big problems we are facing, especially with the continuous rise in fossil fuel combustion engines uses. Presently, the battery electric vehicle BEV is the highly requested vehicle, while fuel cell vehicle FCEV only has an insignificant market share. To justify this, FCEV is analysed and compared to the BEV.This document focus on Matlab-based simulations of electric vehicle systems and fuel cell vehicles in order to predict vehicle performance parameters, Especially the battery, with current demand and charging status depending on the driving cycle.This study also aims to compare battery and hydrogen fuel effects as two sources of power for EVs. We discuss also the benefits of changing a conventional vehicle to an electric vehicle, as well as how we can protect the environment by reducing our gasoline consumption.
{"title":"Modeling, simulation and analysis of BEV and FCEV using Matlab/Simulink","authors":"Oussama Attia, Makram Khalil, Chokri Ben Salah","doi":"10.1109/SETIT54465.2022.9875536","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875536","url":null,"abstract":"nowadays, Climate change is one of the big problems we are facing, especially with the continuous rise in fossil fuel combustion engines uses. Presently, the battery electric vehicle BEV is the highly requested vehicle, while fuel cell vehicle FCEV only has an insignificant market share. To justify this, FCEV is analysed and compared to the BEV.This document focus on Matlab-based simulations of electric vehicle systems and fuel cell vehicles in order to predict vehicle performance parameters, Especially the battery, with current demand and charging status depending on the driving cycle.This study also aims to compare battery and hydrogen fuel effects as two sources of power for EVs. We discuss also the benefits of changing a conventional vehicle to an electric vehicle, as well as how we can protect the environment by reducing our gasoline consumption.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127278507","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}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875643
Salma Laazizi, Jihan Ben Azzouz, A. Jemai
Cyberinfrastructure is characterized by a large amount of emerging and dynamic information, requiring a large number of cyber-criminals trying to acquire information, data mining, machine learning, measurements, and other interdisciplinary skills to meet the cybersecurity issues in Industry 4.0. Machine learning and information mining play an important role in cybersecurity, and unstable information frequently has a high-dimensional feature space. The presence of several noisy characteristics among high-dimensional features might impede and degrade classifier performance. To address this issue, feature selection and subspace methods have been put out and assessed during the past few years. In this paper, four classification techniques and a feature selection strategy are implemented to detect attacks that threaten Industry 4.0. These techniques are Random Forest (RF), Decision Trees (J48), Support Vector Machines (SVM), and Naive Bayes (NB) with Feature Selection Strategy (CFS). Several experiments have been performed using the train and test NSL-KDD datasets with good results. These are based on four categories: Denial of Service (DoS) attack, Probing Attack, User-to-Root (U2R) attack, and Remote-to-Local (R2L) attack. To improve the detection rate of these attacks, a strategy combining multiple classification algorithms is implemented.
{"title":"cybclass: classification approach for cybersecurity in industry 4.0","authors":"Salma Laazizi, Jihan Ben Azzouz, A. Jemai","doi":"10.1109/SETIT54465.2022.9875643","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875643","url":null,"abstract":"Cyberinfrastructure is characterized by a large amount of emerging and dynamic information, requiring a large number of cyber-criminals trying to acquire information, data mining, machine learning, measurements, and other interdisciplinary skills to meet the cybersecurity issues in Industry 4.0. Machine learning and information mining play an important role in cybersecurity, and unstable information frequently has a high-dimensional feature space. The presence of several noisy characteristics among high-dimensional features might impede and degrade classifier performance. To address this issue, feature selection and subspace methods have been put out and assessed during the past few years. In this paper, four classification techniques and a feature selection strategy are implemented to detect attacks that threaten Industry 4.0. These techniques are Random Forest (RF), Decision Trees (J48), Support Vector Machines (SVM), and Naive Bayes (NB) with Feature Selection Strategy (CFS). Several experiments have been performed using the train and test NSL-KDD datasets with good results. These are based on four categories: Denial of Service (DoS) attack, Probing Attack, User-to-Root (U2R) attack, and Remote-to-Local (R2L) attack. To improve the detection rate of these attacks, a strategy combining multiple classification algorithms is implemented.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121872499","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}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875712
Aymen Ramadhan Ghilen, Amel Mohamed Zahou, Wiem Abedelmonem Ben Khalifa
To cope with the unauthorized access to the cloud treasure of resources and services, several cloud service providers implement the API-based access control approach and grant thus a full authentication of any client application. Once called, each cloud API (Application Programming Interface) is required to authenticate through a secret access key, commonly termed API key. A plethora of security risks is associated with these keys whether during their generation, storage, or utilization. The hardware secure element-based proposal aims for an end-to-end security between a cloud service provider and a client application. To ensure a reliable and secure API key exchange, the concerned entities rely on Public Key Infrastructure (PKI). As soon as an adversary acquires an unbounded computing power, it would be easy to intercept the keys and then unlock the gate behind the valuables over the cloud. In this paper, we propose a revised scheme that discards the PKI and installs a quantum mechanism to mutually authenticate the secure element and the cloud service provider by establishing a set of shared keys. A pioneering absolute security of the presented approach is warranted by the principles of quantum physics. To analyze the security of the quantum technology, we establish a formal verification based on PRISM model checking tool. We outstandingly focus on satisfying two prominent properties: (i) both the parties engaged in the quantum protocol are able to detect any disallowed eavesdropping and (ii) the valid amount of information caught by an adversary on the installed key must be negligible.
{"title":"Quantum cryptography for the benefit of API keys safety","authors":"Aymen Ramadhan Ghilen, Amel Mohamed Zahou, Wiem Abedelmonem Ben Khalifa","doi":"10.1109/SETIT54465.2022.9875712","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875712","url":null,"abstract":"To cope with the unauthorized access to the cloud treasure of resources and services, several cloud service providers implement the API-based access control approach and grant thus a full authentication of any client application. Once called, each cloud API (Application Programming Interface) is required to authenticate through a secret access key, commonly termed API key. A plethora of security risks is associated with these keys whether during their generation, storage, or utilization. The hardware secure element-based proposal aims for an end-to-end security between a cloud service provider and a client application. To ensure a reliable and secure API key exchange, the concerned entities rely on Public Key Infrastructure (PKI). As soon as an adversary acquires an unbounded computing power, it would be easy to intercept the keys and then unlock the gate behind the valuables over the cloud. In this paper, we propose a revised scheme that discards the PKI and installs a quantum mechanism to mutually authenticate the secure element and the cloud service provider by establishing a set of shared keys. A pioneering absolute security of the presented approach is warranted by the principles of quantum physics. To analyze the security of the quantum technology, we establish a formal verification based on PRISM model checking tool. We outstandingly focus on satisfying two prominent properties: (i) both the parties engaged in the quantum protocol are able to detect any disallowed eavesdropping and (ii) the valid amount of information caught by an adversary on the installed key must be negligible.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121931630","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}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875471
Intissar Sayahi, Sarra Ismail
Nowadays, the advantages offered by image processing and deep learning increased their efficiency popularity. Thus, vision systems are widely motivating researchers to develop new protocols and features to optimize existing ones. Of course, technical challenges do not lack since the integration of image acquisition and processing units industrial environment poses considerable problems. In context, we adopted in our work the hybrid approach combining hardware design and software development. This approach makes the system compact, robust and reliable, especially in industrial field to ensure several operations quality inspection and verification. The proposed solution is to design an industrial embedded vision system that matches scalable hardware architectures to adaptable algorithms. this paper, we propose an efficient model to automate quality control in an industrial production line. This work aims to integrate the concept of the multi-tasking image processing in the manufacturing field by offering a whole pack of various inspection operations, from surface to dimensional inspections, based on simple hardware implementations, optical setups, and deep learning algorithms.
{"title":"Design and Implementation of an Embedded Vision System for Industrial Inspection","authors":"Intissar Sayahi, Sarra Ismail","doi":"10.1109/SETIT54465.2022.9875471","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875471","url":null,"abstract":"Nowadays, the advantages offered by image processing and deep learning increased their efficiency popularity. Thus, vision systems are widely motivating researchers to develop new protocols and features to optimize existing ones. Of course, technical challenges do not lack since the integration of image acquisition and processing units industrial environment poses considerable problems. In context, we adopted in our work the hybrid approach combining hardware design and software development. This approach makes the system compact, robust and reliable, especially in industrial field to ensure several operations quality inspection and verification. The proposed solution is to design an industrial embedded vision system that matches scalable hardware architectures to adaptable algorithms. this paper, we propose an efficient model to automate quality control in an industrial production line. This work aims to integrate the concept of the multi-tasking image processing in the manufacturing field by offering a whole pack of various inspection operations, from surface to dimensional inspections, based on simple hardware implementations, optical setups, and deep learning algorithms.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114862134","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}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875881
Fatma Hmissi, Sofiane Ouni
MQTT (Message Queuing Telemetry Transport) has become the perfect messaging protocol for IoT (Internet of Things) systems since it is the lightest protocol designed for low bandwidth, high-latency, unreliable networks. Today, the strategy of distributing several MQTT brokers on the networks is widely used because the strategy of using a single broker is no longer efficient. However, in the distributing architectures of MQTT brokers, a subscriber should have prior knowledge about the address of the broker that publishes the data on the topics of interest. In this paper, we tackle this challenge by proposing a mechanism that connects the subscribers to the brokers in a transparent way. The proposed approach, known as TD-MQTT (Transparent Distributed MQTT brokers), requires no prior knowledge of the brokers by the subscribers. The data will be carried automatically from brokers that can change their configuration and location. The transparency will help to use IoT data without worrying about their location and dynamic configuration changes. To evaluate our approach, we compared it with the basic distributed MQTT and the EMMA (MQTT Middle-ware for Edge Computing Applications) approach. The results of the evaluation show that TD-MQTT is much better than the standard MQTT, especially in terms of response time.
{"title":"TD-MQTT: Transparent Distributed MQTT Brokers for Horizontal IoT Applications","authors":"Fatma Hmissi, Sofiane Ouni","doi":"10.1109/SETIT54465.2022.9875881","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875881","url":null,"abstract":"MQTT (Message Queuing Telemetry Transport) has become the perfect messaging protocol for IoT (Internet of Things) systems since it is the lightest protocol designed for low bandwidth, high-latency, unreliable networks. Today, the strategy of distributing several MQTT brokers on the networks is widely used because the strategy of using a single broker is no longer efficient. However, in the distributing architectures of MQTT brokers, a subscriber should have prior knowledge about the address of the broker that publishes the data on the topics of interest. In this paper, we tackle this challenge by proposing a mechanism that connects the subscribers to the brokers in a transparent way. The proposed approach, known as TD-MQTT (Transparent Distributed MQTT brokers), requires no prior knowledge of the brokers by the subscribers. The data will be carried automatically from brokers that can change their configuration and location. The transparency will help to use IoT data without worrying about their location and dynamic configuration changes. To evaluate our approach, we compared it with the basic distributed MQTT and the EMMA (MQTT Middle-ware for Edge Computing Applications) approach. The results of the evaluation show that TD-MQTT is much better than the standard MQTT, especially in terms of response time.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127759912","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}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875912
R. Ayachi, Mouna Afif, Y. Said, A. B. Abdelali
The concept of smart vehicles is becoming an essential feature that ensures driver comfort and security. Smart vehicles are equipped with intelligent systems based on advanced technologies that perform a set of tasks for the mentioned purposes. Recognizing Traffic sign is one the most important systems that guarantee a high-security level. However, it is difficult to develop the best traffic sign recognition system due to numerous obstacles. such as weather conditions, geometric deformation, and most important is the material limitation. In this work, we proposed the implementation of a lightweight convolutional neural network (CNN) model on a mobile device to overcome the mentioned challenges. The proposed CNN combines high performances and low computation complexity. Evaluating the proposed model on publicly available datasets proved its efficiency. Besides, the implementation of the CNN model on the pynq platform demonstrates the possibility of using a wide range of mobile devices for the inference of the proposed model.
{"title":"Traffic Sign recognition for smart vehicles based on lightweight CNN implementation on mobile devices","authors":"R. Ayachi, Mouna Afif, Y. Said, A. B. Abdelali","doi":"10.1109/SETIT54465.2022.9875912","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875912","url":null,"abstract":"The concept of smart vehicles is becoming an essential feature that ensures driver comfort and security. Smart vehicles are equipped with intelligent systems based on advanced technologies that perform a set of tasks for the mentioned purposes. Recognizing Traffic sign is one the most important systems that guarantee a high-security level. However, it is difficult to develop the best traffic sign recognition system due to numerous obstacles. such as weather conditions, geometric deformation, and most important is the material limitation. In this work, we proposed the implementation of a lightweight convolutional neural network (CNN) model on a mobile device to overcome the mentioned challenges. The proposed CNN combines high performances and low computation complexity. Evaluating the proposed model on publicly available datasets proved its efficiency. Besides, the implementation of the CNN model on the pynq platform demonstrates the possibility of using a wide range of mobile devices for the inference of the proposed model.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126550429","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}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875516
Nadia Gargouri, M. Samet, Z. Sakka
This paper presents an Injection Locked Ring Oscillator (ILRO) for use in biomedical implants applications. A pulse injection locking method has been desired for ring oscillator performance enhancement and phase noise cancellation. In addition, the frequency is controlled independently of the biasing circuit using an inversion mode MOS voltage controlled capacitor (varactor).Designed in a 0.18-μm CMOS, this ILRO works inthe ISM band of 902–928 GHz and consumes a lowpower of 4.62 mW under 1.8V power supply. The phase noise of -121.6dBc /Hz at 1MHzoffset frequency was obtained at 76 MHz inputinjection pulse.The proposed circuit can achieve a FoM of -174.19 dBc/Hz.
{"title":"A Low-Noise Injection-Locked Ring Oscillator for biomedical implants applications","authors":"Nadia Gargouri, M. Samet, Z. Sakka","doi":"10.1109/SETIT54465.2022.9875516","DOIUrl":"https://doi.org/10.1109/SETIT54465.2022.9875516","url":null,"abstract":"This paper presents an Injection Locked Ring Oscillator (ILRO) for use in biomedical implants applications. A pulse injection locking method has been desired for ring oscillator performance enhancement and phase noise cancellation. In addition, the frequency is controlled independently of the biasing circuit using an inversion mode MOS voltage controlled capacitor (varactor).Designed in a 0.18-μm CMOS, this ILRO works inthe ISM band of 902–928 GHz and consumes a lowpower of 4.62 mW under 1.8V power supply. The phase noise of -121.6dBc /Hz at 1MHzoffset frequency was obtained at 76 MHz inputinjection pulse.The proposed circuit can achieve a FoM of -174.19 dBc/Hz.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129894902","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}
Pub Date : 2022-05-28DOI: 10.1109/SETIT54465.2022.9875676
Salma Ben Mamia, J. Kasperiūnienė, W. Puech, K. Bouallegue
In this paper, an analyse of scientific journal articles related to chaotic cryptography from 2015 to 2021, is presented. In doing so, the study uses the Web of Science Core Collection database to analyze the data. ‘biblioshiny’ a web-interface of the ‘bibliometrix 3.0’ package of R-studio has been deployed to conduct bibliometric analysis. Also, a graphical mapping of the bibliometric material by using the visualization of similarities (VOS) viewer software, was developed. The study relies on the proposed approach to explore the influence of developing countries on Chaos-based encryption. We found out that developing countries are the most active on chaotic cryptography.
本文对2015年至2021年与混沌密码学相关的科学期刊文章进行了分析。在此过程中，该研究使用Web of Science Core Collection数据库来分析数据。“biblioshiny”是R-studio的“bibliometrix 3.0”软件包的一个网络界面，用于进行文献计量分析。此外，利用相似度可视化(VOS)查看器软件，开发了文献计量资料的图形映射。该研究依赖于提出的方法来探索发展中国家对基于混沌的加密的影响。我们发现，发展中国家对混沌密码最为活跃。
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