Pub Date : 2020-10-24DOI: 10.1109/NILES50944.2020.9257910
Alaa A. Embaby, Zaki B. Nosseir, Hesham Badr
The pancreas of patients with Type 1 Diabetes Mellitus (T1DM) is unable to produce insulin. Thus, insulin therapy is required for T1DM to maintain Blood Glucose (BG) levels within the normal range. The Artificial Pancreas (AP) is a closed-loop control system that is used by T1D patients to maintain their BG levels at the normal range during daily life. In this work, an Adaptive Nonlinear Model Predictive Control (AMPC) algorithm for BG regulation in T1D patients is developed. The proposed technique uses the Feed Forward Neural Network (FFNN) as a nonlinear blood glucose prediction model to handle the delay between the moment of insulin injection and the moment of insulin interaction with the blood glucose. Also, it uses the Fuzzy Logic Controller (FLC) as a control algorithm to determine the amount of insulin required for regulating the BG level. An adaptation method is also included to adjust the proposed system to compensate for physiological differences among patients. The limits of the output membership functions for the FLC are optimized using the Genetic algorithm (GA). Simulation results for a 36h scenario are demonstrated in nine virtual adult patients. The master findings are the average percentages of these patients for the time spent in the normal range, hypo-, and hyperglycemia. Our results indicate that the proposed closed-loop control system increases the time that BG is in the normal range and causes less hyperglycemia as compared to a published technique studied in a similar scenario and population.
{"title":"Adaptive Nonlinear Model Predictive Control algorithm for blood glucose regulation in type 1 diabetic patients","authors":"Alaa A. Embaby, Zaki B. Nosseir, Hesham Badr","doi":"10.1109/NILES50944.2020.9257910","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257910","url":null,"abstract":"The pancreas of patients with Type 1 Diabetes Mellitus (T1DM) is unable to produce insulin. Thus, insulin therapy is required for T1DM to maintain Blood Glucose (BG) levels within the normal range. The Artificial Pancreas (AP) is a closed-loop control system that is used by T1D patients to maintain their BG levels at the normal range during daily life. In this work, an Adaptive Nonlinear Model Predictive Control (AMPC) algorithm for BG regulation in T1D patients is developed. The proposed technique uses the Feed Forward Neural Network (FFNN) as a nonlinear blood glucose prediction model to handle the delay between the moment of insulin injection and the moment of insulin interaction with the blood glucose. Also, it uses the Fuzzy Logic Controller (FLC) as a control algorithm to determine the amount of insulin required for regulating the BG level. An adaptation method is also included to adjust the proposed system to compensate for physiological differences among patients. The limits of the output membership functions for the FLC are optimized using the Genetic algorithm (GA). Simulation results for a 36h scenario are demonstrated in nine virtual adult patients. The master findings are the average percentages of these patients for the time spent in the normal range, hypo-, and hyperglycemia. Our results indicate that the proposed closed-loop control system increases the time that BG is in the normal range and causes less hyperglycemia as compared to a published technique studied in a similar scenario and population.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132648050","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 : 2020-10-24DOI: 10.1109/NILES50944.2020.9257911
Mohamed Maged, Dalia M. Mahfouz, Omar M. Shehata, E. I. Morgan
Over the past few decades, climate change, air pollution and road safety have been classified as vital problems affecting the globe adversely in terms of transportation. To solve these problems, Intelligent Transportation Systems (ITS) are investigated. One of the important ITS applications is vehicle platooning, which is contemplated to enhance road organization and reduce the overall fuel consumption. In this study a cooperative optimal algorithm is adopted to coordinate several vehicles to form platoons that minimize the total fuel cost by maximizing distance vehicles are in platoon, through the adjustment of the vehicles’ speeds. The algorithm is based on pairwise coordination by which the coordination decision is made between each two vehicles or sub-platoons to form a platoon based on fuel-saving potential. The optimization problem outputs the desired optimal speed profiles for each vehicle offline. These speed profiles are then sent to a cruise controller to control each vehicle’s dynamics to reach the desired optimal speeds. A nonlinear vehicle dynamic model including the powertrain dynamics is investigated. A hierarchical speed control approach is used, having an optimal Model predictive Control (MPC) as the upper level controller and a linear Proportional-Integral-Derivative (PID) as the lower level control approach used to manage the vehicles’ velocities. The coordination algorithm and the controller are tested on a scenario of four scattered vehicles moving on a flat road, having same destination point. The simulation scenario is conducted to test the coordination algorithm and demonstrate the performance of the controller in terms of velocity tracking, realistic control effort and reduced fuel consumption. Results show that the optimization and control objectives are achieved successfully.
{"title":"Behavioral Assessment of an Optimized Multi-Vehicle Platoon Formation Control for Efficient Fuel Consumption","authors":"Mohamed Maged, Dalia M. Mahfouz, Omar M. Shehata, E. I. Morgan","doi":"10.1109/NILES50944.2020.9257911","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257911","url":null,"abstract":"Over the past few decades, climate change, air pollution and road safety have been classified as vital problems affecting the globe adversely in terms of transportation. To solve these problems, Intelligent Transportation Systems (ITS) are investigated. One of the important ITS applications is vehicle platooning, which is contemplated to enhance road organization and reduce the overall fuel consumption. In this study a cooperative optimal algorithm is adopted to coordinate several vehicles to form platoons that minimize the total fuel cost by maximizing distance vehicles are in platoon, through the adjustment of the vehicles’ speeds. The algorithm is based on pairwise coordination by which the coordination decision is made between each two vehicles or sub-platoons to form a platoon based on fuel-saving potential. The optimization problem outputs the desired optimal speed profiles for each vehicle offline. These speed profiles are then sent to a cruise controller to control each vehicle’s dynamics to reach the desired optimal speeds. A nonlinear vehicle dynamic model including the powertrain dynamics is investigated. A hierarchical speed control approach is used, having an optimal Model predictive Control (MPC) as the upper level controller and a linear Proportional-Integral-Derivative (PID) as the lower level control approach used to manage the vehicles’ velocities. The coordination algorithm and the controller are tested on a scenario of four scattered vehicles moving on a flat road, having same destination point. The simulation scenario is conducted to test the coordination algorithm and demonstrate the performance of the controller in terms of velocity tracking, realistic control effort and reduced fuel consumption. Results show that the optimization and control objectives are achieved successfully.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115378555","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 : 2020-10-24DOI: 10.1109/NILES50944.2020.9257926
H. Hesham, G. Yasser, M. Ashour, T. Elshabrawy
With the evolution of 5G and the need to provide on demand services anywhere anytime in radio services, offloading the radio processing to a centralized cloud where all the computation and processing occurs gives flexibility in the allocation and re-allocation of resources to users according to their demand and capacity. This concept is the essence of Cloud Radio Access Networks. With this technology comes two main challenges: firstly, how much resources are required given the system traffic load, and secondly, which resources should be assigned to which user to guarantee the best quality of service at the best resource utilization. Resources in this paper are considered as both physical resources, servers in the cloud, lightweight Remote Radio Heads (RRHs) and bandwidth resources presented in Resource Blocks (RBs). The optimal allocation of these resources dependent on the user traffic is a non-linear optimization problem that is computationally challenging and time consuming to solve. In the presence of the high frame rate, the delay associated with this computational complexity may affect the quality of service. This paper explores different supervised machine learning algorithms in order to predict the amount of RRHs, BBUs and RBs the Cloud Radio Access Network needs, then allocate those resources in order to avoid the high level computation resource allocation usually requires, leading to an overall decrease in the latency in the system and hence a more practical use of the optimal solutions. Machine learning techniques considered include linear, logistic regression, k-means clustering and further improving the allocation using neural networks in comparison to logistic regression. Results show that the different machine learning techniques used for prediction and allocation are accurate in comparison to the test data derived analytically using a heuristic approach.
{"title":"Resource Prediction & Allocation in Cloud Radio Access Networks using Machine Learning","authors":"H. Hesham, G. Yasser, M. Ashour, T. Elshabrawy","doi":"10.1109/NILES50944.2020.9257926","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257926","url":null,"abstract":"With the evolution of 5G and the need to provide on demand services anywhere anytime in radio services, offloading the radio processing to a centralized cloud where all the computation and processing occurs gives flexibility in the allocation and re-allocation of resources to users according to their demand and capacity. This concept is the essence of Cloud Radio Access Networks. With this technology comes two main challenges: firstly, how much resources are required given the system traffic load, and secondly, which resources should be assigned to which user to guarantee the best quality of service at the best resource utilization. Resources in this paper are considered as both physical resources, servers in the cloud, lightweight Remote Radio Heads (RRHs) and bandwidth resources presented in Resource Blocks (RBs). The optimal allocation of these resources dependent on the user traffic is a non-linear optimization problem that is computationally challenging and time consuming to solve. In the presence of the high frame rate, the delay associated with this computational complexity may affect the quality of service. This paper explores different supervised machine learning algorithms in order to predict the amount of RRHs, BBUs and RBs the Cloud Radio Access Network needs, then allocate those resources in order to avoid the high level computation resource allocation usually requires, leading to an overall decrease in the latency in the system and hence a more practical use of the optimal solutions. Machine learning techniques considered include linear, logistic regression, k-means clustering and further improving the allocation using neural networks in comparison to logistic regression. Results show that the different machine learning techniques used for prediction and allocation are accurate in comparison to the test data derived analytically using a heuristic approach.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122338046","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 : 2020-10-24DOI: 10.1109/NILES50944.2020.9257885
M. Eltahan, Karim Moharm
Aerosol optical depth (AOD) is one of the most critical indicators for air quality. Estimation of accurate AOD needs to include both dust and chemical reactions in the calculations which are expensive from a computational point of view. In this work, we present a novel and simple model to estimate and predict the temporal trend of AOD based on the well-known algorithm long-short term memory (LSTM). Five domains are the core of this study, Four popular cities Cairo, Alexandria, Aswan, and Hurghada are selected. In addition to one sub-domain which includes one of the most important and internal dust sources for Egypt, Qattara depression. We applied the LSTM algorithm to NASA’s MERRA-2 monthly AOD datasets as training and validation data-set. The algorithms showed a lower root mean square error. The trained models after validation are used to predict the temporal trend of AOD for the period 2020-2022 over the five selected domains.
{"title":"Atmospheric Aerosol Prediction over Egypt with LSTM-RNN using NASA’s MERRA-2","authors":"M. Eltahan, Karim Moharm","doi":"10.1109/NILES50944.2020.9257885","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257885","url":null,"abstract":"Aerosol optical depth (AOD) is one of the most critical indicators for air quality. Estimation of accurate AOD needs to include both dust and chemical reactions in the calculations which are expensive from a computational point of view. In this work, we present a novel and simple model to estimate and predict the temporal trend of AOD based on the well-known algorithm long-short term memory (LSTM). Five domains are the core of this study, Four popular cities Cairo, Alexandria, Aswan, and Hurghada are selected. In addition to one sub-domain which includes one of the most important and internal dust sources for Egypt, Qattara depression. We applied the LSTM algorithm to NASA’s MERRA-2 monthly AOD datasets as training and validation data-set. The algorithms showed a lower root mean square error. The trained models after validation are used to predict the temporal trend of AOD for the period 2020-2022 over the five selected domains.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121060939","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 : 2020-10-24DOI: 10.1109/NILES50944.2020.9257906
Shahd Yaser Nasr, S. Kassem
Production planning and control systems are known for their complexity, especially for large scale production units. The Unified Modeling Language (UML) is known for its efficiency in modeling such complex systems for better visualization and as an initial step for software implementation. In this paper, UML is utilized to model production planning and control systems. The models developed include functional, and behavioral models represented through a use case diagram, an activity diagram, and a communication diagram. The proposed models serve as the first step towards implementing a software for production planning and control systems.
{"title":"Modeling the Production Planning and Control System using UML","authors":"Shahd Yaser Nasr, S. Kassem","doi":"10.1109/NILES50944.2020.9257906","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257906","url":null,"abstract":"Production planning and control systems are known for their complexity, especially for large scale production units. The Unified Modeling Language (UML) is known for its efficiency in modeling such complex systems for better visualization and as an initial step for software implementation. In this paper, UML is utilized to model production planning and control systems. The models developed include functional, and behavioral models represented through a use case diagram, an activity diagram, and a communication diagram. The proposed models serve as the first step towards implementing a software for production planning and control systems.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128553954","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 : 2020-10-24DOI: 10.1109/NILES50944.2020.9257939
A. Shisha, Maha Aboelmaged, Rola Aboshabaan, Amr Hassan, Mariam M. Fouad, M. A. E. Ghany
With the evolution of IoT and its involvement in everyday life, the amount of data collected by IoT devices is increasing drastically and user’s information is becoming more vulnerable to cyber-attacks. Thus, comes the importance of IoT security and the importance to have a flexible security system which doesn’t affect the IoT devices power consumption and efficiency. In this paper an efficient dynamic security system that uses partial reconfiguration concept in zed board is presented to achieve better data security and faster generation of the encrypted data. Algorithm hopping is used to switch between three different cryptographic cyphers which are AEGIS, ASCON and DEOXYS-II. These ciphers are the main finalists in CAESAR competition as they achieved high security, medium security and low security respectively and each of these ciphers has its own significant attributes. The proposed design targets the improvement of reconfiguration time of the zed board using LZ4 (Lempel-Ziv4) compression and decompression techniques for the input bitstream files. Our research shows a decrease in the reconfiguration time of a minimum 61.3% decrease for the largest bit file in comparison to the previous works in the same field.
{"title":"Efficient Hardware Implementation for IoT Security System","authors":"A. Shisha, Maha Aboelmaged, Rola Aboshabaan, Amr Hassan, Mariam M. Fouad, M. A. E. Ghany","doi":"10.1109/NILES50944.2020.9257939","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257939","url":null,"abstract":"With the evolution of IoT and its involvement in everyday life, the amount of data collected by IoT devices is increasing drastically and user’s information is becoming more vulnerable to cyber-attacks. Thus, comes the importance of IoT security and the importance to have a flexible security system which doesn’t affect the IoT devices power consumption and efficiency. In this paper an efficient dynamic security system that uses partial reconfiguration concept in zed board is presented to achieve better data security and faster generation of the encrypted data. Algorithm hopping is used to switch between three different cryptographic cyphers which are AEGIS, ASCON and DEOXYS-II. These ciphers are the main finalists in CAESAR competition as they achieved high security, medium security and low security respectively and each of these ciphers has its own significant attributes. The proposed design targets the improvement of reconfiguration time of the zed board using LZ4 (Lempel-Ziv4) compression and decompression techniques for the input bitstream files. Our research shows a decrease in the reconfiguration time of a minimum 61.3% decrease for the largest bit file in comparison to the previous works in the same field.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127349079","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 : 2020-10-24DOI: 10.1109/NILES50944.2020.9257900
S. Shelan, M. Abozied, H. Hendy, Y. Elhalwagy
The design of missile autopilot is an essential issue as it always shares several challenges. The accuracy of impact is highly affected by designed controller accuracy and robustness against the target maneuver and missile nonlinearity resources. The design of autopilot controllers is highly affected by the derived system transfer function as much as it expresses the detailed system dynamics and nonlinearity resources. In this paper, we highlight the importance of advanced actuation system modeling considering nonlinearity resources. The nonlinear model parameters are derived, identified, and evaluated through simulation of missile roll autopilot. Inserting the developed system model to the three-loop missile autopilot presented a poor performance for the time and frequency stability parameters so in this paper a redesign procedure is proposed for designing a more realized controller. The gain scheduling PID controller based optimized genetic algorithm is developed for selected trim points based on missile dynamic pressure and trajectory parameters. The simulation of roll autopilot loop results presents sufficient efficiency and robustness for the selected trim points different input signals with rapid amplitude and frequency changes for high dynamics systems.
{"title":"Missile Roll Autopilot Redesign Based Advanced Fin Actuation System Modeling","authors":"S. Shelan, M. Abozied, H. Hendy, Y. Elhalwagy","doi":"10.1109/NILES50944.2020.9257900","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257900","url":null,"abstract":"The design of missile autopilot is an essential issue as it always shares several challenges. The accuracy of impact is highly affected by designed controller accuracy and robustness against the target maneuver and missile nonlinearity resources. The design of autopilot controllers is highly affected by the derived system transfer function as much as it expresses the detailed system dynamics and nonlinearity resources. In this paper, we highlight the importance of advanced actuation system modeling considering nonlinearity resources. The nonlinear model parameters are derived, identified, and evaluated through simulation of missile roll autopilot. Inserting the developed system model to the three-loop missile autopilot presented a poor performance for the time and frequency stability parameters so in this paper a redesign procedure is proposed for designing a more realized controller. The gain scheduling PID controller based optimized genetic algorithm is developed for selected trim points based on missile dynamic pressure and trajectory parameters. The simulation of roll autopilot loop results presents sufficient efficiency and robustness for the selected trim points different input signals with rapid amplitude and frequency changes for high dynamics systems.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127511269","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 : 2020-10-24DOI: 10.1109/NILES50944.2020.9257873
A. A. Abbas, H. Ammar, M. Elsamanty
Magnetic Levitation System is one of practical examples which faces some nonlinearities behavior. Such systems require special types of controller parameters consideration for accurate results. In this paper, the process of tuning is to determine the system poles and getting them away from the instability region using state feedback (SF) controller methodology. The resulted controllable system parameters are estimated using LQR controller. Since the desired goal is to minimize vital parameters in the system behavior like the steady state error, settling time, raising time of the system and system overshoot, optimization techniques have been used to minimize cost function of the parameters which need to be optimized and reach for more reliable ones for better performance. Particle swarm optimization (PSO) has been used for tuning process. System operation points should be 0.61 A for electric current and 6 mm distance between coil surface and balanced metal ball, results show that using LQR controller will cause about 33% error percentage as steady state error and about 20% overshoot. Using PSO optimization technique for controller parameters will produce less steady state error of 6.5% with 4% overshoot percentage.
{"title":"Controller Design and Optimization of Magnetic Levitation System (MAGLEV) using Particle Swarm optimization technique and Linear Quadratic Regulator (LQR)","authors":"A. A. Abbas, H. Ammar, M. Elsamanty","doi":"10.1109/NILES50944.2020.9257873","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257873","url":null,"abstract":"Magnetic Levitation System is one of practical examples which faces some nonlinearities behavior. Such systems require special types of controller parameters consideration for accurate results. In this paper, the process of tuning is to determine the system poles and getting them away from the instability region using state feedback (SF) controller methodology. The resulted controllable system parameters are estimated using LQR controller. Since the desired goal is to minimize vital parameters in the system behavior like the steady state error, settling time, raising time of the system and system overshoot, optimization techniques have been used to minimize cost function of the parameters which need to be optimized and reach for more reliable ones for better performance. Particle swarm optimization (PSO) has been used for tuning process. System operation points should be 0.61 A for electric current and 6 mm distance between coil surface and balanced metal ball, results show that using LQR controller will cause about 33% error percentage as steady state error and about 20% overshoot. Using PSO optimization technique for controller parameters will produce less steady state error of 6.5% with 4% overshoot percentage.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116875766","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 : 2020-10-24DOI: 10.1109/NILES50944.2020.9257934
AbdelRahman Hesham, A. Nassar, H. Mostafa
In this paper, a low-energy minimum-area CMOS standard cell library suitable for IoT applications is proposed. Energy consumption reduction is achieved by operating the library in Near-Threshold Voltage (NTV) region, and by designing layout of cells at the minimum possible area for the used technology process. Body biasing technique is proposed to boost pMOS performance. Operating voltage and transistor sizing are also selected to achieve the minimum energy consumption while operating at the frequency range of 1MHz to 20MHz which is suitable for IoT applications. The proposed library was designed and characterized in UMC 130 nm CMOS technology process. The library was modeled to be used in synthesis tools. To prove the benefit for IoT applications, the library was benchmarked by implementing 3 cryptographic algorithms: ASCON, AEGIS-128, and AEZ. Synthesis results are showing that the three cores can operate at 18 MHz, 14 MHz, and 16 MHz respectively, while consuming 0.466 pJ, 3.006 pJ, and 5.064 pJ.
{"title":"Energy-Efficient Near-Threshold Standard Cell Library for IoT Applications","authors":"AbdelRahman Hesham, A. Nassar, H. Mostafa","doi":"10.1109/NILES50944.2020.9257934","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257934","url":null,"abstract":"In this paper, a low-energy minimum-area CMOS standard cell library suitable for IoT applications is proposed. Energy consumption reduction is achieved by operating the library in Near-Threshold Voltage (NTV) region, and by designing layout of cells at the minimum possible area for the used technology process. Body biasing technique is proposed to boost pMOS performance. Operating voltage and transistor sizing are also selected to achieve the minimum energy consumption while operating at the frequency range of 1MHz to 20MHz which is suitable for IoT applications. The proposed library was designed and characterized in UMC 130 nm CMOS technology process. The library was modeled to be used in synthesis tools. To prove the benefit for IoT applications, the library was benchmarked by implementing 3 cryptographic algorithms: ASCON, AEGIS-128, and AEZ. Synthesis results are showing that the three cores can operate at 18 MHz, 14 MHz, and 16 MHz respectively, while consuming 0.466 pJ, 3.006 pJ, and 5.064 pJ.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129578841","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 : 2020-10-24DOI: 10.1109/NILES50944.2020.9257895
Mohamed G. Elsamra, M. Khalil, A. Roshdy, Ahmed E. Amin, O. Elfarouk
Wire-guided missiles in their simplest form are extremely complicated mechanical systems. This is owed to the continuous variations in operating flight conditions and system mass as a consequence of fuel consumption and wire spooling. Thus, modeling and control of such missiles are challenging tasks. The present paper aims to develop a mathematical model for the missile that takes into consideration variations in mass and aerodynamic coefficients. A linearized control and guidance system using rate gyro flight control system is developed to achieve target interception with minimum deviation.
{"title":"Modeling and control of variable-mass flight vehicle","authors":"Mohamed G. Elsamra, M. Khalil, A. Roshdy, Ahmed E. Amin, O. Elfarouk","doi":"10.1109/NILES50944.2020.9257895","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257895","url":null,"abstract":"Wire-guided missiles in their simplest form are extremely complicated mechanical systems. This is owed to the continuous variations in operating flight conditions and system mass as a consequence of fuel consumption and wire spooling. Thus, modeling and control of such missiles are challenging tasks. The present paper aims to develop a mathematical model for the missile that takes into consideration variations in mass and aerodynamic coefficients. A linearized control and guidance system using rate gyro flight control system is developed to achieve target interception with minimum deviation.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128080510","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}
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