Pub Date : 2022-12-27DOI: 10.1109/ICCSPA55860.2022.10019089
Noha H. Khattab, Samy S. Soliman
Unlike conventional cooperative networks, diamond relaying networks can provide higher achievable data rates. In a non-orthogonal multiple access (NOMA) based diamond relaying network (DRN), two relays work together to transmit two messages to the destination within only two-time slots. Power allocation is crucial for improving system performance in NOMA-based networks. Accordingly, the effect of optimizing the power allocation coefficients for the DRN system is investigated. A closed-form analytical solution for obtaining the optimum power allocation coefficients of the DRN model is presented. Simulation results show the superiority of the analytical solution of optimal power allocation assignment over a fixed power allocation. A comparison between the proposed analytical solution with other exhaustive search algorithms is discussed. Finally, the accuracy of the suggested solution is confirmed via simulation results.
{"title":"Optimal Power Allocation in NOMA-Based Diamond Relaying Networks","authors":"Noha H. Khattab, Samy S. Soliman","doi":"10.1109/ICCSPA55860.2022.10019089","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019089","url":null,"abstract":"Unlike conventional cooperative networks, diamond relaying networks can provide higher achievable data rates. In a non-orthogonal multiple access (NOMA) based diamond relaying network (DRN), two relays work together to transmit two messages to the destination within only two-time slots. Power allocation is crucial for improving system performance in NOMA-based networks. Accordingly, the effect of optimizing the power allocation coefficients for the DRN system is investigated. A closed-form analytical solution for obtaining the optimum power allocation coefficients of the DRN model is presented. Simulation results show the superiority of the analytical solution of optimal power allocation assignment over a fixed power allocation. A comparison between the proposed analytical solution with other exhaustive search algorithms is discussed. Finally, the accuracy of the suggested solution is confirmed via simulation results.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114055060","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 : 2022-12-27DOI: 10.1109/ICCSPA55860.2022.10019115
Bassel S. Chawky, M. Hefeida, A. Elbery, A. Noureldin
Autonomous Vehicles (AVs) and smart vehicles rely on cooperative perception to complement the information missed by their sensors due to their limited range and non-line of sight objects. There are many applications that rely on communication leaving sometimes limited resources for cooperative perceptions. Therefore, optimizing the resources and messages sent over the network becomes crucial. An important factor that undermines the potential of vehicular networks is the number of messages containing information about the same objects. Not only this issue exhausts the limited network bandwidth and may lead to dropping some messages in high-density traffics but also prevent other useful information from being shared. This paper suggests a Game Theory Based Transmission (GTBT) to mitigate redundant message content in a fog-based vehicular network. We show that this approach is real-time and achieves 10.5% fewer duplicate messages compared to the Max Score Based Transmission (MSBT) baseline while it does not require any additional communication costs. Moreover, we contrast the pros and cons of our approach against two other baselines: random (Rand) and geo-filtering (Geo) transmission-based approaches, showing that GTBT is able to minimize the lost information (5.3% for GTBT vs 15.2% and 18.3% for Geo and Rand respectively) and is competing with Geo approach in the number of unique messages sent and their value. Lastly, the GTBT is able to provide a gain of 1.7 as calculated by our suggested new metric as a measure of the ability to compensate for the missed information, vs (- 0.5) for the Geo approach. To the best of our knowledge, this is the first study that employs game theory for selecting the content of the message for the cooperative perception problem.
{"title":"Cooperative Perception: Mitigating Messages Content Duplication","authors":"Bassel S. Chawky, M. Hefeida, A. Elbery, A. Noureldin","doi":"10.1109/ICCSPA55860.2022.10019115","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019115","url":null,"abstract":"Autonomous Vehicles (AVs) and smart vehicles rely on cooperative perception to complement the information missed by their sensors due to their limited range and non-line of sight objects. There are many applications that rely on communication leaving sometimes limited resources for cooperative perceptions. Therefore, optimizing the resources and messages sent over the network becomes crucial. An important factor that undermines the potential of vehicular networks is the number of messages containing information about the same objects. Not only this issue exhausts the limited network bandwidth and may lead to dropping some messages in high-density traffics but also prevent other useful information from being shared. This paper suggests a Game Theory Based Transmission (GTBT) to mitigate redundant message content in a fog-based vehicular network. We show that this approach is real-time and achieves 10.5% fewer duplicate messages compared to the Max Score Based Transmission (MSBT) baseline while it does not require any additional communication costs. Moreover, we contrast the pros and cons of our approach against two other baselines: random (Rand) and geo-filtering (Geo) transmission-based approaches, showing that GTBT is able to minimize the lost information (5.3% for GTBT vs 15.2% and 18.3% for Geo and Rand respectively) and is competing with Geo approach in the number of unique messages sent and their value. Lastly, the GTBT is able to provide a gain of 1.7 as calculated by our suggested new metric as a measure of the ability to compensate for the missed information, vs (- 0.5) for the Geo approach. To the best of our knowledge, this is the first study that employs game theory for selecting the content of the message for the cooperative perception problem.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115539587","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 : 2022-12-27DOI: 10.1109/ICCSPA55860.2022.10018983
Ameen Awwad, H.S. Mir, L. Albasha
The development of high-speed analog-to-digital converters (ADCs) facilitates starting the signal processing of received signals closer to the front end of transceivers, such as the ones used in digital radars, without the need of multiple downconversion stages. In this paper, we discuss the design parameters and optimization of a range-selective digital array radar to block interferences around the observed area. The radar system consists of a linear array of transmitters and a receiver where the frequency components received from each transmitter is filtered out and weighted individually to focus the beam at a certain angle and range. The beamforming process used the recursive multibeam techniques and Monte Carlo method and separately to optimize the frequency offsets between the transmitted carriers and, consequently, minimize the sidelobe level (SLL). Simulation results showed that the SLL was decreased to −16.67 dB in the angle domain and below −10 dB in the range domain at the intended location. The experimental testing of the system using a real multicarrier signal and a data acquisition board (DAQ) proved a similar SLL in the range domain below −10 dB.
{"title":"Modeling and Optimization of a Range-Selective Digital Array Radar","authors":"Ameen Awwad, H.S. Mir, L. Albasha","doi":"10.1109/ICCSPA55860.2022.10018983","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10018983","url":null,"abstract":"The development of high-speed analog-to-digital converters (ADCs) facilitates starting the signal processing of received signals closer to the front end of transceivers, such as the ones used in digital radars, without the need of multiple downconversion stages. In this paper, we discuss the design parameters and optimization of a range-selective digital array radar to block interferences around the observed area. The radar system consists of a linear array of transmitters and a receiver where the frequency components received from each transmitter is filtered out and weighted individually to focus the beam at a certain angle and range. The beamforming process used the recursive multibeam techniques and Monte Carlo method and separately to optimize the frequency offsets between the transmitted carriers and, consequently, minimize the sidelobe level (SLL). Simulation results showed that the SLL was decreased to −16.67 dB in the angle domain and below −10 dB in the range domain at the intended location. The experimental testing of the system using a real multicarrier signal and a data acquisition board (DAQ) proved a similar SLL in the range domain below −10 dB.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129872607","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 : 2022-12-27DOI: 10.1109/ICCSPA55860.2022.10019174
Rama Krishna Thelagathoti, Saiteja Malisetty, Hesham H. Ali
Altered mobility patterns are one of the early symptoms of aging. Several motor-related disorders such as Parkinson's disease are also associated with decreased or altered mobility. However, there is no standard clinical test to identify decreased mobility or diagnose movement variations. In recent years, wearable devices have become popular in measuring mobility parameters and quantifying physical activities such as walking and driving. Furthermore, wearable devices have been widely used in the collection and analysis of mobility data because they are small, affordable, and easy to use. The main objective of this research is to develop a data-driven computational model that can analyze mobility data collected from a group of individuals from different age groups and capture potential aging-related movement variabilities. Such a model would also allow computational tools to extract meaningful correlations between age groups and physical activity. In this study, we have analyzed the mobility data collected from 32 healthy adults from different age groups while they are walking outdoors, climbing stairs, and driving. We developed correlation network models and employed a population analysis-based approach to unravel the associations between aging and physical activities; mainly walking and driving. Although our analysis produced interesting results and identified key parameters that impacted the walking and driving patterns, it didn't significantly different between subjects belonging to different age groups, which we believe is mainly due to the limited use dataset in terms of size and variability. However, the proposed model and recommended approach pave the way for future studies that will further explore the relationships between mobility and aging using richer datasets.
{"title":"Analyzing Walking and Driving Behavior Across Different Age Groups Using Population Analysis and Correlation Networks","authors":"Rama Krishna Thelagathoti, Saiteja Malisetty, Hesham H. Ali","doi":"10.1109/ICCSPA55860.2022.10019174","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019174","url":null,"abstract":"Altered mobility patterns are one of the early symptoms of aging. Several motor-related disorders such as Parkinson's disease are also associated with decreased or altered mobility. However, there is no standard clinical test to identify decreased mobility or diagnose movement variations. In recent years, wearable devices have become popular in measuring mobility parameters and quantifying physical activities such as walking and driving. Furthermore, wearable devices have been widely used in the collection and analysis of mobility data because they are small, affordable, and easy to use. The main objective of this research is to develop a data-driven computational model that can analyze mobility data collected from a group of individuals from different age groups and capture potential aging-related movement variabilities. Such a model would also allow computational tools to extract meaningful correlations between age groups and physical activity. In this study, we have analyzed the mobility data collected from 32 healthy adults from different age groups while they are walking outdoors, climbing stairs, and driving. We developed correlation network models and employed a population analysis-based approach to unravel the associations between aging and physical activities; mainly walking and driving. Although our analysis produced interesting results and identified key parameters that impacted the walking and driving patterns, it didn't significantly different between subjects belonging to different age groups, which we believe is mainly due to the limited use dataset in terms of size and variability. However, the proposed model and recommended approach pave the way for future studies that will further explore the relationships between mobility and aging using richer datasets.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122003829","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 : 2022-12-27DOI: 10.1109/ICCSPA55860.2022.10019053
Ahmed Radi, J. Fekry, S. Zahran
Integrated navigation systems are essential for position, velocity, and attitude determination for different applications, including military and civilian ones (drones, UAVs, UGVs, self-driving cars, etc.). The advancements in the Micro-Electromechanical Systems (MEMS) technology have allowed the possibilities of having miniature sensors with a whole wide range of accuracies. Most low-cost navigation system are based on the idea of integrating low-cost GNSS receivers with MEMS-based IMUs using sensor fusion algorithms to provide localization and attitude navigational information with acceptable accuracy at relatively low power consumption and reduced computations as well. This paper aims to propose a low-cost Arduino compatible GNSS-only reduced navigation module that provides eight-out of-nine navigation states (represented in 3D positioning, 3D estimated velocity components in LLF, pitch and heading estimated attitude angles) using a proposed real-time estimation algorithm and without incorporating any inertial sensors. Real in-field data were collected under off-road and urban environments to evaluate the fidelity of the proposed GNSS-only module (including real-time estimation algorithm running on the aforementioned Arduino compatible receiver) using two different GNSS antennas. Such an evaluation was performed by comparing the navigation states acquired from the system undertest with an integrated INS/GPS reference system attached on the same platform. The results showed that the proposed reduced navigation system associated with the real-time estimation algorithm is successfully capable of providing 8-out of-9 navigational states with acceptable accuracy using both antennas. Results also demonstrated a better performance for the reduced GNSS-only system connected to one antenna (Taoglas ADFGP.50A) than the other (Taoglas AGGP.35f) in terms of altitude information and speed recovery of the localization information after GNSS outage periods.
{"title":"Real-Time Position, Velocity and Attitude Estimation Using Low-Cost GNSS Only Reduced Navigation System","authors":"Ahmed Radi, J. Fekry, S. Zahran","doi":"10.1109/ICCSPA55860.2022.10019053","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019053","url":null,"abstract":"Integrated navigation systems are essential for position, velocity, and attitude determination for different applications, including military and civilian ones (drones, UAVs, UGVs, self-driving cars, etc.). The advancements in the Micro-Electromechanical Systems (MEMS) technology have allowed the possibilities of having miniature sensors with a whole wide range of accuracies. Most low-cost navigation system are based on the idea of integrating low-cost GNSS receivers with MEMS-based IMUs using sensor fusion algorithms to provide localization and attitude navigational information with acceptable accuracy at relatively low power consumption and reduced computations as well. This paper aims to propose a low-cost Arduino compatible GNSS-only reduced navigation module that provides eight-out of-nine navigation states (represented in 3D positioning, 3D estimated velocity components in LLF, pitch and heading estimated attitude angles) using a proposed real-time estimation algorithm and without incorporating any inertial sensors. Real in-field data were collected under off-road and urban environments to evaluate the fidelity of the proposed GNSS-only module (including real-time estimation algorithm running on the aforementioned Arduino compatible receiver) using two different GNSS antennas. Such an evaluation was performed by comparing the navigation states acquired from the system undertest with an integrated INS/GPS reference system attached on the same platform. The results showed that the proposed reduced navigation system associated with the real-time estimation algorithm is successfully capable of providing 8-out of-9 navigational states with acceptable accuracy using both antennas. Results also demonstrated a better performance for the reduced GNSS-only system connected to one antenna (Taoglas ADFGP.50A) than the other (Taoglas AGGP.35f) in terms of altitude information and speed recovery of the localization information after GNSS outage periods.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126913521","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 : 2022-12-27DOI: 10.1109/ICCSPA55860.2022.10019210
Nader Abdelaziz, A. El-Rabbany
Most navigation systems in GNSS-challenged environments rely on GNSS/INS integrated navigation system, with the INS potentially providing reliable positioning during short GNSS outages. However, in the event of a prolonged GNSS signal outage, the performance of the system will be solely dependent on the INS solution, which can lead to significant drift over time. As a result, adding more onboard sensors is crucial to mitigate the limitation the GNSS/INS systems, and thereby increase the robustness of the navigation system. This study proposes a loosely-coupled (LC) integration between the INS, LiDAR simultaneous localization and mapping (SLAM), and visual SLAM using an extended Kalman filter (EKF). The developed integrated navigation system is tested on the residential and highway drive segments of the raw KITTI dataset, which simulates various driving outdoor environments in terms of feature density and driving speed. In both cases, a complete artificial GNSS outage is enforced. The results show that the proposed INS/LiDAR/visual SLAM integrated system drastically outperforms the use of INS only. The proposed integrated navigation system yielded an average reduction in the root-mean-square error (RMSE) of approximately 95%, 87%, and 53%, in the east, north, and up directions, respectively. Finally, the proposed algorithm outperformed considered state-of-the-art LiDAR SLAM algorithms.
{"title":"LiDAR/Visual SLAM-Aided Vehicular Inertial Navigation System for GNSS-Denied Environments","authors":"Nader Abdelaziz, A. El-Rabbany","doi":"10.1109/ICCSPA55860.2022.10019210","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019210","url":null,"abstract":"Most navigation systems in GNSS-challenged environments rely on GNSS/INS integrated navigation system, with the INS potentially providing reliable positioning during short GNSS outages. However, in the event of a prolonged GNSS signal outage, the performance of the system will be solely dependent on the INS solution, which can lead to significant drift over time. As a result, adding more onboard sensors is crucial to mitigate the limitation the GNSS/INS systems, and thereby increase the robustness of the navigation system. This study proposes a loosely-coupled (LC) integration between the INS, LiDAR simultaneous localization and mapping (SLAM), and visual SLAM using an extended Kalman filter (EKF). The developed integrated navigation system is tested on the residential and highway drive segments of the raw KITTI dataset, which simulates various driving outdoor environments in terms of feature density and driving speed. In both cases, a complete artificial GNSS outage is enforced. The results show that the proposed INS/LiDAR/visual SLAM integrated system drastically outperforms the use of INS only. The proposed integrated navigation system yielded an average reduction in the root-mean-square error (RMSE) of approximately 95%, 87%, and 53%, in the east, north, and up directions, respectively. Finally, the proposed algorithm outperformed considered state-of-the-art LiDAR SLAM algorithms.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128954410","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 : 2022-12-27DOI: 10.1109/ICCSPA55860.2022.10018985
H. Elghamrawy, A. Noureldin
Recently there is a growing demand for continuous, reliable, and accurate positioning and navigation with the wide spread of a wide range of applications that depend mainly on Global Navigation Satellite System (GNSS), including Global Positioning System (GPS). The widespread of such applications resulted in GPS being an appealing target for jamming. The presence of intentional or unintentional interference is considered one of the major threats to the integrity of the GPS system that not only jeopardizes positioning and timing services but can significantly endanger safety of evolving applications such as autonomous vehicles. This paper aims to propose anti-jamming technique based on wavelet denoising. The proposed technique is assessed based on simulated static and dynamic scenarios obtained from Spirent™ system for a fully controlled environment. The results show that the proposed technique is able to effectively suppress amplitude-modulated (AM) jamming signal.
{"title":"Wavelet Denoising for Amplitude-Modulated Interference Signal Mitigation for GPS Software Receiver","authors":"H. Elghamrawy, A. Noureldin","doi":"10.1109/ICCSPA55860.2022.10018985","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10018985","url":null,"abstract":"Recently there is a growing demand for continuous, reliable, and accurate positioning and navigation with the wide spread of a wide range of applications that depend mainly on Global Navigation Satellite System (GNSS), including Global Positioning System (GPS). The widespread of such applications resulted in GPS being an appealing target for jamming. The presence of intentional or unintentional interference is considered one of the major threats to the integrity of the GPS system that not only jeopardizes positioning and timing services but can significantly endanger safety of evolving applications such as autonomous vehicles. This paper aims to propose anti-jamming technique based on wavelet denoising. The proposed technique is assessed based on simulated static and dynamic scenarios obtained from Spirent™ system for a fully controlled environment. The results show that the proposed technique is able to effectively suppress amplitude-modulated (AM) jamming signal.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132273839","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 : 2022-12-27DOI: 10.1109/ICCSPA55860.2022.10019156
Neda Haghighatpanah, R. Gohary
This paper presents two novel Bayesian learning recovery algorithms for block sparse signals. Two cases are considered. In the first case, the signals within each block are correlated and the block borders are known. In the second case, the block borders are unknown and the signal elements are uncorrelated. Unlike their existing counterparts, the proposed algorithms obtain the optimal block covariances from the data estimated in the previous iterations. Furthermore, the decision to declare a block as zero is based on hypothesis testing. For the second case, we introduce a new prior model which is characterized by elastic dependencies among neighbouring signal elements. Using this model, we develop a novel Bayesian learning algorithm which iterates between estimating the dependencies among the signal elements and updating the Gaussian prior model. Numerical simulations illustrate the effectiveness of the proposed algorithms.
{"title":"Improved Bayesian learning Algorithms for recovering Block Sparse Signals With Known and Unknown Borders","authors":"Neda Haghighatpanah, R. Gohary","doi":"10.1109/ICCSPA55860.2022.10019156","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019156","url":null,"abstract":"This paper presents two novel Bayesian learning recovery algorithms for block sparse signals. Two cases are considered. In the first case, the signals within each block are correlated and the block borders are known. In the second case, the block borders are unknown and the signal elements are uncorrelated. Unlike their existing counterparts, the proposed algorithms obtain the optimal block covariances from the data estimated in the previous iterations. Furthermore, the decision to declare a block as zero is based on hypothesis testing. For the second case, we introduce a new prior model which is characterized by elastic dependencies among neighbouring signal elements. Using this model, we develop a novel Bayesian learning algorithm which iterates between estimating the dependencies among the signal elements and updating the Gaussian prior model. Numerical simulations illustrate the effectiveness of the proposed algorithms.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114070244","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 : 2022-12-27DOI: 10.1109/ICCSPA55860.2022.10019004
Mohamed F ElKhalea, H. Hendy, A. Kamel, I. Arafa, A. Abosekeen
Smartphone positioning using Global Navigation Satellite Systems (GNSS) only is considered a challenge, especially in urban environments. Utilizing Raw GNSS Measurements (RGNSSM) on the android platform made smartphone navigation and tracking easier. Several researches have been conducted to improve the positioning information by removing the noise of pseudorange measurements, generating an optimized weighting covariance matrix, or integrating GNSS data with other sensors. In this paper, a new algorithm based on generating a modified weighting covariance matrix is proposed moreover, it is applied to several satellite selection techniques to improve android smartphone positioning information. This weighting covariance matrix depends on elevation angle, carrier-to-noise density, and also the measurement residual for each satellite. A real road trajectory was conducted to test the performance of the proposed algorithm. The results show that the 3D position was enhanced by 43.41% compared to the method suggested by the Google algorithm.
{"title":"Smartphone Positioning Enhancement Using Several GNSS Satellite Selection Techniques","authors":"Mohamed F ElKhalea, H. Hendy, A. Kamel, I. Arafa, A. Abosekeen","doi":"10.1109/ICCSPA55860.2022.10019004","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019004","url":null,"abstract":"Smartphone positioning using Global Navigation Satellite Systems (GNSS) only is considered a challenge, especially in urban environments. Utilizing Raw GNSS Measurements (RGNSSM) on the android platform made smartphone navigation and tracking easier. Several researches have been conducted to improve the positioning information by removing the noise of pseudorange measurements, generating an optimized weighting covariance matrix, or integrating GNSS data with other sensors. In this paper, a new algorithm based on generating a modified weighting covariance matrix is proposed moreover, it is applied to several satellite selection techniques to improve android smartphone positioning information. This weighting covariance matrix depends on elevation angle, carrier-to-noise density, and also the measurement residual for each satellite. A real road trajectory was conducted to test the performance of the proposed algorithm. The results show that the 3D position was enhanced by 43.41% compared to the method suggested by the Google algorithm.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116249521","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 : 2022-12-27DOI: 10.1109/ICCSPA55860.2022.10019135
Rowan Ihab Halawa, S. Youssef, M. Elagamy
Epileptic seizures are brief disruptions in the electrical activity of the brain. Epilepsy is a central nervous system illness in which people have repeated seizures that happen at random times and usually without warning. Seizures are more likely to cause physical harm and even death in people who have them frequently. Intelligent techniques supporting brain-computer interfaces in seizure control can provide efficient epilepsy detection where seizure events can be identified in the early stages triggering electrical stimulations to be sent to the cortex of the brain. In this paper, a non-patient-specific seizure detection model is introduced. The proposed model integrates wavelet-based electroencephalography EEG brain signal processing with feature extraction to extract combined features from both time and frequency domains. Classification has been applied using different machine learning techniques for efficient early detection of epileptic seizures. In addition, channel selection analysis is implemented to reach an accurate generic model. The experimental comparative study demonstrated that the electroencephalography signals from the frontal lobe channels supply more discriminative features than the other channels, which enhanced the classification accuracy and sensitivity in the proposed model. Experiments have been carried out on the Children's Hospital Boston-Massachusetts Institute of Technology dataset to validate the robustness of the proposed model. The experimental results showed that the proposed model achieved 99.792% accuracy and 99.59% sensitivity in detecting epileptic seizures with improvement in accuracy with rates of 7%, 5%, and 8% compared to [14], [11], and [9], respectively. Experiments showed that the proposed system can detect epileptic seizures effectively, which can give remarkable potential in clinical applications.
{"title":"An Efficient Patient-Independent Epileptic Seizure Assistive Integrated Model in Human Brain-Computer Interface Applications","authors":"Rowan Ihab Halawa, S. Youssef, M. Elagamy","doi":"10.1109/ICCSPA55860.2022.10019135","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019135","url":null,"abstract":"Epileptic seizures are brief disruptions in the electrical activity of the brain. Epilepsy is a central nervous system illness in which people have repeated seizures that happen at random times and usually without warning. Seizures are more likely to cause physical harm and even death in people who have them frequently. Intelligent techniques supporting brain-computer interfaces in seizure control can provide efficient epilepsy detection where seizure events can be identified in the early stages triggering electrical stimulations to be sent to the cortex of the brain. In this paper, a non-patient-specific seizure detection model is introduced. The proposed model integrates wavelet-based electroencephalography EEG brain signal processing with feature extraction to extract combined features from both time and frequency domains. Classification has been applied using different machine learning techniques for efficient early detection of epileptic seizures. In addition, channel selection analysis is implemented to reach an accurate generic model. The experimental comparative study demonstrated that the electroencephalography signals from the frontal lobe channels supply more discriminative features than the other channels, which enhanced the classification accuracy and sensitivity in the proposed model. Experiments have been carried out on the Children's Hospital Boston-Massachusetts Institute of Technology dataset to validate the robustness of the proposed model. The experimental results showed that the proposed model achieved 99.792% accuracy and 99.59% sensitivity in detecting epileptic seizures with improvement in accuracy with rates of 7%, 5%, and 8% compared to [14], [11], and [9], respectively. Experiments showed that the proposed system can detect epileptic seizures effectively, which can give remarkable potential in clinical applications.","PeriodicalId":106639,"journal":{"name":"2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114803605","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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