Pub Date : 2022-12-27DOI: 10.1109/ICCSPA55860.2022.10019023
Mai Kafafy, A. Ibrahim, Mahmoud H. Ismail
The wide deployment of wireless sensor networks has two limiting factors: the power-limited sensors and the congested radio frequency spectrum. A promising way to reduce the transmission power of sensors, and consequently prolonging their lifetime, is deploying reconfigurable intelligent surfaces (RISs) that passively beamform the sensors transmission to remote data centers. Furthermore, spectrum limitation can be overcome by spectrum sharing between sensors and radars. This paper utilizes tools from stochastic geometry to characterize the power reduction in sensors due to utilizing RISs in a shared spectrum with radars. We show that allowing RIS-assisted communication reduces the power consumption of the sensor nodes, and that the power reduction increases with the RISs density. Furthermore, we show that radars with narrow beamwidths allow more power saving for the sensor nodes in its vicinity.
{"title":"Uplink Power Analysis of RIS-assisted Communication Over Shared Radar Spectrum","authors":"Mai Kafafy, A. Ibrahim, Mahmoud H. Ismail","doi":"10.1109/ICCSPA55860.2022.10019023","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019023","url":null,"abstract":"The wide deployment of wireless sensor networks has two limiting factors: the power-limited sensors and the congested radio frequency spectrum. A promising way to reduce the transmission power of sensors, and consequently prolonging their lifetime, is deploying reconfigurable intelligent surfaces (RISs) that passively beamform the sensors transmission to remote data centers. Furthermore, spectrum limitation can be overcome by spectrum sharing between sensors and radars. This paper utilizes tools from stochastic geometry to characterize the power reduction in sensors due to utilizing RISs in a shared spectrum with radars. We show that allowing RIS-assisted communication reduces the power consumption of the sensor nodes, and that the power reduction increases with the RISs density. Furthermore, we show that radars with narrow beamwidths allow more power saving for the sensor nodes in its vicinity.","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":"124394829","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/ICCSPA55860.2022.10019020
Eslam Mounier, M. Korenberg, A. Noureldin
Global Navigation Satellite System (GNSS) and Dead Reckoning (DR) techniques are typically integrated to provide a robust and continuous navigation solution. However, frequent GNSS outages due to signal deterioration and blockage can severely impact the performance of the integrated navigation, which will be deprived of accurate GNSS updates and have to rely solely on the DR solution. The shortcomings of DR navigation solutions are due to the presence of several sensor errors such as biases, scale factor errors, thermal drifts, misalignment errors, and stochastic errors. Despite sensor calibration procedures, the impact of sensor errors may persist, propagating through the DR algorithm and leading to significant drifts, especially with Micro-Electro-Mechanical Systems (MEMS) sensors. In this paper, the objective is to improve the standalone navigation performance of Vehicle Sensors Dead Reckoning (VSDR) during GNSS out-ages. To be specific, the Fast Orthogonal Search (FOS) system identification technique is utilized to model Inertial Measurement Unit (IMU) sensor errors utilizing the availability of the accurate integrated navigation solution. The sensor error models are to be utilized when the integrated solution is compromised (i.e. GNSS outage) to estimate improved sensor measurements, thus reducing drifting navigation errors and achieving robust stan-dalone VSDR operations over extended durations. The proposed method is verified using real data from vehicle motion sensors on real road test experiments performed on a land vehicle in downtown Kingston, Ontario, Canada. Our results demonstrate significant improvements when utilizing the sensor error models for rectifying the raw sensor measurements achieving position accuracy enhancements of 56% on average across different outage durations.
{"title":"Online Motion Sensors Error Modelling for Robust Navigation Using Fast Orthogonal Search","authors":"Eslam Mounier, M. Korenberg, A. Noureldin","doi":"10.1109/ICCSPA55860.2022.10019020","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019020","url":null,"abstract":"Global Navigation Satellite System (GNSS) and Dead Reckoning (DR) techniques are typically integrated to provide a robust and continuous navigation solution. However, frequent GNSS outages due to signal deterioration and blockage can severely impact the performance of the integrated navigation, which will be deprived of accurate GNSS updates and have to rely solely on the DR solution. The shortcomings of DR navigation solutions are due to the presence of several sensor errors such as biases, scale factor errors, thermal drifts, misalignment errors, and stochastic errors. Despite sensor calibration procedures, the impact of sensor errors may persist, propagating through the DR algorithm and leading to significant drifts, especially with Micro-Electro-Mechanical Systems (MEMS) sensors. In this paper, the objective is to improve the standalone navigation performance of Vehicle Sensors Dead Reckoning (VSDR) during GNSS out-ages. To be specific, the Fast Orthogonal Search (FOS) system identification technique is utilized to model Inertial Measurement Unit (IMU) sensor errors utilizing the availability of the accurate integrated navigation solution. The sensor error models are to be utilized when the integrated solution is compromised (i.e. GNSS outage) to estimate improved sensor measurements, thus reducing drifting navigation errors and achieving robust stan-dalone VSDR operations over extended durations. The proposed method is verified using real data from vehicle motion sensors on real road test experiments performed on a land vehicle in downtown Kingston, Ontario, Canada. Our results demonstrate significant improvements when utilizing the sensor error models for rectifying the raw sensor measurements achieving position accuracy enhancements of 56% on average across different outage durations.","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":"125423780","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/ICCSPA55860.2022.10019109
F. Ahmed, Khushboo Singh, K. Esselle, D. Thalakotuna
A metasurface-driven 3D beam-scanning (elevation, azimuth or both) antenna solution is presented in this paper. A pair of novel phase gradient metallic metasurfaces (PGMMs) is designed using the near-electric field phase transformation method operating in the Ku-band. Rotating PGMMs independently atop a static, fixed beam base antenna will enable wide-angle beam-steering in both azimuth and elevation planes. A prototype is made and tested to validate the predicted results. The measured results exhibit excellent beam scanning performance with the highest elevation angle of ±38° and a full 360° in the azimuth. This beam-steering approach does not rely on active radio-frequency components. Moreover, the proposed metasurface obviates costly dielectrics, reducing additional cost and weight, and is suitable for stressed environmental conditions such as high-power systems and inter-satellite or deep-space communication systems.
{"title":"Metasurface-Driven Beam Steering Antenna for Satellite Communications","authors":"F. Ahmed, Khushboo Singh, K. Esselle, D. Thalakotuna","doi":"10.1109/ICCSPA55860.2022.10019109","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019109","url":null,"abstract":"A metasurface-driven 3D beam-scanning (elevation, azimuth or both) antenna solution is presented in this paper. A pair of novel phase gradient metallic metasurfaces (PGMMs) is designed using the near-electric field phase transformation method operating in the Ku-band. Rotating PGMMs independently atop a static, fixed beam base antenna will enable wide-angle beam-steering in both azimuth and elevation planes. A prototype is made and tested to validate the predicted results. The measured results exhibit excellent beam scanning performance with the highest elevation angle of ±38° and a full 360° in the azimuth. This beam-steering approach does not rely on active radio-frequency components. Moreover, the proposed metasurface obviates costly dielectrics, reducing additional cost and weight, and is suitable for stressed environmental conditions such as high-power systems and inter-satellite or deep-space communication systems.","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":"115592282","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/ICCSPA55860.2022.10019216
Aaesha S. Alshehhi, F. Alawadhi, Meera Baqer, R. Dhaouadi
In this paper, we present a low-cost home automation system that can generate an energy-efficient and cost-reducing electricity consumption schedule. The system is comprised of an energy management system running on a raspberry pi 4 called Home Assistant with wireless smart switches connected to the desired home appliances. The communication between all components in the system is configured to be wireless, which means that the control interface for this system is flexible. The proposed optimization method for generating the schedule is based on mixed integer linear programming. The home energy management system is realized on a raspberry pi. The optimization algorithm is implemented using Python and the Gurobi solver package. The optimized scheduling for the home appliances was obtained for different cases of user time preferences and results prove that the proposed method efficiently reduced the total cost of electricity of a typical household.
{"title":"Scheduling Optimization of Household Equipment using a Wireless Home Automation System","authors":"Aaesha S. Alshehhi, F. Alawadhi, Meera Baqer, R. Dhaouadi","doi":"10.1109/ICCSPA55860.2022.10019216","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019216","url":null,"abstract":"In this paper, we present a low-cost home automation system that can generate an energy-efficient and cost-reducing electricity consumption schedule. The system is comprised of an energy management system running on a raspberry pi 4 called Home Assistant with wireless smart switches connected to the desired home appliances. The communication between all components in the system is configured to be wireless, which means that the control interface for this system is flexible. The proposed optimization method for generating the schedule is based on mixed integer linear programming. The home energy management system is realized on a raspberry pi. The optimization algorithm is implemented using Python and the Gurobi solver package. The optimized scheduling for the home appliances was obtained for different cases of user time preferences and results prove that the proposed method efficiently reduced the total cost of electricity of a typical household.","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":"121644676","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/ICCSPA55860.2022.10018988
Ahmed Elsheikh, A. Ibrahim, Mahmoud H. Ismail
Employing 1-bit analog-to-digital converters (ADCs) is necessary for large-bandwidth massive multiple-antenna systems to maintain reasonable power consumption. However, conducting channel estimation with such 1-bit ADCs and with low complexity is a challenging task. In this paper, we propose to employ an Ensemble Regression (ER) model to conduct low-complexity and high-quality channel estimation. The amount of proposed computations are less than 3% of that proposed by similar deep learning (DL) methods, and in turn requires approximately 4% of the power consumed in computations while maintaining the same level of performance.
{"title":"Ensemble Regression for 1-Bit Channel Estimation","authors":"Ahmed Elsheikh, A. Ibrahim, Mahmoud H. Ismail","doi":"10.1109/ICCSPA55860.2022.10018988","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10018988","url":null,"abstract":"Employing 1-bit analog-to-digital converters (ADCs) is necessary for large-bandwidth massive multiple-antenna systems to maintain reasonable power consumption. However, conducting channel estimation with such 1-bit ADCs and with low complexity is a challenging task. In this paper, we propose to employ an Ensemble Regression (ER) model to conduct low-complexity and high-quality channel estimation. The amount of proposed computations are less than 3% of that proposed by similar deep learning (DL) methods, and in turn requires approximately 4% of the power consumed in computations while maintaining the same level of performance.","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":"125705975","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/ICCSPA55860.2022.10019017
S. Youssef, Jomana Ahmed Gaber, Yasmina Ayman Kamal
Early detection of brain tumors is important to increase the rate of complete recovery from it without risking the lives of patients. Nowadays, the medical domain aims to use magnetic resonance to achieve early detection of Brain Tumors (BT), as 40 out of 100 people survive their cancer for 1 year or more[6], therefore the early detection of the tumors helps in the recovery. Magnetic resonance imaging (MRI) and X-Ray images are used in the early diagnosis of BT to eliminate its spreading. In this paper, we build an ensemble classifier model that integrates data augmentation with the VGG16 deep-learning feature extraction model for early detection of multi-class brain tumor types of patient infection. We perform the BT classification using the proposed model on a dataset that has a multiclass classification (Glioma tumor, Meningioma tumor, No tumor, and Pituitary tumor), it will classify the type of the tumor if it exists in the MRI. Our model results in an accuracy of 96.8% using the proposed model.
{"title":"A Computer-Aided Brain Tumor Detection Integrating Ensemble Classifiers with Data Augmentation and VGG16 Feature Extraction","authors":"S. Youssef, Jomana Ahmed Gaber, Yasmina Ayman Kamal","doi":"10.1109/ICCSPA55860.2022.10019017","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019017","url":null,"abstract":"Early detection of brain tumors is important to increase the rate of complete recovery from it without risking the lives of patients. Nowadays, the medical domain aims to use magnetic resonance to achieve early detection of Brain Tumors (BT), as 40 out of 100 people survive their cancer for 1 year or more[6], therefore the early detection of the tumors helps in the recovery. Magnetic resonance imaging (MRI) and X-Ray images are used in the early diagnosis of BT to eliminate its spreading. In this paper, we build an ensemble classifier model that integrates data augmentation with the VGG16 deep-learning feature extraction model for early detection of multi-class brain tumor types of patient infection. We perform the BT classification using the proposed model on a dataset that has a multiclass classification (Glioma tumor, Meningioma tumor, No tumor, and Pituitary tumor), it will classify the type of the tumor if it exists in the MRI. Our model results in an accuracy of 96.8% using the proposed model.","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":"114467033","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/ICCSPA55860.2022.10019188
A. Awang, N. Nayan, N. R. N. Jaafar, Mohd Zubir Suboh, K. A. A. Rahman, Siti Nor Ashikin Ismail
Psychiatric disorders (PDs) interfere with one's functioning and greatly affect a person's quality of life. Prompt diagnosis and intervention at the early stages of these illnesses are important. However, most people are oblivious or unaware of their mental health status as the symptoms may not be easily recognizable. Consequently, complications occur later in life. In this study, a machine learning (ML) approach that distinguishes between case (PD-diagnosed patients) and control (healthy) groups was developed using photoplethysmogram (PPG) morphology. 92 subjects with gender and age-matched PPG data were collected during two phases; baseline and stimulus state of a 10-min experiment. 60 features from PPG morphology were extracted from each phase, and another 30 were obtained from differences between the two phases. A total of 27 out of 90 features exhibited a significant difference. Twelve features extracted by heatmap based on the correlation analysis were fed to five types of ML algorithms: discrimination analysis, k-nearest neighbor, decision tree, support vector machine, and artificial neural network (ANN). The results showed the best performance of 92.86%, 100.00%, and 96.43% for sensitivity, specificity, and accuracy by ANN. Thus, a PD prediction model was developed using machine learning techniques from PPG morphology extraction.
{"title":"Machine Learning Approach on Photoplethysmogram Morphology for Psychiatric Disorders Prediction","authors":"A. Awang, N. Nayan, N. R. N. Jaafar, Mohd Zubir Suboh, K. A. A. Rahman, Siti Nor Ashikin Ismail","doi":"10.1109/ICCSPA55860.2022.10019188","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019188","url":null,"abstract":"Psychiatric disorders (PDs) interfere with one's functioning and greatly affect a person's quality of life. Prompt diagnosis and intervention at the early stages of these illnesses are important. However, most people are oblivious or unaware of their mental health status as the symptoms may not be easily recognizable. Consequently, complications occur later in life. In this study, a machine learning (ML) approach that distinguishes between case (PD-diagnosed patients) and control (healthy) groups was developed using photoplethysmogram (PPG) morphology. 92 subjects with gender and age-matched PPG data were collected during two phases; baseline and stimulus state of a 10-min experiment. 60 features from PPG morphology were extracted from each phase, and another 30 were obtained from differences between the two phases. A total of 27 out of 90 features exhibited a significant difference. Twelve features extracted by heatmap based on the correlation analysis were fed to five types of ML algorithms: discrimination analysis, k-nearest neighbor, decision tree, support vector machine, and artificial neural network (ANN). The results showed the best performance of 92.86%, 100.00%, and 96.43% for sensitivity, specificity, and accuracy by ANN. Thus, a PD prediction model was developed using machine learning techniques from PPG morphology extraction.","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":"117267613","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/ICCSPA55860.2022.10019104
A. Jafarieh, M. Nouri, H. Behroozi, N. K. Mallat
Designing high gain and compact antennas is a very important task in 5G millimeter-wave (mmWave) mobile communication. Microstrip antennas are very popular due to their compact size and ease of fabrication. In this paper, a 5G compact mmWave dipole antenna is proposed. A double dipole is inserted at the ground plane to increase the realized gain and achieve a directive beam. Simulation results show that the proposed 5G antenna has an impedance bandwidth (IBW) of 3.5% and a 5.6 dBi realized gain at a frequency of 28 GHz.
{"title":"A Design of a mmWave Compact Antenna with a Microstrip Line Balun Feed for 5G Communications","authors":"A. Jafarieh, M. Nouri, H. Behroozi, N. K. Mallat","doi":"10.1109/ICCSPA55860.2022.10019104","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019104","url":null,"abstract":"Designing high gain and compact antennas is a very important task in 5G millimeter-wave (mmWave) mobile communication. Microstrip antennas are very popular due to their compact size and ease of fabrication. In this paper, a 5G compact mmWave dipole antenna is proposed. A double dipole is inserted at the ground plane to increase the realized gain and achieve a directive beam. Simulation results show that the proposed 5G antenna has an impedance bandwidth (IBW) of 3.5% and a 5.6 dBi realized gain at a frequency of 28 GHz.","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":"127409906","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/ICCSPA55860.2022.10019148
Ahmed O. El Meligy, L. Albasha
The objective of this paper is to design both a source inductor degenerated low noise amplifier (LNA) and a differential LNA that operate at a radio frequency of 2.4 GHz. The circuit parameters of the LNAs and the test benches are identified by considering the 180 nm generic process design kits (GPDK). The LNAs and test bench schematics are then developed on the Cadence Virtuoso Platform before conducting the simulations and analysis. The obtained results indicate that for the source inductor degenerated LNA, which uses MOSFETs with a gate width of $200 mu mathrm{m}$, a maximum gain of 21.4067 dB is achieved while retaining a minimum noise figure (NF) of 0.367 dB. Furthermore, the 1-dB compression point (PldB) and the input third-order inter-modulation product (IIP3) are found to be −8.172 and −0.513 dBm, respectively. On the other hand, for the differential LNA, using MOSFETs with a gate width of $96 mu mathrm{m}$, the maximum attainable gain is found to be 22.8 dB, and the minimum NF is 2.38 dB. Moreover, −16.634 and −6.547 dBm are obtained for the PldB and the IIP3, respectively.
本文的目的是设计一个源电感退化低噪声放大器(LNA)和一个工作在2.4 GHz射频的差分LNA。采用180 nm通用工艺设计套件(GPDK)确定了LNAs和试验台的电路参数。然后在Cadence Virtuoso平台上开发lna和测试台原理图，然后进行模拟和分析。结果表明，采用栅极宽度为$200 mu maththrm {m}$的mosfet的源电感退化LNA，最大增益为21.4067 dB，同时保持最小噪声系数(NF)为0.367 dB。此外，1 db压缩点(PldB)和输入三阶互调积(IIP3)分别为- 8.172和- 0.513 dBm。另一方面，对于差分LNA，使用栅极宽度为$96 mu mathrm{m}$的mosfet，可获得的最大增益为22.8 dB，最小NF为2.38 dB。PldB和IIP3分别为−16.634和−6.547 dBm。
{"title":"Design and Analysis of Various Narrow-Band LNA Topologies","authors":"Ahmed O. El Meligy, L. Albasha","doi":"10.1109/ICCSPA55860.2022.10019148","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019148","url":null,"abstract":"The objective of this paper is to design both a source inductor degenerated low noise amplifier (LNA) and a differential LNA that operate at a radio frequency of 2.4 GHz. The circuit parameters of the LNAs and the test benches are identified by considering the 180 nm generic process design kits (GPDK). The LNAs and test bench schematics are then developed on the Cadence Virtuoso Platform before conducting the simulations and analysis. The obtained results indicate that for the source inductor degenerated LNA, which uses MOSFETs with a gate width of $200 mu mathrm{m}$, a maximum gain of 21.4067 dB is achieved while retaining a minimum noise figure (NF) of 0.367 dB. Furthermore, the 1-dB compression point (PldB) and the input third-order inter-modulation product (IIP3) are found to be −8.172 and −0.513 dBm, respectively. On the other hand, for the differential LNA, using MOSFETs with a gate width of $96 mu mathrm{m}$, the maximum attainable gain is found to be 22.8 dB, and the minimum NF is 2.38 dB. Moreover, −16.634 and −6.547 dBm are obtained for the PldB and the IIP3, respectively.","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":"125227676","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/ICCSPA55860.2022.10019114
Abeer Elbehery, Yasmine Fahmy, Mai Kafafy
Image inpainting is filling the missing or corrupted pixels in an image in a realistic way that cannot be differentiated by human eye. Deep learning is widely used in image inpainting and it exhibits better performance than classical inpainting methods, but it requires high processing resources and longer time to train the model. In this paper, we propose an autoencoder architecture that outperforms other deep learning techniques in literature methods with lower processing and time complexity.
{"title":"Low Complexity Image Inpainting Using AutoEncoder","authors":"Abeer Elbehery, Yasmine Fahmy, Mai Kafafy","doi":"10.1109/ICCSPA55860.2022.10019114","DOIUrl":"https://doi.org/10.1109/ICCSPA55860.2022.10019114","url":null,"abstract":"Image inpainting is filling the missing or corrupted pixels in an image in a realistic way that cannot be differentiated by human eye. Deep learning is widely used in image inpainting and it exhibits better performance than classical inpainting methods, but it requires high processing resources and longer time to train the model. In this paper, we propose an autoencoder architecture that outperforms other deep learning techniques in literature methods with lower processing and time complexity.","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":"127694333","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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