Pub Date : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101543
Md. Zubaer Alam, T. K. Roy, Subarto Kumar Ghosh, N. Mohammad, L. C. Paul
This research presents an improved backstepping control (IBSC) approach to designing a controller for a DC-DC buck converter to improve output voltage regulation under changing operating conditions. To develop the proposed the proposed controller, a state-space DC-DC buck converter dynamical model in continuous conduction mode is first developed. Secondly, to avoid the complexity of virtual control law derivatives in the traditional BSC method, these terms are treated as uncertain terms during the control law design process. Furthermore, the Lyapunov control theory is used to ensure the closed-loop system's global asymptotic stability. Finally, the performance of the proposed IBSC technique is validated using a simulation study on the MATLAB Simulink platform. A comparison of the simulation results is also presented to show the superiority of the proposed approach as compared to the traditional BSC method. The simulation study and quantitative results reveal that the proposed IBSC method outperforms the traditional BSC method.
{"title":"Output Voltage Stability of a DC-DC Buck Converter via an Improved Backstepping Method","authors":"Md. Zubaer Alam, T. K. Roy, Subarto Kumar Ghosh, N. Mohammad, L. C. Paul","doi":"10.1109/ECCE57851.2023.10101543","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101543","url":null,"abstract":"This research presents an improved backstepping control (IBSC) approach to designing a controller for a DC-DC buck converter to improve output voltage regulation under changing operating conditions. To develop the proposed the proposed controller, a state-space DC-DC buck converter dynamical model in continuous conduction mode is first developed. Secondly, to avoid the complexity of virtual control law derivatives in the traditional BSC method, these terms are treated as uncertain terms during the control law design process. Furthermore, the Lyapunov control theory is used to ensure the closed-loop system's global asymptotic stability. Finally, the performance of the proposed IBSC technique is validated using a simulation study on the MATLAB Simulink platform. A comparison of the simulation results is also presented to show the superiority of the proposed approach as compared to the traditional BSC method. The simulation study and quantitative results reveal that the proposed IBSC method outperforms the traditional BSC method.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114870852","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101516
Mohammad Hanif, N. Mohammad, K. Biswas, Bijoy Harun
To solve the optimization problem of Economic Load Dispatch (ELD), a number of metaheuristic approaches have already been implemented, exhibiting substantial improvement over the conventional technique. Despite this, due to the global energy crisis, research in ELD still continues to garner considerable interest. In this study, the Seagull Optimization Algorithm (SOA), a recently developed swarm intelligence technique, is applied in ELD. As the SOA algorithm has never been utilized in the ELD, it is important to investigate its efficacy and validity in this domain. Here, two case studies of ELD incorporating 6 and 10 generator units are implemented employing SOA. What's more, the performance of SOA in ELD is compared with respect to three other previously applied metaheuristics algorithms. Results indicate that SOA is a potential algorithm capable of handling the practical optimization challenge of ELD problem more effectively, especially in large power plants having more than 6 units.
{"title":"Seagull Optimization Algorithm for Solving Economic Load Dispatch Problem","authors":"Mohammad Hanif, N. Mohammad, K. Biswas, Bijoy Harun","doi":"10.1109/ECCE57851.2023.10101516","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101516","url":null,"abstract":"To solve the optimization problem of Economic Load Dispatch (ELD), a number of metaheuristic approaches have already been implemented, exhibiting substantial improvement over the conventional technique. Despite this, due to the global energy crisis, research in ELD still continues to garner considerable interest. In this study, the Seagull Optimization Algorithm (SOA), a recently developed swarm intelligence technique, is applied in ELD. As the SOA algorithm has never been utilized in the ELD, it is important to investigate its efficacy and validity in this domain. Here, two case studies of ELD incorporating 6 and 10 generator units are implemented employing SOA. What's more, the performance of SOA in ELD is compared with respect to three other previously applied metaheuristics algorithms. Results indicate that SOA is a potential algorithm capable of handling the practical optimization challenge of ELD problem more effectively, especially in large power plants having more than 6 units.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128027491","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101619
Songyang Lyu, M. Chowdhury, Ray C. C. Cheung
ElectroEncephaloGram (EEG) is associated with multiple functions, including communications with neurons, organic monitoring, and interactions with external stimuli. By decoding EEG signals, certain human activities such as sleeping, brain diseases, motor imagery, movement of limbs, and others can be observed and controlled through the brain-computer interface (BCI). Therefore, it is vital to efficiently process EEG signals with a robust and accurate system to build BCI systems with powerful applications. However, as a weak biosignal, EEG demands a fast-reaction system signal processing with high accuracy and sensitivity. In this work, a hardware/software co-design network based on Extreme Learning Machine (ELM) is introduced for the classification of certain actions, motor imagery of the human brain. This system is based on an optimized Hierarchical Extreme Learning Machine (H-ELM) on the software layer. The proposed method has advantages over previous designs with an accuracy of 90.3%. It also improves the training speed by around 25X compared to conventional methods. The software model is also translated into efficient FPGA hardware design to maintain high computation efficiency and reduce power consumption for biomedical applications.
{"title":"Efficient Hardware and Software Co-design for EEG Signal Classification based on Extreme Learning Machine","authors":"Songyang Lyu, M. Chowdhury, Ray C. C. Cheung","doi":"10.1109/ECCE57851.2023.10101619","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101619","url":null,"abstract":"ElectroEncephaloGram (EEG) is associated with multiple functions, including communications with neurons, organic monitoring, and interactions with external stimuli. By decoding EEG signals, certain human activities such as sleeping, brain diseases, motor imagery, movement of limbs, and others can be observed and controlled through the brain-computer interface (BCI). Therefore, it is vital to efficiently process EEG signals with a robust and accurate system to build BCI systems with powerful applications. However, as a weak biosignal, EEG demands a fast-reaction system signal processing with high accuracy and sensitivity. In this work, a hardware/software co-design network based on Extreme Learning Machine (ELM) is introduced for the classification of certain actions, motor imagery of the human brain. This system is based on an optimized Hierarchical Extreme Learning Machine (H-ELM) on the software layer. The proposed method has advantages over previous designs with an accuracy of 90.3%. It also improves the training speed by around 25X compared to conventional methods. The software model is also translated into efficient FPGA hardware design to maintain high computation efficiency and reduce power consumption for biomedical applications.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127990144","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101650
M. Hussain, N. Sharmin, Sumayea Binte Shafiul
Water cycles, climate-related hazards, and agroirrigation are strongly controlled by soil moisture (SM) content. For water resource management, prediction is a key to mitigate and regulate expected economic losses and property damages. This paper compares two supervised machine learning (ML) techniques: support vector regression (SVR) and random forest (RF) to predict SM. In RStudio, various meteorological variables: temperature, relative humidity, wind speed, and rainfall are trained to estimate SM. For eight divisions, SM and weather variables are obtained from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER). The experiments include daily observations for 39 (1982 to 2020) to develop SVR and RF models. To estimate SM from the predictive model, datasets from diverse regions: Rajshahi, Mymensingh, Chittagong, and Sylhet are utilized in training (60%) and Rangpur, Barisal, Khulna, and Dhaka are segregated for validation (40%) resulting in accuracy of 88 to 95.8%. This model further is applied to forecast daily SM for each city including two districts (Bogra and Jessore) and found slightly higher model performance for SVR (90.7%) than RF (90.1%) on average (Year: 2021). For agricultural, industrial and urban water supplies as well as drought, landslides, and river erosions can be mitigated by an accurate estimation of soil moisture. The investigations encourage for providing SM budget to public with supervised ML techniques mostly among data-sparse regions.
{"title":"Estimation of Soil Moisture with Meteorological Variables in Supervised Machine Learning Models","authors":"M. Hussain, N. Sharmin, Sumayea Binte Shafiul","doi":"10.1109/ECCE57851.2023.10101650","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101650","url":null,"abstract":"Water cycles, climate-related hazards, and agroirrigation are strongly controlled by soil moisture (SM) content. For water resource management, prediction is a key to mitigate and regulate expected economic losses and property damages. This paper compares two supervised machine learning (ML) techniques: support vector regression (SVR) and random forest (RF) to predict SM. In RStudio, various meteorological variables: temperature, relative humidity, wind speed, and rainfall are trained to estimate SM. For eight divisions, SM and weather variables are obtained from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER). The experiments include daily observations for 39 (1982 to 2020) to develop SVR and RF models. To estimate SM from the predictive model, datasets from diverse regions: Rajshahi, Mymensingh, Chittagong, and Sylhet are utilized in training (60%) and Rangpur, Barisal, Khulna, and Dhaka are segregated for validation (40%) resulting in accuracy of 88 to 95.8%. This model further is applied to forecast daily SM for each city including two districts (Bogra and Jessore) and found slightly higher model performance for SVR (90.7%) than RF (90.1%) on average (Year: 2021). For agricultural, industrial and urban water supplies as well as drought, landslides, and river erosions can be mitigated by an accurate estimation of soil moisture. The investigations encourage for providing SM budget to public with supervised ML techniques mostly among data-sparse regions.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114192270","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101663
Mohammad A. Islam, Md. Abu Zardar, M. Shafiullah, Awatif Nadia
Energy is an essential factor for power generation, where a community microgrid seeks to integrate renewable energy sources such as solar, wind, tidal, hydropower, and bioenergy into a distribution network along with an energy storage system. This is not only for the security of national growth, but also to minimize electricity costs and availability. The source of renewable energy has no predicted schedule for synchronization of power generation, supporting battery as energy storage for emergency supply or backup. In this paper, predictive scheduling of battery energy considering the cost of degradation due to charging and discharging cycles is proposed. According to the economy, day ahead planning has required a technique for solving the cost efficiency. In this paper, planned has developed by sine-cosine algorithm, which belongs to mathematical trigonometric base populations sector meta-heuristic technique. The sine-cosine algorithm works on the principle of trigonometric mathematical solution to search a target location within an area evaluation.
{"title":"Optimization strategies for Micro-grid energy management and scheduling systems by Sine cosine Algorithm","authors":"Mohammad A. Islam, Md. Abu Zardar, M. Shafiullah, Awatif Nadia","doi":"10.1109/ECCE57851.2023.10101663","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101663","url":null,"abstract":"Energy is an essential factor for power generation, where a community microgrid seeks to integrate renewable energy sources such as solar, wind, tidal, hydropower, and bioenergy into a distribution network along with an energy storage system. This is not only for the security of national growth, but also to minimize electricity costs and availability. The source of renewable energy has no predicted schedule for synchronization of power generation, supporting battery as energy storage for emergency supply or backup. In this paper, predictive scheduling of battery energy considering the cost of degradation due to charging and discharging cycles is proposed. According to the economy, day ahead planning has required a technique for solving the cost efficiency. In this paper, planned has developed by sine-cosine algorithm, which belongs to mathematical trigonometric base populations sector meta-heuristic technique. The sine-cosine algorithm works on the principle of trigonometric mathematical solution to search a target location within an area evaluation.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126165420","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101512
Diba Das, Aditta Chowdhury, A. I. Sanka, M. Chowdhury
Electrooculogram (EOG) is an electrophysiological signal produced around the eyes due to eyeball motion. This signal can be utilized to study eye movements which is bene-ficial in many medical and bio-electrical applications such as controlling human-computer interfaces and diagnosing different ocular diseases. However, the EOG is often contaminated with high-frequency motion artifacts, 50/60 Hz grid interference, and baseline wander. Hence, the collected signals are required to be preprocessed before finally being used in applications. This paper proposes an efficient FPGA-based EOG processor for fast and real-time processing of EOG signals, especially for medical diagnosis. To the best of our knowledge, this is the first work to implement EOG serial preprocessing by FIR and IIR filters on FPGA. MATLAB's FDA tool is used for mathematical validation and primary simulation. The proposed system was implemented on the Xilinx Zynq-7000 FPGA by hardware/software co-design. By statistical analysis, the software and hardware results were found to have the Pearson Correlation Coefficient of 0.99 and a Mean Root Squared Error in the 10–3 range. The resource utilization and power consumption are presented. The on-chip power consumption for this design is 0.271 watts where dynamic power is 0.163 watts (60%), and static power is 0.108 watts (40%). Performance evaluation and comparative study of the software-hardware results revealed the efficacy of the designed EOG preprocessor.
{"title":"Design and Performance Evaluation of an FPGA based EOG Signal Preprocessor","authors":"Diba Das, Aditta Chowdhury, A. I. Sanka, M. Chowdhury","doi":"10.1109/ECCE57851.2023.10101512","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101512","url":null,"abstract":"Electrooculogram (EOG) is an electrophysiological signal produced around the eyes due to eyeball motion. This signal can be utilized to study eye movements which is bene-ficial in many medical and bio-electrical applications such as controlling human-computer interfaces and diagnosing different ocular diseases. However, the EOG is often contaminated with high-frequency motion artifacts, 50/60 Hz grid interference, and baseline wander. Hence, the collected signals are required to be preprocessed before finally being used in applications. This paper proposes an efficient FPGA-based EOG processor for fast and real-time processing of EOG signals, especially for medical diagnosis. To the best of our knowledge, this is the first work to implement EOG serial preprocessing by FIR and IIR filters on FPGA. MATLAB's FDA tool is used for mathematical validation and primary simulation. The proposed system was implemented on the Xilinx Zynq-7000 FPGA by hardware/software co-design. By statistical analysis, the software and hardware results were found to have the Pearson Correlation Coefficient of 0.99 and a Mean Root Squared Error in the 10–3 range. The resource utilization and power consumption are presented. The on-chip power consumption for this design is 0.271 watts where dynamic power is 0.163 watts (60%), and static power is 0.108 watts (40%). Performance evaluation and comparative study of the software-hardware results revealed the efficacy of the designed EOG preprocessor.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134245191","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101595
G. Hasanuzzaman, S. Iezekiel, A. Kanno
A dual-loop optoelectronic oscillator using a polymer-based modulator is demonstrated at 94.5 GHz. The measured single side band phase noise is -70 dBc/Hz at 10kHz offset frequency. A value of 40 dB is achieved for side mode suppression.
{"title":"94.5 GHz Dual-loop Optoelectronic Oscillator","authors":"G. Hasanuzzaman, S. Iezekiel, A. Kanno","doi":"10.1109/ECCE57851.2023.10101595","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101595","url":null,"abstract":"A dual-loop optoelectronic oscillator using a polymer-based modulator is demonstrated at 94.5 GHz. The measured single side band phase noise is -70 dBc/Hz at 10kHz offset frequency. A value of 40 dB is achieved for side mode suppression.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129129031","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}
The spread of false health news and its dis-semination on the internet has become a major concern, due to its potential to have disastrous effects. To detect it, numerous approaches have been attempted. However, we are aware of very few studies that have sought to identify health related false information in Bangla. In this study, we have analyzed the performance of various Machine Learning and Deep Learning approaches in detecting Bangla health-related misinformation that is available online. We have created a comprehensive data repository, consisting more than 5000 data, manually annotated to two fixed categories. Several supervised machine learning classifiers and Deep Learning algorithms have been employed in this experiment to detect fake health news at the textual level. Our experiment achieves maximum accuracy of 88% in the Passive Aggressive approach and 89% in the Bi-LSTM approach. We believe that our dataset is a significant collection of health-related data in Bangla. It may open up new perspectives for the analysis of Bangla-language and health misinformation detection.
{"title":"A Machine Learning and Deep Learning Based Approach to Detect Inaccurate Health Information in Bengali Language","authors":"Nusrat Taki, Eshatur Showan, Umratul Chowdhury, Farzana Tasnim","doi":"10.1109/ECCE57851.2023.10101612","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101612","url":null,"abstract":"The spread of false health news and its dis-semination on the internet has become a major concern, due to its potential to have disastrous effects. To detect it, numerous approaches have been attempted. However, we are aware of very few studies that have sought to identify health related false information in Bangla. In this study, we have analyzed the performance of various Machine Learning and Deep Learning approaches in detecting Bangla health-related misinformation that is available online. We have created a comprehensive data repository, consisting more than 5000 data, manually annotated to two fixed categories. Several supervised machine learning classifiers and Deep Learning algorithms have been employed in this experiment to detect fake health news at the textual level. Our experiment achieves maximum accuracy of 88% in the Passive Aggressive approach and 89% in the Bi-LSTM approach. We believe that our dataset is a significant collection of health-related data in Bangla. It may open up new perspectives for the analysis of Bangla-language and health misinformation detection.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"263 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133579897","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101614
Saika Afrin Sumona, Wahida Binte Naz Aurthy
Sleep apnea resulting from obstructions in the upper respiratory tract during sleep is one of the most common sleep disorders that result in poor sleep and a significant degradation of our quality of life. Sleep apnea patients have frequent pauses in breathing during sleeping and very often face snoring problem. Usually, these short lapses cause a person to wake up at irregular intervals reducing their sleep quality, the older patients, however, find it very difficult to cope with such sleep apnea periods. The traditional monitoring and detection system is both expensive and complicated to be used regularly and at home. This study proposes a novel, low-cost monitoring system for sleep apnea patients which comes in the form of a wearable belt incorporating 3 different sensors to collect physiological signals correlated to sleep apnea. Electrocardiogram (ECG) sensor, photoplethysmog-raphy (PPG) sensor, and accelerometer are used with a bluetooth sensor so that the obtained data can be easily sent to a computer or mobile application where physicians, nurses, caregivers can monitor the patients without being present all the time. Using the assortment of the physiological signals, the onset of sleep apnea can be easily detected and the concerned people can be alerted instantaneously. The proposed system is affordable and can be used at home very easily.
{"title":"A Novel Low-Cost Monitoring System for Sleep Apnea Patients","authors":"Saika Afrin Sumona, Wahida Binte Naz Aurthy","doi":"10.1109/ECCE57851.2023.10101614","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101614","url":null,"abstract":"Sleep apnea resulting from obstructions in the upper respiratory tract during sleep is one of the most common sleep disorders that result in poor sleep and a significant degradation of our quality of life. Sleep apnea patients have frequent pauses in breathing during sleeping and very often face snoring problem. Usually, these short lapses cause a person to wake up at irregular intervals reducing their sleep quality, the older patients, however, find it very difficult to cope with such sleep apnea periods. The traditional monitoring and detection system is both expensive and complicated to be used regularly and at home. This study proposes a novel, low-cost monitoring system for sleep apnea patients which comes in the form of a wearable belt incorporating 3 different sensors to collect physiological signals correlated to sleep apnea. Electrocardiogram (ECG) sensor, photoplethysmog-raphy (PPG) sensor, and accelerometer are used with a bluetooth sensor so that the obtained data can be easily sent to a computer or mobile application where physicians, nurses, caregivers can monitor the patients without being present all the time. Using the assortment of the physiological signals, the onset of sleep apnea can be easily detected and the concerned people can be alerted instantaneously. The proposed system is affordable and can be used at home very easily.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130263037","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 : 2023-02-23DOI: 10.1109/ECCE57851.2023.10101559
Md. Shabir Khan Akash, Md. Al Mamun
It is still challenging to differentiate between normal cells and tumor demarcation in everyday clinical practice. With the use of the FLAIR modality known as Fluid Attenuated Inversion Recovery, a medical professional can learn more about tumor infiltration. Because the preponderance of the cerebrospinal fluid effect can be suppressed by the FLAIR modality. Moreover, one of the advantages of using FLAIR images is that they can be used for both 3D and 2D medical imagery. Therefore, this paper explores the idea of assessing and predicting brain tumors by implementing several types of deep learning CNN architectures, such as VGG16, ResNet50, DenseNet121 and others in a user-friendly functional U-Net architecture. The flexibility of using different pre-trained neural network models in a single architecture is the key advantage of our U-Net architecture. Hyperparameters of the architecture are adjusted and fine-tuned for the segmentation process in order to extract the core features of the tumor contour according to our problem. Having said that, this study's segmentation result on the dice similarity coefficient is 0.9165, 0.9175, 0.9137 and 0.9148 in the BraTS 2018, 2019, 2020 and 2021 datasets respectively.
{"title":"A Comparative Analysis on Predicting Brain Tumor from MRI FLAIR Images Using Deep Learning","authors":"Md. Shabir Khan Akash, Md. Al Mamun","doi":"10.1109/ECCE57851.2023.10101559","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101559","url":null,"abstract":"It is still challenging to differentiate between normal cells and tumor demarcation in everyday clinical practice. With the use of the FLAIR modality known as Fluid Attenuated Inversion Recovery, a medical professional can learn more about tumor infiltration. Because the preponderance of the cerebrospinal fluid effect can be suppressed by the FLAIR modality. Moreover, one of the advantages of using FLAIR images is that they can be used for both 3D and 2D medical imagery. Therefore, this paper explores the idea of assessing and predicting brain tumors by implementing several types of deep learning CNN architectures, such as VGG16, ResNet50, DenseNet121 and others in a user-friendly functional U-Net architecture. The flexibility of using different pre-trained neural network models in a single architecture is the key advantage of our U-Net architecture. Hyperparameters of the architecture are adjusted and fine-tuned for the segmentation process in order to extract the core features of the tumor contour according to our problem. Having said that, this study's segmentation result on the dice similarity coefficient is 0.9165, 0.9175, 0.9137 and 0.9148 in the BraTS 2018, 2019, 2020 and 2021 datasets respectively.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131328107","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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