Pub Date : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136066
D. Varma, M. Murali, M. Krishna, G. Raju
A miniatured Novel Patch antenna for biomedical applications is proposed in this paper. This antenna will work in the ISM band. To compare the SAR value, the proposed antenna is designed in two ways: one without slots and one with slots. The SAR value for lg of tissue in wearable antennas should be less than 1. 6KW. In Computer Simulation Technology (CST), a multilayer phantom model is created to study the characteristics of the antenna when placed on the human body. As a radiating element, a microstrip patch is used, and silk is used as a substrate because it is a common textile material. According to the results, slots in the patch reduce the SAR value (Specific absorption rate). The simulated results, such as return loss, gain, and VSWR, are measured for both slot designs with and without slots. For patch with no interior slots, the return loss is approximately -32dB at 2. 42GHz for freespace and -13.5dB at 2. 42GHz for human phantom model. For patch with interior slots, the return loss is approximately -28dB at 2. 4GHz for freespace and -21dB at 2. 45GHz for human phantom model. The antenna system is simple in design, small in size, has a low SAR value, and has a high gain.
{"title":"Miniaturized Novel Textile Antenna for Biomedical Applications","authors":"D. Varma, M. Murali, M. Krishna, G. Raju","doi":"10.1109/PCEMS58491.2023.10136066","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136066","url":null,"abstract":"A miniatured Novel Patch antenna for biomedical applications is proposed in this paper. This antenna will work in the ISM band. To compare the SAR value, the proposed antenna is designed in two ways: one without slots and one with slots. The SAR value for lg of tissue in wearable antennas should be less than 1. 6KW. In Computer Simulation Technology (CST), a multilayer phantom model is created to study the characteristics of the antenna when placed on the human body. As a radiating element, a microstrip patch is used, and silk is used as a substrate because it is a common textile material. According to the results, slots in the patch reduce the SAR value (Specific absorption rate). The simulated results, such as return loss, gain, and VSWR, are measured for both slot designs with and without slots. For patch with no interior slots, the return loss is approximately -32dB at 2. 42GHz for freespace and -13.5dB at 2. 42GHz for human phantom model. For patch with interior slots, the return loss is approximately -28dB at 2. 4GHz for freespace and -21dB at 2. 45GHz for human phantom model. The antenna system is simple in design, small in size, has a low SAR value, and has a high gain.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132663526","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136076
M. Chaitanya, L. Sharma
The heartbeat, muscle contractions, and other phys- iological functions are examples of biomedical signal sources. Electrocardiograms (ECG), electroencephalograms (EEG), and electromyograms (EMG) are examples of the signals that can be non-invasively recorded and used for diagnosis and as health in- dicators. Hence, timely and accurate diagnosis of the biomedical signals plays a prominent role. Professional healthcare workers assess the signal in search of a clear pattern that would indicate a normal or abnormal heartbeat is a tedious job. Manual inter- pretation of the signals may lead to misdiagnosis. The automated computer-aided diagnosis (CAD) method is one way to support decision-making for the eradication of these deficiencies. The CAD tool should operate as a real-time system for early diagnosis, requiring little time investment, data dependence, and device- specific measurement variances. Deep learning-based methods are becoming more and more common in CAD techniques. Convolutional neural network (CNN), one of the well-known deep learning network, fail of recognise position, texture, and genetic anomalies in the image. A capsule network is one of the newest and most promising deep learning algorithms that tackles CNN’s shortcomings. In this study, we present a thorough analysis of the cutting-edge methodology, tools, and topologies used in current capsule network implementations. The key contribution with this review study is its explanation and summary of major existing Capsule Network implementations and architectures.
{"title":"Capsule Network for 1-D Biomedical signals: A Review","authors":"M. Chaitanya, L. Sharma","doi":"10.1109/PCEMS58491.2023.10136076","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136076","url":null,"abstract":"The heartbeat, muscle contractions, and other phys- iological functions are examples of biomedical signal sources. Electrocardiograms (ECG), electroencephalograms (EEG), and electromyograms (EMG) are examples of the signals that can be non-invasively recorded and used for diagnosis and as health in- dicators. Hence, timely and accurate diagnosis of the biomedical signals plays a prominent role. Professional healthcare workers assess the signal in search of a clear pattern that would indicate a normal or abnormal heartbeat is a tedious job. Manual inter- pretation of the signals may lead to misdiagnosis. The automated computer-aided diagnosis (CAD) method is one way to support decision-making for the eradication of these deficiencies. The CAD tool should operate as a real-time system for early diagnosis, requiring little time investment, data dependence, and device- specific measurement variances. Deep learning-based methods are becoming more and more common in CAD techniques. Convolutional neural network (CNN), one of the well-known deep learning network, fail of recognise position, texture, and genetic anomalies in the image. A capsule network is one of the newest and most promising deep learning algorithms that tackles CNN’s shortcomings. In this study, we present a thorough analysis of the cutting-edge methodology, tools, and topologies used in current capsule network implementations. The key contribution with this review study is its explanation and summary of major existing Capsule Network implementations and architectures.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132727128","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136097
V. Bajaj, Deepali M. Kotambkar Shelke
The leading cause of visual impairment after cataract, is glaucoma and the only way to combat it is to detect it early. It is imperative to develop a system that can work effectively without a lot of equipment, qualified medical personnel, and takes less time in order to address this fundamental issue. A Computer-Aided Diagnosis (CAD) system, which employs different algorithms for medical image processing and analysis, can assist in achieving this. One of the ways to diagnose glaucoma is to calculate Optic Cup to Optic Disc ratio (CDR) and this can be done with the help of CAD algorithms. In medical image processing the primary focus is on image segmentationand its classification in order to obtain a result. In this paper, the exploration the best-known CNN model, U-Net for image segmentation of Optic Disc and Optic Cup from a fundus image and Logistic Regression, a classification model to determine a relationship between these two terms rather than previously used CDR formulas.
{"title":"Fundus Image Classification for Glaucoma using U-Net Architecture and Logistic Regression","authors":"V. Bajaj, Deepali M. Kotambkar Shelke","doi":"10.1109/PCEMS58491.2023.10136097","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136097","url":null,"abstract":"The leading cause of visual impairment after cataract, is glaucoma and the only way to combat it is to detect it early. It is imperative to develop a system that can work effectively without a lot of equipment, qualified medical personnel, and takes less time in order to address this fundamental issue. A Computer-Aided Diagnosis (CAD) system, which employs different algorithms for medical image processing and analysis, can assist in achieving this. One of the ways to diagnose glaucoma is to calculate Optic Cup to Optic Disc ratio (CDR) and this can be done with the help of CAD algorithms. In medical image processing the primary focus is on image segmentationand its classification in order to obtain a result. In this paper, the exploration the best-known CNN model, U-Net for image segmentation of Optic Disc and Optic Cup from a fundus image and Logistic Regression, a classification model to determine a relationship between these two terms rather than previously used CDR formulas.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121403763","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136078
Jay Ram Deepak Tummidi, Rutwij S. Kamble, Sahiesh Bakliwal, Arpan Desai, Bhagyashree V. Lad, A. Keskar
Nowadays the use of Optical remote sensing images (RSIs) for detecting any particular object is being increased.Salient object detection is a fascinating aspect of optical RSIs (SOD). Regarding optical RSIs, there are a plethora of problems, like crowded backdrops, diverse object orientations, different object scales, etc. As a result, the execution of the current salient object detection models frequently suffers greatly. The relevance of information about edges, which is essential for producing correct saliency maps, is frequently overlooked by existing SOD models. To overcome this issue, this model uses Spatial Channel Attention U-Net (SCAU-Net) for detecting the edge maps. This model pop-out aircraft in optical RSIs using SOD. First, the input is sent into Encoder and SCAUNet simultaneously. The SCAU-Net provides salient edge cues and the encoders are used to give a good feature representation for salient objects. Then the output of the encoder is sent to the decoders. The decoders of the feature-merge module gives position attention to salient objects. The efficient edge map provided by SCAU-Net is used for improving the position attention cues. The final step is to combine all position attention cues to obtain the final output. The final output contains the aircraft detected in it. By seeing the obtained results we can say that our model can precisely and accurately detect the aircrafts present in the given input.
{"title":"Salient Object Detection based Aircraft Detection for Optical Remote Sensing Images","authors":"Jay Ram Deepak Tummidi, Rutwij S. Kamble, Sahiesh Bakliwal, Arpan Desai, Bhagyashree V. Lad, A. Keskar","doi":"10.1109/PCEMS58491.2023.10136078","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136078","url":null,"abstract":"Nowadays the use of Optical remote sensing images (RSIs) for detecting any particular object is being increased.Salient object detection is a fascinating aspect of optical RSIs (SOD). Regarding optical RSIs, there are a plethora of problems, like crowded backdrops, diverse object orientations, different object scales, etc. As a result, the execution of the current salient object detection models frequently suffers greatly. The relevance of information about edges, which is essential for producing correct saliency maps, is frequently overlooked by existing SOD models. To overcome this issue, this model uses Spatial Channel Attention U-Net (SCAU-Net) for detecting the edge maps. This model pop-out aircraft in optical RSIs using SOD. First, the input is sent into Encoder and SCAUNet simultaneously. The SCAU-Net provides salient edge cues and the encoders are used to give a good feature representation for salient objects. Then the output of the encoder is sent to the decoders. The decoders of the feature-merge module gives position attention to salient objects. The efficient edge map provided by SCAU-Net is used for improving the position attention cues. The final step is to combine all position attention cues to obtain the final output. The final output contains the aircraft detected in it. By seeing the obtained results we can say that our model can precisely and accurately detect the aircrafts present in the given input.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123930613","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136088
Kranti S. Kamble, Joydeep Sengupta
The dual-stage correlation and instantaneous frequency (CIF) thresholding approach for retrieval of noise-free desired frequency band of EEG signal is proposed for affective emotion identification task. Initially, the raw electroencephalogram (EEG) signals are breakdown applying the empirical mode decomposition technique to produce intrinsic mode functions (IMFs). The noisy IMFs are eliminated by applying correlation thresholding. Secondly, these noise-free EEG signals are divided into several modes using a non-linear chirp variational mode decomposition approach to retrieve desired frequency bands (4-30Hz) by applying the IF-based filtering method on the modes. The power spectral densities extracted from filtered modes are fed to ML-based classifiers to classify emotions into arousal, valence, and dominance groups. This study also shows the efficacy of ensemble ML (EML): random forest (RF) and bagging over conventional ML (CML): support vector machine and logistic regression classifiers. The RF reported the highest average F1-scores using 10-fold cross-validation for arousal, valence, and dominance are 83.99%,75.94%, and 88.86% respectively. Similarly, the respective average accuracies of two-EML are~1.47%, ~1.27%, and~0.3% higher compared to two-CML classifiers. To summarize, the proposed CIF-based filtering approach is useful for affective emotion identification under the framework of EML classifiers.
{"title":"Affective computing for emotion identification using dual-stage filtered multi-channel EEG signals","authors":"Kranti S. Kamble, Joydeep Sengupta","doi":"10.1109/PCEMS58491.2023.10136088","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136088","url":null,"abstract":"The dual-stage correlation and instantaneous frequency (CIF) thresholding approach for retrieval of noise-free desired frequency band of EEG signal is proposed for affective emotion identification task. Initially, the raw electroencephalogram (EEG) signals are breakdown applying the empirical mode decomposition technique to produce intrinsic mode functions (IMFs). The noisy IMFs are eliminated by applying correlation thresholding. Secondly, these noise-free EEG signals are divided into several modes using a non-linear chirp variational mode decomposition approach to retrieve desired frequency bands (4-30Hz) by applying the IF-based filtering method on the modes. The power spectral densities extracted from filtered modes are fed to ML-based classifiers to classify emotions into arousal, valence, and dominance groups. This study also shows the efficacy of ensemble ML (EML): random forest (RF) and bagging over conventional ML (CML): support vector machine and logistic regression classifiers. The RF reported the highest average F1-scores using 10-fold cross-validation for arousal, valence, and dominance are 83.99%,75.94%, and 88.86% respectively. Similarly, the respective average accuracies of two-EML are~1.47%, ~1.27%, and~0.3% higher compared to two-CML classifiers. To summarize, the proposed CIF-based filtering approach is useful for affective emotion identification under the framework of EML classifiers.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127376846","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136089
Swarnima Prabhune Wankar
In this project, a detailed description of using a 3-lead ECG sensor, pulse oximeter probe, temperature sensor and respiration rate sensor is being used for transmitting the data acquired via microcontroller through Bluetooth wireless link, and the data is received at a mobile using android application is illustrated. The acquired data is processed and reconstructed as a reading or waveform. Lastly, the waveform can be displayed on a personal computer (PC) screen. The implementation of wireless technology in the existing monitoring system eliminates the physical constraints imposed by hard-wired link. A wireless communication protocol is developed using the Bluetooth module for short-distance data transmission.
{"title":"Remote Health Monitoring System using Android Application","authors":"Swarnima Prabhune Wankar","doi":"10.1109/PCEMS58491.2023.10136089","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136089","url":null,"abstract":"In this project, a detailed description of using a 3-lead ECG sensor, pulse oximeter probe, temperature sensor and respiration rate sensor is being used for transmitting the data acquired via microcontroller through Bluetooth wireless link, and the data is received at a mobile using android application is illustrated. The acquired data is processed and reconstructed as a reading or waveform. Lastly, the waveform can be displayed on a personal computer (PC) screen. The implementation of wireless technology in the existing monitoring system eliminates the physical constraints imposed by hard-wired link. A wireless communication protocol is developed using the Bluetooth module for short-distance data transmission.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121641806","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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