Pub Date : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150807
Mohd Mansoor, Pankajbhai R. Prajapati, M. Suhaib
The robotic gripper plays an important role in the robot functioning. It’s the end effector which does the end job for the robot, like pick-place, manipulating and grasping. The dexterity and manipulation depend upon the number of fingers, joints, and degree of freedom (DOF). Multi-finger robotic gripper mimics the human hand design and function. They are used in industries and as well as prosthetics to give rehabilitation to the person with disability. The components of the multi fingered robotic hand consists of palm, actuator, link, joint, tendon, controller, and sensor. It works like the human hand. Several robotic hands have been developed with three, four and five fingers. They have been tested on various objects to determine their efficiency and accordingly modifications have been done. There are various actuation methods and number of actuators that determine the DOF in the robotic gripper. This review article primarily focuses on the robotic hand/gripper along with their design and applications.
{"title":"Some Study on Multifinger Robotic Gripper","authors":"Mohd Mansoor, Pankajbhai R. Prajapati, M. Suhaib","doi":"10.1109/REEDCON57544.2023.10150807","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150807","url":null,"abstract":"The robotic gripper plays an important role in the robot functioning. It’s the end effector which does the end job for the robot, like pick-place, manipulating and grasping. The dexterity and manipulation depend upon the number of fingers, joints, and degree of freedom (DOF). Multi-finger robotic gripper mimics the human hand design and function. They are used in industries and as well as prosthetics to give rehabilitation to the person with disability. The components of the multi fingered robotic hand consists of palm, actuator, link, joint, tendon, controller, and sensor. It works like the human hand. Several robotic hands have been developed with three, four and five fingers. They have been tested on various objects to determine their efficiency and accordingly modifications have been done. There are various actuation methods and number of actuators that determine the DOF in the robotic gripper. This review article primarily focuses on the robotic hand/gripper along with their design and applications.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117162188","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151287
Shivani Pandey, Satanand Mishra, R. Jain
Water pollution is a serious problem in different parts of the world. In addition, water quality must be monitored to ensure that the water is provided safely for drinking and other purposes. Too high a concentration of Arsenic ions in drinking water is the cause of many health problems, including heart problems, neurological problems, etc. Water sampling and laboratory analysis are required for traditional water quality monitoring. In this paper, we discussed an IoT-based interfacing sensor device for sensing arsenic contaminants in water where IoT cloud computing networks enable the integration of a variety range of mechanical and electronic devices. A Node MCU device is used for data transmission which emphasizes on Wi-Fi-controlled interface devices and IoT-enabled communication protocol for the detection of water contaminants. This system is connected to an IoT cloud platform to store the data for analyzing purposes where Red-Green-Blue (RGB) color detection occurs by identifying the wavelength of contaminants. The system makes use of IoT to display the output in real-time for on-site and off-site monitoring via mobile phone. The system makes use of IoT to display the output in real-time for on-site and off-site monitoring via mobile phone, The major advantage of IoT technology is that it easily connects devices and stores the generated data in the cloud. With the help of command control systems, data can be used for appropriate applications to make human life easier and safer while considering Industry's impact. The acceptable limits set by WHO and the Bureau of Indian Standards for Arsenic are 0.05 mg/litres and 0.01 mg/l respectively. Therefore, a smart and intelligent device that can be used for measuring Arsenic content which is very necessary today to ensure the health of human life in society.
{"title":"IoT Interface Device for Sensing Arsenic in Contaminated Water","authors":"Shivani Pandey, Satanand Mishra, R. Jain","doi":"10.1109/REEDCON57544.2023.10151287","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151287","url":null,"abstract":"Water pollution is a serious problem in different parts of the world. In addition, water quality must be monitored to ensure that the water is provided safely for drinking and other purposes. Too high a concentration of Arsenic ions in drinking water is the cause of many health problems, including heart problems, neurological problems, etc. Water sampling and laboratory analysis are required for traditional water quality monitoring. In this paper, we discussed an IoT-based interfacing sensor device for sensing arsenic contaminants in water where IoT cloud computing networks enable the integration of a variety range of mechanical and electronic devices. A Node MCU device is used for data transmission which emphasizes on Wi-Fi-controlled interface devices and IoT-enabled communication protocol for the detection of water contaminants. This system is connected to an IoT cloud platform to store the data for analyzing purposes where Red-Green-Blue (RGB) color detection occurs by identifying the wavelength of contaminants. The system makes use of IoT to display the output in real-time for on-site and off-site monitoring via mobile phone. The system makes use of IoT to display the output in real-time for on-site and off-site monitoring via mobile phone, The major advantage of IoT technology is that it easily connects devices and stores the generated data in the cloud. With the help of command control systems, data can be used for appropriate applications to make human life easier and safer while considering Industry's impact. The acceptable limits set by WHO and the Bureau of Indian Standards for Arsenic are 0.05 mg/litres and 0.01 mg/l respectively. Therefore, a smart and intelligent device that can be used for measuring Arsenic content which is very necessary today to ensure the health of human life in society.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115140289","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150677
Yusuf Ahmed Khan, Madiha Tahreem, Omar Farooq
This paper proposes a novel method for binary sleep and Wake classification using entropy-based features extracted from a single-channel electroencephalogram (EEG). This study aims to improve the accuracy of sleep and Wake classification, which has several applications such as in sleep research, sleep tracking, diagnosis of sleep disorders, human performance assessment, human factors engineering. The proposed method is evaluated using the publicly available UCDDB dataset. Results show that the method achieved high classification accuracy, with the Ensemble subspace KNN classifier achieving the highest accuracy of 94.3%, followed by the fine KNN classifier with an accuracy of 92%. A significant improvement in performance can be attributed to the use of entropy-based features in the proposed method. Based on the promising results of this study, it is evident that the proposed method can be applied to sleep medicine for the classification of sleep stages, which can potentially lead to better diagnosis and treatment of sleep disorders.
{"title":"Single Channel EEG Based Binary Sleep and Wake Classification using Entropy Based Features","authors":"Yusuf Ahmed Khan, Madiha Tahreem, Omar Farooq","doi":"10.1109/REEDCON57544.2023.10150677","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150677","url":null,"abstract":"This paper proposes a novel method for binary sleep and Wake classification using entropy-based features extracted from a single-channel electroencephalogram (EEG). This study aims to improve the accuracy of sleep and Wake classification, which has several applications such as in sleep research, sleep tracking, diagnosis of sleep disorders, human performance assessment, human factors engineering. The proposed method is evaluated using the publicly available UCDDB dataset. Results show that the method achieved high classification accuracy, with the Ensemble subspace KNN classifier achieving the highest accuracy of 94.3%, followed by the fine KNN classifier with an accuracy of 92%. A significant improvement in performance can be attributed to the use of entropy-based features in the proposed method. Based on the promising results of this study, it is evident that the proposed method can be applied to sleep medicine for the classification of sleep stages, which can potentially lead to better diagnosis and treatment of sleep disorders.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122681959","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150650
Seema Khan, Asif Jamil Ansari, S. Kazmi
The present work deals with synthesizing copper oxide (CuO) nanoparticles using sol-gel technique, the material characterization was performed to find out its optical and structural properties, absorbance of 288 nm, band gap of 2.84 eV, and particle size of 15.4 nm were observed. The particle size of pure TiO2 was 18 nm and increases to 21.2 nm for the CuO-TiO2 nanocomposite. The absorbance of 302 nm, 322 nm and band gap 3.01 eV, 2.63 eV for TiO2 and CuO-TiO2 were observed respectively. TiO2 and CuO-TiO2 nanocomposites were used as anode material for fabrication of dye sensitized solar cells (DSSC) using N719 dye. Potassium iodide electrolyte and platinum counter electrode is used for DSSCs development. To prepare DSSC anode doctor blading technique was used on FTO glass followed by sintering up to 450 °C. PV characterization was performed in standard test conditions of 100 mW/cm2 and T=25°C. The efficiency of 4.33 % was observed for pure TiO2 anode with N719 dye which increases to 7.73 % for CuO-TiO2 nanocomposites anode.
{"title":"CuO-TiO2 nanocomposite anode for Efficiency Enhancement of Dye Sensitized solar cell","authors":"Seema Khan, Asif Jamil Ansari, S. Kazmi","doi":"10.1109/REEDCON57544.2023.10150650","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150650","url":null,"abstract":"The present work deals with synthesizing copper oxide (CuO) nanoparticles using sol-gel technique, the material characterization was performed to find out its optical and structural properties, absorbance of 288 nm, band gap of 2.84 eV, and particle size of 15.4 nm were observed. The particle size of pure TiO2 was 18 nm and increases to 21.2 nm for the CuO-TiO2 nanocomposite. The absorbance of 302 nm, 322 nm and band gap 3.01 eV, 2.63 eV for TiO2 and CuO-TiO2 were observed respectively. TiO2 and CuO-TiO2 nanocomposites were used as anode material for fabrication of dye sensitized solar cells (DSSC) using N719 dye. Potassium iodide electrolyte and platinum counter electrode is used for DSSCs development. To prepare DSSC anode doctor blading technique was used on FTO glass followed by sintering up to 450 °C. PV characterization was performed in standard test conditions of 100 mW/cm2 and T=25°C. The efficiency of 4.33 % was observed for pure TiO2 anode with N719 dye which increases to 7.73 % for CuO-TiO2 nanocomposites anode.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129384939","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150505
Som Kumar Basnat, M. W. Akram, M. Nizamuddin
Challenges faced by the MOSFETs by scaling down further and further has led to the consideration of novel device (Ambipolar CNTFET) in which channel is intrinsic and has Schottky barrier contacts. Ambipolar CNTFET has back gate which can control the polarity of the device. This in field polarity control can make efficient reconfigurable logic circuits. This work presents the implementation of Ambipolar CNTFET based logic gates such as Inverter, NOR and NAND Gate and extracted different performance parameters such as average power, delay and power delay product and compared it with the conventional CNTFET and CMOS technology. The results show reduction in delay of Ambipolar CNTFET based NOR and NAND gate in comparison to CMOS NOR and NAND gate by 15.48% and 76.93% respectively. An Ambipolar CNTFET is modelled by a circuit consisting of two CNTFETs and two inverters. All the simulation are performed using HSPICE software at 32nm technology node.
{"title":"Implementation of Ambipolar CNTFET based logic gates and their performance comparison with CNTFET and CMOS based logic gates","authors":"Som Kumar Basnat, M. W. Akram, M. Nizamuddin","doi":"10.1109/REEDCON57544.2023.10150505","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150505","url":null,"abstract":"Challenges faced by the MOSFETs by scaling down further and further has led to the consideration of novel device (Ambipolar CNTFET) in which channel is intrinsic and has Schottky barrier contacts. Ambipolar CNTFET has back gate which can control the polarity of the device. This in field polarity control can make efficient reconfigurable logic circuits. This work presents the implementation of Ambipolar CNTFET based logic gates such as Inverter, NOR and NAND Gate and extracted different performance parameters such as average power, delay and power delay product and compared it with the conventional CNTFET and CMOS technology. The results show reduction in delay of Ambipolar CNTFET based NOR and NAND gate in comparison to CMOS NOR and NAND gate by 15.48% and 76.93% respectively. An Ambipolar CNTFET is modelled by a circuit consisting of two CNTFETs and two inverters. All the simulation are performed using HSPICE software at 32nm technology node.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129146836","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151279
Disha Singh, Mohammad Ammar, Kushagra Varshney, Y. Khan
Diabetic retinopathy is a diabetes complication that affects the retina of the eye. Conditions like Hyperglycemia and Diabetes can damage the blood vessels in the retina, causing vision problems or even blindness. Early on, the condition often has no symptoms, but it can be detected through regular eye exams. The paper focuses on identifying the various cases of this impairment like Diabetic Macular Edema (DME) and Agerelated Macular Degeneration (AMD) implications like Choroidal Neovascularization (CNV) and Drusen, present in Optical Coherence Tomography images using very lightweight, data-efficient, CNN-based transformer, namely MobileVit. The classification results were obtained using the MobileVit-XXS, the lightest variant of the MobileVit. A balanced, publicly accessible dataset was used to train the model, which was then fine-tuned for optimum performance. This work proposes a CAD methodology using a lightweight CNN-based Transformer network. The accuracy generated by the model is 98.86% and the F1-score is 93.50%. A simple application is developed to test the deployability of the model.
{"title":"Optical Coherence Tomography Image Classification using Light-weight Hybrid Transformers","authors":"Disha Singh, Mohammad Ammar, Kushagra Varshney, Y. Khan","doi":"10.1109/REEDCON57544.2023.10151279","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151279","url":null,"abstract":"Diabetic retinopathy is a diabetes complication that affects the retina of the eye. Conditions like Hyperglycemia and Diabetes can damage the blood vessels in the retina, causing vision problems or even blindness. Early on, the condition often has no symptoms, but it can be detected through regular eye exams. The paper focuses on identifying the various cases of this impairment like Diabetic Macular Edema (DME) and Agerelated Macular Degeneration (AMD) implications like Choroidal Neovascularization (CNV) and Drusen, present in Optical Coherence Tomography images using very lightweight, data-efficient, CNN-based transformer, namely MobileVit. The classification results were obtained using the MobileVit-XXS, the lightest variant of the MobileVit. A balanced, publicly accessible dataset was used to train the model, which was then fine-tuned for optimum performance. This work proposes a CAD methodology using a lightweight CNN-based Transformer network. The accuracy generated by the model is 98.86% and the F1-score is 93.50%. A simple application is developed to test the deployability of the model.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130233444","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151234
Manju Dabass, Anuj Chandalia, H. Gupta, R. Senasi
Lung segmentation is considered as prerequisite step in medical image analysis, particularly for the diagnosis formulation and treatment plan of lung diseases. Hence, we are proposing a residual convolutional and attention learning-based U-Net model for precise and proficient lung segmentation in CT scans. The proposed model incorporates a residual convolutional learning block in place of conventional convolutional layer that is utilized in encoder and decoder and an attention mechanism implemented in skip connections of the conventional U-Net architecture, which resulted in augmenting feature representational capability and advancing the discriminative competence of the model. The model is trained and evaluated on a very well-known public dataset named Lung Image Database Consortium (LIDC) dataset and a private dataset taken from a hospital. Experimental outcomes reveal that the presented model accomplishes state-of-the-art performance in terms of Dice Similarity Coefficient as 0.981 for LIDC and 0.987 for private dataset and outperforms several existing methods. The proposed model has the capability to be employed in various clinical applications including lung disease diagnosis and treatment planning and hence, can assist radiologists in enhancing patient survival rate.
{"title":"Lung Segmentation in CT scans with Residual Convolutional and Attention Learning-based U-Net","authors":"Manju Dabass, Anuj Chandalia, H. Gupta, R. Senasi","doi":"10.1109/REEDCON57544.2023.10151234","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151234","url":null,"abstract":"Lung segmentation is considered as prerequisite step in medical image analysis, particularly for the diagnosis formulation and treatment plan of lung diseases. Hence, we are proposing a residual convolutional and attention learning-based U-Net model for precise and proficient lung segmentation in CT scans. The proposed model incorporates a residual convolutional learning block in place of conventional convolutional layer that is utilized in encoder and decoder and an attention mechanism implemented in skip connections of the conventional U-Net architecture, which resulted in augmenting feature representational capability and advancing the discriminative competence of the model. The model is trained and evaluated on a very well-known public dataset named Lung Image Database Consortium (LIDC) dataset and a private dataset taken from a hospital. Experimental outcomes reveal that the presented model accomplishes state-of-the-art performance in terms of Dice Similarity Coefficient as 0.981 for LIDC and 0.987 for private dataset and outperforms several existing methods. The proposed model has the capability to be employed in various clinical applications including lung disease diagnosis and treatment planning and hence, can assist radiologists in enhancing patient survival rate.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"45 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120882904","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151098
N. Rafiuddin, Y. Khan, Omar Farooq
This study proposes a statistical approach to examine the pre-ictal period before the onset of seizures. The study employs the multidepth wavelet packet (MDWP) approach by excavating through the wavelet packet tree to the eighth level of decomposition. Numerous statistical measures were chosen to extract features over raw signal and the retained wavelet packets from the MDWP approach. This extensive process extracted more than twelve thousand features from every five-minute window taken two hours before to five minutes before the seizure onset. Ranking the features extracted from each five-minute window separately revealed the feature of mode computed on the 11th packet of the 4th level of decomposition, 6th packet of the 3rd level of decomposition and 3rd packet of the 2nd level of decomposition among the top three features during the pre-ictal duration. Moreover, the rank of these features shows a drooping nature around 70 minutes before seizure onset. This indicates the sign of prediction horizon to be close to 70 minutes before seizure onset for patient-1 of the CHB-MIT scalp EEG dataset. MATLAB installed on Workstation with 24 cores was used to process the enormous data involved in this study.
{"title":"Analysis of Seizure Prediction Horizon on Scalp EEG Using MDWP Approach","authors":"N. Rafiuddin, Y. Khan, Omar Farooq","doi":"10.1109/REEDCON57544.2023.10151098","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151098","url":null,"abstract":"This study proposes a statistical approach to examine the pre-ictal period before the onset of seizures. The study employs the multidepth wavelet packet (MDWP) approach by excavating through the wavelet packet tree to the eighth level of decomposition. Numerous statistical measures were chosen to extract features over raw signal and the retained wavelet packets from the MDWP approach. This extensive process extracted more than twelve thousand features from every five-minute window taken two hours before to five minutes before the seizure onset. Ranking the features extracted from each five-minute window separately revealed the feature of mode computed on the 11th packet of the 4th level of decomposition, 6th packet of the 3rd level of decomposition and 3rd packet of the 2nd level of decomposition among the top three features during the pre-ictal duration. Moreover, the rank of these features shows a drooping nature around 70 minutes before seizure onset. This indicates the sign of prediction horizon to be close to 70 minutes before seizure onset for patient-1 of the CHB-MIT scalp EEG dataset. MATLAB installed on Workstation with 24 cores was used to process the enormous data involved in this study.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125799210","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151323
Akash Pundir, Manmohan Sharma, Ankita Pundir
Recognizing a person's gait is a challenging task because there are so many factors to consider, such as obstructions due to clothing and bags. As a solution to this problem, a system is proposed for identifying gaits that is based on deep learning and random forests. For feature extraction from video frames, the system employs two popular pretrained models, MobileNetV1 and VGG19. The dimensionality of features is minimized using PCA and mean-based feature fusion is used to combine the reduced features. Six angles were selected from the dataset, and Random Forest was used for classification. The proposed method is put to the test on the CASIA-B dataset, and the results obtained show a mean accuracy of 93.1% for six angles. Experimental findings prove that deep learning and random forests are useful tools for gait recognition.
{"title":"Multiview Human Gait Recognition using a Hybrid CNN Approach","authors":"Akash Pundir, Manmohan Sharma, Ankita Pundir","doi":"10.1109/REEDCON57544.2023.10151323","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151323","url":null,"abstract":"Recognizing a person's gait is a challenging task because there are so many factors to consider, such as obstructions due to clothing and bags. As a solution to this problem, a system is proposed for identifying gaits that is based on deep learning and random forests. For feature extraction from video frames, the system employs two popular pretrained models, MobileNetV1 and VGG19. The dimensionality of features is minimized using PCA and mean-based feature fusion is used to combine the reduced features. Six angles were selected from the dataset, and Random Forest was used for classification. The proposed method is put to the test on the CASIA-B dataset, and the results obtained show a mean accuracy of 93.1% for six angles. Experimental findings prove that deep learning and random forests are useful tools for gait recognition.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131971286","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151371
Sathish S, Aneesa Farhan M A
Microgrids have become a global trending topic such that there is a plethora of research undergoing in different aspects that include operations like energy management, stability, protection and control. A DC microgrid has done a revolution in the power distribution network and it improves power reliability and quality. Also, DC microgrids have significant benefits over the AC grid in terms of size, price, and efficiency, thereby becoming a preferred network for modern power systems. However, there are no design guidelines or standard models to study the DC microgrid. In this paper, a DC microgrid has been modelled with renewable sources, distributed loads, and an AC grid. The proposed system is modelled using MATLAB/Simulink software platform. Additionally, the work also emphasizes the design and operational features of a DC microgrid, including an investigation of fault characteristics for low resistance faults, high resistance faults and faults at different locations of the DC microgrid.
{"title":"Modelling of DC Microgrid for Fault Analysis","authors":"Sathish S, Aneesa Farhan M A","doi":"10.1109/REEDCON57544.2023.10151371","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151371","url":null,"abstract":"Microgrids have become a global trending topic such that there is a plethora of research undergoing in different aspects that include operations like energy management, stability, protection and control. A DC microgrid has done a revolution in the power distribution network and it improves power reliability and quality. Also, DC microgrids have significant benefits over the AC grid in terms of size, price, and efficiency, thereby becoming a preferred network for modern power systems. However, there are no design guidelines or standard models to study the DC microgrid. In this paper, a DC microgrid has been modelled with renewable sources, distributed loads, and an AC grid. The proposed system is modelled using MATLAB/Simulink software platform. Additionally, the work also emphasizes the design and operational features of a DC microgrid, including an investigation of fault characteristics for low resistance faults, high resistance faults and faults at different locations of the DC microgrid.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133687215","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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