2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)最新文献

Person Attribute Recognition using Hybrid Transformers for Surveillance Scenarios 基于混合变压器的监视场景人物属性识别

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974664

S. Abhilash, Venu Madhav Nookala

引用次数: 1

Adaptive Filter for the Removal of Baseline Wander and Detection of Pulse Event in Wrist Pulse Acquisition using Piezoresistive Pressure Sensors 压阻式压力传感器腕部脉冲采集中基线漂移去除与脉冲事件检测的自适应滤波

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974704

Sukesh Rao M, Sanith C Bangera, N. C, A. K.

{"title":"Adaptive Filter for the Removal of Baseline Wander and Detection of Pulse Event in Wrist Pulse Acquisition using Piezoresistive Pressure Sensors","authors":"Sukesh Rao M, Sanith C Bangera, N. C, A. K.","doi":"10.1109/DISCOVER55800.2022.9974704","DOIUrl":"https://doi.org/10.1109/DISCOVER55800.2022.9974704","url":null,"abstract":"In this paper, we report an interesting finding of the adaptive filter characteristic that aids in the accurate detection of the pulse event information. Accurate pulse detection is extremely critical as it provides sufficient cardia information. This will further enhance the chances of wearable cardiac activity monitors. The signal is acquired using the using MPVZ5004G6U piezo-resistive (PZR) sensors from the wrist of humans. The signal conditioning is performed and an adaptive FIR filter that uses the least mean squared (LMS) algorithm is designed and deployed for the system identification application. Upon careful examination of the experimental results, the error signal is found to provide the pulse event information. This is due to the sudden change in the pulse activity that causes the error signal to exhibit impulse train like characteristics. The baseline wander and low frequency motion artifacts are alleviated from the signal. The study is conduced using the LabVIEW Virtual Instruments(VI).","PeriodicalId":264177,"journal":{"name":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132349274","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}

引用次数: 0

Statistical Analysis of Deep Learning Models for Diabetic Macular Edema Classification using OCT Images 深度学习模型在糖尿病黄斑水肿OCT图像分类中的统计分析

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974917

K. Pavithra, Preetham Kumar, M. Geetha, S. Bhandary

{"title":"Statistical Analysis of Deep Learning Models for Diabetic Macular Edema Classification using OCT Images","authors":"K. Pavithra, Preetham Kumar, M. Geetha, S. Bhandary","doi":"10.1109/DISCOVER55800.2022.9974917","DOIUrl":"https://doi.org/10.1109/DISCOVER55800.2022.9974917","url":null,"abstract":"Diabetic macular edema (DME) is a potentially blinding complication of Diabetic retinopathy (DR) and indeed the main cause of visual impairment in diabetic patients. DME can indeed be diagnosed in varying levels of severity by employing Optical Coherence Tomography (OCT), which is a standard imaging modality to capture the 3D view of the retina. Computerized detection of DME is beneficial, and automated identification can assist doctors in their daily activities. Deep Learning (DL), a widely recognized method in this regard, has contributed to improving the effectiveness of classification algorithms. The focus of this research is to use a standard OCT dataset to test and analyze two DL models, Optic Net and DenseNet for DME classification. A statistical analysis of the accuracy measures collected during the experiments is performed to evaluate the performance of the two models. The statistical findings suggest that the model Optic Net (Accuracy-98%, Specificity-100%) outperforms DenseNet (Accuracy-94%, Specificity-96%) in terms of accuracy, and the results could be used to choose an optimal model for DME detection.","PeriodicalId":264177,"journal":{"name":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124416697","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}

引用次数: 0

Tracing and decoding of covert phonemes using single channel Electroencephalogram with Machine Learning Techniques 利用机器学习技术的单通道脑电图追踪和解码隐蔽音素

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974955

Varalakshmi Perumal, Jeevan Medikanda

{"title":"Tracing and decoding of covert phonemes using single channel Electroencephalogram with Machine Learning Techniques","authors":"Varalakshmi Perumal, Jeevan Medikanda","doi":"10.1109/DISCOVER55800.2022.9974955","DOIUrl":"https://doi.org/10.1109/DISCOVER55800.2022.9974955","url":null,"abstract":"A Brain-computer interface BCI is a technology that interfaces the brain and computer for communication without the person expressing it. Amongst concepts of reading thoughts of the brain, decoding covert speech is a popular application in BCI which can be able to translate the imagined voice inside a person. In this study, Electroencephalogram (EEGs) has been used to interpret the covert speech of a person. On the other hand, reading the brain with EEG is a complicated task to use in daily life applications as it needs multichannel spatial information to be extracted by connecting leads all over the scalp. In the direction of overcoming this complexity, this study uses only single-channel EEG Fpz, which is much easier to access than channels. In this study, Multilayer Perceptron (MLP), K-nearest neighbour Classifier (KNN), Support Vector Classifier (SVC), and Random Forest (RF) models are proposed to classify a single channel Fpz of EEG by extracting spectral information in form of wavelet decomposition coefficients and an energy level over Alpha, Beta, Gamma, Delta and Theta bands to show the evidence that covert speech can be derived through single channel EEG with basics classifiers.","PeriodicalId":264177,"journal":{"name":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","volume":"078 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124067743","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}

引用次数: 0

The Design and Implementation of Synchronous and Asynchronous NoC Router 同步与异步NoC路由器的设计与实现

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974626

S. Chaitra, R. Shruthi, S. R. Shreyas, Shashidhara H R

引用次数: 0

AI-enabled Clinical Decision Support System 支持人工智能的临床决策支持系统

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974639

Pv Vasudeva Rao, Ashwija, Kanmani, Sk Kavana Tilak, Brinda Kulal, Rs Jyothika

{"title":"AI-enabled Clinical Decision Support System","authors":"Pv Vasudeva Rao, Ashwija, Kanmani, Sk Kavana Tilak, Brinda Kulal, Rs Jyothika","doi":"10.1109/DISCOVER55800.2022.9974639","DOIUrl":"https://doi.org/10.1109/DISCOVER55800.2022.9974639","url":null,"abstract":"Each person’s life is extremely important and vital for the country’s development. Helping to limit the number of misdiagnoses can save many lives and strengthen families, as some families would lose their primary source of income as a result of misdiagnosis. Misdiagnosis is one of the significant errors in the medical field due to misjudgments by medical professionals eading to increased harm to patients. With 72 percent of errors occurring during the patient-practitioner encounter, it becomes increasingly important to reduce the error in real time. Lowering the mortality rate as a result of misdiagnosis would enhance and build the social well-being of the country. The area of general medicine is very vast and has more than 400 diseases and conditions under it. However, with differing and misleading symptoms for the diseases, it becomes rather confusing for a recent medical graduate to diagnose an individual within the limiting time frame for testing the patient. The developed solution is an artificial intelligence-based system that uses traditional machine learning algorithms and deep learning techniques to help new medical graduates and practitioners with limited experience reliably diagnose a patient’s medical condition based on the patient’s symptoms recorded during the clinical confrontation. The proposed solution is an AI-enabled Clinical Decision Support System in the form of a web application intended to assist medical graduates and healthcare professionals in accurately diagnosing a patient’s health condition based on the symptoms observed during doctor-patient encounters. The developed system has achieved the highest accuracy by using the Ensemble technique which is a combination of Support Vector Classifier, Random Forest, and Naive Bayes technique for textual data analysis and ResNet architecture for analyzing image data.","PeriodicalId":264177,"journal":{"name":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","volume":"53 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130682113","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}

引用次数: 0

Parkinson’s Disease Stage Classification with Gait Analysis using Machine Learning Techniques and SMOTE-based Approach for Class Imbalance Problem 基于机器学习技术的步态分析与帕金森病分期分类和基于smote的分类不平衡问题

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974754

Aishwarya Balakrishnan, Jeevan Medikonda, Pramod K. Namboothiri, Manikandan Natarajan

{"title":"Parkinson’s Disease Stage Classification with Gait Analysis using Machine Learning Techniques and SMOTE-based Approach for Class Imbalance Problem","authors":"Aishwarya Balakrishnan, Jeevan Medikonda, Pramod K. Namboothiri, Manikandan Natarajan","doi":"10.1109/DISCOVER55800.2022.9974754","DOIUrl":"https://doi.org/10.1109/DISCOVER55800.2022.9974754","url":null,"abstract":"High variability in symptom severity and progression rate roots the need for a diverse training dataset, to build an efficient Parkinson’s Disease (PD) severity prediction model. The Physionet database comprises gait signals of PD subjects belonging to various H&Y score-based severity levels but forms an imbalanced dataset. A dataset is said to be imbalanced if the representation of the classification categories within a dataset is not equal. The severity of misclassifying abnormal cases as normal is high and thus is a matter of concern. This paper shows how a technique called Synthetic Minority Oversampling Technique (SMOTE) deals with the class imbalance problem in PD stage-wise classification by improving minority class recognition. The method is validated by quantifying the dissimilarity among samples generated showing the non-existence of overlapping or replication. Spatiotemporal gait parameters along with their regularity and symmetry features are the attributes considered. Classifiers are trained with balanced & imbalanced datasets and their predictive accuracy attributes are compared. Results show an improvement in determining the minority class by the model trained with the balanced dataset, thus improving the generalizability of the model.","PeriodicalId":264177,"journal":{"name":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130222600","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}

引用次数: 1

Sub-Axial Vertebral Column Fracture CT Image Synthesis by Progressive Growing Generative Adversarial Networks (PGGANs) 渐进式生长生成对抗网络(Progressive growth Generative Adversarial Networks, PGGANs)合成亚轴椎骨折CT图像

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974676

D. Sindhura, R. Pai, Shyamasunder N. Bhat, M. M. Manohara Pai

{"title":"Sub-Axial Vertebral Column Fracture CT Image Synthesis by Progressive Growing Generative Adversarial Networks (PGGANs)","authors":"D. Sindhura, R. Pai, Shyamasunder N. Bhat, M. M. Manohara Pai","doi":"10.1109/DISCOVER55800.2022.9974676","DOIUrl":"https://doi.org/10.1109/DISCOVER55800.2022.9974676","url":null,"abstract":"Orthopaedicians need the assistance of the Deep Learning (DL) model for easy Vertebral Column Fracture Type identification. Deep Learning models require large datasets. Due to the non-availability of large annotated data sets, the DL model needs intensive data augmentation methods. In this proposed research work, Progressive Growing Generative Adversarial Networks (PGGANs) are used to generate synthetic Vertebral Column Fracture (VCF) CT images. The synthetic CT images of VCF generated by PGGANs are high resolution, realistic yet wholly different from the real images. The PGGANs is a multi-stage generative model that generates 512 X 512 CT images that increases the accuracy of the VCF Type identification system. A total of375 vertebral column CT images were utilized for training the model, which were collected from the Spine Clinic, Orthopaedics Department, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal. Among 375 images, 275 Chance fractures and 100 posterior tension band disruption fracture images were present. To analyse the effect of PGGAN augmentation on VCF type identification, lately VGG16 pre-trained model is implemented. The VGG16 model with PGGAN augmentation got an accuracy of 87.01%, which is more when compared to the model without augmentation. In conclusion, PGGAN generated VCF images are realistic and can be used for data augmentation without privacy restrictions and in VCF type identification DL models for increased performance.","PeriodicalId":264177,"journal":{"name":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131327295","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}

引用次数: 0

ECC-based Authenticated Key-Agreement Algorithm using Time-stamps for IoD networks 基于ecc的IoD网络时间戳认证密钥协议算法

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974860

S. Samanth, P. V., Mamatha Balachandra

{"title":"ECC-based Authenticated Key-Agreement Algorithm using Time-stamps for IoD networks","authors":"S. Samanth, P. V., Mamatha Balachandra","doi":"10.1109/DISCOVER55800.2022.9974860","DOIUrl":"https://doi.org/10.1109/DISCOVER55800.2022.9974860","url":null,"abstract":"Internet of Things (IoT) networks have advanced and have made the lives of humans much easier, during the past few decades. Raspberry Pi (RP) is a type of IoT device with memory constraints. Both military and civilian applications have used drones or Unmanned Aerial Vehicles (UAVs) over the past several decades. Internet of Drones (IoD) networks are subsets of IoT networks. Drones are resource-constrained devices. Moreover, IoD networks are vulnerable to different security attacks. Hence, 2 RP 3B+ boards are used for the network model to be treated like a drone and a Ground Control Station (GCS) respectively. Moreover, an Authenticated Key-Agreement (AKA) algorithm is designed based on Elliptic Curve Cryptography (ECC). The proposed algorithm has been designed using Python programming language, and the performance metrics analysis is done using Jupyter-Notebook. An idea of integration of timestamps and trigonometric concepts has been introduced to improve the security of the designed ECC-based AKA algorithm. The designed AKA algorithm provides different properties in terms of security, and can also resist some known attacks, as shown by the algorithm’s security analysis. The proposed AKA algorithm’s analysis in terms of performance shows that it outperforms 3 recent related security mechanisms in terms of performance metrics like Total Computation Cost, Total Storage Cost, and Total Communication Cost.","PeriodicalId":264177,"journal":{"name":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","volume":"7 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131725531","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}

引用次数: 0

Improving the Performance of Sleep Apnea Screening System using Wavelets and Bottleneck Feature Fusion 利用小波和瓶颈特征融合提高睡眠呼吸暂停筛查系统的性能

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974829

C. Srinidhi, C. Santhosh Kumar, Mrudula G. B, P. Muralidharan, S. Gopinath, A. Anand Kumar

{"title":"Improving the Performance of Sleep Apnea Screening System using Wavelets and Bottleneck Feature Fusion","authors":"C. Srinidhi, C. Santhosh Kumar, Mrudula G. B, P. Muralidharan, S. Gopinath, A. Anand Kumar","doi":"10.1109/DISCOVER55800.2022.9974829","DOIUrl":"https://doi.org/10.1109/DISCOVER55800.2022.9974829","url":null,"abstract":"S1eep apnea is the one of the most prevalent sleep disorder caused due to obstruction in breathing. Sleep apnea detection is usually done using polysomnography (PSG) which is not available for rural health care. The main objective of this work is to develop an affordable sleep apnea screening system using electrocardiography (ECG) signals as input.The baseline system was built using statistical features extracted from the time domain, frequency domain, and wavelet decomposed signals as input to a support vector machine (SVM) backend classifier. The baseline showed an accuracy of 86%, specificity of 83%, and sensitivity of 88%. Further, a Convolutional neural network (CNN) model is also implemented to check the performance of the system on wavelet decomposed signals. The best CNN model gave an accuracy of 86.6%, a sensitivity of 84.01%, and a specificity of 84.1%.To enhance the performance further, bottleneck features were extracted from the bottleneck layer of a CNN and the features thus derived are combined for feature fusion. The bottleneck layer compresses the model aiding in the extraction of lower dimensionality information. The bottleneck features from the best-performing models are fused together. The performance of the fused bottleneck features was found to show an accuracy of 87.6%, sensitivity of 86.4%, and specificity of 86.49%.","PeriodicalId":264177,"journal":{"name":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126627379","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}

引用次数: 1