2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)最新文献

{"title":"Orchestrate -A GAN Architectural-Based Pipeline for Musical Instrument Chord Conversion","authors":"S. G, Sriraman S, Sruthilaya S, Ulagaraja J","doi":"10.1109/ICEEICT56924.2023.10157056","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157056","url":null,"abstract":"Acoustic instruments produce sounds that are characterized by specific patterns and qualities, including harmonic content, attack, and decay, vibrato, resonance, and timbre. The creation and manipulation of instrumental sounds in various musical contexts are one of the most important features of acoustic instruments. Acoustic music is unamplified music that produces sound only by vibrating air and acoustic means, instead of through electronic or virtual instruments. Acoustic music emphasizes simplicity in its lyrics, harmonies, and melodies. The conversion of one musical instrumental chord to another musical instrumental chord is possible in acoustic instruments. In this paper, the Differentiable Digital Signal Processing technique is employed as a new approach to the realistic neural audio synthesis of musical instruments that combines the efficiency and interpretability of classical DSP elements such as filters, oscillators, reverberation, etc. The deep learning techniques are incorporated to train the model and produce harmonious music patterns. The generated music preserves the feature of the real play. The method also allows non-instrumentalists to process music. The model can be further developed to feed existing music. The preprocessed data is fed as input to obtain the desired instrumental chord or music.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"28 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":"124636324","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}

{"title":"Ensemble deep learning prediction of STEMI using ECG signals","authors":"Kanimozhi J, Hemalatha Karnan, UmaMaheshwari Durairaj","doi":"10.1109/ICEEICT56924.2023.10157721","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157721","url":null,"abstract":"Myocardial infarction or heart attack is caused due to atherosclerotic plaque deposition in the coronary arteries thereby occluding the artery, which leads to decrease in blood flow and oxygen supply to the specific regions of the heart muscles. For diagnostic purpose, ECG is used which shows the ST elevation, negative T wave and pathologic Q wave. Classification of myocardial infarction from the normal ECG is handled in this work using the ensemble model of CNN, LSTM and BiLSTM algorithm. The myocardial infarction dataset [10506X188] and normal ECG dataset [4046X188] are retrieved from the PTB Diagnostic ECG Database. The tabular datasets in the size of [14553X191] consisting of abnormal and normal signals and the labels are generated prior to classification. Preprocessing steps involve the signal extraction and signal denoising of both the signal types. The tabular datasets are k-fold cross- validated for training, validation and testing. The split data are trained using CNN, LSTM and BiLSTM network layers individually. The ensemble model, thenceforth, combining all these three networks consecutively is evaluated for the performance in terms of training accuracy 100% and confusion chart for all the four models is also compared.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"50 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":"114569904","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}

{"title":"VLSI Architecture of a High Speed Polar Code Decoder using Finite Length Scaling LDPC Codes","authors":"Kandi Naveen, Vishnubhatla Sai Lakshmi Manonmai, Murala Sri Jaya Nikhitha, Vasireddy Pradeep, G. Kumar","doi":"10.1109/ICEEICT56924.2023.10157091","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157091","url":null,"abstract":"In this concise a polar decoder form propagation based on belief is formulated which employ finite length LDPC systems. Here the belief sum-product Propagation (BP) is designed for LDPC system beyond affecting the binary communication erasure channels. Belief decoding is parallel and iterative in nature, as it own iteratively nature the required idleness and energy dissemination increments straightly. The prompt report stated that unstable node (VNs) is reduced during individual iteration than as BP. Declination of erased VNs reduces decoding process cause a forceful decrease in complexity, compared among polar decoder that is designed based on CSFG. CSFG is implemented with Quarter-way scheduling algorithm, a sub-factor graph reduces valuations of taken by belief decoder but due to different variable nodes used in the process it has large complication during design. To overcome this BPD with LDPC codes is designed. Simulation and synthesis results in the progressive art reveal that LDPC system drawn better in performance in contrast with belief based propagation.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"36 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":"126990702","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}

{"title":"Design and Simulation of Fault Tolerances in Combinational Circuits Using CMOS 45nm Technology","authors":"Vanga Karunakar Reddy, Ravi Kumar Av","doi":"10.1109/ICEEICT56924.2023.10157552","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157552","url":null,"abstract":"In this paper,. As we designed complexity circuit there may be any errors or faults. The faults may be stuck at ‘0’, stuck at ‘1'or may be a bridge fault. In order to identify the fault or error in circuit we need to verify each and every block to identify whether error or fault occurred. So to decrease the complexity of circuit and to identify error once after the design of circuit is completed. So to overcome the faults in circuit we are using self-checking multiplexer. Here we are designing self- repairing 2:1 multiplexer. By designing this circuit we can find and repair all kind of faults. In this paper proposing two methods in that the first method is detected and correct fault in Mux. Detect and correct faults in basic gates. New models can give 100% error accuracy. To design the proposed fast full adder here we are using hybrid logic style. This proposed method has been designed and analyzed using CMOS Mentor graphics 45nm Technology.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"56 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":"127693162","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}

{"title":"Detection and Analysis of Cardiac Arrhythmias from Heartbeat Classification","authors":"Yesudasu Paila, Ravi Raja A, N. S. P. Revathi Nuvvula, R. L. Durga Prasad Pandi, Pujitha Kodali, Sivarama Krishna Reddy Vanga","doi":"10.1109/ICEEICT56924.2023.10156983","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10156983","url":null,"abstract":"The Electrocardiogram (ECG), one of the biological signals, can be utilized to identify heart arrhythmias. Detecting a single irregular heartbeat that can occur alone or in repetition helps in discovering an arrhythmia. Early detection of arrhythmias and taking necessary precautions can help cure or prevent life-threatening arrhythmias. Depending on the shape and features of ECG, they are categorized into multiple arrhythmias and grouped as classes based on their threat level, such as Unknown Beats (Q), Supraventricular Ectopic Beat (SVEB), Fusion Beat (F), Ventricular Ectopic Beat (VEB) and Non-ectopic Beat (N). The openly accessible Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH) database is considered in this paper. Three stages are suggested for detection. The first stage is pre-processing, which is done by the 1-Dimensional Wavelet Discrete Transform (1D-DWT) method. The second stage is feature extraction, carried out by the Empirical Mode Decomposition (EMD) method. Features now extracted are then fed for the classifiers. Deep Neural Network (DNN) is capable of automatically extracting features and analyzing data patterns, eliminating the need for complex signal processing. For the classification stage, the dataset considered has 20% test data and 80% trained data. The Deep Learning (DL) originated Convolutional Neural Network (CNN) is compared with K-Nearest Neighbor (KNN) algorithm, which is originated from Machine Learning (ML) for secondary confirmation. These classifiers achieved a Maximum Accuracy (MAAC) of 90.87%, Maximum Sensitivity (MASE) of 90.56%, and Maximum Specificity (MASP) of 91.18% with KNN, and a MAAC of 93.8%, MASE of 92.52%, and MASP of 95.08% with the CNN classifier.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"6 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":"126493953","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}

{"title":"An Innovative Method for Optimizing Photovoltaic Array Design in Partially Shaded Environments","authors":"G. Shanmugapriya, Nikale Vasant Muralidhar, Ravindra R Solankce, Subash Ranjan Kabat, R. Jeevalatha, Pandit S. Patil","doi":"10.1109/ICEEICT56924.2023.10157043","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157043","url":null,"abstract":"Several factors contribute to the gradual decrease in energy production from Photovoltaic (PV) systems. Partial shading is a key contributing element. Clouds and the presence of structures like buildings and tall trees cast partial shadows. Shade can reduce the quantity of energy generated by a photovoltaic system. Even in shady situations, it must be propped up to live. By removing the load from the PV array and taking a look at the open-circuit and short- circuit currents and voltages partial shading may be determined in the traditional manner. However, the suggested solution uses a regular camera to identify the partial shadowing without disconnecting the PV array from the load. There is a higher degree of sensitivity to variations in system performance using this approach. Photographs of the PV array are taken using a digital camera, and then partial shading is determined using standard image processing methods. During times of partial shadowing, the suggested technology reconfigures the electrical current in order to maximize power production by using the relay circuit. Partial shading data is used to create the control signals for the relays. The suggested approach involves interfacing a camera with MATLAB and immediately processing the collected pictures to identify the error or partial shading. The PV array is reconfigured for increased output power generation after reconfiguring signals are sent from a computer using a MATLAB-Arduino connection to the switch circuits in the array. The proposed approach has been tested in a solar PV system with a power output of 80 W, with results showing a 15 percent increase in output. It works well for 1-5-kilowatt solar photovoltaic power systems.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"11 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":"131408053","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}

{"title":"Driver Behavior Management in Supply Chain Industries across Southeast Asia","authors":"Anuradha Das, Sabarirajan K, Selvakuberan Karuppasamy, Subhashini Lakshminarayanan","doi":"10.1109/ICEEICT56924.2023.10157716","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157716","url":null,"abstract":"There has been a rapid increase in the traffic concentration in southeast Asian countries in the past decade. The impact of risky driving attitude on businesses is monumental both in terms of road safety and cost optimization. The purpose of this article was to identify potentially risky driving pattern and factors influencing them including geographical factors. We analyzed the telematics data generated by an In-Vehicle-Monitoring-System installed in vehicles operating in supply chain industries. With the findings we propose the framework of a Risk Management platform which can be used by fleet managers to provide constructive feedbacks to drivers. Telematics data gathered after the implementation of the RMP shows up to 20% decrease in one of the key harsh driving indicators i.e. Harsh Acceleration.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"42 4 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":"122854994","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}

{"title":"Energy Disaggregation of Residential House via Event Based Optimization Technique","authors":"Prabhash Kumar Sonwani, A. Swarnkar, Gurpinder Singh, N. Gupta, K. R. Niazi","doi":"10.1109/ICEEICT56924.2023.10157394","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157394","url":null,"abstract":"Non-intrusive load monitoring (NILM) is a technique for disaggregating the total energy consumption of a building into individual appliance-level energy consumption. Event detection is a critical component of NILM systems as it involves the identification and classification of different electrical events from the aggregate power signal. In this article an event detection method for NILM systems has been proposed that is based on the analysis of the statistical properties of the aggregate power signal. Specifically, we use a sliding window approach and K-Means clustering to detect number of devices from the power signal and then apply a threshold-based algorithm to detect electrical events. We evaluate the proposed method on a public dataset and demonstrate its effectiveness in accurately detecting electrical events. The proposed method has the potential to improve the accuracy with recall of 98.84% carried out on Pecan Street Datanort Inc.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"2 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":"128484705","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}

{"title":"Big Data Analytics and Educational Sustainability-A Malaysian Scenario","authors":"S. Jayashree, Mohammad Nurul Hassan Reza, C. Malarvizhi, Mazni Binti Alias","doi":"10.1109/ICEEICT56924.2023.10157261","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157261","url":null,"abstract":"Despite the increased interest in higher learning institutions (HLIs), research on big data analytics (BDA) focusing on sustainability in the education sector is still in its infancy. Therefore, this study proposes a BDA-centric framework, emphasizing sustainability in higher education by integrating the Technology, Organization, and Environment (TOE) theory and the Diffusion of Innovation (DOI) model. The framework will be tested to examine the impact of the factors on BDA adoption and sustainable education. It also aims to determine how BDA assists HLIs in attaining sustainability. Moreover, whether BDA mediates the relationship between the factors and sustainable education will also be tested. The study will employ a questionnaire-based survey to validate the model. Data will be collected from public and private universities located in various states in Malaysia. Structural Equation Modeling (SEM) will be employed to examine the model and proposed hypotheses. The study's findings offer essential insights for adopting BDA successfully in HLIs. This study may be helpful for educators, policymakers, and big data vendors in adopting big data successfully among HLIs to ensure sustainability in educational systems.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","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":"117100253","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}

{"title":"Deep learning-based Object Detection in Underwater Communications System","authors":"M. Sangari, K. Thangaraj, U. Vanitha, N. Srikanth, J. Sathyamoorthy, K. Renu","doi":"10.1109/ICEEICT56924.2023.10157072","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157072","url":null,"abstract":"Being at the nexus of robotics and ocean engineering, underwater robots have been a developing research area. They can be used for deep sea infrastructure inspections, oceanographic mapping, and environmental monitoring. Autonomous navigation skills are essential for doing these activities successfully, especially given the poor communication conditions in underwater locations. Autonomous navigation technologies, such as path planning and tracking, have been one of the fascinating but difficult issues in the field of study due to the extremely dynamic and three-dimensional settings. Due to their short detection ranges and poor visibility, cameras have not received much attention as an underwater sensor. However, using visual data from cameras is still a popular technique for underwater sensing, and it works particularly well for close-range detections. In this study, the enhancement of underwater vision is achieved by combining the max-RGB and shades of grey methods. Then, to solve the problem of poorly illuminated underwater images, a technique known as RCNN (Region-based Convolutional Neural Network) is proposed. This procedure tells the mapping relationship how to create the illumination map. Following image processing, an RCNN strategy for underwater detection and classification is recommended. Two improved strategies are then used to change the RCNN structure in accordance with the properties of underwater vision. In order to deal with the challenges of object tracking and detection in underwater communication, a correlation filter tracking algorithm (CFTA) method was created. The properties of the invariant moment and area were looked at after the object's region had been extracted using a threshold segment and morphological technique. The findings show that the suggested method is effective for underwater target tracking based on RCNN-CFTA in the aquatic environment. Simulated evaluation of these methods' performance demonstrates the potency of the suggested strategies.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"18 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":"117324327","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}