2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)最新文献

Estimating Depth Map of an RGB image using Encoders and Decoders 使用编码器和解码器估计RGB图像的深度图

2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) Pub Date : 2022-12-02 DOI: 10.1109/UPCON56432.2022.9986392

{"title":"Estimating Depth Map of an RGB image using Encoders and Decoders","authors":"","doi":"10.1109/UPCON56432.2022.9986392","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986392","url":null,"abstract":"Depth estimation from a single RGB picture has emerged as one of the most significant study areas in recent years because of the wide variety of applications, from robotics to medical sciences. Monocular depth estimation has often had low resolution and blurry depth maps, which are not usable for further training of models with specific applications. The main drawback of generic depth estimation models is that they take an object and its environment into consideration. Because of this, traditional deep learning-based systems often experience severe setbacks in forecasting depths. This paper proposes an encoder-decoder network that, using transfer learning, can forecast high-quality depth pictures from a single RGB image. After initialising the encoder using augmentation algorithms and significant feature extraction from pre-trained networks, the decoder predicts the high-end depth maps. We have also used several boundary detection techniques to remove the object from its environment without losing the object's pixel information. Our network performs comparable to the state-of-the-art on two datasets and also generates qualitatively better results that more accurately represent object boundaries which can be further used in 6D pose estimation to perform robotic grasping.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"914 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123275616","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

Object Detection based Approach for an Efficient Video Summarization with System Statistics over Cloud 基于目标检测的云上系统统计高效视频摘要方法

2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) Pub Date : 2022-12-02 DOI: 10.1109/UPCON56432.2022.9986376

Alok Negi, Krishan Kumar, Parul Saini, Shamal Kashid

{"title":"Object Detection based Approach for an Efficient Video Summarization with System Statistics over Cloud","authors":"Alok Negi, Krishan Kumar, Parul Saini, Shamal Kashid","doi":"10.1109/UPCON56432.2022.9986376","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986376","url":null,"abstract":"The tremendous volume of video data generated by industrial surveillance networks presents a number of difficulties when examining such videos for a variety of purposes, including video summarization (VS), analysis, indexing and retrieval. The task of creating video summaries is extremely difficult because of the huge amount of data, redundancy, interleaved views and light variations. Multiple object detection and identification in video is difficult for machines to recognize and classify. To address all such issues, multiple low-feature and clustering-based machine learning strategies that fail to completely exploit VS are recommended. In this work, we achieved VS by embedding deep neural network-based soft computing methods. Firstly, the objects in extracted frames are detected using YOLOv5, and then the frames without objects (useless frames) are removed. Video summary generation occurs with the help of frames containing Objects. To check the quality of the proposed work Summary length, precision, recall, PR curve, and mean average precision (mAP) are used and system resource utilization during the model training are also tracked. As a result, the proposed work was able to identify the most effective video summarization framework with best summary length under varying conditions.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122735176","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: 2

Meta-learning with Hopfield Neural Network Hopfield神经网络的元学习

2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) Pub Date : 2022-12-02 DOI: 10.1109/UPCON56432.2022.9986399

Sambhavi Tiwari, Manas Gogoi, S. Verma, Krishna Pratap Singh

{"title":"Meta-learning with Hopfield Neural Network","authors":"Sambhavi Tiwari, Manas Gogoi, S. Verma, Krishna Pratap Singh","doi":"10.1109/UPCON56432.2022.9986399","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986399","url":null,"abstract":"In this paper, we propose a novel meta-learning method that leverages the advantages of both meta-learning and storage. In meta-learning, the neural network tries to learn parameters distributed across multiple tasks. Meta-learning provides quick learning with unseen meta-testing tasks. In model-based meta-learning methods, an external memory module is used to retain a memory of important parameters from one task to the other, enabling meta-learning. The model proposed in this work consists of a long short-term memory(LSTM) neural network with an external memory network known as Hopfield neural network. Hopfield neural network is a single-layer, non-linear, auto-associative model that uses an external memory network. Unlike previous methods, our proposed model $LSTM_{HAM}$, i.e., long short term memory with Hopfield associative memory focuses on storing knowledge that uses an additional memory network to store and retrieve patterns using different location-based access mechanisms. Our model extends the capabilities of the LSTM and performs meta-learning best on 5-way 10-shot task setting with an average accuracy of approximately 60 percent.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128491371","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: 2

16 Watt S-Band GaN Based Power Amplifier Using Replicating Stages 基于复制级的16瓦s波段GaN功率放大器

2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) Pub Date : 2022-12-02 DOI: 10.1109/UPCON56432.2022.9986379

Mohammad Zaid, A. Pampori, Y. Chauhan

引用次数: 0

Small signal modelling and Parameter Analysis of Virtual Synchronous Generator Based Control in Isolated Microgrid 孤立微电网虚拟同步发电机控制的小信号建模与参数分析

2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) Pub Date : 2022-12-02 DOI: 10.1109/UPCON56432.2022.9986455

Nidhi Dubey, S. K.

引用次数: 1

Analyzing Binary Tree based Topology Configuration for Energy Efficient Multicore Architectures 基于二叉树的节能多核结构拓扑结构分析

2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) Pub Date : 2022-12-02 DOI: 10.1109/UPCON56432.2022.9986479

Aarya Chaumal, Amit M. Joshi

{"title":"Analyzing Binary Tree based Topology Configuration for Energy Efficient Multicore Architectures","authors":"Aarya Chaumal, Amit M. Joshi","doi":"10.1109/UPCON56432.2022.9986479","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986479","url":null,"abstract":"Modern computer architectures are moving towards domain-specific designs of processors. ARM-based processors domi-nate the embedded domain due to their compact and energy-efficient design. x86 processors have higher computing capabilities but at the cost of high energy consumption. This work tries to improve the Network-on-Chip design of x86 processors to have an energy-efficient Chip Multi-Processor configuration with similar computing power. Network-on-Chip is a significant part of modern computer architecture. It helps to efficiently navigate on-chip traffic on current Chip Multi-Processors where the number of cores is increasing rapidly. The topology of a Network-on-Chip significantly impacts system performance as it directly affects the network bandwidth and the area of the system. Therefore, Network-on-Chip topology affects the system's execution time, area, and energy consumption. This work proposes a novel topology to improve performance in terms of energy consumption and execution time, and affects L1D miss rate of the considered specification with few benchmark programs. The proposed topology is inspired from the traditional binary tree topology and tries to overcome its shortcomings to improve the system performance. The experiment results suggest that the proposed topology improves system performance on applications belonging to domains that are suited for embedded class processors.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121662733","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

Solar Photovoltaic Energy Forecasting Using Machine Learning and Deep Learning Technique 利用机器学习和深度学习技术预测太阳能光伏能源

2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) Pub Date : 2022-12-02 DOI: 10.1109/UPCON56432.2022.9986446

Prashant Singh, N. Singh, A. K. Singh

{"title":"Solar Photovoltaic Energy Forecasting Using Machine Learning and Deep Learning Technique","authors":"Prashant Singh, N. Singh, A. K. Singh","doi":"10.1109/UPCON56432.2022.9986446","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986446","url":null,"abstract":"The whole world is going through electrical fuel transition, from traditional to renewable energy (RE) sources. Natural resources like coal, natural gas, fossil fuels are still dominant energy sources to produce electrical energy throughout the world. If the switching towards RE source does not take place, these natural sources will deplete sooner, and heavy energy crises will come into picture. The paper addresses the issue of forecasting short-term renewable energy supply. The stochastic nature of RE sources has an impact on power system planning procedures, lowering the reliability as well as security of power supply for end users [1]. In this paper solar photovoltaic (PV) energy forecasting is performed using two dependent data variables such as (a) solar irradiance and (b) temperature, and past solar PV energy output using machine learning and deep learning (DL) algorithms. DL is a kind of complex learning inspired by human learning. Long Short Term Memory (LSTM) network and Gated Recurrent Unit (GRU) network are the examples of it. The paper investigates the issue of identifying features and determining suitable error metrics. DL model was developed and tested on real solar PV energy produced on MNNIT Allahabad, India campus. The forecasting performance of developed models is evaluated in terms of three important measures, (a) mean absolute error (MAE), (b) mean squared error (MSE), and (c) root mean square error (RMSE).","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125259834","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

Power Quality Improvement of 3-phase Solar Energy Conversion System using MOFSE 利用MOFSE改善三相太阳能转换系统电能质量

2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) Pub Date : 2022-12-02 DOI: 10.1109/UPCON56432.2022.9986393

Rituvic Pandey, Tripurari Nath Gupta, M. Rawat

引用次数: 0

Performance of Resnet-16 and Inception-V4 Architecture to Identify Covid-19 from X-Ray Images Resnet-16和Inception-V4架构从x射线图像中识别Covid-19的性能

2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) Pub Date : 2022-12-02 DOI: 10.1109/UPCON56432.2022.9986372

Aayush Sharma, Ashwini Kodipalli, T. Rao

引用次数: 0

Substrate Integrated Waveguide Antenna with Slow Wave Effect to Minimize Dimensions 最小尺寸的慢波效应基板集成波导天线

2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) Pub Date : 2022-12-02 DOI: 10.1109/UPCON56432.2022.9986414

Vaibhav Pratap Singh, Vivek Chand, Yash Pal

{"title":"Substrate Integrated Waveguide Antenna with Slow Wave Effect to Minimize Dimensions","authors":"Vaibhav Pratap Singh, Vivek Chand, Yash Pal","doi":"10.1109/UPCON56432.2022.9986414","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986414","url":null,"abstract":"On the basis of a previously developed slow wave substrate integrated waveguide, this study offers a new design, simulation, and measurement results of a circularly polarized square slot antenna backed by cavities (SW-SIW). 11.2 GHz is the intended operating frequency for the planned antenna. The size of the proposed antenna has been reduced to 13% when compared to the reference design and 49% when compared to the traditional SIW version of antenna thanks to the internal metallized via that creates the slow wave effect for the physical separation of the electric and magnetic field in the designed antenna structure. The designed antenna has been tested and simulated, and the results show that it performs better than expected in terms of gain, radiation loss, and return loss. According to the suggested design, the maximum return loss achieved by the proposed antenna is 37 dB at 11.2 GHz frequency, and the return loss is less than 10 dB in the frequency range of 11 to 11.3 GHz. With proposed antenna we got the gain of 5.9 dB as compare to reference antenna with 4.8 dBi of gain.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133583159","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