2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)最新文献

FPGA Implementation of Reversible LFSR with Primitive Polynomial using Verilog HDL 基于Verilog HDL的可逆原始多项式LFSR FPGA实现

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793134

H. P., K. Bailey

{"title":"FPGA Implementation of Reversible LFSR with Primitive Polynomial using Verilog HDL","authors":"H. P., K. Bailey","doi":"10.1109/icdcece53908.2022.9793134","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9793134","url":null,"abstract":"Linear Feedback Shift Register (LFSRs) based pseudo-random number generators (PRNGs) are used in test pattern generators (TPGs). Because FPGAs are reprogrammable, you can implement designs of LFSR on FPGA and simulate different lengths. It is important to test and validate the simulated results and synthesis results. The primitive polynomial determines the total number of pseudo-random output states of LFSRs. If the polynomial used is a primitive, the output random state is up to 2n- 1 state. This paper presented the designed, developed, and implemented 4, 8, 16, and 32 bit reversible LFSRs in FPGAs by performing, analyzing, and investigating random behavior using the Verilog HDL code. The analysis is performed to find the design speed requirements in FPGA when the number of constant inputs, the number of garbage outputs, the number of reversible logic gates, and the increased number of bits. In this paper, shows a comparative study of 4, 8, 16, and 32 bit reversible LFSRs on FPGAs to understand their performance. According to the results, the proposed reversible LFSR design using the Pareek gate approach reduces power by 10% when compared to the irreversible approach. As a result, the proposed design is used to develop Built-In-Self-Test (BIST) module.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124878048","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: 4

Deep Learning Long Short-Term Memory based Automatic Music Transcription System for Carnatic Music 基于深度学习长短期记忆的卡纳蒂克音乐自动转录系统

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792867

B. Gowrishankar, Nagappa U. Bhajantri

{"title":"Deep Learning Long Short-Term Memory based Automatic Music Transcription System for Carnatic Music","authors":"B. Gowrishankar, Nagappa U. Bhajantri","doi":"10.1109/icdcece53908.2022.9792867","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9792867","url":null,"abstract":"Automatic music transcription uses computational algorithms to covert a music audio into some form of music notation. Automatic music transcription is very important for applications like music mixing, song recommendation and music information retrieval. It is a challenging task involving the domains of signal processing and artificial intelligence. Though there are many works on automatic music transcription for western music, there are very few works for automatic music transcription for Indian classical music, especially Carnatic music. Automatic music transcription for Carnatic music is very challenging due to variations in the swars/note frequencies. Due to the variations, it becomes difficult to detect the Raga and transcribe the music. In this work, a deep learning LSTM based automatic music transcription system is proposed for Carnatic music. The note detection is solved as the image classification problem using a modified Visual Geometry Group Network (VGGNet). The sequence of notes is classified into 72 Melakartha ragas using an LSTM classifier and provides better accuracy when compared to existing methods.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125133762","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: 5

Autonomous Library System 自治图书馆系统

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792872

B. Yik, Tham Hoong Ching, Zety Marlia Zainal Abidin

引用次数: 2

Image Classification Using Deep Learning Algorithms for Cotton Crop Disease Detection 基于深度学习算法的图像分类棉花病害检测

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792911

Shubham Bavaskar, V. Ghodake, Gayatri Deshmukh, Pranav Chillawar, Atul B. Kathole

{"title":"Image Classification Using Deep Learning Algorithms for Cotton Crop Disease Detection","authors":"Shubham Bavaskar, V. Ghodake, Gayatri Deshmukh, Pranav Chillawar, Atul B. Kathole","doi":"10.1109/icdcece53908.2022.9792911","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9792911","url":null,"abstract":"Agriculture plays an important role in the development of the economy of a nation. Many diseases caused by pathogens and pests hamper agricultural production. If not detected earlier the diseases can cause severe damage, that is why the most important step in the prevention of damage is to detect the disease as early as possible. Traditionally the diseases are detected on basis of past knowledge using bare eyes. The traditional process can be harmful as incorrect detection can account for the wrong and excess use of pesticides harming plants. This paper presents a system for detecting crop diseases using deep learning-based image classification of crop leaves. The system can detect three cotton diseases- Bacterial Blight, Curl Virus, and Fusarium Wilt - by scanning cotton plant leaves. The paper also compares performances of 4 different deep learning architectures. The highest accuracy of the system obtained using ResNet152 V2 architecture is 99.12% on the training dataset and 98.26% on the testing dataset.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126093889","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

Deep Residual Network for Image Recognition 图像识别的深度残差网络

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792645

Satnam Singh Saini, P. Rawat

{"title":"Deep Residual Network for Image Recognition","authors":"Satnam Singh Saini, P. Rawat","doi":"10.1109/icdcece53908.2022.9792645","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9792645","url":null,"abstract":"Training of a neural network is easier than it goes deeper. Deeper architecture makes neural networks more difficult to train because of vanishing gradient and complexity problems, and via this training, deeper neural networks become much time taking and high utilization of computer resources. Introducing residual blocks in neural networks train specifically deeper architecture networks than those used previously. Residual networks gain this achievement by attaching a trip connection to the layers of artificial neural networks. This paper is about showing residual networks and how they work like formulas, we will see residual networks obtain good accuracy, and as well as the model is easier to optimize because Res Net makes training of large structured neural networks more efficient. We will check residual nets on the Image Net dataset with a depth of 152 layers which is 8x more intense than VGG nets yet very less complex. After building this architecture of residual nets gets error up to 3.57% on the Image Net test dataset. We also compare the Res Net result to its equivalent Convolutional Network without residual connection. Our results show that ResNet provides higher accuracy but apart from that, it is more prone to over fitting. Stochastic augmentation of training datasets and adding dropout layers in networks are some of the over fitting prevention methods.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"575 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123404532","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: 5

Detection of Brain Tumor using a Feature Optimization using Particle Swarm Optimization and Ensemble Classifier 基于粒子群优化和集成分类器的特征优化脑肿瘤检测

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792822

A. Bhatt, Vineeta Saxena Nigam

{"title":"Detection of Brain Tumor using a Feature Optimization using Particle Swarm Optimization and Ensemble Classifier","authors":"A. Bhatt, Vineeta Saxena Nigam","doi":"10.1109/icdcece53908.2022.9792822","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9792822","url":null,"abstract":"The brain tumor is a damning disease after cardiovascular diseases across the globe. However, brain tumor detection's accurate and early-stage saves millions of lives worldwide. This research introduced an ensemble-based classifiers in tumor detection, which was created using the bagging approach. The primary classifier in the ensemble classifier is the support vector machine and random forest classifier. Furthermore, the proposed ensemble classifier uses particle swarm optimization to work with the feature optimization method. The process of feature optimization enhances the feature selection process for the classifier. The brain tumor images are captured by magnetic Resonance imaging (MRI). The MRI images are rich in texture features, and now discrete wavelet transform applies for feature extraction. The BRATS dataset is being used to evaluate the suggested classification technique, which was implemented in MATLAB software. Extreme learning (EL) and CNN are compared to two existing algorithms in the suggested algorithm. The study of the results indicates that the suggested algorithm outperformed existing algorithms by 2%.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115499326","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

A Deep-Learning Based Approach to Resource Allocation in NOMA Based Cognitive Radio Network with Heterogeneous IoT Users 基于深度学习的异构物联网用户NOMA认知无线网络资源分配方法

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793269

S. Devipriya, J. M. Leo Manickam, K. Jasmine Mystica

{"title":"A Deep-Learning Based Approach to Resource Allocation in NOMA Based Cognitive Radio Network with Heterogeneous IoT Users","authors":"S. Devipriya, J. M. Leo Manickam, K. Jasmine Mystica","doi":"10.1109/icdcece53908.2022.9793269","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9793269","url":null,"abstract":"In 5G mobile technology, the expansion of Internet of Things (IoT) has created a huge need for a wide variety of latency, dependability, and energy efficiency requirements, etc… Spectrum efficiency (SE) of such large scale network needs to be improved with an economical power consumption. The non-orthogonal multiple access (NOMA) technique is utilized to enhance system efficiency (SE) by merging several users in the same frequency. An energy efficient (EE) resource allocation (RA) problem has been formulated for NOMA based heterogeneous IoT networks. Using the examining technique of Cognitive Radio (CR) Network, a stepwise RA scheme is assigned for IoT Users (IoTUs) and Mobile Users (MUs) with the mutual interference management. Later, to find a solution quickly and flawlessly, a deep recurrent neural network (RNN) based mechanism has been proposed. Furthermore, to systemize the approach of heterogeneous users, a rate and precedence demands based scheduling method has been implemented. Extensive results demonstrate that the deep learning based framework performs better than traditional RA methods in terms of computational complexity. On comparing with the prevailing OFDMA technique, the NOMA system with the imperfect SIC provides an acceptable performance on the EE at the cost of low EE and high power consumption.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"354 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122796609","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

Hyperspectral Image Classification using Digital Signature Comparison based Classifier 基于数字签名比较分类器的高光谱图像分类

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793137

Renuvenkataswamy Sunkara, A. K. Singh, G. Kadambi, Prameela Kumari N

{"title":"Hyperspectral Image Classification using Digital Signature Comparison based Classifier","authors":"Renuvenkataswamy Sunkara, A. K. Singh, G. Kadambi, Prameela Kumari N","doi":"10.1109/icdcece53908.2022.9793137","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9793137","url":null,"abstract":"In this paper, a pixel-based supervised classifier based on Digital Signature Comparison (DSC) for hyperspectral image classification is proposed. The classifier is conceptually simple, easy to implement, and requires less memory space for storing training parameters when compared to the widely used and popular Support Vector Machine (SVM) classifier and the Naive Bayes (NB) classifier. In the proposed classifier, the digital signatures are generated by successive comparison of the Digital Number (DN) of the present spectral band to the DN of its adjacent spectral band from the set of original spectral bands of the hyperspectral image, for both training data (for each class label) and test data. Then, a comparison of the digital signatures of training data with test data and finding the number of matches of comparison are performed to assign a test pixel to the class for which it has the highest or majority of votes (or matches). Performance accuracy derived through simulation results on two hyperspectral images, namely Washington-DC Mall (WDC-M) and Salinas-A, substantiate the effectiveness of the proposed classifier (with lesser training parameters’ storage).","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117262765","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

Identification and Classification of Melanoma Using Deep Learning Algorithm 基于深度学习算法的黑色素瘤识别与分类

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792698

Jaynab Sultana, Binoy Saha, Shuvo Khan, T.M Sanjida, Madina Hasan, Mohammad Monirujjaman Khan

{"title":"Identification and Classification of Melanoma Using Deep Learning Algorithm","authors":"Jaynab Sultana, Binoy Saha, Shuvo Khan, T.M Sanjida, Madina Hasan, Mohammad Monirujjaman Khan","doi":"10.1109/icdcece53908.2022.9792698","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9792698","url":null,"abstract":"Melanoma also referred to as the most dangerous skin cancer, grow in the melanocytes that generate the pigment melanin. These cancers spread to different parts of the human skin in different ways and gradually lead to death. So, this cancer needs to be detected soon and easily. People often ignore skin problems and do not want to take complex medical tests. The aim of this article is to classify melanoma and non-melanoma by applying a deep learning-based model through dermoscopic images from a lesion dataset of Kaggle. Transfer learning with inception v3 model is utilized for melanoma classification. Convolutional neural networking (CNN) helps to achieve an encouraging result. A dataset from Kaggle containing total 2,750 dermoscopic images was being used. It consisted of three classes. The test accuracy is 77.25% in Jupyter notebook and 96.19% in colab google drive was achieved. The model can distinguish melanoma and non-melanoma (nevus and seborrheic keratosis) with satisfactory level of prediction. People need to take any dermoscopic image of their lesion and the get result can be generated within 5 minutes. Thus, the system can be used in a simple way to detect and distinguish between melanoma and non-melanoma.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128707624","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: 3

An Efficient Deep Learning Based Chatbot for GRIET 基于深度学习的高效GRIET聊天机器人

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792978

A. N, Sai Sravya Thumati, Sandhya Reyya

引用次数: 1