2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)最新文献_第3页

FPGA Implementation of High Speed 64-Bit Data Width True Random Number Generator using Clock Managers With Metastability 基于亚稳态时钟管理器的高速64位数据宽度真随机数发生器的FPGA实现

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2023-02-22 DOI: 10.1109/ICECCT56650.2023.10179671

C. Marimuthu, B. Priyanka

{"title":"FPGA Implementation of High Speed 64-Bit Data Width True Random Number Generator using Clock Managers With Metastability","authors":"C. Marimuthu, B. Priyanka","doi":"10.1109/ICECCT56650.2023.10179671","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179671","url":null,"abstract":"Currently, cybersecurity plays a crucial role in various fields to ensure secure data communication. True Random Number Generators (TRNGs) are crucial components for many critical security applications. While analog-based entropy sources are often used in digital-based solutions, there is a high demand for digital-based solutions, especially for Field Programmable Gate Array (FPGA)-based digital systems. A unique technique has been developed to simplify the design of TRNGs on FPGA devices. This technique utilizes the runtime capabilities of the hardware primitives provided by the Digital Clock Manager (DCM) to adjust the phase shift between two clock signals. An auto-tuning approach automatically adjusts the phase difference between the clock signals to force one or more flip-flops (FFs) to enter the metastability zone, which is used as a source of unpredictability in the system. Additionally, the fast carry-chain hardware primitive is uniquely used to further enhance the randomness of the generated bits. Lastly, a powerful on-chip post-processing strategy is employed to prevent any interference with the TRNG output. This work was implemented in verilog HDL, with 32 and 64 data width, and synthesized in Xilinx Zynq FPGA. The characteristics of the TRNG design were evaluated based on area, delay, and power consumption.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131283552","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

LRDADF: An AI Enabled Framework for Detecting Low-Rate DDoS Attacks in Cloud Computing Environments LRDADF:基于AI的云计算环境下低速率DDoS攻击检测框架

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2023-02-22 DOI: 10.1109/ICECCT56650.2023.10179834

V. Venkateshwarlu, Durgunala Ranjith, Amireddy Raju

{"title":"LRDADF: An AI Enabled Framework for Detecting Low-Rate DDoS Attacks in Cloud Computing Environments","authors":"V. Venkateshwarlu, Durgunala Ranjith, Amireddy Raju","doi":"10.1109/ICECCT56650.2023.10179834","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179834","url":null,"abstract":"In cloud computing environment DDoS attacks are continually evolving with intelligent strategies. Low-rate DDoS attack is one such strategy that make it difficult to detect attack. At the same time, cloud infrastructure is also evolving rapidly. Container based technology enables cloud computing to have lightweight approaches in resource utilization and flexibility in scaling services. The existing DDoS attack detection methods used in cloud computing are not adequate when adversaries employ the modality known low-rate DDoS attack. There is need for an approach that not only detects the attack but also defeat the attack as much as possible. Towards this end, in this paper, we proposed a framework named Low-Rate DDoS Attack Detection Framework (LRDADF). Since low-rate DDoS attacks are difficult to be defeated, we proposed a mathematical model to realize mitigation strategy besides employing deep learning methods to have effective means of detecting such attacks. We proposed an algorithm named Hybrid Approach for Low-Rate DDoS Detection (HA-LRDD). The algorithm uses an Artificial Intelligence (AI) enabled methods comprising of deep Convolutional Neural Network (CNN) and deep autoencoder. Another algorithm known as Dynamic Low-Rate DDoS Mitigation (DLDM) is used to minimize the effect of the attack after detection or even defeat the attack by ensuring the smooth functioning of cloud infrastructure which is under attack. Extensive simulation study revealed that the proposed framework is able to detect low-rate DDoS attacks and also mitigate the attacks to ensure there is acceptable quality of service in cloud computing environments.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"277 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124230828","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

Dual-band Patch Antenna for Next Generation Indoor Access Point Receiver 新一代室内接入点接收机的双频贴片天线

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2023-02-22 DOI: 10.1109/ICECCT56650.2023.10179703

Thirunavukkuarasu G, Nithin R, N. S, Rajalingam J

引用次数: 0

New versions Elliptic Curve Group Ring Public Key Cryptosystems 新版本椭圆曲线群环公钥密码系统

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2023-02-22 DOI: 10.1109/ICECCT56650.2023.10179798

Qasim Mohsin Luhaib, R. K. Ajeena

引用次数: 0

An overview of GeoSpatial Artificial Intelligence technologies for city planning and development 城市规划与发展的地理空间人工智能技术综述

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2023-02-22 DOI: 10.1109/ICECCT56650.2023.10179796

Hafsa Ouchra, A. Belangour, Allae Erraissi

{"title":"An overview of GeoSpatial Artificial Intelligence technologies for city planning and development","authors":"Hafsa Ouchra, A. Belangour, Allae Erraissi","doi":"10.1109/ICECCT56650.2023.10179796","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179796","url":null,"abstract":"Geo-spatial artificial intelligence (GeoAI) is an interdisciplinary field that combines techniques and methods from engineering, computer science, statistics, and space science to analyze and model spatial and temporal phenomena using artificial intelligence (AI) methods. This subject has a large influence on society and the economy since it focuses on real-world challenges. Artificial intelligence is fast developing in the automatic recognition of characteristics in geospatial data such as satellite photos, aerial photographs, etc. The computer community is very interested in satellite imagery because it wants to help machines understand their surroundings by analyzing satellite data. This type of treatment has the potential to test large areas at low cost. Remote sensing and geographic data enable collection, analysis, and processing of global observation data for civilian and military applications. This document provides an overview of GeoAI methods in urban planning, including the definition of GeoSpatial Artificial Intelligence and the distinctions between GeoAI and traditional AI. Different types of geospatial data satellites are also depicted. Integrating Geographic Information System (GIS) with Artificial Intelligence and using GeoAI tools and techniques are critical steps in successful geographic data analysis.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123596601","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

A Comparative Performance Analysis of CBIR using Wavelet and Contourlet Transform 基于小波变换和Contourlet变换的CBIR性能对比分析

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2023-02-22 DOI: 10.1109/ICECCT56650.2023.10179614

Parmeshwar Birajadar, Abhijit Shete

引用次数: 0

Enhanced Time Slotted Channel Scheduling for Constrained Networks 约束网络的改进时隙信道调度

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2023-02-22 DOI: 10.1109/ICECCT56650.2023.10179825

N. Pradhan, B. Chaudhari, P. Khandekar

引用次数: 0

Enhanced Design of PI-PD Controller based on Smith Predictor for Integrating Processes with Time Delay 基于Smith预测器的PI-PD控制器对时滞过程集成的改进设计

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2023-02-22 DOI: 10.1109/ICECCT56650.2023.10179733

V. Sreevalli, M. Rathaiah, R. Kiranmayi, P. Bharath Kumar, K. Nagabhushanam, Ahamadia. K Firdouse Kowsar

{"title":"Enhanced Design of PI-PD Controller based on Smith Predictor for Integrating Processes with Time Delay","authors":"V. Sreevalli, M. Rathaiah, R. Kiranmayi, P. Bharath Kumar, K. Nagabhushanam, Ahamadia. K Firdouse Kowsar","doi":"10.1109/ICECCT56650.2023.10179733","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179733","url":null,"abstract":"Proportional-Integral-Derivative (PID) controller is the most popular technique used in several industrial applications and has done more research work throughout many years. Being popular, the PID controller exhibits undesirable output response for some open loop unstable process such as integrating ones. This paper projects a design of smith predictor with an outer loop Proportional-Integral (PI) controller and interior loop Proportional-Derivative (PD) controller. The smith predictor uses plant process as a predictive model and it eliminates the delay part of the closed loop characteristic equation. Three various examples of integrating plants with time delay are carried out in simulation study to evaluate the obtained performance of projected method. The results from the simulation study are compared with the optimal PI-PD method. To illustrate the robustness of projected method, parameter uncertainties are included to the process and the following results are exhibited. The projected method is more robust to such dissimilarities. Disturbance of magnitude 0.1 and a white noise of 0.0001 magnitude are added to the system and the improved performance are shown in the simulation results. The proposed control algorithm will be designed and implemented in MATLAB/SIMULINK environment.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128540429","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

Batch Normalized Siamese Network Deep Learning Based Image Similarity Estimation 基于批归一化Siamese网络深度学习的图像相似度估计

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2023-02-22 DOI: 10.1109/ICECCT56650.2023.10179689

M. Devi, J. Pandian, Aparna Joshi, Yeluri Praveen

{"title":"Batch Normalized Siamese Network Deep Learning Based Image Similarity Estimation","authors":"M. Devi, J. Pandian, Aparna Joshi, Yeluri Praveen","doi":"10.1109/ICECCT56650.2023.10179689","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179689","url":null,"abstract":"The assessment of how two distinct images are equal are indeed called image similarity and consistency. In other words, it measures how much the intensity patterns in two images are comparable to one another. In order to achieve this, researchers examine the image descriptors recursively in order to identify descriptor pairs that are comparable. The two images are deemed comparable if the number of related descriptors exceeds a predetermined threshold and both images exhibit the very same entity. The computation of image similarity is used for various applications which graves to be the mandatory process for production of the application. With this intent, the Fashion MNIST dataset from KAGGLE is used for implementing the image similarity estimation. This paper proposes Batch Normalized Siamese Network (BNSN) deep learning based model for computing the image similarity. The BNSN model is designed with two subnetworks that generates feature vectors of two input images. The lambda batch normalization is performed with single dense layer to predict the image similarity with label 0 indicating the identical images and label 1 denoting the different images. The 30,000 training images were fitted with BNSN and tested with 30,000 images. Python was implemented on a Geforce Tesla V100 NVidia Graphics card webserver with a batch size of 64 and 30 training epochs. The training images are also tested with traditional image similarity method and implementation of proposed BNSN shows the accuracy of 91.91%, Precision of 92.93%, Recall of 90.72% and FScore of 91.81%.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"65 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129817669","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

Building Accessible Video Games for Children with an Autism Spectrum Disorder 为患有自闭症谱系障碍的儿童制作无障碍电子游戏

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT) Pub Date : 2023-02-22 DOI: 10.1109/ICECCT56650.2023.10179616

Akash Bagchi, Akshaya Nadathur, Pranjal Padakannaya, Siddiq Khan, Siddiq Khan

引用次数: 0