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A Proficient and Economical Approach for IoT-based Smart Doorbell System 1

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international conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2022

作者： Thakkar, Abhi K. Ukani, Vijay Institute of Technology Nirma University Gujarat Ahmedabad382421 India

ISBN: (数字)9789819932504

ISBN: (纸本)9789819932498

The internet of Things has expanded and is now being used in electronic devices of daily use because of the developments in technology and connectivity. The concept is to employ a computing device to automatically or remotely monitor the essential functions and properties of these things. The primary concern of a homeowner is that he/she remains safe from intruders or strangers;therefore, smart doorbells may be a solution to alleviate this issue. Depending on the system design, smart doorbells provide a variety of security measures such as biometric authentication, remote access and more. This paper discusses an effective design approach to IoT-based smart doorbell systems by using a framework that employs well-established algorithms for face detection, liveness detection and face recognition as well as some minor aspects of enhancing security. Another aspect is to have low-cost, readily available components so that individuals can build their own smart doorbell security devices by following a similar approach. The smart doorbell system is able to recognise the face, differentiate between a live person and a fake image and classify the person as either an owner or a visitor. Depending on the classification, the door is unlocked for the owner and, in the presence of a visitor, the owner is notified on the mobile application. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： internet of things

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A novel heating load forecasting method based on information entropy attention mechanism 3

A novel heating load forecasting method based on information...

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3rd international conference on Advanced Algorithms and signal image Processing, AASIP 2023

作者： Yin, Jianhua Zhang, Guobin Huo, Hongyan Zhang, Qian Inner Mongolia Electric Power Science and Research Institute Huhhot010020 China

ISBN: (纸本)9781510668522

Due to the high delay and large inertia characteristics, there are issues with inefficient regulation and energy waste in district heating systems. Improving the predictive ability of regional heating load can help improve the accuracy of thermal network control. In order to improve the accuracy of heating load prediction, this paper proposes a new heating load prediction algorithm, STL-IEA-LSTM, which combines LSTM model with information entropy attention mechanism to separately model the data modeling of heating load after STL algorithm decomposition, so as to enhance the prediction ability of heating load. The experimental evaluation conducted on the operating data of a heating station in Shijiazhuang during the heating season demonstrated that the STL-IEA-LSTM combination model constructed in this study achieved a MAPE of 0.7031%, indicating its superior performance in predicting heating load. Comparative analysis with other models, including STL-LSTM, entropy-based LSTM, classic LSTM, XGBoost, and MLP, further confirmed the effectiveness of the proposed method in achieving high prediction accuracy. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Forecasting

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3rd international Workshop on New Approaches for Multidimensional signal Processing, NAMSP 2022

3rd International Workshop on New Approaches for Multidimens...

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3rd international Workshop on New Approaches for Multidimensional signal Processing, NAMSP 2022

ISBN: (纸本)9789811978418

The proceedings contain 23 papers. The special focus in this conference is on New Approaches for Multidimensional signal Processing. The topics include: Development of IoT Indoor Monitoring System for Independent Elderly;improving the Process of Evaluating User Stories Using the Paraconsistent Annotated Evidential Logic Eτ;neural Network Algorithm Applied in Electrical Engineering Automation;Truss Structure Optimization Design based on FE-PSO-SQP Algorithm;practice System of Ant Colony Optimization Algorithm in Business Administration;literature Review of Smart Contracts Using Blockchain technology;a Comprehensive Study of 5th Generation Scheduling Algorithms;a Comparative Analysis of Homogeneous and Heterogeneous Protocols to Maximize the Lifetime of Wireless Sensor Network for Precision Agriculture;the Integration Development and Upgrading Path of Industry 4.0 Architecture Industrial Engineering Network Driven by Big Data;video Tracing of Moving Objects by Fusing Three-Term Decompositions;SAAS Application Prospect Analysis in Hrm and Methods to Upgrade the Contemporary System;All Digital Phase Locked Loop (ADPLL) and Its Blocks—A Comprehensive Knowledge;on Realization of Smart Logistic Warehouse Management with internet of Things;development and Design of Intelligent Traction System for Bulk Cargo Terminal;deep Learning Approaches for Classroom Audio Classification Using Mel Spectrograms;Tensor Spectral Pyramid for Color Video Sequences Representation, based on 3D FO-AHKLT;electromyography signal Acquisition, Processing, Optimization and Its Applications;research on the Radar signal Classification Method based on the Deep Faith Network Model;measuring Machine Intelligence Using Black-Box-based Universal Intelligence Metrics;COVID Detection Using ECG image Reports: A Survey;disease Detection Techniques in Plants: Transition from Manual to Automation.

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Novel Framework for image Classification based on Patch-based CNN Model

Novel Framework for Image Classification Based on Patch-Base...

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international conference on Data Analytics and Management, ICDAM 2023

作者： Gour, Ayush Bhanodia, Praveen Kumar Sethi, Kamal K. Rajput, Shivashankar Acropolis Institute of Technology and Research Indore Indore India

ISBN: (纸本)9789819965465

image data is being developed at a rapid speed every day due to the speedy growth of internet;resultantly, image classification has become very crucial topic in solving computer vision-related tasks like images segmentation and object detection, etc. The convolutional neural networks (CNN) have become the preferred choice for categorizing images since 2012, because of its capability to find out hierarchical representations from the original image data without human intervention. CNNs are excellent at deciphering the intricate visual features present in images because they can capture local patterns and gradually build up more abstract and high-level representations. In several image processing tasks, patch-based solutions performed better than rival approaches. This research work illustrates the patch-based CNN model to increase picture classification accuracy, which may also serves as a model assembly without supplementary cost of model training. At the same time, applying data augmentation techniques throughout the training as well as testing phases helps assure network optimization as well as improvise generalization of the model. This methodology can transmit specific domain information effectively by processing individual patches instead of the entire image. To increase training samples, essential patches of appropriate size were extracted from the original images, improving overall model performance. Additionally, our approach is applicable to various network architectures and data augmentation techniques, making it versatile for deep learning tasks. Thorough evaluations on a real-world dataset demonstrate the superior performance of our proposed framework which also achieves an accuracy of 96.21% on the CIFAR-10 dataset, surpassing the state-of-the-art results (93.41%). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Object detection

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Linear DOA Estimation Method based on NGO-SVM 23

Linear DOA Estimation Method Based on NGO-SVM

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23rd IEEE international conference on Communication technology, ICCT 2023

作者： Zhang, Baojie Wang, Zhongxun Cai, Zhuoran He, Pengfei Liu, Congguang School of Physics and Electronic Information Yantai University Yantai China

ISBN: (纸本)9798350325959

In recent years, it has become a major trend to use DOA (Direction of Arrival) estimation method to process array signals in large array systems. DOA estimation methods have shown remarkable capabilities in spatial signal resolution and improving system capacity. However, traditional DOA estimation methods suffer from severe performance degradation in low signal-to-noise ratio conditions. To address this issue, this paper proposes a linear DOA estimation method based on NGO-SVM. The algorithm combines the NGO (Northern Goshawk Optimization) algorithm with SVM (Support Vector Machines) to enhance the performance of the NGO algorithm. By training the extracted features from the samples, the algorithm improves the accuracy of DOA linear estimation. Simulation experiments demonstrate that under identical conditions, the proposed algorithm outperforms the comparison algorithms in terms of Root Mean Square Error (RMSE) performance and exhibits superior angular resolution. © 2023 IEEE.

关键词： direction of arrival estimation northern goshawk algorithm stochastic matrix theory support vector machine

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An IoT-based Pest Detection and Alert System for Farmers Using CNN-LSTM Approach

An IoT-Based Pest Detection and Alert System for Farmers Usi...

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2023 international conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2023

作者： Laxmi, M. Koshariya, Ashok Kumar Hazela, Bramah Singh, Prashant Manju, M. Jeyaselvi, M. Geethanjali College of Engineering and Technology Ece Hyderabad Telangana Cheeryal India Collage of Agriculture and Research Station Indira Gandhi Krishi Vishwavidyalaya Department of Plant Pathology Fingeshwar Chhattisgarh Gariyaband India Amity School of Engineering & Technology Amity University Uttar Pradesh India Amity School of Engineering & Technology Lucknow Amity University Uttar Pradesh India School of Agriculture Sr University Telangana Warangal India Srm Institute of Science and Technology Department of Networking and Communications Chennai Kattankulathur India

ISBN: (纸本)9798350300857

Insects and other pests are a major cause of agricultural economic loss in India. Consequently, farmers heavily rely on pesticides to combat weeds, insects, and plant diseases. Misusing pesticides is bad for people and the economy. This research study proposes a novel method for controlling pests that takes use of image processing and the internet of Things in order to cut down on the application of harmful chemicals. Insects can be detected with the help of an infrared sensor (PIR) in the suggested system. To verify the pest's existence in the field, photos are captured using image processing. After receiving an input image, the suggested process includes four steps: preprocessing, segmentation, feature extraction, and model performance evaluation. Processing in grayscale and improving individual features are among the preprocessing methods employed. It employs a histogram to perform the segmentation. Extraction of Region of Interest (ROI), Extraction of Detailed image, Extraction of ORB Key Points, and Extraction of Feature Descriptor are all used in the process of feature extraction. CNN, LSTM, and CNN-LSTM make up the performance evaluation suite. When compared to those other two tried-and-true approaches, the proposed CNN-LSTM strategy fares quite well. © 2023 IEEE.

关键词： Discrete wavelet transforms

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Research on Underwater image Restoration Algorithm based on Generative Adversarial Network 6

Research on Underwater Image Restoration Algorithm Based on ...

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6th IEEE international conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Wang, Ruijie Feng, Peng Qi, Wei He, Peng Liu, Yanan Chongqing University Ministry of Education Key Laboratory of Optoelectronics Technology and Systems Ministry of Education Engineering Research Center for Nondestructive Testing with Industrial CT Chongqing China Chongqing Technology and Business Institute School of Electronics and Information Engineering Chongqing China

ISBN: (纸本)9798350325485

Underwater optical imaging is prone to image distortion, blurring and other problems arising from thermal disturbance. To tackle down the above-mentioned problems, underwater images affected by thermal disturbance are analyzed in this study from two perspectives (i.e., image gray histogram distribution and similarity assessment). Subsequently, a deep learning-based underwater thermal disturbance image restoration algorithm is developed based on generative adversarial networks. The U-Net structure is employed in the generator network, and MultiRes blocks are adopted in the contracting and expanding paths for extracting image feature information on different scales maximally. Moreover, a Bottleneck Attention Module (BAM) is introduced in the contracting path to place a full focus on the distorted image information. High-precision image restoration is achieved using the Wasserstein adversarial loss function in the adversarial process between the generator and discriminator networks. To validate the algorithm, an underwater thermal imaging experimental platform is independently built, and a dataset is established. As indicated by the experimental results, the proposed algorithm is capable of effectively restoring images while preserving key edge features, such that a Structural Similarity (SSIM) index of 0.7749 and a Peak signal to Noise Ratio (PSNR) index of 21.28 are generated. This study can lay a solid foundation for further processing in underwater image restoration. © 2023 IEEE.

关键词： image reconstruction

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Development of a Verilog-based System for Pattern Detection Using Convolutional Neural Networks 2

Development of a Verilog-Based System for Pattern Detection ...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Kandula, Bala Sindhuri Singh, Sangeeta Tata, Subhashini Manasaveena, T. Bolisetty, V Vijayasri Sadguna Kumari, V. S.R.K.R Engineering College Department of E.C.E Bhimavaram India Vardhaman College of Engineering Hyderabad Department of E.C.E Telangana India SR Gudlavalleru Engineering College Department of E.C.E Gudlavalleru India NRI Institute of Technology Department of E.C.E Vijayawada India Aditya College of Engineering and Technology Department of E.C.E Surampalem India Pydah College of Engineering Department of E.C.E Kakinada India

ISBN: (纸本)9798350366570

Pattern detection is a foundational element in computer vision, crucial for advancements in autonomous driving, surveillance, and numerous other fields. Convolutional Neural Networks (CNNs) have significantly enhanced pattern detection by improving both accuracy and efficiency. This paper aims to develop a Verilog-based system for pattern detection using CNNs, focusing on detecting and counting occurrences of a specified test pattern within input images. The CNN architecture employs Convolutional Layers for feature extraction and Max Pooling Layers for spatial downsampling, both essential in image processing. Through iterative weight adjustments during training, the CNN learns to recognize patterns similar to the test pattern. After training, the system evaluates unseen images, predicting the presence and count of test pattern instances. To optimize performance, the system incorporates approximate adders to reduce area and gate count, thereby enhancing efficiency. These optimizations enable the system to achieve high performance while maintaining computational efficiency, providing detection and counting of specified patterns in images. This capability offers valuable insights and practical solutions for various real-world applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Android Application-based Security Surveillance Implementing Machine Learning 2nd

Android Application-Based Security Surveillance Implementing...

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2nd international conference on Big Data, Machine Learning, and Applications, BigDML 2021

作者： Das, Henakshi Meher, Preetisudha National Institute of Technology Arunachal Pradesh791119 India

ISBN: (纸本)9789819934805

Usually, home automation is constructed to control the appliances even when the owner is not at home. Our system is designed in such a way that the implementation should be cost-effective and powerful using the internet of Things. This system can be controlled via our developed Android-based mobile application. Using a camera inside home, an automatic identification of intruder as a part of intelligence access control system. Once the intruder gets detected by infrared sensor and our systems will compare the intruder face with existing data set, then it will match the face, if matched occur system will consider the face otherwise, an alert notification will be sent to owner android device. Then the owner can switch on the appliances (light and fans) using the mobile application [4]. This paper combines machine learning and image processing which are powerful modern technologies. This paper basically deals with the integration of all three criteria: Android application, machine learning, and home automation. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Android (operating system)

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Automatic closed-loop test technology of smart gateway based on mobile internet technology 4

Automatic closed-loop test technology of smart gateway based...

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4th IEEE international conference on Power, Intelligent Computing and systems, ICPICS 2022

作者： Quan, Xu Qiyuan, Xu Xinhao, Lin Nan, Ma Lei, Yu Yinliang, Liu Electric Power Research Institute CSG Guangzhou510663 China Shenzhen Power Supply Bureau Shenzhen518022 China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

The application of mobile internet technology in the test of power distribution terminal is analyzed, and the mobile system architecture of the test terminal is explained. The closed loop test system of the intelligent network is analyzed, and the system architecture, test process, hardware principle, and test function are proposed. Specific analysis is carried out on the test method of smart gateway based on mobile internet, including the test method and process of single smart gateway, the test method and process of multiple smart gateways, and the modular test method of smart gateway. The three-remote test of the power distribution terminal is used as a case for simulation calculation. Through the voltage signal, current signal and switch position of the test results, the effectiveness of the proposed mobile interconnection test method is demonstrated. © 2022 IEEE.

关键词： Gateways (computer networks)

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