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IoT-based Agriculture Farm Monitoring System and Development of Plant Leaf Disease Detection Using Deep Neural Network

IoT-Based Agriculture Farm Monitoring System and Development...

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2024 international conference on signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Mim, Tanvir Islam Fahim, Ivtiaz Ahkam Islam, Nymul Rahman, Mizanur Rahman, Yasin Rouf, Nirjhor Anowar, Tanbir Ibne Mondal, Shuvra Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798331540685

Throughout history, agriculture or farming has played a vital role in human civilization by providing people's fundamental necessities and significant contributions to a country's economy. Although small-scale smart farming has started in a few nations, it has yet to be adopted scientifically globally. The rapid growth of the world population along with urbanization has rendered modern agriculture increasingly essential day by day. Towards multi-domain modernization, the internet of Things (IoT) technology is a developing paradigm that aims to link many smart physical parameters in smart agriculture. In this paper, an IoT-based smart agricultural farm monitoring system prototype has been proposed which has been designed considering different environmental parameters. Additionally, image processing-based plant disease detection model has been developed in two different methods. The system incorporates various sensors, actuators, and a microcontroller to monitor and control temperature, humidity, soil moisture, and rainfall. It also includes a water pump motor for automated irrigation where a Wi-Fi module allows remote monitoring and controlled by a developed smartphone app. To validate, the proposed hardware model was simulated using Electronic Design Automation (EDA) tools. There have been notable outcomes observed on temperature, humidity, soil moisture, and other monitoring parameters data reading from LCD. Also, deep learning-based image processing algorithm has been proposed using Feature extraction and Convolutional Neural Network (CNN) methods and achieved a high accuracy of about 96%-98% in plant disease detection. The overall proposed system offers improved resource utilization and smart agricultural practices for expanded functionalities and long-term sustainability to ensure food security for the growing population. © 2024 IEEE.

关键词： Soil moisture

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Visual-auditory substitution device for indoor navigation based on fast visual marker detection

Visual-auditory substitution device for indoor navigation ba...

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international IEEE conference on signal-image Technologies and internet-based System

作者： Florian Scalvini Camille Bordeau Maxime Ambard Cyrille Migniot Stéphane Argon Julien Dubois ImViA EA 7535 Univ. Bourgogne-Franche-Comté Dijon France LEAD CNRS UMR 5022 Univ. Bourgogne-Franche-Comté Dijon France

This paper proposes a new navigation device to assist visually impaired people reach a defined destination safely in the indoor environment. This approach based on visual-auditory substitution provides the user a 2D spatial sound perception of the destination and of nearby and dangerous obstacles. Visual markers are placed at several relevant locations to create a mesh of the building where each marker is visually accessible from another marker. A graph representation of markers locations and their connection to each other defines by a way finding algorithm the shortest path reach to the wished position. The navigation task is achieved by moving from visual marker to visual marker until the desired destination is reached. These markers can be used independently of any other system or in addition to other solutions based on geolocalisation and/or a digital building model. Moreover, further information can be associated to the markers, and therefore verbalize to the user for instance a temporary hazards, a door presence or any other usual displayed information. The passive visual markers enables to deploy easily and quickly a scalable and low-cost solution to "signpost" the environment for users. Combined with our realtime implemented obstacle detection, their analysis enables the navigational abilities of visually impaired people to be improved.

关键词： Visualization Indoor navigation Geology Buildings Hazards Indoor environment Task analysis

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Adaptive blockage detection and prevention for radar chip-based autonomous driving systems 3

Adaptive blockage detection and prevention for radar chip-ba...

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

作者： Chen, Chao Dai, Mian Liu, Yuying Du, Juan Zhao, Shuai Huacheng Certificate CATARC Dongli Tianjin300399 China CICT CATARC Tianjin Xiqing300393 China ATIRI CATARC Tianjin Dongli300300 China

ISBN: (纸本)9781510668522

This paper presents an radar blockage recognition approach along with blindness prevention measures utilizing intelligent computing methods to address the challenges in radar chip-based autonomous driving systems. A process and algorithm are initially created for detection, enabling real-Time and precise identification. The adaptive blockage performance controller and the automatic diagnosis and recovery strategy are then proposed as part of the adaptive blockage prevention strategy, which enables the system to quickly return to its normal operating state by intelligently adjusting parameters when the sensor blockage occurs. The conventional linear threshold methods are replaced with nonlinear machine learning to deal with complex performance variations, with the aim of lowering false positives and negatives through real-Time optimization. A comparative experiment is conducted to show the effectiveness. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Autonomous vehicles

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3rd international conference on Advanced Computing and Applications, ICACA 2024

3rd International Conference on Advanced Computing and Appli...

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3rd international conference on Advanced Computing and Applications, ICACA 2024

ISBN: (纸本)9789819747986

The proceedings contain 47 papers. The special focus in this conference is on Advanced Computing and Applications. The topics include: Optimizing Task Offloading in internet of Medical Things systems: A Hybrid Fog-Cloud Approach with Actor-Critic Decision Making;solving Delivery Allocation for Logistic Network Using Quantum Annealing;The Combined ECC Scheme with Outer 3-Bit BEC and Inner SEC-DAEC Codes for FSO Communication System;analyzing Multisignature Scheme of Bitcoin based on Threshold Cryptography Guidelines;a Public Key Exchange Protocol Using Tropical Determinant for IoT Environment;Certificateless Verifiable Data Integrity Checking Scheme in Fog-CPSs Architecture;noPass—A Novel Passwordless Multi-authentication-based Approach for Secure Login;an Overview of the Discrete Logarithm Problem in Cryptography;QoS-Aware Decentralized Trustworthy Forensic Evidence Management Framework Using the internet of Vehicle Things (IoVT);implementation of Ethical Hacking—The Importance of Protecting User and System Data;Cryptanalysis of Two Correlation-based Digital Watermarking Schemes in DCT Domain;cryptanalysis of a Histogram-based Watermarking Framework;a Hybrid Deep Learning Framework for Text-Independent Automatic Speaker Recognition System;AMBTC-based High Capacity Data Hiding Scheme Exploiting PVD and BRP;The Impact of UX/UI Usability Constructs on Purchase Decisions for Mobile Food Ordering Applications in India;Breast Cancer Detection and Classification Using MF-FRFCM Segmentation and IGOA-ELM Model;audio Encryption Scheme Applying a 2D Cosine Sine Logistic Map;noisy Electronic Tongue signal Prediction for Tea Quality Estimation Using Autoencoder;a Chain Code-based Methodology for Loop Closure Detection in Digital images;A Hybrid Watermarking Scheme Using DWT and Haar Transform for image Authentication;leveraging Transfer Learning for Screening of Geriatric Depression;emotion-based Song Categorization with Support Vector Machines;filtering-based Movie Recommendati

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An image Scrambling Encryption Algorithm based on Combinatorial Chaos Mapping

An Image Scrambling Encryption Algorithm Based on Combinator...

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2023 international conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Luo, Yuan Zhou, Ying Hou, Yongxu Ye, Jun School of Cyberspace Security Hainan University Key Laboratory of Internet Information Retrieval of Hainan Province Haikou China

ISBN: (纸本)9798350313413

With the development and application of network technology, digital images, as a form of information transmission, are at high risk of being stolen or decrypted. Our scheme utilizes the nature of low dimensional chaos to generate a high dimensional chaotic system with more variables and higher complexity by mixing two different low dimensional chaotic systems to protect digital images and confirms that it outperforms the original system through simulation experiments. In this paper, a new image scrambling encryption algorithm for multi chaotic system is designed by combining improved tent mapping and high-dimensional chaotic system, and compared with the existing encryption scheme in terms of simulation experiments and security performance parameters, which proves that the scheme has better comprehensive ability. © 2023 IEEE.

关键词： Chaotic systems digital images DNA coding encryption algorithms scrambling and diffusion

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Spider Monkey Optimization-based image Data Forgery Detection Over Vehicular Cloud Computing 2nd

Spider Monkey Optimization-Based Image Data Forgery Detectio...

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2nd international conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Manasa, M. Chaudhari, Shilpa S. Ramaiah Institute of Technology Bangalore India Visvesvaraya Technological University Karnataka Belagavi India

ISBN: (纸本)9789819977109

images are commonly shared through various mediums such as newspapers, magazines, internet, and scientific journals. But with the accessibility of software like Photoshop, GIMP, and Coral Draw, it has become increasingly difficult to differentiate between an original and a forged image. Traditional methods for detecting image forgery rely on handcrafted features, but they are limited in their ability to identify specific types of tampering based on certain features in the image. To overcome this obstacle, deep learning methods have been employed. Increased vehicular networking image data faces this challenge. The proposed image forgery detection uses hybrid model of convolution neural networks (CNN), spider monkey optimization technique (SMO), and support vector machines (SVMs). CNN extracts deep semantic features of images transferred as vehicular data over the vehicular cloud using error level analysis (ELA) techniques. SMO chooses the optimal features from deep semantic features. Binary classifications based on SVM provide whether the image is original or forged. The results demonstrate that the proposed method achieves a recognition accuracy of 96%, which is significantly higher than the same model without SMO, which achieved an accuracy of 93%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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A Fault Diagnosis Method based on TCN-LSTM-SE Neural Networks for Distributed PV systems 2

A Fault Diagnosis Method based on TCN-LSTM-SE Neural Network...

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2nd IEEE international conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

作者： Wang, Zhao Wang, Chao Ke, Qiwen Zhang, Baoqun Wang, Yaqun Zeng, Shuang Kang, Taifeng Lan, Tian Liu, Zihao Liu, Congwei State Grid Beijing Electric Power Company Beijing China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China North China University of Technology Department of Electrical Engineering Beijing China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373646

Distributed photovoltaic power generation grid-connected systems add the variety and complexity of faults to the grid, so the research on fault diagnosis method with high diagnostic accuracy and practicality is of great significance for its safe and stable operation. The traditional modelling, control and detection methods are limited by the construction cost and susceptible to parameter fluctuations, which are difficult to adapt to future development needs. In this paper, we propose a fault diagnosis method based on the TCN-LSTM-SE model for distributed PV grid-connected systems and use it for three-phase unbalance and harmonic voltage prediction and fault diagnosis. Firstly, the SE attention mechanism is used to highlight fault features in the input data, then the TCN is chosen to perform feature extraction while maintaining the signal timing characteristics, the LSTM network finally memorizes the key features to complete the timing modeling. Simulation results show that the method has lower prediction error and higher diagnostic accuracy than mainstream network models, and the fault location and severity can be accurately known. © 2024 IEEE.

关键词： Photovoltaic systems Adaptation models Accuracy Attention mechanisms Neural networks Predictive models Feature extraction Harmonic analysis Timing Long short term memory

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Chaotic Technique for High Information Security based on Dual-Hiding Asynchronous-Logic AES Accelerator with High Resistance to Prevent Side-Channel Attacks 10

Chaotic Technique for High Information Security based on Dua...

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10th international conference on Communication and signal Processing, ICCSP 2024

作者： Prasanth, V.S. Munikumar, M. Ganesh, Maddigiri Kumar, Gulimi Ranjith Parveen Akhther, A. Madanapalle Institute of Technology & Science Dept. of ECE Andhra Pradesh 517 325 India Sathyabama Institute of Science & Technology Chennai600 119 India Sathyabama Institute of Science & Technology Dept. of Cse Chennai600 119 India

ISBN: (纸本)9798350353068

In the majority of applications, including e-commerce, online banking, satellite, wireless communications, electronic devices, appliances, signal and digital image processing, etc., data security is regarded as a critical component. Cryptography is a technique which is used to keep the data safe and secret by changing it into a form so that unnecessary users cannot understand. This technique provides a strong, economical way to keep our data as a secret and helps in data integrity verification. Cryptography technique is facing issue from side channel attacks and unable to provide error free data. The proposed accelerator leverages chaotic-based dual-hiding techniques combined with asynchronous logic design to enhance security while maintaining efficiency Chaos-based encryption systems can avoid the difference between power factor (DPA) and differential failure factor (DFA) output by introducing randomness and non-uniformity into the encryption process. Additionally, asynchronous logic design mitigates timing-based side-channel vulnerabilities by eliminating clock signals, thereby disrupting attackers' ability to extract sensitive information through timing analysis. Also, a chaotic sequence is used in between the process of generating cipher text. Thus, the design will decrease the area overhead and provide strong resistance from side channel attacks and make the system more reliable. The proposed design is executed in Xilinx Vivado 2018.3 and the output is verified using simulation results. © 2024 IEEE.

关键词： Digital devices

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Machine Learning based Approach by image Recognition for Detection of Islanding

Machine Learning Based Approach by Image Recognition for Det...

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2024 international conference on Smart systems for Applications in Electrical Sciences, ICSSES 2024

作者： Pal, Kumaresh Goswami, Subhadip Benia, Tapas Kumar Bhuyan, Anshuman Manna, Pranendu Kumbhakar, Shyamal National Institute of Technology Department of Electrical Engineering Jamshedpur India National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Techno India University Department of Electrical Engineering Kolkata India

ISBN: (纸本)9798350364040

Excessive penetration of DG sources poses a significant difficulty known as islanding. The act of islanding has the potential to inflict harm upon both customers and their equipment. As per the IEEE 1547 DG interconnection criteria, it is necessary to identify islanding within a two-second timeframe and then deactivate the DG. The present study introduces a novel methodology for identifying islanding through the utilisation of visual pattern recognition and Machine Learning classification techniques. The Histogram of Oriented Gradient (HOG) is employed as a feature descriptor in order to perform pattern recognition on images, with the objective of identifying both non-islanding and islanding occurrences. Therefore, the spectrogram images are acquired from the time-series signal that exists at the point of common coupling. The provided photos are employed for the purpose of extracting features using the HOG algorithm. These extracted features are subsequently used as input for the k-Nearest Neighbors (KNN) classifier, both for training and testing purposes. The spectrogram image is obtained by using the rate of change of the negative sequence voltage component. The efficacy of the KNN classifier is evaluated using 5,10,20, and 25 -fold cross-validations. The classification findings indicate that the utilisation of the KNN classifier and HOG feature for visual pattern recognition yielded exceptional outcomes, with an accuracy rate of 96.75% and a detection time of 211 ms. © 2024 IEEE.

关键词： image recognition

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Unsupervised Real-Time Flow Data Drift Detection based on Model Logits for internet of Things Network Traffic Classification 17

Unsupervised Real-Time Flow Data Drift Detection Based on Mo...

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IEEE Congress on Cybermatics: 17th IEEE international conference on internet of Things, iThings 2024, 20th IEEE international conference on Green Computing and Communications, GreenCom 2024, 17th IEEE international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE international conference on Smart Data, SmartData 2024

作者： Wang, Pan Liu, Minyao Li, Zeyi Wang, Zixuan Chen, Xuejiao Nanjing University of Posts and Telecommunications School of Modern Posts Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China Nanjing Vocational College of Information Technology School of Communications Nanjing China

ISBN: (纸本)9798350351637

In recent years, numerous internet of Things (IoT) traffic classification techniques based on deep learning have been proposed, demonstrating impressive classification performance. With ubiquitous access to massive heterogeneous IoT endpoints, networks exhibit high levels of dynamism, heterogeneity, and complexity, leading to potential data distribution drift (DDD), which will significantly degrade the performance of IoT network traffic classification. Although numerous scholars have explored various drift detection methods, most studies require real-time stream data labeling. Meanwhile, some unsupervised methods incur high resource consumption and latency when applied to network traffic. To address this issue, we propose an unsupervised real-time drift detection method based on Model Logits (ML-UDD). based on the Logit scores from the deep learning model's traffic classification/inference process, the model's Maximum Logit Score (MLS) will be calculated as the key metric for detection DDD. This method does not need to rely on the data labels, thus it can explore in an unsupervised manner, which is superior to many other supervised methods in terms of expert experience and labor. Furthermore, an adaptive weighted CUSUM-type rapid detection approach is proposed to detect the DDD based on a sliding time window during the traffic flow proceeding. In addition, this method can further locate the 'Drift Flow' where DDD happened, namely Flow Drift Location. This offers a real-time and efficient method to monitor and identify DDD with minimal resource consumption. The experiments have been conducted on two public and private network traffic datasets. The comparative experiments with other SOTA DDD detection methods have demonstrated that the proposed ML-UDD method can surpass these methods in terms of different performance metrics. © 2024 IEEE.

关键词： image thinning

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