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3rd International Conference on electronics and Sustainable Communication Systems, ICESC 2022

作者： Sasikala, G. Srinivasan, Sathiyandrakumar Navarajan, J. Theresa, M. Mercy VelTech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Department of Computer Application India Panimalar Institute of Technology Department of Electronics and Communication Engineering India Prince Shri Venkateshwara Padmavathi Engineering College Department of Electronics and Communication Engineering India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

A vital need exists to safeguard the safety of dams and reservoirs' water levels against natural or human threats. Therefore, this paper designs an efficient IoT-based Water Level Management system that may be used for this purpose. Owing to the existing flood gate management mechanism, uncontrolled water levels might cause a reservoir to overflow due to the lack of information to the flood gate supervisor. The Internet of Things (IoT) is one of the newest and fastest-growing disciplines of technology. Using a web server as an interface, the IoT on this prototype allows for real-time management and monitoring of water level. Ultrasonic sensors for reading water level data and stepper motor actuators for sluice gate control are utilized in conjunction with an ESP 32 micro-controller. Each of the 20 experiments shows an error rate of 0% and a data transmission latency of 2 seconds based on the readings from the IoT-based sensor © 2022 IEEE.

关键词： Water levels

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Self-Guided Autoencoders for Unsupervised Change Detection in Heterogeneous Remote Sensing Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2458-2471页

作者： Shi, Jiao Wu, Tiancheng Kai Qin, Alex Lei, Yu Jeon, Gwanggil Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Northwestern Polytechnical University in Shenzhen Research and Development Institute Shenzhen518057 China Chongqing Innovation Center of Northwestern Polytechnical University Chongqing401135 China Swinburne University of Technology Department of Computer Science and Software Engineering HawthornVIC3122 Australia Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this article proposes self-guided autoencoders (SGAE) for unsupervised change detection (CD) in heterogeneous remote sensing images (RSIs). Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudolabeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudolabeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method. © 2020 IEEE.

关键词： Feature extraction

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Automated Aeroponics System IoT-Based Monitoring and Control for Optimized Plant Growth

Automated Aeroponics System IoT-Based Monitoring and Control...

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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Rajesh Tanna Talada Santhosh Sanjay Kumar Gollavilli Lagudu Suri Demudu Ratala Koteswara Naik Cheepurupalli Raghuram Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology Visakhapatnam India Department of Electrical and Electronics Engineering Visakha Institute of Engineering & Technology Visakhapatnam India Department of Electrical and Electronics Engineering A. M. Reddy Memorial College of Engineering and Technology Narasaraopet India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College Bhimavaram India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

Challenges of farming are getting worse due to pests, other environmental issues, and changes in the climate. Several novel technologies have been developed to improve plant growth and productivity, yet there are still numerous issues in this field. This suggests that daily improvements are needed. This paper provides an innovative automated system that uses a variety of environmental sensors, including as $\mathbf{p H}$, TDS, and humidity, for hydroponics and aeroponics. These sensors continuously monitor and modify crucial components to provide the perfect growth environment. In addition, the system has autonomous nutrient dosing, which reduces the need for human intervention and boosts overall effectiveness. Better control over the farming process is made possible by the system’s ability to scale and monitor in real-time thanks to the integration of IoT technologies. The study also looks at how machine learning (ML) and artificial intelligence (AI) might improve crop health monitoring and growth forecasts. Additionally, an LED-lit indoor farming setup is included in the system.

关键词： Fault detection Aeroponics Hydroponics Nutrients Inverters Sensors Protection Voltage control Monitoring Farming

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Advancements in Binary Number Processing: Analyzing a CNTFET-Based Digital Circuit for 3-Bit Up and Down Counting with Challenges in Artificial Intelligence

Advancements in Binary Number Processing: Analyzing a CNTFET...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Khan, Imran Ahmed Shah, Owais Ahmad Vats, Satvik Arora, Sujata Mahmood, Md Rashid Whig, Pawan Jamia Millia Islamia University Department of Electronics & Communication Engineering New Delhi India School of Engineering & Technology K. R. Mangalam University Giurugram India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Noida International University Department of Electrical & Electronics Engineering Gautam Budh Nagar India Guru Nanak Institutions Technical Campus Department of Electronics & Communication Engineering Hyderabad India Vivekananda Institute of Professional Studies Department of Electronics & Communication Engineering New Delhi India

ISBN: (纸本)9798350313987

Carbon Nanotube Field- Effect Transistors (CNTFETs) have emerged as a promising alternative to traditional silicon-based transistors, offering enhanced electrical properties and potential integration with existing silicon technology. This research study presents a design and performance analysis of a digital circuit based on CNTFET technology, specifically tailored for precise computation involving 3-bit up and down binary numbers. The circuit's ability to accurately compute additions and subtractions is pivotal for various computational applications. Additionally, this study explores the challenges and implications of integrating Artificial Intelligence (AI) algorithms within the context of this emerging semiconductor technology. The findings shed light on the prospects of utilizing CNTFETs in arithmetic operations, providing insights into the intersection of advanced semiconductor technologies and the field of AI. The comprehensive analysis presented herein establishes a foundation for future research and advancements at the convergence of CNTFET -based digital circuits and AI methodologies. © 2023 IEEE.

关键词： Artificial Intelligence CMOS digital circuit CNTFET Counter

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Construction, modeling, and control of a three-beam prototype using interactive fiber rubber composites

Construction, modeling, and control of a three-beam prototyp...

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93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2023

作者： Acevedo-Velazquez, Aline Iobana Keshtkar, Najmeh Mersch, Johannes Katzer, Konrad Cherif, Chokri Zimmermann, Martina Gerlach, Gerald Röbenack, Klaus Institute of Control Theory Faculty of Electrical and Computer Engineering TUD Dresden University of Technology Dresden Germany Institute of Solid-State Electronics Faculty of Electrical and Computer Engineering TUD Dresden University of Technology Dresden Germany Fraunhofer Institute for Material and Beam Technology IWS Dresden Germany Institute for Material Science Faculty of Mechanical Engineering TUD Dresden University of Technology Dresden Germany Institute of Textile Machinery and High Performance Material Technology Faculty of Mechanical Engineering TUD Dresden University of Technology Dresden Germany

In this contribution, we discuss the construction, modeling, and control of a three-beam prototype that utilizes interactive fiber rubber composites integrated with shape memory alloys (SMAs) as actuators. These actuators are integrated into a textile layer and covered with an elastomer layer, providing flexibility and protection to the prototype. A mathematical model was developed to describe the behavior of the prototype, employing system identification techniques. However, precise position control of systems actuated by SMAs is challenging due to their inherent nonlinearities and hysteretic behavior. To tackle this challenge, a proportional-integral (PI) controller was designed based on robust stability conditions. The effectiveness of the designed PI controller was validated through both numerical simulations and real experiments. © 2023 The Authors. Proceedings in Applied Mathematics & Mechanics published by Wiley-VCH GmbH.

关键词： Actuators

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Leveraging Recurrent Neural Networks (RNN) for Workforce Planning and Optimization 2

Leveraging Recurrent Neural Networks (RNN) for Workforce Pla...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Machikuri, Santoshi Kumari Yadav, Kanchan Adnan, Myasar Mundher Krishnaiah, Pokala Subburam, S. Sambath Kumar, K. GRIET Department of AIML Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq CMR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of IT Tamil Nadu Chennai600073 India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798350368109

This paper looks at the use of Recurrent Neural Networks;particularly LSTM and GRU in workforce management. The task of Workforce planning is an important process of confronting employee need and requirement along with the use of minimum workforce and cost. It is important here to appreciate that the conventional techniques of forecasting like the linear regression model do not capture the intricate dynamics of labor demand in relation to time. On the other hand, RNNs are effective for time-series data and can therefore forecast the demand of the workforce in the future based on the data obtained. In this research, it describes how to train and build an RNN model and then combine it with optimization algorithms for generating the best staffing schedules. Using real-world forc data, the model was compared and shown to make more accurate labour forecasts and increase utilisation by 13% over traditional methods. Also it minimized the cases of understaffing and overstaffing and also cut down on employee overtime. Hence, there is confidence that RNN-based systems are capable of improving operational flexibility, productivity through increased employee satisfaction, and contain cost benefits. The further work will be directed to the inclusion of more external conditions and investigation of the reinforcement learning for the improvement of the results. According to this study, application of RNNs is a sign of a shift to a new level of managing workforce through intelligent demand forecast and improving scheduling. © 2024 IEEE.

关键词： Recurrent neural networks

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Key-Independent Image Encryption using DNA Encoding and Chaotic Dynamics

Key-Independent Image Encryption using DNA Encoding and Chao...

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International Conference on Communications and Signal Processing

作者： V Maharaja Vannia Moorthi Megha R Krishna Gopika Anil Kumar V Thanikaiselvan S Subashanthini Rengarajan Amirtharajan School of Electronics Engineering Vellore Institute of Technology Vellore India School Of Computer Science Engineering And Information Systems Vellore Institute of Technology Vellore India School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Privacy violations are common in our technology-driven world, where almost everyone interacts with internet-connected electronic devices. To address this, cryptography has emerged, concealing data during transmission to deter hackers. Cryptography involves basic mathematical operations, exchanging data based on a shared key known only to the sender and receiver. However, this method suffers from key dependency at the receiver’s end, requiring constant key knowledge. To overcome this issue, a novel image encryption method has been introduced in this paper that is independent of the key on the receiver side. This method leverages techniques such as DNA Encoding, Chaotic Dynamics, and Key Hiding Mechanisms to increase the security of the sent image. The proposed method has been tested experimentally, and the results demonstrate its effectiveness with entropy values exceeding 7.995 and correlation coefficient values below 1% for the vertical, horizontal, and diagonal directions. Overall, the proposed image encryption scheme offers a promising solution for protecting sensitive image data in various applications, combining advanced encryption techniques with key management enhancements to ensure confidentiality and integrity.

关键词： Correlation coefficient Privacy Image coding Chaotic communication computer hacking DNA Receivers

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Design and Implementation of a System for Vehicle Accident Reporting and Tracking 7

Design and Implementation of a System for Vehicle Accident R...

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7th International Conference on Communication and electronics Systems, ICCES 2022

作者： Gowda, V. Dankan Sharma, Avinash Naik, Anil S. Meena, Radhey Shyam Kudari, Jayashree M. Purushotham, S. BMSITandM Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi Markandeshwar Engineering College Department of Computer Science Engineering Haryana India National Institute of Technology Karnataka Surathkal India Indian Institute of Technology Department of Industrial and Management Engineering Uttar Pradesh Kanpur India Jain Deemed to Be University Department of Computer Science and Information Technology Karnataka Bangalore India Sona College of Technology Department of Electrical and Electronics Engineering Tamilnadu Salem India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Population growth and per capita income have contributed to increasing vehicle ownership and presence on roads worldwide. Increasing traffic on roads is one of the prime causes of road accidents. A large proportion of people die prematurely in road accidents. It is almost impossible to predict before the accident happens. Also, there may be some accident prevention mechanisms and early warning systems, but it is hard to avert road accidents wholly. This paper has explained in detail, the designing and implementation of a crash notifier system. © 2022 IEEE.

关键词： Roads and streets

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Fine-Tuning Prophet model for application in Agro-Industry to forecast Pesticide consumption 1

Fine-Tuning Prophet model for application in Agro-Industry t...

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1st IEEE International Conference on Green Industrial electronics and Sustainable Technologies, GIEST 2024

作者： Dey, Snehasis De, Utpal Chandra Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Samant, Tapaswini Banerjee, Shobhan College of Engineering Electronics and Telecommunication Engineering Bhubaneswar India Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Indian Institute of Information Technology Dept. of Computer Science & Engr. Ranchi India

ISBN: (纸本)9798331540142

Forecasting plays a key role in several industries to predict the probable requirement of a certain product or service over a period of time. This may or may not be dependent on certain external factors depending upon which, a suitable method needs to be chosen to perform the forecasting task. For a given data distribution, the best-fit model needs to be figured out after fine-tuning its hyperparameters to acquire the most optimal forecasts. In this paper, we have used the Prophet model of Facebook, an additive regression model to forecast the requirement of pesticides to be used for agricultural needs, and presented an overview of how fine-tuning the parameters leads to optimal curve fits, hence resulting in better-quality forecasts. We have also studied the impact of the size of the train data on the overall quality of predictions. Hence after combining both the data size and parameter values, the curve fits and predictions have been visually illustrated. © 2024 IEEE.

关键词： Pesticides

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Analyze the Difference between Rotational and Translational Motions Produced by High-speed Train 10

Analyze the Difference between Rotational and Translational ...

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10th International Conference on Environmental and engineering Geophysics, ICEEG 2023

作者： Rong, Chao Zhang, Dingfan Cao, Yuwen Li, Zhengbin Youfu Technology Co. Ltd Hangzhou311200 China Photoelectric Inertial Sensing Laboratory Advanced Institute of Information Technology Peking University Zhejiang China School of Electronics Engineering and Computer Science Peking University Beijing China

The development of fiber optic gyroscope technology makes it possible to directly measure the rotational ground-motion. The joint observation of the three-component rotational seismometer and the traditional three-component translational seismometer is a trend of future. In this paper, the translational and rotational signals generated during the high-speed trainway are studied, and the three-component translational and rotational seismometers are fixed together to realize the joint recording of the high-speed trainway signal. Comparing the translational and rotational three-component data, we find that only one component waveforms and the spectrum have certain consistency. However, the difference between them indicates that the rotation and translation signals generated by the shallow surface wave signal have different frequency bands. © Published under licence by IOP Publishing Ltd.

关键词： Gyroscopes

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