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IoT based on Smart Traffic Lights and Streetlight System

IoT based on Smart Traffic Lights and Streetlight System

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international conference on Edge computing and Applications (ICECAA)

作者： Shobana S Shakunthala M C. Vimala Josphine M. Theodore Kingslin S. Sivarajan Chairma Lakshmi K R Department of Information Technology R.M.K Engineering College Chennai India Department of Electronics and Communication Engineering R.M.D Engineering College Chennai Department of Artificial Intelligence and Data Science R.M.K Engineering College Chennai Department of Electronics and Communication Engineering R.M.K College of Engineering College & Technology Department of Electrical and Electronics Engineering Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Electronics and Instrumentation Engineering R.M.K. Engineering College Chennai India

Energy wastage, traffic jams, and traffic monitoring are the three main problems the world's population is confronting. The traditional roadway management method is ineffective at controlling the flow of traffic when one lane has significantly more traffic than the others. Additionally, the present street light controller uses lights that are not turned off in the morning even though it is sufficiently light outside and that are turned on in the late afternoon before even the sun sets, which causes some power loss. This proposed system will offer an advanced smart traffic management system and intelligent street lighting system to bring a standardized approach to solving this issue. The enhanced smart traffic control system is accomplished by modifying the signal timings based on the comparison of the densities on all roads, i.e., when a specific lane has much more density compared to the other lanes, that specific lane is modified to a green signal for a specified length of time. By computerizing solar-powered street lights with IoT, energy wastage in the traditional street lighting system is prevented. The street lights are activated whenever an item moves, or they remain off otherwise. This proposed method reduces traffic issues, and energy wastage and improves the traffic monitoring process also.

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Hybrid adaptive sliding mode technique for cascaded H bridge inverter 5

Hybrid adaptive sliding mode technique for cascaded H bridge...

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5th international conference on Intelligent computing and control Systems, ICICCS 2021

作者： Bright, E. Immanuvel Sundramurthy, Venkatesa Prabhu Chaturvedi, Abhay Gurunathan, Sabarinathan Shabaz, Mohammad Poonkuzhali, B. Erode Sengunthar Engineering College Department of Electrical and Electronics Engineering Perundurai Erode Tamil Nadu India College of Biological and Chemical Engineering Addis Ababa Science and Technology University Department of Chemical Engineering Addis Ababa Ethiopia GLA University Department of ECE Mathura U.P. India PSNA College of Engineering and Technology Department of Mathematics Dindigul Tamil Nadu India Lovely Professional University Department of Computer Science Engineering India

ISBN: (纸本)9781665412728

The proposed technique introduces a multi-porter hybrid PWM technology based on the adaptive sliding mode due to the deficit in modulation techniques, which have been used for Cascaded H-bridge (CHB) Multi-level inverters in the Carrier Phase Shift and Carrier In-Phase Disposition phases. This research is mainly focused on release of low harmonic distortion sinusoidal voltages (THD). When CHB Multi-level Inverter uses the Hybrid-Multicarrier PWM technology, as with Carrier Phase Shift PWM, then the power stability is obtained naturally links the cascading H Bridge cells. At the similar time, the harmonic vary of the output voltage and the invertors' harmonic output line voltage is the same as that of the IPD-PWM technology. The system performance is calculated by means of the MATLAB/SIMULINK/Sim Power Systems setting. The results show that, the controller proposed has satisfied its objectives and the selected control strategy remains more effective and robust. This paper gives an example of the seven-level CHB inverter and verifies the correctness of the relevant assumptions through simulation and experimental results. © 2021 IEEE.

关键词： Pulse width modulation

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TYLE: Tile-based Dynamic Quality Enhancement for 360-degree Video Streaming

TYLE: Tile-based Dynamic Quality Enhancement for 360-degree ...

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IEEE international conference on Performance, computing and Communications (IPCCC)

作者： Sonali Keshava Murthy Naik Mea Wang Diwakar Krishnamurthy Department of Computer Science University of Calgary Calgary Canada Department of Electrical and Software Engineering University of Calgary Calgary Canada

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

In recent years, live streaming of 360° videos has increased in popularity due to the emergence of virtual and mixed reality (VR/MR) applications. The high quality and bird-eye view characteristics of VR/MR pose real-time challenges for 360° video streaming. The tile-based 360° video streaming has been created based on Dynamic Adaptive Streaming over HTTP (DASH), mainly focusing on adaptation algorithms but ignoring the impact of coding properties and variations in video content on streaming quality. In this paper, we take on the challenge in a new direction through the exploration of the potential of CRF rate control. Our deep quality inspection of CRF transcoded videos lead to the proposal of a dynamic quality ladder for a more continuous quality provisioning in contrast to the conventional discrete quality levels. Our quality selection is based on visual quality and CRF rate control rather than just the resolution and bitrate in conventional DASH. We propose TYLE, a Tile-based dynamic quality enhancement for 360° video streaming. Without increasing the bandwidth demand, TYLE serves the content in the Field-of-View (FoV) at the highest quality level and content in the near-FoV region with improved visual quality compare to conventional DASH. TYLE maintains smoother and stabler playback, especially under the condition challenged by bandwidth under-provisioning and high motion-activity videos.

关键词： Visualization Smoothing methods Motion segmentation Heuristic algorithms Dynamics Bit rate Bandwidth Transcoding Streaming media Videos

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Transfer Learning in Ensemble Convolutional Neural Networks Toward the Detection of Microscopic Fusarium Oxysporum 3

Transfer Learning in Ensemble Convolutional Neural Networks ...

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3rd international conference on electrical, Communication and Computer engineering, ICECCE 2021

作者： Ong, Josh Daniel L. Abigan, Erinn Giannice T. Cajucom, Luis Gabriel A. Abu, Patricia Angela R. Estuar, Ma. Regina Justina E. Ateneo de Manila University Dept. of Information Systems and Computer Science Quezon City Philippines

ISBN: (纸本)9781665438971

The Panama disease, also known as the Fusarium wilt, is a deadly disease known to affect banana plants all over the world. Caused by a fungal pathogen known as Fusarium oxysporum f. sp. cubense (Foc), the disease has been a constant threat to banana producers considering that it cannot be eradicated once it has infected the soil. A new strain that emerged in 1989 called Tropical Race 4 (TR4) is now threatening the Cavendish cultivar, the most popular banana variety being grown today. Furthermore, symptoms of the disease are not visible until late stages of the infection. While there are methods that accurately determine the presence of Foc in a soil sample, these are costly and inaccessible to most banana producers. Thus, we propose the use of convolutional neural networks in the automatic detection of Foc TR4 in soil samples with the use of microscopy. This study utilized a dataset containing microscopy images of various fungal species captured using three distinct microscopy techniques: brightfield, darkfield, and fluorescent. Transfer learning has shown to make significant improvements to the performance of the models in classifying microscopic fungi. The best performing individual model was trained exclusively on brightfield images and has achieved an accuracy score of 93.92% while the best ensemble model was able to achieve an accuracy of 97.55%. Furthermore, tests on a subset meant to simulate realistic appearances of Foc have shown that the final model is viable for actual field use. © 2021 IEEE.

关键词： Convolutional neural networks

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Greenhouse Monitoring and controlling using Cloud-Based Android Application

Greenhouse Monitoring and Controlling using Cloud-Based Andr...

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international conference for Convergence of Technology (I2CT)

作者： Tarun Kumar Samli Dilip Kumar Department of Electronics and Communication Engineering Sant Longowal Institute of Engineering and Technology Longowal India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida

Wireless sensor network (WSN) technology is the amalgamation of numerous sensors exploited for monitoring and controlling the physical climate conditions. The integration of WSN technology in greenhouse farming has offered a new direction towards the production of crops. Greenhouse presents a protected environment for the plants/crops and facilitates the farmers in boosting their production. The paper presents a real-time greenhouse monitoring and controlling system. Humidity-Temperature sensor (DHT11), Soil Moisture sensor and Light sensor (LDR) are explored to measure the climate parameters accurately. To collect the real-time monitoring data from these sensors and transmit the data to the Firebase Cloud, the Wi-Fi module ESP8266 is attached to Arduino UNO. Further, an android application is developed for monitoring and controlling climate parameters via smart devices. The proposed model is efficient in terms of services and cost. The proposed automatic greenhouse monitoring and control system can facilitate the farmers in monitoring and controlling the climate conditions automatically as well as manually if the need arises. The developed application is user-friendly and can be efficiently utilized by farmers.

关键词： Wireless sensor networks Cloud computing Atmospheric modeling Green products Prototypes control systems Real-time systems

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Robust Object Recognition in Open Environments Based on Causal Inference

Robust Object Recognition in Open Environments Based on Caus...

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Robotics, control and Automation engineering (RCAE), international conference on

作者： Ruonan Liu Shuai Wu Ping Kong Wei Xie Weidong Zhang Department of Automation Shanghai Jiao Tong University Shanghai China College of Intelligence and Computing Tianjin University Tianjin China School of Information and Communication Engineering Hainan University Hainan China Department of Automation Shanghai Jiaotong University Shanghai China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

The rapid development of artificial intelligence technology has drawn widespread attention to the problem of target recognition in open environments. Traditional target recognition methods are easily affected by environmental changes and interference noise, leading to insufficient recognition accuracy and robustness. This paper proposes a robust target recognition method based on causal inference, aiming to improve the robust performance of image recognition models in open environments. The method first extracts features from images, analyzes the impact of noise features such as the environment in image features, and derives the essential feature properties of the target. Then, the causal relationship is introduced into the target recognition, and by analyzing the relationship between the environment, the target, and the observation data, the features of the target are combined with the observation data to achieve accurate and stable recognition of the target. The proposed method has achieved good target recognition effects on multiple open datasets, and the experimental results show that the algorithm can effectively solve the problem of poor robustness of the target recognition model in open environments, providing strong support for practical applications. Through the application of this method, the stability and robustness of the target recognition model can be significantly improved, which has important practical application value.

关键词： Accuracy Image recognition Automation Target recognition Noise Interference Feature extraction Robustness Stability analysis Robots

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Fuzzy controller for Indoor Air Quality control: A Sport Complex Case Study 4th

Fuzzy Controller for Indoor Air Quality Control: A Sport Com...

引用

4th international conference on Advanced Informatics for computing Research, ICAICR 2020

作者： Omarov, Bakhytzhan Altayeva, Aigerim Demeuov, Akhan Tastanov, Adilbay Kassymbekov, Zhakipbek Koishybayev, Arman International University of Tourism and Hospitality Turkistan Kazakhstan International Information Technology University Almaty Kazakhstan Auezov South Kazakhstan University Shymkent Kazakhstan Khoja Akhmet Yassawi International Kazakh-Turkish University Turkistan Kazakhstan

ISBN: (纸本)9789811636592

Currently used automatic control systems for electric drives of ventilation systems do not fully meet the requirements for the quality of performance of heat engineering production processes. It is necessary to develop new management systems through the use of intelligent technologies. The mathematical description of the control object is obtained by the methods of the classical theory of automatic control. Optimization of control loops is performed using fuzzy algorithm synthesis methods. A model of a ventilation system using a fuzzy controller is considered. Methods for developing intelligent control systems for electric drives of ventilation and air conditioning systems are proposed. It is established that the developed intelligent methods of control of electric drives of ventilation systems can be used under strict requirements to the microclimate in the room to ensure comfortable conditions. The developed fuzzy model was applied in a university sport complex, and the results showed the effectiveness of the proposed model when comparing traditional systems. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Air quality

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Automated Reinforcement Learning Based on Parameter Sharing Network Architecture Search 6

Automated Reinforcement Learning Based on Parameter Sharing ...

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6th international conference on Robotics and Automation engineering, ICRAE 2021

作者： Wang, Zhaolei Zhang, Jun Li, Yue Gong, Qinghai Luo, Wuyi Zhao, Jikang Beijing Aerospace Automatic Control Institute Science and Technology on Aerospace Intelligent Control Laboratory Beijing China Beijing Aerospace Automatic Control Institute China Academy of Launch Vehicle Technology Beijing China

ISBN: (纸本)9781665406970

The performance of machine learning depends on the choice of hyperparameters to a great extent. Only by choosing the appropriate hyperparameters can we learn the desired learning results. At present, the end-to-end learning algorithm is widely concerned in the academic circles, and realizes the agile design from the demand end to the execution end at the design task level, which can dramatically reduce the complexity of the design. However, there are still a large number of hyperparameters, which need to be tuned manually, increasing the difficulty of machine learning application. Thus, with the continuous development of high-performance parallel computing, automated machine learning method arises. In this paper, aiming at the automatic design of the hyperparameter, the neural network architecture of deep reinforcement learning in the field of motion control, LSTM recurrent neural network topology generation algorithm, parameter sharing based fast reinforcement learning and evaluation mechanism, and graph generator parameter learning algorithm based on policy gradient are combined. An automated search and optimization framework of neural network architecture in the deep reinforcement learning is proposed, realizing the automated generation of network architecture. Finally, the effectiveness of the proposed approach is verified by taking the lunar lander landing control problem as an example. © 2021 IEEE.

关键词： Network architecture

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SPDID: A Secure and Privacy-Preserving Decentralized Identity utilizing Blockchain and PUF

SPDID: A Secure and Privacy-Preserving Decentralized Identit...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yueyue He Wenxuan Fan Koji Inoue Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

For internet users, using digital identities has become increasingly essential. Traditional systems often rely on centralized control, which no longer meets privacy demands. Decentralized identity systems offer a more transparent and resilient solution by giving users control over their identity data through distributed ledgers, decentralized identifiers, and verifiable credentials. However, existing schemes are often based on assumptions such as the presence of a reliable anonymous credential issuer, making them challenging to implement in real-world scenarios. Furthermore, they overlook handling user credentials securely and struggle with ensuring user authentication while providing privacy. These systems are unable to provide public audits of issued credentials without jeopardizing sensitive data. This paper introduces SPDID, a novel decentralized identity system based on blockchain technology is introduced. First, it transforms legacy documents into anonymous credentials without interaction or alteration using zero-knowledge proofs and Pedersen commitments. SPDID also employs the physical unclonable function (PUF) to design a secure key management system resilient to physical attacks. Additionally, it suggests a user authentication method that is unlinkable and resistant to Sybil without the need for new trusted third parties. Furthermore, SPDID uses Merkle trees to construct a credential issuance list that is publicly auditable on the blockchain. The system’s security and practical performance are demonstrated through security analysis and a prototype implementation on Hyperledger Fabric.

关键词： Performance evaluation Resistance Privacy Distributed ledger Authentication Prototypes Transforms Physical unclonable function Blockchains Security

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Quadrotor novel model for trajectory tracking using neural sliding modes in discrete-Time 17

Quadrotor novel model for trajectory tracking using neural s...

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17th international conference on electrical engineering, computing science and automatic control, CCE 2020

作者： Florez, Julio A. Vega, Luz A. Sanchez, Edgar N. Loukianov, Alexander G. CINVESTAV-IPN Electrical Engineering Department Zapopan Jalisco45019 Mexico Industrial University of Santander School of Mechanical Engineering Bucaramanga Santander680003 Colombia

ISBN: (纸本)9781728189871

This paper addresses the solution of a trajectory tracking a quadrotor considering plant parameter variations, proposing a novel model. First a novel model of the quadrotor is developed, considering different phenomena such as: blade flapping and drag force. By means of a discrete-Time Recurrent High-Order Neural Network trained with an extended Kalman filter algorithm, neural model is identified for the quadrotor then, it is transformed using the block control technique in order to design a sliding surface, which depends on the tracking errors. Finally, a non-switching discrete-Time sliding mode control is applied to ensure the aforementioned sliding surface be attractive the effectiveness of the proposed based on simulations. © 2020 IEEE.

关键词： Recurrent neural networks

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