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Smartphone-based system for measuring liquid refractive index

Smartphone-based system for measuring liquid refractive inde...

2023 Applied Optics and Photonics China: Optical Sensing, Imaging, and Display technology and applications;and Biomedical Optics, AOPC 2023

作者： Deng, Dingnan Yu, Yechang Wang, Chengguo Huang, Bin Fu, Xin Zhong, Yuancong Chen, Ruikai School of Physics and Electronic Engineering Jiaying University Meizhou514015 China Meizhou Intelligent Photoelectric Detection Application Engineering Technology Research Center Jiaying University Meizhou514015 China School of Computer Jiaying University Meizhou514015 China

ISBN: (纸本)9781510672321

The liquid refractive index is one of the important optical parameters reflecting the properties of solution. A simple scheme based on shading effect for measuring liquid refractive index is presented. When a light beam passes through the liquid, the total reflection occurs at the interface with the air to produce a shading effect, forming a circular shading pattern, and its radius is related to the refractive index of the liquid. Based on this principle, a liquid refractive index measurement system based on smartphone is built. A rectangle colorimetric dish with tested liquid is illuminated by He-Ne laser. A white stickable label paper is attached to one side of colorimetric dish. Then the circular shading pattern displayed on label paper can be recorded by smartphone. A smartphone data processing program was developed to calculate the liquid refractive index from the image taken by smartphone. System calibration and smartphone shooting parameters are discussed in detail. The refractive index measurement experiments of sucrose solution and sodium chloride solution with different concentrations demonstrate the feasibility and effectiveness of the proposed method. © 2023 SPIE. All rights reserved.

关键词： Smartphones

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Development of an Electric Automation Control Model Using Artificial Intelligence 2

Development of an Electric Automation Control Model Using Ar...

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2nd International Conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024

作者： Srikanth, V. Aswini, P. Asha, V. Pithamber, Khobragade Sobti, Rajeev Salman, Z. School of Sciences and Computer Studies Bengaluru India Hyderabad India New Horizon College of Engineering Department of Master of Computer Application Bangalore India Mlr Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (纸本)9798350364699

The design of an AI-based electrical computerization control system is implemented to efficiently address the challenges encountered by contemporary electrical engineering. A model of an AI-based control system for electrical automation is presented. To achieve optimal control settings, the control strategy makes use of an artificial intelligence system. Even in the presence of a twenty per cent load intervention and 2.1 Hz wavelength interference, the research demonstrates that the organisation has significant anti-interference capabilities, as shown by an acceptable fail rate of 0.02 for each at the system management level. Consequently, using an AI algorithm in automated electrification control may lead to a significant improvement in control reaction time, cost savings, and efficient construction. © 2024 IEEE.

关键词： AI Artificial Intelligence Control System Cost Savings Electrical Engineering

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IoT based Truck Wheel Temperature Monitoring System

IoT based Truck Wheel Temperature Monitoring System

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Zaid, Mohd Ahmad, Nadeem Aljamal, Mohammed Patel, Sarosh Khan, Asif Dept. of Computer Science and Engineering Integral University Lucknow India Dept. of Electronics and Communication Engg. Goel Institute of Technology and Managment Lucknow India School of Engineering University of Bridgeport United States Integral Robotics Lab Dept. of Computer Application Integral University Lucknow India

ISBN: (纸本)9798350374933

The main objective of this paper is to design an IoT based truck wheel temperature monitoring system using Arduino board with the aim to overcome endanger and raise the safety measures for truck and goods. By designing and implementing a temperature-detecting system, we can detect the temperature of the wheels trucks. By the use of such detecting device we can prevent the major problem associated with wheel’s temperature and may avoid the major accidents. This monitoring system senses the temperature data of the wheel and transmits through the Wi-Fi module. In addition, it upgrades Thingspeak channel that can collect the data and it can easily be accessed by the transport agency and the truck driver. This can further be analysed using any computer system or mobile with internet connectivity anywhere across the globe to take real-time decision. The core function of the proposed detecting system is to monitor the real- time value of truck wheel temperature and to inform the concerned people. © 2024 IEEE.

关键词： Wi-Fi

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A collaborative empirical analysis on machine learning based disease prediction in health care system

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International Journal of Information technology (Singapore) 2024年第1期16卷 261-270页

作者： Das, Ayushi Choudhury, Deepjyoti Sen, Arpita Department of IT Techno India University West Bengal Kolkata 700091 India Department of AIML School of CSE Manipal University Jaipur Rajasthan Jaipur 303007 India Department of Computer Application B.P. Podder Institute of Management and Technology West Bengal Kolkata 700091 India

The adaptation of Artificial Intelligence can radically reshape the entire healthcare industry. This paper proposes a comparative analysis of four Machine Learning algorithms namely, k-Nearest Neighbour, Naive Bayes, Decision Tree, and Random Forest. These supervised classifiers are used to predict widely identified diseases based on one’s conspicuous symptoms among a given data-set of common symptoms. In our experiment comparing the different Machine Learning models, Random Forest had obtained the highest accuracy of 99.5%, followed by Decision Tress at 95.8%, K-Nearest Neighbor at 93.4%, and Naive Bayes at 87.7%. This paper represents a state-of-the art comparative analysis which has achieved the higher accuracy than the existing analytical results carried out in earlier research. Finally, a web-based application has also been developed to visualize the predictions in a better way. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Decision Tree Healthcare K-Nearest Neighbour Naive Bayes Random Forest

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Fall Detection and Alerts in Hearing Aids for Deaf using Generative AI

Fall Detection and Alerts in Hearing Aids for Deaf using Gen...

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Computational Intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Bharat Tripathi Madan Lal Saini Manish Jain Apoorva Saxena Narendra Pal Department of Computer Application Allenhouse Institute of Technology Kanpur India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab India Department of Computer Application Allenhouse Business School Kanpur India Department of Computer Science & Engineering Axis Institute of Technology & Management Kanpur India Department of Computer Science & Engineering Invertis University Bareilly India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with different types of background sounds. Additionally, the section explores the contributing factors to this challenge such as reduced audibility, reduced frequency selectivity, loudness recruitment, and dead regions in the cochlea where there are no surviving inner hair cells and or neurons. The sections evaluate different techniques for compensating for the effects of these factors. While signal-processing methods using the output of a single microphone to compensate for reduced frequency selectivity have had limited success, techniques utilizing multiple microphones have proven effective. Amplitude compression has also been effective in compensating for the effects of loudness recruitment, enabling individuals to understand speech across a wide range of sound levels. Next, the paper delves into the technical aspects of implementing fall detection systems in hearing aids. learning algorithms are also investigated for their potential in enhancing fall detection accuracy and minimizing false alarms. Furthermore, the importance of user-friendly interfaces and customization features in ensuring the effectiveness and acceptance of fall detection alerts is also highlighted. The paper also addresses the ethical and privacy considerations associated with fall detection in hearing aids. It emphasizes the need for transparent data handling practices and user consent, while also addressing potential concerns related to user autonomy and intrusion.

关键词： Hair Privacy Neurons Signal processing algorithms Signal processing Hearing aids Fall detection Transient analysis Recruitment Microphones

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An Optimised Deep Learning Approach of Lane Detection in Complex Indian Environment 1

An Optimised Deep Learning Approach of Lane Detection in Com...

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1st IEEE International Conference on Industrial Electronics: Developments and applications, ICIDeA 2022

作者： Rath, Manas Kumar Swain, Prasanta Kumar Banerjee, Shobhan School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India Maharaja Sriram Chandra Bhanja Deo University Department of Computer Application India BITS - Pilani Department of Engineering Technology Rajasthan India

ISBN: (数字)9781665421492

ISBN: (纸本)9781665421492

Lane detection systems are useful to avoid road accidents that are due to wrong-lane driving. Such systems have the target to detect the lanes and warn the driver in case the vehicle enters the wrong lane. In this paper, we've proposed a lane detection technique that increases the accuracy and stability of the vehicle while tracing the position of the lane in a complex driving environment. Based on the existing methods like Canny edge detection and the Hough transform method, the proposed technique has an excellent detection ability and can detect key lanes in complex road environments such as darkness, traffic jams, and uneven lighting. © 2022 IEEE.

关键词： Feature extraction

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Particle Swarm Optimization Technique for Current Equalization of PV Systems to Achieve Higher GMPP Under PSCs

Particle Swarm Optimization Technique for Current Equalizati...

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International Conference on Smart Energy and Advancement in Power Technologies, ICSEAPT 2021

作者： Pachauri, Rupendra Kumar Prasad, Hanuman Gupta, Pankaj Kumar Sharma, Manish Electrical and Electronics Engineering Department School of Engineering University of Petroleum and Energy Dehradun248007 India Electrical Engineering Department Model Institute of Technology Jammu181123 India School of Computer Science and Application IIMT University Meerut250001 India

ISBN: (纸本)9789811949708

The sun irradiations levels are always vary at different times, which affect the performance of solar photovoltaic (PV) systems in terms of power losses (PL). But many natural and un-natural causes exist and behave an obstruction to extract best performance from PV systems. The popular unacceptable causes are partial shading conditions (PSCs), which affects the PV performance highly. To diminish the effect of PSCs, relevant PV module rearrangement method is another good solution. In this paper, particle swarm optimization (PSO) technique demonstrates the current equalization of PV array systems under PSCs. In that method, a particle reaches the optimum value depending on its present velocity, past experience, and the experience of its nearby group members. Also, traditional arrangement techniques such as the series–parallel (SP) and total cross-tied (TCT) configurations have been used. As a result, performance parameters such as power and voltage at global maximum power point (GMPP), PL, and fill factor (FF) have been assessed. This paper displays the methods of implementation of the techniques to avert the effect of PSCs and to improve the energy output of the PV array under non-uniform irradiation circumstances. MATLAB/Simulink modelling is carried out to compare the results of various reconfiguration techniques and to find out which one is the best one under considered PSCs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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Specific Emitter Identification Based on ACGAN and STFT 7

Specific Emitter Identification Based on ACGAN and STFT

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7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Wang, Bin Xie, Jun Wang, Feng The 38th Research Institute Of China Electronics Technology Group Corporation China Key Laboratory Of Aperture Array And Space Application China Key Laboratory Of Intelligent Information Processing Anhui Hefei230088 China School Of Computer And Information Hefei University Of Technology Hefei230601 China

ISBN: (纸本)9798350361445

Deep learning methodologies are gaining popularity for enhancing the efficiency of Specific Emitter Identification (SEI). Nevertheless, the lack of labeled datasets continues to pose a challenge in most non-cooperative scenarios for SEI. Addressing this challenge, a framework for SEI is proposed, leveraging Short-Time Fourier Transform (STFT) in conjunction with auxiliary classifier GAN(ACGAN). The Short-Time Fourier Transform of emitters is utilized as radio-frequency fingerprint for the emitters. The input latent layer is infused with label information to steer the training process of the Generative Adversarial Network. Through semi-supervised training, shared weight updates are implemented for the classification network. Experimental results demonstrate that our model can achieve superior or competitive performances in several datasets compared with the former models. © 2024 IEEE.

关键词： Deep learning

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Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning

arXiv

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arXiv 2025年

作者： Yan, Xingrun Zuo, Shiyuan Lyu, Yifeng Fan, Rongfei Hu, Han School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Institute of Computer Application Technology Norinco Groups Beijing China School of Information and Electronics Beijing Institute of Technology Beijing China

Semantic communication is designed to tackle issues like bandwidth constraints and high latency in communication systems. However, in complex network topologies with multiple users, the enormous combinations of client data and channel state information (CSI) pose significant challenges for existing semantic communication architectures. To improve the generalization ability of semantic communication models in complex scenarios while meeting the personalized needs of each user in their local environments, we propose a novel personalized federated learning framework with dual-pipeline joint source-channel coding based on channel awareness model (PFL-DPJSCCA). Within this framework, we present a method that achieves zero optimization gap for non-convex loss functions. Experiments conducted under varying SNR distributions validate the outstanding performance of our framework across diverse datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Latent semantic analysis

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Exploring the Power of Deep Learning in Anomaly Detection: A Comprehensive Review and Analysis 12th

Exploring the Power of Deep Learning in Anomaly Detection: A...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Kumar, Gaurav Kumar, Ankit Pandy, Saroj Kumar Singh, Kamred Udham Singh, Teekam Kumar, Lalan Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

This paper aims to provide a comprehensive overview of existing and state-of-the-art fraud and intrusion detection strategies, specifically focusing on incorporating neural networks. We begin by discussing the fundamental concept of data mining and its significance in detecting anomalies. By leveraging advanced machine learning and neural network techniques, we can effectively uncover subtle patterns and irregularities within complex datasets. We explore how the development of anomalies has enhanced our ability to identify and mitigate various threats, such as malware attacks and unlawful practices. Traditional detection strategies have proven effective, but as deep learning progresses, we uncover new opportunities and insights that can significantly enhance the accuracy and efficiency of anomaly detection systems. Throughout this study, we delve into the most cutting-edge approaches, ranging from broad neural networks to shallower architectures, and their applicability to fraud and intrusion detection. We analyze the strengths and limitations of these strategies, shedding light on their performance characteristics, scalability, and interpretability. By addressing these key issues, we aim to provide a comprehensive summary of the latest fraud and intrusion detection advancements driven by deep learning methodologies. This research contributes to the broader understanding of data mining's potential in uncovering anomalies and bolstering security measures across diverse domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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