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arXiv 2024年

作者： Tsang, Mankei Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore Department of Physics National University of Singapore 2 Science Drive 3 Singapore117551 Singapore

Using quantum information geometry, I derive quantum generalizations of the Onsager rate equations, which model the dynamics of an open system near a steady state. The generalized equations hold for a flexible definition of the forces as well as a large class of statistical divergence measures and quantum-Fisher-information metrics beyond the conventional definition of entropy production. I also derive quantum Onsager-Casimir relations for the transport tensors by proposing a general concept of time reversal and detailed balance for open quantum systems. The results establish a remarkable connection between statistical mechanics and parameter estimation theory. Copyright © 2024, The Authors. All rights reserved.

关键词： Statistical mechanics

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Goat Feeding Behavior Modeling and Monitoring Utilizing Livestock Wearable Device and Machine Learning Algorithm

Goat Feeding Behavior Modeling and Monitoring Utilizing Live...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Melchizedek Alipio Gianne Marcus Obusan Julianne Abbe Ong Samuel Perez Anton Luis Garces Miroslav Bures Department of Computer Science System Testing Intelligent Laboratory FEE Czech Technical University in Prague Prague 2 Czech Republic Department of Electronics and Computer Engineering De La Salle University Manila Philippines

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Goat farming shows potential as one of the so-lutions for providing quality livestock products in developing countries. Goat health is essential in determining the quality of goods obtained from the goat. Smart livestock farming has been a reliable solution to achieve high-yield and high-quality goat products. However, current smart livestock monitoring systems do not consider the feeding pattern and behavior of the animal as a factor in its overall health. Hence, this study aims to develop a system that effectively monitors the feeding behavior of the goat using wearable devices, machine learning, and a visualization interface. The wearable device consists of an accelerometer, gyrometer, and temperature sensors to monitor the goat's feeding behavior and movement. The data collected by the wearable device are wirelessly transmitted to a base station to build a sensor-based dataset. This dataset is used to generate a k-means clustering model to classify the feeding behavior of the goats. Results show that the system was able to classify the feeding behavior of the goat with an accuracy of 98.02 % and a reliability of 100% in comparison with the actual goat behavior on the farm. Lastly, a graphical user interface is developed to provide monitoring and other relevant information to help the farmers in grazing the goats.

关键词： Temperature sensors Temperature measurement Temperature distribution Animals Agriculture Reliability Wearable devices

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Selecting and Evaluating Key MDS-UPDRS Activities Using Wearable Devices for Parkinson's Disease Self-Assessment

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 177-189页

作者： Zhao, Yuting Wang, Xulong Peng, Xiyang Li, Ziheng Nan, Fengtao Zhou, Menghui Qi, Jun Yang, Yun Zhao, Zhong Xu, Lida Yang, Po Yunnan University National Pilot School of Software Kunming650106 China University of Sheffield Department of Computer Science SheffieldS10 2TN United Kingdom Northwest A&F University College of Information Engineering Xianyang712100 China Xi'an JiaoTong-Liverpool University Department of Computing Suzhou215000 China Yunnan First People's Hospital Yunnan650021 China Old Dominion University NorfolkVA23529 United States

Parkinson's disease (PD) is a complex neurodegenerative disease in the elderly. This disease has no cure, but assessing these motor symptoms will help slow down that progression. Inertial sensing-based wearable devices, such as mobile phones and smartwatches have been widely employed to analyze the condition of PD patients. However, most studies purely focused on a single activity or symptom, which may ignore the correlation between activities and complementary characteristics. In this article, a novel technical pipeline is proposed for fine-grained classification of PD severity grades, which identify the most representative activities. We also propose a multiactivities combination scheme based on MDS-UPDRS. Utilizing this scheme, symptom-related and complementary activities are captured. We collected 85 PD subjects of different severity grades using a single wrist sensor. Our best results demonstrate F1 scores of 95.75% for PD diagnosis and the fine-grained classification accuracy of PD disease grade is 82.41% when combing four activities which improved by 11.02% over a single activity. The experiments and theoretical analyses can serve as a useful foundation for future investigations into the effect of proposed solutions for PD diagnosis in uncontrolled environment setup, ultimately leading to self-PD assessment in the home environment. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Learning the Optimal Energy-based Control Strategy for Port-Hamiltonian Systems

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IFAC-PapersOnLine 2024年第6期58卷 208-213页

作者： Riccardo Zanella Alessandro Macchelli Stefano Stramigioli University of Twente Faculty of Electrical Engineering Mathematics and Computer Science P.O. Box 217 7500 AE Enschede The Netherlands Department of Electrical Electronic and Information Engineering University of Bologna viale del Risorgimento 2 40136 Bologna Italy

This paper describes a synthesis and tuning procedure of discrete-time, energy-based regulators for port-Hamiltonian systems. Based on a discrete-time approximation of the plant, the control system is designed within the energy-shaping plus damping injection paradigm. This approach guarantees asymptotic stability, but it is not able “as is” to meet other requirements, such as task performance optimisation. The contribution is integrating the power of artificial neural networks as parametric function approximators and passivity-based control to enhance the performance of an asymptotically stable controlled system. The idea is to employ artificial neural networks that are optimally shaped to enhance the performances during task execution through the solution of an optimisation problem.

关键词： port-Hamiltonian systems passivity-based control reinforcement learning

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High Harmonic Generation by Bright Squeezed Vacuum

High Harmonic Generation by Bright Squeezed Vacuum

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Rasputnyi, Andrei Birk, Michael Chen, Zhaopin Cohen, Oren Krüger, Michael Kaminer, Ido Chekhova, Maria Tani, Francesco Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Friedrich-Alexander Universität Erlangen-Nürnberg Staudtstraße 7/B2 Erlangen91058 Germany Physics Department and Solid-State Institute Technion - Israel Institute of Technology Haifa32000 Israel Department of Electrical and Computer Engineering Solid State Institute Technion Israel Institute of Technology Haifa32000 Israel

ISBN: (纸本)9781957171395

We present the first observation of non-perturbative high-harmonic generation driven by nonclassical light, namely bright squeezed vacuum. Compared to classical light, bright squeezed vacuum leads to higher efficiency, different power scaling, and unique photon-number distributions. © 2024 The Author(s) © Optica Publishing Group 2024.

关键词： Squeezed light

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Femtosecond Laser based Post-Processing Free True Random Number Generator

Femtosecond Laser based Post-Processing Free True Random Num...

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Frontiers in Optics + Laser science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser science 2023

作者： Sharma, Amritash Paithankar, Prasanna Varshney, Shailendra K. Group Advanced Photonics Laboratory Department of Electronics and Electrical Communication Engineering Indian Institute of Technology West Bengal Kharagpur721302 India Department of Applied Optics and Photonics University of Calcutta Technology Campus JD 2 Salt Lake Kolkata700098 India Department of Computer Science and Engineering Indian Institute of Technology West Bengal Kharagpur721302 India Authors contributed equally

We experimentally demonstrate a high-entropy source based on the intensity fluctuation in a femtosecond laser. The setup successfully passes statistical tests without any post-processing. The generator can be potentia... 详细信息

ISBN: (纸本)9781957171296

关键词： Femtosecond lasers Laser sources Mode-locked fiber lasers Quantum communications Quantum cryptography Ultrafast lasers

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Efficiently improving the Wi-Fi-based human activity recognition, using auditory features, autoencoders, and fine-tuning

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computers in Biology and Medicine 2024年 172卷 108232-108232页

作者： Rahdar, Amir Chahoushi, Mahnaz Ghorashi, Seyed Ali AIFA Dotin *** Tehran Iran Department of Computer Science & Digital Technologies School of Architecture Computing and Engineering University of East London LondonE16 2RD United Kingdom

Human activity recognition (HAR) based on Wi-Fi signals has attracted significant attention due to its convenience and the availability of infrastructures and sensors. Channel State Information (CSI) measures how Wi-Fi signals propagate through the environment. However, many scenarios and applications have insufficient training data due to constraints such as cost, time, or resources. This poses a challenge for achieving high accuracy levels with machine learning techniques. In this study, multiple deep learning models for HAR were employed to achieve acceptable accuracy levels with much less training data than other methods. A pretrained encoder trained from a Multi-Input Multi-Output Autoencoder (MIMO AE) on Mel Frequency Cepstral Coefficients (MFCC) from a small subset of data samples was used for feature extraction. Then, fine-tuning was applied by adding the encoder as a fixed layer in the classifier, which was trained on a small fraction of the remaining data. The evaluation results (K-fold cross-validation and K = 5) showed that using only 30% of the training and validation data (equivalent to 24% of the total data), the accuracy was improved by 17.7% compared to the case where the encoder was not used (with an accuracy of 79.3% for the designed classifier, and an accuracy of 90.3% for the classifier with the fixed encoder). While by considering more calculational cost, achieving higher accuracy using the pretrained encoder as a trainable layer is possible (up to 2.4% improvement), this small gap demonstrated the effectiveness and efficiency of the proposed method for HAR using Wi-Fi signals. © 2024 The Authors

关键词： Channel state information

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Optimized Convolutional Neural Network for Automatic Detection of COVID-19

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computers, Materials & Continua 2022年第1期70卷 1159-1175页

作者： K.Muthumayil M.Buvana K.R.Sekar Adnen El Amraoui Issam Nouaouri Romany F.Mansour Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622TamilnaduIndia Department of Computer Science&Engineering PSNA College of Engineering and TechnologyDindigul624622India School of Computing SASTRA Deemed UniversityThanjavur613401India Univ.Artois U.R.3926Laboratoire de Génie Informatique et d’Automatique de l’Artois(LGI2A)F-62400BéthuneFrance Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the *** use X-Rays or Computed Tomography(CT)images to confirm the presence of ***,image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical *** current research work introduces Multi-objective Black Widow Optimization(MBWO)-based Convolutional Neural Network i.e.,MBWOCNN technique for diagnosis and classification of *** model involves four steps such as preprocessing,feature extraction,parameter tuning,and *** the beginning,the input images undergo preprocessing followed by CNN-based feature ***,Multi-objective Black Widow Optimization(MBWO)technique is applied to fine tune the hyperparameters of ***,Extreme Learning Machine with autoencoder(ELM-AE)is applied as a classifier to confirm the presence of COVID-19 and classify the disease under different class *** proposed MBWO-CNN model was validated experimentally and the results obtained were compared with the results achieved by existing *** experimental results ensured the superior results of the ELM-AE model by attaining maximum classification performance with the accuracy of 96.43%.The effectiveness of the technique is proved through promising results and the model can be applied in diagnosis and classification of COVID-19.

关键词： COVID-19 classification CNN hyperparameter tuning black widow optimization

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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Diabetes Prediction using Data Mining Techniques: A state-of-the-art Survey

Diabetes Prediction using Data Mining Techniques: A state-of...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Ritu Dewan Rohith Polishetty Kiran Kumar Ravulakollu Naveen Jagadam Mayank Kumar Goyal Bhagwati Sharan Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Migo India limited. Hyderabad India School of Technology Woxsen University Hyderabad India N2force Technologies Inc. Naperville IL USA Department of Computer Science & Engineering School of Engineering & Technology Sharda University Greater Noida India Department of Computer Science and Engineering School of Engineering and Sciences SRM University Amaravati AP India

Diabetes is a major concern, that arises with persistently elevated blood sugar levels leading to many health issues such as kidney damage, eyesight loss, and heart diseases, and also acting as a reason for death. These all risks are associated with both type 1 and type 2 diabetes. Data mining techniques will help us in the prediction and classification of diabetes. So, we can take the proper assistance from the consultant and the medicine depending upon the prediction of the level of diabetes. Multiple data mining techniques have been proposed by various researchers to get information at the early stages of diabetes with higher performance metrics. In this paper, state-of-the-art and in-use data processing techniques used for the prediction of diabetes have been presented. Along with the comparison of their respective gaps, datasets, models, and the accuracy of the models.

关键词： Measurement Analytical models Computational modeling Medical services Predictive models Feature extraction Diabetes

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