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UKACC International Conference on Control (CONTROL)

作者： Paulo S.P. Pessim Felipe A. Silva Márcia L.C. Peixoto Reinaldo M. Palhares Márcio J. Lacerda Graduate Program in Electrical Engineering Federal University of Minas Gerais Brazil Department of Electrical Engineering Federal University of São João del-Rei - UFSJ Brazil LAMIH laboratory UMR CNRS 8201 Université Polytechnique Hauts-de-France Valenciennes France INSA Hauts-de-France Valenciennes France Department of Electronics Engineering Federal University of Minas Gerais Minas Gerais Brazil Centre for Communications Technology School of Computing and Digital Media London Metropolitan University UK

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

This paper presents conditions in the form of Linear Matrix Inequalities to design state-feedback controllers for discrete-time uncertain positive linear systems in the presence of denial of service (DoS) attacks. The system under attack is modeled after a switched model that allows us to derive the synthesis conditions. Two strategies for control are considered, a hold strategy and a packet-based approach. Numerical experiments illustrate the efficacy of the proposed method of keeping the positiveness and stability of the closed-loop system under the presence of DoS attacks.

关键词： Linear systems Switched systems Design methodology Switches Stability analysis Numerical models Safety

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An Explainable Deep Learning Model for Prediction of Severity of Alzheimer’s Disease

An Explainable Deep Learning Model for Prediction of Severit...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Godwin Ekuma Daniel B. Hier Tayo Obafemi-Ajayi Computer Science Dept. Missouri State University Springfield MO USA Electrical & Computer Eng. Dept Missouri University of Science & Technology Rolla MO USA Engineering Program Missouri State University Springfield MO USA

Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. Despite the high predictive accuracy, usability lags in practical applications due to the black-box model perception. Model explainability and interpretability are essential for successfully integrating artificial intelligence into healthcare practice. This work addresses the challenge of an explainable deep learning model for the prediction of the severity of Alzheimer’s disease (AD). AD diagnosis and prognosis heavily rely on neuroimaging information, particularly magnetic resonance imaging (MRI). We present a deep learning model framework that integrates a local data-driven interpretation method that explains the relationship between the predicted AD severity from the CNN and the input MR brain image. The deep explainer uses SHapley Additive exPlanation values to quantity the contribution of different brain regions utilized by the CNN to predict outcomes. We conduct a comparative analysis of three high-performing CNN models: DenseNet121, DenseNet169, and Inception-ResNet-v2. The framework shows high sensitivity and specificity in the test sample of subjects with varying levels of AD severity. We also correlated five key AD neurocognitive assessment outcome measures and the APOE genotype biomarker with model misclassifications to facilitate a better understanding of model performance.

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Modeling of Measurement Error in Financial Returns Data

arXiv

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arXiv 2024年

作者： Jasra, Ajay Maama, Mohamed Mijatović, Aleksandar School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Statistics University of Warwick United Kingdom

In this paper we consider the modeling of measurement error for fund returns data. In particular, given access to a time-series of discretely observed log-returns and the associated maximum over the observation period, we develop a stochastic model which models the true log-returns and maximum via a Lévy process and the data as a measurement error there-of. The main technical difficulty of trying to infer this model, for instance Bayesian parameter estimation, is that the joint transition density of the return and maximum is seldom known, nor can it be simulated exactly. Based upon the novel stick breaking representation of [12] we provide an approximation of the model. We develop a Markov chain Monte Carlo (MCMC) algorithm to sample from the Bayesian posterior of the approximated posterior and then extend this to a multilevel MCMC method which can reduce the computational cost to approximate posterior expectations, relative to ordinary MCMC. We implement our methodology on several applications including for real data. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov chains

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Interferometry Among Strongly Driven Quasiparticles with Distinct Dynamical and Geometrical Phases

Interferometry Among Strongly Driven Quasiparticles with Dis...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Seamus O'Hara Joe Costello Qi Tian Qile Wu Ken West Loren Pfeiffer Mark Sherwin Liang Wu Department of Physics and Institute of Terahertz Science and Technology University of California Santa Barbara Santa Barbara CA Intelligence Community Post-Doctoral Research Fellowship Program Philadelphia PA Department of Physics University of Pennsylvania Philadelphia PA Department of Electrical and Computer Engineering Princeton University Princeton NJ

ISBN: (纸本)9798350369311

In this proceeding, we present polarization data of sidebands emitted from strongly driven quasiparticles, demonstrate the dependence of these data on various parameters of the external driving field, and describe the data using an analytical expression, dependent on driving field parameters and the effective Hamiltonian of the system. We propose using this method on spin-momentum locked systems, such as altermagnets, to observe and reconstruct sidebands which are influenced by the topology of individual bands.

关键词： Electro-optic effects Amplitude modulation Topology Interferometry

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Directional Neural Connectivity during Robot Mirror Therapy in Patients with Stroke

Directional Neural Connectivity during Robot Mirror Therapy ...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Yuma Kanaizuka Takahiro Manabe Jian-Jia Huang Jen-Wen Hung Yumie Ono Electrical Engineering Program Graduate School of Science and Technology Meiji University Japan School of Science and Technology Meiji University Japan Department of Rehabilitation Chang Gung Memorial Hospital-Kaohsiung Medical Center Kaohsiung Taiwan School of Medicine College of Medicine Chang Gung University Taoyuan Taiwan

This study explored directional connectivity networks during hand movement training in stroke patients using 8 conditions combining mirror therapy, robot-assisted bimanual therapy, and object manipulation. The findings revealed that mirror therapy and robot-assisted bimanual therapy decreased interhemispheric inward connectivity to the affected motor cortex in the left-hand paralyzed patient, reducing inhibitory control. Conversely, these interventions increased interhemispheric inward connectivity in the right-hand paralyzed patient, suggesting enhanced excitatory connectivity. The results emphasized the influence of neurorehabilitation methods, hemiparesis severity, and affected side on interhemispheric connectivity. Further research is needed to develop personalized rehabilitation strategies based on directional connectivity measures.

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IoT–based tire pressure monitoring system for air and temperature pressure using MPX5500D and LM35 sensor

IoT–based tire pressure monitoring system for air and tempe...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Agung Pangestu Iing Sodikin Muhammad Yusro Rum Sapundani Rosyid Ridlo Al Hakim Sinka Wilyanti Electrical Engineering Department Faculty of Engineering and Computer Science Jakarta Global University Depok Indonesia Department of Electronic Engineering Faculty of Engineering State University of Jakarta Jakarta Indonesia Electromedical Engineering Technology Faculty of Health Science University of Muhammadiyah Purwokerto Purwokerto Indonesia Graduate Program in Electrical Engineering Faculty of Engineering and Computer Science Jakarta Global University Depok Indonesia

ISBN: (纸本)9781665453905

Research has been carried out to monitor vehicle tires before they are used and can reduce damage, including overcoming vehicle fuel waste because air pressure is continuously monitored. This research aims to utilize the MPX5500DP sensor as an air pressure device, the LM35 sensor as a temperature reader, and a buzzer based on IoT to build a tire pressure monitoring system (TPMS). The MPX5500DP and LM35 sensor inputs to the Arduino Uno microcontroller are distributed by the NodeMCU, fitted with a Wi-Fi module. The Blynk application sends and displays the data on a smartphone using the IoT-based. Based on this research, data on the percentage of errors in monitoring air pressure and tire temperature on vehicles were obtained by comparing the data to the pressure gauge and thermometer: 1—the results of the average reading of the sensor error value. MPX5500DP air pressure against pressure gauge is 5.3%. 2—the average reading of the LM35 sensor error value on the temperature thermometer is 6.8%. With this research, the air pressure and temperature in the tires can be monitored in real-time via a smartphone using the IoT-based.

关键词： Temperature measurement Temperature distribution Thermometers Microcontrollers Tire pressure Real-time systems Pressure gauges

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Is Attention All You Need for EEG to Predict Neurological Outcomes in Cardiac Arrest?

Is Attention All You Need for EEG to Predict Neurological Ou...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Dionisio, Jefferson Rizwan, Muhammad Suzuki, Hidetaka Hassan, Kazi Mahmudul Chanpornpakdi, Ingon Lin, Lian-Yu Tanaka, Toshihisa Lin, Che Tokyo University of Agriculture and Technology Tokyo Japan Japanese Red Cross Musashino Hospital Department of Emergency and Critical Care Tokyo Japan National Taiwan University College of Medicine and Hospital Division of Cardiology Department of Internal Medicine Taipei Taiwan Artificial Intelligence of Things Taiwan International Graduate Program Academia Sinica Taipei Taiwan National Taiwan University Department of Electrical Engineering Taipei Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan National Taiwan University Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan Department of Smart Medicine and Health Informatics National Taiwan University Taipei Taiwan

ISBN: (纸本)9798350351552

Sudden cardiac arrest (SCA) poses a significant health challenge, necessitating accurate predictions of neurological outcomes in comatose patients, where good outcomes are defined as the recovery of most cognitive functions. Electroencephalogram (EEG) serves as a valuable biomarker for monitoring neurological states due to its rich, time-dependent information. This study aims to predict neurological outcomes using early EEG data by employing the Transformer model, which leverages multi-headed attention to identify patterns in lengthy sequences such as hour-long EEG recordings. Unlike traditional methods that use subsampled EEG epochs, we utilize the entire EEG sequences, subdivided into time steps, allowing our model to capture detailed temporal patterns via the attention mechanism. Moreover, we trained our proposed model using each EEG recording as an individual data sample but evaluated our model through aggregated patient-wise predictions. This approach allows us to boost the data sample size. Our results demonstrate promising predictive performance, achieving an AUROC of 0.82 and AUPRC of 0.90 on the holdout test set and an AUROC of 0.73 and AUPRC of 0.93 on an external test set with patient-wise predictions. This study highlights the potential of utilizing attention mechanisms to capture important time series progressions across EEG sequences for improving SCA prognosis. © 2024 IEEE.

关键词： Electroencephalography

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Protecting our little ones: A GPS-based child supervision tool with 99% accuracy rate

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AIP Conference Proceedings 2024年第1期3167卷

作者： Syamsudduha Syahrorini Salsabillah Jamaaluddin Jamaaluddin Arief Wisaksono Shazana Dyah Ayuni Agus Hayatal Electrical Engineering Study Program Faculty of Science and Technology Universitas Muhammadiyah Sidoarjo Sidoarjo Indonesia

This study aimed to develop a child supervision tool with a sound system to help parents quickly detect and locate danger to their children. The tool utilizes a Push Button or a microphone that, when activated by the child saying "Danger," sends the GPS coordinates to a NodeMCU ESP8266 microcontroller that sends location signals and notifications to the parent’s smartphone. The buzzer on the device also activates to alert the parent of danger. The study found that Voice Recognition as a sender of Blynk notifications and activating the buzzer function properly, and the GPS U-Blox Neo 6m accurately captured locations with an accuracy rate above 99%, with a sensor reading error rate below 0.1%. The implication is that this tool can help parents supervise their children and prevent them from becoming victims of kidnapping.

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Development of an Optical Biosensor Based on Gold Nanoparticles

Development of an Optical Biosensor Based on Gold Nanopartic...

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IEEE Conference on Nanotechnology

作者： Amanda Anatasya Yong-Joon Choi Tsugumi Sakae Gilang Gumilar Ni Luh Wulan Septiani Go Kawamura Kazuaki Sawada Brian Yuliarto Faculty of Industrial Technology Master's Program in Engineering Physics Institut Teknologi Bandung Bandung Indonesia Department of Electrical and Electronic Information Engineering Toyohashi University of Technology Aichi Japan Engineering Physics Department Advanced Functional Materials (AFM) Laboratory Institut Teknologi Bandung Bandung Indonesia Research Center for Advanced Materials National Research and Innovation (BRIN) Komplek Puspiptek South Tangerang Indonesia

This paper describes an experimental study of SARS-CoV-2 detection using plasmonic gold nanoparticles, which overcomes the limitations of traditional detection techniques known for their time-consuming and a tendency to produce high false negative results. The transmission spectrum of gold nanoparticles (AuNPs) was recently estimated to be in filter-free wavelength sensors as a miniaturization detection system. Additionally, we demonstrate that wide-ranging absorption will manifest within the visible spectrum when the virus is partially coated with gold nanoparticles at a particular percentage of coverage. This extensive absorption can be harnessed to direct the advancement of a reliable and precise colorimetric plasmon sensor for the detection of COVID-19. The transmission value resulting from the gold nano seed is around 507–540 nm. In addition, plasmonic gold nanoparticles can detect SARS-CoV-2 well from the range of 100-1,000 ng/ml. We expect a miniaturization biosensor system with qualitative diagnosis to be realized by the development method.

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Lighting the way: Enhancing telephone notification with DC lamps in noisy settings

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AIP Conference Proceedings 2024年第1期3167卷

作者： Priaji Hardimita Arief Wisaksono Syamsudduha Syarorini Jamaaluddin Jamaaluddin Shazana Dhiya Ayuni Electrical Engineering Study Program Faculty of Science and Technology Universitas Muhammadiyah Sidoarjo Sidoarjo Indonesia

This study aims to develop a device that uses DC lamps as a notification of a telephone ringing in a noisy room. The research method involves observing the Cikande electronic ambassador shop and proposing the use of a DC lamp as a replacement for the speaker output of the telephone. The device includes a PNP transistor type as an amplifier and multiple LEDs to ensure visibility in a noisy environment. The result is a functional device that uses DC lamps to notify the ringing of a telephone in a noisy room. The implication of this research is that it provides a solution for situations where conventional ringing tones from telephones are not audible due to noise pollution. The device can be useful in various settings, including busy offices, factories, and public spaces.

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