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Correction of Italian under-reporting in the first COVID-19 wave via age-specific deconvolution of hospital admissions

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arXiv 2023年

作者： Milanesi, Simone De Nicolao, Giuseppe Department of Mathematics University of Pavia Pavia Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Division of Infectious Diseases I Fondazione IRCCS Policlinico San Matteo Pavia Italy

When the COVID-19 pandemic first emerged in early 2020, healthcare and bureaucratic systems worldwide were caught off guard and largely unprepared to deal with the scale and severity of the outbreak. In Italy, this led to a severe underreporting of infections during the first wave of the spread. The lack of accurate data is critical as it hampers the retrospective assessment of nonpharmacological interventions, the comparison with the following waves, and the estimation and validation of epidemiological models. In particular, during the first wave, reported cases of new infections were strikingly low if compared with their effects in terms of deaths, hospitalizations and intensive care admissions. In this paper, we observe that the hospital admissions during the second wave were very well explained by the convolution of the reported daily infections with an exponential kernel. By formulating the estimation of the actual infections during the first wave as an inverse problem, its solution by a regularization approach is proposed and validated. In this way, it was possible to computed corrected time series of daily infections for each age class. The new estimates are consistent with the serological survey published in June 2020 by the National Institute of Statistics (ISTAT) and can be used to speculate on the total number of infections occurring in Italy during 2020, which appears to be about double the number officially recorded. © 2023, CC BY.

关键词： Inverse problems

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound optimization

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Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao ...

2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Song, Xianxin Xu, Jie Liu, Fan Han, Tony Xiao Eldar, Yonina C. School of Science and Engineering Future Network of Intelligence Institute Guangdong Provincial Key Laboratory of Future Networks of Intelligence China Southern University of Science and Technology Department of Electrical and Electronic Engineering China Wireless Technology Lab 2012 Laboratories Huawei China Weizmann Institute of Science Faculty of Mathematics and Computer Science Israel

ISBN: (纸本)9781665459754

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. The AP aims to estimate the target's direction-of-arrival (DoA) with respect to the IRS, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the CramérRao lower bound (CRLB) on estimation error. Towards this end, we first obtain the CRLB expression for estimating the DoA in closed form. Next, we optimize the joint beamforming design to minimize the CRLB, via alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design based on CRLB minimization achieves improved sensing performance in terms of mean squared error, as compared to the traditional schemes with signal-to-noise ratio maximization and separate beamforming. © 2022 IEEE.

关键词： Beamforming

Soliton-mean field interaction in Korteweg-de Vries dispersive hydrodynamics

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arXiv 2022年

作者： Ablowitz, Mark J. Cole, Justin T. El, Gennady A. Hoefer, Mark A. Luo, Xu-Dan Department of Applied Mathematics University of Colorado Boulder United States Department of Mathematics University of Colorado Colorado Springs United States Department of Mathematics Physics and Electrical Engineering Northumbria University United Kingdom Chinese Academy of Sciences China

The propagation of localized solitons in the presence of large-scale waves is a fundamental problem, both physically and mathematically, with applications in fluid dynamics, nonlinear optics and condensed matter physics. Here, the evolution of a soliton as it interacts with a rarefaction wave or a dispersive shock wave, examples of slowly varying and rapidly oscillating dispersive mean fields, for the Korteweg-de Vries equation is studied. Step boundary conditions give rise to either a rarefaction wave (step up) or a dispersive shock wave (step down). When a soliton interacts with one of these mean fields, it can either transmit through (tunnel) or become embedded (trapped) inside, depending on its initial amplitude and position. A comprehensive review of three separate analytical approaches is undertaken to describe these interactions. First, a basic soliton perturbation theory is introduced that is found to capture the solution dynamics for soliton-rarefaction wave interaction in the small dispersion limit. Next, multiphase Whitham modulation theory and its finite-gap description are used to describe soliton-rarefaction wave and soliton-dispersive shock wave interactions. Lastly, a spectral description and an exact solution of the initial value problem is obtained through the Inverse Scattering Transform. For transmitted solitons, far-field asymptotics reveal the soliton phase shift through either type of wave mentioned above. In the trapped case, there is no proper eigenvalue in the spectral description, implying that the evolution does not involve a proper soliton solution. These approaches are consistent, agree with direct numerical simulation, and accurately describe different aspects of solitary wave-mean field interaction. Copyright © 2022, The Authors. All rights reserved.

关键词： Solitons

Reinforcement Learning-Based Optimal Control for Multiplicative-Noise Systems with Input Delay

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arXiv 2023年

作者： Wang, Hongxia Zhao, Fuyu Zhang, Zhaorong Xu, Juanjuan Li, Xun The School of Electrical and Automation Engineering Shandong University of Science and Technology Qingdao30332 China The Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Shandong University Jinan250061 China

In this paper, the reinforcement learning (RL)-based optimal control problem is studied for multiplicative-noise systems, where input delay is involved and partial system dynamics is unknown. To solve a variant of Riccati-ZXL equations, which is a counterpart of standard Riccati equation and determines the optimal controller, we first develop a necessary and sufficient stabilizing condition in form of several Lyapunov-type equations, a parallelism of the classical Lyapunov theory. Based on the condition, we provide an offline and convergent algorithm for the variant of Riccati-ZXL equations. According to the convergent algorithm, we propose a RL-based optimal control design approach for solving linear quadratic regulation problem with partially unknown system dynamics. Finally, a numerical example is used to evaluate the proposed algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

Inverse Kalman Filtering for Systems with Correlated Noises

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Inverse Kalman Filtering for Systems with Correlated Noises

IEEE Conference on Decision and Control

作者： Yibei Li Xiaoming Hu Bo Wahlberg Lihua Xie School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Mathematics Division of Optimization and Systems Theory KTH Royal Institute of Technology Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Sciencem KTH Royal Institute of Technology Sweden

This paper focuses on two inverse problems of the Kalman filter in which the process and measurement noises are correlated. The unknown covariance matrix in a stochastic system is reconstructed from observations of its posterior beliefs. For the standard inverse Kalman filtering problem, a novel duality-based formulation is proposed, where a well-defined inverse optimal control (IOC) problem is solved instead. Identifiability of the underlying model is proved, and a least squares estimator is designed that is statistically consistent. The time-invariant case using the steady-state Kalman gain is further studied. Since this inverse problem is ill-posed, a canonical class of covariance matrices is constructed, which can be uniquely identified from the dataset with asymptotic convergence. Finally, the performances of the proposed methods are illustrated by numerical examples.

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Pioneering Privacy-First Approaches in IoT Healthcare with Secure Wearable Monitoring Systems

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Pioneering Privacy-First Approaches in IoT Healthcare with S...

2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Najm, Rusul Awasthy, Neeta Kumari, E.N.Vijaya Lakhannal, Sorabh Sharma, Tarunika Singh, Anurag Prashanthi, Bh. College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Gl Bajaj Group of Institutions Department of Computer Science and Engineering U.P. Mathura India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India New Horizon College of Engineering Department of Applied Sciences Bangalore India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Griet Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798331527495

In the world of IoT healthcare right now, protecting the privacy of patients' medical data collected by wearable tracking devices is an urgent and important issue. It is very important that this condition be met. The Secure Health Data Aggregation and Homomorphic Encryption (SHAHE) method is being thought about as a possible alternative while this investigation is going on. We thought about the need for processing, data collection, and access control as we worked on the SHAHE approach. We can fully reach this goal by using homomorphic cryptography and safe aggregation methods. The main goal of the program is to protect people's privacy by making data value analysis better and reducing unauthorized access to important health information. This is the reason why the program was made. © 2025 IEEE.

关键词： Authentication

Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants

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arXiv 2022年

作者： Paul, Ankita Tajin, Md Abu Saleh Das, Anup Mongan, William Dandekar, Kapil Department of Electrical and Computer Engineering Drexel University PhiladelphiaPA19104 United States Department of Mathematics and Computer Science Ursinus College CollegevillePA19426 United States

Precise monitoring of respiratory rate in premature newborn infants is essential to initiating medical interventions as required. Wired technologies can be invasive and obtrusive to the patients. We propose a Deep Learning enabled wearable monitoring system for premature newborn infants, where respiratory cessation is predicted using signals that are collected wirelessly from a non-invasive wearable Bellypatch put on the infant's body. We propose a five-stage design pipeline involving data collection and labeling, feature scaling, deep learning model selection with hyper parameter tuning, model training and validation, and model testing and deployment. The machine learning model used is a 1-D Convolutional Neural Network (1DCNN) architecture with 1 convolution layer, 1 pooling layer, and 3 fully-connected layers, achieving 97.15% classification accuracy. To address energy limitations of wearable processing, several quantization techniques are explored and their performance and energy consumption are analyzed for the respiratory classification task. Results demonstrate a reduction of energy footprints and model storage overhead with a considerable degradation of the classification accuracy, meaning that quantization and other model compression techniques are not the best solution for respiratory classification problem on wearable devices. Hence,to improve classification accuracy, yet reduce the energy consumption we propose a novel Spiking Neural Network (SNN)-based respiratory classification solution, which can be implemented on event-driven neuromorphic hardware platforms. To this end, we propose an approach to convert the analog operations of our baseline trained 1DCNN to their spiking equivalent. We perform a design-space exploration using the parameters of the converted SNN to generate inference solutions having different accuracy and energy footprints. We select a solution that achieves an accuracy of 93.33% with 18x lower energy compared to the baseline 1DCNN

关键词： Energy utilization

An Efficient Wireless Sensor Network based Intrusion Detection System

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An Efficient Wireless Sensor Network based Intrusion Detecti...

Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Dinesh Rajassekharan V Ramachandran Dilip Kumar Sharma Thirumoorthy Palanisamy Y. Varatharaj Myilsam Ramesh Chandra Poonia Faculty of Computer Science Peninsula College Malaysia Department of Computer Science and Engineering GITAM School of Computing GITAM Deemed to be University Bengaluru India Department of Mathematics Jaypee University of Engineering and Technology Guna Madhya Pradesh India Department of Computer Science and Engineering Nandha Engineering College Erode Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science CHRIST (Deemed to be University) Delhi-NCR Ghaziabad Uttar Pradesh India

Wireless Sensor Networks (WSNs) are widely used in diverse applications due to their cost-effectiveness and versatility. However, they face substantial difficulties because of their innate resource constraints and susceptibility to security attacks. A possible method to improve the security of WSNs is clustering-based intrusion detection and responding mechanisms. An in-depth analysis of the clustering-based intrusion detection and response method for WSNs is presented in this study. The suggested method efficiently uses data mining and machine learning techniques to identify unusual behaviour and probable intrusions. The system effectively analyses data inside clusters by grouping Sensor Nodes (SN) into clusters, allowing it to differentiate between legitimate patterns and insecure activity. The network may respond promptly to identified breaches and react to the responsive mechanism, which reduces their impact and protects network integrity. The proposed Mathematically Modified Gene Populated Spectral Clustering Based Intrusion Detection System and Responsive Mechanism (MMMMGPSC-IDS-RM) is compared with existing state-of-art techniques, and MMMMGPSC-IDS-RM outperforms with the highest detection rate of 96%.

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Application of Machine Learning in Mental Diseases Diagnosis

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Application of Machine Learning in Mental Diseases Diagnosis

Information Technology (ACIT)

作者： Rahaf Alsawi Raed Alazaidah Ghassan Samara Haneen Alzoui Omar Alidmat Esraa Abu Elsoud Mohammed Talat Khouj Anwar Katrawi Mo’Ath Alluwaici Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Mental diseases, mental disorders, or psychological disorders is a large group of symptoms which causes changes to the state of mood and reflect this on the behavior of the person with the mental disorder. It affects the patient’s way of thinking, which limits his ability to perform his simple daily needs, and thus, becomes a burden on the individual, and on the surrounding society as well. There are common types of mental disorders such as depression, anxiety disorders, schizophrenia, and bipolar disorder. In this research, two main objectives are considered. The first objective is determining which is the best classifier for predicting a person’s mental disorder among six well-known classifiers. The second objective is determining what is the best feature selection technique to use with mental disorders datasets considering six popular techniques, and with respect to several evaluation metrics. The results revealed that Logistic Regression and Naïve Bayes are the two best classification models, while Info Gain Ratio is the best feature selection method.

关键词： Measurement Logistic regression Mood Machine learning Schizophrenia Feature extraction Depression Bayes methods Medical diagnosis Information technology