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Hydrological and Structural Monitoring for Flood Risk Mitigation in Cosenza Urban Area (Southern Italy)

Hydrological and Structural Monitoring for Flood Risk Mitiga...

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International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Daniela Biondi Francesco S. Liguori Antonio Bilotta Antonio Madeo Giovanna Capparelli Dept. of Computer Engineering Modelling Electronics and Systems University of Calabria Rende Italy

The evaluation of flood risk in urban areas characterized by high vulnerability indices is a fundamental task in order to undertake any mitigation action. When flood-induced phenomena affect structures (buildings, bridges, river banks, etc.) particularly in areas adjacent to rivers, as for the city of Cosenza, the real time detection of any change in the structural response is crucial for a proper safety level assessment. In the present work, a framework for remote monitoring of hydrological events and related structural behaviour is proposed. Data pertaining to the hydro-meteorological event are collected mainly through a series of ultrasonic hydrometers, different types of rain gauges and webcams, to allow a rough estimate of the expected action on the structures. The structural response is monitored through accelerometers (or other instruments) which allows identifying the evolution of the dynamic behaviour, in order to detect insurgent damage phenomena. The acquired structural measurements are employed to tune both fine structural models or rough simplified models. Moreover, the structural monitoring system allows to estimate damages due to other natural events, such as earthquakes, which well suits current philosophies of multi-risk assessment. This study is carried out within the “Smart integrated monitoring system for safe and resilient communities” action of the NRRP Tech4you project.

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Union Bound for Generalized Duplication Channels with DTW Decoding

Union Bound for Generalized Duplication Channels with DTW De...

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IEEE International Symposium on Information Theory

作者： Adrian Vidal V. B. Wijekoon Emanuele Viterbo Dept. Electrical & Computer Systems Engineering Monash University Clayton VIC Australia

In this paper, we calculate a union bound for dynamic time warping (DTW)-based decoding of piecewise constant signals corrupted by additive noise and time stretching due to sample duplications, as observed in raw measurement signals obtained from nanopore sequencers. We consider both finitely- and infinitely-supported duplications with geometric-like characteristic, which include discrete uniform distributions as a special case. First, we provide explicit algorithms that calculate the union bound in O(αk 2 ) time for the infinite-support case and in O(β 2 k 2 ) for the finite-support case, where k is the codeword length, α is the minimum duplication, and β is the maximum duplication. Next, we show that a multi-read union bound exhibits a thresholding effect, where the error probability can be made arbitrarily close to zero by aggregating DTW distances from sufficiently many independent reads. Finally, we validate the calculated bounds relative to simulation results.

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A Systematic Literature Review of the Application of Agile Applied to Large-Scale, Safety-Critical, Cyber-Physical systems

A Systematic Literature Review of the Application of Agile A...

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International Conference on Data and software engineering (ICODSE)

作者： Robin J. Yeman Yashwant K. Malaiya Dept. Systems Engineering Colorado State University Fort Collins USA Dept. Computer Science Colorado State University Fort Collins USA

ISBN: (数字)9798331506407

ISBN: (纸本)9798331506414

The application of Agile methodologies to large-scale, safety-critical cyber-physical systems (LS/SC/CPS) has shown significant interest over the last 5 years. Although there has been limited research into each of the individual attributes for de-velopment projects that are large-scale, safety-critical, or cyber-physical systems, there is a scarcity of research when all three attributes exist simultaneously in a development endeavor. This systematic literature review explores the extent of Agile adoption in projects where all three attributes are present. It begins by examining the extent of Agile adoption by domain, identifying the drivers for change, reviewing the challenges encountered, and investigating the augmentations used to address these challenges. The findings indicate that there has been growth in adoption across the medical, aerospace, automotive, and robotics indus-tries, with limited adoption in energy and transportation. Key drivers include speed, adaptability, and cost. Several challenges have been identified, with regulatory/compliance requirements being the most significant. Multiple augmentations to Agile have been applied to overcome the identified challenges, ranging from incorporating safety frameworks and specialized artifacts to leveraging modeling and simulation, as well as reimagining pro-grammatics. This review highlights industry efforts to apply Agile to LS/SC/CPS, while identifying several gaps in the literature. The largest gap is empirical validation in real-world settings. Future research should focus on improving the existing Agile scaling frameworks with guidance and resources to help achieve the benefits of Agile in LS/SC/CPS development while ensuring regulatory and safety compliance.

关键词： Industries Technological innovation Costs Transportation Cyber-physical systems Complexity theory Standards Aerospace industry Systematic literature review software engineering

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Utilizing Deep Neural Networks to Improve Intrusion Detection System(IDS)

Utilizing Deep Neural Networks to Improve Intrusion Detectio...

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Telecommunications Conference (ITC-Egypt), International

作者： Ahmed Mahmoud Saad Mohammed Awad Meekay Mostafa Samy El Sayed Mansour Electronics and Computer and Systems Engineering Dept. Mansoura University Cairo Egypt Mansour Electronics and Communications Dept. Mansoura University Cairo Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Internet services are now accessible everywhere in the world due to the rapid advancement of networks and technology in recent decades. The development of information security solutions that can recognize novel threats is now critical due to the surge in piracy and the compromising of many modern systems. An intrusion detection system (IDS) is one of the most important information security technologies. It uses machine learning and deep learning algorithms to locate anomalies in the network. The main goal of this paper is to identify the unknown assault package by utilizing a novel intrusion detection method that offers excellent network performance. In this model, two techniques-binary classification and multiclass classification-that make use of a deep neural network algorithm are used to detect attacks. The suggested's outstanding accuracy

关键词： Deep learning Machine learning algorithms Accuracy Web and internet services Supervised learning Intrusion detection Artificial neural networks

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Investigating the Significance of Surface Roughness for Screen-printed Silver ink on Kapton Substrate

Investigating the Significance of Surface Roughness for Scre...

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IISE Annual Conference and Expo 2022

作者： Abdelatty, Mohamed Y. Umar, Ashraf Khinda, Gurvinder S. Poliks, Mark D. Dept. of Systems Science and Industrial Engineering State University of New York at Binghamton BinghamtonNY United States Dept. of Electrical and Computer Engineering State University of New York at Binghamton BinghamtonNY United States

ISBN: (纸本)9781713858072

Minor changes between the designed Flexible hybrid electronic (FHE) devices and the actual realized printed devices are very crucial for the development and maturity of this technology. The quality of printed electronics depends on mechanical and chemical factors such as ink properties and the printing technique [1], [2]. Fluctuations in the quality of printed electronic devices, such as thickness, width, waviness, and surface roughness, affect their radio frequency (RF) performance [2]. In this paper, we investigate statistically, using design of experiments (DoE) techniques, the significance of surface roughness of screen-printed silver ink traces on the RF performance. Ninety-six microstrip lines were printed;we measured the average thickness, average width, average roughness, and average waviness, for all the samples. Also, we measured the S-parameters for all the samples;then, we extracted Insertion-loss at four different frequencies. We performed simple statistical analysis and checked independence and normality assumptions for our data. We considered the thickness, width, roughness, and waviness to be our independent variables. We performed regression analysis and best subsets regression four times each time we consider the insertion-loss as the dependent variable. We were able to conclude that surface roughness becomes more significant as the frequency increases for screen-printed FHEs. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

关键词： Surface roughness

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Exercise and Sedentary Activity Recognition Using Late Fusion: Building Adaptable Uncertain Models

Exercise and Sedentary Activity Recognition Using Late Fusio...

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International Conference on Information Fusion

作者： Ezequiel Juarez Garcia Victoria R. Rodrigues Mehrdad Fazli Laura E. Barnes Nicholas J. Napoli Dept. of Electrical and Computer Engineering University of Florida Gainesville FL Dept. of Systems and Information Engineering University of Virginia Charlottesville VA

Wearable smart devices are capable of capturing a variety of information from their users using a multitude of noninvasive sensing modalities. Using features from the raw measurements of wearable devices, sensor fusion enables us to obtain a holistic picture of the users’ context and monitor their activity state with increased accuracy. Human activity recognition using noninvasive sensors allows us to capture the natural behavior of users in their day-to-day lives. This in-the-wild activity recognition, however, poses several key challenges that must be addressed to create effective classification models. The main challenges are class imbalance, uncertainty in classifier decisions, and large feature spaces. To address them, this study further explores a probabilistic sensor fusion method called Naive Adaptive Probabilistic Sensor (NAPS) Fusion. In doing so, we establish the viability of NAPS Fusion for natural human activity recognition using noninvasive sensing modalities. NAPS Fusion handles dimensionality reduction by creating reduced feature sets and mitigates the class imbalance issue through the use of Synthetic Minority Oversampling Technique (SMOTE). Moreover, NAPS Fusion addresses uncertainty in the decisions of classifiers using a Dempster-Shafer theoretic late fusion framework. Our empirical evaluation demonstrates that NAPS Fusion has broad applications beyond its original design for cognitive state detection. It outperforms similar decision level sensor fusion methods (late fusion using averaging, LFA, and late fusion using learned weights, LFL) in the detection of exercise and sedentary activities such as walking, running, lying down, and sitting. We observe improvements of up to 56% in F1 score and up to 59% in precision with NAPS Fusion over the compared methods.

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Multispectral Image Segmentation in Agriculture: Evaluating Deep Learning Models with Train-Test Split and Cross-Validation Strategies

Multispectral Image Segmentation in Agriculture: Evaluating ...

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Iberian Robotics Conference (ROBOT)

作者： Wilgo Cardoso Tiago Barros Gil Gonçalves Cristiano Premebida Urbano J. Nunes Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal Dept. of Mathematics Institute for Systems Engineering and Computers at Coimbra University of Coimbra Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

In agricultural robotics, the integration of multi-spectral image processing and deep learning (DL) has become the state-of-the-art (SOTA) in crop monitoring, yield estimation, and efficient land management. This work addresses the impact of different DL-segmentation models and evaluation protocols on multispectral imagery datasets collected by a UAV over vineyards. In terms of evaluation protocols, we have considered train-test split, standard k-fold cross-validation, and group k-fold cross-validation. While the first two assume that the training and test data are drawn from the same underlying distribution, the group k-fold cross-validation protocol assumes that each fold represents distinct distributions. Most works either adopt train-test split or k-fold cross-validation under the assumption that both the training and test sets are drawn from the same distribution. However, this assumption is rarely met in real-world applications. Therefore, the objective of this study is to evaluate and compare different evaluation protocols within the context of a real-world agricultural task, highlighting their limitations and weaknesses. Two SOTA DL-based segmentation models, SegNet and DeepLabV3, are employed to perform semantic segmentation on datasets of three Vineyards. The models have been trained and tested considering single-modality representations. In addition to the RGB modality, models trained on NDVI, GNDVI and early fusion are also evaluated. The performance of the models are evaluated using the IoU metric across different dataset configurations. The results indicate that the early fusion representation achieves the highest performance across the various splitting protocols, compared to the single-input representations. The results also show that the train-test and random k-fold splitting approaches report similar results. However, when employing group k-fold the performance drops consistently across both models and the modalities. This indicates that the models

关键词： Deep learning Training Precision agriculture Measurement Protocols Data models Yield estimation Robots Monitoring Standards

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Comparison of Whole Blood Coagulation Profiles in COVID-19 and Sepsis Patients Using a Handheld Dielectric Coagulometer

Comparison of Whole Blood Coagulation Profiles in COVID-19 a...

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2023 IEEE Biomedical Circuits and systems Conference, BioCAS 2023

作者： Pourang, Sina Suster, Michael A. Thomas, Asha Lapping, Stephanie D. Nayak, Lalitha V. Mohseni, Pedram Case Western Reserve University Dept. of Electrical Computer and Systems Engineering ClevelandOH United States University Hospitals Cleveland Medical Center Division of Hematology/Oncology ClevelandOH United States

ISBN: (纸本)9798350300260

Rapid point-of-care (POC) assessment of thrombosis is clinically important in patients who develop significant blood coagulation abnormalities such as noted with sepsis or COVID-19. In this work, we compare the coagulation profiles of whole blood in sepsis and COVID-19 patients using a handheld dielectric coagulometer termed ClotChip®. ClotChip® is a three-dimensional, parallel-plate, capacitive sensor integrated into a single-use microfluidic channel with a total volume of © 2023 IEEE.

关键词： Blood coagulation COVID-19 dielectric spectroscopy microfluidics point-of-care testing

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Optimizing Driver Identification Using Adaboost and Vehicular Telemetric Data Analysis

Optimizing Driver Identification Using Adaboost and Vehicula...

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International Conference on Emerging Technologies, ICET

作者： Ahmad Mustafa Khurram Shehzad Khattak Zawar Hussain Khan Dept. of Computer Systems Engineering UET Peshawar Peshawar Pakistan Electrical Engineering Department University of Hail Hail Saudi Arabia

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

In Intelligent Transportation systems (ITS), Artificial Intelligence (AI) has transformed transportation network management. This paper explores driver identification using machine learning to enhance vehicle security and efficiency. AI applications in ITS enable real-time data processing, predictive maintenance, and better traffic management, promoting a safer and more efficient ecosystem. Driver identification, or finger-printing, utilizes machine learning to identify drivers based on their unique driving behaviors, helping prevent unauthorized vehicle use, reducing theft, ensuring regulatory compliance, and optimizing resource allocation. Our research employs the AdaBoost algorithm, leveraging OBD- II data such as vehicle speed, RPM, coolant temperature, torque, accelerometer, and GPS for accurate driver identification with an accuracy of 96%. This approach capitalizes on AdaBoost's real-time processing capability, enhancing prediction accuracy, reducing overfitting, and improving generalization to new data. The results highlight the significant potential of AI -driven methods to advance ITS capabilities.

关键词： Adaptation models Accuracy Torque Biological system modeling Machine learning Real-time systems Security Resource management Driver behavior Vehicles

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Forecasting Unemployment Recession Rates: A Logistic Regression Approach Utilizing Comprehensive Economic Indicators

Forecasting Unemployment Recession Rates: A Logistic Regress...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Skandarsini R R Lakshmi Narayanaa T S. Jhansi Ida Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India

This paper presents a predictive model using logistic regression to forecast unemployment recession rates, utilizing a range of variables such as GDP, inflation rates, employment rates, and trade activities. The model aims to provide accurate predictions of recession occurrence, utilizing historical data and rigorous cross-validation techniques. The model's significance lies in its potential applications in financial prudence and risk management, enabling job seekers to plan their personal finances and career trajectories, while companies can integrate these insights into strategic planning and supply chain optimization. The model's interpretability provides actionable insights by highlighting the significance and direction of each variable's impact on the predicted recession rate. The paper contributes to a deeper understanding of economic recessions and empowers stakeholders to navigate challenges with resilience and confidence. The model's deployment offers a practical tool for individuals and organizations to make informed choices during economic uncertainties, ultimately fostering prudent and sustainable economic strategies.

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