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WARA-PS:a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation

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Autonomous Intelligent Systems 2021年第1期1卷 104-134页

作者： Olov Andersson Patrick Doherty Mårten Lager Jens-Olof Lindh Linnea Persson Elin A.Topp Jesper Tordenlid Bo Wahlberg Department of Computer and Information Science Linköping UniversityLinköpingSweden Saab Kockums MalmöSweden Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholmSweden Department of Computer Science Lund UniversityLundSweden Saab Combitech AB LinköpingSweden Division of Automatic Control Department of Electrical EngineeringLinköping UniversityLinköpingSweden

A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is *** objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal *** enabler is a computational infrastructure with a core system architecture for industrial and academic *** includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial *** motivating application for the demonstration is marine search and rescue operations.A state-of-art delegation framework for the mission planning together with three specific applications is also *** first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface *** second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles,and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine *** research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test *** would be most difficult to do experiments on this large scale without the WARA-PS research ***,these demonstrator activities have resulted in effective research dissemination with high public visibility,business impact and new research collaborations between academia and industry.

关键词： Autonomous systems Intelligent system architectures Research demonstration arena Autonomous drones Autonomous marine vessels Public safety and security Collaborative robotics

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Multiscale Modelling of Curved Pixel Arrays

Multiscale Modelling of Curved Pixel Arrays

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International Conference on Numerical Simulation of Optoelectronic Devices, (NUSOD)

作者： Enrico Bellotti John Glennon Mike Zhu Alex Kyrtsos Andreu Glasmann Electrical and Computer Engineering Department Boston University Boston MA USA Division of Material Science and Engineering Boston University Boston MA USA

Large format infrared (IR) detector arrays that provide high image resolution over a wide field of view (WFOV), are of critical importance for both defense and civilian applications, for example surveillance, astronomy, and remote sensing. Reduction of pixel pitch has been aggressively pursued to increase the array format, and the pixel dimension is approaching a size comparable to the wavelength. While significant progress has been made to increase the resolution of IR focal plane arrays (FPA), a considerable effort is still required to design the optics needed to implement the camera. For applications where a WFOV is needed, the camera optics has to be designed to mitigate the optical aberrations that degrade illumination and resolution uniformity across the FPA. This is currently achieved by employing many optical elements to flatten the Petzval field curvature to match the footprint of the planar image sensor. As a result of the complex optics needed to achieve field flattening, and at the same time to minimize other aberrations, the size, weight, and cost of the entire camera increases considerably.

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The Internet of Things and Its Intervention For Waste Management in Smart Cities 3

The Internet of Things and Its Intervention For Waste Manage...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Malik, Sarthak Malik, Praveen Kumar Kumar, Gotte Ranjith Singh, Rajesh Naim, Arshi School of Computer Science and Engineering Lovely Professional University Punjab Jalandhar India School of Electronics and Electrical Engineering Lovely Professional University Punjab Jalandhar India School of Computer Science & Artificial Intelligence Sr University Warangal India Uttaranchal University Division of Research & Innvovation Dehradun India King Khalid University Department of Business Management AlSamer University campus Aseer Abha Saudi Arabia

ISBN: (纸本)9798350330724

Smart cities are becoming increasingly popular in modern culture as towns strive to embrace new technologies and approaches to enhance infrastructure and services for citizens. One of the key factors in creating a smart city is efficient waste management, and the Internet of Things (IoT) is playing a major role in this process. The IoT refers to the interconnectivity of everyday devices and appliances, allowing them to communicate and share data. This technology is being leveraged in waste management to increase efficiencies and reduce costs through the use of sensors and real-time monitoring. The intervention of IoT in smart cities to automate and improvise various waste management processes can pave the way for a better, cleaner city. IoT can be leveraged in smart cities for various aspects of waste management including waste bins monitoring, waste bin collection planning, waste segregation and recycling, etc. In this study we will be discussing the role of IoT in a few of the aspects and its potential impact in the pursuit of cleaner cities. © 2023 IEEE.

关键词： Internet of things

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Detection of Bleeding Events In EHR Notes Using A Graph Dynamic Autoencoder Based Riemannian Residual Neural Network 4

Detection of Bleeding Events In EHR Notes Using A Graph Dyna...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Niveditha, V.R. Ali, H. Mohammed Rammohan, S. Radha Rajababu, D. Maranan, R. Kaliappan, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Semmancheri Chennai600119 India Srm Institute of Science and Technology Department of Mechanical Engineering Ramapuram Campus Chennai89 India Hindustan University Center for Open and Digital Education Padur Chennai India Sr University Department of Electrical and Electronics Engineering Telangana Warangal506371 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India Lovely Professional University Division of Research and Development Jalandhar - Delhi G.T.Road Punjab Phagwara144411 India

ISBN: (纸本)9798331518981

Identifying occurrences of abnormal bleeding based on unstructured and structured medical data, such as patient notes, vitals, and laboratory findings, is the problem of recognising bleeding events in Electronic Health Records (EHR). The problem is in accurately recognising these occurrences by analysing temporal patterns, extracting significant information, and distinguishing catastrophic bleeding occasions from typical medical fluctuations given the complexity of EHR data, which is frequently longitudinal, heterogeneous, and packed with noise. The goal is to create a Graph Dynamic Autoencoder combined with a Riemannian Residual Neural Network (GDA-RRNN) with Elk Herd optimizer (EHO) model that can identify and display bleeding incidents in electronic medical data. The EHO optimizes the hyperparameters GDA-RRNN to improve the performance of the suggested method. We extracted 300 electronic health records that contained leukaemia or bleeding diagnostic codes from the International categorization of Diseases, Tenth Revision. The electronic health record has annotations indicating whether a sentence was positive or negative for bleeding. A proposed model that can identify bleeding at the sentence and note levels was created using the annotated texts. The best-performing suggested approach achieved a sensitivity of 0.99, specificity of 0.99, and a negative prediction value of 0.99 on an equal test set of 1178 phrases. © 2024 IEEE.

关键词： Electronic health record

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Energy-Efficient TDMA-NOMA for RIS-Assisted Ultra-Dense VLC Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Ihsan, Asim Asif, Muhammad Khan, Wali Ullah Osahon, Isaac N. O. Rajbhandari, Sujan University of Cambridge Electrical Engineering Division Department Of Engineering Cambridge United Kingdom Jiangsu University School of Computer Science And Communication Engineering Zhenjiang China Luxembourg City1855 Luxembourg University of Strathclyde Institute of Photonics Scotland Glasgow United Kingdom

This paper proposes an energy-efficient optimization technique for downlink indoor visible light communication (VLC) systems using hybrid non-orthogonal multiple access (NOMA) and reconfigurable intelligent surfaces (RIS). The approach considers a hybrid time division multiple access-NOMA (TDMA-NOMA) to provide massive connectivity to multi-clusters. Clusters of users are formed using NOMA while TDMA is used to allocate a specific time slot within a communication frame. The proposed technique optimizes the precoding at the multi-LED transmitter, RIS tuning parameters, and time-slot allocation parameters for each cluster to maximize the system's energy efficiency (EE). The EE optimization problem is solved through the block coordinate descent (BCD) framework, which splits the optimization problem into two blocks. An alternating optimization (AO) framework is used in the first block to optimize the transmit precoding through conic quadratic programming (CQP) and RIS tuning parameters through a semidefinite programming (SDP) technique based on the surrogate optimization method. The second block allocates energy-efficient time-slot for each cluster through linear programming (LP) approach to further improve the EE of the system. The simulation results indicate that the proposed BCD framework achieves fast convergence and excellent performance in terms of the EE of the system while maintaining low computational complexity. © 2017 IEEE.

关键词： Quadratic programming

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Towards Non-Invasive Swallowing Assessment: an AI-Powered Interface for Swallowing Kinematic Analysis using High-Resolution Cervical Auscultation

Towards Non-Invasive Swallowing Assessment: an AI-Powered In...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yuewen Luo Ayman Anwar Siyi Ren James L. Coyle Ervin Sejdic Division of Engineering Science Faculty of Applied Science & Engineering University of Toronto Toronto ON Canada Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto Toronto ON Canada Department of Communication Science and Disorders School of Health and Rehabilitation Sciences University of Pittsburgh Pittsburgh PA USA Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto and North York General Hospital Toronto ON Canada

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Swallowing is a pivotal physiological function for human sustenance and hydration. Dysfunctions, termed dysphagia, necessitate prompt and precise diagnosis. Videofluoroscopic swallowing studies (VFSS) remain the gold standard for swallowing assessment but pose accessibility and radiation exposure concerns. High-resolution cervical auscultation (HRCA) presents a non-invasive alternative with comparable accuracy. Yet, existing studies have only assessed HRCA in tandem with VFSS, without applying it to clinical settings. We propose an AI-enhanced PC software deploying HRCA for real-time bedside swallowing screening. This software proficiently detects abnormalities and precisely measures key kinematic events, including upper esophageal sphincter opening and laryngeal vestibule closure duration. The system was evaluated through two studies, encompassing a VFSS comparative analysis and a usability study. Our findings underscored the software’s superior operational efficiency and diagnostic accuracy, positioning our HRCA-based system as a refined and pragmatic alternative for dysphagia assessment.

关键词： Accuracy Software algorithms Kinematics Software Real-time systems Physiology Software reliability Software measurement Usability Pragmatics

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Multi to binary class size based imbalance handling technique in wireless sensor networks

Multi to binary class size based imbalance handling techniqu...

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作者： Singh, Neha Virmani, Deepali Dhiman, Gaurav Vimal, S. University School of Information Communication and Technology Guru Gobind Singh Indraprastha University Delhi Dwarka110086 India Department of Computer Science Engineering VIPS-TC School of Engineering and Technology Delhi110034 India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Computer Science and Engineering University Centre for Research and Development Chandigarh University Mohali Gharuan140413 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Division of Research and Development Lovely Professional University Phagwara144402 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140601 India Department of Computer Science Government Bikram College of Commerce Punjab Patiala147001 India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Tamil Nadu Rajapalayam626117 India

Wireless sensor network (WSN) has entered various disciplines including healthcare, banking, transportation, ocean and wildlife monitoring, earthquake monitoring, and numerous military applications. Now days, there is escalation in size of data which makes it unfeasible to analyse it with accuracy. There are numerous problems that are faced when detecting a pattern between structured and unstructured data which are unworkable by humans, so to make computation fast, easy and accurate, Machine Learning came in existence. Machine Learning is extensively used in WSNs. To make a machine learn, a training dataset is required and output is predicted by testing the dataset. A dataset in WSN has multi-class in its dependent variable. This multiclass classification causes class imbalance problem. The paper proposes multi to binary class size based imbalance handling technique in wireless sensor networks (MBSCIH) technique to solve class imbalance problem in multi-class classification. The technique MBSCIH converts multi-class classification into binary-class classification. The proposed technique MBSCIH is applied on WSN-DS, NSL-KDD and KDD-cup 99 datasets and is tested with five major machine learning algorithm: Naive Bayes, Random Forest, decision tree, support vector machine (SVM) and k-nearest neighbour (KNN). The test method used for testing is 10-fold cross validation. Results infer that the proposed method increases the existing efficiency by 15.13%, 0.28%, 0.01%, 0.01%, 0.12% for Naïve Bayes, Decision Tree, SVM, Random Forest and KNN Algorithms respectively for KDD Cup99 dataset. The proposed method increases the efficiency by 43.1%, 2.79%, 0.01%, 0.53% for Naïve Bayes, decision tree, SVM and KNN algorithms respectively for NSL-KDD dataset. Also, the proposed method increases the efficiency by 0.13%, 0.08%, 0.29%, 0.29% for Naïve Bayes, decision tree, SVM and KNN algorithms respectively for WSN-DS *** the experimental analysis proves that solving multiclas

关键词： Wireless sensor networks

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Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals

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IFAC-PapersOnLine 2024年第24期58卷 579-583页

作者： Israel Jesus Santos Filho Muhammad Mahboob Ur Rahman Taous-Meriem Laleg-Kirati Tareq Al-Naffouri Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia The National Institute for Research in Digital Science and Technology Paris-Saclay France

We present for the first time a novel method that utilizes the chest movement-modulated radio signals for non-contact acquisition of the photoplethysmography (PPG) signal. Under the proposed method, a software-defined radio (SDR) exposes the chest of a subject sitting nearby to an orthogonal frequency division multiplexing signal with 64 sub-carriers at a center frequency 5.24 GHz, while another SDR in the close vicinity collects the modulated radio signal reflected of the chest. This way, we construct a custom dataset by collecting 160 minutes of labeled data (both raw radio data as well as the reference PPG signal) from 16 healthy young subjects. With this, we first utilize principal component analysis for dimensionality reduction of the radio data. Next, we denoise the radio signal and reference PPG signal using wavelet technique, followed by segmentation and Z-score normalization. We then synchronize the radio and PPG segments using cross-correlation method. Finally, we proceed to the waveform translation (regression) task, whereby we first convert the radio and PPG segments into frequency domain using discrete cosine transform (DCT), and then learn the non-linear regression between them. Eventually, we reconstruct the synthetic PPG signal by taking inverse DCT of the output of regression block, with a mean absolute error of 8.1294. The synthetic PPG waveform has a great clinical significance as it could be used for non-contact performance assessment of cardiovascular and respiratory systems of patients suffering from infectious diseases, e.g., covid19.

关键词： Non-contact methods RF-based methods software-defined radio PPG deep learning

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Smart Diaper Embedded with Fully Printed Sensors for Wireless Healthcare and Monitoring

Smart Diaper Embedded with Fully Printed Sensors for Wireles...

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IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS)

作者： Shawkat Ali Arshad Khan Amine Bermak Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

In this research, a real-time healthcare and monitoring wireless system is developed to monitor vital signs of a wearer i.e., temperature, respiration rate, and wetting volume on a smartphone. The smart diaper consists of two main units, a detachable electronic module and a disposable printed biosensors patch deployed on a commercial diaper. Custom sensors array was designed and fabricated in a single patch through printed electronics technology by using biocompatible materials on a thermal transfer substrate. The sensors patch was designed in such a way to accurately collect the vital signs data by placing each sensor at appropriate site. Individual sensors were characterized for their electrical and mechanical properties. The overall system was tested, and data was wirelessly transmitted to a smartphone via Bluetooth. The main purpose of the proposed smart diaper is to minimize the effort of caregiver and avoid infection, rashes, and discomfort. We believe this work will open a new window for the printed electronics systems for special needs, elderly, and infant healthcare.

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Adaptive Sampling for Efficient Softmax Approximation 38

Adaptive Sampling for Efficient Softmax Approximation

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Baharav, Tavor Z. Kang, Ryan Sullivan, Colin Tiwari, Mo Luxenberg, Eric Tse, David Pilanci, Mert Eric and Wendy Schmidt Center Broad Institute CambridgeMA02142 United States Department of Computer Science Stanford University StanfordCA94305 United States AI Division Software Engineering Institute PittsburghPA15213 United States Gridmatic CupertinoCA95014 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States

The softmax function is ubiquitous in machine learning and optimization applications. Computing the full softmax evaluation of a matrix-vector product can be computationally expensive in high-dimensional settings. In many applications, however, it is sufficient to calculate only the top few outputs of the softmax function. In this work, we present an algorithm, dubbed AdaptiveSoftmax, that adaptively computes the top k softmax values more efficiently than the full softmax computation, with probabilistic guarantees. We demonstrate the sample efficiency improvements afforded by AdaptiveSoftmax on real and synthetic data to corroborate our theoretical results. AdaptiveSoftmax yields > 10x gain over full softmax computation on most datasets, yielding up to 30x improvement for Mistral7B evaluated on the Wikitext dataset. The adaptive method we propose for estimating the partition function (the softmax denominator) is of independent interest and can be used in other applications such as kernel density estimation. © 2024 Neural information processing systems foundation. All rights reserved.

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