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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Packy, Anna L. Jayahankar, Jayesh Teymourlouei, Arya Stone, Joshua Oh, Hyuk Katz, Garrett E. Reggia, James A. Gentili, Rodolphe J. University of Maryland Neuroscience and Cognitive Science Program College Park United States University of Maryland Department of Computer Science College Park United States University of Maryland Department of Electrical & Computer Engineering College Park United States University of Maryland Department of Kinesiology College Park United States Syracuse University Department of Electrical Engineering & Computer Science Syracuse United States

ISBN: (纸本)9798350371499

Human-robot teaming has become increasingly important with the advent of intelligent machines. Prior efforts suggest that performance, mental workload, and trust are critical elements of human-robot dynamics that can be altered by the robot's behavior. Most prior human-robot teaming studies used behavioral analyses, but a limited number used neural markers, without the use of physical robots and complex tasks. Here we combine behavioral and EEG cortical dynamics to examine cognitive-motor processes when individuals complete a complex task under various team environments with a robot. The results revealed that altering the robot quality affected both behavioral and EEG dynamics. Task completion with an experienced robot led to greater team performance and human trust along with lower mental workload compared to an inexperienced teammate or when individuals performed alone. EEG changes suggest that different attentional processes were engaged when humans worked with the robot and performed alone, and that visual processing was more prominent when teaming with an inexperienced teammate. This work can inform human cognitive-motor processes and the design of robotic controllers in human-robot teams. © 2024 IEEE.

关键词： Man machine systems

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electrical programmable multilevel nonvolatile photonic random-access memory

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Light(science & Applications) 2023年第11期12卷 2325-2334页

作者： Jiawei Meng Yaliang Gui Behrouz Movahhed Nouri Xiaoxuan Ma Yifei Zhang Cosmin-Constantin Popescu Myungkoo Kang Mario Miscuglio Nicola Peserico Kathleen Richardson Juejun Hu Hamed Dalir Volker J.Sorger Department of Electrical and Computer Engineering George Washington UniversityWashington DC 20052USA Florida Semiconductor Institute University of FloridaGainesvilleFL 32603USA Department of Electrical and Computer Engineering University of FloridaGainesvilleFL 32603USA Department of Materials Science&Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA CREOL The College of Optics&PhotonicsUniversity of Central FloridaOrlandoFL 32816USA

Photonic Random-Access Memories(P-RAM)are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data *** Phase-Change Materials(PCMs)have been showed multilevel memory capability,but demonstrations still yield relatively high optical loss and require cumbersome WRITE-ERASE approaches increasing power consumption and system package *** we demonstrate a multistate electrically programmed low-loss nonvolatile photonic memory based on a broadband transparent phase-change material(Ge2Sb2Se5,GSSe)with ultralow absorption in the amorphous state.A zero-staticpower and electrically programmed multi-bit P-RAM is demonstrated on a silicon-on-insulator platform,featuring efficient amplitude modulation up to 0.2 dB/μm and an ultralow insertion loss of total 0.12 dB for a 4-bit memory showing a 100×improved signal to loss ratio compared to other phase-change-materials based photonic *** further optimize the positioning of dual microheaters validating performance *** we demonstrate a half-a-million cyclability test showcasing the robust approach of this material and ***-loss photonic retention-of-state adds a key feature for photonic functional and programmable circuits impacting many applications including neural networks,LiDAR,and sensors for example.

关键词： retention random eliminating

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Eliminations of sulphur and fluoride from contaminated water by the applications of various types of biosorbents materials

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Water-Energy Nexus 2025年 8卷 73-92页

作者： Dey, Subhashish Veerendra, G.T.N. Phani Manoj, A.V. Anjaneya Babu, P.S.S. Jagadeesh, Gogineni Venkata Swarup Kumar, J.N.V.R. Civil Engineering Department Gudlavalleru Engineering College Andhra Pradesh Gudlavalleru India Mechanical Engineering Department Gudlavalleru Engineering College Andhra Pradesh Gudlavalleru India Computer Science Engineering Department Gitam University Andhra Pradesh Vishakapatnam India

Industrial and human waste releases sulphur and fluoride metals into the water sources, polluting them and endangering humans and the environment. There are various methods available for removals of the pollutants from the water. The membrane filtering and Chemical precipitation are more expensive, useless at low metal concentrations, and produce vast amounts of sludge and toxic by products that must be disposed of when treating big volumes of water. Alternative the wastewater treatment using the biosorption technology is environmentally friendly method. These methods are cheaper, more accessible, and reusable than traditional ones. Biomass can be treated physically and chemically treated before usage. The other parameters included the contact time, agitation speed, adsorbent dosage, pH, and temperature also affects the biosorption performances. After the removal of sulphur and fluorides from water, the bio-sorbent can be regenerated and reused to save money. Wastewater pollution removal using adsorption method is generally accepted. In the present scenario, the magnetic biosorbents and nanoparticles biosorbents are used for wastewater treatment have been developed. The magnetic biosorbents are attractive because to their various active sites, large specific surface area, easy separation, and lower cost. Chemical activations like acid, alkali, and salt improve biosorbents adsorption. In this observe, the various biosorbents have been developed for their better performance on defluoridation and desulphuridation. Due to different scientific barriers in the biosorption procedures those obstruct its commercialization;there has been a gradually rising consideration in this area of study. Of late more consideration is being paid in the formation of cost-effective adsorbents using various agricultural wastes, plant biomass, bacteria, algae and fungi. This review article highlights the applications of different biosorbents, sorption isotherm, and kinetics. In addition, the

关键词： Wastewater treatment

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Relevant Musical Schema-based Human Emotion Controller using Deep Learning Techniques 3

Relevant Musical Schema-based Human Emotion Controller using...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Nagaraj, P. Sudar, K. Muthamil Krishna, Gollamandala Vamsi Yeswanth, Grandhi Srinivas Charan, Gurrala Sai Charan, Nidiganti Kalasalingam Academy of Research and Education Krishnankoil Department of Computer Science and Engineering Virudhunagar India Mepco Schlenk Engineering College Department of Computer Science and Engineering Virudhnagar India

ISBN: (纸本)9798350361186

Musical Schema negotiation describes how a user's facial expressions may convey their emotional state or mood. You may see these expressions in the live video from the system camera. Many efforts are being made to teach machines to recognize various human emotions or moods in the field of computer vision and machine learning (ML). There are several techniques available in machine learning to identify human emotions. We propose a system that detects mood in real time and generates a mood-based music player that recommends songs depending on the observed mood. This is now a function that comes pre-installed on the traditional music player applications on our phones. One major benefit of mood detection integration is customer satisfaction. Among this system's objectives are to analyze the user's look, forecast their expression, and suggest music based on how they seem. © 2024 IEEE.

关键词： Deep learning

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Design of a High-Efficiency Temporal Engine for Real-Time Spatial Satellite Image Classification Using Augmented Incremental Transfer Learning for Crop Analysis

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SN computer science 2024年第5期5卷 585页

作者： Dhande, Akshay Pramodrao Malik, Rahul Saini, Dipen Garg, Rachit Jha, Sudan Nazeer, Jabeen Ahmad, Sultan Department of Electronics and Telecommunication Engineering Lovely Professional University Punjab Phagwara India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Kathmandu University Dhulikhel Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India

Satellite image processing is a multidomain task which involves design of image capturing, denoising, segmentation, feature extraction, feature reduction, classification, and post-processing tasks. A wide variety of satellite image processing models are proposed by researchers, and each of them has different data and process requirements. For instance, the image capturing module might obtain images in layered form, while feature extraction module might require data in 2D or 3D forms. Moreover, performance of these models also varies due to changes in internal process parameters and dataset parameters, which limits their accuracy and scalability when applied to real-time scenarios. To reduce the probability of these limitations, a novel high-efficiency temporal engine for real-time satellite image classification using augmented incremental transfer learning is proposed and discussed in this text. The model initially captures real-time satellite data using Google’s Earth Engine and processes it using a transfer learning-based convolutional neural network (CNN) via backscatter coefficient analysis. These coefficients indicate average intensity value of Precision Image (PRI) when evaluated over a distributed target. Due to extraction of backscattering coefficients, the model is capable of representing crop images in VV (vertical transmit, vertical receive), and HV (horizontal transmit vertical receive) modes. Thereby assisting the CNN model to extract a wide variety of features from input satellite image, which classifies these datasets (original, VV, and VH) into different crop categories. The classified images are further processed via an incremental learning layer, which assists in visual identification of affected regions. Due to use of incremental learning and CNN for classification, the proposed TRSAITL model is capable of achieving an average accuracy of 97.8% for crop type and severity of damage detection, with an average PSNR (Peak Signal-to-Noise Ratio) of 29.

关键词： Accuracy Classification Crop Damage Deep learning Earth Incremental Precision Recall Satellite Transfer Type

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An Explainable AI for Prostate Cancer Analysis 5th

An Explainable AI for Prostate Cancer Analysis

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Kumar, M. Rakesh Athish, B.S. Devi, A. Chandini Kumar, P. Department of Computer Science and Engineering Rajalakshmi Engineering College TN Chennai India

ISBN: (纸本)9789819771776

Prostate cancer comes under SDG 3—Good Health and Well-Being is a global health concern affecting millions of men, demanding precise analysis for early detection and effective treatment. This project pioneers, innovative techniques for prostate cancer analysis, utilizing AI-driven segmentation and advanced analytical methods to address this pressing concern. Prostate cancer poses a significant worldwide health challenge, potentially metastasizing to neighboring organs and bones, leading to pain and fractures. The primary aim of this project is to enhance the precision and efficiency of prostate cancer analysis for early detection and timely intervention. Approximately one in eight men receive a prostate cancer diagnosis during their lifetime, with a higher prevalence among older individuals and non-Hispanic black men, particularly those aged 65 and older. Awareness levels vary, with 47.3% of survey respondents aware of the condition, primarily through media outlets (21.2%) and healthcare professionals (9%). This project addresses varying prostate cancer rates in distinct regions (peripheral zone: 70–80%, transitional zone: 10–20%, central zone: 5% or less). It employs AI-driven segmentation, image processing, and machine learning for accurate tumor detection, classification, and risk assessment. An innovative aspect is developing an explainable AI system for transparent and expert-empowered analysis. The project uses AI to diagnose prostate cancer accurately and early. It combines AI-driven segmentation, advanced analytical methodologies, refined algorithms, and a user-friendly interface. The aim is to enhance patient care through accurate and interpretable prostate cancer analyses in underserved areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Formal Modeling of Hybrid System Based on Semi-continuous Colored Petri Net: A Case Study of Adaptive Cruise Control System

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ACM Transactions on Embedded Computing Systems 2025年第3期24卷 1-21页

作者： Yu, Wangyang Guo, Qi Cheng, Yumeng Liu, Lu Hao, Fei Zhai, Xiaojun Chen, Minsi The School of Computer Science Shaanxi Normal University Shaanxi Xi'an China The School of Computer Science Shaanxi Normal University Xi'an China University of Exeter Department of Computer Science Exeter United Kingdom University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom

Many Next-Generation consumer electronic devices would be distributed hybrid electronic systems, such as UAVs (Unmanned Aerial Vehicles) and smart electronic cars. The safety and risk control are the key issues for the sustainability of such consumer electronic systems. The modeling of hybrid electronic systems is difficult to be abstracted by traditional Petri Nets. This also makes the reachable marking graph unable to be applied to Petri Nets of the hybrid electronic systems. This paper proposes a novel Petri Net to model and analyze the hybrid electronic systems. We name it a Semi-continuous Colored Petri Net (SCPN) that inherits the excellent modeling capabilities and analysis methods of Petri Nets, and can formally depict hybrid quantities. In addition, we propose the construction algorithm for an SCPN reachable marking graph and prove its finiteness. Finally, we model and analyze an Adaptive Cruise Control (ACC) system of smart electronic cars as an example to prove the validity of SCPN. We use the proposed SCPN to model and analyze the running process of an ACC system under the continuous deceleration scenario of the front vehicle. The application study shows that the ACC system has logic flaws under the constant headway strategy when the front vehicle continues to decelerate. Based on this analysis, improvements to the SCPN of the ACC system are made, effectively enhancing its safety and logical correctness. Copyright © 2025 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adaptive cruise control

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Enhancing Urban Traffic Management Through Machine Learning Prediction Models for Sensor-Less Roads 8

Enhancing Urban Traffic Management Through Machine Learning ...

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8th International Conference on Intelligent Traffic and Transportation, ICITT 2024 - incorporating the 8th International Conference on Communication and Network Technology, ICCNT 2024

作者： de Souza Oliveira, Thamires Di Grande, Sarah Berlotti, Mariaelena Cavalieri, Salvatore Torrisi, Vincenza Calabrò, Giovanni Inturri, Giuseppe Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9781643685601

The process of urbanization on a global scale has generated a significant increase in metropolitan populations, which in turn brings with it a series of challenges for the management of transport infrastructure. In this context, the importance of accurate traffic flow prediction in urban traffic management systems stands out, with the city of Catania being used as the focus of this study. However, predicting traffic flow faces challenges, such as the high costs associated with acquiring extensive real traffic data, particularly when installing sensors on every road is not feasible. To address the issue of missing traffic flow data on roads without sensors, this study proposes an innovative approach based on machine learning, using the Long Short-Term Memory architecture and incorporating both sensor and floating car data. The results obtained are thoroughly analyzed and discussed, highlighting the ability of the LSTM model to provide accurate predictions of urban traffic, even with a limited amount of data available. These findings highlight the relevance of employing advanced machine learning techniques in the efficient management of urban mobility, aiming to improve the quality of life in cities and deal more effectively with the challenges of contemporary urban traffic. © 2024 The Authors.

关键词： Prediction models

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Automating Boundary Filling in Cubical Agda 9

Automating Boundary Filling in Cubical Agda

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9th International Conference on Formal Structures for Computation and Deduction, FSCD 2024

作者： Doré, Maximilian Cavallo, Evan Mörtberg, Anders Department of Computer Science University of Oxford United Kingdom Department of Computer Science and Engineering University of Gothenburg Sweden Department of Mathematics Stockholm University Sweden

ISBN: (纸本)9783959773232

When working in a proof assistant, automation is key to discharging routine proof goals such as equations between algebraic expressions. Homotopy Type Theory allows the user to reason about higher structures, such as topological spaces, using higher inductive types (HITs) and univalence. Cubical Agda is an extension of Agda with computational support for HITs and univalence. A difficulty when working in Cubical Agda is dealing with the complex combinatorics of higher structures, an infinite-dimensional generalisation of equational reasoning. To solve these higher-dimensional equations consists in constructing cubes with specified boundaries. We develop a simplified cubical language in which we isolate and study two automation problems: contortion solving, where we attempt to "contort" a cube to fit a given boundary, and the more general Kan solving, where we search for solutions that involve pasting multiple cubes together. Both problems are difficult in the general case – Kan solving is even undecidable – so we focus on heuristics that perform well on practical examples. We provide a solver for the contortion problem using a reformulation of contortions in terms of poset maps, while we solve Kan problems using constraint satisfaction programming. We have implemented our algorithms in an experimental Haskell solver that can be used to automatically solve goals presented by Cubical Agda. We illustrate this with a case study establishing the Eckmann-Hilton theorem using our solver, as well as various benchmarks – providing the ground for further study of proof automation in cubical type theories. © Maximilian Doré, Evan Cavallo, and Anders Mörtberg.

关键词： Automation

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring 5

DL-Based Somnolence Detection for Improved Driver Safety and...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Neeraja, Peddinti. Kumar, R.G. Kumar, M. Sunil Liyakat, Kazi Kutubuddin Sayyad Vani, M. Sowmya School of Computing Department of Computer Application AP Tirupathi India Siddharth Institute of Engineering & Technology Computer Science and Engineering AP Puttur India School of Computing Department of Computer Science and Engineering AP Tirupathi India Brahmdevdada Mane Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798350383522

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads. © 2024 IEEE.

关键词： Convolution neural network deep learning Drowsiness detection transfer learning

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