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Technologies Applied to Electronics Teaching (TAEE)

作者： André s Garcí a Ló pez Miguel Damas Hermoso Claudia Villalonga Palliser Oresti Bañ os Legrá n Department of Electrical Engineering Automation and Electronic Technology Polytechnic University of Cartagena Cartagena Spain Department of Architecture and Computer Technology University of Granada Granada Spain

ISBN: (数字)9781665421614

ISBN: (纸本)9781665421621

This paper describes an academic project building on various state-of-the-art Industry 4.0 technologies. The project considers as the primary scenario the current problem behind the accumulation of biological waste in the vicinity of hospitals due to the COVID-19. To that end, a fully automated personal protective equipment recycling plant is developed using the Factory I/O simulation tool, TIA Portal, and Ignition. The project is considered to serve well as a reference for other Electrical Engineering undergraduates while complementing their training in skills such as design, automation, and supervision of industrial processes.

关键词： Training Personal protective equipment Industries Hospitals Ignition Production facilities Recycling

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Emerging Techniques for Online Learning Analytics 6th

Emerging Techniques for Online Learning Analytics

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6th International Conference on technology in Education, ICTE 2023

作者： Wang, Yidan Huang, Xiaoran Yu, Qijing Lai, YuXuan School of Computer Science The Open University of China Beijing China School of Architecture and Arts North China University of Technology Beijing China Academic Affairs Department The Open University of China Beijing China

ISBN: (纸本)9789819982547

The rapid growth of online learning platforms presents both challenges and opportunities for educational institutions. Online learning analytics has the potential to extract valuable insights from student learning behaviours, and such insights could offer learners flexible access to educational resources and foster lifelong learning opportunities. This paper reviews the research progress and emerging techniques in online learning analytics. We first outline the objectives of learning analytics, emphasising its potential to enhance personalised learning experiences, and examine key challenges faced in such platforms. We also review state-of-the-art techniques and explore emerging trends, natural language processing and multimodal learning analytics in particular. The paper discusses the implications of online learning analytics for educators and instructional designers, identifies open research challenges, and inspires further research and innovation to support learners in their educational journey. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data handling

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Development of Reflex technology of Action Identification in Project Planning Systems

Development of Reflex Technology of Action Identification in...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Teslia, Iurii Yehorchenkova, Nataliia Khlevna, Iulia Yehorchenkov, Oleksii Kataieva, Yevheniia Klevanna, Ganna Cherkasy State Technological University Department Of Information Technology Project Cherkasy Ukraine Kyiv National University Of Construction And Architecture Department Of Project Management Kyiv Ukraine Taras Shevchenko National University Of Kyiv Department Of Technology Management Kyiv Ukraine Cherkasy State Technological University Department Of Automated Systems Software Cherkasy Ukraine Cherkasy State Technological University Department Of Computer Science And Systems Analysis Cherkasy Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The need for intellectualization of project management processes is shown, in particular, the creation of intelligent systems capable of effectively plan projects in the process of natural-language interaction with the project team. The analysis of existing theoretical and practical developments in this field is carried out. It is shown that the issue of intellectualization of the project planning process is still open. It is proposed to use a reflex approach to building an intelligent project planning system. The technology of reflex identification of actions in project planning systems, based on the model of non-force (information) interaction, is proposed. Scenarios of actions have been developed and their distribution has been performed in accordance with the content of natural language requests to the project planning system. Experimental studies are shown a high probability of correct recognition of actions in project planning systems from natural language requests of project team members. The results of the conducted experiments unequivocally testify to the effectiveness of the developed information technology of reflex identification of action scenarios in project planning systems. © 2022 IEEE.

关键词： Intelligent systems

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Design and Experimental Investigation of Trikarenos: A Fault-Tolerant 28nm RISC-V-based SoC

arXiv

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arXiv 2024年

作者： Rogenmoser, Michael Wiese, Philip Forlin, Bruno Endres Gürkaynak, Frank K. Rech, Paolo Menicucci, Alessandra Ottavi, Marco Benini, Luca The Integrated Systems Laboratory ETH Zurich Switzerland The Computer Architecture for Embedded Systems The University of Twente Netherlands The Department of Industrial Engineering The University of Trento Italy The Department of Space Engineering The Delft University of Technology Netherlands The Computer Architecture for Embedded Systems The University of Twente Netherlands The Department of Electronic Engineering The University of Rome Tor Vergata Italy The Integrated Systems Laboratory ETH Zurich Switzerland The Department of Electrical Electronic and Information Engineering The University of Bologna Italy

We present a fault-tolerant by-design RISC-V SoC and experimentally assess it under atmospheric neutrons and 200 MeV protons. The dedicated ECC and Triple-Core Lockstep countermeasures correct most errors, guaranteein... 详细信息

关键词： Fault tolerance

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Modulation Recognition of the OFDM Signals in the UAV-RIS Based AI-enabled 6G Network for Smart Agriculture

Modulation Recognition of the OFDM Signals in the UAV-RIS Ba...

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International Bhurban Conference on Applied Sciences & technology, IBCAST

作者： Adil Sheraz Muhammad Farrukh Shahid Zuhaib Ashfaq Khan Tariq Jamil. S. Khanzada Department of Computer Science FAST-NUCES Karachi Pakistan Department of Electronic and Communication Engineering (ECE) School of Architecture Technology and Engineering (ATE) University of Brighton UK Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Computer Systems Engineering Department Mehran UET Jamshoro Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The integration of artificial intelligence (AI) and 6G networks aims to enhance agricultural applications by enabling adaptive and efficient communication systems. Fusing the potential and strengths of machine learning (ML) technology and the high-speed, low-latency communication capabilities of 6G networks, we can create intelligent farming systems that can enhance productivity, reduce resource wastage, and ensure sustainable agricultural practices. To tackle resource wastage and power efficiency of 6G-enabled devices, we present an AI-based solution that utilizes pre-trained Deep Learning (DL) models to classify Orthogonal Frequency Division Multiplexing (OFDM) modulated Quadrature Amplitude Modulation (QAM) signals. The proposed approach involves recognizing signals based on the classification results at the receiving nodes, prompting them to switch their radio modulation schemes to options such as 4-QAM, 16-QAM, and 64-QAM. Taking advantage of DL models for QAM classification and dynamically adjusting modulation schemes, the proposed solution can optimize resource utilization, improve data transmission quality, and enhance overall network performance in agricultural applications. We used pre-trained ResNet50, MobileNetV2, and a scratch CNN model. Our results show that pretrained models performed better than scratch CNN models and demonstrate the feasibility of using pretrained DL models for modulation scheme classification, offering promising opportunities for the development of adaptive and intelligent agricultural systems.

关键词： 6G mobile communication Smart agriculture Adaptation models Adaptive systems Quadrature amplitude modulation OFDM Modulation Switches Resource management Residual neural networks

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An Efficient Timing System for IFMIF-DONES Facility Based on Ethernet Time Transfer Protocols

An Efficient Timing System for IFMIF-DONES Facility Based on...

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2022 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium (EFTF/IFCS)

作者： Carlos Megí as Ví ctor Vá zquez Eduardo Ros Javier Dí az Mauro Cappelli Department of Computer Architecture and Technology Universidad de Granada Granada Spain Department of Fusion and Nuclear Safety ENEA Rome Italy

ISBN: (数字)9781665497183

ISBN: (纸本)9781665497190

This work analyses the synchronization requirements of the IFMIF-DONES infrastructure systems and proposes a cost-effective solution based on standard protocols: Network Time Protocol (NTP) and IEEE-1588 profiles, which are compliant with Ethernet networks. This approach simplifies the deployment of the timing system and its integration in already existing networks, being able to provide high scalability in the number of devices and limiting the number of time-aware devices or independent timing networks in the solution. The proposal takes full advantage of the capabilities of White-Rabbit technology to disseminate low phase noise frequency over the network without requiring wired radiofrequency solutions. Final considerations are made to understand the simplicity and viability of this solution.

关键词： Radio frequency Phase noise Time-frequency analysis Protocols Scalability Ethernet Synchronization

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CONSTRUCTION PROGRESS MONITORING IN SMART CITIES USING DEEP ALEXNET

CONSTRUCTION PROGRESS MONITORING IN SMART CITIES USING DEEP ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Rajavel, A. Karkuzhali, S. Sharma, Sarvdaman Singh, Sitesh Kumar Senthilkumar, P. Ida, S. Jhansi Electronics and Instrumentation Engineering Kamaraj College of Engineering and Technology Virudhnagar India Department of Electrical and Electronics Engineering R.M.D. Engineering College Chennai India Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Nekemte Oromia Ethiopia KL School of Architecture Koneru Lakshmaiah Education Foundation Vaddeswaram Greenfield AP Guntur India Department of Computer Science and Business Systems R.M.K Engineering College India

ISBN: (纸本)9798350305708

Construction progress monitoring plays a crucial role in ensuring the timely and efficient completion of infrastructure projects within smart cities. This paper proposes a novel approach utilizing the Deep AlexNet architecture for construction progress assessment in smart cities. The Deep AlexNet model is trained on a diverse dataset of images captured from construction sites, enabling it to accurately analyze the visual cues indicative of construction advancement. By leveraging deep learning techniques, our system extracts and processes features from images, enabling automated and real-time progress evaluation. The proposed method contributes to enhancing construction management practices by providing stakeholders with a reliable tool to monitor and manage construction projects efficiently. Experimental results demonstrate the effectiveness and robustness of the Deep AlexNet-based approach in accurately assessing construction progress, thereby facilitating informed decision-making throughout the construction lifecycle. © 2023 IEEE.

关键词： Construction progress monitoring Deep AlexNet Deep learning Infrastructure projects Smart cities

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Cox-ResNet: A Survival Analysis Model Based on Residual Neural Networks for Gene Expression Data

Cox-ResNet: A Survival Analysis Model Based on Residual Neur...

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IEEE International Conference on Networking, Sensing and Control

作者： Qingyan Yin Wangwang Chen Ruiping Wu Zhi Wei School of Science Xi'an University of Architecture and Technology Xi'an China Department of Computer Science New Jersey Institute of Technology Newark USA

ISBN: (纸本)9781665472449

Survival analysis with genomics data provides a deep understanding of biological processes related to prognosis and disease progression at the molecular level. However, high-dimensional small sample genome data causes computational challenges in survival analysis. To address this problem of overfitting and poor interpretation of existing models, we applied the deep learning technology to genome data and proposed a survival analysis model based on an image-based residual neural network model, called Cox-ResNet. High-dimensional gene expression data was embedded into 2D images according to gene positions on chromosomes, and then a residual network model based on Cox proportional hazards was introduced to perform survival analysis. We demonstrated the performance of Cox-ResNet on five datasets of different cancer types from TCGA, comparing it with the cutting-edge survival analysis methods. The Cox-ResNet model not only shows better performance in prediction accuracy, but also biologically interpretable, by generating heat-maps and prognostic genes for high-risk groups with the guided Grad-Cam visualization method. By performing protein-protein interaction network analysis, we examined hub genes and their biological functions for the bladder cancer. These findings confirm that Cox-ResNet model provides a new solution for discovering the driver genes of poor cancer prognosis.

关键词： Analytical models Biological system modeling Computational modeling Genomics Data models Gene expression Bioinformatics

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Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Robust Global Localization in Autonomous Mobile Robotics

Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Ro...

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International Conference on Advanced Robotics (ICAR)

作者： Nikolaos Stathoulopoulos Emanuele Pagliari Luca Davoli George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden Department of Engineering and Architecture Internet of Things (IoT) Lab University of Parma Parma Italy

This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi finger-printing in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems able to overcome limitations faced by traditional Global Navigation Satellite System (GNSS)-based localization methods. By leveraging the complementary strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate the confidence of each prediction as an indicator of potential degradation, we propose a redundancy-based approach that enhances the system's overall robustness and accuracy. The proposed framework allows independent operation of the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the predictions while considering their confidence levels, we achieve enhanced and consistent performance in localization tasks.

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AgileWatts: An Energy-Efficient CPU Core Idle-State architecture for Latency-Sensitive Server Applications 22

AgileWatts: An Energy-Efficient CPU Core Idle-State Architec...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Jawad Haj Yahya Haris Volos Davide B. Bartolini Georgia Antoniou Jeremie S. Kim Zhe Wang Kleovoulos Kalaitzidis Tom Rollet Zhirui Chen Ye Geng Onur Mutlu Yiannakis Sazeides Rivos Inc Mountain View California United States Computer Architecture Laboratory Department of Computer Science University of Cyprus Computing Systems Lab Huawei Zurich Research Center Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

ISBN: (纸本)9781665462723

User-facing applications running in modern datacenters exhibit irregular request patterns and are implemented using a multitude of services with tight latency requirements (30--250μs). These characteristics render existing energy-conserving techniques ineffective when processors are idle due to the long transition time (order of 100μs) from a deep CPU core idle power state (C-state). While prior works propose management techniques to mitigate this inefficiency, we tackle it at its root with AgileWatts (AW): a new deep CPU core C-state architecture optimized for datacenter server processors targeting latency-sensitive *** drastically reduces the transition latency from deep CPU core idle power states while retaining most of their power savings based on three key ideas. First, AW eliminates the latency (several microseconds) of saving/restoring the core context when powering-off/-on the core in a deep idle state by i) implementing medium-grained power-gates, carefully distributed across the CPU core, and ii) retaining context in the power-ungated domain. Second, AW eliminates the flush latency (several tens of microseconds) of the L1/L2 caches when entering a deep idle state by keeping L1/L2 content power-ungated. A small control logic also remains ungated to serve cache coherence traffic. AW implements cache sleep-mode and leakage reduction for the power-ungated domain by lowering a core's voltage to the minimum operational level. Third, using a state-of-the-art power efficient all-digital phase-locked loop (ADPLL) clock generator, AW keeps the PLL active and locked during the idle state, cutting microseconds of wake-up latency at negligible power *** evaluation with an accurate industrial-grade simulator calibrated against an Intel Skylake server shows that AW reduces the energy consumption of Memcached by up to 71% (35% on average) with <1% end-to-end performance degradation. We observe similar trends for other evaluated services (MySQL and Kafka).

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