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arXiv 2024年

作者： Kamahi, Sepehr Yaghoobzadeh, Yadollah School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Tehran Institute for Advanced Studies Khatam University Tehran Iran

Despite the widespread adoption of autoregressive language models, explainability evaluation research has predominantly focused on span infilling and masked language models. Evaluating the faithfulness of an explanation method—how accurately it explains the inner workings and decision-making of the model—is challenging because it is difficult to separate the model from its explanation. Most faithfulness evaluation techniques corrupt or remove input tokens deemed important by a particular attribution (feature importance) method and observe the resulting change in the model’s output. However, for autoregressive language models, this approach creates out-of-distribution inputs due to their next-token prediction training objective. In this study, we propose a technique that leverages counterfactual generation to evaluate the faithfulness of attribution methods for autoregressive language models. Our technique generates fluent, in-distribution counterfactuals, making the evaluation protocol more reliable. © 2024, CC BY.

关键词： Modeling languages

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Evolution of Artificial Intelligence and Machine Learning in Betterment of Healthcare Services 1

Evolution of Artificial Intelligence and Machine Learning in...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Abu Eid, Aws Ismail Maula, Shabana Wagh, Aparna Vyas, Vibha Patil, Prakash Sridhar, P. Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant College of Management Karad India College of Engineering Pune Department of Electronics and Telecommunication Pune India Department of Radio-Diagnosis Karad India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350360707

Artificial Intelligence usually comprises a system that is composed of hardware and software. Machine learning is a subset of artificial intelligence (AI). The study discovered that the environment and contemporary technology on the current healthcare services have created new demands, which has led to the formation of these five categories of research. By matching eligible patients with clinical trials, AI technologies in the healthcare industry may also be beneficial for medical research. There are numerous other fascinating uses for AI in the biomedical field. It is evident that AI is becoming more and more relevant in the field of biomedicine. AI technologies may be able to assist in addressing significant health issues, but its potential may be constrained by the caliber of the health data that is now available and by the fact that AI cannot exhibit certain human traits, like empathy. Numerous ethical and societal concerns are brought up by the use of AI, many of which are also brought up by the use of data and healthcare technologies more generally. © 2024 IEEE.

关键词： Adversarial machine learning

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Parametric Optimization and Mathematical Analysis of Dual Halide Lead Free Perovskite Solar Cell 2

Parametric Optimization and Mathematical Analysis of Dual Ha...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Sharma, Sakshi Kumar, Kaushal Khan, Baseem Pachauri, Rupendra Kumar Kumar Gupta, Ankur Hawassa University Department of Electrical and Computer Engineering Hawassa05 Ethiopia Upes School of Advanced Engineering Dehradun248007 India International University if Business Agriculture and Technology Miyan Research Institute of Technology Dhaka1230 Bangladesh Iimt University R&d Department Meerut India

ISBN: (纸本)9798350364699

The scientific community has recently taken an interest in perovskite solar cells, often known as PSCs. Dual halide PSC has the best efficiency reported in the literature at 20.9%. The purpose of this work is to improve a lead-free dual halide photovoltaic (PSC) by modifying the material of the hole transport layer (HTL) and the electron transport layer (ETL), as well as regulating the thickness of the absorber layer. In order to evaluate the device, the following metrics are taken into consideration: fill factor (FF), power conversion efficiency (PCE), open circuit voltage (VOC), and short circuit current density (JSC). With optimization, the gadget can reach an efficiency level of 24.03%. Further investigation into a tandem or dual structure method may be possible with this work. © 2024 IEEE.

关键词： Perovskite solar cells

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Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

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computers, Materials & Continua 2024年第1期78卷 1363-1379页

作者： Kwok Tai Chui Brij B.Gupta Varsha Arya Miguel Torres-Ruiz School of Science and Technology Hong Kong Metropolitan UniversityHong Kong999077China Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Center for Advanced Information Technology Kyung Hee UniversitySeoul02447Korea Symbiosis Centre for Information Technology Symbiosis International UniversityPuneMaharashtra411042India Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon School of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Business Administration Asia UniversityTaichung41354Taiwan Center for Interdisciplinary Research University of Petroleum and Energy StudiesDehradun248007India Department of Computer Science&Engineering Chandigarh UniversityChandigarh160036India Centro de Investigación en Computación Instituto Politécnico NacionalUPALM-ZacatencoMexico City07320Mexico

The visions of Industry 4.0 and 5.0 have reinforced the industrial *** have also made artificial intelligence incorporated as a major *** machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe *** learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two *** response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning *** is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative *** algorithm also adaptively adjusts the portion of training data to balance the learning rate and training *** proposed algorithm is evaluated and analyzed using ten benchmark *** with other algorithms from existing works,SA-ITL improves the accuracy of all *** studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

关键词： Deep learning incremental learning machine fault diagnosis negative transfer transfer learning

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Penta - Transistor Integrate & Fire (PTIF) Spiking Neuron with an Ultra-Low Energy Consumption of 0.045 fJ per Spike

Penta - Transistor Integrate & Fire (PTIF) Spiking Neuron wi...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Shelby Williams Kasem Khalil Magdy Bayoumi Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette LA USA Department of Electrical and Computer Engineering Univ. of Mississippi Oxford MS USA Department of Electrical Engineering Assiut Univ. Assiut Egypt Department of Electrical and Computer Engineering University of Louisiana at Lafayette Lafayette LA USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

The human brain's computational efficiency reigns supreme. There is no known device that even comes close to its low-power, massive parallelism or its data-processing capabilities. The ultimate goal of neuroscience is to mimic the brain. The proposed analog circuit is one such attempt. It utilizes an artificial, silicon-based, neuronal implementation of the widely recognized Integrate & Fire (I&F) spiking model. Its topology exhibits superior structural simplicity, comprising only five equivalently-sized MOSFET transistors. This Penta Transistor Integrate & Fire (PTIF) model uses a positive-feedback design. Energy efficiency is the key distinguishing hallmark of this PTIF circuit, with an energy consumption of only 0.045 femtojoules (fJ) per spike. In fact, to the best of our knowledge, the proposed PTIF implementation has the lowest energy usage per spike than any previously reported Integrate & Fire (I&F) neuron design by one magnitude (1Ox). Linear and quadratic energy-saving performances stem from the exclusion of external capacitors and the utilization of a subthreshold supply voltage, respectively. Additionally, no complex transistor resizing is necessary, allowing for easy transfer to much smaller technology nodes. The aforementioned attributes of the PTIF design make it highly advantageous for ultra-low-power applications, especially well- suited for Edge Computing (EC), Federated Learning (FL), and Internet of Things (IoT).

关键词： Semiconductor device modeling MOSFET Energy consumption Neuroscience Computational modeling Neurons Brain modeling

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Sub-terahertz transmissive reconfigurable intelligent surface for integrated beam steering and self-OOK-modulation

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Light(Science & Applications) 2025年第1期14卷 131-141页

作者： Dongfang Shen Feng Lan Luyang Wang Tianyang Song Munan Yang Tianyu Hu Yueting Li Xiaolei Nie Jiayao Yang Shixiong Liang Hongxin Zeng Hui-Fang Zhang Pinaki Mazumder Ziqiang Yang Yaxin Zhang Tie Jun Cui Sichuan THz Communication Technology Engineering Research Center School of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou313000China Zhangjiang Laboratory Shanghai201204China School of Microelectronics Tianjin UniversityTianjin300072China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen518100China Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI48109USA Southeast University Nanjing210096China

Boasting superior flexibility in beam manipulation and a simpler framework than traditional phased arrays,terahertz metasurface-based phased arrays show great promise for 5G-A/6G communication *** with the reflective reconfigurable intelligent surface(reflective RIS),the transmissive RIS(TRIS)offers more feasibility for transceiver multiplexing systems to meet the growing demand for high-performance beam tracking in terahertz communication and radar ***,the terahertz TRIS encounters greater challenges in phase shift,beam efficiency,and complex ***,we propose a sub-terahertz TRIS based on the phase shift via Pancharatnam-Berry(PB)metasurface and self-on-off keying(OOK)modulation via Schottky *** electrically reconfigurable unit cell consists of a column-wise phase resonator and a rectangular *** experimental retrieved equivalent lumped-element circuit model is implemented in joint field-circuit simulations and is validated by experiments.A fabricated prototype demonstrates excellent performance of TRIS with the minimum insertion loss of 2.8 dB for operational states,large bandwidth nearly covering the entire W-band for 1-bit phase shift,deep OOK amplitude modulation of 12 dB,and wide scanning range of±60°with low specular *** further implement an integrated platform combining high-speed beam steering and spatial-light modulation,verifying the point-to-point signal transmissions in different directions using the TRIS *** proposed TRIS with high-performance and cost-effective fabrication makes it a promising solution to terahertz minimalist communication systems,radar,and satellite communication systems.

关键词： reconfigurable reflective resonator

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A Hybrid Random Number Generator Based on MetaStability-Ring Oscillator Linear Feedback Shift Registers (MSRO- LFSR)

A Hybrid Random Number Generator Based on MetaStability-Ring...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Sonia Akter Shelby Williams Kasem Khalil Magdy Bayoumi Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette LA USA Department of Electrical and Computer Engineering Univ. of Mississippi Oxford MS USA Department of Electrical Engineering Assiut Univ. Assiut Egypt Department of Electrical and Computer Engineering University of Louisiana at Lafayette Lafoyette LA USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

Robust Random Number Generators (RNGs) are indispensable components in modern cryptographic systems, secure communications, and various other applications where unpredictability and randomness are paramount. Their resilience and robustness are critical for safeguarding sensitive data from security risks and potential breaches. In contrast, Pseudoran-dom Number Generators (PRNGs) are not as secure because their generated sequence of bits will eventually repeat them-selves, rendering PRNGs predictable and vulnerable. Despite this limitation, PRNGs prove to be extremely useful in many aspects of security applications. Because of the aforementioned observations, there is a need to enhance the security of PRNGs to make them statistically robust. However, only a limited number of research efforts attempt to transform PRN Gs, particularly Linear Feedback Shift Registers (LFSRs), into reliable Hybrid RNGs (HRNGs). The proposed framework undergoes synthesis on the Altera Cyclone V FPG A, successfully passing all the National institute and Standards Technology (NIST) SP800-22 test suites for randomness. By leveraging the concepts of metastability, jitter, and phase noise, the proposed design achieves high degree of randomness. Additionally, at technology node 14nm, this design is synthesized using Synopsys Design Compiler (SDC) and achieves high speed, minimal silicon area, and low energy consumption.

关键词： Linear feedback shift registers Transforms Side-channel attacks NIST Jitter Generators Hybrid power systems

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The Role of 5G Networks in Energy-Efficient Smart Cities Development using IoT 2

The Role of 5G Networks in Energy-Efficient Smart Cities Dev...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Vasu, V. Tamilarasi, A. Prakash, R.B.R. Kalaivani, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science India St. Claret College Department of Commerce Jalahalli Karnataka Bengaluru560013 India Kongu Engineering College Department of Computer Applications Perundurai India Electrical and Electronics Engineering Koneru Lakshmaiah Education Foundation vaddeswaram522302 India AP/ECE Erode Sengunthar Engineering College India

ISBN: (纸本)9781665456302

In theory, residents in 'smart cities' would have access to a wide range of amenities designed to enhance their daily lives by making better use of public resources and providing a more satisfying experience overall. The efficiency of these services depends on the information sharing across disparate systems serving similar functions. Large amounts of data with a variety of sophisticated, application-specific needs will likely be sent throughout the information exchange process. By better management of public resources and a focus on resident comfort, infrastructure upkeep, and environmental sustainability, 'smart cities,' which rely on ICT, aim to raise the bar on service quality. Fifth-generation (5G) wireless mobile communication paves the way for a new kind of communication network that can connect everyone and everything. Impacting economies and communities, 5G will offer the connectivity infrastructure required by many smart city applications. A glimpse into the future of urban areas may be seen in the rise of 'smart cities' and the IoT. The integration of many systems aimed to monitor varied components of the smart city may be utilized to generate a more sustainable and securer city. This research study presents a construction that mixes the data from diverse systems. In order to lessen the quantity of produced traffic in the 5G network and subsequently the energy consumption, suggest the usage of data aggregation in each antenna. A summary of 5G communication networks and different 5G technologies utilized in smart cities to promote sustainability is provided. The sustainability metrics for 5G networks are then examined across the environmental, social policy, and economic aspects, as well as sub-dimensions such as energy efficiency, energy usage, carbon footprint, contamination, cost, health, safety, and security. The findings indicate that while in an effort to address sustainability in 5G technology and intelligent buildings, the bulk of research publicat

关键词： 5G mobile communication systems

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Neural Network Implementation for Battery Failure Detection in Electric Vehicles

Neural Network Implementation for Battery Failure Detection ...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart electrical Systems, ICSES 2023

作者： Vijitha, S. Hebri, Dheeraj Singh, Sangeeta Manohara, M. Ishrat, Mohammad Joseph, D Raja Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai600117 India Srinivas Institute of Technology Department of Master of Computer Application Karnataka Mangaluru574143 India JSS Academy of Technical Education Department of Electrical Engineering Uttar Pradesh Noida201301 India Mohan Babu University School of Engineering Andhra Pradesh Tirupati517102 India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Andhra Pradesh Vaddeswaram522502 India Hindustan Institute of Technology and Science School of Aeronautical Sciences Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350319200

Soil destruction and global warming have recently come to the forefront of discussion in industrialised nations as governments strive to accommodate the rising demands of their citizens. The demand for zero-emission electric cars has increased as a result of international competitiveness and technological advancements (EVs). Concerns about the high voltage of increasing numbers of electric cars are shared by an increasing number of individuals. Since the system of batteries may be at responsible for over 30% of EV accidents, it is vital to investigate how problems with LIBs are recognized. Many different kinds of problems make it hard to fix EV's LIB. Fast and precise diagnosis of battery pack problems is crucial for the immediate and ongoing safety of EV operation. Utilizing models of neural networks like multiple hidden layers (MLP) or nonlinear activation functions, this research provides a mechanism for identifying and fixing problems with electric vehicle batteries (RBF). To generate information for the BFD system, battery simulations are done in MATLAB. Accuracy may be improved by performing pre-processing steps on the information once it has been generated. After training, the two models are put to the test to see how well they perform. There are both positive and negative measures that may be used to determine which model is the best. © 2023 IEEE.

关键词： Accuracy Battery Electric Vehicle Fault Model Neural Network

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Physics-informed tracking of qubit fluctuations

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Physical Review Applied 2024年第1期22卷 014033-014033页

作者： Fabrizio Berritta Jan A. Krzywda Jacob Benestad Joost van der Heijden Federico Fedele Saeed Fallahi Geoffrey C. Gardner Michael J. Manfra Evert van Nieuwenburg Jeroen Danon Anasua Chatterjee Ferdinand Kuemmeth Center for Quantum Devices Lorentz Institute and Leiden Institute of Advanced Computer Science Center for Quantum Spintronics Department of Physics QDevil Quantum Machines 2750 Ballerup Denmark Department of Engineering Science Department of Physics and Astronomy Birck Nanotechnology Center Elmore Family School of Electrical and Computer Engineering School of Materials Engineering

Environmental fluctuations degrade the performance of solid-state qubits but can in principle be mitigated by real-time Hamiltonian estimation down to timescales set by the estimation efficiency. We implement a physics-informed and an adaptive Bayesian estimation strategy and apply them in real time to a semiconductor spin qubit. The physics-informed strategy propagates a probability distribution inside the quantum controller according to the Fokker-Planck equation, appropriate for describing the effects of nuclear spin diffusion in gallium arsenide. Evaluating and narrowing the anticipated distribution by a predetermined qubit probe sequence enables improved dynamical tracking of the uncontrolled magnetic field gradient within the singlet-triplet qubit. The adaptive strategy replaces the probe sequence by a small number of qubit probe cycles, with each probe time conditioned on the previous measurement outcomes, thereby further increasing the estimation efficiency. The combined real-time estimation strategy efficiently tracks low-frequency nuclear spin fluctuations in solid-state qubits, and can be applied to other qubit platforms by tailoring the appropriate update equation to capture their distinct noise sources.

关键词： Quantum control Quantum fluctuations & noise Quantum information with solid state qubits Double quantum dots Bayesian methods Fokker–Planck equation Machine learning Radio frequency techniques

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