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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Abidin, Nabila Ameera Zainal Yassin, Ahmad Ihsan Mohd Ali, Megat Syahirul Amin Megat Mansor, Wahidah Azhan, Mirsa Nurfarhan Mohd Rozlan, Muhammad Fakharul Radzy Mohd Jahidin, Aisyah Hartini Mahmoodin, Zulkifli Eskandari, Farzad Universiti Teknologi Mara College Of Engineering Shah Alam Malaysia Microwave Research Institute Universiti Teknologi Mara Shah Alam Malaysia Sdn. Bhd. Shah Alam Malaysia University Of Malaya Centre For Foundation Studies In Science Kuala Lumpur Malaysia Universiti Kuala Lumpur British Malaysian Institute Medical Engineering Technology Section Kuala Lumpur Malaysia Allameh Tabataba'ie University Department Of Statistics Mathematics And Computer Science Tehran Iran

ISBN: (纸本)9798350382310

This paper provides a comprehensive overview of working memory, a crucial cognitive construct for learning, reasoning, and intellectual abilities. It introduces Baddeley and Hitch's multi-component model, highlighting the construct's essential role in facilitating learning, comprehension, problem-solving, and attention. The paper then analyzes four key models: Baddeley's multicomponent framework, Cowan's integrated memory network model, Engle's attentional control-based model, and Oberauer's tripartite structure, exploring their shared and differing perspectives. Recent studies on working memory using electroencephalogram are reviewed, identifying core frequency bands associated with cognitive states and predicting individual working memory capacity from electroencephalogram. Conventional working memory assessment methods are also discussed, emphasizing their relative advantages and limitations based on specific goals and practical considerations. Overall, this paper integrates theoretical models, neural correlates, and practical applications to provide a comprehensive overview of working memory research, highlighting its interdisciplinary nature for future studies. © 2024 IEEE.

关键词： Electroencephalography

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ARiViT: attention-based residual-integrated vision transformer for noisy brain medical image classification

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European Physical Journal Plus 2024年第5期139卷 440页

作者： Hameed, Madiha Zameer, Aneela Khan, Saddam Hussain Raja, Muhammad Asif Zahoor Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan Department of Computer System Engineering The University of Engineering and Applied Science (UEAS) Swat Pakistan Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan

Brain tumor detection in medical image processing presents a formidable challenge due to the complex behavior exhibited by these tumors. Their intricate nature arises from a variety of shapes and textures within brain tumor images, exacerbated by their diverse cellular origins. This complexity is further compounded by the presence of noisy images, which add an additional layer of difficulty. The overlapping image intensities in tumor and non-tumor areas pose a significant hurdle in extracting meaningful insights from raw data using predictive models. In response to these challenges, we introduce ARiViT, a novel framework based on residual learning. ARiViT merges vision transformers, convolutions, and adversarial learning to tackle the complexities of brain tumor detection. ARiViT’s core generator integrates RiT blocks, merging residual convolutions and transformers, with residual connections, channel compression, and strategic weight sharing for enhanced capabilities and computational efficiency. ARiViT demonstrates exceptional robustness in handling noisy images and adapts well to various modality setups. Our systematic approach involves dataset splitting into training, testing, and validation sets, maintaining distributions of 80:20 and subsequently 70:30, ensuring rigorous evaluation. Additionally, our data preprocessing strategies are tailored to handle noisy and low-quality images, as well as those with unusual tumor shapes. The GAN architecture embedded in ARiViT effectively manages the complexities inherent in such image variations, ensuring reliable tumor detection even in challenging scenarios. Through exhaustive comparative evaluations, ARiViT proves its superiority over existing CNN and transformer techniques, as evidenced by both qualitative and quantitative analyses. Our efforts culminate in remarkable achievements, including an F1-score of 98.09%, establishing a pioneering solution in the demanding field of brain tumor detection. © The Author(s), under

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Novel design of recurrent neural network for the dynamical of nonlinear piezoelectric cantilever mass–beam model

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European Physical Journal Plus 2024年第1期139卷 16-16页

作者： Kausar, Aneela Chang, Chuan-Yu Raja, Muhammad Asif Zahoor Zameer, Aneela Shoaib, Muhammad Graduate School of Engineering Science and Technology (Computer Science and Information Engineering) National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Islamabad Nilore 45650 Pakistan AI Center Yuan Ze University Taoyüan 320 Taiwan

Piezoelectricity spans a wide range of fields, showcasing its practical significance. In the realm of consumer electronics, piezoelectric materials are utilized in buzzers, sensors, and actuators. These components find applications in everyday devices such as mobile phones, wearable gadgets, and home appliances. The aim of study is to present a novel design of layered recurrent neural network (LRNNs) for solving nonlinear piezoelectric cantilever mass–beam (PE-CMB) model representing the excitation frequency dynamics. The proposed PE-CMB model is mathematically represented with highly nonlinear second-order differential system. The synthetic dataset is created using Adams numerical scheme for piezoelectric cantilever beam system, and the data are randomly segmented for training samples to formulate the network and testing samples to record performance on unbiased data and validation samples for controllability of adaptive procedure of training the weights to approximate the solutions of PE-CMB with LRNNs. The efficiency and consistent performance of the LRNNs design are validated through an evaluation process encompassing measures such as achieved accuracy using mean squared error-derived cost functions, plots illustrating learning curves, adaptively controlled parameters, solution dynamics along with associated error analysis, and a statistical analysis involving distribution of error for each instances via histograms and regression metric. © 2024, The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature.

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Shaping of Time-Resolved Biphoton Correlations with a Microresonator-Based Spectral Shaper

Shaping of Time-Resolved Biphoton Correlations with a Micror...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Cohen, Lucas M. Wu, Kaiyi Myilswamy, Karthik V. Lingaraju, Navin B. Lu, Hsuan-Hao Lukens, Joseph M. Weiner, Andrew M. School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States The Johns Hopkins University Applied Physics Laboratory LaurelMD20723 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States

We report on the manipulation of the time-resolved biphoton correlation function using a sub-GHz resolution silicon nitride microresonator-based spectral shaper capable of programmable amplitude and phase modulation. ... 详细信息

ISBN: (纸本)9781957171395

关键词： Silicon nitride

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Design of Digital Phase Frequency Detector in ADPLL RF Transceiver

Design of Digital Phase Frequency Detector in ADPLL RF Trans...

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Agrosystem engineering, technology & Applications (AGRETA), IEEE International Conference on

作者： Abdul Khaliq Jahariah Sampe Fazida Hanim Hashim Huda Abdullah Noor Hidayah Mohd Yunus Muhammad Abuzar Baqir Institute of Microengineering & Nanoelectronics Universiti Kebangsaan Malaysia Bangi Malaysia Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Bangi Malaysia Advanced Telecommunication Technology Communication Technology Section Universiti Kuala Lumpur Malaysia Electrical & Computer Engineering Department COMSATS University Islamabad Sahiwal Punjab Pakistan

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

CMOS technology advancements have significantly enhanced smart farming monitoring system (SFMS) miniaturization and efficiency, especially in transceivers using All-Digital Phase-Locked Loops (ADPLLs). The ADPLL's phase-frequency detector (PFD) evaluates two digital inputs to identify frequency and phase errors. A time-to-digital converter (TDC), typically used by the PFD block, has high power usage and slow phase-frequency acquisition. Therefore, the study aims to minimize the high-power consumption and improve the fast phase-frequency acquisition of traditional Phase-Frequency Detectors (PFDs) by developing a Verilog HDL-optimized digital PFD explicitly designed for ultra-low power ADPLLs. The simulation results revealed a duty cycle of 34.2%, no frequency difference, and a time difference of 0.05 ns, suggesting accurate phase error detection and correction. The PFD's fast response time and minimal dead zone impact assure fast phase lock, which is crucial for SFMS in real-time. Furthermore, the ultra-low power and fast locking range required by the Verilog-simulated PFD design make it ideal for SFMS applications. This research highlights how improved PFD designs may develop high-performance, low-power ADPLL transceivers.

关键词： Smart agriculture Radio frequency Time-frequency analysis Low power electronics Phase frequency detectors Transceivers Real-time systems Time factors Hardware design languages Monitoring

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Quantum nonlocal modulation cancelation with distributed clocks

Optica Quantum

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Optica Quantum 2025年第1期3卷 45-54页

作者： Stephen D Chapman Suparna Seshadri Joseph M Lukens Nicholas A Peters Jason D McKinney Andrew M Weiner Hsuan-Hao Lu Elmore Family School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona 85287 USA Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA

We demonstrate nonlocal modulation of entangled photons with truly distributed radio frequency (RF) clocks. Leveraging a custom radio-over-fiber (RFoF) system characterized via classical spectral interference, we validate its effectiveness for quantum networking by multiplexing the RFoF clock with one photon from a frequency-bin-entangled pair and distributing the coexisting quantum-classical signals over fiber. Phase modulation of the two photons reveals nonlocal correlations in excellent agreement with theory: in-phase modulation produces additional sidebands in the joint spectral intensity, while out-of-phase modulation is nonlocally canceled. Our simple, feedback-free design attains subpicosecond synchronization—namely, drift less than ∼0.5 ps in a 5.5 km fiber over 30 min (fractionally only ∼2×10 −8 of the total fiber delay)—and should facilitate frequency-encoded quantum networking protocols such as high-dimensional quantum key distribution and entanglement swapping, unlocking frequency-bin qubits for practical quantum communications in deployed metropolitan-scale networks.

关键词： Entanglement distribution Laser communications Phase modulation Quantum communications Quantum key distribution Quantum teleportation

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Low-loss Cd-substituted Mg ferrites with matching impedance for high-frequency-range antennas

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Journal of Magnesium and Alloys 2021年第4期9卷 1403-1413页

作者： Gongwen Gan Dainan Zhang Jie Li Gang Wang Xin Huang Yan Yang Yiheng Rao Xueying Wang Huaiwu Zhang Ray T.Chen State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of ChinaNo.4Section 2North Jianshe RoadChengdu 610054China Microelectronic Research Center Department of Electrical and Computer EngineeringUniversity of Texas at AustinAustinTX 78758USA

The effects of Cd^(2+) ions on the microstructure,magnetic properties,and dielectric properties of Bi_(2)O_(3)-added MgFe_(2)O_(4) ferrites(Cd_(x)Mg_(1-x)Fe_(2)O_(4),x=0.00,0.15,0.30 and 0.45)are obtained by adopting the solid-state reaction method at a low temperature(910℃).The objective is to achieve matching impedances,low magnetic and dielectric losses(tanδμand tanδε,respectively),and a relatively large miniaturization factor to reduce antenna *** results indicate that the cations occupying the tetrahedral(A)and octahedral(B)ion sites are redistributed,resulting in an enhanced super-exchange interaction between the two *** a result,improved magnetization,including the increase in saturation magnetization(41.74 emu/g)and decrease in coercivity(63.75 Oe),is *** real part of permeability(μ')also increases with increasing concentration of Cd^(2+) *** x is 0.15,matching impedances with equivalent μ'and ε'values are obtained over a long frequency range(1–150MHz).Moreover,the formation of a dense microstructure guarantees that losses occur at low orders of magnitude(tanδμ≈10−2 and tanδε≈10−3).Accordingly,these properties afford wide application perspectives for the proposed compounds in the high-frequency region,i.e.,from high-frequency to very-high-frequency bands.

关键词： Cd-substituted Mg compounds Magnetic properties Dielectric properties Matching impedance Low loss High frequency antenna

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IT-Security Risk Based Approach for Secure Operation of Distributed Data Platforms in Supply Chains

IT-Security Risk Based Approach for Secure Operation of Dist...

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IEEE International Conference on Industrial engineering and engineering Management

作者： Marvin Voß Jonas Kallisch Maxim Runge Tobias Theus Karl-Heinz Niemann Christoph Wunck Faculty I - Electrical Engineering and Information Technology University of Applied Sciences and Arts Hannover Germany Department of Engineering Section Electrical Engineering and Computer Science University of Applied Sciences Emden/Leer Germany Faculty of Mathematics Informatics and Statistics Ludwig Maximilian University Munich Germany

ISBN: (数字)9798350386097

ISBN: (纸本)9798350386103

This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to ease secure information exchange while maintaining the sovereignty of each participant, leveraging standardized security features and advanced technologies such as federated learning. Security measures are based on a comprehensive risk analysis, which methodically identifies, assesses, and mitigates potential threats. Moreover, federated learning models that are not transparent are utilized. This allows data owners to maintain control over sensitive information within the consortium. The described approach markedly enhances both security and operational collaboration across the industry, representing a crucial stride towards more integrated and efficient supply chain management in manufacturing.

关键词： Industries Supply chain management Federated learning Supply chains Collaboration Particle measurements Manufacturing Security Risk analysis Information exchange

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Fault tolerance in fog-based Social Internet of Things

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Knowledge-Based Systems 2023年第1期265卷

作者： Mohammadi, Venus Rahmani, Amir Masoud Darwesh, Aso Sahafi, Amir Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Information Technology University of Human Development Sulaimanyah Iraq Department of Computer Engineering South Tehran Branch Islamic Azad University Tehran Iran

The Social Internet of Things (SIoT) is a glimpse into the future for self-establishment of inter-things social relationships that guarantee object-to-object transactions. This scenario adopted an intelligent strategy by the trust characteristics and typology of leveraging objects to eradicate the interference of defective nodes. In addition, SIoT networks are error-prone due to heterogeneity and potential resource constraints on the devices, which necessitates the inclusion of a mechanism to enhance the lifetime and dependability of devices. Therefore, fault tolerance mechanisms are indispensable to ensure optimal performance through fault detection and recovery. Although fog architecture and SIoT are two stand-alone paradigms, we extend fog competence into SIoT to empower resource-poor objects for handling intensive tasks. Hence a fault-tolerant model based on a clustering algorithm for fault detection and retrieval is presented. In the proposed scheme, the Markov chains model all faults diagnosed by the fog node. In the recovery stage, the status of the nodes is determined based on the type of faults and the assigned threshold to make a substitution by hot standby nodes. The four performance criteria, namely the reliability, availability, probability density function, the average time to failure, and their mathematical equations were developed on the Markov model. Furthermore, the achievements were demonstrated through either theoretical analysis or simulation results. Then, the proposed scheme increases the number of live nodes, improves the detection accuracy in forwarded requests, lowers the number of invalid requests, and enhances availability and reliability compared to networks with analogous properties. © 2023 Elsevier B.V.

关键词： Internet of things

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Green Leaf Disease Detection Using Raspberry Pi and Random Forest Machine Learning Algorithm: An Automated Approach for Precision Agriculture

Green Leaf Disease Detection Using Raspberry Pi and Random F...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Chitra Sabapathy Ranganathan Amjath Ali Jakul Basha A Vijayalakshmi N. Mohankumar S. Sujatha C. Sasikala Mphasis Corporation Chennai Tamil Nadu India Department of Engineering Computer Engineering Electrical and Electronic Section University of Technology and Applied Sciences Ibra Sultanate of Oman Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

Farm production and food security are affected by green leaf diseases. Modern disease detection technologies may discover plant health issues early. This work uses Raspberry Pi, a cheap and flexible single-board computer, along the advanced Random Forest machine learning technique to identify a green leaf disease. To identify crop diseases, the study uses automated, non-invasive technologies. From labeled images of good and sickly leaves, the algorithm extracts color histograms and texture, and form. It feeds a Random Forest classifier these properties for classifying data with high dimensions. Several green leaf diseased plant species were examined. This method detected plant health concerns with high precision. Movement and low cost make the Raspberry Pi ideal for remote and resource-limited agriculture, increasing farming accuracy. Helping farmers and users identify and develop green leaf diseases improves efficient farming. This methodology has the potential to improve crop management, optimize pesticide usage, and increase agricultural productivity. These results might contribute to the development of advanced automatic disease detection techniques to address green leaf diseases in modern agriculture.

关键词： Productivity Precision agriculture Machine learning algorithms Accuracy Plants (biology) Crops Real-time systems Random forests Diseases Farming

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