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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Patrícia U. L. da Costa Guilherme Paim Leandro Rocha Eduardo da Costa Sérgio Almeida Sergio Bampi Graduate Program on Electronic Engineering and Computing - Catholic University of Pelotas (UCPel) Brazil Graduate Program on Microelectronics (PGMicro) - Federal University of Rio Grande do Sul (UFRGS) Brazil

ISBN: (数字)9781728160443

ISBN: (纸本)9781728160450

This work presents an efficient NLMS-based VLSI architecture to extract the fetal electrocardiogram (FECG) and detect the fetal heart rate (FHR), using the adaptive filter strategy. The efficient NLMS-based architecture herein investigated can robustly cancel the high-noised mother-related ECG signals, enabling the FHR measurement. We used the Improved Fetal Pan and Tompkins Algorithm (IFPTA) to detect fetal R-peak and calculate the FHR. Our NLMS-based VLSI architecture effectively detects the R-peaks in the extracted FECG with 93.2% accuracy with the only 2.4 mW of total power dissipation.

关键词： Fetal heart rate Very large scale integration Computer architecture Electrocardiography Hardware Thorax Power dissipation

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Optimizing Iterative-based Dividers for an Efficient Natural Logarithm Operator Design

Optimizing Iterative-based Dividers for an Efficient Natural...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Patrícia Ücker Miguel R. Weirich Guilherme Paim Eduardo A. C. da Costa Sergio Bampi Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Brazil Graduate Program on Microelectronics (PGMicro) Federal University of Rio Grande do Sul (UFRGS) Brazil

ISBN: (数字)9781728134277

ISBN: (纸本)9781728134284

This work proposes to optimize iterative-based dividers for a natural logarithm operator design. The logarithm operator approximates the function at the base e. Such an operator is implemented using Taylor Series approximation. In this approximation, the division is a costly operation. In this work, we explore two iterative-based divider circuits for logarithm operator. We also offer an algorithm responsible for allowing input values different from the ones in the convergence region of the Taylor Series, with a reduced number of iterations. Through co-simulation using both the Matlab ® and ModelSim ® softwares, it was possible to determine the approximation quality of the implemented circuits, with a curve response very close to the Matlab one. Moreover, the Goldschmidt divider presents a slightly fewer relative error, mainly for higher input values comparing with Newton-Raphson. Synthesis results show that although the logarithm circuit with Newton-Raphson divider has marginally more area, it is more power-efficient than the one using Goldschmidt divider.

关键词： Approximation algorithms Taylor series Mathematical model Newton method Convergence Hardware Matlab

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Detection of features from the internet of things customer attitudes in the hotel industry using a deep neural network model

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Measurement: Sensors 2022年 22卷

作者： Rajesh, Sudha Abd Algani, Yousef Methkal Al Ansari, Mohammed Saleh Balachander, Bhuvaneswari Raj, Roop Muda, Iskandar Kiran Bala, B. Balaji, S. Dept. of Computational Intelligence College of Engineering and Technology School of Computing SRMIST Kattankulathur Chennai India Department of Mathematics Sakhnin College Israel College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Lecturer in Economics Education Department Government of Haryana Haryana Panchkula India Department of Doctoral Program Faculty Economic and Business Universitas Sumatera Utara 20222 Jl. Prof TM Hanafiah 12 USU Campus Padang bulan Medan20155 Indonesia Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India Department of CSE Panimalar Engineering College Chennai India Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel

Tourism and the hotel business have benefited greatly from the use of digital social networking. Using social big data research, the application of deep learning seems to have been beneficial in a marketing strategies and customer preference estimate. Recognizing human psychology, which is critical to industrial success, has benefited greatly from digital technology and social media. The Internet of Things (IoT) provides a chance for a hotel sector to improve the customer experience although lowering operational expenses. The ratings are determined by the following factors: Value, Apartment, Location, Hygiene, Front Office, Facilities, Professional Service, Internet, and Parking. Traditional techniques which anticipate hotel evaluations through minimal precision add difficulty to the rating assessment. As a result, efficient deep learning algorithms are employed to evaluate reviews designed to help consumers in selecting better hotels. To predict qualities, multiple classification techniques, including convolutional neural network-based deep learning (CNN-DL) and support vector machine (SVM) network-based deep learning, were used in this research. The system examines system efficiency by using the TripAdvisor website, this is a well American database. The research results reveal that the CNN-DL method outperforms another method in terms of classification efficiency and failure *** graphical results could also be utilized to enhance the effectiveness of the suggested model and offer insights into response tactics, demonstrating the study's academic and conceptual achievements. Although it is feasible to conclude from such research that the possibility of IoT within the hotel industry has not yet been fully investigated, as researchers commonly speculate on using IoT for implementations that might quickly be of involvement to a hotel sector, but refuse to recognize that possibility as a massive market. © 2022 The Author(s)

关键词： Hotels

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Author Correction: Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex

Communications Biology

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Communications Biology 2023年第1期6卷 1页

作者： Thomas F. Varley Maria Pope Olaf Sporns Joshua Faskowitz School of Informatics Computing & Engineering Indiana University Bloomington IN 47405 USA Department of Psychological & Brain Sciences Indiana University Bloomington IN 47405 USA Program in Neuroscience Indiana University Bloomington IN 47405 USA

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Loss tangent fluctuations due to two-level systems in superconducting microwave resonators

arXiv

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arXiv 2024年

作者： Vallières, André Russell, Megan E. You, Xinyuan Garcia-Wetten, David A. Goronzy, Dominic P. Walker, Mitchell J. Bedzyk, Michael J. Hersam, Mark C. Romanenko, Alexander Lu, Yao Grassellino, Anna Koch, Jens McRae, Corey Rae H. Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Electrical Computing and Energy Engineering Department University of Colorado BoulderCO80309 United States Department of Physics University of Colorado BoulderCO80309 United States National Institute of Standards and Technology BoulderCO80305 United States

Superconducting microwave resonators are critical to quantum computing and sensing technologies. Additionally, they are common proxies for superconducting qubits when determining the effects of performance-limiting loss mechanisms such as from two-level systems (TLS). The extraction of these loss mechanisms is often performed by measuring the internal quality factor Qi as a function of power or temperature. In this work, we investigate large temporal fluctuations of Qi at low powers over periods of 12 to 16 hours (relative standard deviation σQi/Qi = 13%). These fluctuations are ubiquitous across multiple resonators, chips and cooldowns. We are able to attribute these fluctuations to variations in the TLS loss tangent due to two main indicators. First, measured fluctuations decrease as power and temperature increase. Second, for interleaved measurements, we observe correlations between low- and medium-power Qi fluctuations and an absence of correlations with high-power fluctuations. Agreement with the TLS loss tangent mean is obtained by performing measurements over a time span of a few hours. We hypothesize that, in addition to decoherence due to coupling to individual near-resonant TLS, superconducting qubits are affected by these observed TLS loss tangent fluctuations. © 2024, CC BY.

关键词： Qubits

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Theoretical foundations of studying criticality in the brain

arXiv

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arXiv 2023年

作者： Tian, Yang Tan, Zeren Hou, Hedong Li, Guoqi Cheng, Aohua Qiu, Yike Weng, Kangyu Chen, Chun Sun, Pei Department of Psychology Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing100084 China Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing100084 China Institute for Interdisciplinary Information Science Tsinghua University Beijing100084 China UFR de Mathématiques Université de Paris Paris75013 France Institute of Automation Chinese Academy of Science Beijing100190 China University of Chinese Academy of Science Beijing100049 China Tsien Excellence in Engineering Program School of Aerospace Engineering Tsinghua University Beijing100084 China

Criticality is hypothesized as a physical mechanism underlying efficient transitions between cortical states and remarkable information processing capacities in the brain. While considerable evidence generally supports this hypothesis, non-negligible controversies persist regarding the ubiquity of criticality in neural dynamics and its role in information processing. Validity issues frequently arise during identifying potential brain criticality from empirical data. Moreover, the functional benefits implied by brain criticality are frequently misconceived or unduly generalized. These problems stem from the non-triviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data. To help solve these problems, we present a systematic review and reformulate the foundations of studying brain criticality, i.e., ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), using the terminology of neuroscience. We offer accessible explanations of the physical theories and statistic techniques of brain criticality, providing step-by-step derivations to characterize neural dynamics as a physical system with avalanches. We summarize error-prone details and existing limitations in brain criticality analysis and suggest possible solutions. Moreover, we present a forward-looking perspective on how optimizing the foundations of studying brain criticality can deepen our understanding of various neuroscience questions. © 2023, CC BY.

关键词： Brain

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Addressing the Digital Gap for the Older Persons and their caregivers in the State of Qatar: A Stakeholders' Perspective

Addressing the Digital Gap for the Older Persons and their c...

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International Conference on Information and Communication Technology and Accessibility (ICTA)

作者： Dena Al Thani Asma Hassan Hajer Chalghoumi Achraf Othman Suzanne Hammad Information and Computing Technology Division College of Science and Engineering Hamad Bin Khalifa University Doha Qatar AGE-WELL NCE Canada Mada Center Doha Qatar Adjunct Lecturer Liberal Arts Program Northwestren University in Qatar Doha Qatar

ISBN: (纸本)9781665466424

The demographics in the Arab world are expected to go through a change in the coming decades. It is projected that the older population in Qatar will increase. Access to and use of information communication technology (ICT) by older persons and their caregivers has rarely been discussed. This study exposes the perspectives of the policy-level stakeholders and non-profit organizations working with older persons regarding the access and use of ICT. Through focus groups, the study investigates existing services and identifies the challenges encountered within the ICT Ecosystem. The study also attempts to explore their personal perspectives toward the access and use of older persons and their caregivers to ICT Thematic analysis was employed to capture the main themes emerging from exchanges with the stakeholders. Five themes emerged to stress the importance of existing efforts, the effects of the COVID-19 pandemic, and the extent of technology acceptance. The findings revealed the extent of partnership between the different institutes when providing the different services, conducting raising awareness activities, and training campaigns, and supporting the technology acceptance. The paper concludes by providing recommendations for stakeholders on ways to address the digital gap.

关键词： Training Pandemics Ecosystems Organizations Information and communication technology Stakeholders Digital divide

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CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data

CompressedMediQ: Hybrid Quantum Machine Learning Pipeline fo...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2025

作者： Chen, Kuan-Cheng Li, Yi-Tien Li, Tai-Yu Liu, Chen-Yu Lee, Po-Heng Henry Chen, Cheng-Yu Imperial College London Department of Electrical and Electronic Engineering London United Kingdom Imperial College London Centre for Quantum Engineering London United Kingdom Taipei Medical University Hospital Translational Imaging Research Center Taipei Taiwan Taipei Medical University Research Center for Neuroscience Taipei Taiwan Taipei Medical University Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Taiwan NARlabs National Center for High-Performance Computing Hsinchu Taiwan National Taiwan University Graduate Institute of Applied Physics Taipei Taiwan Taipei Medical University Hospital Department of Medical Imaging Taipei Taiwan National Defense Medical Center Department of Radiology Taipei Taiwan College of Medicine School of Medicine Department of Radiology Taipei Taiwan

ISBN: (纸本)9798331519315

This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer's Disease Neuroimaging Initiative and Neuroimaging in Frontotemporal Dementia, present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical HPC nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline's superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. Despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing. © 2025 IEEE.

关键词： Network coding

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CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data

CompressedMediQ: Hybrid Quantum Machine Learning Pipeline fo...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Kuan-Cheng Chen Yi-Tien Li Tai-Yu Li Chen-Yu Liu Po-Heng Henry Lee Cheng-Yu Chen Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Translational Imaging Research Center Taipei Medical University Hospital Taipei Taiwan Research Center for Neuroscience Taipei Medical University Taipei Taiwan Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan National Center for High-Performance Computing NARlabs Hsinchu Taiwan Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Department of Medical Imaging Taipei Medical University Hospital Taipei Taiwan Department of Radiology National Defense Medical Center Taipei Taiwan Department of Radiology School of Medicine College of Medicine Taipei Taiwan

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer’s Disease Neuroimaging Initiative and Neuroimaging in Frontotemporal Dementia, present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical HPC nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline’s superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. Despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing.

关键词： Neuroimaging Support vector machines Quantum computing Accuracy Magnetic resonance imaging Pipelines Machine learning Medical services Signal processing Standards

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Towards Automatic Cetacean Photo-Identification: A Framework for Fine-Grain, Few-Shot Learning in Marine Ecology

arXiv

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arXiv 2022年

作者： Trotter, Cameron Wright, Nick McGough, A. Stephen Sharpe, Matt Cheney, Barbara Civil, Mònica Arso Moore, Reny Tyson Allen, Jason Berggren, Per School of Engineering Newcastle University United Kingdom School of Computing Newcastle University United Kingdom School of Natural & Environmental Sciences Newcastle University United Kingdom School of Biological Sciences University of Aberdeen United Kingdom School of Biology University of St Andrews United Kingdom Chicago Zoological Society’s Sarasota Dolphin Research Program SarasotaFL United States

Photo-identification (photo-id) is one of the main non-invasive capture-recapture methods utilised by marine researchers for monitoring cetacean (dolphin, whale, and porpoise) populations. This method has historically been performed manually resulting in high workload and cost due to the vast number of images collected. Recently automated aids have been developed to help speed-up photo-id, although they are often disjoint in their processing and do not utilise all available identifying information. Work presented in this paper aims to create a fully automatic photo-id aid capable of providing most likely matches based on all available information without the need for data pre-processing such as cropping. This is achieved through a pipeline of computer vision models and post-processing techniques aimed at detecting cetaceans in unedited field imagery before passing them downstream for individual level catalogue matching. The system is capable of handling previously uncatalogued individuals and flagging these for investigation thanks to catalogue similarity comparison. We evaluate the system against multiple real-life photo-id catalogues, achieving mAP@IOU[0.5] = 0.91, 0.96 for the task of dorsal fin detection on catalogues from Tanzania and the UK respectively and 83.1, 97.5% top-10 accuracy for the task of individual classification on catalogues from the UK and USA. Copyright © 2022, The Authors. All rights reserved.

关键词： Mammals

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