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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Daniel Junehee Lee Fei Yuan Yushi Zhou Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada Department of Electrical and Computer Engineering Lakehead University Thunder Bay ON Canada

This paper presents an 8-bit cyclic Vernier digital-to-time converter (DTC) for time-mode successive approximation register time-to-digital converters (SAR TDCs). The DTC offers a low degree of mismatch-induced nonlinearity hence a small lower bound of the dynamic range, a virtually unlimited upper bound of the dynamic range, and a small silicon area. The principle, operation, design, and calibration of the DTC are presented. The DTC is designed in TSMC 130 nm 1.2 V technology and analyzed using Spectre with BSIM3v3 device models. Simulation results show that the DTC achieves a dynamic range of 2.31 ~ 591.4 ps, a DNL of 0.37 LSB, an INL of 0.66 LSB, and consumes 1.49 mW.

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1-Bit Reconfigurable Phase Shifter for mm-Wave Antenna Beam-Steering Applications

1-Bit Reconfigurable Phase Shifter for mm-Wave Antenna Beam-...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Bilal, Asif Quddious, Abdul Kanno, Atsushi Kawanishi, Tetsuya Antoniades, Marco A. Iezekiel, Stavros EMPHASIS Research Centre University of Cyprus Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Technische Universität Dresden RF and Photonics Engineering Dresden Germany Nagoya Institute of Technology Aichi Japan National Institute of Information and Communications Technology Tokyo Japan Waseda University Tokyo Japan Computer and Biomedical Engineering Department of Electrical Toronto Metropolitan University TorontoON Canada

ISBN: (纸本)9781665442282

A 1-bit reconfigurable phase shifter for 28 GHz mm-wave beam steering antennas is proposed. The design uses two beam-lead GaAs PIN diodes loaded on a microstrip transmission line in parallel, to provide two discrete phases for the transmitted signal. When the diodes are switched OFF, no shift in the phase of the transmitted signal is observed while a phase shift of 90° is achieved when the diodes are switched ON. The total length of the phase shifter is ∼λ/2. The compact size, low cost, reduced complexity and easy adaptation in antenna and phased array technology makes it a suitable candidate for 5G mm-wave beam steering applications. © 2023 IEEE.

关键词： Phase shifters

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Cancelable Biometrics Based on Quaternion Adaptive Filter Optimization for Security Applications 6

Cancelable Biometrics Based on Quaternion Adaptive Filter Op...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Nasr, Mahmoud Piórkowski, Adam Mah, Pascal Muam Abd El-Samie, Fathi E. Department of Biocybemetics and Biomedical Engineering AGH University of Krakow Cracow Poland Faculty of Engineering and Technology Future University in Egypt Cairo Egypt Department of Technical Informatics and Telecommunications AGH University of Krakow Cracow Poland Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

ISBN: (纸本)9798350373875

This study introduces a unique cancelable face recognition technique that depends on adaptive filters and quaternion mathematics. The whale optimization is also applied to determine the optimal parameter settings for best performance. The primary concept of the suggested model revolves around the concealment of facial characteristics before their utilization in the face recognition process. The primary aim of this procedure is to guarantee the confidentiality of users' personal information throughout the enrollment and verification stages of the biometric system, particularly when the database is compromised and the stored biometric templates are altered. In this context, the color facial image is utilized to construct a quaternion. After that, a mask image is employed in a separate quaternion. The cancelable templates are obtained by using the input and mask images represented in quaternion format and applying an adaptive filter on both of them. This process involves preserving the weights of the adaptive filter. Investigation of the suggested tehnique performance demonstrates that the obtained templates lead to low (EER) and a large Area under the ROC curve. © 2024 IEEE.

关键词： Adaptive filters

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Instance Segmentation of Damaged Insulators for Power System Inspection and Maintenance

Instance Segmentation of Damaged Insulators for Power System...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Merin, Jovencio Alon, Alvin Sarraga Pinkihan, Joel Sandil, Ricky C. Delos Santos Ligayo, Michael Angelo Arago, Ryan Flores Sevilla, Rovenson Cunanan, Christopher Technological University of the Philippines Dept. of Electrical Engineering Manila Philippines Center for AI Research Department of Trade and Industry Makati City Philippines College of Computing Sciences Ifugao State University Ifugao Philippines College Preparatory Program Kingdom of Saudi Arabia Saudi Arabia College of Engineering Quezon City University Quezon City Philippines Technological University of the Philippines Department of Electrical Engineering Manila Philippines Bulacan State University Christopher Franco Cunanan Computer Engineering Department Bulacan Philippines

ISBN: (纸本)9798350369106

This paper introduces a novel approach for power system inspection using Mask-RCNN, an advanced model for instance segmentation. The accuracy of our model in identifying and diagnosing damaged low- to medium-voltage insulators is exceptional, as evidenced by its Mean Average Precision (mAP) score of 98.7%• Throughout the training process, the model exhibited minimal loss metrics, indicating its proficiency in predicting bounding boxes precisely, segmenting, classifying, and aligning masks. The model exhibited consistent reliability in distinguishing between undamaged and damaged components during the inference process, as seen by its consistently high detection rates for both 'normal intact' and 'damaged broken' insulators. The results demonstrate the practical usefulness of the model and offer promising prospects for enhancing power system maintenance and inspection protocols. The model's consistent and accurate identification of damaged insulators has the potential to significantly enhance the safety and efficiency of power systems, thereby revolutionizing the industry. © 2024 IEEE.

关键词： Insulator contamination

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Impact of Environmental Conditions on the Remaining Useful Lifetime of SiC MOSFET

Impact of Environmental Conditions on the Remaining Useful L...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Md Zakir Hasan Ashik Amin Md Moniruzzaman Seungdeog Choi Prashant Singh Chun-Hung Liu Department of Electrical and Computer Engineering Mississippi State University Starkville USA Department of Mechanical Aerospace & Biomedical Engineering University of Tennessee Knoxville USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Silicon carbide (SiC) power MOSFETs are widely applied to critical infrastructure in modern energy systems. Thus, accurately predicting its remaining useful lifetime (RUL) in real-world applications has become crucial. State-of-the-art explored its RUL through a power cycling test mostly considering constant environmental conditions (e.g., fixed temperature and humidity), which has resulted in considerable RUL prediction errors in real-world applications. This study directly integrates environmental factors into the RUL modeling to address this issue. Specifically, the junction temperature (T j ), on-state voltage (V ds,on ), on-state resistance (R ds,on ), and case temperature (T C ) have been explored in various environmental conditions to understand their tight correlations with the RUL in the real world. Time series statistics models Autoregressive (AR) and Autoregressive Integrated Moving Average (ARIMA) have been used to predict SiC MOSFET RUL to gain new insights systematically.

关键词： Resistance MOSFET Temperature Correlation Silicon carbide Time series analysis Voltage Mathematical models Environmental factors Reliability

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RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier

arXiv

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arXiv 2024年

作者： Memar, Maryam Ostadsharif Ziaei, Navid Nazari, Behzad Yousefi, Ali Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Biomedical Engineering University of Houston HoustonTX United States

Intracranial electroencephalography (iEEG) is increasingly used for clinical and brain-computer interface applications due to its high spatial and temporal resolution. However, inter-subject variability in electrode implantation poses a challenge for developing generalized neural decoders. To address this, we introduce a novel decoder model that is robust to inter-subject electrode implantation variability. We call this model RISE-iEEG, which stands for Robust to Inter-Subject Electrode Implantation Variability iEEG Classifier. RISE-iEEG employs a deep neural network structure preceded by a participant-specific projection network. The projection network maps the neural data of individual participants onto a common low-dimensional space, compensating for the implantation variability. In other words, we developed an iEEG decoder model that can be applied across multiple participants’ data without requiring the coordinates of electrode for each participant. The performance of RISE-iEEG across multiple datasets, including the Music Reconstruction dataset, and AJILE12 dataset, surpasses that of advanced iEEG decoder models such as HTNet and EEGNet. Our analysis shows that the performance of RISE-iEEG is about 7% higher than that of HTNet and EEGNet in terms of F1 score, with an average F1 score of 0.83, which is the highest result among the evaluation methods defined. Furthermore, Our analysis of the projection network weights reveals that the Superior Temporal and Postcentral lobes are key encoding nodes for the Music Reconstruction and AJILE12 datasets, which aligns with the primary physiological principles governing these regions. This model improves decoding accuracy while maintaining interpretability and generalization. © 2024, CC BY-NC-ND.

关键词： Deep neural networks

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Cochceps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-Based Masking for Speech Emotion Recognition

Cochceps-Augment: A Novel Self-Supervised Contrastive Learni...

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

作者： Ioannis Ziogas Hessa Alfalahi Ahsan H. Khandoker Leontios J. Hadjileontiadis Department of Biomedical Engineering Khalifa University of Science and Technology Abu Dhabi UAE Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

Self-supervised learning (SSL) for automated speech recognition in terms of its emotional content, can be heavily degraded by the presence noise, affecting the efficiency of modeling the intricate temporal and spectral informative structures of speech. Recently, SSL on large speech datasets, as well as new audio-specific SSL proxy tasks, such as, temporal and frequency masking, have emerged, yielding superior performance compared to classic approaches drawn from the image augmentation domain. Our proposed contribution builds upon this successful paradigm by introducing CochCeps-Augment, a novel bio-inspired masking augmentation task for self-supervised contrastive learning of speech representations. Specifically, we utilize the newly introduced bio-inspired cochlear cepstrogram (CC-GRAM) to derive noise robust representations of input speech, that are then further refined through a self-supervised learning scheme. The latter employs SimCLR to generate contrastive views of a CC-GRAM through masking of its angle and quefrency dimensions. Our experimental approach and validations on the emotion recognition K-EmoCon benchmark dataset, for the first time via a speaker-independent approach, features unsupervised pre-training, linear probing and fine-tuning. Our results potentiate CochCeps-Augment to serve as a standard tool in speech emotion recognition analysis, showing the added value of incorporating bio-inspired masking as an informative augmentation task for self-supervision. Our code for implementing CochCeps-Augment will be made available at: https://***/GiannisZgs/CochCepsAugment.

关键词： Emotion recognition Frequency-domain analysis Noise Speech recognition Contrastive learning Image augmentation Noise robustness

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Sub-Daily Temporal Disaggregation of Regional Climate Models with Artificial Neural Networks: An Application for Power System Planning and Operation

Sub-Daily Temporal Disaggregation of Regional Climate Models...

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International Conference on Smart Energy Systems and Technologies (SEST)

作者： Rouzbeh Shirvani Alessandro Bosisio Christian Bovo Saeed D. Manshadi Roberto Bolis Manuel Eduardo Ferro De Pina Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Department of Electrical & Computer Engineering San Diego State University San Diego CA USA Department of Engineering EDP Renewables S.p.A Milan Italy

ISBN: (数字)9798350386493

ISBN: (纸本)9798350386509

Long-term climate forecasts are vital in supporting energy companies in many aspects of their energy systems planning and operations, such as dispatch scheduling and demand management. Regional climate models can provide such multi-decade forecasts; however, these models are mainly available with a daily resolution, while a higher level of temporal resolution is usually needed for energy systems planning and operation. This paper aims to develop a tool to aid power system planning and operation by disaggregating climate models with daily resolution into sub-daily. To this end, a machine learning approach based on artificial neural networks is adopted to disaggregate the daily values of climate variables into hourly. A separate artificial neural network for average temperature, shortwave radiation, humidity, and wind speed is trained on the historical values from bias-corrected hourly reanalysis WFDE5 (WATCH Forcing Data methodology applied to surface meteorological variables of ERA5). The result suggests that the average temperature model demonstrates the best performance, while the wind speed model shows less accurate performance in comparison with other models, mainly due to its inherent stochastic nature. Finally, as a study case, the trained artificial neural networks are used to downscale the Coupled Model Intercomparison Project Phase 5 and create an hourly dataset of the model in the Basilicata region, south of Italy.

关键词： Temperature measurement Training Wind speed Energy resolution Stochastic processes Artificial neural networks Power system planning Predictive models Planning Meteorology

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Time-Interpolated Vernier Digital-to-Time Converter with Applications in Time-Mode SAR TDC

Time-Interpolated Vernier Digital-to-Time Converter with App...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Daniel Junehee Lee Fei Yuan Yushi Zhou Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON> Canada Department of Electrical and Computer Engineering Lakehead University Thunder Bay ON Canada

This paper proposes an 8 bit time-interpolated Vernier digital-to-time converter (DTC) consisting of a 4-bit Vernier delay line coarse DTC and a 4 bit time interpolator fine DTC with applications in a time-based successive approximation register time-to-digital converter (SAR TDC) for power and area efficiency. The design, operation, calibration, and error analysis of the DTC are presented. The DTC is designed in a TSMC 130 nm 1.2 V CMOS technology and analyzed using Spectre with BSIM3 device models. Simulation results show that the DTC achieves a resolution of 0.57-0.69 ps, a dynamic range of 145.4-176 ps, and DNL and INL of 0.5 LSB and 0.52 LSB, respectively and consumes 1.1 mW.

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A Performance Study of Selected Machine Learning Techniques for Predicting Heart Diseases 2nd

A Performance Study of Selected Machine Learning Techniques ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Olorunfemi, Blessing Oluwatobi Ogunde, Adewale Opeoluwa Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Li, Chun-Ta Faculty of Natural Sciences Department of Computer Science Redeemer’s University Osun Nigeria Department of Computer Science Bowen University Iwo Nigeria Faculty of Information and Communication Sciences Department of Computer Science University of Ilorin Ilorin240003 Nigeria Faculty of Engineering Department of Electrical and Electronics Engineering University of Lagos Lagos Akoka100213 Nigeria Bachelor’s Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City24206 Taiwan

ISBN: (纸本)9789819777853

Heart Disease remains a leading cause of mortality worldwide. It alarmingly rises at a quick rate, making early heart disease prediction crucial for effective prevention and timely intervention. Heart disease diagnosis is a difficult process that requires technical skills and accuracy to complete. With improvements in technology, computing has lent its voice to simplify the diagnosis of various health problems. Machine learning uses past or existing history to predict future results. Various machine learning techniques have been developed over the years and used in predicting heart diseases with various levels of performance. Identifying the best-suited machine learning technique to use for prediction purposes can be a challenging task. This research work analyses the performance of seven (7) machine learning techniques, comprising AdaBoost Algorithm, KNN, Logistic Regression, Naïve Bayes Classifier, Random Forest, SVM, and XGBoost. The heart disease dataset was downloaded from the UCI repository and analysed using Python programming language in the Jupyter Notebook environment. A comparative analysis of the seven (7) techniques was performed based on Accuracy, Precision, and Recall. From the results obtained, KNN, Random Forest, and XGBoost showed superior performance over the others with an accuracy of 100%, AdaBoost Algorithm followed with an accuracy of 92.2%, SVM followed with an accuracy of 91.71%, Naïve Bayes Classifier followed with an accuracy of 88.29% while Logistic Regression has the least accuracy of 86.34%. KNN, RF, and XGBoost outperformed AdaBoost, SVN, and LR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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