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Dual Dynamic Consistency Regularization for Semi-Supervised Domain Adaptation

IEEE Access 2024年 12卷 36267-36279页

作者： Ngo, Ba Hung Lam, Ba Thinh Nguyen, Thanh Huy Dinh, Quang Vinh Choi, Tae Jong Chonnam National University Graduate School of Data Science Gwangju61186 Korea Republic of University of Science Faculty of Information Technology Ho Chi Minh City700000 Viet Nam National Cheng Kung University Department of Biomedical Engineering Tainan70101 Taiwan Vietnamese-German University School of Electrical Engineering and Computer Science Ho Chi Minh City72000 Viet Nam

The Vision Transformer (ViT) model serves as a powerful model to capture and comprehend global information, particularly when trained on extensive datasets. Conversely, the Convolutional Neural Network (CNN) model is beneficial to training with small datasets for retaining essential local information. Inspired by these properties of ViT and CNN models, we introduce a hybrid framework that smoothly increases the cross-domain generalization in Semi-supervised Domain Adaptation (SSDA). To achieve this goal, we first train the ViT model on abundant labeled source data, while the CNN model is trained on a few labeled target samples. Then, these models dynamically exchange their knowledge for potential generalization to unlabeled target data via the proposed method, named Dual Dynamic Consistency Regularization (D2CR). Specifically, the ViT model provides its pseudo labels to update the global perspective for the CNN model. Similarly, the CNN model offers pseudo labels to complement the local perspective for the ViT model. The previous methods use a fixed threshold algorithm for the pseudo-labeling process. However, we utilize the dynamic threshold strategy to create pseudo labels for the bi-directional consistency regularization learning between the ViT and CNN models. We verify our approach across several SSDA benchmark datasets. The outstanding experimental results provide strong evidence of the effectiveness and superiority of our approach over previous state-of-the-art SSDA methods. © 2013 IEEE.

关键词： Neural network models

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Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication

Digital Twin-Based User-Centric Edge Continual Learning in I...

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IEEE International Conference on Communications (ICC)

作者： Shisheng Hu Jie Gao Xinyu Huang Mushu Li Kaige Qu Conghao Zhou Xuemin Shen Department of Electrical and Computer Engineering University of Waterloo Canada School of Information Technology Carleton University Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, we propose a digital twin (DT)-based user-centric approach for processing sensing data in an integrated sensing and communication (ISAC) system. The considered scenario involves an ISAC device with a lightweight deep neural network (DNN) and a mobile edge computing (MEC) server with a large DNN. After collecting sensing data, the ISAC device either processes the data locally or uploads them to the server for higher-accuracy data processing. To cope with data drifts, the server updates the lightweight DNN when necessary, referred to as continual learning. Our objective is to minimize the long-term average computation cost of the MEC server by jointly optimizing two decisions, i.e., sensing data offloading and sensing data selection for the DNN update. A DT of the ISAC device is constructed to predict the impact of potential decisions on the long-term computation cost of the server, based on which the decisions are made with closed-form formulas. Experiments on executing DNN-based human motion recognition tasks are conducted to demonstrate the outstanding performance of the proposed DT-based approach in computation cost minimization.

关键词： Continuing education Performance evaluation Costs Multi-access edge computing Training data Artificial neural networks Integrated sensing and communication

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A Compact-Area Low-Power Temperature Sensor Featuring High Supply Voltage Scalability

A Compact-Area Low-Power Temperature Sensor Featuring High S...

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

作者： Elisabetta Moisello Piero Malcovati Edoardo Bonizzoni Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

This paper proposes a highly supply voltage-scalable, low-power and compact-area temperature sensor, which satisfies the requirements for Systems-on-Chip (SoCs) and microprocessors hotspots monitoring. The proposed novel sensor structure employs diode-connected NMOS devices operating in sub-thresold, featuring different sizes, as sensing elements, in order to implement a proportional-to-absolute-temperature (PTAT) sensor output voltage. The proposed sensor does not require any additional biasing or start-up circuits and, since it mostly processes the signals generated by the sensing elements in the current domain, enables high supply voltage scalability. Furthermore, a low-cost 1-point temperature calibration algorithm can be employed for trimming the sensor sensitivity. The proposed sensor circuit was designed in a 130-nm CMOS process and its performance was verified through extensive simulations in the Cadence Virtuoso environment.

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Adaptive Thermal Clothing with IoT and Random Forest Regression for Dynamic Outdoor Comfort

Adaptive Thermal Clothing with IoT and Random Forest Regress...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Srinivasan, S. Sridevi, V. Saravanan, K. Murugan, S. Srinivasan, C. Muthulekshmi, M. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Biomedical Engineering Tamil Nadu Chennai India Academy Of Maritime Education And Training Deemed To Be University Department Of Electrical And Electronics Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department Of Information Technology Tamil Nadu Chennai India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371567

This research presents a novel method for improving outdoor comfort by combining adaptive thermal apparel with Internet of Things (IoT) and Random Forest Regression. Wearing inflexible traditional outdoor gear may be a real pain for those living in different parts of the world. As a solution, we present an intelligent garment system that can adapt its thermal insulation in real-time using data gathered from IoT sensors. A Random Forest Regression model is used to ascertain the most appropriate thermal settings for the garments based on the gathered data, which encompasses environmental factors such as humidity, temperature, and wind speed. By taking each user's tastes and physiological needs into account, the adaptive system strives to provide individualized comfort. The suggested technique successfully maintains the target comfort levels throughout varied outside situations, according to the experimental findings. In an age of rising climatic fluctuation, our study offers a potential answer for dynamic outdoor comfort to the expanding area of smart fabrics and wearable technologies. © 2024 IEEE.

关键词： Energy efficiency

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Microfluidic-organic thin film transistor coupled platform for simple solution exposure

RSC Applied Interfaces

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RSC Applied Interfaces 2024年第4期1卷 771-778页

作者： Boileau, Nicholas T. King, Benjamin Kapar, Sparsh Sohi, Ali Najafi Manion, Joseph G. Godin, Michel Lessard, Benoît H. Department of Chemical and Biological Engineering University of Ottawa 161 Louis Pasteur Pvt Ottawa ON Canada Department of Physics University of Ottawa 150 Louis Pasteur Pvt Ottawa ON Canada Ottawa-Carleton Institute for Biomedical Engineering University of Ottawa 161 Louis Pasteur Pvt Ottawa ON Canada School of Electrical Engineering and Computer Science University of Ottawa 800 King Edward Ave. Ottawa ON Canada

Efforts to combine organic thin film transistors (OTFTs) within microfluidic networks to create sensitive, versatile, and low-cost sensors for rapid chemical analysis have been limited by the need for complex equipment and by the sensitivity of OTFTs to common processing techniques used in traditional microfluidic fabrication. We designed and validated a robust, easy to use, and simple to manufacture prototype microfluidic-OTFT pressure coupled system. Our design enables multiple OTFT architectures to be combined with microfluidic analyte delivery, eliminates common processing steps that can alter OTFT performance, and only requires easily-accessible equipment. As a proof of concept, we demonstrate the exposure of aluminium chloride phthalocyanine (AlClPc) based OTFTs to different solutions of sodium fluoride (NaF). This work could accelerate the design of more versatile, rapid, and reliable OTFT based liquid chemical sensors. © 2024 RSC.

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Two-step registration of rigid and non-rigid MR-iUS for brain shift compensation using transfer learning

Two-step registration of rigid and non-rigid MR-iUS for brai...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Amir Hossein Jalalzadeh Seyed Saeid Talebi Mehrdad Hashemi Kamangar Department of Biomedical Engineering Engineering faculty Shahed University Tehran Iran Department of Electrical and Computer Engineering Noshirvani University Babol Iran Department of Electrical and Electronic Engineering Engineering Faculty Shomal University Amol Iran

Accurate removal of brain tumors is always one of the most important challenges for surgeons, as the continuous change of the brain state after opening the skull and releasing the resulting pressure causes the tumor state to change. By registration of preoperative MR images on intraoperative ultrasound images, the extent of this change is estimated and a new image of the brain is created. The result shows the changes and shifts in the brain, and the surgeon removes the tumor based on this image. Image registration using new deep learning methods has attracted the attention of many researchers due to its high efficiency and accuracy. In this paper, the images of 22 male and female patients with grade 2 glioma tumors were used to evaluate the proposed method. MR images of the patients were taken before surgery, while ultrasound images were taken during surgery and after cranial incision. The deep network used in this paper to compensate for non-rigid changes is voxel morph. All images were fed to the pre-trained network in pairs, and the results are reported for each individual. The average error of all images for the proposed method is 3.56 ± 1.72. This shows the improvement in performance compared to the previous methods since in this work the landmarks were not used in training phase.

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Multi-bit Receivers for High-Speed Communication over CAN Bus

Multi-bit Receivers for High-Speed Communication over CAN Bu...

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

作者： Andrea Gallone Piero Malcovati Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

This paper presents a new CAN (Controller Area Network) receiver, which is suited for multi-bit communication. Two alternative designs are described. The former provides better performance in terms of threshold voltage accuracy and pulse width symmetry, the latter is optimized in terms of area, but provides worse results regarding all the other key parameters. In both receivers, flash analog-to-digital conversion is used, which is performed during the data transmission phase and gives 4 pulses at the output that can be converted to a binary representation by an encoder. During arbitration the receivers behave conventionally. A chip of the second design described was produced using a $0.18-{\mu} \mathrm{m}$ high voltage BCD technology. The circuit draws an average current of 5 mA from a 5-V supply.

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Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting 38

Introducing Spectral Attention for Long-Range Dependency in ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kang, Bong Gyun Lee, Dongjun Kim, HyunGi Chung, DoHyun Yoon, Sungroh Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Department of Future Automotive Mobility Seoul National University Korea Republic of

Sequence modeling faces challenges in capturing long-range dependencies across diverse tasks. Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. However, they are constrained by their inherent inability to effectively address long-range dependencies in time series data, primarily due to using fixed-size inputs for prediction. Furthermore, they typically sacrifice essential temporal correlation among consecutive training samples by shuffling them into mini-batches. To overcome these limitations, we introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples and facilitates the handling of long-range information while maintaining the base model structure. Spectral Attention preserves long-period trends through a low-pass filter and facilitates gradient to flow between samples. Spectral Attention can be seamlessly integrated into most sequence models, allowing models with fixed-sized look-back windows to capture long-range dependencies over thousands of steps. Through extensive experiments on 11 real-world time series datasets using 7 recent forecasting models, we consistently demonstrate the efficacy of our Spectral Attention mechanism, achieving state-of-the-art results. © 2024 Neural information processing systems foundation. All rights reserved.

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A D-Band Low-Noise-Amplifier in SiGe BiCMOS with Broadband Multi-Resonance Matching Networks

A D-Band Low-Noise-Amplifier in SiGe BiCMOS with Broadband M...

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European (Formerly European Gallium Arsenide and Other Semiconductors Application Symposium - EGASS) Microwave Integrated Circuit Conference (EuMIC)

作者： Guglielmo De Filippi Lorenzo Piotto Andrea Bilato Andrea Mazzanti Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

Silicon amplifiers in D-Band are required to operate at high gain-bandwidth products and close to the cutoff frequency $f_{\max }$. Multi-stage amplifiers commonly employ stagger-tuning to meet the desired bandwidth, but with sub-optimal noise and linearity. Better performance is achieved with broadband inter-stage matching and gain progressively distributed among the stages. This work proposes a design flow for broadband matching networks approximating the response of a doubly-tuned transformer. The technique is applied to design a 3-stage D-band LNA in BiCMOS 55 nm technology. Measurements show 28 dB gain, 127-168 GHz bandwidth, NF down to 5.2 dB and >2dBm output compression point with 30 mA DC current from 2V supply. The performance compare favorably against previous works.

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Location-Based Medium Access Control for Next-Generation Industrial IoT Networks

Location-Based Medium Access Control for Next-Generation Ind...

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IEEE International Conference on Communications (ICC)

作者： Ahmed Ajeena Jie Gao Majeed M. Hayat Lian Zhao Xuemin Shen Department of Electrical and Computer Engineering Marquette University Milwaukee USA School of Information Technology Carleton University Ottawa Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

A medium access control (MAC) protocol design is proposed in this paper for next-generation industrial Internet of Things (IIoT) networks. Considering a nonfully connected network with multiple access points (APs), we aim to connect a massive number of IIoT devices densely populating the network and minimize the delay in channel access without packet collisions. To achieve this objective, we propose a device location-based medium access control design, which integrates scheduled access and carrier sensing. In our design, devices are assigned to time slots based on their locations, and the assignments are coordinated among APs to eliminate collisions while maximizing channel utilization. To analyze the performance of the proposed design, we derive the average delay each device experiences with the proposed scheduling scheme and verify our analysis via simulations of an IIoT network with 19 APs and over 17000 devices. The results show the effectiveness of the proposed design in supporting massive connections while at the same time achieving low delay.

关键词： Performance evaluation Analytical models Job shop scheduling Simulation Media Access Control Delays Sensors

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