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arXiv 2023年

作者： Liu, Fei Lin, Xi Wang, Zhenkun Yao, Shunyu Tong, Xialiang Yuan, Mingxuan Zhang, Qingfu Department of Computer Science City University of Hong Kong Hong Kong School of System Design and Intelligent Manufacturing the Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China Huawei Noah’s Ark Lab Canada

Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of which the search operators need a carefully handcrafted design with domain knowledge. Recently, some attempts have been made to replace the manually designed operators in MOEAs with learning-based operators (e.g., neural network models). However, much effort is still required for designing and training such models, and the learned operators might not generalize well on new problems. To tackle the above challenges, this work investigates a novel approach that leverages the powerful large language model (LLM) to design MOEA operators. With proper prompt engineering, we successfully let a general LLM serve as a black-box search operator for decomposition-based MOEA (MOEA/D) in a zero-shot manner. In addition, by learning from the LLM behavior, we further design an explicit white-box operator with randomness and propose a new version of decomposition-based MOEA, termed MOEA/D-LO. Experimental studies on different test benchmarks show that our proposed method can achieve competitive performance with widely used MOEAs. It is also promising to see the operator only learned from a few instances can have robust generalization performance on unseen problems with quite different patterns and settings. The results reveal the potential benefits of using pre-trained LLMs in the design of MOEAs. To foster reproducibility and accessibility, the source code is https://***/FeiLiu36/LLM4MOEA. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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A 2-channel time-to-digital converter with 20-ps resolution for pico-second timing application 11

A 2-channel time-to-digital converter with 20-ps resolution ...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Du, Zhouji Ke, Qin Yu, Hang Li, Yan Ge, Binjie Yong, Shanshan Cheng, Yonghong Shenzhen Laboratory of IC Design for IOT College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Lab. of IC Design for IOT College of CS & SE Shenzhen University Shenzhen China Dept. of Electronics and Computer Engineering SHENZHEN MCU-BIT University Shenzhen China Peking University Shenzhen Graduate School Shenzhen China Hubble Sensor Solution Shenzhen Co. LTD Shenzhen China

ISBN: (纸本)9781665492324

Time-to-Digital Converter (TDC) provides picosecond accuracy timing and is widely used in many applications such as Light Detection and Ranging (LIDAR), 3-D vision, and ultrasonic flow sensors. This paper introduces a 2-channel TDC with two-level resolution control architecture. designed in a 180 nm CMOS process, the TDC occupies an area of 2.3 mm ×1.3 mm and achieves 20 ps resolution at 2 Msps measurement rate. © 2022 IEEE.

关键词： Frequency converters

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Performance Evaluation of a Skin Conformable Polymer-based Flexible Temperature Sensor

Performance Evaluation of a Skin Conformable Polymer-based F...

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IEEE SENSORS

作者： Athul Krishnan Karthick Thiyagarajan Mitradip Bhattacharjee Yang An i-Labs Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Madhya Pradesh India Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith NSW Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

In today's modern age, there is a growing need for temperature sensors that can conform to the skin and monitor body temperature continuously. Achieving this requires the miniaturization of the sensor and careful selection of the active layer stacking. Further, it is important to check the effect of the active layer stacking on the sensor performance. In this context, we present the performance evaluation results for a polymer-based flexible temperature sensor designed to conform to the skin. This sensor utilizes PEDOT:PSS + GO composition as the temperature-sensitive material developed on top of a flexible PET substrate. The experimental results demonstrate that the performance of the sensor is dependent on the active layer stacking and electrode separation. An impressive sensor sensitivity of $1.205 \%/^\circ\mathrm{C}$ was achieved through sensor miniaturization and a single active layer stacking. This shows the performance tuning of a temperature sensor using active layer stacking.

关键词： Temperature sensors Performance evaluation Temperature measurement Sensitivity Stacking Skin Polymers Tuning Substrates Monitoring

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StreamFP: Learnable Fingerprint-guided Data Selection for Efficient Stream Learning

arXiv

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arXiv 2024年

作者： Shi, Tongjun Zhang, Shuhao Chen, Binbin He, Bingsheng National Engineering Research Center for Big DataTechnology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Singapore University of Technology and Design Singapore National University of Singapore Singapore

Stream Learning (SL) requires models that can quickly adapt to continuously evolving data, posing significant challenges in both computational efficiency and learning accuracy. Effective data selection is critical in SL to ensure a balance between information retention and training efficiency. Traditional rule-based data selection methods struggle to accommodate the dynamic nature of streaming data, highlighting the necessity for innovative solutions that effectively address these challenges. Recent approaches to handling changing data distributions face challenges that limit their effectiveness in fast-paced environments. In response, we propose StreamFP, a novel approach that uniquely employs dynamic, learnable parameters called fingerprints to enhance data selection efficiency and adaptability in stream learning. StreamFP optimizes coreset selection through its unique fingerprint-guided mechanism for efficient training while ensuring robust buffer updates that adaptively respond to data dynamics, setting it apart from existing methods in stream learning. Experimental results demonstrate that StreamFP outperforms state-of-the-art methods by achieving accuracy improvements of 15.99%, 29.65%, and 51.24% compared to baseline models across varying data arrival rates, alongside a training throughput increase of 4.6x. © 2024, CC BY.

关键词： Data accuracy

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Data Quality Assessment of Substation Data in Bangladesh: Insights from Handwritten Data Digitization

Data Quality Assessment of Substation Data in Bangladesh: In...

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International Conference on Power Systems (ICPS)

作者： Murshed Al Amin MD. Jawad-Al-Mursalin Hoque Zakia Nazzum Md Abu Sayed Sarker Tanveer Ahmed Rumee Moinul Islam Zaber Data and Design Lab Dhaka Bangladesh Military Institute of Science and Technology Independent University Bangladesh Department of Computer Science & Engineering University of Dhaka Dhaka Bangladesh United Nations University Guimarães Portugal

The assessment of power quality is vital for evaluating the existing state of the electricity supply, pinpointing its deficiencies, and guiding enhancements to guarantee a stable and consistent power supply, particularly in the face of rising demands from both industrial and residential sectors. In the context of Bangladesh, however, most substation data are still recorded manually, despite ongoing digitization efforts. This paper focuses on the quality of digitized transcription of handwritten substation data to determine its suitability for further analytical use. Our analysis revealed that the paper-based data from the substations exhibited significant gaps, undermining its reliability and accuracy. Through comprehensive evaluation, we concluded that the data, in its current state, is inadequate for in-depth analysis without the application of robust imputation methods. By tackling these challenges, our research aims to contribute to more informed decision-making and the efficient management of Bangladesh’s electrical infrastructure. This study not only diagnoses the current shortcomings in data quality but also lays the groundwork for future improvements in the country’s approach to energy data management.

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Assessment of Serum Creatinine and Serum Sodium Prognostic Potential in Heart Failure Patients Using Machine Learning

Assessment of Serum Creatinine and Serum Sodium Prognostic P...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Sona Alyounis Ahsan Khandoker Cesare Stefanini Leontios Hadjileontiadis Department of Biomedical Engineering Healthcare Engineering Innovation Center Khalifa University of Science and Technology Abu Dhabi UAE Creative Engineering Design Lab at the BioRobotics Institute Applied Experimental Sciences Scuola Superiore Sant’Anna Italy Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Heart failure (HF) is the leading etiology for hospital admissions and ranks among the foremost contributors to mortality. This complex clinical syndrome with various phenotypes is categorized by left ventricle ejection fraction levels (LVEF), namely preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF). This study investigates the prognostic impact of serum creatinine and serum sodium levels in HF patients across these three classes using machine learning tools. A comprehensive dataset of HF patients’ medical records including serum sodium and serum creatinine was utilized. Machine learning regression models were employed to predict the LVEF levels. Additionally, classification models were implemented to categorize patients into HFpEF, HFmEF, and HFrEF classes. Regression analyses revealed the predictive capabilities of serum sodium and serum creatinine in estimating the progression of HF severity. Furthermore, classification models successfully differentiated between the three EF classes, providing valuable insights into the classification patterns of HF patients based on these biomarkers. The results demonstrated the significance of serum sodium serum creatinine as prognostic markers in HF, and this contributes to a more nuanced approach to HF management, paving the way for targeted interventions and improved patient outcomes. Moreover, this study highlights the potential of machine learning techniques to enhance risk stratification and classification in HF patients, enabling personalized prognostication and treatment strategies.

关键词： Heart Maximum likelihood estimation Phenotypes Hospitals Sodium Machine learning Predictive models Regression analysis Cardiovascular diseases Medical diagnostic imaging

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Flexible Tactile Sensing Using PDMS/TiO2 Based Capacitor with MOSFET Structure

Flexible Tactile Sensing Using PDMS/TiO2 Based Capacitor wit...

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IEEE SENSORS

作者： Kamalesh Tripathy Dhanranjan Kumar Karthick Thiyagarajan Mitradip Bhattacharjee Department of Electrical Engineeringand Computer Science i-Lab Indian Institute of Science Education and Research Bhopal Bhopal Madhya Pradesh India Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith NSW Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

In this paper, we present a flexible capacitive tactile sensor based on a polydimethylsiloxane (PDMS) elastomer, which is well-suited for a wide range of applications. The sensor incorporates an n-channel depletion-mode MOSFET to achieve an extended gate architecture. To enhance the sensing performance, TiO 2 nanoparticles are incorporated as nanofillers, resulting in an improved dielectric material ideal for highly sensitive capacitive pressure and tactile sensors. The 99PDMS-01TiO 2 nanocomposite (comprising 99 wt % PDMS and 1 wt % TiO 2 ) is utilized for promising flexible tactile sensor applications due to the synergistic effects between the PDMS matrix and TiO 2 nanoparticles, which enhance both the mechanical and sensing properties of the composite. The fabricated sensor exhibits a sensitivity of $\mathbf{30} \boldsymbol{\mu}\mathbf{A}/\mathbf{kPa}$ within the range of 0-10.5 kPa and a sensitivity of $\mathbf{9} \boldsymbol{\mu}\mathbf{A}/\mathbf{kPa}$ in the range of 10.5-30 kPa.

关键词： Nanoparticles MOSFET Sensitivity Nanocomposites Capacitors Tactile sensors Logic gates Robustness Sensors Titanium dioxide

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Triplet-Attention Residual Network for Breast Cancer Histopathology Image Classification 21

Triplet-Attention Residual Network for Breast Cancer Histopa...

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Proceedings of the 5th International Conference on Advances in Image Processing

作者： Lu Cao Shan Huang Jianxin Zhang School of Computer Science and Engineering Dalian Minzu University China School of Computer Science and Engineering Dalian Minzu University China and Key Lab of Advanced Design and Intelligent Computing(Ministry of Education) Dalian University China

ISBN: (纸本)9781450385183

Recently, breast cancer histopathology image classification using convolutional neural networks has achieved more and more attentions with the great progress. To capture more discriminant deep features for the classification, this paper proposes a novel triplet-attention residual network, i.e., TAResNet, to distinguish the breast cancer histopathology image. TAResNet employs the representative ResNet18 model to extract deep features of histopathology images, followed by a triplet-attention module to further boost the discriminability of deep features through expanding feature diversity and enhancing inter-dimensional dependency. Extensive experiments carried out on the public BreakHis dataset well evaluate the effectiveness the given TAResNet model. More specifically, TAResNet achieves its optimal classification accuracy of 98.34% and 98.77% at the image level and patient level, respectively.

关键词： Breast cancer Triplet attention Histopathological image classification Convolutional neural network

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FPGA for secured hardware & IP ownership

FPGA for secured hardware & IP ownership

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IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Suman Lata Tripathi Abhishek Kumar Mufti Mahmud VLSI Design Lab Lovely Professional University Punjab NTU India VLSI Design Lab Lovely Professional University Punjab India Department of Computer Science & Engineering Nottingham Trent University UK

ISBN: (纸本)9781665472067

Reconfigurable nature of embedded FPGAs (EFPGA) are commonly used hardware for system-on-chip design due to additional features of upgradability, and security with high computational powers. SoC design with high portability EFPGA has the potential to replace time-consuming, less compatible full custom SoC design. Security is a major concern of designers to avoid illegal use of IPs for SoC design from external attacks or treats without paying charges to the owner. Security challenges are associated with bitstream loading on FPGA, design functionality, and onboard memory blocks. Several techniques are used to generate secured IP are explored mainly secured bitstream generation, generating physical unclonable functions (PUF), security protocol etc. Xilinx FPGA boards are frequently used for IP design prototyping with software like Vivado tool using HDLs. This paper is a comprehenssive study of current state of art of secured hardware design with FPGA.

关键词： Protocols Loading Watermarking Physical unclonable function SRAM cells Software System-on-chip

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Wander 22

Wander

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Proceedings of the SIGGRAPH Asia 2022 Art Gallery

作者： Yuqian Sun Chang Hee Lee Chenhang Cheng Ali Asadipour Ying Xu Yihua Li Computer Science Research Centre Royal College of Art London United Kingdom Affective Systems and Cognition Lab Industrial Design Department College of Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Unaffiliated Shanghai China Department of Industrial Design School of Art and Design Wuhan University of Technology Wuhan China Department of Product Design College of Fashion and Design Donghua University Shanghai China

This is a public art project centred on an AI chatbot called Wander. She can generate interactive fiction based on realworld locations' knowledge graphs. Through public participation, the stories are visualised on...

ISBN: (纸本)9781450394710

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