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15th International Conference on Body Area Networks, BodyNets 2020

作者： Särestöniemi, Mariella Pomalaza-Raez, Carlos Hakala, Jaakko Myllymäki, Sami Kilpijärvi, Joni Iinatti, Jari Hämäläinen, Matti Myllylä, Teemu Centre for Wireless Communications University of Oulu Oulu Finland Department of Electrical and Computer Engineering Purdue University West Lafayette United States Optoelectronics and Measurement Techniques Research Unit Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland Microelectronics Research Unit Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland Research Unit of Medical Imaging Physics and Technology Faculty of Medicine University of Oulu Oulu Finland

ISBN: (纸本)9783030649906

This paper presents a simulation-based study on detection of stroke/brain hemorrhage even in the white matter using radio channel characteristics analysis. The idea is to utilize the fact that blood has different dielectric properties than brain’s white and grey matters and, thus, additional blood areas inside the brain change radio channel characteristics between the transmitter and receiver antennas located on the opposite sides of the head. The antennas should be strongly directive and designed to work attached to the body surface so that hemorrhages even in the white matter could be detected. The study is conducted using the electromagnetic simulation software CST and two different simulation models: a spherical tissue layer model and an anatomical voxel model. The antennas used in this study are bio-matched mini-horn antennas designed for implant communications at 1–4 GHz frequency range. Different sizes of the blood areas are evaluated. This initial study shows how even small sizes of hemorrhage can change radio channel even as the hemorrhage is located in the middle of the brain, in the white matter. The path loss difference is 0.5–10 dB between the hemorrhage and reference cases depending on the size and location of the hemorrhage. A practical solution of this hemorrhage detection technique could be a portable helmet type of structure having several small sized antennas around the internal part of the helmet. Such a helmet would be easy to use e.g. in ambulance, which would enable early detection of hemorrhage in its early phase and, hence, improve prospects of the cure significantly. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2020.

关键词： computer software

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Simultaneous semantic alignment network for heterogeneous domain adaptation

arXiv

引用

arXiv 2020年

作者： Li, Shuang Xie, Binhui Wu, Jiashu Zhao, Ying Liu, Chi Harold Ding, Zhengming School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Computing and Information Systems The University of Melbourne Melbourne Australia Department of Computer Information and Technology Indiana University-Purdue University Indianapolis United States

Heterogeneous domain adaptation (HDA) transfers knowledge across source and target domains that present heterogeneities e.g., distinct domain distributions and difference in feature type or dimension. Most previous HDA methods tackle this problem through learning a domain-invariant feature subspace to reduce the discrepancy between domains. However, the intrinsic semantic properties contained in data are under-explored in such alignment strategy, which is also indispensable to achieve promising adaptability. In this paper, we propose a Simultaneous Semantic Alignment Network (SSAN) to simultaneously exploit correlations among categories and align the centroids for each category across domains. In particular, we propose an implicit semantic correlation loss to transfer the correlation knowledge of source categorical prediction distributions to target domain. Meanwhile, by leveraging target pseudo-labels, a robust triplet-centroid alignment mechanism is explicitly applied to align feature representations for each category. Notably, a pseudo-label refinement procedure with geometric similarity involved is introduced to enhance the target pseudo-label assignment accuracy. Comprehensive experiments on various HDA tasks across text-to-image, image-to-image and text-to-text successfully validate the superiority of our SSAN against state-of-the-art HDA methods. The code is publicly available at https://***/BIT-DA/SSAN. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Bi-directional generation for unsupervised domain adaptation

arXiv

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arXiv 2020年

作者： Yang, Guanglei Xia, Haifeng Ding, Mingli Ding, Zhengming School of Instrument science and engineering Harbin Institute of Technology Department of Electrical & Computer Engineering Indiana University-Purdue University Indianapolis United States Department of Computer Information and Technology Indiana University-Purdue University Indianapolis United States

Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. The conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. To balance the mitigation of domain gap and the preservation of the inherent structure, we propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains. Specifically, two cross-domain generators are employed to synthesize one domain conditioned on the other. The performance of our proposed method can be further enhanced by the consistent classifiers and the cross-domain alignment constraints. We also design two classifiers which are jointly optimized to maximize the consistency on target sample prediction. Extensive experiments verify that our proposed model outperforms the state-of-the-art on standard cross domain visual benchmarks. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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A Primer on Rate-Splitting Multiple Access: Tutorial, Myths, and Frequently Asked Questions

arXiv

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arXiv 2022年

作者： Clerckx, Bruno Mao, Yijie Jorswieck, Eduard A. Yuan, Jinhong Love, David J. Erkip, Elza Niyato, Dusit The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom GrazA-8010 Austria The School of Information Science and Technology ShanghaiTech University Shanghai201210 China The Institute for Communications Technology TU Braunschweig Brunswick Germany The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia The School of Electrical and Computer Engineering Purdue University West LafayetteIN United States The Electrical and Computer Engineering Department New York University Tandon School of Engineering BrooklynNY11201 United States The School of Computer Engineering Nanyang Technological University Singapore

Rate-Splitting Multiple Access (RSMA) has emerged as a powerful multiple access, interference management, and multi-user strategy for next generation communication systems. In this tutorial, we depart from the orthogonal multiple access (OMA) versus non-orthogonal multiple access (NOMA) discussion held in 5G, and the conventional multi-user linear precoding approach used in space-division multiple access (SDMA), multiuser and massive MIMO in 4G and 5G, and show how multi-user communications and multiple access design for 6G and beyond should be intimately related to the fundamental problem of interference management. We start from foundational principles of interference management and rate-splitting, and progressively delineate RSMA frameworks for downlink, uplink, and multicell networks. We show that, in contrast to past generations of multiple access techniques (OMA, NOMA, SDMA), RSMA offers numerous benefits: 1) enhanced spectral, energy and computation efficiency;2) universality by unifying and generalizing OMA, SDMA, NOMA, physical-layer multicasting, multi-user MIMO under a single framework that holds for any number of antennas at each node (SISO, SIMO, MISO, and MIMO settings);3) flexibility by coping with any interference levels (from very weak to very strong), network loads (underloaded, overloaded), services (unicast, multicast), traffic, user deployments (channel directions and strengths);4) robustness to inaccurate channel state information (CSI) and resilience to mixed-critical quality of service;5) reliability under short channel codes and low latency. We then discuss how those benefits translate into numerous opportunities for RSMA in over forty different applications and scenarios of 6G, e.g., multi-user MIMO with statistical/quantized CSI, FDD/TDD/cell-free massive MIMO, millimeter wave and terahertz, cooperative relaying, physical layer security, reconfigurable intelligent surfaces, cloud-radio access network, internet-of-things, massive access, jo

关键词： Space division multiple access

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Rethink Maximum Mean Discrepancy for Domain Adaptation

arXiv

引用

arXiv 2020年

作者： Wang, Wei Li, Haojie Ding, Zhengming Wang, Zhihui DUT-RU International School of Information Science & Engineering Dalian University of Technology DalianLiaoning116000 China Department of Computer Information and Technology Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis IndianapolisIN46202 United States

Existing domain adaptation methods aim to reduce the distributional difference between the source and target domains and respect their specific discriminative information, by establishing the Maximum Mean Discrepancy (MMD) and the discriminative distances. However, they usually accumulate to consider those statistics and deal with their relationships by estimating parameters blindly. This paper theoretically proves two essential facts: 1) minimizing the MMD equals to maximize the source and target intra-class distances respectively but jointly minimize their variance with some implicit weights, so that the feature discriminability degrades;2) the relationship between the intra-class and inter-class distances is as one falls, another rises. Based on this, we propose a novel discriminative MMD. On one hand, we consider the intra-class and inter-class distances alone to remove a redundant parameter, and the revealed weights provide their approximate optimal ranges. On the other hand, we design two different strategies to boost the feature discriminability: 1) we directly impose a trade-off parameter on the implicit intra-class distance in MMD to regulate its change;2) we impose the similar weights revealed in MMD on inter-class distance and maximize it, then a balanced factor could be introduced to quantitatively leverage the relative importance between the feature transferability and its discriminability. The experiments on several benchmark datasets not only prove the validity of theoretical results but also demonstrate that our approach could perform better than the comparative state-of-art methods substantially. Copyright © 2020, The Authors. All rights reserved.

关键词： Economic and social effects

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RF-Rate Hybrid CNN Accelerator Based on Analog-CMOS and Xilinx RFSoC

RF-Rate Hybrid CNN Accelerator Based on Analog-CMOS and Xili...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Udara De Silva Soumyajit Mandal Arjuna Madanayake Jin Wei-Kocsis Leonid Belostotski Electrical and Computer Engineering Florida International University Electrical Engineering & Computer Science Case Western Reserve University Computer and Information Technology Purdue University Electrical and Computer Engineering University of Calgary

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

The superior performance of deep learning (DL) has sent shock waves in the machine learning community. The high adoption rate of DL has set new demands on computational throughput, latency, and power efficiency of the computing infrastructure. In addition to conventional approaches to acceleration of the inference component of DL systems based on GPUs, cloud computing, ASIC/FPGAs and custom vector processors (such as tensor processing units), there is renewed interest in high-frequency analog circuits for DL inference. Analog computing is a potential candidate for meeting challenging requirements in throughput, latency and power efficiency. Because DL inference has superior noise resilience and relatively low accuracy needs (typically less than 8 bits), analog circuits can provide a promising alternative to all-digital accelerators. This paper presents early work on the design of an analog CMOS accelerator that performs analog convolution and decision operations in parallel and in real-time by pairing a high-frequency operational amplifier-based CNN filtering kernel with a rectified linear unit (ReLu) non-linearity based on an active precision rectifier circuit. The analog accelerator was designed in a 45 nm CMOS process and simulated in Cadence Spectre. Image convolution results are presented and compared with MATLAB simulations. The proposed solution also employs Xilinx RF System-on-Chip (SoC) devices based on the Xilinx ZCU1285 RFSoC platform to interface digital inputs and outputs with the proposed RF-rate analog inference accelerator.

关键词： Convolution Kernel Delays Bandwidth Resistors Machine learning Nonvolatile memory

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Single-Photon Emitters in Aluminum Nitride by Zr ion Implantation

Single-Photon Emitters in Aluminum Nitride by Zr ion Implant...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Alexander Senichev Zachariah O. Martin Yongqiang Wang Han Htoon Alexei S. Lagutchev Alexandra Boltasseva Vladimir M. Shalaev School of Electrical and Computer Engineering Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette IN USA U.S. Department of Energy (DOE) National Quantum Information Science Research Center Quantum Science Center (QSC) Oak Ridge TN USA Materials Science and Technology Division Los Alamos National Laboratory Ion Beam Materials Laboratory Los Alamos NM USA Center for Integrated Nanotechnologies Materials Physics and Applications Division Los Alamos National Laboratory Los Alamos NM USA

We report on the generation of single-photon emitters in aluminum nitride films through Zr-ion implantation, which was predicted to form optically addressable spin defects. We studied implantation conditions, post-imp... 详细信息

ISBN: (纸本)9781957171258

关键词： Chemical vapor deposition Emission Fluorescence Ion beams Ions Photoluminescence

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Applications of Raman Spectroscopy in Clinical Medicine

arXiv

引用

arXiv 2023年

作者： Qi, Yaping Chen, Esther Xinyi Hu, Dan Yang, Ying Wu, Zhenping Zheng, Ming Sadi, Mohammad A. Jiang, Yucheng Zhang, Kang Chen, Zi Chen, Yong P. Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Av. Wai Long China Tohoku University Sendai980-8577 Japan Faculty of Medicine Macau University of Science and Technology Av. Wai Long China State Key Laboratory of Information Photonics and Optical Communications School of Science Beijing University of Posts and Telecommunications Beijing100876 China School of Materials Science and Physics China University of Mining and Technology Xuzhou221116 China Department of Physics and Astronomy Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Jiangsu Suzhou215009 China Division of Thoracic Surgery Brigham and Women's Hospital Harvard Medical School 75 Francis Street BostonMA02115 United States Institute of Physics and Astronomy Villum Center for Hybrid Quantum Materials and Devices Aarhus University AarhusC 8000 Denmark

Raman spectroscopy provides spectral information related to the specific molecular structures of substances and has been well established as a powerful tool for studying biological tissues and diagnosing diseases. This article reviews recent advances in Raman spectroscopy and its applications in diagnosing various critical diseases, including cancers, infections, and neurodegenerative diseases, and in predicting surgical outcomes. These advances are explored through discussion of state-of-the-art forms of Raman spectroscopy, such as surface-enhanced Raman spectroscopy, resonance Raman spectroscopy, and tip-enhanced Raman spectroscopy employed in biomedical sciences. We discuss biomedical applications, including various aspects and methods of ex vivo and in vivo medical diagnosis, sample collection, data processing, and achievements in realizing the correlation between Raman spectra and biochemical information in certain diseases. Finally, we present the limitations of the current study and provide perspectives for future research. © 2023, CC BY.

关键词： COVID-19

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Discriminative cross-domain feature learning for partial domain adaptation

arXiv

引用

arXiv 2020年

作者： Jing, Taotao Shao, Ming Ding, Zhengming Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis IndianapolisIN46202 United States Department of Computer and Information Science University of Massachusetts Dartmouth MA02747 United States Department of Computer Information and Technology Indiana University-Purdue University Indianapolis IndianapolisIN46202 United States

Partial domain adaptation aims to adapt knowledge from a larger and more diverse source domain to a smaller target domain with less number of classes, which has attracted appealing attention. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain to better fight off the cross-domain distribution divergences. However, it is essential to align target data with only a small set of source data. In this paper, we develop a novel Discriminative Cross-Domain Feature Learning (DCDF) framework to iteratively optimize target labels with a cross-domain graph in a weighted scheme. Specifically, a weighted cross-domain center loss and weighted cross-domain graph propagation are proposed to couple unlabeled target data to related source samples for discriminative cross-domain feature learning, where irrelevant source centers will be ignored, to alleviate the marginal and conditional disparities simultaneously. Experimental evaluations on several popular benchmarks demonstrate the effectiveness of our proposed approach on facilitating the recognition for the unlabeled target domain, through comparing it to the state-of-the-art partial domain adaptation approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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