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arXiv 2020年

作者： Sriram, Shreeya Avlani, Shitij Ward, Matthew P. Sen, Shreyas School of Electrical and Computer Engineering Purdue University West LafayetteIN47906 United States Weldon School of Biomedical Engineering Purdue University West LafayetteIN47906 United States Indiana University School of Medicine IndianapolisIN46202 United States

Continuous multi-channel monitoring of biopotential signals is vital in understanding the body as a whole, facilitating accurate models and predictions in neural research. The current state of the art in wireless technologies for untethered biopotential recordings rely on radiative electromagnetic (EM) fields. In such transmissions, only a small fraction of this energy is received since the EM fields are widely radiated resulting in lossy inefficient systems. Using the body as a communication medium (similar to a'wire') allows for the containment of the energy within the body, yielding order(s) of magnitude lower loss than radiative EM communication. In this work, we introduce Animal Body Communication (ABC), which utilizes the concept of using the body as a medium into the domain of chronic animal biopotential recording. This work, for the first time, develops the theory and models for animal body communication circuitry and channel loss. Using this theoretical model, a sub-inch3, custom-designed sensor node is built using off the shelf components which is capable of sensing and transmitting biopotential signals, through the body of the rat at significantly lower powers compared to traditional wireless transmissions. In-vivo experimental analysis proves that ABC successfully transmits acquired electrocardiogram (EKG) signals through the body with correlation accuracy >99% when compared to traditional wireless communication modalities, with a 50x reduction in power consumption. Copyright © 2020, The Authors. All rights reserved.

关键词： Sensor nodes

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On sparse identification of complex dynamical systems: a study on discovering influential reactions in chemical reaction networks

arXiv

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arXiv 2020年

作者： Harirchi, Farshad Kim, Doohyun Khalil, Omar Liu, Sijia Elvati, Paolo Baranwal, Mayank Hero, Alfred Violi, Angela Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109–2125 United States Department of Mechanical Engineering University of Michigan Ann ArborMI48109–2125 United States Departments of Chemical Engineering Biomedical Engineering Macromolecular Science and Engineering Biophysics Program University of Michigan Ann ArborMI48109–2125 United States

A wide variety of real life complex networks are prohibitively large for modeling, analysis and control. Understanding the structure and dynamics of such networks entails creating a smaller representative network that preserves its relevant topological and dynamical properties. While modern machine learning methods have enabled identification of governing laws for complex dynamical systems, their inability to produce white-box models with sufficient physical interpretation renders such methods undesirable to domain experts. In this paper, we introduce a hybrid black-box, white-box approach for the sparse identification of the governing laws for complex, highly coupled dynamical systems with particular emphasis on finding the influential reactions in chemical reaction networks for combustion applications, using a data-driven sparse-learning technique. The proposed approach identifies a set of influential reactions using species concentrations and reaction rates, with minimal computational cost without requiring additional data or simulations. The new approach is applied to analyze the combustion chemistry of H2 and C3H8 in a constant-volume homogeneous reactor. The influential reactions determined by the sparse-learning method are consistent with the current kinetics knowledge of chemical mechanisms. Additionally, we show that a reduced version of the parent mechanism can be generated as a combination of the significantly reduced influential reactions identified at different times and conditions and that for both H2 and C3H8 fuel, the reduced mechanisms perform closely to the parent mechanisms as a function of the ignition delay time over a wide range of conditions. Our results demonstrate the potential of the sparse-learning approach as an effective and efficient tool for dynamical system analysis and reduction. The uniqueness of this approach as applied to combustion systems lies in the ability to identify influential reactions in specified conditions and times dur

关键词： Learning systems

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Low-cost and cleanroom-free fabrication of microneedles

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Microsystems & Nanoengineering 2018年第1期4卷 518-524页

作者： Hojatollah Rezaei Nejad Aydin Sadeqi Gita Kiaee Sameer Sonkusale Department of Electrical and Computer Engineering Nano LabTufts UniversityMedfordMA 02155USA Department of Biomedical Engineering Tufts UniversityMedfordMA 02155USA

We present a facile,low-cost and cleanroom-free technique for the fabrication of microneedles using molds created by laser *** mold with high aspect ratios is achieved on acrylic sheet by engraving a specific pattern of crossover lines(COL)using CO2 laser *** COL pattern on the acrylic sheet creates a sharp conical shape in the center of the *** have shown that a variety of microneedle shapes with different heights and tip angles can be easily achieved by changing the number and the length of the ***(PDMS)microneedles were fabricated by casting the PDMS on the *** resulted PDMS microneedles are oxygen plasma treated and then *** PDMS layer is casted on PDMS microneedles and detached after *** silanization prevents those two layers of PDMS from bonding to each other and makes them easily *** detachment of the PDMS mold of microneedles,the mold is used to fabricate degradable polyvinyl alcohol microneedle patch suitable for transdermal drug *** release kinetics of the needles are also shown and discussed in order to prove the applicability of the needles.

关键词： drug delivery laser ablation microneedles

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Preliminary experimental results for imaging at millimetre-wave frequencies in breast phantoms

Preliminary experimental results for imaging at millimetre-w...

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European Microwave Conference in Central Europe (EuMCE)

作者： S. Di Meo D. Conforti M. Pasian G. Matrone Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9781728112404;9782874870675

Breast cancer is one of the main causes of death among women worldwide. Microwave and millimetre wave imaging systems for breast cancer detection have been proposed as alternative or complementary techniques to the actual diagnostic devices. Several microwave prototypes and breast tissue-mimicking phantoms have been proposed to scan the breast in the low microwave regime; however, when cancers within very fat tissues are targeted the resolution at microwave frequencies is not satisfying for an early diagnosis. A possible solution to this problem could be the use of higher frequencies, at the cost of a relatively lower penetration depth. In this paper, for the first time, a preliminary image at mm-waves of a fatty breast phantom with an embedded 4-mm metallic target is presented. The recipe and the procedure for phantom preparation is explained, and dielectric properties are measured and compared to those of ex-vivo human tissues, in the [0.5-50] GHz frequency range. The proposed preliminary mm-wave system architecture is composed by two mono-modal truncated double-ridge waveguides), and a synthetic linear aperture is created by shifting these radiators with two microstep actuators in 10 different positions. Data were acquired in the [18]-[40] GHz frequency range and calibrated. The image was finally obtained through Delay and Sum beamforming with coherence-factor weighting.

关键词： Phantoms Dielectrics Microwave imaging Microwave theory and techniques Breast cancer

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Stochastic Exceptional Points for Noise-Assisted Sensing

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Physical Review Letters 2023年第22期130卷 227201-227201页

作者： Zhipeng Li Chenhui Li Ze Xiong Guoqiang Xu Yongtai Raymond Wang Xi Tian Xin Yang Zhu Liu Qihang Zeng Rongzhou Lin Ying Li Jason Kai Wei Lee John S. Ho Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Wireless and Smart Bioelectronics Lab School of Biomedical Engineering ShanghaiTech University Shanghai 201210 China Department of Physiology Yong Loo Lin School of Medicine National University of Singapore Singapore 117593 Singapore School of Physics and Electronics Hunan Normal University Changsha Hunan 410081 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China International Joint Innovation Center Key Lab of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining 314400 China Heat Resilience and Performance Centre Yong Loo Lin School of Medicine National University of Singapore Singapore 119228 Singapore Human Potential Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore 119228 Singapore

Noise is a fundamental challenge for sensors deployed in daily environments for ambient sensing, health monitoring, and wireless networking. Current strategies for noise mitigation rely primarily on reducing or removing noise. Here, we introduce stochastic exceptional points and show the utility to reverse the detrimental effect of noise. The stochastic process theory illustrates that the stochastic exceptional points manifest as fluctuating sensory thresholds that give rise to stochastic resonance, a counterintuitive phenomenon in which the added noise increases the system’s ability to detect weak signals. Demonstrations using a wearable wireless sensor show that the stochastic exceptional points lead to more accurate tracking of a person’s vital signs during exercise. Our results may lead to a distinct class of sensors that overcome and are enhanced by ambient noise for applications ranging from healthcare to the internet of things.

关键词： Noise Non-Hermitean systems PT-symmetry Noise measurements

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Development of handheld near-infrared spectroscopic medical imaging system

Development of handheld near-infrared spectroscopic medical ...

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Bio-Optics: Design and Application, BODA 2019 - Part of Biophotonics Congress: Optics in the Life Sciences Congress 2019

作者： Saikia, Manob Jyoti Kanhirodan, Rajan Dept. of Physics Indian Institute of Science Bangalore560012 India Dept. of Electrical Computer and Biomedical Engineering University of Rhode Island KingstonRI02881 United States

We present a handheld near-infrared spectroscopic imaging system. The system has a sensor pad and a controller for the high-speed 3D imaging of tissue wirelessly. Experimental results on a phantom prove working of the... 详细信息

ISBN: (纸本)9781943580545

关键词： Infrared devices

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The role of empathy in choosing majors 126

The role of empathy in choosing majors

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126th ASEE Annual Conference and Exposition: Charged Up for the Next 125 Years, ASEE 2019

作者： Jacobs, Eddie de Jongh Curry, Amy L. Astorne-Figari, Carmen Deaton, Russell J. Salem, Wesam M. Xu, Yonghong Jade Roberts, Shelby G. Department of Electrical and Computer Engineering University of Memphis United States Department of Biomedical Engineering University of Memphis United States Department of Economics University of Memphis United States Department of Counseling Educational Psychology and Research University of Memphis United States

In this research, the role empathy plays in women's choice of major is investigated with particular emphasis on engineering disciplines. We began by formulating a survey instrument that measured the level of empathy of the survey participant, the perceived level of empathy of various academic disciplines, the likelihood of majoring in a discipline, and the perception of empathy among the faculty and fellow students within their current major, along with demographic information. To measure the level of empathy of an individual, a modified form of the Empathizing/Systematizing Quotient (EQ-SQ) developed by Simon Baron-Cohen was used. The survey produced 517 valid responses. Of those responses, 46% were female. In addition, 257 were STEM majors with 231 of those being engineering majors. Of the engineering majors, 69 were female. Results from this survey indicate that 1) women are more empathetic than men, 2) students in engineering and other STEM majors are less empathetic than those in non-STEM majors, 3) engineering disciplines are perceived as less empathetic than other majors, and 4) empathy is negatively correlated with choosing engineering as a major. Following these quantitative results, a qualitative focus group study was developed to delve deeper into some of the reasons behind the quantitative results. The focus groups were women in STEM (non-engineering) disciplines, women in engineering disciplines, and women in non-STEM disciplines. These studies are ongoing but to date, we have had three engineering women and two non-STEM women participate in the study. Using thematic analysis, an early result emerging from this part of the study is the role that fathers and math teachers play in the choices women make to study engineering. © American Society for engineering Education, 2019.

关键词： Surveys

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Design Space Exploration of Embedded Applications on Heterogeneous CPU-GPU Platforms

Design Space Exploration of Embedded Applications on Heterog...

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International Conference on High Performance Computing & Simulation (HPCS)

作者： Abdullah Siddiqui Gul N. Khan Electrical Computer and Biomedical Engineering Ryerson University Toronto Ontario Canada

ISBN: (数字)9781728144849

ISBN: (纸本)9781728144856

CPU-GPU platforms possess the potential of enhancing the performance of applications through some unique and diverse capabilities of both CPU-GPU devices. As a result, the methodologies for CPU/GPU system design space exploration for various applications are now considerably more challenging on these heterogeneous platforms. In this paper, we present a heuristic algorithm for partitioning the computation of applications between a CPU and GPU, while satisfying the user-defined constraints. Our methodology leverages the SIMD-related computing and hierarchical memory model of GPUs to optimize application mapping and allocation to CPU-GPU systems. The algorithm partitions the application, which is specified as a Directed Acyclic Graph (DAG), for a CPU-GPU platform to meet the objectives specified by the user. The effectiveness of our methodology is demonstrated by efficiently partitioning and executing MJPEG decoder and benchmark applications on a CPU-GPU system.

关键词： Graphics processing units Task analysis Partitioning algorithms Genetic algorithms Computational modeling Resource management Parallel processing

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Correction to: Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data

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Neuroinformatics 2023年第1期21卷 143页

作者： Irina Belyaeva Ben Gabrielson Yu-Ping Wang Tony W Wilson Vince D Calhoun Julia M Stephen Tülay Adali Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA. irinbel1@umbc.edu. Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA. Department of Biomedical Engineering Tulane University New Orleans LA USA. The Institute for Human Neuroscience Boys Town National Research Hospital Boys Town NE 68010 USA. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Atlanta GA USA. The Mind Research Network Lovelace Biomedical Research Institute Albuquerque NM USA.

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Technical report: Quality assessment tool for machine learning with clinical CT

arXiv

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arXiv 2021年

作者： Gao, Riqiang Khan, Mirza S. Tang, Yucheng Xu, Kaiwen Deppen, Steve Huo, Yuankai Sandler, Kim L. Massion, Pierre P. Landman, Bennett A. Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37235 United States Vanderbilt University Medical Center NashvilleTN37235 United States Department of Biomedical Informatics Vanderbilt University NashvilleTN37212 United States U.S. Department of Veterans Affairs NashvilleTN37212 United States

Image Quality Assessment (IQA) is important for scientific inquiry, especially in medical imaging and machine learning. Potential data quality issues can be exacerbated when human-based workflows use limited views of the data that may obscure digital artifacts. In practice, multiple factors such as network issues, accelerated acquisitions, motion artifacts, and imaging protocol design can impede the interpretation of image collections. The medical image processing community has developed a wide variety of tools for the inspection and validation of imaging data. Yet, IQA of computed tomography (CT) remains an under-recognized challenge, and no user-friendly tool is commonly available to address these potential issues. Here, we create and illustrate a pipeline specifically designed to identify and resolve issues encountered with large-scale data mining of clinically acquired CT data. Using the widely studied National Lung Screening Trial (NLST), we have identified approximately 4% of image volumes with quality concerns out of 17,392 scans. To assess robustness, we applied the proposed pipeline to our internal datasets where we find our tool is generalizable to clinically acquired medical images. In conclusion, the tool has been useful and time-saving for research study of clinical data, and the code and tutorials are publicly available at https://***/MASILab/QA_tool. © 2021, CC BY-NC-ND.

关键词： computerized tomography

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