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MO653: High-Flux Haemodialysis and Haemodiafiltration: A Comparative Study Based on 1-H NMR Serum Metabolic Profile

Nephrology Dialysis Transplantation 2022年第SUPPLEMENT_3期37卷

作者： Flores Santos, Andressa Manfron Schiefer, Elberth Sassaki, Guilherme Menezes, Leociley Fonseca, Renato Cunha, Regiane Souza, Wesley Pecoits-Filho, Roberto Stinghen, Andréa E M Universidade Federal do Paraná Clinical Analysis Department Curitiba Brazil Universidade Tecnológica Federal do Paraná Graduate Program in Electrical and Computer Engineering Curitiba Brazil Universidade Federal do Paraná Biochemistry Department Curitiba Brazil Universidade Federal do Paraná Basic Pathology Department Curitiba Brazil Pontifícia Universidade Católica do Paraná Programa de Pós-Graduação em Ciências da Saúde Curitiba Brazil

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Aperture-Coupled Koch Fractal on-Chip Antennas for 60 GHz ISM Band

Aperture-Coupled Koch Fractal on-Chip Antennas for 60 GHz IS...

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IEEE MTT-S Latin America Microwave Conference (LAMC)

作者： Samuel Medeiros Araújo Morais Alexandre Jean René Serres Ivana Soares Guarany Raimundo Carlos da Silverio Freire Paulo Fernandes da Silva Ewaldo Elder Carvalho Santana Paulo Henrique da Fonseca Silva Post-Graduation Program in Electrical Engineering Federal University of Campina Grande Campina Grande Brazil Post-Graduation Program in Computer Engineering and Systems State University of Maranhão São Luis Brazil Post-Graduation Program in Electrical Engineering Federal Institute of Education Science and Technology of Paraíba João Pessoa Brazil

ISBN: (纸本)9781538673348;9781538673331

This paper presents Koch fractal aperture-coupled patch antennas operating in industrial scientific and medical band at 60 GHz (57 GHz to 64 GHz), for on-chip application. This work is the continuation of the development of aperture-coupled antennas with aperture and patch element built in 2 μm-thick aluminum lying on two layers of silicon with a height of 200 μm. The use of Koch fractal allowed the development of aperture-coupled antennas of greater perimeter with more metal in the patch elements, which promoted an increase in the gain. The observed gain for Koch fractal level 2 was of 10.55 dBi, with a bandwidth of 7.23 GHz, covering the ISM 60 GHz Band, and an increase of less than 10% in total patch dimension. For Koch fractal levels 0 (square), a polarization close to circular was achieved, with an axial ratio less than 6 at resonant frequencies.

关键词： Fractals Patch antennas Resonant frequency Aperture antennas Bandwidth Gain

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Author Correction: Cortical astrocytes modulate dominance behavior in male mice by regulating synaptic excitatory and inhibitory balance

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Nature neuroscience 2023年第12期26卷 2250页

作者： Kyungchul Noh Woo-Hyun Cho Byung Hun Lee Dong Wook Kim Yoo Sung Kim Keebum Park Minkyu Hwang Ellane Barcelon Yoon Kyung Cho C Justin Lee Bo-Eun Yoon Se-Young Choi Hye Yoon Park Sang Beom Jun Sung Joong Lee Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. Department of Physics and Astronomy Seoul National University Seoul Republic of Korea. Department of Molecular Biology Dankook University Cheonan Republic of Korea. Department of Electronic and Electrical Engineering Ewha Womans University Seoul Republic of Korea. Center for Cognition and Sociality Institute for Basic Science (IBS) Daejeon Republic of Korea. Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA. Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea. Department of Brain & Cognitive Sciences Ewha Womans University Seoul Republic of Korea. Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. sjlee87@snu.ac.kr.

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Optomechanically Induced Birefringence and Optomechanically Induced Faraday Effect

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Physical Review Letters 2019年第2期123卷 023602-023602页

作者： Robert Duggan Javier del Pino Ewold Verhagen Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Center for Nanophotonics AMOLF Science Park 104 1098 XG Amsterdam The Netherlands Photonics Initiative Advanced Science Research Center City University of New York New York 10031 USA Physics Program Graduate Center City University of New York New York 10016 USA Department of Electrical Engineering City College of The City University of New York New York 10031 USA

We demonstrate an optomechanical platform where optical mode conversion mediated by mechanical motion enables the arbitrary tailoring of polarization states of propagating light fields. Optomechanical interactions are realized in a Fabry-Pérot resonator, which naturally supports two polarization-degenerate states while an optical control field induces rotational symmetry breaking. Applying such principles, the entire Poincaré sphere is spanned by just optical control of the driving field, realizing reciprocal and nonreciprocal optomechanically induced birefringence for linearly polarized and circularly polarized control driving. A straightforward extension of this setup also enables all-optical tunable isolation and circulation. Our findings open new avenues to exploit optomechanics for the arbitrary manipulation of light polarization.

关键词： Nonlinear optics Optomechanics

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Nano functional neural interfaces

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Nano Research 2018年第10期11卷 5065-5106页

作者： Yongchen Wang Hanlin Zhu Huiran Yang Aaron D. Argall Lan Luan Chong Xie Liang Guo Department of Biomedical Engineering The Ohio State University Columbus 43210 USA Department of Biomedical Engineering The University of Texas at Austin Austin 78712 USA Department of Electrical and Computer Engineering The Ohio State University Columbus 43210 USA Key Laboratory of Flexible Electronics and Institute of Advanced Materials Jiangsu National Synergetic Innovation Center for Advanced Materials Nanjing Tech University Nanjing 211816 China Biomedical Sciences Graduate Program The Ohio State University Columbus 43210 USA Department of Neuroscience The Ohio State University Columbus 43210 USA

Engineered functional neural interfaces （fNIs） serve as essential abiotic-biotic transducers between an engineered system and the nervous system. They convert external physical stimuli to cellular signals in stimulation mode or read out biological processes in recording mode. Information can be exchanged using electricity, light, magnetic fields, mechanical forces, heat, or chemical signals. fNIs have found applications for studying processes in neural circuits from cell cultures to organs to whole organisms, fNI-facilitated signal transduction schemes, coupled with easily manipulable and observable external physical signals, have attracted considerable attention in recent years. This enticing field is rapidly evolving toward miniaturization and biomimicry to achieve long-term interface stability with great signal transduction efficiency. Not only has a new generation of neuroelectrodes been invented, but the use of advanced fNIs that explore other physical modalities of neuromodulation and recording has begun to increase. This review covers these exciting developments and applications of fNIs that rely on nanoelectrodes, nanotransducers, or bionanotransducers to establish an interface with the nervous system. These nano fNIs are promising in offering a high spatial resolution, high target specificity, and high communication bandwidth by allowing for a high density and count of signal channels with minimum material volume and area to dramatically improve the chronic integration of the fNI to the target neural tissue. Such demanding advances in nano fNIs will greatly facilitate new opportunities not only for studying basic neuroscience but also for diagnosing and treating various neurological diseases.

关键词： neural interface neurotechnology nanoelectrode nanomaterial neural recording neural stimulation

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Human brain mapping with multi-thousand channel PtNRGrids resolves novel spatiotemporal dynamics

arXiv

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arXiv 2021年

作者： Tchoe, Youngbin Bourhis, Andrew M. Cleary, Daniel R. Stedelin, Brittany Lee, Jihwan Tonsfeldt, Karen J. Brown, Erik C. Siler, Dominic Paulk, Angelique C. Yang, Jimmy C. Oh, Hongseok Ro, Yun Goo Choi, Woojin Lee, Keundong Russman, Samantha Ganji, Mehran Galton, Ian Ben-Haim, Sharona Raslan, Ahmed M. Dayeh, Shadi A. Integrated Electronics and Biointerfaces Laboratory Department of Electrical and Computer Engineering University of California San Diego San DiegoCA92093 United States Department of Neurological Surgery University of California San Diego San DiegoCA92093 United States Department of Neurological Surgery Oregon Health & Science University Mail Code CH8N 3303 SW Bond Avenue PortlandOR97239-3098 United States Department of Obstetrics Gynecology and Reproductive Sciences Center for Reproductive Science and Medicine University of California San Diego San DiegoCA92093 United States Department of Neurology Massachusetts General Hospital BostonMA02114 United States Department of Neurosurgery Massachusetts General Hospital BostonMA02114 United States Graduate Program of Materials Science and Engineering University of California San Diego San DiegoCA92093 United States

Electrophysiological devices are critical for mapping eloquent and diseased brain regions and for therapeutic neuromodulation in clinical settings and are extensively utilized for research in brain-machine interfaces. However, the existing devices are often limited in either spatial resolution or cortical coverage, even including those with thousands of channels used in animal experiments. Here, we developed scalable manufacturing processes and dense connectorization to achieve reconfigurable thin-film, multi-thousand channel neurophysiological recording grids using platinum-nanorods (PtNRGrids). With PtNRGrids, we have achieved a multi-thousand channel array of small (30 μm) contacts with low impedance, providing unparalleled spatial and temporal resolution over a large cortical area. We demonstrate that PtNRGrids can resolve sub-millimeter functional organization of the barrel cortex in anesthetized rats that captured the histochemically-demonstrated structure. In the clinical setting, PtNRGrids resolved fine, complex temporal dynamics from the cortical surface in an awake human patient performing grasping tasks. Additionally, the PtNRGrids identified the spatial spread and dynamics of epileptic discharges in a patient undergoing epilepsy surgery at 1 mm spatial resolution, including activity induced by direct electrical stimulation. Collectively, these findings demonstrate the power of the PtNRGrids to transform clinical mapping and research with brain-machine interfaces and highlights a path toward novel therapeutics. Copyright © 2021, The Authors. All rights reserved.

关键词： Mapping

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Training of Physical Neural Networks

arXiv

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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A micromirror array with annular partitioning for high-speed random-access axial focusing

arXiv

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arXiv 2020年

作者： Ersumo, Nathan Tessema Yalcin, Cem Antipa, Nick Pégard, Nicolas Waller, Laura Lopez, Daniel Muller, Rikky University of California Berkeley - University of California San Francisco Graduate Program in Bioengineering United States Departement of Electrical Engineering & Computer Sciences University of California BerkeleyCA94720 United States Department of Applied Physical Sciences University of North Carolina at Chapel Hill Chapel HillNC27514 United States Chan Zuckerberg Biohub San FranciscoCA94158 United States Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States

Dynamic axial focusing functionality has recently seen widespread incorporation in microscopy, augmented/virtual reality (AR/VR), adaptive optics, and material processing. Yet the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such functionality. The varifocal tools that are the least burdensome to drive (ex: liquid crystal, elastomeric or optofluidic lenses) suffer from low (~100 Hz) refresh rates. Conversely, the fastest devices sacrifice either critical capabilities such as dwelling capacity (ex: acoustic gradient lenses or monolithic micromechanical mirrors) or low operating overhead (ex: deformable mirror arrays). Here, we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed, dwelling capacity and lightweight drive by employing low-rigidity micromirrors that exploit the robustness of defocusing phase profiles. Geometrically, the device consists of an 8.2 mm diameter array of piston-motion, 48 μm-pitch micromirror pixels that provide 2π phase shifting for wavelengths shorter than 1 100 nm with 10-90% settling in 64.8 μs (i.e. 15.44 kHz refresh rate). The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires less than 30 V/channel. Optical experiments demonstrated the array’s wide focusing range with a measured ability to target 29 distinct, resolvable depth planes. Overall, the features of the proposed array offer the potential for compact, straightforward methods of tackling bottlenecked applications including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR. Copyright © 2020, The Authors. All rights reserved.

关键词： Pixels

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Design and Implementation of OLED Driving and OPD Readout Circuitry for an Optical Vibration Sensor 17

Design and Implementation of OLED Driving and OPD Readout Ci...

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17th IEEE SENSORS Conference, SENSORS 2018

作者： Wu, Yi-Cheng Kao, Yung-Hua Chao, Paul C.-P. Wey, Chin-Long Sauter, Thilo Eka, Fitrah P. Pandey, Rajeev Department of Electrical Engineering National Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Chiao Tung University Hsinchu City Taiwan EECS International Graduate Program National Chiao Tung University Hsinchu City Taiwan Department for Integrated Sensor Systems Danube University Krems Austria Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu City Taiwan

ISBN: (纸本)9781538647073

This study presents an organic light-emitting diode (OLED) drive circuit and an organic photo detector (OPD) readout circuits for a vibration sensor. Vibration sensor consist of micro-electro-mechanical systems (MEMS), which are OLED and OPD needs to be integrated for many applications in automation or consumer electronics. The light flux is generated by an OLED located on top of the structure and the modulated light flux is measured by an OPD. The OLED drive circuit contains pulse width modulation (PWM) driver to control PWM output for optimization of voltage operation is designed in order to minimize power consumption efficiency of device. The readout circuit includes a trans-impedance amplifier (TIA) to convert incident light of OPD to voltage output, a 14-bit analog-to-digital converter (ADC) to extract amplitude of vibration level, circuit to generate clock for time-to-digital converter (TDC) which detect frequency of vibration sensor. The designed circuit has been successfully fabricated by using TSMC 0.18 μm CMOS process, the chip area is 1.012 mm2. The power consumption of chip is about 6.2 mW. The measured frequency of PWM is about 200 kHz with 50% duty cycle. The frequency range of the vibration can be sensed well as about 10 Hz1.5 kHz, and resulting in high SNR at frequencies under 60 Hz. © 2018 IEEE.

关键词： Electric power utilization

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Application of a Robust Relay Feedback Structure for TITO Processes

Application of a Robust Relay Feedback Structure for TITO Pr...

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IEEE Conference on Control Technology and Applications

作者： Moisés T. da Silva Péricles R. Barros Post-Graduate Program in Electrical Engineering Federal University of Campina Grande Campina Grande PB Brazil Electrical Engineering Department Federal University of Campina Grande Campina Grande PB Brazil

In this paper, a relay feedback structure which is robust to disturbances is applied to estimate multiple frequency points of processes with two-inputs two-outputs (TITO). The relay structure is composed of a block to remove static disturbance or drift followed by a relay. This block consists of a simple high-pass filter followed by a relay plus an integrator. Since the relay structure used is able to reject disturbance, during the relay feedback test, a step change and a periodic square wave with frequency lower than the ultimate frequency are applied at the process input. Thus, these frequencies information are used to estimate a FOPTD model in a wider frequency region. The proposed approach is illustrated using the Woody-Berry distillation column model. Simulation examples illustrate that the robust relay structure ensures a symmetric oscillation of the TITO process output, even under disturbances.

关键词： Application Robust Relay Feedback Structure TITO Processes

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