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Annual IEEE Systems Conference

作者： Ryuji Nagazawa Haruto Ota Kien Nguyen Hiroo Sekiya Won-Joo Hwang Hiroyuki Torikai Mikio Hasegawa Department of Informatics Graduate School of Informatics Chiba University Chiba Japan Department of Biomedical Convergence Engineering Pusan National University Yangsan-si Korea Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Electrical Engineering Tokyo University of Science Tokyo Japan

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

The estimation of indoor occupancy rates has become increasingly important, especially for accident prevention at events and providing critical rescue information during disasters. However, conventional systems require significant effort and cost for installation, such as the need for dedicated servers and internet connectivity, making them difficult to deploy conveniently. This study proposes the design of an indoor occupancy estimation system based on the Wireless Brain-Inspired Computing (WiBIC) platform. By equipping each IoT device with neuron functionality, environmental data is compressed into spike signals directly at the source. These signals are processed using intelligent information processing techniques based on the principles of brain-inspired computing, enabling the estimation of indoor occupancy. The WiBIC platform's serverless architecture and low-power characteristics allow for easy and efficient deployment of the system in various indoor environments. The proposed system further emphasizes energy efficiency by incorporating FPGA implementation and the Asynchronous Pulse Code Multiple Access (APCMA) protocol. Experimental evaluations were conducted using data collected from illuminance sensors, human detection sensor, and current sensors to estimate occupancy rates. The results show that the system achieves practical accuracy, validating its effectiveness. The accuracy achieved is sufficient for practical applications, demonstrating the effectiveness of the proposed system.

关键词： Wireless communication Performance evaluation Wireless sensor networks Accuracy Neurons Estimation Information processing Sensor phenomena and characterization Internet of Things Servers

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A 4-Channel 0.23$mm^{2}$ Voltage-to-Time Converter AFE with $3.7\mu V_{rms}$ Noise and $480nW$ Galvanic Impulse Uplink

A 4-Channel 0.23$mm^{2}$ Voltage-to-Time Converter AFE with ...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Mehdi Bandali Morgan Riley Benjamin C. Johnson Dept. of Electrical and Computer Engineering Boise State University Boise ID USA Morgan Riley is with Biomedical Engineering Doctoral Program Boise State Universitvy Boise ID USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

We present a 4-channel 15 kS/s Voltage-to-Time Converter (VTC) analog front-end (AFE) with a $0.49\mu W$ impulse-based galvanic uplink for a peripheral nerve interface. Multiple, low-noise, high-data-rate channels are needed to sense compound action potentials and measure their conduction velocity as they propagate down a peripheral nerve. To achieve high energy efficiency for these constraints, the AFE encodes and transmits data with time-domain charge-balanced impulses through an implantable galvanic link. Each channel consists of an integrator with charge-based sampling and amplification for rapid multiplexing. A shared VTC encodes the amplitude-domain outputs of each integrator into differential time-domain impulses. Since the timing can be synchronized with stimulation, this AFE achieves instant artifact recovery after rail-to-rail stimulation events. We designed this AFE in a 180nm CMOS process, and the simulation results show an SNDR of 60dB and noise of $3.7\mu Vrms$ . Thanks to the new galvanic uplink protocol, this front-end only consumes $11.28\mu W$ including wireless data transmission for four channels.

关键词： Wireless communication Wireless sensor networks Protocols Simulation Noise Data communication Velocity measurement

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Matrix confinement modulates 3D spheroid sorting and burst-like collective migration

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Acta Biomaterialia 2024年 179卷 192-206页

作者： Cai, Grace Li, Xinzhi Lin, Shan-Shan Chen, Samuel J. Rodgers, Nicole C. Koning, Katherine M. Bi, Dapeng Liu, Allen P. Applied Physics Program University of Michigan Ann ArborMI United States Department of Physics Carnegie Mellon University PittsburghPA United States Institute of Molecular Medicine College of Medicine National Taiwan University Taipei Taiwan Department of Mechanical Engineering University of Michigan Ann ArborMI United States Cellular and Molecular Biology Program University of Michigan Ann ArborMI United States Department of Physics Northeastern University BostonMA United States Department of Biomedical Engineering University of Michigan Ann ArborMI United States Department of Biophysics University of Michigan Ann ArborMI United States

While it is known that cells with differential adhesion tend to segregate and preferentially sort, the physical forces governing sorting and invasion in heterogeneous tumors remain poorly understood. To investigate this, we tune matrix confinement, mimicking changes in the stiffness and confinement of the tumor microenvironment, to explore how physical confinement influences individual and collective cell migration in 3D spheroids. High levels of confinement lead to cell sorting while reducing matrix confinement triggers the collective fluidization of cell motion. Cell sorting, which depends on cell-cell adhesion, is crucial to this phenomenon. Burst-like migration does not occur for spheroids that have not undergone sorting, regardless of the degree of matrix confinement. Using computational Self-Propelled Voronoi modeling, we show that spheroid sorting and invasion into the matrix depend on the balance between cell-generated forces and matrix resistance. The findings support a model where matrix confinement modulates 3D spheroid sorting and unjamming in an adhesion-dependent manner, providing insights into the mechanisms of cell sorting and migration in the primary tumor and toward distant metastatic sites. Statement of significance: The mechanical properties of the tumor microenvironment significantly influence cancer cell migration within the primary tumor, yet how these properties affect intercellular interactions in heterogeneous tumors is not well understood. By utilizing calcium and calcium chelators, we dynamically alter collagen-alginate hydrogel stiffness and investigate tumor cell behavior within co-culture spheroids in response to varying degrees of matrix confinement. High confinement is found to trigger cell sorting while reducing confinement for sorted spheroids facilitates collective cell invasion. Notably, without prior sorting, spheroids do not exhibit burst-like migration, regardless of confinement levels. This work establishes that matrix confin

关键词： Tumors

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Tracking of Thermal Ablation Using US B-Mode and Elastography

Tracking of Thermal Ablation Using US B-Mode and Elastograph...

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Ultrasonics Symposium (LAUS), IEEE UFFC Latin America

作者： Edgar Taka Gonzalo Garay Wagner Pereira Guillermo Cortela Biomedical Engineering Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Ultrasound Laboratory University of the Republic Montevideo Uruguay

ISBN: (数字)9798350349085

ISBN: (纸本)9798350349092

High Intensity Focused Ultrasound (HIFU), application minimizes the need for invasive surgery. This focused emitted energy can generate enough heat to cause irreversible protein denaturation of cells, making a thermal ablation injury. Tissue damages depend on the thermal dose promoted by HIFU. US elastography can provide information about changes in the stiffness of the affected volume (change on shear elastic modulus - μ). This fact allows an evaluation of the degree and extent of tissue ablation, which helps the conduct of treatment. Experimental setup consists of a high-power single element focused transducer to generate an intense ultrasonic field. A commercial elastography Supersonic Image Aixplorer with a SL10-2 transducer was used both to generate the US image and to emit the elastography pushes. Ex-vivo bovine skeletal muscle tissue (SMT) as sample was used with the fibers arranged along their length and the US transducer parallel to the fibers. The HIFU transducer placed perpendicular to the muscle fibers, applying energy four shots with PRF=60 s, and duty of 50 %. The elastography area was 28×28 mm2 and area of interest (ROI) was Ø10 mm. After HIFU shots, elastography evaluation at the focal point revealed changes in tissue elasticity (elastic modulus) that initially drops, then increases only after the fourth shot. That is when thermal ablation occurs (protein denaturation of the affected tissues).

关键词： Proteins Heating systems Transducers Ultrasonic imaging Tissue damage Surgery Muscles

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Predicting Potential Fires in Indonesia by Analyzing VIIRS Night Data during 2018 – 2022

Predicting Potential Fires in Indonesia by Analyzing VIIRS N...

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International Symposium on Grids and Clouds 2023 in conjunction with HEPiX Spring Workshop, ISGC and HEPiX 2023

作者： Suhardiman, Basuki Hermanto, Benni Rio Directorate of Information Technology Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Master’s Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

Forest fires have been detected to occur in Indonesia since 1998. The forest fires mostly resulted from human activities in order to expand their land, especially oil palm lands. Once a land clearing is carried out, it covers areas of tens to hundreds of thousands of hectares. The peak of the forest fires occurred in 2015 when almost half of the world was affected especially by smoke from the fire. In 2011, VIIRS (Visible Infrared Imaging Radiometer Suite) was launched by NOAA (National Oceanic and Atmospheric Administration). In their collected data, there is data on Indonesia issued daily for areas in the islands of Java, Sumatra, Kalimantan, Sulawesi, and the general areas of Indonesia. The VIIRS night data shows the possibility of some burning at night. We need to identify burnings that potentially become fires, which are potentially producing smoke. It is important to observe areas where large-scale forest fires frequently occur, so the authority could do some prevention beforehand. Using existing data released per day per area, we utilize an ANN (Artificial Neural Network) to identify a potential fire. The processes start with data cleaning and processing, Neural Network creation, and finally ANN training and testing. By using the ANN prediction model with the MinMax Scaler, we choose variables Temperature, Radiant Heat Intensity, and Source Footprint. Simulations are made to show the ranges of these variables that predict the possibility of’flame’ status occurring. We conclude that the ANN method can give more accurate results than the existing classification. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Fires

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Fully Elman Neural Network: A Novel Deep Recurrent Neural Network Optimized by an Improved Harris Hawks Algorithm for Classification of Pulmonary Arterial Wedge Pressure

TechRxiv

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TechRxiv 2023年

作者： Fetanat, Masoud Stevens, Michael Jain, Pankaj Hayward, Christopher Meijering, Erik Lovell, Nigel H. Graduate School of Biomedical Engineering UNSW Sydney Australia Cardiology Department St Vincent’s Hospital Sydney Australia Victor Chang Cardiac Research Institute Sydney Australia School of Medicine UNSW Sydney Australia Graduate School of Biomedical Engineering School of Computer Science and Engineering UNSW Sydney Australia

Heart failure (HF) is one of the most prevalent life-threatening cardiovascular diseases in which 6.5 million people are suffering in the USA and more than 23 million worldwide. Mechanical circulatory support of HF patients can be achieved by implanting a left ventricular assist device (LVAD) into HF patients as a bridge to transplant, recovery or destination therapy and can be controlled by measurement of normal and abnormal pulmonary arterial wedge pressure (PAWP). While there are no commercial long-term implantable pressure sensors to measure PAWP, real-time non-invasive estimation of abnormal and normal PAWP becomes vital. In this work, first an improved Harris Hawks optimizer algorithm called HHO+ is presented and tested on 24 unimodal and multimodal benchmark functions. Second, a novel fully Elman neural network (FENN) is proposed to improve the classification performance. Finally, four novel 18-layer deep learning methods of convolutional neural networks (CNNs) with multilayer perceptron (CNN-MLP), CNN with Elman neural networks (CNN-ENN), CNN with fully Elman neural networks (CNN-FENN), and CNN with fully Elman neural networks optimized by HHO+ algorithm (CNN-FENN-HHO+) for classification of abnormal and normal PAWP using estimated HVAD pump flow were developed and compared. The estimated pump flow was derived by a non-invasive method embedded into the commercial HVAD controller. The proposed methods are evaluated on an imbalanced clinical dataset using 5-fold cross-validation. The proposed CNN-FENN-HHO+ method outperforms the proposed CNN-MLP, CNN-ENN and CNN-FENN methods and improved the classification performance metrics across 5-fold cross-validation with an average sensitivity of 79%, accuracy of 78% and specificity of 76%. The proposed methods can reduce the likelihood of hazardous events like pulmonary congestion and ventricular suction for HF patients and notify identified abnormal cases to the hospital, clinician and cardiologist for emergency actio

关键词： Left ventricular assist devices

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Effect of High-Flow Nasal Cannula on Capnogram Waveform for Studying Non-Invasive Human Ventilation

Effect of High-Flow Nasal Cannula on Capnogram Waveform for ...

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biomedical engineering Conference (NBEC), IEEE National

作者： Tjokorda Gde Tirta Nindhia Nursyasa Aqila Mohammed Julia Jais Jamaludin Pagupathi Devandran Nabiel Rafa Angel Bhaswara Thalitakum Hutajulu M.B. Malarvili Study Program of Mechanical Engineering Engineering Faculty Udayana University Jimbaran Bali Indonesia School of Biomedical and Health Science Engineering Universiti Teknologi Malaysia Johor Malaysia

This study is conducted to compare three different cannulas; all purpose nasal cannula with 2 prongs, nasal pressure cannula with 2 prongs and single port nasal cannula which are commonly used for capnography device. These cannulas or sometimes called as nasal prong is connected to the device via nose of subjects studied. This procedure is a part of respiratory study to monitor lungs condition and breathing pattern. This study involves 47 subject where the exhaled breath was recorded to make comparison and choose the most suitable and effective for capnography waveform analysis. Utilizing Statistical Package for the Social Sciences (SPSS) to find the normality of all the nasal cannula data based on capnogram waveform. The result from the analysis shows that the frequency distribution for type 1 nasal cannula which is also known as all-purpose nasal cannula had the feature values closer to the standard value. Our study proofs that all purpose nasal cannula sampling is the most suitable to capture exhaled breath for monitoring respiratory conditions.

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Organic-inorganic hybrid piezotronic bipolar junction transistor for pressure sensing

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Microsystems & Nanoengineering 2024年第3期10卷 229-244页

作者： Emad Iranmanesh Zihao Liang Weiwei Li Congwei Liao Shunyu Jin Chuan Liu Kai Wang Shengdong Zhang Charalampos Doumanidis Gehan A.J.Amaratunga Hang Zhou Guangdong Provincial Key Laboratory of In-Memory Computing Chips School of Electronic and Computer EngineeringPeking University Shenzhen Graduate SchoolShenzhen 518055P.R.China School of Mechanical Engineering Guangdong Technion-Israel Institute of TechnologyShantou 515063P.R.China State Key Laboratory of Microelectronics Device and Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029P.R.China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of ChinaHefei 23000PR China School of Electronics and Information Technology Sun Yat-sen UniversityNo.132 East Waihuan RoadGuangzhou 510006P.R.China Department of Mechanical Aerospaceand Biomedical EngineeringUniversity of South AlabamaShelby Hall3128MobileAL 36688USA Electrical Engineering Division Department of EngineeringUniversity of Cambridge9 JJ Thomson AvenueCambridge CB30FAUnited Kingdom Zhejiang University International CampusHainingChina

With the rapid development of the Internet of Things(IoTs),wearable sensors are playing an increasingly important role in daily monitoring of personal health and *** signal-to-noise-ratio has become the most critical performance factor to *** enhance it,on the one hand,good sensing materials/devices have been employed;on the other hand,signal amplification and noise reduction circuits have been ***,most of these devices and circuits work in an active sampling mode,requiring frequent data acquisition and hence,entailing high-power *** this scenario,a flexible and wearable event-triggered sensor with embedded signal amplification without an external power supply is of great ***,we report a flexible two-terminal piezotronic n-p-n bipolar junction transistor(PBJT)that acts as an autonomous and highly sensitive,current-and/or voltage-mediated pressure *** PBJT is formed by two back-to-back piezotronic diodes which are defined as emitter-base and collectorbase *** force exertion on the emitter side,as a result of the piezoelectric effect,the emitter-base diode is forward biased while the collector-base diode is reverse *** to the inherent BJT amplification effect,the PBJT achieves record-high sensitivities of 139.7 kPa^(-1)(current-based)and 88.66 kPa^(-1)(voltage-based)in sensing *** PBJT also has a fast response time of<110 ms under exertion of dynamic stimuli ranging from a flying butterfly to a gentle finger ***,the PBJT advances the state of the art not only in terms of sensitivity but also in regard to being self-driven and autonomous,making it promising for pressure sensing and other IoT applications.

关键词： bipolar transistor IoT

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Learning Priority Rule using Neural Network for Job Shop Scheduling with regard to Weighted Tardiness 10

Learning Priority Rule using Neural Network for Job Shop Sch...

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10th International Conference on Leading Edge Manufacturing Technologies in 21st Century, LEM 2021

作者： Eguchi, Toru Hayashi, Eitetsu Murayama, Takeshi Graduate School of Advanced Science and Engineering Hiroshima University Japan Graduate School of Biomedical and Health Sciences Hiroshima University Japan

This paper deals with job shop scheduling using priority rules. Because of its computational efficiency, applying priority rules is the most practical approach for real-world practical scheduling problems. However, effective priority rules have been developed by trial and error, and scheduling performance generated by simple priority rules is not always good. This paper examines learning priority rules for job shop scheduling with regard to weighed tardiness using the data obtained from optimal or near-optimal schedules. The optimal or near-optimal schedules are generated using an IP solver or a genetic algorithm. Numerical experiments show that it is possible to learn effective priority rules if training data of enough quality are used for learning. © 2021 The Japan Society of Mechanical Engineers.

关键词： Genetic algorithms

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Application of ShearWave™ Elastography to evaluate heat-induced changes in the Young’s modulus of fresh bovine muscle: a preliminary study

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Research on biomedical engineering 2021年第2期37卷 157-169页

作者： Costa-Júnior, José Francisco Silva de Oliveira, Viviane Bastos De Matheo, Lucas Lobianco de Albuquerque Pereira, Wagner Coelho Brazilian Air Force Academy PirassunungaSP Brazil Biomedical Engineering Program - COPPE Federal University of Rio de Janeiro Rio de JaneiroRJ Brazil

Purpose: The purpose of this study was to investigate the variation in the Young’s modulus (E) of bovine muscle samples as a function of temperature change generated by therapeutic ultrasound using ShearWave™ Elastography. Methods: Initially, the bovine muscle was heated via therapeutic ultrasound with a frequency of 3 MHz, nominal intensity of 2 W·cm−2, and application time of 2 min. Immediately following cessation of therapeutic irradiation, an E image was recorded and the stiffness was measured in circular area positioned at six depths (from 0.4 to 2.9 cm) in the center of the region of interest. Next, an E image was recorded every minute for the first 5 min. Over the next 30 min, an image was recorded every 5 min. Finally, an image was acquired 60 min after cessation of therapeutic irradiation. In the second test, the same experimental procedure was performed 60 min later with the physiotherapy equipment configured with a 10-min application time. Finally, during the ultrasonic irradiation of a new bovine muscle sample, the physiotherapeutic transducer was applied in a circular motion and with an angular velocity of 3.6 ± 0.3 rad·s−1. Results: In the first test, the bovine muscle E decreased from 212.2 ± 32.8 to 80.1 ± 13.8 kPa at 0.4-cm depth, as the temperature increased from 18.2 to 44.9 °C. This effect was reversed when the temperature decreased. In the second test, denaturation and cell death occurred, so an artifact appeared in the elastographic image and the ShearWave™ Elastography did not capture the E from the depth of 1.9 cm. Conclusion: We confirmed that it is possible to use ShearWave™ Elastography to investigate heat-induced changes in the elastic modulus of biological tissue. © 2021, Sociedade Brasileira de Engenharia Biomedica.

关键词： Cell death

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