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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Saifullin, Talgat E. Grakhova, Elizaveta P. Ishmiyarov, Arsen A. Katerinkina, Ekaterina N. Letavin, Denis A. Kutluyarov, Ruslan V. Research Lab. "Sensor Systems Based on the Integrated Photonics Devices" Ufa University of Science and Technology Ufa Russia Telecommunication systems Dept. Ufa University of Science and Technology Ufa Russia Institute of Radio Electronics Information Technologies dept Ural Federal University Yekaterinburg Russia

ISBN: (纸本)9798350389234

The paper showcases experimental testing of a phased array antenna for connecting and tracking vehicles in Vehicle-to-Everything technology applications. The study involved measuring the performance of a microstrip half-octagon shaped emitter and a 14-element linear antenna array with a main lobe width of no more than 8 degrees. Both the emitter and antenna array were designed and manufactured using WL-CT350 laminate material. The antenna system operates within the frequency range of 5614 to 5943 MHz. To control the main lobe position of the antenna array, a beamforming circuit was developed, which consisted of a splitter and a phase control unit utilizing planar phase shifters. These phase shifters were controlled by Arduino Mega 2560 microcontrollers, which implemented machine-learning algorithms to determine the optimal service sector based on the location of the vehicle. Experimental tests were conducted in an anechoic chamber to demonstrate the antenna's ability to steer a focused radio beam across ten different sectors within a scanning range of 90 degrees. © 2024 IEEE.

关键词： Antenna lobes

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A Lightweight Robust Approach for Automatic Heart Murmurs and Clinical Outcomes Classification from Phonocardiogram Recordings

A Lightweight Robust Approach for Automatic Heart Murmurs an...

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2022 Computing in Cardiology, CinC 2022

作者： Lu, Hui Yip, Julia Beatriz Steigleder, Tobias Griesshammer, Stefan Heckel, Maria Jami, Naga Venkata Sai Jitin Eskofier, Bjoern Ostgathe, Christoph Koelpin, Alexander Chair of Electronics and Sensor Systems Brandenburg University of Technology Cottbus Germany Department of Palliative Medicine Universitätsklinikum Erlangen Erlangen Germany Institute of High-Frequency Technology Hamburg University of Technology Hamburg Germany Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (纸本)9798350300970

Cardiac auscultation provides an efficient and cost-effective way for cardiac disease pre-screening. The George B. Moody PhysioNet Challenge 2022 aimed to detect heart murmurs and clinical outcomes with heart sound recordings from multiple auscultation locations. Our team HearHeart proposed a lightweight convolutional neural network (CNN) to detect heart murmurs and a random forest model to classify clinical outcomes. 128 Melspectrogram features and wide features like the socio-demographic data and statistical features are extracted. Different techniques are employed to migrate the data imbalance and model the overfitting problem. We used two data augmentation methods, noise injection and spectrogram augmentation in time and frequency domain to increase the training samples and avoid overfitting during training. Besides, weighted loss functions are applied to both tasks to deal with data imbalance. In the end, we ensembled the models from cross-validation and used voting for the final classification. We achieved a murmur score of 0.791, and a clinical outcome score of 11731.64 on 5-fold cross-validation in the hidden validation set. While on the hidden test set, we achieved a murmur score of 0.780, and a clinical outcome score of 12110, placing our team 1st and 10th in the challenge tasks, respectively. © 2022 Creative Commons.

关键词： Cardiology

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Diaper Waste Recycling Towards a Triboelectric Nanogenerator Based Weather Station: An Innovative Solution for Sustainable Energy Generation

SSRN

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SSRN 2024年

作者： Basith, Sayyid Abdul Ramadoss, Ananthakumar Khandelwal, Gaurav Jacob, George Chandrasekhar, Arunkumar Nanosensors and Nanoenergy Lab Sensor Systems Lab Department of Sensor and Biomedical Technology School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Chennai600032 India Materials and Manufacturing Research Group James Watt School of Engineering University of Glasgow G128QQ United Kingdom Centre for Nanotechnology Research Vellore Institute of Technology Tamilnadu Vellore632014 India

The escalating concerns surrounding waste management worldwide and the pressing need for sustainable energy sources have prompted innovative solutions at the nexus of resource recycling and self-powered applications. Diapers are disposed of after use in modern society, and widely available diapers are not environmentally friendly, thus posing a significant threat to the environment. According to the United Nations Environment Programme (UNEP) 2023 report, around 250 million single-use diapers are thrown out daily across the globe. This research presents a pioneering approach to recycling super absorbent polymer (SAP) gels from waste diapers and using them alongside discarded baking sheets to fabricate a maraca-resembled Diaper Waste-based Triboelectric Nanogenerator (DW-TENG). The fabricated DW-TENG is demonstrated for developing a self-powered weather station, enabling real-time wind speed, humidity, and temperature monitoring. The manuscript details the intricate fabrication of the DW-TENG, elucidating design considerations, assembly processes, and the unique roles of SAP powder and baking sheets in the triboelectric energy generation mechanism. In addition, our study quantifies the potential global impact of diaper recycling on energy generation. Based on the calculations, a 4.72 MW power generation is expected yearly, considering the yearly diaper disposal rates. The work highlights the transformative impact of recycling practices for energy generation and represents a promising stride toward a circular and sustainable future. © 2024, The Authors. All rights reserved.

关键词： Energy conservation

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Experimental Testing of the Phased Array Antenna Beamsteering for the V2X Applications

Experimental Testing of the Phased Array Antenna Beamsteerin...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Talgat E. Saifullin Elizaveta P. Grakhova Arsen A. Ishmiyarov Ekaterina N. Katerinkina Denis A. Letavin Ruslan V. Kutluyarov Research Lab. “Sensor Systems Based on the Integrated Photonics Devices” Ufa University of Science and Technology Ufa Russian Federation Telecommunication systems dept Ufa University of Science and Technology Ufa Russian Federation Information Technologies dept. Ural Federal University Institute of Radio Electronics Ekatirenburg Russian Federation

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The paper showcases experimental testing of a phased array antenna for connecting and tracking vehicles in Vehicle-to-Everything technology applications. The study involved measuring the performance of a microstrip halfoctagon shaped emitter and a 14 -element linear antenna array with a main lobe width of no more than 8 degrees. Both the emitter and antenna array were designed and manufactured using WL-CT350 laminate material. The antenna system operates within the frequency range of 5614 to 5943 MHz. To control the main lobe position of the antenna array, a beamforming circuit was developed, which consisted of a splitter and a phase control unit utilizing planar phase shifters. These phase shifters were controlled by Arduino Mega 2560 microcontrollers, which implemented machine-learning algorithms to determine the optimal service sector based on the location of the vehicle. Experimental tests were conducted in an anechoic chamber to demonstrate the antenna’s ability to steer a focused radio beam across ten different sectors within a scanning range of 90 degrees.

关键词： Phased arrays Antenna measurements Microstrip antenna arrays Array signal processing Phase shifters Adaptive arrays Microstrip antennas

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Smart Maracas: An Innovative Triboelectric Nanogenerator for Earthquake Detection and Energy Harvesting

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SSRN 2023年

作者： Chandrasekhar, Arunkumar Basith, Sayyid Abdul Vivekananthan, Venkateswaran Khandelwal, Gaurav Maria Joseph Raj, Nirmal Prashant Purusothaman, Yuvasree Kim, Sang Jae Nanosensors and Nanoenergy Lab Sensor Systems Lab Department of Sensor and Biomedical Technology School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Center for Flexible Electronics Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh 522302 India James Watt South Building School of Engineering University of Glasgow G12 8QQ United Kingdom Energy Harvesting Research Group School of Physics and Astronomy SUPA University of St Andrews United Kingdom Department of Materials Science and Engineering The Pennsylvania State University University ParkPennsylvania16802 United States Nanomaterials and Systems Laboratory Faculty of Applied Energy Systems Major in Mechatronics Engineering Jeju National University Korea Republic of Jeju National University Jeju63243 Korea Republic of

In an era marked by a growing demand for sustainable energy solutions and resilient disaster management systems, the convergence of innovative technologies holds the promise of addressing multifaceted challenges. This manuscript explores the multifunctional capabilities of the "smart maracas", a novel triboelectric nanogenerator (TENG) designed to harvest mechanical energy and simultaneously serve as an earthquake sensor. The smart maracas is a striking example of the potential of TENGs to harness mechanical motion for practical applications. The device converts mechanical energy into electrical power through meticulous engineering, opening avenues for self-sustaining power sources in various domains. The manuscript outlines the device's structural design, working principle, and real-time applications, spanning bio-mechanical energy harvesting, vibrational energy scavenging, rotational energy harvesting, and a unique sensing application for door monitoring. A pivotal aspect of this research revolves around the smart maracas' role as an earthquake sensor. Rigorous experiments were conducted to assess the device's responsiveness to simulated seismic forces. Notably, a linear relationship with an R² value of 0.9989 was established between the voltage generated by the smart maracas and seismic acceleration. This remarkable correlation underscores the device's precision and reliability in detecting seismic events, opening doors for cost-effective earthquake monitoring solutions. © 2023, The Authors. All rights reserved.

关键词： Earthquakes

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A Lightweight Robust Approach for Automatic Heart Murmurs and Clinical Outcomes Classification from Phonocardiogram Recordings

A Lightweight Robust Approach for Automatic Heart Murmurs an...

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Computers in Cardiology (CinC)

作者： Hui Lu Julia Beatriz Yip Tobias Steigleder Stefan Grießhammer Maria Heckel Naga Venkata Sai Jitin Jami Bjoern Eskofier Christoph Ostgathe Alexander Koelpin Chair of Electronics and Sensor Systems Brandenburg University of Technology Cottbus Germany Institute of High-Frequency Technology Hamburg University of Technology Hamburg Germany Department of Palliative Medicine Universitätsklinikum Erlangen Erlangen Germany Machine Learning and Data Analytics (MaD) Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

关键词： Heart Training Frequency-domain analysis Feature extraction Data models Recording Convolutional neural networks

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The LPGBT PLL and CDR architecture, performance and see robustness

The LPGBT PLL and CDR architecture, performance and see robu...

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2019 Topical Workshop on electronics for Particle Physics, TWEPP 2019

作者： Biereigel, Stefan Kulis, Szymon Francisco, Rui Leitao, Pedro Leroux, Paul Moreira, Paulo Prinzie, Jeffrey CERN European Organisation for Nuclear Research Switzerland ESAT-ADVISE research lab KU Leuven university Belgium Electronics and Sensor Systems Brandenburg University of Technology Cottbus Germany

We present the design, architecture and experimental results of the low jitter Clock and Data Recovery (CDR) and Phase Locked Loop (PLL) circuit in the Low-Power Gigabit Transceiver (lpGBT) ASIC. This circuit includes a low noise radiation-tolerant integrated LC-oscillator with a nominal frequency of 5.12 GHz to support a 10.24 Gbps uplink and a 2.56 Gbps downlink CDR. This CDR employs a novel loop architecture with a high-speed feed forward loop stabilization technique. A test circuit was fabricated in a 65 nm CMOS technology and has been tested experimentally for correct operation in the foreseen radiation environment. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Phase locked loops

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Correction: contactless radar-based heart rate Estimation in palliative care- a feasibility study and possible use in symptom management

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BMC palliative care 2025年第1期24卷 113页

作者： Stefan G Grießhammer Anke Malessa Hui Lu Julia Yip Julie Leuschner Florian Christgau Nils C Albrecht Marie Oesten Thanh Truc Tran Robert Richer Maria Heckel Bjoern M Eskofier Alexander Koelpin Tobias Steigleder Christoph Ostgathe Department of Palliative Medicine Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany. stefan.griesshammer@uk-erlangen.de. Department of Palliative Medicine Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany. Institute of High-Frequency Technology Hamburg University of Technology (TUHH) Hamburg Germany. Chair of Electronics and Sensor Systems Brandenburg University of Technology Cottbus-Senftenberg Cottbus Germany. Machine Learning and Data Analytics Lab Friedrich-Alexander- Universität Erlangen-Nürnberg (FAU) Erlangen Germany.

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A Smart sensor Architecture for eHealth Applications

A Smart Sensor Architecture for eHealth Applications

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Ermanno Cordelli Giorgio Pennazza Marco Sabatini Marco Santonico Luca Vollero Computational Systems and Bioinformatics Lab Università Campus Bio-Medico di Roma Electronics for Sensor Systems Lab Università Campus Bio-Medico di Roma

The diffusion of cheap sensing and programmable hardware systems enables today the quick design of complex smart sensor devices, i.e. sensing systems equipped with computational and communication functionalities, ready to be included in IoT systems. In this paper we present a general software architecture for the implementation of such smart sensors. The goal of the proposed model is to provide a reference framework usable in multiple scenarios, suitable for the management of different sensors, and providing a general and standard interface and simple computational functionalities. The paper presents our model, examples of different smart sensors based on our model and the implementation, test and performance assessment of a simple analogue smart sensor. The implementation of the proposed model is available online as a template for the design and development of smart sensors equipped with updatable edge computing functionalities. The performance evaluation of our analogue smart sensor shows that the application of our model has limited overhead which is highly compensated by its flexibility.

关键词： Micromechanical devices Intelligent sensors Computer architecture Computational modeling Transducers Microcontrollers

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Detecting Respiratory Effort-Related Arousals in Polysomnographic Data Using LSTM Networks

Detecting Respiratory Effort-Related Arousals in Polysomnogr...

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Computers in Cardiology (CinC)

作者： Sven Schellenberger Kilin Shi Melanie Mai Jan P Wiedemann Tobias Steigleder Björn Eskofier Robert Weigel Alexander Kölpin Chair of Electronics and Sensor Systems Brandenburg University of Technology Cottbus Germany Institute for Electronics Engineering Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany Department of Palliative Care Universitätsklinikum Erlangen Erlangen Germany Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9781728109244

To diagnose sleep disorders, hours of sleep data from lots of different physiological sensors have to be analyzed. To do so, experts have to look through all the data which is time-consuming and error-prone. Automatic detection and classification of sleep related breathing disorders and arousals would significantly simplify this task. This years Physionet/CinC Challenge deals with this topic. This paper examines the use of a Long Short-Term Memory network for automatic arousal detection. On the test set, an AUPRC score of 0.14 was achieved.

关键词： Training Feature extraction Sleep Electroencephalography Physiology Electromyography Electrocardiography

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