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Lu G, Wang J, Zhou R, Xie Z, Yuan Y, Huang L, Yeow JTW. Terahertz communication: detection and signal processing. NANOTECHNOLOGY 2024; 35:352002. [PMID: 38768574 DOI: 10.1088/1361-6528/ad4dad] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 05/20/2024] [Indexed: 05/22/2024]
Abstract
The development of 6 G networks has promoted related research based on terahertz communication. As submillimeter radiation, signal transportation via terahertz waves has several superior properties, including non-ionizing and easy penetration of non-metallic materials. This paper provides an overview of different terahertz detectors based on various mechanisms. Additionally, the detailed fabrication process, structural design, and the improvement strategies are summarized. Following that, it is essential and necessary to prevent the practical signal from noise, and methods such as wavelet transform, UM-MIMO and decoding have been introduced. This paper highlights the detection process of the terahertz wave system and signal processing after the collection of signal data.
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Affiliation(s)
- Guanxuan Lu
- Advanced Micro-/Nano- Devices Lab, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Jiaqi Wang
- Advanced Micro-/Nano- Devices Lab, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Rui Zhou
- Advanced Micro-/Nano- Devices Lab, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Zhemiao Xie
- Advanced Micro-/Nano- Devices Lab, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Yifei Yuan
- Advanced Micro-/Nano- Devices Lab, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Lin Huang
- Advanced Micro-/Nano- Devices Lab, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - John T W Yeow
- Advanced Micro-/Nano- Devices Lab, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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Rusanen M, Korkalainen H, Gretarsdottir H, Siilak T, Olafsdottir KA, Töyräs J, Myllymaa S, Arnardottir ES, Leppänen T, Kainulainen S. Self-applied somnography: technical feasibility of electroencephalography and electro-oculography signal characteristics in sleep staging of suspected sleep-disordered adults. J Sleep Res 2024; 33:e13977. [PMID: 37400248 DOI: 10.1111/jsr.13977] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/05/2023] [Accepted: 06/12/2023] [Indexed: 07/05/2023]
Abstract
Sleep recordings are increasingly being conducted in patients' homes where patients apply the sensors themselves according to instructions. However, certain sensor types such as cup electrodes used in conventional polysomnography are unfeasible for self-application. To overcome this, self-applied forehead montages with electroencephalography and electro-oculography sensors have been developed. We evaluated the technical feasibility of a self-applied electrode set from Nox Medical (Reykjavik, Iceland) through home sleep recordings of healthy and suspected sleep-disordered adults (n = 174) in the context of sleep staging. Subjects slept with a double setup of conventional type II polysomnography sensors and self-applied forehead sensors. We found that the self-applied electroencephalography and electro-oculography electrodes had acceptable impedance levels but were more prone to losing proper skin-electrode contact than the conventional cup electrodes. Moreover, the forehead electroencephalography signals recorded using the self-applied electrodes expressed lower amplitudes (difference 25.3%-43.9%, p < 0.001) and less absolute power (at 1-40 Hz, p < 0.001) than the polysomnography electroencephalography signals in all sleep stages. However, the signals recorded with the self-applied electroencephalography electrodes expressed more relative power (p < 0.001) at very low frequencies (0.3-1.0 Hz) in all sleep stages. The electro-oculography signals recorded with the self-applied electrodes expressed comparable characteristics with standard electro-oculography. In conclusion, the results support the technical feasibility of the self-applied electroencephalography and electro-oculography for sleep staging in home sleep recordings, after adjustment for amplitude differences, especially for scoring Stage N3 sleep.
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Affiliation(s)
- Matias Rusanen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Henri Korkalainen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Heidur Gretarsdottir
- Reykjavik University Sleep Institute, School of Technology, Reykjavik University, Reykjavik, Iceland
- Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Tiina Siilak
- Reykjavik University Sleep Institute, School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Kristin Anna Olafsdottir
- Reykjavik University Sleep Institute, School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Juha Töyräs
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Science Service Center, Kuopio University Hospital, Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Sami Myllymaa
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Erna Sif Arnardottir
- Reykjavik University Sleep Institute, School of Technology, Reykjavik University, Reykjavik, Iceland
- Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Timo Leppänen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Samu Kainulainen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
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UFS-LSTM: unsupervised feature selection with long short-term memory network for remote sensing scene classification. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-021-00660-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano. ADVANCES IN HUMAN-COMPUTER INTERACTION 2021. [DOI: 10.1155/2021/6483003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
The history of medicine shows that myocardial infarction is one of the significant causes of death in humans. The rapid evolution in autonomous technologies, the rise of computer vision, and edge computing offers intriguing possibilities in healthcare monitoring systems. The major motivation of the work is to improve the survival rate during a cardiac arrest through an automatic emergency recognition system under ambient intelligence. We present a novel approach to chest pain and fall posture-based vital sign detection using an intelligence surveillance camera to address the emergency during myocardial infarction. A real-time embedded solution persuaded from “edge AI” is implemented using the state-of-the-art convolution neural networks: single shot detector Inception V2, single shot detector MobileNet V2, and Internet of Things embedded GPU platform NVIDIA’s Jetson Nano. The deep learning algorithm is implemented for 3000 indoor color image datasets: Nanyang Technological University Red Blue Green and Depth, NTU RGB + D dataset, and private RMS dataset. The research mainly pivots on two key factors in creating and training a CNN model to detect the vital signs and evaluate its performance metrics. We propose a model, which is cost-effective and consumes low power for onboard detection of vital signs of myocardial infarction and evaluated the metrics to achieve a mean average precision of 76.4% and an average recall of 80%.
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