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15th German Microwave Conference (GeMiC)

作者： Tchameni, Michelle R. Schiffer, Christian Luecken, Volker Diewald, Andreas R. Schroeder, Udo Univ Appl Sci Lab Radar Technol & Opt Syst Trier Germany IEE SA Basics & Math Models Bissen Luxembourg

ISBN: (纸本)9783982039732

Non-contact methods for monitoring vital signs, like the heartbeat, offer convenience and hygiene benefits but face challenges in achieving accurate and reliable results. In this paper, we propose a novel approach for estimating heartbeat peaks in radar signals using a cross-correlation method based on a template function. By applying this correlative method to phase-extracted radar signals recorded from breathing individuals, cardiac information is isolated and heartbeat peaks are identified over time. Experiments using a 24 GHz FMCW radar system have been conducted, comparing the results with synchronized ECG data. Our approach not only demonstrates the effectiveness of non-contact cardiac monitoring but also showcases its potential for providing valuable insights into improved vital signs monitoring. This work showcases the potential of radar technology for real-time vital sign monitoring and healthcare applications.

关键词： FMCW radar time measurement vital signs monitoring cross correlation detection algorithm

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Application of peak based-Bayesian statistical method for isotope identification and categorization of depleted, natural and low enriched uranium measured by LaBr3:Ce scintillation detector

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第10期55卷 3913-3923页

作者： Yucel, Haluk Tuzuner, Selin Saatci Massey, Charles Ankara Univ Inst Nucl Sci Besevler 10 Yil Campus TR-06100 Ankara Turkiye Int Atom Energy Agcy IAEA Vienna Int Ctr VIC Div Nucl Secur Detect Sci & Technol Dept Nucl Safety & SecurNucl Secur Mat Outside Re POB 100 A-1400 Vienna Austria

Todays, medium energy resolution detectors are preferably used in radioisotope identification devices(RID) in nuclear and radioactive material categorization. However, there is still a need to develop or enhance << automated identifiers >> for the useful RID algorithms. To decide whether any material is SNM or NORM, a key parameter is the better energy resolution of the detector. Although masking, shielding and gain shift/stabilization and other affecting parameters on site are also important for successful operations, the suitability of the RID algorithm is also a critical point to enhance the identification reliability while extracting the features from the spectral analysis. In this study, a RID algorithm based on Bayesian statistical method has been modified for medium energy resolution detectors and applied to the uranium gamma-ray spectra taken by a LaBr3:Ce detector. The present Bayesian RID algorithm covers up to 2000 keV energy range. It uses the peak centroids, the peak areas from the measured gamma-ray spectra. The extraction features are derived from the peak-based Bayesian classifiers to estimate a posterior probability for each isotope in the ANSI library. The program operations were tested under a MATLAB platform. The present peak based Bayesian RID algorithm was validated by using single isotopes(Am-241,Co- 57, Cs-137,Mn-54, Co-60), and then applied to five standard nuclear materials(0.32-4.51% at.U-235), as well as natural U- and Th-ores. The ID performance of the RID algorithm was quantified in terms of F-score for each isotope. The posterior probability is calculated to be 54.5-74.4% for U-238 and 4.7-10.5% for U-235 in EC-NRM171 uranium materials. For the case of the more complex gamma-ray spectra from CRMs, the total scoring (S-T) method was preferred for its ID performance evaluation. It was shown that the present peak based Bayesian RID algorithm can be applied to identify U-235 and U-238 isotopes in LEU or natural U-Th samples if a medium ene

关键词： LaBr 3 (Ce) Scintillation detectors Gamma ray spectrum Isotope identification Bayesian statistical method detection algorithm Nuclear security U-235

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On the Performance of RIS-assisted Networks with HQAM

On the Performance of RIS-assisted Networks with HQAM

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Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)

作者： Oikonomou, Thrassos K. Tyrovolas, Dimitrios Tegos, Sotiris A. Diamantoulakis, Panagiotis D. Sarigiannidis, Panagiotis Liaskos, Christos Karagiannidis, George K. Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece Univ Western Macedonia Dept Elect & Comp Engn Kozani 50100 Greece Univ Ioannina Comp Sci Engn Dept Ioannina Greece Lebanese Amer Univ LAU Artificial Intelligence & Cyber Syst Res Ctr Beirut Lebanon

ISBN: (纸本)9798350344998;9798350345001

In this paper, we investigate the application of hexagonal quadrature amplitude modulation (HQAM) in reconfigurable intelligent surface (RIS)-assisted networks, specifically focusing on its efficiency in reducing the number of required reflecting elements. Specifically, we present analytical expressions for the average symbol error probability (ASEP) and propose a new metric for conditioned energy efficiency, which assesses the network's energy consumption while ensuring the ASEP remains below a certain threshold. Additionally, we introduce an innovative detection algorithm for HQAM constellations, which demonstrates a substantial reduction in computational complexity. Finally, our study reveals that HQAM significantly enhances both the ASEP and energy efficiency compared to traditional quadrature amplitude modulation (QAM) schemes.

关键词： HQAM Reconfigurable Intelligent Surfaces (RISs) Energy Efficiency Average Symbol Error Probability detection algorithm

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Improved YOLOv5s Network for Multi-scale safety Helmet detection 11

Improved YOLOv5s Network for Multi-scale safety Helmet Detec...

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11th International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Zhao, Lin Zhang, Dalong Liu, Yifa Guo, Junli Shi, Zhanhao Zhengzhou Univ Inst Cyberspace Secur Zhengzhou Peoples R China Shandong Agr & Engn Univ Inst Informat Sci & Engn Jinan Peoples R China

ISBN: (纸本)9781665469890

A safety helmet detection model has been developed based on the original YOLOv5 network model, which addresses the problems of low accuracy and poor robustness for small targets in complex natural environments. Firstly, we add the layer to detect small targets, resulting in the shallow detection scale, which makes the model better able to detect small objects. Secondly, introducing the transformer self-attention module in the backbone network, which can extract global features with rich semantic information, improves the detection performance. Thirdly, The Ghost module demands fewer parameters and less computational complexity for features to be generated, reducing the pressure to learn model parameters. Finally, the loss function EIoU Loss is applied to improve the detection accuracy and accelerate the convergence of the model. Experimental results show that the average accuracy means (MAP), accuracy rates (P), and recall rates (R) of the improved algorithm are increased by 1.2%, 0.4%, and 0.2%, respectively while reducing the model size, which proves that the algorithm is more effective than the original algorithm at detecting safety helmets.

关键词： safety helmet small targets YOLOv5 algorithm detection algorithm

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3D Camera and Pulse Oximeter for Respiratory Events detection

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第1期25卷 181-188页

作者： Coronel, Carmina Wiesmeyr, Christoph Garn, Heinrich Kohn, Bernhard Wimmer, Markus Mandl, Magdalena Glos, Martin Penzel, Thomas Kloesch, Gerhard Stefanic-Kejik, Andrijana Boeck, Marion Kaniusas, Eugenijus Seidel, Stefan AIT Austrian Inst Technol GmbH A-1210 Vienna Austria Kepler Univ Hosp Dept Neurol 2 A-4020 Linz Austria Oberosttereich Gesundheitsholding Salzkammergut Klinikum Voklabruck A-4840 Vocklabruck 4840 Austria Adv Sleep Res GmbH Berlin Germany Charite Univ Med Berlin Interdisciplinary Sleep Med Ctr D-10117 Berlin Germany Saratov NG Chernyshevskii State Univ Saratov 410012 Russia Med Univ Vienna Dept Neurol A-1090 Vienna Austria Vienna Univ Technol TU Wien Inst Electrodynam Microwave & Circuit Engn A-1040 Vienna Austria

Objective: The purpose of this study was to derive a respiratory movement signal from a 3D time-of-flight camera and to investigate if it can be used in combination with SpO2 to detect respiratory events comparable to polysomnography (PSG) based detection. Methods: We derived a respiratory signal from a 3D camera and developed a new algorithm that detects reduced respiratory movement and SpO2 desaturation to score respiratory events. The method was tested on 61 patients synchronized 3D video and PSG recordings. The predicted apnea-hypopnea index (AHI), calculated based on total sleep time, and predicted severity were compared to manual PSG annotations (manualPSG). Predicted AHI evaluation, measured by intraclass correlation (ICC), and severity classification were performed. Furthermore, the results were evaluated by 30-second epoch analysis, labelled either as respiratory event or normal breathing, wherein the accuracy, sensitivity, specificity and Cohens kappa were calculated. Results: The predicted AHI scored an ICC r 0.94 (0.90 0.96 at 95 confidence interval, p < 0.001) compared to manualPSG. Severity classification scored 80 accuracy, with no misclassification by more than one severity level. Based on 30-second epoch analysis, the method scored a Cohens kappa 0.72, accuracy 0.88, sensitivity 0.80, and specificity 0.91. Conclusion: Our detection method using SpO2 and 3D camera had excellent reliability and substantial agreement with PSG-based scoring. Significance: This method showed the potential to reliably detect respiratory events without airflow and respiratory belt sensors, sensors that can be uncomfortable to patients and susceptible to movement artefacts.

关键词： Cameras Three-dimensional displays Sleep apnea Sensors Biomedical measurement Manuals Biomedical computing detection algorithm biomedical signal processing biomedical monitoring

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Modeling of infrared scattering signatures of liquid and solid aerosol clouds 25

Modeling of infrared scattering signatures of liquid and sol...

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Conference on Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXV

作者： Furstenberg, Robert Shabaev, Andrew Huffman, Tyler J. Kendziora, Christopher A. McGill, R. Andrew US Naval Res Lab 4555 Overlook Ave SW Washington DC 20375 USA

ISBN: (纸本)9781510674301;9781510674318

The size of particles typically present in aerosol clouds are in the range of 0.1-10 mu m, which is within one order of magnitude of the infrared (IR) wavelengths in the molecular "fingerprint" region (approx. 6-12 mu m). This length scale is also close to the optical absorption depth for materials of interest. Consequently, IR scattering signatures of aerosols differ from those associated with reflectance from surfaces of bulk materials. Furthermore, the shape of particles is also a factor that affects IR scattering spectra. Accordingly, both aerosol particle size and morphology must be considered in the development of accurate models and reliable detection algorithms. This report describes recent advances concerning modeling of IR scattering signatures of micron-sized spherical particles (found in liquid aerosols) and irregularly-shaped particles ( found in solid aerosols). In our model, spherical particles are modeled using Mie scattering theory while non-spherical ones require numerical modeling, using finite-difference time-domain (FDTD) solvers. The model inputs involve particle optical constants (complex index of refraction - n and k), aerosol concentration, particle size (diameter) distribution and various shape parameters (for solid aerosols only). Our model addresses two detection scenarios: one where the signatures consist of back-scattered light only and the other where the portion of the forward scattered light which (diffusely) reflects off background surfaces is also collected. We discuss the effect of particle size and shape distribution on the IR signatures of aerosol clouds. We report on efforts to optimize our model such that a large number of spectra can be generated quickly and efficiently, which is a requirement for use in detection algorithms, both for training and usage. We also present preliminary results on machine learning approaches to develop detection algorithms capable of detecting aerosol clouds that have variable IR signatures due to

关键词： Aerosols IR scattering Mie scattering Modeling detection algorithm Standoff detection Synthetic data Machine learning

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Novel ventricular tachyarrhythmia detection enhancement detects undertreated life-threatening arrhythmias

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HEART RHYTHM O2 2022年第1期3卷 70-78页

作者： Wilkoff, Bruce L. Sterns, Laurence D. Katcher, Michael S. Upadhyay, Gaurav Seizer, Peter Kang, Chaoyi Rhude, Jennifer Davis, Kevin J. Fischer, Avi Cleveland Clin Robert & Suzanne Tomsich Sydell & Arnold Miller Family Heart Vasc & Thorac Dept Cardiovasc Med Cleveland OH 44106 USA Royal Jubilee Hosp Div Cardiol Victoria BC Canada Massachusetts Gen Brigham Salem Hosp Salem MA USA Univ Chicago Med Pritzker Sch Med Ctr Arrythmia Care Chicago IL 60637 USA Ostalbklinikum Aalen Dept Cardiol & Angiol Aalen Germany Abbott Sylmar CA USA

BACKGROUND Ventricular tachyarrhythmias (VTA) with low and varying signal amplitudes and morphologies may not be successfully identified utilizing traditional implantable cardioverter-defibrillator algorithms. OBJECTIVE Develop and validate a novel algorithm (VF Therapy Assurance, VFTA) to improve detection and timely delivery of high-voltage therapy (HVT) for these arrhythmias. METHODS Arrhythmia detection was simulated on recorded VTA electrograms (EGMs) utilizing Abbott's *** database. EGMs where an HVT occurred only when VFTA was enabled, or where VFTA provided an HVT > 30 seconds earlier than without VFTA, were readjudicated with physician review. As VFTA never prevents detection or therapy, EGMs where VFTA did not activate or alter HVT were not adjudicated. RESULTS Among 564,353 recorded VTA EGMs from 20,000 devices, VFTA altered HVT in 105 EGMs from 67 devices. Physician adjudication determined that 81.9% (86/105) of these EGMs were true undertreated VTA episodes and would have received appropriate HVT with VFTA enabled. Furthermore, 65% of the episodes (56/86) were ventricular fibrillation, were polymorphic, did not selfterminate during the recording window, or were not amenable antitachycardia pacing. Of those, 87.5% (49/56) would not have elicited HVT without VFTA. Overall, VFTA provided new or earlier appropriate HVT in 0.27% (53/20,000) of devices with an increase in inappropriate HVT in 0.07% (14/20,000) devices. CONCLUSION The VFTA algorithm successfully identifies VTA missed by traditional detection algorithms, owing to undersensed ventricular signals resulting in the rate falling below the programmed detection rate. The use of VFTA increases the likelihood of delivering life-saving HVT.

关键词： detection algorithm Implantable cardioverter-defibrillator High-voltage therapy Undersensing Ventricular tachyarrhythmia

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Constructing Arbitrary-Shape Scatterer Models for Analysis of Optical Spectral Data 25

Constructing Arbitrary-Shape Scatterer Models for Analysis o...

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Conference on Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXV

作者： Shabaev, A. Furstenberg, R. Huffman, T. Kendziora, C. A. McGill, R. A. Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)9781510674301;9781510674318

This study examines an application of simulated single-particle spectra for analysis of IR scattering by suspensions of small particles. Particularly, the case of aerosols comprised of solid particles is explored. These particles vary in size, shape, and orientation with respect to the incident light. Single particle scattering is numerically calculated and the simulated data is surveyed. The scattering of particle ensembles is considered and a prototype system consisting of a sparse distribution of particles is evaluated.

关键词： infrared spectroscopy Mie scattering spectral analysis detection algorithm standoff detection synthetic data aerosols

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Hybrid Quantum-Classical Neural Network For Diagnosis of Autism Spectrum Disorder 5

Hybrid Quantum-Classical Neural Network For Diagnosis of Aut...

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2024 International Conference on Quantum Computing and Engineering

作者： Kim, Anthony Bergen Cty Acad Dept Phys Hackensack NJ 07601 USA

ISBN: (纸本)9798331541378

Autism Spectrum Disorder (ASD) affects almost 3% of children worldwide, and due to the complexity and heterogeneity of ASD, accurate and efficient diagnosis is difficult to achieve. Qualitative diagnosis based on observation of behavior has a major risk of misdiagnosis. Meanwhile, the most efficient quantitative algorithms in modern literature are similarly problematic, with misdiagnosis rates of up to 44%, execution times of over 7 hours, and all algorithms are purely classical. This project is an implementation of a hybrid quantum classical neural network (HQCNN) to improve upon classical neural networks by applying quantum algorithms while only using fMRI data. The proposed neural network implements a variational quantum deflation (VQD) algorithm, an extension of the variational quantum eigensolver (VQE) framework for finding the first k excited states of a Hamiltonian. This study shows that the VQD algorithm produces eigenvalues effectively equivalent to the classically computed ones, and the general algorithm has up to 70% accuracy with less than an hour of execution time, a major improvement from the 56% accuracy and over 7 hour runtimes of most classical algorithms. With the development of quantum computing technology, the VQD algorithm will continue to be optimized. Furthermore, the HQCNN is generally scalable, and only requires fMRI data for analysis. This study shows a faster and more accurate diagnosis of ASD using quantum algorithms.

关键词： neural network quantum network variational quantum eigensolver autism spectrum disorder detection algorithm IBM quantum lab

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Research on Extracting Potential DC Arc Fault Features Based on Data Mining Methods 68

Research on Extracting Potential DC Arc Fault Features Based...

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68th IEEE Holm Conference on Electrical Contacts (HOLM)

作者： Wu, Hancong Ge, Shiwei Zhou, Yingqing Meng, Yu Li, Xingwen Chen, Silei Wang, Xiaoshuai Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian Peoples R China Zhejiang Tengen Elect Co Ltd Jiaxing Peoples R China Xian Univ Technol Sch Elect Engn Xian Peoples R China

ISBN: (纸本)9798350342468

Due to the great difference in the properties of cable materials used in DC lines, the time-frequency features of DC arc fault that are easy to extract are difficult to take into account various cable materials. Therefore, it is necessary to mine potential arc fault features of different materials from the arc fault signals to meet the needs of distinguishing fault state from normal state. Firstly, the current waveforms of DC arc fault are obtained from the experiments of different electrode materials, four type of data mining methods are used to mine the potential DC arc fault information in the current signals to extract the DC arc fault detection features. Then, based on the principles of the proposed data mining methods, the mining results are compared to obtain the optimal mining features for various electrode materials. Finally, the construction of DC arc fault detection algorithm for different electrode materials is realized based on SVM model. The detection results indicate that the potential arc fault features can increase the accuracy of arc fault detection.

关键词： DC arc fault data mining detection algorithm PV system

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