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19th IEEE International Multi-Conference on systems, Signals and Devices, SSD 2022

作者： Hafsa, Mariem Bchir, Ons Bader, Oumaima Ben Amara, Najoua Essoukri Kanoun, Olfa Université de Sousse Ecole Nationale d' Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany University of Sousse National EngineeringSchool of Sousse Computer Engineering Department Sousse Tunisia

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Electrical Impedance Tomography (EIT) is a non-invasive, non-ionizing medical imaging method that has been on the rise in biomedical field. For lung monitoring, electrodes are attached around patient's thorax. Several boundary voltages are measured on the surface of these electrodes, which result from the injection of a small alternating current. Based on these measurements, the inner conductivity distribution of the patient's lung can be determined and used for monitoring and diagnosis. EIT can be used for extended periods of time, which makes it ideal for continuous monitoring, especially for intensive care patients. Several stationary EIT-based lung monitoring platforms have been developed for this reason. In this work, however, we propose a web and cloud-based monitoring solution that allows remote access for the doctors and medical staff. The implemented solution provides real-time, continuous monitoring as well as a visualization of extracted lung features that aid the diagnosis, © 2022 IEEE.

关键词： Electrodes

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Post-processing for Thorax Imaging based on Electrical Impedance Tomography using Deep Learning 19

Post-processing for Thorax Imaging based on Electrical Imped...

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19th IEEE International Multi-Conference on systems, Signals and Devices, SSD 2022

作者： Haddad, Hamdi Hafsa, Mariem Bader, Oumaima Ben Amara, Najoua Essoukri Kanoun, Olfa University of Sousse National Engineering School of Sousse Computer Engineering Department Sousse Tunisia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Electrical Impedance Tomography (EIT) for lung monitoring is a non-ionizing medical imaging technique that is on the rise in biomedical engineering research in the recent decades. It reflects the inner conductivity distribution of biological tissue based on boundary voltages that appear on the electrodes, after injecting an alternating current of a certain amplitude into the human thorax. EIT, however, has a low spatial resolution and is susceptible to errors caused by electrode movement and poor contact quality, which can lead to the generation of blurry images. Post-processing imaging techniques can, in fact, be a beneficial tool for fine-tuning this issue. The focus of this work is to implement a U-NET multi-class semantic segmentation deep learning model to post-process reconstructed EIT thorax images to enhance its characteristics such as contrast, resolution... The model has achieved an overall accuracy of 72.43%. The resulting reconstructed images have reflected the physical changes during the breathing cycle, have deleted artifacts, and have made the shapes of the lungs and heart clearer. © 2022 IEEE.

关键词： Electrodes

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A Robust Convolutional Neural Network for Electrical Impedance Tomography Image Reconstruction

A Robust Convolutional Neural Network for Electrical Impedan...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Mariem Hafsa Kailun Lin Najoua Essoukri Ben Amara Olfa Kanoun Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany Department of Mechanical Engineering Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Electrical Impedance Tomography (EIT) is a widely used non-invasive imaging method in a variety of application fields. Most EIT applications require an accurate and high-quality reconstructed inner conductivity distribution image. Several solutions have been developed for reliable EIT image reconstruction, but they still suffer from limitations such as low spatial resolution and inconsistency in real measurement scenarios. In this paper, we propose using physically based, extensive simulation data to train a Convolutional Neural Network (CNN) model with 5-fold cross-validation. We generate a set of 10.000 data samples, which builds a comprehensive basis for its training and ensures reliability and generalization capability. The quantitative evaluation demonstrates good reconstruction performance, achieving a Mean Squared Error (MSE) of 0.0151 and an Image Correlation Coefficient (ICC) of 0.97 in the best-performing fold for the simulated data. The method's effectiveness is particularly validated through experimental measurements, where it significantly outperforms traditional Gauss-Newton reconstruction in terms of image sharpness and boundary definition. The algorithm is robust and easily applicable for EIT imaging.

关键词： Training Image quality Electrical impedance tomography Voltage measurement Impedance measurement Imaging Robustness Convolutional neural networks Spatial resolution Image reconstruction

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A Low-Power Voltage-Controlled Current Source for Wearable Bioimpedance Applications

A Low-Power Voltage-Controlled Current Source for Wearable B...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Cherif Ouni Bilel Ghoul Hamdi Al-Shaikhli Jumana Ma'touq Ahmed Fakhfakh Olfa Kanoun Measurement and Sensor Technology Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Centre of Sfax National School of Electronics and Telecommunications of Sfax Sfax Tunisia Department of Biomedical Engineering School of Applied Medical Sciences (SAMS) German Jordanian University Amman Jordan

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Bioimpedance spectroscopy (BIS) has gained prominence as a non-invasive diagnostic tool for characterizing biological tissues, offering critical insights into physiological and pathological conditions. At the core of BIS systems lies the Voltage-Controlled Current Source (VCCS), which is essential for ensuring precise and stable current delivery across varying load conditions. This paper investigates a modified dual Howland configuration with load-in-loop compensation designed for low-power applications. By operating at reduced supply voltages (5V) instead of the conventional ±12V, the proposed design addresses the limitations of existing VCCS circuits in portable and wearable medical devices. The study evaluates the performance of the proposed VCCS circuit in a wide range of frequencies (50 kHz to 1 MHz), load conditions (1000 to 18kO), and input signal types (sine, multi-sine, and sine-sweep). Key findings demonstrate stable current output, efficient power consumption, and robust performance at reduced power levels, making it suitable for bioimpedance applications requiring portable operation.

关键词： Performance evaluation Spectroscopy Pathology Power demand Medical devices Circuits Bioimpedance Physiology Wearable devices Voltage control

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Full-Waveform Modeling for Time-of-Flight measurements based on Arrival Time of Photons

arXiv

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arXiv 2022年

作者： Fink, Maximilian Schardt, Michael Baier, Valentin Wang, Kun Jakobi, Martin Koch, Alexander W. Technical University of Munich Department of Electrical and Computer Engineering Institute for Measurement Systems and Sensor Technology Munich80333 Germany Blickfeld GmbH Munich80339 Germany

Modern LiDAR sensors find increasing use in safety-critical applications. Therefore, highly accurate modeling of the system’s behavior under demanding environmental conditions is necessary. In this paper, we present a modular structure to accurately simulate the amplified raw detector signal of a direct time-of-flight LiDAR system for coaxial transmitter-receiver optics. Our model describes, a measurement system based on standard optical components and a detector able of converting single photons to an electrical signal. To verify the model’s predictions, single-point measurements for targets of different reflectivity at defined distances were performed. Statistical analysis shows an R-squared value greater than 0.990 for simulated and measured signal amplitude levels. Noise modeling shows good accordance with the performed measurements for different target irradiance levels. The presented results have a guiding significance in the modeling of the complex signal processing chain of LiDAR systems, as it enables the prediction of key parameters of the system early in the development process. Hence, unnecessary costs by design flaws can be mitigated. The modular structure allows easy adaption for arbitrary LiDAR systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical radar

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Fast processing and classification of epileptic seizures based on compressed EEG signals

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Computers in Biology and Medicine 2025年 184卷 109346-109346页

作者： Djemal, Achraf Kallel, Ahmed Yahia Ouni, Cherif El Baccouch, Rihem Bouchaala, Dhouha Kammoun Feki, Fatma Charfi Triki, Chahnez Fakhfakh, Ahmed Kanoun, Olfa Professorship Measurement and Sensor Technology Chemnitz University of Technology Chemnitz Germany Child Neurology Department Hedi Chaker Sfax University Hospital and Research Laboratory LR19ES15 University of Sfax Sfax Tunisia Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Centre of Sfax National School of Electronics and Telecommunications of Sfax Sfax3021 Tunisia

The diagnosis of epilepsy based on visual inspection of electroencephalogram (EEG) signals is inherently complex and prone to error, even for physicians, mainly due to the large number of signals involved and the variability between individuals. These same challenges make the development of portable epilepsy diagnostic systems for everyday use difficult. Key obstacles include the immense complexity of signal processing and the inherent ambiguity in accurately classifying disease. For these reasons, we propose in this paper the deployment of compressive sensing to condense EEG signals while preserving relevant information, allowing seizure classification based on systematically selected features of the reconstructed signals. Based on a dataset comprising EEG recordings from 13 epileptic patients with various seizure types, we explore the deployment of the discrete cosine transform (DCT) and random matrix multiplication for compression ratios ranging from 5% to 70%, balancing data reduction with signal fidelity. Following the extraction of relevant features, selection was performed based on mutual information and a correlation matrix to preserve only the most relevant features for analysis. For classification, following a comparison of adequate machine learning models, XGBoost is chosen as it realizes a classification accuracy of 98.78%. The CS method was implemented on an STM32 microcontroller and a Raspberry Pi for reconstruction and classification, to demonstrate feasibility as an embedded system. At 70% compression, significant improvements have been observed: 70% file size reduction, 84% decrease in transmission time (from 2518.532s to 400.392s), and substantial energy savings (e.g., from 11.5±0.707 mWh to 4.5±0.707 mWh for Patient 12). Thereby, the signal quality was maintained with PSNR of 16.15±3.98 and Pearson correlation coefficient of 0.68±0.15. The proposed system highlights the potential for efficient, portable, real-time epilepsy diagnosis systems that a

关键词： Cosine transforms

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Quantitative phase imaging through an ultra-thin lensless fiber endoscope

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Light(Science & Applications) 2022年第8期11卷 1843-1852页

作者： Jiawei Sun Jiachen Wu Song Wu Ruchi Goswami Salvatore Girardo Liangcai Cao Jochen Guck Nektarios Koukourakis Juergen W.Czarske Laboratory of Measurement and Sensor System Technique(MST) TU DresdenHelmholtzstrasse 1801069DresdenGermany Competence Center for Biomedical Computational Laser Systems(BIOLAS) TU DresdenDresdenGermany State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentsTsinghua University100084BeijingChina Institute for Integrative Nanosciences IFW DresdenHelmholtzstraße 2001069DresdenGermany Max Planck Institute for the Science of Light&Max-Planck-Zentrum für Physik und Medizin 91058ErlangenGermany Cluster of Excellence Physics of Life TU DresdenDresdenGermany Institute of Applied Physics TU DresdenDresdenGermany

Quantitative phase imaging(QPI)is a label-free technique providing both morphology and quantitative biophysical information in ***,applying such a powerful technique to in vivo pathological diagnosis remains ***-core fiber bundles(MCFs)enable ultra-thin probes for in vivo imaging,but current MCF imaging techniques are limited to amplitude imaging *** demonstrate a computational lensless microendoscope that uses an ultra-thin bare MCF to perform quantitative phase imaging with microscale lateral resolution and nanoscale axial sensitivity of the optical path *** incident complex light field at the measurement side is precisely reconstructed from the far-field speckle pattern at the detection side,enabling digital refocusing in a multi-layer sample without any mechanical *** accuracy of the quantitative phase reconstruction is validated by imaging the phase target and hydrogel beads through the *** the proposed imaging modality,three-dimensional imaging of human cancer cells is achieved through the ultra-thin fiber endoscope,promising widespread clinical applications.

关键词： fiber phase thin

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Improved Particle Swarm Optimization Algorithm for EIT Image Reconstruction

Improved Particle Swarm Optimization Algorithm for EIT Image...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Oumayma Kahouli Mariem Hafsa Hiba Hellara Imed Eddine Bennour Najoua Essoukri Ben Amara Olfa Kanoun Department of Computer Science National Engineering School of Sousse University of Sousse Sousse Tunisia Laboratory of Advanced Technology and Intelligent Systems National Engineering School of Sousse Laboratory of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Center of Sfax University of Sfax Tunisia

Electrical Impedance Tomography (EIT) is a clinical imaging technique that gained a lot of attention because it is a non-invasive and radiation-free method. To obtain an inner image of the domain under study, a specific number of electrodes is attached to the surface of the object, then through a pair of electrodes, a low alternative current is injected. Potentials produced by this injection are measured with the remaining electrodes. Based on these boundary voltages measurements, the inner conductivity can be inferred, thus the image is reconstructed. The focus of this paper is to overcome the major weakness of the EIT image reconstruction technique, which is the low spatial resolution of the reconstructed images caused by the non-linearity of EIT inverse problem. This paper considers this problem, an optimization problem that is solved using the Particle Swarm Optimization (PSO) meta-heuristic. A variant of the standard PSO is proposed and validated, first on a circular mesh composed of 686 triangles then on a lung mesh composed of 2707 triangles.

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Lensless Near-Field Imaging with Enhanced Deep Super-Resolution Towards Real-Time Intraoperative Tumor Classification

Lensless Near-Field Imaging with Enhanced Deep Super-Resolut...

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Imaging systems and Applications, ISA 2022

作者： Czarske, J.W. Wang, T. Wu, J. Uckermann, O. Galli, R. Kuschmierz, R. Laboratory of Measurement and Sensor Systems TU Dresden Helmholtzstrasse 18 Dresden01069 Germany TU Dresden Dresden Germany State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision Instruments Tsinghua University Beijing100084 China Department of Neurosurgery University Hospital Carl Gustav Carus TU Dresden Dresden Germany Division of Medical Biology Department of Psychiatry Faculty of Medicine University Hospital Carl Gustav Carus TU Dresden Germany Department of Medical Physics and Biomedical Engineering Faculty of Medicine Carl Gustav Carus TU Dresden Germany

A deep learning assisted fiber endoscopy is proposed. Resolutions beyond physical limitations are achieved, resulting in increased tumor diagnostic accuracy. This method is promising for rapid minimal-invasive intraop... 详细信息

ISBN: (纸本)9781557528209

关键词： Tumors

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Mapping the optical dielectric response of isolated monolayer MoS2by push-broom microspectroscopy 2

Mapping the optical dielectric response of isolated monolaye...

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Unconventional Optical Imaging II 2020

作者： Dong, Xingchen Köhler, Michael H. Wang, Kun Jakobi, Martin Koch, Alexander W. Institute for Measurement Systems and Sensor Technology Department of Electrical and Computer Engineering Technical University of Munich Arcisstraße 21 Munich80333 Germany

ISBN: (数字)9781510634756

ISBN: (纸本)9781510634749

Two-dimensional van der Waals materials are attractive for photonics and optoelectronics due to distinctive layerdependent optical properties. Optical properties based on light-matter interactions have been revealed by modern imaging and spectroscopy techniques. Hyperspectral imaging microscopy working in line-scan mode (push-broom microspectroscopy) can provide abundant spectral information covering a large area compared to conventional spectroscopy techniques, with a higher acquisition speed than point-scan techniques such as atomic force microscopy and Raman imaging microscopy. This contribution studies in-depth the reconstruction of 3D datacubes and the extraction of optical responses of the sample. Monolayer MoS2, a subclass of semiconducting two-dimensional materials, is fabricated by the mechanical exfoliation method on the SiO2/Si substrate with an oxide thickness of 285 nm. The isolated monolayer MoS2 is observed and identified by a conventional optical microscope. The custom-built push-broom microspectroscope is utilized to scan the region of interest, with the whole spectrum of every line recorded at each frame. The spectral information of every point is collected and 3D spectral data sets are reconstructed for feature extraction and property analysis. To realize the thickness mapping of flakes, linear unmixing is employed to calculate the abundance of isolated monolayer MoS2 on the SiO2/Si substrate, improving flake identification performances. The characteristic spectrum of monolayer MoS2 is acquired by averaging the spectrum from the monolayer MoS2 flake. Furthermore, the optical dielectric response is further analyzed by Kramers-Kronig constrained analysis and Fresnel-law-based analysis. The optical dielectric function is calculated and compared based on the refractive index and medium thickness. This detailed analysis of optical dielectric responses highlights the feasibility of push-broom microspectroscopy for two-dimensional materials characterizat

关键词： Refractive index

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