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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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Remote platform for lung monitoring based on Electrical Impedance Tomography measurements 19

Remote platform for lung monitoring based on Electrical Impe...

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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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Features Selection for Force Myography Based Hand Gesture Recognition

Features Selection for Force Myography Based Hand Gesture Re...

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IEEE Jordan International Joint Conference on Electrical engineering and Information technology (JEEIT)

作者： Malak Fora Manar Jaradat Bilel Ben Atitallah Congyu Wu Olfa Kanoun Department of Systems Science and Industrial Engineering State University of New York Binghamton United States of America Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany

Hand gesture recognition has a wide range of applications in robotics, game control, and in communication with the deaf and people with trouble hearing. Recognition of American sign language (ASL) hand gestures has been extensively studied in the literature. Multiple data sources and different features extracted from these data were used to classify ASL gestures. In this study, we examined the features used in previous research to determine the minimum number of features that can give an accurate classification of ASL hand gestures. Force myography (FMG) signals captured for ASL gestures of digits 0–9 were used in this analysis of the selected features. Extracted features from the raw FMG signals were applied to K-nearest neighbors (KNN) and Extreme Learning Machine (ELM) to evaluate their efficiency in identifying the corresponding hand gesture. Results show that using only the mean value as input to classification algorithms yields the highest classification accuracy. The classification accuracy was 90% and 96.9% for KNN and ELM, respectively.

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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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sensory Modulation of Gait Balance in Human Locomotion: A Neuromusculoskeletal Modeling Study

Sensory Modulation of Gait Balance in Human Locomotion: A Ne...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Vahid Firouzi Omid Mohseni Oskar Von Stryk Andre Seyfarth Maziar A. Sharbafi Laufiabor Locomotion Laboratory Centre for Cognitive Science Technische Universität Darmstadt Darmstadt Germany Department of Computer Science Technische Universität Darmstadt Darmstadt Germany Department of Electrical Engineering and Information Technology Measurement and Sensor Technology Group Technische Universität Darmstadt Darmstadt Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Maintaining balance during gait relies on intricate sensorimotor processes, engaging compensatory muscle actions and reflex modulations to counteract external perturbations and prevent falls. This study aims to identify possible muscle reflex parameters that are modulated to maintain balance in the face of external perturbations through neuromechanical simulations. A reflex-based neuromusculoskeletal model is utilized and subjected to four distinct sagittal-plane perturbation types (two whole body linear momentum (WBLM) and two whole body angular momentum (WBAM) applied to the torso at five different timings. Analysis of the correlation between reflex parameters and their impact on perturbation recovery indicates seven key reflex parameters as strong candidates for modulating gait balance. For WBLM perturbations, our findings indicated the contribution of both stance and swing phase strategies by modulating Soleus positive force feedback and Iliopsoas activity. In WBAM perturbations, Vasti positive force feedback emerged as a crucial reflex contributing to knee joint stability in backward torque perturbation. The study suggests leg adjustments as a primary strategy for recovering from forward torque perturbations, achieved by modulating hamstring positive force feedback and increasing Iliopsoas muscle activation during the swing phase. These findings provide new insights into the underlying mechanisms of reflex modulation and its significance in balancing and recovering from perturbations during locomotion.

关键词： Torso Legged locomotion Torque Perturbation methods Force feedback Modulation Muscles Robot sensing systems Stability analysis Timing

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Bioimpedance Spectroscopy Improves Insulin Absorption measurement Method: A Feasibility In-Vivo Study Based on Saline

Bioimpedance Spectroscopy Improves Insulin Absorption Measur...

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Impedance Spectroscopy (IWIS), International Workshop on

作者： Pasquale Arpaia Ornella Cuomo Fortuna Galdieri Olfa Kanoun Francesca Mancino Nicola Moccaldi Hanen Nouri Department of Electrical Engineering and Information Technology (DIETI) University of Naples Federico II Naples Italy Department of Neuroscience and Reproductive and Odontostomatological Sciences University of Naples Federico II Naples Italy Department of Engineering for Innovation University of Salento Lecce Italy Department of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany Measurement and Sensor Technology Chemnitz University of Technology Chemnitz Germany

A feasibility of a method based on impedance spectroscopy and equivalent circuit approach to assess insulin bioavailability is realized in-vivo by exploiting a vehicle as insulin emulator. The vehicle bioavailability is indirectly assessed by measuring the local impedance variation due to the drug disappearance from the injection volume. An experimental campaign was carried out on a healthy subject to acquire bioimpedance spectra in the frequency range of [1-100] kHz during controlled administration of vehicle. A modified Tregear’s model is proposed to model the investigated phenomena. In particular, a linear relationship between the resistor used for modelling the hypodermis and the amount of vehicle injected was found. The method based on equivalent circuit exhibited an improvement in sensitivity (70 %) with respect to the approach based on percentage variation of impedance magnitude.

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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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Pluggable multitask diffractive neural networks based on cascaded metasurfaces

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Opto-Electronic Advances 2024年第2期7卷 23-31页

作者： Cong He Dan Zhao Fei Fan Hongqiang Zhou Xin Li Yao Li Junjie Li Fei Dong Yin-Xiao Miao Yongtian Wang Lingling Huang Beijing Engineering Research Center of Mixed Reality and Advanced Display Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of ChinaSchool of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China Institute of Modern Optics Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network TechnologyNankai UniversityTianjin 300350China Department of Physics and Optoelectronics Faculty of ScienceBeijing University of TechnologyBeijing 100124China Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing 100191China Beijing Aerospace Institute for Metrology and Measurement Technology Beijing 100076China

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering *** has been considered as one of the powerful tools in promoting the fields of imaging processing and object ***,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems *** push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal *** the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared ***,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

关键词： optical neural networks diffractive deep neural networks cascaded metasurfaces

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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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A Polynomial Curve Fitting of Bioimpedance Data for Parameter Estimation of the Cole-Impedance Model

A Polynomial Curve Fitting of Bioimpedance Data for Paramete...

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Impedance Spectroscopy (IWIS), International Workshop on

作者： Mitar Simić Todd J. Freeborn Fernando Seoane Olfa Kanoun Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia Department of Electrical and Computer Engineering University of Alabama Tuscaloosa Alabama USA Department of Clinical Science Intervention and Technology Karolinska Institutet Stockholm Sweden Department of Clinical Physiology Karolinska University Hospital Stockholm Sweden Department of Medical Technology Karolinska University Hospital Stockholm Sweden Department of Textile Technology University of Borås Borås Sweden Chair of Measurement and Sensor Technology Chemnitz University of Technology Chemnitz Germany

ISBN: (数字)9798331507626

ISBN: (纸本)9798331507633

The rapid advancements in technology have led to a significant increase in the number of portable and wearable devices, driving the demand for efficient and reliable methods for bioimpedance analysis suitable for compact embedded-hardware platforms. In this paper, a method for complex bioimpedance curve fitting with the second order polynomial is derived and validated with synthetic datasets as well as experimentally obtained bioimpedance. The main advantages are independence of the frequency data (which results in reduction of the dataset by 1/3), and the elimination of dependence on characteristic frequency estimation. More importantly, it is possible to estimate parameters of the Cole-impedance model (R∞, R1 and $a$ ) from obtained polynomial coefficients in a very efficient micro controller-based realization. Raspberry Pi Pico-based execution time was around 9 ms for 256 datapoints, which is more than 1000 times shorter when compared to the relevant references. Moreover, here presented method required 22.05% less flash memory and 43.64% less SRAM. Experimental bioimpedance of human volunteer was obtained using the ImpediMed SFB7 device. Relative deviations of the estimated values of the Cole-impedance model with the proposed method and BioImp software were around 5%. The proposed method's suitability for applications involving portable and cost-effective bioimpedance measurement and processing units was demonstrated by achieving a mean relative deviation of less than 2 % between the fitted and measured bioimpedance of pear slices over a three-hour period.

关键词： Spectroscopy Biological system modeling Bioimpedance Polynomials Frequency estimation Data models Software Curve fitting Wearable devices Synthetic data

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