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Highly Efficient IPT Transmitter Circuit Based on a Novel Enhanced Class B Amplifier for Consumer Applications

IEEE Transactions on Consumer Electronics 2025年

作者： Adawy, Abdallah Bouattour, Ghada Ibbini, Mohammed Kanoun, Olfa Technische Universität Chemnitz Professorship Measurement and Sensor Technology Chemnitz Germany Leuphana University of Lüneburg Measurement and Sensor Technology in Production Engineering Lüneburg Germany Jordan University of Science and Technology Irbid Jordan

Designing an efficient Inductive Power Transfer (IPT) system involves significant challenges. On the transmitter side, it is essential to provide the transmitter coil with an alternating current (AC) operating within the kHz-MHz frequency range, which is generally generated by inverters linked to an intermediate direct current (DC) bus. While methods such as Zero Voltage Switching (ZVS) and Zero Current Switching (ZCS) can effectively minimize losses, they frequently lead to increased design complexity and voltage distortion. This paper introduces a compact and highly efficient transmitter circuit, with dimensions of 40 x 60 mm, utilizing an enhanced Class B AC-AC amplifier. This amplifier incorporates an operational amplifier with adjustable gain to deliver a trapezoidal waveform to the gates of the connected MOSFETs. The specific waveform effectively reduces crossover distortion and enhances the overall efficiency of the converter. Experimental results indicate that the efficiency of the proposed circuit surpasses 98%, which is suitable for powering small to medium load levels. © 1975-2011 IEEE.

关键词： Operational amplifiers

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Robust real-time model parameter extraction in inductive spectroscopy based on Artificial Neural Networks

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IFAC-PapersOnLine 2025年第1期59卷 121-126页

作者： Frank Wendler Ahmed Yahia Kallel Jeannette Boll Birgit Awiszus Till Clausmeyer Sebastian Härtel Olfa Kanoun Professorship Measurement and Sensor Technology Chemnitz University of Technology Reichenhainer Str. 70 09126 Chemnitz Germany Professorship Forming Technology Chemnitz University of Technology Reichenhainer Str. 70 09126 Chemnitz Germany Chair of Hybrid Manufacturing Brandenburg University of Technology Konrad-Wachsmann-Allee 17 03046 Cottbus Germany

Impedance spectrum modelling requires parameter extraction by solving the corresponding inverse identification problem, for which both gradient-based and stochastic methods have been proposed. Stochastic methods are more robust and have a lower risk of getting trapped in local minima but need a long calculation time. In this study, we examine the implementation of artificial neural networks (ANNs) in solving inverse identification problems in inductance spectroscopy, contrasting their performance with robust stochastic methods. In order to overcome the shortage of a representative amount of experimental data, we propose to use the analytically based model to generate accurate enough labelled data for the training process of the ANN. The artificial data have been structured in a homogeneous and tightly spaced grid in the parameter space, thus supporting the model’s generalisation and suppressing overfitting. ANNs with varied degrees of complexity have been investigated by modifying the number of neurons and evaluated by training and comparison with stochastic parameter extraction methods. The investigation concludes that, for the presented application in inductive spectroscopy, the neural networks can provide comparable parameter extraction results with a relative deviation of 0.03 % of the parameter value and a significant reduction in runtime from 60 s to 8 ms.

关键词： Frequency domain identification measurement instrumentation Data-fusion Process observation parameter estimation Induction spectroscopy Machine learning Neural networks

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Simulation Study of Surface Acoustic Wave sensors for Soil Parameter Monitoring

Simulation Study of Surface Acoustic Wave Sensors for Soil P...

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

作者： Zhenyu Wu Tianqi Lu Ammar Al-Hamry Anurag Adiraju Ge Shi Siqi Liu Swaranpreet Kaur Surinder Singh Olfa Kanoun Professorship of Measurement and Sensor Technology Chemnitz University of Technology Chemnitz Germany Department of Physics Sant Longowal Institute of Engineering & Technology Longowal Sangrur Punjab India Department of Electronics and Communication Engineering Sant Longowal Institute of Engineering & Technology Longowal Sangrur Punjab India

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Soil parameters such as moisture, pH, and salinity are essential for agricultural productivity and environmental management. However, traditional monitoring methods, while accurate, are time-consuming and lack real-time capabilities, limiting their suitability for precision agriculture. Surface acoustic wave (SAW) sensors offer a promising alternative due to their high sensitivity, robustness, and real-time monitoring capabilities. In this study, a SAW sensor was designed and simulated using COMSOL Multiphysics to detect soil moisture. The sensor design employs a lithium tantalate piezoelectric substrate and interdigitated transducers (IDTs), with graphene oxide (GO) as a functional coating to enhance sensitivity. Simulation results showed that at a GO coating thickness of 350 nm, the sensor achieved the highest frequency shift at 70% relative humidity, demonstrating optimal sensitivity. Additionally, dynamic response analysis revealed that as humidity increased from 0% to 100%, the response time grew nonlinearly from 2.5 seconds to 42.2 seconds. The simulation results of the nonlinear growth trend of the optimal coating thickness and response time provide a clear direction for practical humidity sensor design optimization, dynamic environmental adaptation analysis, and future experimental validation and development. This study provides a theoretical foundation for the development of SAW-based soil sensors and outlines future directions for expanding functionality to monitor pH and salinity. Future work will focus on transitioning to 3D FEM models, experimental validation, and advanced material development. Fabricating actual sensors and validating simulations with experimental data will ensure reliability, supporting the broader application of SAW sensors in precision agriculture and environmental management.

关键词： Temperature sensors Precision agriculture Solid modeling Sensitivity Simulation Surface acoustic waves Humidity sensors Finite element analysis Monitoring

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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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Microcontroller-Based Implementation of the DRT for Bioimpedance Spectroscopy

Microcontroller-Based Implementation of the DRT for Bioimped...

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

作者： Ayoub Trabelsi Nour Ammar Cherif Ouni Ahmed Yahia Kallel Ahmed Fakhfakh Salwa Sahnoun Nabil Derbel Olfa Kanoun Professorship Measurement and Sensor 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 Technopole of Sfax Ons City Tunisia Control and Energy Management Laboratory School of Sfax University of Sfax Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Bio-Impedance Spectroscopy (BIS) is a non-invasive method for characterizing biological systems. Converting impedance spectra into the distribution of relaxation times (DRT) allows the separation of overlapping effects without relying on equivalent circuits as impedance models. This transformation significantly reduces signal processing time from several seconds to milliseconds. Therefore, there is a significant need for embedded solutions that implement DRT in microcontroller-based systems. Following a detailed evaluation of various methodologies, NNLS combined with Tikhonov regularization was selected for its ability to achieve an 83.33% reduction in execution time. This paper presents a novel approach for implementing this optimized DRT extraction in STM32-based embedded systems. The novel implementation achieves processing times of 74 ms for a 101-frequency data set on the STM32 while utilizing only 2.69 kB of RAM. The system successfully extracts DRT peaks with different magnitudes and their relative relaxation times for 51-frequency and 101-frequency datasets. We demonstrate equivalent circuit parameter extraction with relative errors below 3.5%. This work establishes a foundation for portable BIS analysis, with potential applications in areas such as medical diagnostics, food state characterization, and plant monitoring.

关键词： Spectroscopy Embedded systems Microcontrollers Signal processing algorithms Random access memory Bioimpedance Biological systems Signal processing Impedance Equivalent circuits

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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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FINITE ELEMENT METHOD BASED INVESTIGATION OF SUBSTRATES EFFEKT ON SURFACE-ENHANCED RAMAN SPECTROSCOPY 9

FINITE ELEMENT METHOD BASED INVESTIGATION OF SUBSTRATES EFFE...

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9th EnvIMEKO Symposium of Environmental Instrumentation and measurements, EnvIMEKO 2022

作者： Adiraju, Anurag Al-Hamry, Ammar Kanoun, Olfa Professorship of Measurement and sensor technology Chemnitz University of Technology Chemnitz Germany Professorship of Measurement and sensor technology Chemnitz University of Technology Germany

ISBN: (纸本)9789299009031

Surface-enhanced Raman spectroscopy (SERS) is a widely used method for the detection of molecules of environmental concern such as pesticides. One of the key factors to achieve high sensitivity in SERS is the high electromagnetic enhancement formed between plasmonic nanoparticles which is dependent on the wavelength used, shape, size, distance, and type of nanoparticles. In this regard, the scope of this paper is to investigate the enhancement factor achieved between adjacent silver or gold nanoparticles of different geometrical configurations by changing the shapes and sizes of the particles. Simulations based on finite element methods (FEM) were carried out to investigate the effect of different excitation laser wavelengths. The obtained results show the dependency of the enhancement factor, size, type of nanoparticles, excitation wavelength of the laser, dielectric properties of the surrounding medium and the type of substrate used. © 2022 9th EnvIMEKO Symposium of Environmental Instrumentation and measurements, EnvIMEKO 2022. All Rights Reserved.

关键词： Finite element method

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Transmissive Eddy Current sensor for Gap Detection Between Sheet Metal Materials 19

Transmissive Eddy Current Sensor for Gap Detection Between S...

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

作者： Hu, Zheng Wendler, Frank Kanoun, Olfa Chemnitz University of Technology Professorship of Measurement and Sensor Technology Chemnitz Germany

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Path control is decisive for the quality of welding processes. In this work, an eddy current sensor system based on double coils is investigated for gap detection between two sheet metal materials. The coupling inductances of the sending and the receiving coils are collected in the frequency range from 10 kHz to 1 MHz, in cases of different gap widths and measurement distances. The experimental results show that the developed system is able to measure gaps with a width from 0 mm to 12 mm at various distances from 3 mm to 6 mm. This method is therefore suitable for precise location in welding processes, especially in the plasma welding process facing great noisy disruptions. © 2022 IEEE.

关键词： Inductance

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Low-Cost and Easily Manufactured Flexible All-Solid-State Supercapacitors Based on Laser-Induced Graphene 17

Low-Cost and Easily Manufactured Flexible All-Solid-State Su...

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17th International Workshop on Impedance Spectroscopy, IWIS 2024

作者： Lu, Tianqi Adiraju, Anurag Li, Yinting Mo, Haoran Wu, Zhenyu Kanoun, Olfa Professorship of Measurement and Sensor Technology Chemnitz University of Technology Chemnitz Germany

ISBN: (纸本)9798331507626

Supercapacitors (SCs) are critical energy storage devices that bridge the gap between traditional capacitors and batteries, offering high power density, rapid charge-discharge cycles, and long operational lifespans. As the demand for flexible and wearable electronics continues to grow, the development of low-cost, easily manufacturable, and high-performance supercapacitors has become increasingly important. This paper presents the development and characterization of a flexible supercapacitor based on laser-induced graphene (LIG). The LIG interdigital electrodes were fabricated on polyimide film using UV laser technology, while a PVA H2 SO4 polymer electrolyte was prepared and applied to the electrodes. The electrochemical performance of the supercapacitors (LIG-SCs) was evaluated through cyclic voltammetry and galvanostatic charge-discharge tests. The study examined the influence of key fabrication parameters, such as vacuum drying time and electrolyte volume, on areal capacitance, and further explored the device's performance under mechanical bending. The LIGSCs demonstrated a maximum areal capacitance of 13.27 mF cm2, retaining 39.03% of the initial capacitance at a higher current density of 0.5 mA cm2. Furthermore, the supercapacitors exhibited exceptional stability under mechanical deformation, with an RSD of 2.81%, indicating minimal variation in electrochemical performance across different bending angles. After 1000 charge-discharge cycles, the capacitance retention increased to 1 0 3. 5%, suggesting enhanced cycling stability. Additionally, the reproducibility of the fabrication process was validated, as the areal capacitance of multiple LIG-SC samples ranged with an RSD of only 0. 9 6%. The results demonstrate the high reliability and scalability of the LIG-SC fabrication process, making it a promising approach for cost-effective, flexible supercapacitors suitable for wearable electronics and energy storage applications. © 2024 IEEE.

关键词： Cyclic voltammetry

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Improving the accuracy of hand sign recognition by chaotic swarm algorithm-based feature selection applied to fused surface electromyography and force myography signals

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Engineering Applications of Artificial Intelligence 2025年 154卷

作者： Hellara, Hiba Barioul, Rim Sahnoun, Salwa Fakhfakh, Ahmed Kanoun, Olfa National School of Electronics and Telecommunications of Sfax University of Sfax Rue de Tunis Sfax Ons City3018 Tunisia Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Reichenhainer Str 70 Saxony Chemnitz09126 Germany Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Centre of Sfax Technopole of Sfax Sfax Ons City3021 Tunisia

This study addresses the challenge of distinguishing ambiguous American Sign Language (ASL) gestures by integrating surface electromyography (sEMG) and force myography (FMG) signals. A Chaotic Salp Swarm Algorithm (CSSA) with six chaotic maps was employed for feature selection, followed by k-Nearest Neighbors (kNN) classification. The objective of this approach was to enhance the classification accuracy of 20 American Sign Language (ASL) hand signs using individual and combined sEMG and FMG signals. The experiment involved the collection of data from ten participants, with each participant repeating the ASL postures ten times. The results of the study demonstrated that the CSSA effectively reduced the number of features required for classification while slightly improving the classification accuracy. The fusion of sEMG and FMG achieved an average accuracy of 88.5%, surpassing the accuracy of individual sEMG (81%) and FMG (76.75%) signals. Moreover, after systematically eliminating eight ambiguous gestures, the model attained 100% accuracy. These findings underscore the potential of integrating sEMG and FMG signals with CSSA-based feature selection to enhance ASL gesture recognition. Furthermore, the study identifies specific ambiguous postures that must be excluded to achieve perfect classification. © 2025 The Authors

关键词： Feature Selection

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