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Detection of Magnetite Rust Using Circular Microstrip Patch Antenna-based Chipless RFID Sensor

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Detection of Magnetite Rust Using Circular Microstrip Patch ...

International Electrical Engineering Congress (iEECON)

作者： Sirisak Nongpromma Suwan Janin Kamol Kaemarungsi Akkarat Boonpoonga Nonchanutt Chudpooti Lakkhana Bannawat Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Engineering and Technology Faculty of Engineering Rajamangala University of Technology Lanna Lampang Lampang Thailand National Science and Technology Development Agency National Electronics and Computer Technology Center Pathumthani Thailand Department of Electrical and Computer Engineering Faculty of Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Computer Engineering Faculty of Engineering Rajamangala University of Technology Rattanakosin Nakhon Pathom Thailand

In this paper, detection of the magnetite rust is presented by using a chipless RFID sensor based on a circular microstrip patch antenna (CMPA). The sensor is designed at the resonant frequency of 4.89 GHz by using a single-layer PCB with its dielectric constant of 4.3. Simulations were conducted to verify that the designed sensor can detect the magnetite rust through shifted resonant frequency. In simulations, the sensor was mounted on the iron with its magnetite rust. The width and depth of iron rust were varied in order to investigate the effect of iron rust on the resonant frequency. Simulation results have been shown that the proposed chipless RFID sensor and RCS’ can be utilized to detect the effect of the iron rust.

关键词： Radar cross-sections Patch antennas Simulation Magnetic resonance Microstrip antennas Radar antennas Iron

Entanglement duality in spin-spin interactions

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Physical Review A 2022年第3期106卷 032407-032407页

作者： Vahid Azimi-Mousolou Anders Bergman Anna Delin Olle Eriksson Manuel Pereiro Danny Thonig Erik Sjöqvist Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan 81746-73441 Iran Department of Physics and Astronomy Uppsala University Box 516 SE-751 20 Uppsala Sweden Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology AlbaNova University Center SE-10691 Stockholm Sweden Swedish e-Science Research Center (SeRC) KTH Royal Institute of Technology SE-10044 Stockholm Sweden School of Science and Technology Örebro University SE-701 82 Örebro Sweden

We examine entanglement of thermal states for spin-1/2 dimers in external magnetic fields. Entanglement transition in the temperature-magnetic-field plane demonstrates a duality in spin-spin interactions. This identifies a pair of dual categories of symmetric and antisymmetric dimers with each category classified into toric entanglement classes. The entanglement transition line is preserved from each toric entanglement class to its dual toric class. The toric classification is an indication of the topological signature of the entanglement, which bring about topological stability that could be relevant for quantum information processing.

关键词： Quantum correlations in quantum information Quantum entanglement Quantum information theory Spin dynamics

Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches

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arXiv 2024年

作者： Saifullah, Shoffan Pranolo, Andri Dreżewski, Rafal Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia College of Computer and Information Hohai University Nanjing211100 China Department of Informatics Faculty of Industrial Technology Universitas Ahmad Dahlan Yogyakarta55166 Indonesia Informatics Department Faculty of Industrial Technology Universitas Ahmad Dahlan Yogyakarta55166 Indonesia

This study systematically investigates the impact of image enhancement techniques on Convolutional Neural Network (CNN)-based Brain Tumor Segmentation, focusing on Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and their hybrid variations. Employing the U-Net architecture on a dataset of 3064 Brain MRI images, the research delves into preprocessing steps, including resizing and enhancement, to optimize segmentation accuracy. A detailed analysis of the CNN-based U-Net architecture, training, and validation processes is provided. The comparative analysis, utilizing metrics such as Accuracy, Loss, MSE, IoU, and DSC, reveals that the hybrid approach CLAHE-HE consistently outperforms others. Results highlight its superior accuracy (0.9982, 0.9939, 0.9936 for training, testing, and validation, respectively) and robust segmentation overlap, with Jaccard values of 0.9862, 0.9847, and 0.9864, and Dice values of 0.993, 0.9923, and 0.9932 for the same phases, emphasizing its potential in neuro-oncological applications. The study concludes with a call for refinement in segmentation methodologies to further enhance diagnostic precision and treatment planning in neuro-oncology. © 2024, CC BY-SA.

关键词： Image enhancement

Imprinting of Antiferromagnetic Vortex States in NiO-Fe Nanostructures

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arXiv 2024年

作者： Ślęzak, M. Wagner, T. Bharadwaj, V.K. Gomonay, O. Koziol-Rachwal, A. Menteş, T.O. Locatelli, A. Zając, M. Wilgocka-Ślęzak, D. Dróżdż, P. Ślęzak, T. AGH University of Krakow Faculty of Physics and Applied Computer Science Kraków Poland Institute of Physics Johannes Gutenberg-University Mainz Mainz55099 Germany Elettra - Sincrotrone Trieste S.C.p.A. Basovizza Trieste Italy National Synchrotron Radiation Centre SOLARIS Jagiellonian University Kraków Poland Jerzy Haber Institute of Catalysis and Surface Chemistry PAS Kraków Poland Institute of Physics Maria Curie-Sklodowska University Lublin Poland

Magnetic vortices are topological spin structures frequently found in ferromagnets, yet novel to antiferromagnets. By combining experiment and theory, we demonstrate that in a nanostructured antiferromagnetic-ferromagnetic NiO(111)-Fe(110) bilayer, a magnetic vortex is naturally stabilized by magnetostatic interactions in the ferromagnet and is imprinted onto the adjacent antiferromagnet via interface exchange coupling. We use micromagnetic simulations to construct a corresponding phase diagram of the stability of the imprinted antiferromagnetic vortex state. Our in depth analysis reveals that the interplay between interface exchange coupling and the antiferromagnet magnetic anisotropy plays a crucial role in locally reorienting the Néel vector out-of-plane in the prototypical in-plane antiferromagnet NiO and thereby stabilizing the vortices in the antiferromagnet. © 2024, CC BY.

关键词： Nickel oxide

Role of deep learning in cognitive healthcare: Wearable signal analysis, algorithms, benefits, and challenges

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Digital Communications and Networks 2025年第3期11卷 642-670页

作者： Md. Sakib Bin Alam Aiman Lameesa Senzuti Sharmin Shaila Afrin Shams Forruque Ahmed Mohammad Reza Nikoo Amir H. Gandomi Department of Information Technology University of Information Technology and Sciences (UITS) Dhaka 1212 Bangladesh Department of Computer Science American International University - Bangladesh (AIUB) Dhaka 1229 Bangladesh Science and Math Program Asian University for Women Chattogram 4000 Bangladesh School of Mathematical Sciences Sunway University Bandar Sunway Petaling Jaya 47500 Selangor Darul Ehsan Malaysia Department of Mathematics and Physics North South University Dhaka 1229 Bangladesh Department of Civil and Architectural Engineering Sultan Qaboos University Muscat Oman Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia University Research and Innovation Center (EKIK) Óbuda University 1034 Budapest Hungary Department of Computer Science Khazar University Mahsati 41 Baku Azerbaijan

Deep Learning (DL) offers promising solutions for analyzing wearable signals and gaining valuable insights into cognitive disorders. While previous review studies have explored various aspects of DL in cognitive healthcare, there remains a lack of comprehensive analysis that integrates wearable signals, data processing techniques, and the broader applications, benefits, and challenges of DL methods. Addressing this limitation, our study provides an extensive review of DL's role in cognitive healthcare, with a particular emphasis on wearables, data processing, and the inherent challenges in this field. This review also highlights the considerable promise of DL approaches in addressing a broad spectrum of cognitive issues. By enhancing the understanding and analysis of wearable signal modalities, DL models can achieve remarkable accuracy in cognitive healthcare. Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-term Memory (LSTM) networks have demonstrated improved performance and effectiveness in the early diagnosis and progression monitoring of neurological disorders. Beyond cognitive impairment detection, DL has been applied to emotion recognition, sleep analysis, stress monitoring, and neurofeedback. These applications lead to advanced diagnosis, personalized treatment, early intervention, assistive technologies, remote monitoring, and reduced healthcare costs. Nevertheless, the integration of DL and wearable technologies presents several challenges, such as data quality, privacy, interpretability, model generalizability, ethical concerns, and clinical adoption. These challenges emphasize the importance of conducting future research in areas such as multimodal signal analysis and explainable AI. The findings of this review aim to benefit clinicians, healthcare professionals, and society by facilitating better patient outcomes in cognitive healthcare.

关键词： Cognitive healthcare Deep learning Wearable sensor Convolutional neural network Recurrent neural network

Impact of the spin-orbit interaction on the phase diagram and anisotropy of the in-plane critical magnetic field in superconducting LaAlO3/SrTiO3interface

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arXiv 2021年

作者： Wójcik, P. Nowak, M.P. Zegrodnik, M. Agh University of Science and Technology Faculty of Physics and Applied Computer Science Al. Mickiewicza 30 Krakow30-059 Poland Agh University of Science and Technology Academic Centre for Materials and Nanotechnology Al. Mickiewicza 30 Krakow30-059 Poland

The two-dimensional electron gas at the interface between LaAlO3and SrTiO3(LAO/STO) exhibits gate tunable superconductivity with a characteristic dome-like shape of the critical temperature (Tc) in the phase diagram. As shown recently [Phys. Rev. B 102, 085420 (2020)], such an effect can be explained as a consequence of the extended s-wave symmetry of the gap within an intersite real space pairing scenario, leading to a good agreement between the experiment and theory. In this work, we turn to a detailed analysis of the inuence of spin-orbit coupling on the LAO/STO phase diagram by considering the atomic and the Rashba components. In particular, we analyze the optimal carrier concentration for which the maximal Tcis reached relative to the Lifshitz transition point. We find that the a misalignment between the two can be significantly enhanced by the spin-orbit splitting of the bands, combined with the fact that superconductivity sets in when the Fermi level passes the anticrossing induced by the spin-orbital hybridization. In the presence of the external in-plane magnetic field, our calculations show four-fold anisotropy with the paramagnetic limit largely exceeded for B||directed along the high symmetry points [01] and [01]. The obtained electron concentration dependence of Bc||reproduces the characteristic dome-like shape reported in experiments and the estimated value of Bc||corresponds to that measured experimentally. Copyright © 2021, The Authors. All rights reserved.

关键词： Anisotropy

Radiation-dominated bouncing model with slow contraction and inflation

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Physical Review D 2024年第4期109卷 043531-043531页

作者： Piero A. P. Molinari Paola C. M. Delgado Rodrigo F. Pinheiro Nelson Pinto-Neto Centro Brasileiro de Pesquisas Físicas - CBPF Rua Dr. Xavier Sigaud 150 Urca 22290-180 Rio de Janeiro RJ Brazil Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University 30-348 Krakow Poland

A very simple nonsingular inflationary model is presented where the unique matter content is a radiation fluid. The model slowly contracts from a very large, almost empty and flat spacetime and realizes a bounce. It is then launched to a quasi-de Sitter inflationary expansion with more than sixty e-folds, which smoothly changes to the usual classical, decelerated radiation-dominated expansion before nucleosynthesis. The initial contracting and final expanding phases are classical, but the intermediate bounce and inflationary phases are induced by quantum cosmological effects emerging from a Gaussian wave function quickly moving in configuration space. During this quantum era, a huge number of photons is created. The scale factor describing all this rich evolution is a surprisingly simple analytic function of conformal time. The cosmological scalar perturbations arising from quantum vacuum fluctuations in the far past of the model present an almost scale invariant spectrum with an amplitude compatible with observations for reasonable values of the free parameters of the model.

关键词： Canonical quantum gravity Cosmology Inflation Quantum cosmology

SLOVA: Uncertainty Estimation Using Single Label One-Vs-All Classifier

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arXiv 2022年

作者： Wójcik, Bartosz Grela, Jacek Smieja, Marek Misztal, Krzysztof Tabor, Jacek Faculty of Mathematics and Computer Science Jagiellonian University Lojasiewicza 6 Kraków30-348 Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Lojasiewicza 11 Kraków30-348 Poland

Deep neural networks present impressive performance, yet they cannot reliably estimate their predictive confidence, limiting their applicability in high-risk domains. We show that applying a multi-label one-vs-all loss reveals classification ambiguity and reduces model overconfidence. The introduced SLOVA (Single Label One-Vs-All) model redefines typical one-vs-all predictive probabilities to a single label situation, where only one class is the correct answer. The proposed classifier is confident only if a single class has a high probability and other probabilities are negligible. Unlike the typical softmax function, SLOVA naturally detects out-of-distribution samples if the probabilities of all other classes are small. The model is additionally fine-tuned with exponential calibration, which allows us to precisely align the confidence score with model accuracy. We verify our approach on three tasks. First, we demonstrate that SLOVA is competitive with the state-of-the-art on in-distribution calibration. Second, the performance of SLOVA is robust under dataset shifts. Finally, our approach performs extremely well in the detection of out-of-distribution samples. Consequently, SLOVA is a tool that can be used in various applications where uncertainty modeling is required. © 2022, CC BY.

关键词： Probability distributions

Architecture Reduction of a Probabilistic Neural Network by Merging K–Means and K–Nearest Neighbor Algorithms

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SSRN

SSRN 2022年

作者： Kusy, Maciej Kowalski, Piotr A. Faculty of Electrical and Computer Engineering Rzeszow University of Technology al. Powstancow Warszawy 12 Rzeszow35–959 Poland Faculty of Physics and Applied Computer Science AGH University of Science and Technology al. A. Mickiewicza 30 Cracow30–059 Poland Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw01–447 Poland

Probabilistic neural network (PNN) has a sizable structure since it requires all training records in the activation of its hidden layer. Therefore, in order to manage large data classification problems, it is recommended to minimize its inner design. In this paper, the algorithm for the PNN’s architecture reduction is presented. It is organized as follows. First, a k-means data clustering is conducted and the obtained centers are stored. Next, one selects a single nearest neighbor to the determined centers considering each class separately. The pattern neurons of a PNN are then established using both (i) the clusters centers and (ii) the records closest to the obtained centroids. The algorithm is applied to the classification tasks of seven repository data sets. Utilized PNN is trained by means of four training procedures with different kernel functions in each case. A 10–fold cross validation method is applied to assess the performance of the original and reduced networks. The results are also compared to the ones provided by methods existing in literature. It is shown that in the majority classification cases, it is possible to achieve a higher accuracy of reduced PNN compared to the original network and the approaches introduced in literature. © 2022, The Authors. All rights reserved.

关键词： K-means clustering