Search results for: muscle magnetic signal

Authors: Hamdi Omar Omar, Hela Fakhfakh, Chokri Yaich

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This work reflects the integration of different techniques, such as field sampling and observations, magnetic susceptibility measurement, cyclostratigaraphy and sequence stratigraphy. The combination of these results allows us to reconstruct the environmental evolution of the Sidi Khalif Formation in the North-South Axis (NOSA), aged of Upper Tithonian, Berriasian and Lower Valanginian. Six sedimentary facies were identified and are primarily influenced by open marine sedimentation receiving increasing terrigenous influx. Spectral analysis, based on MS variation (for the outcropped section) and wireline logging gamma ray (GR) variation (for the sub-area section) show a pervasive dominance of 405-kyr eccentricity cycles with the expression of 100-kyr eccentricity, obliquity and precession. This study provides (for the first time) a precise duration of 2.4 myr for the outcropped Sidi Khalif Formation with a sedimentation rate of 5.4 cm/kyr and the sub-area section to 3.24 myr with a sedimentation rate of 7.64 cm/kyr. We outlined 27 5th-order depositional sequences, 8 Milankovitch depositional sequences and 2 major 3rd-order cycles for the outcropping section, controlled by the long eccentricity (405 kyr) cycles and the precession index cycles. This study has demonstrated the potential of MS and GR to be used as proxies to develop an astronomically calibrated time-scale for the Mesozoic era.

Keywords: Berriasian, magnetic susceptibility, orbital tuning, Sidi Khalif Formation

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Authors: Satyabrata Bera, Mintu Mondal

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Two-dimensional (2D) ferromagnetic materials have garnered significant attention due to their potential to host intriguing scientific phenomena such as the anomalous Hall effect, anomalous Nernst effect, and high transport spin polarization. This study focuses on the investigation of air-stable van der Waals(vdW) ferromagnets, FeGeTe₂ (FₙGT with n = 3, 4, and 5). Particular emphasis is placed on the Fe4GeTe2 (F4GT) compound, which exhibits a complex and fascinating magnetic behavior characterized by two distinct transitions: (i) paramagnetic (PM) to ferromagnetic (FM) around T C ∼ 270 K, and (ii) another spins reorientation transition (SRT) at T SRT ∼ 100 K . Scaling analysis of magnetocaloric effect confirms the second-order character of the ferromagnetic transition, while the same analysis at T SRT suggests that SRT is first-order phase transition. Moreover, the F4GT exhibits a large anomalous Hall conductivity (AHC), ∼ 490 S/cm at 2 K . The near-quadratic behavior of the anomalous Hall resistivity with the longitudinal resistivity suggests that a dominant AHC contribution arises from an intrinsic Berry curvature (BC) mechanism. Electronic structure calculations reveal a significant BC resulting from SOC-induced gapped nodal lines around the Fermi level, thereby giving rise to large AHC. Additionally, we reported exceptionally large anomalous Hall angle (≃ 10.6%) and Hall factor (≃ 0.22 V −1 ) values, the largest observed within this vdW family. The findings presented here, provide valuable insights into the fascinating magnetic and transport properties of 2D ferromagnetic materials, in particular, FₙGT family.

Keywords: 2D vdW ferromagnet, spin reorientation transition, anomalous hall effect, berry curvature

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Authors: S. H. Bae, D. W. Cho, W. B. Jeong, J. R. Cho

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Fatigue fracture of an aluminum welded structure is a phenomenon frequently occurring from pores in a weld. In order to grasp the state of the welded structure in operation in real time, the acceleration signal of the structure is measured. At this time, the vibration characteristic of the signal according to the fatigue load is an important parameter of the state diagnosis. This paper was an experimental study on the variation of vibration characteristics of welded beams with vibration aging (especially bending vibration). First simple beams were produced according to welding conditions. Each beam was vibrated and measured beam's PSD (power spectral density) according to the degree of aging. Also, modal testing was conducted to compare the transfer functions of welded beams. Testing result shows that the natural frequencies of the beam changed with the vibration aging due to the change of stiffness in welding part and its stiffness was estimated by the finite element method.

Keywords: modal testing, natural frequency, vibration aging, welded structure

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Authors: Yuyan Yi, Jingyi Zheng, Nedret Billor

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Independent component analysis (ICA) is one of the most commonly used blind source separation (BSS) techniques for signal pre-processing, such as noise reduction and feature extraction. The main parameter in the ICA method is the number of independent components (IC). Although there have been several methods for the determination of the number of ICs, it has not been given sufficient attentionto this important parameter. In this study, wereview the mostused methods fordetermining the number of ICs and providetheir advantages and disadvantages. Further, wepropose an improved version of column-wise ICAByBlock method for the determination of the number of ICs.To assess the performance of the proposed method, we compare the column-wise ICAbyBlock with several existing methods through different ICA methods by using simulated and real signal data. Results show that the proposed column-wise ICAbyBlock is an effective and stable method for determining the optimal number of components in ICA. This method is simple, and results can be demonstrated intuitively with good visualizations.

Keywords: independent component analysis, optimal number, column-wise, correlation coefficient, cross-validation, ICAByblock

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Wayan Widhiada, Cok Indra Partha, Gusti Ngurah Nitya Santhiarsa

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The purpose of this paper is to investigate the sophistication of the use of Proportional Integral and Derivative Control to control the kinematic motion of the mobile robot manipulator. Simulation and experimental methods will be used to investigate the sophistication of PID control to control the mobile robot arm in the collection and placement of several kinds of objects quickly, accurately and correctly. Mathematical modeling will be done by utilizing the integration of Solidworks and MATLAB / Simmechanics software. This method works by converting the physical model file into the xml file. This method is easy, fast and accurate done in modeling and design robotics. The automatic control design of this robot manipulator will be validated in simulations and experimental in control labs as evidence that the mobile robot manipulator gripper control design can achieve the best performance such as the error signal is lower than 5%, small overshoot and get steady signal response as quickly.

Keywords: control analysis, kinematics motion, mobile robot manipulator, performance

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Authors: Matej Patzelt, Jana Mrzilkova, Jan Dudak, Frantisek Krejci, Jan Zemlicka, Zdenek Wurst, Petr Zach, Vladimir Musil

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Introduction: Micro-CT is well used for examination of bone structures and teeth. On the other hand visualization of the soft tissues is still limited. The goal of our study was to elaborate methodology for soft tissue samples imaging in micro-CT. Methodology: We used organs of rats and mice. We either did a preparation of the organs and fixation in contrast solution or we did cannulation of blood vessels and their injection for imaging of the vascular system. First, we scanned native specimens, then we created corrosive specimens by resins. In the next step, we injected vascular system either by Aurovist contrast agent or by Exitron. In the next step, we focused on soft tissues contrast increase. We scanned samples fixated in Lugol solution, samples fixated in pure ethanol and in formaldehyde solution. All used methods were afterwards compared. Results: Native specimens did not provide sufficient contrast of the tissues in any of organs. Corrosive samples of the blood stream provided great contrast and details; on the other hand, it was necessary to destroy the organ. Further examined possibility was injection of the AuroVist contrast that leads to the great bloodstream contrast. Injection of Exitron contrast agent comparing to Aurovist did not provide such a great contrast. The soft tissues (kidney, heart, lungs, brain, and liver) were best visualized after fixation in ethanol. This type of fixation showed best results in all studied tissues. Lugol solution had great results in muscle tissue. Fixation by formaldehyde solution showed similar quality of contrast in the tissues like ethanol. Conclusion: Before imaging, we need to, first, determinate which structures of the soft tissues we want to visualize. In the case of the bloodstream, the best was AuroVist and corrosive specimens. Muscle tissue is best visualized by Lugol solution. In the case of the organs containing cavities, like kidneys or brain, the best way was ethanol fixation.

Keywords: experimental imaging, fixation, micro-CT, soft tissues

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Authors: Jiaying Zhang, Xin Mu, Chang Ni, Jeff L. Zhang

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Magnetic resonance elastography (MRE) non-invasively assesses tissue elastic properties, such as shear modulus, by measuring tissue’s displacement in response to mechanical waves. The estimated metrics on tissue elasticity or stiffness have been shown to be valuable for monitoring physiologic or pathophysiologic status of tissue, such as a tumor or fatty liver. To quantify tissue shear modulus from MRE-acquired displacements (essentially an inverse problem), multiple approaches have been proposed, including Local Frequency Estimation (LFE) and Direct Inversion (DI). However, one common problem with these methods is that the estimates are severely noise-sensitive due to either the inverse-problem nature or noise propagation in the pixel-by-pixel process. With the advent of deep learning (DL) and its promise in solving inverse problems, a few groups in the field of MRE have explored the feasibility of using DL methods for quantifying shear modulus from MRE data. Most of the groups chose to use real MRE data for DL model training and to cut training images into smaller patches, which enriches feature characteristics of training data but inevitably increases computation time and results in outcomes with patched patterns. In this study, simulated wave images generated by Finite Differential Time Domain (FDTD) simulation are used for network training, and U-Net is used to extract features from each training image without cutting it into patches. The use of simulated data for model training has the flexibility of customizing training datasets to match specific applications. The proposed method aimed to estimate tissue shear modulus from MRE data with high robustness to noise and high model-training efficiency. Specifically, a set of 3000 maps of shear modulus (with a range of 1 kPa to 15 kPa) containing randomly positioned objects were simulated, and their corresponding wave images were generated. The two types of data were fed into the training of a U-Net model as its output and input, respectively. For an independently simulated set of 1000 images, the performance of the proposed method against DI and LFE was compared by the relative errors (root mean square error or RMSE divided by averaged shear modulus) between the true shear modulus map and the estimated ones. The results showed that the estimated shear modulus by the proposed method achieved a relative error of 4.91%±0.66%, substantially lower than 78.20%±1.11% by LFE. Using simulated data, the proposed method significantly outperformed LFE and DI in resilience to increasing noise levels and in resolving fine changes of shear modulus. The feasibility of the proposed method was also tested on MRE data acquired from phantoms and from human calf muscles, resulting in maps of shear modulus with low noise. In future work, the method’s performance on phantom and its repeatability on human data will be tested in a more quantitative manner. In conclusion, the proposed method showed much promise in quantifying tissue shear modulus from MRE with high robustness and efficiency.

Keywords: deep learning, magnetic resonance elastography, magnetic resonance imaging, shear modulus estimation

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Authors: Vineet Barwal, Sajid Husain, Nanhe Kumar Gupta, Soumyarup Hait, Sujeet Chaudhary

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High perpendicular magnetic anisotropy (PMA) and low damping constant (α) in ferromagnets are one of the few necessary requirements for their potential applications in the field of spintronics. In this regards, ferromagnetic τ-phase of MnAl possesses the highest PMA (Ku > 107 erg/cc) at room temperature, high saturation magnetization (Ms~800 emu/cc) and a Curie temperature of ~395K. In this work, we have investigated the magnetotransport behaviour of this potentially useful binary system MnₓAl₁₋ₓ films were synthesized by co-sputtering (pulsed DC magnetron sputtering) on Si/SiO₂ (where SiO₂ is native oxide layer) substrate using 99.99% pure Mn and Al sputtering targets. Films of constant thickness (~25 nm) were deposited at the different growth temperature (Tₛ) viz. 30, 300, 400, 500, and 600 ºC with a deposition rate of ~5 nm/min. Prior to deposition, the chamber was pumped down to a base pressure of 2×10⁻⁷ Torr. During sputtering, the chamber was maintained at a pressure of 3.5×10⁻³ Torr with the 55 sccm Ar flow rate. Films were not capped for the purpose of electronic transport measurement, which leaves a possibility of metal oxide formation on the surface of MnAl (both Mn and Al have an affinity towards oxide formation). In-plane and out-of-plane transverse magnetoresistance (MR) measurements on films sputtered under optimized growth conditions revealed non-saturating behavior with MR values ~6% and 40% at 9T, respectively at 275 K. Resistivity shows a parabolic dependence on the field H, when the H is weak. At higher H, non-saturating positive MR that increases exponentially with the strength of magnetic field is observed, a typical character of hopping type conduction mechanism. An anomalous decrease in MR is observed on lowering the temperature. From the temperature dependence of reistivity, it is inferred that the two competing states are metallic and semiconducting, respectively and the energy scale of the phenomenon produces the most interesting effects, i.e., the metal-insulator transition and hence the maximum sensitivity to external fields, at room temperature. Theory of disordered 3D systems effectively explains the crossover temperature coefficient of resistivity from positive to negative with lowering of temperature. These preliminary findings on the MR behavior of MnAl thin films will be presented in detail. The anomalous large MR in mixed phase MnAl system is evidently useful for future spintronic applications.

Keywords: magnetoresistance, perpendicular magnetic anisotropy, spintronics, thin films

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

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Authors: Pranav Gulati, Isha Sharma

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Hypertension and hypotension are known to have repercussions on the health of an individual, with hypertension contributing to an increased probability of risk to cardiovascular diseases and hypotension resulting in syncope. This prompts the development of a non-invasive, non-intrusive, continuous and cuff-less blood pressure monitoring system to detect blood pressure variations and to identify individuals with acute and chronic heart ailments, but due to the unavailability of such devices for practical daily use, it becomes difficult to screen and subsequently regulate blood pressure. The complexities which hamper the steady monitoring of blood pressure comprises of the variations in physical characteristics from individual to individual and the postural differences at the site of monitoring. We propose to develop a continuous, comprehensive cardio-analysis tool, based on reflective photoplethysmography (PPG). The proposed device, in the form of an eyewear captures the PPG signal and estimates the systolic and diastolic blood pressure using a sensor positioned near the temporal artery. This system relies on regression models which are based on extraction of key points from a pair of PPG wavelets. The proposed system provides an edge over the existing wearables considering that it allows for uniform contact and pressure with the temporal site, in addition to minimal disturbance by movement. Additionally, the feature extraction algorithms enhance the integrity and quality of the extracted features by reducing unreliable data sets. We tested the system with 12 subjects of which 6 served as the training dataset. For this, we measured the blood pressure using a cuff based BP monitor (Omron HEM-8712) and at the same time recorded the PPG signal from our cardio-analysis tool. The complete test was conducted by using the cuff based blood pressure monitor on the left arm while the PPG signal was acquired from the temporal site on the left side of the head. This acquisition served as the training input for the regression model on the selected features. The other 6 subjects were used to validate the model by conducting the same test on them. Results show that the developed prototype can robustly acquire the PPG signal and can therefore be used to reliably predict blood pressure levels.

Keywords: blood pressure, photoplethysmograph, eyewear, physiological monitoring

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Authors: Salah Al-Din I. Badran, Samad Ahmadi, Ismail Shahin

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A blind source separation method is proposed; in this method we use a non-uniform filter bank and a novel normalisation. This method provides a reduced computational complexity and increased convergence speed comparing to the full-band algorithm. Recently, adaptive sub-band scheme has been recommended to solve two problems: reduction of computational complexity and increase the convergence speed of the adaptive algorithm for correlated input signals. In this work the reduction in computational complexity is achieved with the use of adaptive filters of orders less than the full-band adaptive filters, which operate at a sampling rate lower than the sampling rate of the input signal. The decomposed signals by analysis bank filter are less correlated in each sub-band than the input signal at full bandwidth, and can promote better rates of convergence.

Keywords: blind source separation, computational complexity, subband, convergence speed, mixture

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Authors: Chatchaval Pornpatkul, Wipawan Suksathid

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This paper is to study and control the pressure of the water inside the open tank using a cascade control with the communication in the process by fieldbus system for the pressure calibration. The plant model is to be used in experiments to control the level and flow process of the water by using Syscon program to create functions. We used to control by Intouch runtime program to create the graphic display on the screen. In this case we used PI control the level and the flow process of water in the open tank in the range of 0 – 10 L/m. The output signal of the level and the flow transmitter are the digital standard signal by fieldbus system. And all information displayed on the computer with the communication between the computer and plant model can be communication to each other through just one cable pair. And in this paper, the PI tuning, we used calculate by Ziegler-Nichols reaction curve method to control the plant model by PI controller.

Keywords: cascade control, fieldbus system, pressure calibration, microelectronics systems

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Authors: Hassan Almoslim

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Background: The machine resistance training is an exercise that uses the equipment as loads to strengthen and condition the musculoskeletal system and improving muscle tone. The machine resistance training is easy to use, allow the individual to train with heavier weights without assistance, useful for beginners and elderly populations and specific muscle groups. Purpose: The purpose of this study was to examine the impact of nine weeks of machine resistance training on maximum strength among lean and normal weight male college students. Method: Thirty-six male college students aged between 19 and 21 years from King Fahd University of petroleum & minerals participated in the study. The subjects were divided into two an equal groups called Lean Group (LG, n = 18) and Normal Weight Group (NWG, n = 18). The subjects whose body mass index (BMI) is less than 18.5 kg / m2 is considered lean and who is between 18.5 to 24.9 kg / m2 is normal weight. Both groups performed machine resistance training nine weeks, twice per week for 40 min per training session. The strength measurements, chest press, leg press and abdomen exercises were performed before and after the training period. 1RM test was used to determine the maximum strength of all subjects. The training program consisted of several resistance machines such as leg press, abdomen, chest press, pulldown, seated row, calf raises, leg extension, leg curls and back extension. The data were analyzed using independent t-test (to compare mean differences) and paired t-test. The level of significance was set at 0.05. Results: No change was (P ˃ 0.05) observed in all body composition variables between groups after training. In chest press, the NWG recorded a significantly greater mean different value than the LG (19.33 ± 7.78 vs. 13.88 ± 5.77 kg, respectively, P ˂ 0.023). In leg press and abdomen exercises, both groups revealed similar mean different values (P ˃ 0.05). When the post-test was compared with the pre-test, the NWG showed significant increases in the chest press by 47% (from 41.16 ± 12.41 to 60.49 ± 11.58 kg, P ˂ 001), abdomen by 34% (from 45.46 ± 6.97 to 61.06 ± 6.45 kg, P ˂ 0.001) and leg press by 23.6% (from 85.27 ± 15.94 to 105.48 ± 21.59 kg, P ˂ 0.001). The LG also illustrated significant increases by 42.6% in the chest press (from 32.58 ± 7.36 to 46.47 ± 8.93 kg, P ˂ 0.001), the abdomen by 28.5% (from 38.50 ± 7.84 to 49.50 ± 7.88 kg, P ˂ 0.001) and the leg press by 30.8% (from 70.2 ± 20.57 to 92.01 ± 22.83 kg, P ˂ 0.001). Conclusion: It was concluded that the lean and the normal weight male college students can benefit from the machine resistance-training program remarkably.

Keywords: body composition, lean, machine resistance training, normal weight

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Authors: V. V. Srinivasu, Jayashree Das

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Owing to the industrial and technological importance, transition metal (TM) doped ZnO has been widely chosen for many practical applications in electronics and optoelectronics. Besides, though still a controversial issue, the reported room temperature ferromagnetism in transition metal doped ZnO has added a feather to its excellence and importance in current semiconductor research for prospective application in Spintronics. Anticipating non controversial and improved optical and magnetic properties, we adopted co doping method to synthesise polycrystalline Mn:TM (Fe,Ni) and Mn:RE(Gd,Sm) co doped ZnO samples by solid state sintering route with compositions Zn1-x (Mn:Fe/Ni)xO and Zn1-x(Mn:Gd/Sm)xO and sintered at two different temperatures. The structure, composition and optical changes induced in ZnO due to co doping and sintering were investigated by XRD, FTIR, UV, PL and ESR studies. X-ray peak profile analysis (XPPA) and Williamson-Hall analysis carried out shows changes in the values of stress, strain, FWHM and the crystallite size in both the co doped systems. FTIR spectra also show the effect of both type of co doping on the stretching and bending bonds of ZnO compound. UV-Vis study demonstrates changes in the absorption band edge as well as the significant change in the optical band gap due to exchange interactions inside the system after co doping. PL studies reveal effect of co doping on UV and visible emission bands in the co doped systems at two different sintering temperatures, indicating the existence of defects in the form of oxygen vacancies. While the TM: TM co doped samples of ZnO exhibit ferromagnetism at room temperature, the TM: RE co doped samples show paramagnetic behaviour. The magnetic behaviours observed are supported by results from Electron Spin resonance (ESR) study; which shows sharp resonance peaks with considerable line width (∆H) and g values more than 2. Such values are usually found due to the presence of an internal field inside the system giving rise to the shift of resonance field towards the lower field. The g values in this range are assigned to the unpaired electrons trapped in oxygen vacancies. TM: TM co doped ZnO samples exhibit low field absorption peaks in their ESR spectra, which is a new interesting observation. We emphasize that the interesting observations reported in this paper may be considered for the improved futuristic applications of ZnO based materials.

Keywords: co-doping, electro spin resonance, microwave absorption, spintronics

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Authors: Ju-Hong Lee, Ching-Wei Liao, Kun-Che Lee

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This paper deals with the problem of using antenna sensors for adaptive beamforming in the presence of random steering mismatch. We present an efficient adaptive array beamformer with robustness to deal with the considered problem. The robustness of the proposed beamformer comes from the efficient designation of the steering vector. Using the received array data vector, we construct an appropriate correlation matrix associated with the received array data vector and a correlation matrix associated with signal sources. Then, the eigenvector associated with the largest eigenvalue of the constructed signal correlation matrix is designated as an appropriate estimate of the steering vector. Finally, the adaptive weight vector required for adaptive beamforming is obtained by using the estimated steering vector and the constructed correlation matrix of the array data vector. Simulation results confirm the effectiveness of the proposed method.

Keywords: adaptive beamforming, antenna array, linearly constrained minimum variance, robustness, steering vector

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Authors: Sanghamitra Acharya, Suwarna Datar

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Extensive use of digital and smart communication createsprolong expose of unwanted electromagnetic (EM) radiations. This harmful radiation creates not only malfunctioning of nearby electronic gadgets but also severely affects a human being. So, a suitable microwave absorbing material (MAM) becomes a necessary urge in the field of stealth and radar technology. Initially, Aluminum based hexa ferrite was prepared by sol-gel technique and for carbon derived composite was prepared by the simple one port chemical reduction method. Finally, composite films of Poly (Vinylidene) Fluoride (PVDF) are prepared by simple gel casting technique. Present work demands that aluminum-based hexaferrite phase conjugated with graphene in PVDF matrix becomes a suitable candidate both in commercially important X and Ku band. The structural and morphological nature was characterized by X-Ray diffraction (XRD), Field emission-scanning electron microscope (FESEM) and Raman spectra which conforms that 30-40 nm particles are well decorated over graphene sheet. Magnetic force microscopy (MFM) and conducting force microscopy (CFM) study further conforms the magnetic and conducting nature of composite. Finally, shielding effectiveness (SE) of the composite film was studied by using Vector network analyzer (VNA) both in X band and Ku band frequency range and found to be more than 30 dB and 40 dB, respectively. As prepared composite films are excellent microwave absorbers.

Keywords: carbon nanocomposite, microwave absorbing material, electromagnetic shielding, hexaferrite

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Authors: Vinay Kant Shankhdhar

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Purpose: Maxillary defects are three dimensional and require complex bone and soft tissue reconstruction. Maxillary reconstruction using fibula osteocutaneous flaps in situation requiring orbital floor, orbital wall, palatal defects, and external skin, all at the same time require special planning and multiple osteotomies. We tried to improvise our reconstruction using multiple osteotomies and skin paddle designs for fibula and Flexor Hallucis Longus Muscle. This study aims at discussing the planning and outcome in complex maxillary reconstructions using fibula flaps and soft tissue flaps with or without bone grafts. Material and Methods: From 2011 to 2017 a total of 129 Free fibula flaps were done, 67 required two or more struts, 164 Anterolateral Thigh Flaps, 11 Deep Inferior Epigastric Artery perforator flaps and 3 vertical rectus abdominis muscle flaps with iliac crest bone graft. The age range was 2 to 70 years. The reconstruction was evaluated based on the post-operative rehabilitation including orbital support (prevention of diplopia), oral diet, speech and cosmetic appearance. Results: The follow- up is from 5 years to 1 year. In this series, we observed that the common complications were the de-vascularisation of most distal segment of osteotomised fibula and native skin necrosis. Commonest area of breakdown is the medial canthal region. Plate exposure occurs most commonly at the pyriform sinus. There was extrusion of one non-vascularized bone graft. All these complications were noticed post-radiotherapy. Conclusions: The use of free fibula osteocutaneous flap gives very good results when only alveolar reconstruction is required. The reconstruction of orbital floor with extensive skin loss with post operative radiotherapy has maximum complication rate in long term follow up. A soft tissue flap with non vascularized bone graft may be the best option in such cases.

Keywords: maxilla reconstruction, fibula maxilla, post cancer maxillary reconstruction

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Authors: Nacera Yassa, Abdelmalek Saidoune, Ghania Ouadfel, Hamza Houassine

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The rapid advancement in electrical technologies has underscored the increasing importance of multiphase machines across various industrial sectors. These machines offer significant advantages in terms of efficiency, compactness, and reliability compared to their single-phase counterparts. However, early detection and diagnosis of electrical faults remain critical challenges to ensure the durability and safety of these complex systems. This paper presents an advanced analytical model for multiphase machines, with a particular focus on dual stator asynchronous machines. The primary objective is to develop a robust diagnostic tool capable of effectively detecting and locating electrical faults in these machines, including short circuits, winding faults, and voltage imbalances. The proposed methodology relies on an analytical approach combining electrical machine theory, modeling of magnetic and electrical circuits, and advanced signal analysis techniques. By employing detailed analytical equations, the developed model accurately simulates the behavior of multiphase machines in the presence of electrical faults. The effectiveness of the proposed model is demonstrated through a series of case studies and numerical simulations. In particular, special attention is given to analyzing the dynamic behavior of machines under different types of faults, as well as optimizing diagnostic and recovery strategies. The obtained results pave the way for new advancements in the field of multiphase machine diagnostics, with potential applications in various sectors such as automotive, aerospace, and renewable energies. By providing precise and reliable tools for early fault detection, this research contributes to improving the reliability and durability of complex electrical systems while reducing maintenance and operation costs.

Keywords: faults, diagnosis, modelling, multiphase machine

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Authors: S. Hosseini, M. Ahmadi Najafabadi, M. Akhlaghi

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The health of the journal bearings is very important in preventing unforeseen breakdowns in rotary machines, and poor lubrication is one of the most important factors for producing the bearing failures. Hydrodynamic lubrication (HL), mixed lubrication (ML), and boundary lubrication (BL) are three regimes of a journal bearing lubrication. This paper uses acoustic emission (AE) measurement technique to correlate features of the AE signals to the three lubrication regimes. The transitions from HL to ML based on operating factors such as rotating speed, load, inlet oil pressure by time domain and time-frequency domain signal analysis techniques are detected, and then metal-to-metal contacts between sliding surfaces of the journal and bearing are identified. It is found that there is a significant difference between theoretical and experimental operating values that are obtained for defining the lubrication regions.

Keywords: acoustic emission technique, pressure fed journal bearing, time and frequency signal analysis, metal-to-metal contact

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Authors: Rupesh K. Mishra, Gaëlle Catanante, Akhtar Hayat, Jean-Louis Marty

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Ochratoxins (OTA) are secondary metabolites present in a wide variety of food stuff. They are dangerous by-products mainly produced by several species of storage fungi including the Aspergillus and Penicillium genera. OTA is known to have nephrotoxic, immunotoxic, teratogenic and carcinogenic effects. Thus, needs a special attention for a highly sensitive and selective detection system that can quantify these organic toxins in various matrices such as cocoa beans. This work presents a folding based aptasensors by employing an aptamer conjugated redox probe (methylene blue) specifically designed for OTA. The aptamers were covalently attached to the screen printed carbon electrodes using diazonium grafting. Upon sensing the OTA, it binds with the immobilized aptamer on the electrode surface, which induces the conformational changes of the aptamer, consequently increased in the signal. This conformational change of the aptamer before and after biosensing of target OTA could produce the distinguishable electrochemical signal. The obtained limit of detection was 0.01 ng/ml for OTA samples with recovery of up to 88% in contaminated cocoa samples.

Keywords: ochratoxin A, cocoa, DNA aptamer, labelled probe

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Authors: Pejman Taghibeikzadehbadr

Abstract:

Background and Aim: PGC-1 alpha is a transcription factor that was first detected in brown adipose tissue. Since its discovery, PGC-1 alpha has been known to facilitate beneficial adaptations such as mitochondrial biogenesis and increased angiogenesis in skeletal muscle following aerobic exercise. Therefore, the purpose of this study was to investigate the expression of PGC-1 alpha isoforms in response to eccentric and concentric resistance training in healthy subjects. Materials and Methods: Ten healthy men were randomly divided into two groups (5 patients in eccentric group - 5 in eccentric group). Isokinetic contraction protocols included eccentric and concentric knee extension with maximum power and angular velocity of 60 degrees per second. The torques assigned to each subject were considered to match the workload in both protocols, with a rotational speed of 60 degrees per second. Contractions consisted of a maximum of 12 sets of 10 repetitions for the right leg, a rest time of 30 seconds between each set. At the beginning and end of the study, biopsy of the lateral broad muscle tissue was performed. Biopsies were performed in both distal and proximal directions of the lateral flank. To evaluate the expression of PGC1α-1 and PGC1α-4 genes, tissue analysis was performed in each group using Real-Time PCR technique. Data were analyzed using dependent t-test and covariance test. SPSS21 software and Exell 2013 software were used for data analysis. Results: The results showed that intra-group changes of PGC1α-1 after one session of activity were not significant in eccentric (p = 0.168) and concentric (p = 0.959) groups. Also, inter-group changes showed no difference between the two groups (p = 0.681). Also, intra-group changes of PGC1α-4 after one session of activity were significant in an eccentric group (p = 0.012) and concentric group (p = 0.02). Also, inter-group changes showed no difference between the two groups (p = 0.362). Conclusion: It seems that the lack of significant changes in the desired variables due to the lack of exercise pressure is sufficient to stimulate the increase of PGC1α-1 and PGC1α-4. And with regard to reviewing the answer, it seems that the compatibility debate has different results that need to be addressed.

Keywords: eccentric contraction, concentric contraction, PGC1α-1 و PGC1α-4, human subject

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Authors: Somayeh Komeylian

Abstract:

Array signal processing techniques have been recently developing in a variety application of the performance enhancement of receivers by refraining the power of jamming and interference signals. In this scenario, biases induced to the antenna array receiver degrade significantly the accurate estimation of the carrier phase. Owing to the integration of frequency becomes the carrier phase, we have obtained the unbiased doppler frequency for the high precision estimation of carrier phase. The unbiased characteristic of Doppler frequency to the power jamming and the other interference signals allows achieving the highly accurate estimation of phase carrier. In this study, we have rigorously investigated the unbiased characteristic of Doppler frequency to the variation of the antenna array geometries. The simulation results have efficiently verified that the Doppler frequency remains also unbiased and accurate to the variation of antenna array geometries.

Keywords: array signal processing, unbiased doppler frequency, GNSS, carrier phase, and slowly fluctuating point target

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Authors: Tao Lin, Shanghong Zhao, Yufu Yin, Zihang Zhu, Wei Jiang, Xuan Li, Qiurong Zheng

Abstract:

A simple operated, stable and compact multi-band frequency conversion and multi-phase shift is proposed to satisfy the demands of multi-band communication and radar phase array system. The dual polarization quadrature phase shift keying (DP-QPSK) modulator is employed to support the LO sideband and the optical frequency comb simultaneously. Meanwhile, the fiber is also used to introduce different phase shifts to different sidebands. The simulation result shows that by controlling the DC bias voltages and a C band microwave signal with frequency of 4.5 GHz can be simultaneously converted into other signals that cover from C band to K band with multiple phases. It also verifies that the multi-band and multi-phase frequency conversion system can be stably performed based on current manufacturing art and can well cope with the DC drifting. It should be noted that the phase shift of the converted signal also partly depends of the length of the optical fiber.

Keywords: microwave photonics, multi-band frequency conversion, multi-phase shift, conversion efficiency

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Authors: Rashid Ahmed , John N. Avaritsiotis

Abstract:

Many signal subspace-based approaches have already been proposed for determining the fixed Direction of Arrival (DOA) of plane waves impinging on an array of sensors. Two procedures for DOA estimation based neural networks are presented. First, Principal Component Analysis (PCA) is employed to extract the maximum eigenvalue and eigenvector from signal subspace to estimate DOA. Second, minor component analysis (MCA) is a statistical method of extracting the eigenvector associated with the smallest eigenvalue of the covariance matrix. In this paper, we will modify a Minor Component Analysis (MCA(R)) learning algorithm to enhance the convergence, where a convergence is essential for MCA algorithm towards practical applications. The learning rate parameter is also presented, which ensures fast convergence of the algorithm, because it has direct effect on the convergence of the weight vector and the error level is affected by this value. MCA is performed to determine the estimated DOA. Preliminary results will be furnished to illustrate the convergences results achieved.

Keywords: Direction of Arrival, neural networks, Principle Component Analysis, Minor Component Analysis

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Authors: Abdullah Alnutayfat, Alexander Sutin, Dong Liu

Abstract:

Wind turbine has become one of the most popular energy productions. However, failure of blades and maintenance costs evolve into significant issues in the wind power industry, so it is essential to detect the initial blade defects to avoid the collapse of the blades and structure. This paper aims to apply modulation of high-frequency blade vibrations by low-frequency blade rotation, which is close to the known Vibro-Acoustic Modulation (VAM) method. The high-frequency wideband blade vibration is produced by the interaction of the surface blades with the environment air turbulence, and the low-frequency modulation is produced by alternating bending stress due to gravity. The low-frequency load of rotational wind turbine blades ranges between 0.2-0.4 Hz and can reach up to 2 Hz for strong wind. The main difference between this study and previous ones on VAM methods is the use of a wideband vibration signal from the blade's natural vibrations. Different features of the vibroacoustic modulation are considered using a simple model of breathing crack. This model considers the simple mechanical oscillator, where the parameters of the oscillator are varied due to low-frequency blade rotation. During the blade's operation, the internal stress caused by the weight of the blade modifies the crack's elasticity and damping. The laboratory experiment using steel samples demonstrates the possibility of VAM using a probe wideband noise signal. A cycle load with a small amplitude was used as a pump wave to damage the tested sample, and a small transducer generated a wideband probe wave. The received signal demodulation was conducted using the Detecting of Envelope Modulation on Noise (DEMON) approach. In addition, the experimental results were compared with the modulation index (MI) technique regarding the harmonic pump wave. The wideband and traditional VAM methods demonstrated similar sensitivity for earlier detection of invisible cracks. Importantly, employing a wideband probe signal with the DEMON approach speeds up and simplifies testing since it eliminates the need to conduct tests repeatedly for various harmonic probe frequencies and to adjust the probe frequency.

Keywords: vibro-acoustic modulation, detecting of envelope modulation on noise, damage, turbine blades

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Authors: Olawale J. Olaluyi, Ayodele S. Oluwole, O. Akinsanmi, Johnson O. Adeogo

Abstract:

Communication using visible light VLC is considered a cutting-edge technology used for data transmission and illumination since it uses less energy than radio frequency (RF) technology and has a large bandwidth, extended lifespan, and high security. The room's irregular distribution of small base stations, or LED array distribution, is the cause of the obscured area, minimum signal-to-noise ratio (SNR), and received power. In order to maximize the received power distribution and SNR at the center of the room for an indoor VLC system, the researchers offer an innovative model for the placement of eight LED array distributions in this work. We have investigated the arrangement of the LED array distribution with regard to receiving power to fill the open space in the center of the room. The suggested LED array distribution saved 36.2% of the transmitted power, according to the simulation findings. Aside from that, the entire room was equally covered. This leads to an increase in both received power and SNR.

Keywords: visible light communication (VLC), light emitted diodes (LED), optical power distribution, signal-to-noise ratio (SNR).

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Authors: S. M. Mabandla, P. Loveday, C. Gomes, D. T. Maiga, T. T. Phadi

Abstract:

Lead zirconate titanate (PZT) piezoelectric ceramics are widely used in ultrasonic applications due to their ability to effectively convert electrical energy into mechanical vibrations and vice versa. This paper presents a study on the behaviour of various combinations of pairs of PZT piezoelectric ceramic materials positioned adjacent to each other with an intermediate gap submerged in water, where one piezoelectric ceramic material is excited by a cyclic electric field with constant frequency and amplitude displacement. The transmitted ultrasonic sound propagates through the medium and is received by the PZT ceramic at the other end, the ultrasonic sound signal amplitude displacement experiences attenuation during propagation due to acoustic impedance. The investigation focuses on understanding the causes of extremely high amplitude displacement attenuation that have been observed in various combinations of piezoelectric ceramic pairs that are submerged in water arranged in a manner stipulated earlier. by examining various combinations of pairs of these piezoelectric ceramics, their physical, electrical, and acoustic properties, and behaviour and attributing them to the observed significant signal attenuation. The experimental setup involves exciting one piezoelectric ceramic material at one end with a burst square cyclic electric field signal of constant frequency, which generates a burst of ultrasonic sound that propagates through the water medium to the adjacent piezoelectric ceramic at the other end. Mechanical vibrations of a PZT piezoelectric ceramic are measured using a double-beam laser Doppler vibrometer to mimic the incident ultrasonic waves generated and received ultrasonic waves on the other end due to mechanical vibrations of a PZT. The measured ultrasonic sound wave signals are continuously compared to the applied cyclic electric field at both ends. The impedance matching networks are continuously tuned at both ends to eliminate electromechanical impedance mismatch to improve ultrasonic transmission and reception. The study delves into various physical, electrical, and acoustic properties of the PZT piezoelectric ceramics, such as the electromechanical coupling factor, acoustic coupling, and elasticity, among others. These properties are analyzed to identify potential factors contributing to the unusually high acoustic impedance in the water medium between the ceramics. Additionally, impedance-matching networks are investigated at both ends to offset the high signal attenuation and improve overall system performance. The findings will be reported in this paper.

Keywords: acoustic impedance, impedance mismatch, piezoelectric ceramics, ultrasonic sound

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Authors: Ching-Yu Liao, Ju-Joan Wong

Abstract:

Recent years have shown a trend of younger stroke patients and an increase in ischemic strokes with the rise in stroke incidence. This has led to a growing demand for telemedicine, particularly during the COVID-19 pandemic, which has made the need for telemedicine even more urgent. This shift in healthcare is also closely related to advancements in Internet of Things (IoT) technology. Stroke-induced hemiparesis is a significant issue for patients. The medical community believes that if intervention occurs within three to six months of stroke onset, 80% of the residual effects can be restored to normal, a period known as the stroke golden period. During this time, patients undergo treatment and rehabilitation, and neural plasticity is at its best. Lower limb rehabilitation for stroke generally includes exercises such as support standing and walking posture, typically involving the healthy limb to guide the affected limb to achieve rehabilitation goals. Existing gait training aids in hospitals usually involve balance gait, sitting posture training, and precise muscle control, effectively addressing issues of poor gait, insufficient muscle activity, and inability to train independently during recovery. However, home training aids, such as braced and wheeled devices, often rely on the healthy limb to pull the affected limb, leading to lower usage of the affected limb, worsening circular walking, and compensatory movement issues. IoT technology connects devices via the internet to record, receive data, provide feedback, and adjust equipment for intelligent effects. Therefore, this study aims to explore how IoT can be integrated into existing gait training aids to monitor and sensor home rehabilitation movements, improve gait training compensatory issues through real-time feedback, and enable healthcare professionals to quickly understand patient conditions and enhance medical communication. To understand the needs of hemiparetic patients, a review of relevant literature from the past decade will be conducted. From the perspective of user experience, participant observation will be used to explore the use of home training aids by stroke patients and therapists, and interviews with physical therapists will be conducted to obtain professional opinions and practical experiences. Design specifications for home training aids for hemiparetic patients will be summarized. Applying IoT technology to lower limb training aids for stroke hemiparesis can help promote walking function recovery in hemiparetic patients, reduce muscle atrophy, and allow healthcare professionals to immediately grasp patient conditions and adjust gait training plans based on collected and analyzed information. Exploring these potential development directions provides a valuable reference for the further application of IoT technology in the field of medical rehabilitation.

Keywords: stroke, hemiplegia, rehabilitation, gait training, internet of things technology

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

Abstract:

In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

Procedia PDF Downloads 386