Use of Non-Destructive Ultrasonic Techniques as Characterization Tools for Different Varieties of Wine.

In this work, we have verified how non-destructive ultrasonic evaluation allows for acoustically characterizing different varieties of wine. For this, a 3.5 MHz transducer has been used by means of an immersion technique in pulse-echo mode. The tests were performed at various temperatures in the range 14-18 °C. The evaluation has been carried out studying, on the one hand, conventional analysis parameters (velocity and attenuation) and, on the other, less conventional parameters (frequency components). The experimental study comprised two stages. In the first, the feasibility of the study was checked by inspecting twelve samples belonging to six varieties of red and white wine. The results showed clearly higher ultrasonic propagation velocity values in the red wine samples. In the second, nine samples of different monovarietal wine varieties (Grenache, Tempranillo and Cabernet Sauvignon) were analyzed. The results show how ultrasonic velocity makes it possible to unequivocally classify the grape variety used in winemaking with the Cabernet Sauvignon variety having the highest values and the Grenache the lowest. In addition, the wines of the Tempranillo variety are those that present higher values of the attenuation coefficient, and those from the Grenache variety transmit higher frequency waves.

Behavioral skill practice as a predictor of mood and family functioning in adolescents with bipolar and depressive mood disorders: Results of a 6-month randomized trial of family-focused therapy.

OBJECTIVE: Behavioral interventions require considerable practice of treatment skills in between therapy sessions. The effects of these treatments may vary with the degree to which patients are able to implement these practices. In offspring of parents with bipolar and major depressive disorders, we examined whether youth who frequently practiced communication and problem-solving skills between family-focused therapy (FFT) sessions had less severe mood symptoms and better psychosocial functioning over 6 months than youth who practiced less frequently. METHODS: We randomly assigned offspring (ages 12-19) of parents with mood disorders to 12 sessions of FFT plus a mobile app that encouraged the practice of communication, problem-solving and mood management skills (FFT-MyCoachConnect [MCC] condition) or 12 sessions of FFT with an app that only allowed for tracking of symptoms and stress (FFT-Track condition). Independent evaluators assessed youths' mood and psychosocial functioning at 9-week intervals over 27 weeks. Clinicians rated participants' between-session skill practice at each FFT session. RESULTS: FFT-MCC was associated with more frequent skill practice than FFT-Track over 18 weeks of treatment. Skill practice was associated with reductions in youths' mood instability and perceptions of family conflict over 27 weeks in both app conditions. Skill practice mediated the effects of app condition on youths' mood instability and family functioning. CONCLUSIONS: Mobile applications as adjuncts to family therapy for youth with mood disorders can help increase skill practice. These findings provide preliminary causal evidence for behavioral skill practice improving mood symptoms and family functioning among youth with mood disorders.

An ensemble deep learning models approach using image analysis for cotton crop classification in AI-enabled smart agriculture.

BACKGROUND: Agriculture is one of the most crucial assets of any country, as it brings prosperity by alleviating poverty, food shortages, unemployment, and economic instability. The entire process of agriculture comprises many sectors, such as crop cultivation, water irrigation, the supply chain, and many more. During the cultivation process, the plant is exposed to many challenges, among which pesticide attacks and disease in the plant are the main threats. Diseases affect yield production, which affects the country's economy. Over the past decade, there have been significant advancements in agriculture; nevertheless, a substantial portion of crop yields continues to be compromised by diseases and pests. Early detection and prevention are crucial for successful crop management. METHODS: To address this, we propose a framework that utilizes state-of-the-art computer vision (CV) and artificial intelligence (AI) techniques, specifically deep learning (DL), for detecting healthy and unhealthy cotton plants. Our approach combines DL with feature extraction methods such as continuous wavelet transform (CWT) and fast Fourier transform (FFT). The detection process involved employing pre-trained models such as AlexNet, GoogLeNet, InceptionV3, and VGG-19. Implemented models performance was analysed based on metrics such as accuracy, precision, recall, F1-Score, and Confusion matrices. Moreover, the proposed framework employed ensemble learning framework which uses averaging method to fuse the classification score of individual DL model, thereby improving the overall classification accuracy. RESULTS: During the training process, the framework achieved better performance when features extracted from CWT were used as inputs to the DL model compared to features extracted from FFT. Among the learning models, GoogleNet obtained a remarkable accuracy of 93.4% and a notable F1-score of 0.953 when trained on features extracted by CWT in comparison to FFT-extracted features. It was closely followed by AlexNet and InceptionV3 with an accuracy of 93.4% and 91.8% respectively. To further improve the classification accuracy, ensemble learning framework achieved 98.4% on the features extracted from CWT as compared to feature extracted from FFT. CONCLUSION: The results show that the features extracted as scalograms more accurately detect each plant condition using DL models, facilitating the early detection of diseases in cotton plants. This early detection leads to better yield and profit which positively affects the economy.

Non-invasive and probeless rapid in-vitro monitoring and quantification of HUVECs counts based on FFT impedimetery.

Introduction: The endothelial cells derived from the human vein cord (HUVECs) are used as in-vitro models for studying cellular and molecular pathophysiology, drug and hormones transport mechanisms, or pathways. In these studies, the proliferation and quantity of cells are important features that should be monitored and assessed regularly. So rapid, easy, noninvasive, and inexpensive methods are favorable for this purpose. Methods: In this work, a novel method based on fast Fourier transform square-wave voltammetry (FFTSWV) combined with a 3D printed electrochemical cell including two inserted platinum electrodes was developed for non-invasive and probeless rapid in-vitro monitoring and quantification of human umbilical vein endothelial cells (HUVECs). The electrochemical cell configuration, along with inverted microscope images, provided the capability of easy use, online in-vitro monitoring, and quantification of the cells during proliferation. Results: HUVECs were cultured and proliferated at defined experimental conditions, and standard cell counts in the initial range of 12 500 to 175 000 were prepared and calibrated by using a hemocytometer (Neubauer chamber) counting for electrochemical measurements. The optimum condition, for FFTSWV at a frequency of 100 Hz and 5 mV amplitude, were found to be a safe electrochemical measurement in the cell culture medium. In each run, the impedance or admittance measurement was measured in a 5 seconds time window. The total measurements were fulfilled at 5, 24, and 48 hours after the seeding of the cells, respectively. The recorded microscopic images before every electrochemical assay showed the conformity of morphology and objective counts of cells in every plate well. The proposed electrochemical method showed dynamic linearity in the range of 12 500-265 000 HUVECs 48 hours after the seeding of cells. Conclusion: The proposed electrochemical method can be used as a simple, fast, and noninvasive technique for tracing and monitoring of HUVECs population in in-vitro studies. This method is highly cheap in comparison with other traditional tools. The introduced configuration has the versatility to develop electrodes for the study of various cells and the application of other electrochemical designations.

Frequency analysis of ankle joint quasi-stiffness during quiet unperturbed standing in Chiari Malformation.

Chiari Malformation (Chiari) is a congenital condition occurring from an inferior herniation of the cerebellar tonsils into the foramen magnum. Given the role of the cerebellum in postural control, it is reasonable to expect joint motion to be affected in this patient population. In fact, joint stiffness is a common self-reported symptom of Chiari, however it has never been assessed in these individuals. This study aimed to examine if ankle joint quasi-stiffness is correlated with Chiari severity. The human body was considered as an inverted oscillating pendulum without damping. A Fast Fourier Transform was used to extract natural frequency from the center of pressure trajectories during upright standing. Ankle joint quasi-stiffness was then calculated using the relationship between natural frequency and moment of inertia. Twelve Chiari participants (Chiari), six with decompression surgery (Chiari-D) and six without (Chiari-ND), and eight control individuals (Control) participated. Participants completed three, 30-second quiet standing trials on a force plate, focused on a target three meters in front of them. Chiari, regardless of surgery, had significantly lower quasi-stiffness than controls (Chiari-D vs. Control p = 0.0011, Chiari-ND vs. Control, p < 0.001). The proposed method is advantageous as it incorporates the entire center of pressure signal, minimizes error from instantaneous muscular dynamics, and does not require motion capture.

Enhancement of Classifier Performance with Adam and RanAdam Hyper-Parameter Tuning for Lung Cancer Detection from Microarray Data-In Pursuit of Precision.

Microarray gene expression analysis is a powerful technique used in cancer classification and research to identify and understand gene expression patterns that can differentiate between different cancer types, subtypes, and stages. However, microarray databases are highly redundant, inherently nonlinear, and noisy. Therefore, extracting meaningful information from such a huge database is a challenging one. The paper adopts the Fast Fourier Transform (FFT) and Mixture Model (MM) for dimensionality reduction and utilises the Dragonfly optimisation algorithm as the feature selection technique. The classifiers employed in this research are Nonlinear Regression, Naïve Bayes, Decision Tree, Random Forest and SVM (RBF). The classifiers' performances are analysed with and without feature selection methods. Finally, Adaptive Moment Estimation (Adam) and Random Adaptive Moment Estimation (RanAdam) hyper-parameter tuning techniques are used as improvisation techniques for classifiers. The SVM (RBF) classifier with the Fast Fourier Transform Dimensionality Reduction method and Dragonfly feature selection achieved the highest accuracy of 98.343% with RanAdam hyper-parameter tuning compared to other classifiers.

Efficient water-related failure detection in PEM fuel cells: Combining a PEMFCs fractional order impedance model with FFT-PWM techniques and artificial neural network classification.

Water management and early detection of faults in proton exchange membrane fuel cells (PEMFCs) are among the most critical constraints that limit the optimal spread of this type of energy. Consequently, it is necessary to enhance the reliability and durability of PEMFCs by developing an approach to diagnose and identify water failure modes. This paper proposes an effective and simple method to detect, diagnose, and classify various water failure modes in PEMFCs using a hybrid diagnostic approach. This approach combines the PEMFC fractional order impedance model (FOIM) with fast Fourier transform pulse width modulation (FFT-PWM) techniques and artificial neural network pattern recognition (ANN-PR) classification. The results show an accurate match between the electrochemical impedance spectroscopy (EIS) experimental data, the Nyquist impedance spectra of FOIM, and the FFT-PWM algorithm as a proposed alternative technique to EIS measurements. Learning of ANN-PR was performed using the frequency spectrum amplitude (FSA) database of the voltage and current signals produced by the PEMFCs FOIM DC/DC boost converter, which was generated using the FFT-PWM algorithm. The ANN-PR achieved low values for error accuracy, with the Low Square Error and Learning Error reaching 6.676 × 10-19 and 1.888 × 10-16, respectively. The elements inside the confusion matrix and the rest of the matrices confirm that the proposed model's accuracy, precision, recall, and high F1 score reached 100%. Furthermore, all predictions made by the ANN-PR model were consistently accurate across all areas of failure detection. Overall, the proposed method helps in analyzing, diagnosing, and classifying fuel cell failure modes such as flooding and drying, which may simplify the health assessment of PEMFC.

Machine learning-enhanced drug testing for simultaneous morphine and methadone detection in urinary biofluids.

The simultaneous identification of drugs has considerable difficulties due to the intricate interplay of analytes and the interference present in biological matrices. In this study, we introduce an innovative electrochemical sensor that overcomes these hurdles, enabling the precise and simultaneous determination of morphine (MOR), methadone (MET), and uric acid (UA) in urine samples. The sensor harnesses the strategically adapted carbon nanotubes (CNT) modified with graphitic carbon nitride (g-C3N4) nanosheets to ensure exceptional precision and sensitivity for the targeted analytes. Through systematic optimization of pivotal parameters, we attained accurate and quantitative measurements of the analytes within intricate matrices employing the fast Fourier transform (FFT) voltammetry technique. The sensor's performance was validated using 17 training and 12 test solutions, employing the widely acclaimed machine learning method, partial least squares (PLS), for predictive modeling. The root mean square error of cross-validation (RMSECV) values for morphine, methadone, and uric acid were significantly low, measuring 0.1827 µM, 0.1951 µM, and 0.1584 µM, respectively, with corresponding root mean square error of prediction (RMSEP) values of 0.1925 µM, 0.2035 µM, and 0.1659 µM. These results showcased the robust resiliency and reliability of our predictive model. Our sensor's efficacy in real urine samples was demonstrated by the narrow range of relative standard deviation (RSD) values, ranging from 3.71 to 5.26%, and recovery percentages from 96 to 106%. This performance underscores the potential of the sensor for practical and clinical applications, offering precise measurements even in complex and variable biological matrices. The successful integration of g-C3N4-CNT nanocomposites and the robust PLS method has driven the evolution of sophisticated electrochemical sensors, initiating a transformative era in drug analysis.

College students' satisfaction with online teaching during the COVID-19 pandemic: Evidence from Hubei Province, China.

To evaluate the performance of online teaching during the COVID-19 period, we collected 1886 survey data from college students in Hubei Province, China. The scoring rules of the Framework for Teaching were used to measure college students' satisfaction with online teaching, and an econometric model was constructed to empirically validate its dynamic influences. We found that college students' satisfaction with online teaching during the COVID-19 pandemic was lower than that with offline teaching. Online teaching satisfaction was significantly affected by variables of class size, proportion of online teaching, epidemic severity, college grade, network, course classification, major classification, and the teacher's age and skills. It was further found that as COVID-19 gradually dissipated, offline teaching should be resumed as soon as possible. These findings objectively evaluate the teaching performance of college students during the COVID-19 pandemic and can provide suggestions for optimizing online teaching during future emergencies.

The effects of multi-colour light filtering glasses on human brain wave activity.

The prevalence of electronic screens in modern society has significantly increased our exposure to high-energy blue and violet light wavelengths. Accumulating evidence links this exposure to adverse visual and cognitive effects and sleep disturbances. To mitigate these effects, the optical industry has introduced a variety of filtering glasses. However, the scientific validation of these glasses has often been based on subjective reports and a narrow range of objective measures, casting doubt on their true efficacy. In this study, we used electroencephalography (EEG) to record brain wave activity to evaluate the effects of glasses that filter multiple wavelengths (blue, violet, indigo, and green) on human brain activity. Our results demonstrate that wearing these multi-colour light filtering glasses significantly reduces beta wave power (13-30 Hz) compared to control or no glasses. Prior research has associated a reduction in beta power with the calming of heightened mental states, such as anxiety. As such, our results suggest that wearing glasses such as the ones used in this study may also positively change mental states, for instance, by promoting relaxation. This investigation is innovative in applying neuroimaging techniques to confirm that light-filtering glasses can induce measurable changes in brain activity.

Analysis of Unique Motility of the Unicellular Green Alga Chlamydomonas reinhardtii at Low Temperatures down to -8 °C.

Previous studies of motility at low temperatures in Chlamydomonas reinhardtii have been conducted at temperatures of up to 15 °C. In this study, we report that C. reinhardtii exhibits unique motility at a lower temperature range (-8.7 to 1.7 °C). Cell motility was recorded using four low-cost, easy-to-operate observation systems. Fast Fourier transform (FFT) analysis at room temperature (20-27 °C) showed that the main peak frequency of oscillations ranged from 44 to 61 Hz, which is consistent with the 60 Hz beat frequency of flagella. At lower temperatures, swimming velocity decreased with decreasing temperature. The results of the FFT analysis showed that the major peak shifted to the 5-18 Hz range, suggesting that the flagellar beat frequency was decreasing. The FFT spectra had distinct major peaks in both temperature ranges, indicating that the oscillations were regular. This was not affected by the wavelength of the observation light source (white, red, green or blue LED) or the environmental spatial scale of the cells. In contrast, cells in a highly viscous (3.5 mPa·s) culture at room temperature showed numerous peaks in the 0-200 Hz frequency band, indicating that the oscillations were irregular. These findings contribute to a better understanding of motility under lower-temperature conditions in C. reinhardtii.

Through the wall human heart beat detection using single channel CW radar.

Single-channel continuous wave (CW) radar is widely used and has gained popularity due to its simple architecture despite its inability to measure the range and angular location of the target. Its popularity arises in the industry due to the simplicity of the required components, the low demands on the sampling rate, and their low costs. Through-the-wall life signs detection using microwave Doppler Radar is an active area of research and investigation. Most of the work in the literature focused on utilizing multi-channel frequency modulated continuous wave (FMCW), CW, and ultra-wideband (UWB) radar for their capability of range and direction of arrival (DOA) estimation. In this paper, through-the-wall single-subject and two-subject concurrent heart rate detection using single-channel 24-GHz CW radar leveraged with maximal overlap discrete wavelet transform (MODWT) is proposed. Experimental results demonstrated that the repetitive measurement of seven different subjects at a distance of 20 cm up to 100 cm through two different barriers (wood and brick wall) showed an average accuracy of heart rate extraction of 95.27% for varied distances (20-100 cm) in comparison with the Biopac ECG acquisition signal. Additionally, the MODWT method can also isolate the independent heartbeat waveforms from the two subjects' concurrent measurements through the wall. This involved four trials with eight different subjects, achieving an accuracy of 97.04% for a fixed distance of 40 cm from the Radar without estimating the angular location of the subjects. Notably, it also superseded the performance of the direct FFT method for the single subject after 40 cm distance measurements. The proposed simpler architecture of single-channel CW radar leveraged with MODWT has several potential applications, including post-disaster search and rescue scenarios for finding the trapped, injured people under the debris, emergency evacuation, security, surveillance, and patient vital signs monitoring.

LieWaves: dataset for lie detection based on EEG signals and wavelets.

This study introduces an electroencephalography (EEG)-based dataset to analyze lie detection. Various analyses or detections can be performed using EEG signals. Lie detection using EEG data has recently become a significant topic. In every aspect of life, people find the need to tell lies to each other. While lies told daily may not have significant societal impacts, lie detection becomes crucial in legal, security, job interviews, or situations that could affect the community. This study aims to obtain EEG signals for lie detection, create a dataset, and analyze this dataset using signal processing techniques and deep learning methods. EEG signals were acquired from 27 individuals using a wearable EEG device called Emotiv Insight with 5 channels (AF3, T7, Pz, T8, AF4). Each person took part in two trials: one where they were honest and another where they were deceitful. During each experiment, participants evaluated beads they saw before the experiment and stole from them in front of a video clip. This study consisted of four stages. In the first stage, the LieWaves dataset was created with the EEG data obtained during these experiments. In the second stage, preprocessing was carried out. In this stage, the automatic and tunable artifact removal (ATAR) algorithm was applied to remove the artifacts from the EEG signals. Later, the overlapping sliding window (OSW) method was used for data augmentation. In the third stage, feature extraction was performed. To achieve this, EEG signals were analyzed by combining discrete wavelet transform (DWT) and fast Fourier transform (FFT) including statistical methods (SM). In the last stage, each obtained feature vector was classified separately using Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and CNNLSTM hybrid algorithms. At the study's conclusion, the most accurate result, achieving a 99.88% accuracy score, was produced using the LSTM and DWT techniques. With this study, a new data set was introduced to the literature, and it was aimed to eliminate the deficiencies in this field with this data set. Evaluation results obtained from the data set have shown that this data set can be effective in this field.

Sacroiliitis diagnosis based on interpretable features and multi-task learning.

Objective.Sacroiliitis is an early pathological manifestation of ankylosing spondylitis (AS), and a positive sacroiliitis test on imaging may help clinical practitioners diagnose AS early. Deep learning based automatic diagnosis algorithms can deliver grading findings for sacroiliitis, however, it requires a large amount of data with precise labels to train the model and lacks grading features visualization. In this paper, we aimed to propose a radiomics and deep learning based deep feature visualization positive diagnosis algorithm for sacroiliitis on CT scans. Visualization of grading features can enhance clinical interpretability with visual grading features, which assist doctors in diagnosis and treatment more effectively.Approach.The region of interest (ROI) is identified by segmenting the sacroiliac joint (SIJ) 3D CT images using a combination of the U-net model and certain statistical approaches. Then, in addition to extracting spatial and frequency domain features from ROI according to the radiographic manifestations of sacroiliitis, the radiomics features have also been integrated into the proposed encoder module to obtain a powerful encoder and extract features effectively. Finally, a multi-task learning technique and five-class labels are utilized to help with performing positive tests to reduce discrepancies in the evaluation of several radiologists.Main results.On our private dataset, proposed methods have obtained an accuracy rate of 87.3%, which is 9.8% higher than the baseline and consistent with assessments made by qualified medical professionals.Significance.The results of the ablation experiment and interpreting analysis demonstrated that the proposed methods are applied in automatic CT scan sacroiliitis diagnosis due to their excellently interpretable and portable advantages.

Use of 2D FFT and DTW in Protein Sequence Comparison.

Protein sequence comparison remains a challenging work for the researchers owing to the computational complexity due to the presence of 20 amino acids compared with only four nucleotides in Genome sequences. Further, protein sequences of different species are of different lengths; it throws additional changes to the researchers to develop methods, specially alignment-free methods, to compare protein sequences. In this work, an efficient technique to compare protein sequences is developed by a graphical representation. First, the classified grouping of 20 amino acids with a cardinality of 4 based on polar class is considered to narrow down the representational range from 20 to 4. Then a unit vector technique based on a two-quadrant Cartesian system is proposed to provide a new two-dimensional graphical representation of the protein sequence. Now, two approaches are proposed to cope with the varying lengths of protein sequences from various species: one uses Dynamic Time Warping (DTW), while the other one uses a two-dimensional Fast Fourier Transform (2D FFT). Next, the effectiveness of these two techniques is analyzed using two evaluation criteria-quantitative measures based on symmetric distance (SD) and computational speed. An analysis is performed on five data sets of 9 ND4, 9 ND5, 9 ND6, 12 Baculovirus, and 24 TF proteins under the two methods. It is found that the FFT-based method produces the same results as DTW but in less computational time. It is found that the result of the proposed method agrees with the known biological reference. Further, the present method produces better clustering than the existing ones.

Temperature dependence of acoustic parameters in pure and blended edible oils: Implications for characterization and authentication.

This research investigates the temperature-dependent variation of diverse acoustic parameters in samples of edible oils. It further supplements previous studies on the effectiveness of non-destructive ultrasonic inspection in the authentication of edible oils. The oils under examination consist of pure samples of olive, sunflower, and corn oils, as well as variable mixtures ranging from 20 % to 80 % of the more expensive one (olive oil) with the other two, simulating a hypothetical adulteration scenario. The studied acoustic parameters are related to the velocity, attenuation, and frequency components present in 2.25 MHz ultrasonic waves propagating through the oil samples within a temperature range of 24 °C to 34 °C. The results confirm the suitability of non-destructive ultrasonic inspection in evaluating and detecting the adulteration of olive oil with economically inferior oils such as sunflower and corn. Additionally, this study provides added value by laying the groundwork for a non-destructive and innovative determination of the fatty acid profile of an edible oil based on the evolution of the aforementioned ultrasonic parameters with temperature. The findings hold potential for enhancing the authenticity assessment and quality control of edible oils in the food industry.

A Two-Dimensional Precision Level for Real-Time Measurement Based on Zoom Fast Fourier Transform.

This paper proposes a two-dimensional precision level for real-time measurement using a zoom fast Fourier transform (zoom FFT)-based decoupling algorithm that was developed and integrated in an FPGA. This algorithm solves the contradiction between obtaining high resolution and obtaining high measurement speed, and achieves both high angle-resolution measurement and real-time measurement. The proposed level adopts a silicone-oil surface as the angle-sensitive interface and combines the principle of homodyne interference. By analyzing the frequency of the interference fringes, the angle variation can be determined. The zoom-FFT-based decoupling algorithm improves the system's frequency resolution of the interference fringes, thereby significantly enhancing the angle resolution. Furthermore, this algorithm improves the efficiency of angle decoupling, while the angle decoupling process can also be transplanted to the board to realize real-time measurement of the level. Finally, a prototype based on the level principle was tested to validate the effectiveness of the proposed method. The principle analysis and test results showed that the angle resolution of the prototype improved from 9 arcsec to about 0.1 arcsec using this angle-solution method. At the same time, the measurement repeatability of the prototype was approximately ±0.2 arcsec. In comparison with a commercial autocollimator, the angle measurement accuracy reached ±0.6 arcsec.

Inspection of Spot Welded Joints with the Use of the Ultrasonic Surface Wave.

Spot welded joints play a crucial role in the construction of modern automobiles, serving as a vital method for enhancing the structural integrity, strength, and durability of the vehicle body. Taking into account spot welding process in automotive bodies, numerous defects can arise, such as insufficient weld nugget diameter. It may have evident influence on vehicle operation or even contribute to accidents on the road. Hence, there is a need for non-invasive methods that allow to assess the quality of the spot welds without compromising their structural integrity and characteristics. Thus, this study describes a novel method for assessing spot welded joints using ultrasound technology. The usage of ultrasonic surface waves is the main component of the proposed advancement. The study employed ultrasonic transducers operating at a frequency of 10 MHz and a specially designed setup for testing various spot welded samples. The parameters of the spot welding procedure and the size of the weld nugget caused differences in the ultrasonic surface waveforms that were recorded during experiments. One of the indicators of weld quality was the amplitude of the ultrasonic pulse. For low quality spot welds, the amplitude amounted to around 25% of the maximum value when using single-sided transducers. Conversely, for high-quality welds an amplitude of 90% was achieved. Depending on the size of the weld nugget, a larger or smaller amount of wave energy is transferred, which results in a smaller or larger amplitude of the ultrasonic pulse. Comparable results were obtained when employing transducers on both sides of the tested joint, as an amplitude ranging from 13% for inferior welds to 97% for superior ones was observed. This research confirmed the feasibility of employing surface waves to assess the diameter of the weld nugget accurately.

Compressed SVD-based L + S model to reconstruct undersampled dynamic MRI data using parallel architecture.

BACKGROUND: Magnetic Resonance Imaging (MRI) is a highly demanded medical imaging system due to high resolution, large volumetric coverage, and ability to capture the dynamic and functional information of body organs e.g. cardiac MRI is employed to assess cardiac structure and evaluate blood flow dynamics through the cardiac valves. Long scan time is the main drawback of MRI, which makes it difficult for the patients to remain still during the scanning process. OBJECTIVE: By collecting fewer measurements, MRI scan time can be shortened, but this undersampling causes aliasing artifacts in the reconstructed images. Advanced image reconstruction algorithms have been used in literature to overcome these undersampling artifacts. These algorithms are computationally expensive and require a long time for reconstruction which makes them infeasible for real-time clinical applications e.g. cardiac MRI. However, exploiting the inherent parallelism in these algorithms can help to reduce their computation time. METHODS: Low-rank plus sparse (L+S) matrix decomposition model is a technique used in literature to reconstruct the highly undersampled dynamic MRI (dMRI) data at the expense of long reconstruction time. In this paper, Compressed Singular Value Decomposition (cSVD) model is used in L+S decomposition model (instead of conventional SVD) to reduce the reconstruction time. The results provide improved quality of the reconstructed images. Furthermore, it has been observed that cSVD and other parts of the L+S model possess highly parallel operations; therefore, a customized GPU based parallel architecture of the modified L+S model has been presented to further reduce the reconstruction time. RESULTS: Four cardiac MRI datasets (three different cardiac perfusion acquired from different patients and one cardiac cine data), each with different acceleration factors of 2, 6 and 8 are used for experiments in this paper. Experimental results demonstrate that using the proposed parallel architecture for the reconstruction of cardiac perfusion data provides a speed-up factor up to 19.15× (with memory latency) and 70.55× (without memory latency) in comparison to the conventional CPU reconstruction with no compromise on image quality. CONCLUSION: The proposed method is well-suited for real-time clinical applications, offering a substantial reduction in reconstruction time.

Robust Symbol Timing Synchronization for Initial Access under LEO Satellite Channel.

This paper proposes a robust symbol timing synchronization scheme for return link initial access based on the Digital Video Broadcasting-Return Channel via Satellite 2nd generation (DVB-RCS2) system for the Low Earth Orbit (LEO) satellite channel. In most cases, the feedforward estimator structure is considered for implementing Time Division Multiple Access (TDMA) packet demodulators such as the DVB-RCS2 system. More specifically, the Non-Data-Aided (NDA) approach, without using any kind of preamble, pilot, and postamble symbols, is applicable for fine symbol timing synchronization. However, it hinders the improvement in estimation accuracy, especially when dealing with short packet lengths during the initial access from the User Terminal (UT) to the Gateway (GW). Moreover, when a UT sends a short random access packet for initial access or resource request to the LEO satellite channel, the conventional schemes suffer from a large Doppler error depending on UT's location in a beam and satellite velocity. To ameliorate these problems, we propose a novel symbol timing synchronization algorithm for GW, and its advantage is confirmed through computer simulation.