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This paper presents a simplified approach to introducing the Fast Fourier Transform (FFT) as a graphical tool in high school physics. By combining the use of smartphones, interactive Jupyter Notebooks, and widely available applications, the module supports the study of wave phenomena in both time and frequency domains. We describe the design and implementation of the intervention, outline the learning goals, detail the module structure, and report on the data collection methods and outcomes. In addition, we discuss practical observations and challenges, and we propose future improvements. Overall, the results show that students develop both conceptual understanding and technical skills

Annotated datasets play a significant role in developing advanced Artificial Intelligence (AI) models that can detect bridge structure defects autonomously. Most defect datasets contain visual images of surface defects; however, subsurface defect data such as delamination which are critical for effective bridge deck evaluations are typically rare or limited to laboratory specimens. Three Non-Destructive Evaluation (NDE) methods (Infrared Thermography (IRT), Impact Echo (IE), and Ground Penetrating Radar (GPR)) were used for concrete delamination detection and reinforcement corrosion detection. The authors have developed a unique NDE dataset, Structural Defect Network 2021 (SDNET2021), which consists of IRT, IE, and GPR data collected from five in-service reinforced concrete bridge decks. A delamination survey map locating the areas, extent and classes of delamination served as the ground truth for annotating IRT, IE and GPR field tests’ data in this study. The IRT were processed to create an ortho-mosaic maps for each deck and were aligned with the ground truth maps using image registration, affine transformation, image binarization, morphological operations, connected components and region props techniques to execute a semi-automatic pixel–wise annotation. Conventional methods such as Fast Fourier transform (FFT)/peak frequency and B-Scan were used for preliminary analysis for the IE and GPR signal data respectively. The quality of NDE data was verified using conventional Image Quality Assessment (IQA) techniques. SDNET2021 dataset consists of 557 delaminated and 1379 sound IE signals, 214,943 delaminated and 448,159 sound GPR signals, and about 1,718,083 delaminated and 2,862,597 sound IRT pixels. SDNET2021 addresses one of the major gaps in benchmarking, developing, training, and testing advanced deep learning models for concrete bridge evaluation by providing a publicly available annotated and validated NDE dataset.

Internal erosion, characterized by the migration of soil particles within hydraulic earth structures due to seepage, is a significant global concern for risk management and maintenance. Among various mechanisms contributing to internal erosion, suffusion emerges as a prominent process. It involves the simultaneous detachment, transport, and potential self-filtration of fine particles through the pore network, leading potentially to a change in permeability and shear strength. Thus, investigating the link between permeability and microstructure is a key to achieve a better understanding of suffusion and to predict its consequences on the soil’s permeability. The proposed methodology involves generating discrete element method-based samples, characterizing their constriction size distribution, and computing permeability using fast Fourier transform. While the Kozeny-Carman model was initially developed for stable microstructures, it may not apply to suffusion due to microstructural evolution. Thus, a modified approach is introduced, incorporating a characteristic constriction diameter computed from the constriction size distribution. This modified model is being compared against the original Kozeny-Carman one on fourteen gap-graded specimens. Encouraging results are herein being obtained so that the modified approach will be later used on flow modified specimens.

The deconvolution approach for the mapping of acoustic sources (DAMAS) based on orthogonal matching pursuit (OMP-DAMAS) has attracted much attention due to its advantages of high spatial resolution and excellent capability to suppress spurious sources. In this paper, we propose an improved version of OMP-DAMAS based on fast Fourier transformation (FFT), abbreviated as FFT-OMP-DAMAS. This method assumes that the array point spread functions (PSFs) are spatially shift-invariant. The sum of the product of the acoustic source distribution and PSFs is converted into a convolution form, which is further converted into a product in the wave number domain. With these conversions, FFT can be used to improve the solving speed. Both simulations and experiments show that the proposed method not only inherits the advantages of OMP-DAMAS in terms of spatial resolution and spurious source suppression but also significantly improves the computational efficiency compared with OMP-DAMAS.

Predicting international rankings has always been a demanding area for Universities and Higher Educational Institutions (HEIs) all over the world in the recent decade. In this research work, a novel tool EnFftRP (Ensembled Fast Fourier Transformed Ranking Prediction) is developed for predicting international ranks of various universities and HEIs. It uses a hybrid ensembled model in duology with the Fast Fourier Transformation (FFT). Ensemble model improves the prediction accuracies which are elevated further using FFT. The fourier processing algorithm, being an influential computational concept for data anatomy is a novel approach applied to the ensembled model. A combination of six base models Decision Tree, Support Vector Machine, Multilayer Perceptron, K-Nearest Neighbour, Random Forest and Logistic Regression are deployed for the construction of ensembled model. The data set being used is Shanghai World Ranking University Dataset for 14 years ranging from 2005 to 2018. It is split into training and test data set. The training data set is considered from year 2005–2014 and the test dataset from 2015 to 2018. It is empirically established that proposed tool produces highly promising prediction parameters as accuracies (95%), specificities (94.41%), sensitivities (95.54%), Productively Predicted Values (94.94%), Non-Productively Predicted Values (95.07%), F1-score (97.40%) and Kappa score (0.90) as compared to obtained by similar models like RSFT and others. To the best of our knowledge, till now no tool exists which can predict the ranks of HEIs with this much high predictive power.

BackgroundOver the past decades, extensive research has examined the interactions between space weather, solar activity, and the biosphere, particularly their implications for agricultural productivity. However, the specific mechanisms linking solar activity to agricultural performance remain underexplored, particularly in the context of bioeconomic efficiency. Understanding these connections is crucial for mitigating adverse environmental impacts and optimizing agricultural productivity. Research has highlighted the synchronization between multi-year solar activity cycles, particularly the 11-year solar cycles, and agricultural performance. Forecasting these cycles holds the potential to anticipate fluctuations in agricultural productivity, thus enabling more effective resource planning and enhancing profitability within the agri-food industry.MethodologyThis study employs Fast Fourier Transform (FFT) and advanced statistical tools from Microcal Origin 6.0 to analyze time-series data for 10 key agricultural products in Germany over a 61-year period. The study integrates solar radiation data, meteorological variables, and regional agricultural production data to investigate the relationship between solar activity and crop yields.ResultsThe analysis reveals significant temporal associations between solar activity cycles and agricultural yields, identified through both cross-correlation and spectral analysis. These findings indicate that solar activity, particularly during the 11-year solar cycle, plays a notable role in shaping agricultural productivity.ConclusionThe results confirm the synchronization between solar activity and agricultural performance. These insights have significant implications for the agri-food sector, suggesting that incorporating solar activity forecasts into agricultural management strategies could enhance resource allocation, improve crop yield predictions, and promote sustainable agricultural practices.

Marine gravity anomalies are crucial parameters and elements for determining coastal and ocean geoid, tectonics and crustal structures, as well as offshore studies. This study aims to derive and develop a marine gravity anomaly model over Malaysian seas from multi-mission altimetry data. Universiti Teknologi Malaysia 2020 Mean Sea Surface Model is computed based on along-track data from nine satellite missions, incorporating TOPEX, Jason-1, Jason-2, ERS-2, Geosat Follow on (GFO), Envisat-1, CryoSat-2, SARAL/AltiKa, and Sentinel-3A. The data exploited are from 1993 to 2019 (27 years). Residual gravity anomaly is computed using Gravity Software, and two-dimensional planar Fast Fourier Transformation method is applied. The evaluation, selection, blunder detection, combination, and re-gridding of the altimetry-derived gravity anomalies and Global Geopotential Model data are demonstrated. Cross-validation procedure is employed for data cleaning and quality control using the Kriging interpolation method. Then, cross-validation procedure is applied to the tapering window width 200, which adopting the GECO model denotes the optimum gravity anomaly with root mean square errors in the range of ± 4.2472 mGal to ± 6.0202 mGal. The findings suggest that the estimated marine gravity anomaly is acceptable to be implemented in the marine geoid determination and bathymetry estimation over Malaysian seas. In addition, the results of this study are valuable for geodetic and geophysical applications in marine areas. Key points Along-track altimetry data are used for mean sea surface derivation. Mean sea surface model is utilised in the estimation of marine gravity anomalies. Global Geopotential Model is crucial in the marine gravity estimation of a region.

This paper proposes an approach to extract the mode shapes of beam-like structures from the dynamic response of a moving mass. When a mass passes through a beam containing several artificially installed humps, its vertical acceleration can be recorded. After applying fast Fourier transformation to the dynamic response, one can extract the mode shapes of the beam. The surface roughness was neglected compared to the humps and its adverse effect on the extraction was reduced. The passing mass performs as both “exciter” and “massage receiver”; therefore, this method requires only one single accelerometer, making it more convenient and time saving in practice. Moreover, to estimate the possible error in extracting mode shapes, a wavenumber domain filtering technique is used to reconstruct the general profiles of mode shapes. Experimental validation of this approach in laboratory scale was conducted. The experimental results show that the proposed method performs well in extracting lower order mode shapes. It should also be noted that the passing mass can not have a very high velocity (e.g. 80 mm/s), otherwise the mass may jump and separate from the beam, and the proposed method may fail to identify mode shapes.

This work investigates the effects of tool wear on surface roughness (R a ), chip formation mechanisms and chip morphology in the microendmilling of aluminum alloy (AA 1100) using acoustic emission (AE) signals. The acquired AE signals are analysed in the time domain, frequency domain using fast Fourier transformation (FFT) and the discrete wavelet transformation (DWT) technique. The time domain analysis indicates that the root mean square of the AE (AE RMS ) signals is sensitive to the formation of the buildup edge apart from effective machining. The frequency domain analysis indicates that the dominant frequency of the AE signals lies between 150 and 300 kHz. The AE-specific energies are computed by decomposing the AE signals in different frequency bands, using the DWT technique. The higher and lower orders of AE-specific energies are obtained. The higher order of AE-specific energies indicates chip formation mechanisms such as shearing and microfracture. Chip morphology studies are carried out using the FFT analysis. The FFT indicates that low-frequency and low-amplitude AE lead to tight curl chips, while high-frequency and high-amplitude AE lead to elemental/short comma chips. This work provides new significant inferences on tool wear, chip formation mechanisms and chip morphology in the microendmilling of AA 1100.

Intelligent fault diagnosis methods based on signal analysis have been widely used for bearing fault diagnosis. These methods use a pre-determined transformation (such as empirical mode decomposition, fast Fourier transform, discrete wavelet transform) to convert time-series signals into frequency domain signals, the performance of dignostic system is significantly rely on the extracted features. However, extracting signal characteristic is fairly time consuming and depends on specialized signal processing knowledge. Although some studies have developed highly accurate algorithms, the diagnostic results rely heavily on large data sets and unreliable human analysis. This study proposes an automatic feature learning neural network that utilizes raw vibration signals as inputs, and uses two convolutional neural networks with different kernel sizes to automatically extract different frequency signal characteristics from raw data. Then long short-term memory was used to identify the fault type according to learned features. The data is down-sampled before inputting into the network, greatly reducing the number of parameters. The experiment shows that the proposed method can not only achieve 98.46% average accuracy, exceeding some state-of-the-art intelligent algorithms based on prior knowledge and having better performance in noisy environments.