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The ability to sense propagating electromagnetic plane waves based on their directions of arrival (DOAs) is fundamental to a range of radio frequency (RF) sensing, communications, and imaging applications. This paper introduces an algorithm for the wideband true time delay digital delay Vandermonde matrix (DVM), utilizing Thiran fractional delays that are useful for realizing RF sensors having multiple look DOA support. The digital DVM algorithm leverages sparse matrix factorization to yield multiple simultaneous RF beams for low-complexity sensing applications. Consequently, the proposed algorithm offers a reduction in circuit complexity for multi-beam digital wideband beamforming systems employing Thiran fractional delays. Unlike finite impulse response filter-based approaches which are wideband but of a high filter order, the Thiran filters trade usable bandwidth in favor of low-complexity circuits. The phase and group delay responses of Thiran filters with delays of a fractional sampling period will be demonstrated. Thiran filters show favorable results for sample delay blocks with a temporal oversampling factor of three. Thiran fractional delays of orders three and four are mapped to Xilinx FPGA RF-SoC technologies for evaluation. The preliminary results of the APF-based Thiran fractional delays on FPGA can potentially be used to realize DVM factorizations using application-specific integrated circuit (ASIC) technology. [ABSTRACT FROM AUTHOR]

The Laplace transform is a central mathematical tool for analysing 1D/2D signals and for the solution to PDEs; however, its definition and computation on arbitrary data is still an open research problem. We introduce the Laplace transform on arbitrary domains and focus on the spectral Laplace transform, which is defined by applying a 1D filter to the Laplacian spectrum of the input domain. The spectral Laplace transform satisfies standard properties of the 1D and 2D transforms, such as dilation, translation, scaling, derivation, localisation, and relations with the Fourier transform. The spectral Laplace transform is enough general to be applied to signals defined on different discrete data, such as graphs, 3D surface meshes, and point sets. Working in the spectral domain and applying polynomial and rational polynomial approximations, we achieve a stable computation of the spectral Laplace Transform. As main applications, we discuss the computation of the spectral Laplace Transform of functions defined on arbitrary domains (e.g., 2D and 3D surfaces, graphs), the solution of the heat diffusion equation, and graph signal processing. [ABSTRACT FROM AUTHOR]

Due to the construction technology, material characteristics, long-term coupling of environment and load, and other factors, quality defects produced in rock bolts, posed a potential threat to the safety of public infrastructure. Non-destructive testing for rock bolt structure in-service is an important part of anchoring quality evaluation and reinforcement. The stress wave method has strong adaptability and rapid development, which is the main method for non-destructive testing technology of bolt anchorage quality. The research state-of-the-art of non-destructive testing from the aspects of the theory of anchoring bolt quality detection based on stress wave, model test, stress wave excitation and reception, signal processing and analysis, and numerical simulation were systematically reviewed. The results show that the research on non-destructive testing technology of bolt anchorage quality in China is relatively late, but developed rapidly. The existing non-destructive testing technology for bolt anchorage quality is relatively mature, which can realize the detection of the length and grouting compactness of grouted bolts. However, it still has shortcomings, which limit the detection accuracy and detection length of the bolt. The non-destructive testing method for the service state of old bolts has yet rarely been reported, and further research is still needed. Finally, the future potential development direction has been prospected combined with the future development trend. In the follow-up study, the existing theory should be further improved and perfected, and the non-destructive testing technology and equipment should be developed and upgraded to expand the application scope of non-destructive testing technology for bolt anchorage quality. At the same time, the intelligent evaluation system for the bolt service state based on non-destructive testing technology should be constructed and the remaining service life prediction method needs to be proposed. [ABSTRACT FROM AUTHOR]

The ability to sense propagating electromagnetic plane waves based on their directions of arrival (DOAs) is fundamental to a range of radio frequency (RF) sensing, communications, and imaging applications. This paper introduces an algorithm for the wideband true time delay digital delay Vandermonde matrix (DVM), utilizing Thiran fractional delays that are useful for realizing RF sensors having multiple look DOA support. The digital DVM algorithm leverages sparse matrix factorization to yield multiple simultaneous RF beams for low-complexity sensing applications. Consequently, the proposed algorithm offers a reduction in circuit complexity for multi-beam digital wideband beamforming systems employing Thiran fractional delays. Unlike finite impulse response filter-based approaches which are wideband but of a high filter order, the Thiran filters trade usable bandwidth in favor of low-complexity circuits. The phase and group delay responses of Thiran filters with delays of a fractional sampling period will be demonstrated. Thiran filters show favorable results for sample delay blocks with a temporal oversampling factor of three. Thiran fractional delays of orders three and four are mapped to Xilinx FPGA RF-SoC technologies for evaluation. The preliminary results of the APF-based Thiran fractional delays on FPGA can potentially be used to realize DVM factorizations using application-specific integrated circuit (ASIC) technology.

Traditional methods detect wear by interpreting mathematical models and tire characteristics; however, these methods struggle to accurately reflect the actual rolling condition of the tire. In this study, we propose a machine learning-based tire wear detection module that can provide accurate results under tire test rig conditions. To develop this module, we designed three key components: integrated acceleration and PVDF sensors within the tire to capture vibration and deformation data; signal preprocessing algorithms to highlight multi-source signal differences under varying wear conditions; and deep learning algorithms to achieve precise tire wear grade identification. Experimental results demonstrate that, under different tire pressures, loads, speeds, and wear levels, the system can accurately identify tire wear grades with 99.99% accuracy by combining data from both sensors. • Developed a multi-sensor intelligent tire module for systematic data collection, exploring acceleration and strain signals under various conditions (pressure, load, speed, wear levels). • Conducted comprehensive comparisons of tire signals in time and frequency domains, revealing key vibration and deformation characteristics for different wear levels. • Proposed an algorithm framework for multi-source signal preprocessing and wear level recognition. • Demonstrated high accuracy, robustness, and practicality in tire wear detection and classification. [ABSTRACT FROM AUTHOR]

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We propose a novel method for measuring the pitch of a speech signal based on the calculation and subsequent processing of the autocorrelation function of the signal, emphasizing its peak associated with the signal's period. The proposed processing prevents gross errors in pitch measurement and represents a type of clipping of the positive peaks in the autocorrelation function. [ABSTRACT FROM AUTHOR]

Nowadays, the impact of the ships on the World economy is enormous, considering that every ship needs fuel to sail from source to destination. It requires a lot of fuel, and therefore, there is a need to monitor and predict a ship’s average fuel consumption. However, although there are much models available to predict a ship’s consumption, most of them rely on a uniform time set. Here we show the model of predicting external influences to ship’s average fuel consumption based on a non-uniform time set. The model is based on the numeric fitting of recorded data. The first set of recorded data was used to develop the model, while the second set was used for validation. Statistical quality measures have been used to choose the optimal fitting function for the model. According to statistical measures, the Gaussian 7, Fourier 8, and smoothing spline fitting functions were chosen as optimal algorithms for model development. In addition to extensive data analysis, there is an algorithm for filter length determination for the preprocessing of raw data. This research is of interest to corporate logistics departments in charge of ensuring adequate fuel for fleets when and where required.

Infrared (IR) spectrometers, particularly the aging ones, often suffer from the band overlap and random noise. In this paper, a blind estimation method based on discrete cosine transform (DCT) regularization is proposed for IR spectrum measured from an aging spectrometer instrument. Motivated by the observation that the DCT coefficient distribution of the ground-truth spectrum is sparser than that of the observed spectrum, an IR spectral deconvolution model is formulated in our method to regularize the distribution of the observed spectrum by total variation regularization. Then, the split Bregman method is exploited to solve the resulting optimization problem. The experimental results demonstrate an encouraging performance of the proposed approach to suppress noise and preserve spectral details. The novelty of our method lies on its ability to estimate instrument function and latent spectrum in a joint framework; thus, mitigating the effects of instrument aging to a large extent. The recovered IR spectra can efficiently capture the spectral features and interpret the unknown chemical mixture in industrial applications. [ABSTRACT FROM AUTHOR]

Thermography is an established technique in Non-Destructive Testing and Evaluation (NDT&E) for detecting and visualizing defects. The acquired thermal image sequences contain valuable information in the spatial, time and frequency domains. Many thermal feature extraction methods have been previously proposed to detect and quantify defects. However, the quantitative assessment and error analysis remain as challenges. In the present work, a quantitative analysis strategy is proposed for validating detection performance of various thermal feature extraction techniques based on Eddy Current Stimulated Thermography. F-score is adopted as a global quantitative evaluation indicator to assess the crack detectability. The quantitative validation is carried out based on a large number of simulations and experiments for cracks on the nonferromagnetic material. The detectability in relation to the variations in crack size and the different thermal feature extraction methods have been critically analyzed. The insights obtained from the present findings can serve as guidelines to other types of thermal-based NDT&E to improve the defect visibility and detectability. [ABSTRACT FROM AUTHOR]

This paper proposes a new system for the unsupervised diagnostic and monitoring of defects in waveguide imaging. The proposed method is automatic and does not require manual selection of specific frequencies for defect diagnostics. The core of the method is a computational intelligent machine learning algorithm based on sparse non-negative matrix factorization. An internal functionality is built into the machine learning algorithm to adaptively learn and control the sparsity of the factorization, and to render better accuracy in detecting defects. This is achieved by using Bayesian statistics methodology. The proposed method is demonstrated on automatic detection of defect in metals. In addition, we show that the extraction of the spectrum signature corresponding to the defect is significantly more efficient with the proposed optimal sparsity, which subsequently led to better detection performance. Experimental tests and comparisons with other sparse factorization methods have been conducted to verify the efficacy of the proposed method. [ABSTRACT FROM PUBLISHER]

In dental medicine, the study of mandibular movement has an important role in oral rehabilitation as it allows diagnosis of pathologies in the temporomandibular joint and the definition of adequate treatment plans. In this paper, a new device specially developed for the acquisition and analysis of 3D mandibular movement is presented. A facial arc is adopted as its main support structure and electromagnetic sensors are employed to acquire the mandibular movement. A neural network is used to transform the electrical signals output by the sensors into 3D Cartesian coordinates. A custom-made computer application is developed to display and analyse the movement acquired. The device is shown to be easy to use, comfortable for patients and capable of being produced at an affordable price. [ABSTRACT FROM AUTHOR]

Nowadays, the impact of the ships on the World economy is enormous, considering that every ship needs fuel to sail from source to destination. It requires a lot of fuel, and therefore, there is a need to monitor and predict a ship's average fuel consumption. However, although there are much models available to predict a ship's consumption, most of them rely on a uniform time set. Here we show the model of predicting external influences to ship's average fuel consumption based on a non-uniform time set. The model is based on the numeric fitting of recorded data. The first set of recorded data was used to develop the model, while the second set was used for validation. Statistical quality measures have been used to choose the optimal fitting function for the model. According to statistical measures, the Gaussian 7, Fourier 8, and smoothing spline fitting functions were chosen as optimal algorithms for model development. In addition to extensive data analysis, there is an algorithm for filter length determination for the preprocessing of raw data. This research is of interest to corporate logistics departments in charge of ensuring adequate fuel for fleets when and where required. [ABSTRACT FROM AUTHOR]