Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
Ultrasonic signal processing methodologies use many signal parameters to be investigated, one of which is time-of-flight (ToF). There are many and various methods used to determine ToF, such as threshold detection, peak-based methods, cross-correlation, zero-crossing tracking algorithms, etc. The application of most of these methods becomes problematic when the background noise becomes high and the signal amplitude, frequency, or propagation velocity changes. In order to partially solve these problems, this paper proposes a new and simple method to determine the time-of-flight and center frequency of signals based on the use of zero-crossing times of filtered signals to calculate these parameters. Taking advantage of the idea that these zero-crossing times are concentrated around the maximum of the signal envelope, they were used as the time-of-flight of the signal. Together with the ToF, the center frequency of the signal was also determined. The proposed method was adapted to the processing of experimental signals obtained during various ultrasound investigations. By processing S0 mode signals propagating in the sheet molding compound plate, the propagation velocity of this mode was calculated. Its value was compared with the value obtained by the 2D FFT method. The obtained results differed by 0.9%. Using simulated signals propagating in 1 mm-thick aluminum, the phase and group velocity segments of the A0 mode were calculated. Their values differed by 0.7% from the theoretically calculated values of the dispersion curves by the SAFE method....
Topological signals are variables or features associated with both nodes and edges of a network. Recently, in the context of topological machine learning, great attention has been devoted to signal processing of such topological signals. Most of the previous topological signal processing algorithms treat node and edge signals separately and work under the hypothesis that the true signal is smooth and/or well approximated by a harmonic eigenvector of the higher-order Laplacian, which may be violated in practice. Here, we propose Dirac-equation signal processing, a framework for efficiently reconstructing true signals on nodes and edges, also if they are not smooth or harmonic, by processing them jointly. The proposed physics-inspired algorithm is based on the spectral properties of the topological Dirac operator. It leverages the mathematical structure of the topological Dirac equation to boost the performance of the signal processing algorithm. We discuss how the relativistic dispersion relation obeyed by the topological Dirac equation can be used to assess the quality of the signal reconstruction. Finally, we demonstrate the improved performance of the algorithm with respect to previous algorithms. Specifically, we show that Dirac-equation signal processing can also be used efficiently if the true signal is a nontrivial linear combination of more than one eigenstate of the Dirac equation, as it generally occurs for real signals....
This article explores the evolution of enterprise media processing systems from basic storage repositories to intelligent, AIpowered platforms that deliver significant business value across industries. Modern image and document processing pipelines leverage advanced computer vision and deep learning technologies to transform what was once an operational burden into a strategic competitive advantage. The discussion encompasses the architectural components of scalable media pipelines, including robust ingestion systems, optimized processing cores, and intelligent storage architectures that handle diverse visual inputs at enterprise scale. The article explores how convolutional neural networks enable automated document classification, real-time damage detection, and intelligent visual enhancement across finance, insurance, transportation, and e-commerce sectors. Additionally, it addresses critical challenges in scaling these systems, including petabyte-scale cloud migration strategies, data integrity preservation techniques, and performance SLA maintenance approaches. The article concludes by exploring emerging trends such as multimodal intelligence integration, edge computing for latency reduction, and explainable AI for regulated industries, illustrating how the transformation of raw media into actionable insights drives operational efficiency and creates new business capabilities....
This study presents an innovative approach to processing vibration signals in bridge structures, with a focus on enhancing the accuracy of dynamic response measurements and structural health assessments. It addresses key challenges in signal processing, particularly the uncertainties in selecting filtering parameters for isolating dynamic components from static displacements. A novel method for adaptive filter parameter selection is proposed, which considers variations in resonant frequencies and the non-linearity of quasi-static displacements caused by moving loads. This approach significantly reduces errors in determining forced and natural vibration parameters, leading to more accurate assessments of the bridge’s mechanical characteristics. The study introduces an optimized algorithm for processing acceleration and velocity signals, improving the resolution of natural frequency identification. This method combines traditional Fast Fourier Transform (FFT) techniques with an innovative spectral analysis approach, enabling precise identification of resonant frequencies and damping coefficients. A comprehensive evaluation framework is developed, integrating vibration amplitude, frequency, and damping ratio analyses. This framework enhances structural health assessments, improving the detection and characterization of potential defects and changes in load-bearing capacity. The practical significance of this research lies in its real-world application to bridge diagnostics. The study provides guidelines for sensor selection and configuration, adapted for various bridge types and sizes. The proposed methods demonstrate notable improvements in dynamic coefficient determination and overall structural assessments, offering the potential to reduce maintenance costs and enhance bridge safety....
Power signal processing is a specialized domain within signal processing that focuses on the analysis, interpretation, and manipulation of signals in electrical power systems. In modern smart grids, Power Quality Disturbances (PQDs) can result in considerable operational disruptions and financial losses for energy stakeholders. Accurate and timely identification of these disturbances is critical to maintaining grid reliability, efficiency, and energy stability. To overcome these challenges, the research proposes a comprehensive framework for PQD identification by leveraging advanced power signal processing techniques and time-frequency-based feature extraction. A Short-Time Fourier Transform fused Efficient Natural Gradient Boosting (STFT-ENGB) model is introduced for robust recognition of power quality disturbances with energy grid applications. To improve computational efficiency and decrease redundant data collection, a signal-piloted gain device is employed. This device continuously monitors power signals and initiates data acquisition only when abnormalities or potential disturbances are detected. The Z-score normalization is a preprocessing technique for reducing noise. The STFT is utilized to extract discriminative, time-localized features from the power signals, effectively characterizing voltage fluctuations and transient energy anomalies. These extracted features are subsequently used to train and evaluate the ENGB classifier. The proposed STFT-ENGB approach achieves high accuracy (98.75%). Experimental results demonstrate that the proposed framework achieves high classification accuracy while significantly reducing data volume and computational load. The reduction in processing overhead and latency underscores the system's suitability for real-time smart grid applications. The proposed approach offers a promising solution for real-time power signal monitoring in smart grid environments, facilitating intelligent fault diagnosis and improving the overall resilience and responsiveness of modern electrical infrastructure....
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