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As the number of electronic gadgets in our daily lives is increasing and most of them require some kind of human interaction, this demands innovative, convenient input methods. There are limitations to state-of-the-art (SotA) ultrasound-based hand gesture recognition (HGR) systems in terms of robustness and accuracy. This research presents a novel machine learning (ML)-based end-to-end solution for hand gesture recognition with low-cost micro-electromechanical (MEMS) system ultrasonic transducers. In contrast to prior methods, our ML model processes the raw echo samples directly instead of using pre-processed data. Consequently, the processing flow presented in this work leaves it to the ML model to extract the important information from the echo data. The success of this approach is demonstrated as follows. Four MEMS ultrasonic transducers are placed in three different geometrical arrangements. For each arrangement, different types of ML models are optimized and benchmarked on datasets acquired with the presented custom hardware (HW): convolutional neural networks (CNNs), gated recurrent units (GRUs), long short-term memory (LSTM), vision transformer (ViT), and cross-attention multi-scale vision transformer (CrossViT). The three last-mentioned ML models reached more than 88% accuracy. The most important innovation described in this research paper is that we were able to demonstrate that little pre-processing is necessary to obtain high accuracy in ultrasonic HGR for several arrangements of cost-effective and low-power MEMS ultrasonic transducer arrays. Even the computationally intensive Fourier transform can be omitted. The presented approach is further compared to HGR systems using other sensor types such as vision, WiFi, radar, and state-of-the-art ultrasound-based HGR systems. Direct processing of the sensor signals by a compact model makes ultrasonic hand gesture recognition a true low-cost and power-efficient input method. [ABSTRACT FROM AUTHOR]

From the beginning of COVID-19 pandemic, numerous mutants of SARS-CoV-2 have since been evolved owing to high transmissibility and virulence. Due to the limited effectiveness of previously imposed vaccines and preventive therapies, these strains are still causing concern. This paper proposes comparative evaluation of three novel genomic signal processing-based methods employing discrete wavelet decomposition with lifting (DWT), discrete Fourier transform (DFT), and singular value decomposition (SVD) for the classification of emerging SARS-CoV-2 variants utilizing feature extraction from collected SARS-CoV-2 variants acquired from the NCBI virus database. The efficiency and accuracy of the proposed alignment-free algorithms have been tested using three Coronavirus datasets including human Coronavirus (HCoV), SARS-CoV-2 variants (CoV-Variants and Omicron). The viral nucleotide sequences which are converted into numerical representation leveraging purine-pyrimidine mapping, DNA walk & Z-curve are fed into DWT, SVD, & DFT processors, respectively. In the approach with DWT, the second-generation wavelet transform employs two best wavelet bases Daubechies (Db) and Biorthogonal (Bior) based on the validation of the HCoV dataset for the feature extraction of the CoV-Variants dataset. Various machine learning algorithms, such as Support Vector Machine, K-nearest neighbors, and ensemble, are used to classify the virus strains and evaluate the efficacy of the algorithm. Finally, hyper-parametric tuning is done utilizing the Bayesian optimization technique to select the best fit model for KNN and SVM. The proposed algorithm has successfully classified the CoV-Variants dataset with an average accuracy of 98.76% utilizing the DWT, DFT, and SVD, while the best-achieved accuracy for this dataset is 98.9% using the DWT technique employing purine–pyrimidine mapping. The best-achieved accuracy rate for predicting Omicron is 99.8% using SVD-based technique. The best-obtained accuracy for HCoV dataset is 100% resulted in all three methods. [ABSTRACT FROM AUTHOR]

Fault diagnosis of analog circuits is a classical problem, and its difficulty lies in the similarity between fault features. To address the issue, an end-to-end mutually exclusive autoencoder (EEMEAE) fault diagnosis method for analog circuits is proposed. In order to make full use of the advantages of Fourier transform(FT) and wavelet packet transform(WPT) for extracting signal features, the original signals processed by FT and WPT are fed into two autoencoders respectively. The hidden layers of the autoencoders are mutually exclusive by Euclidean distance restriction. And the reconstruction layer is replaced by a softmax layer and 1-norm combined with cross-entropy that can effectively enhance the discriminability of features. Finally, the learning rate is adjusted adaptively by the difference of loss function to further improve the convergence speed and diagnostic performance of the model. The proposed method is verified by the simulation circuit and actual circuit and the experimental results illustrate that it is effective. [ABSTRACT FROM AUTHOR]

The utilization of broadband quantization data increases the possibility of practical applications for fish target recognition based on the acoustic scattering theory. However, the quantification of broadband data is more complex than that of narrowband systems, requiring consideration of the broadband characteristics of split-beam transducers, seawater absorption, and circuits. This paper elucidates the scatterer acoustic field equation, transducer power equation, and signal processing flow in split-beam broadband quantization technology for engineering applications. A broadband calculation model based on transducer parameters is proposed to enhance the generalization ability of broadband quantization technology to different types of sonar. The classical echo integration method is combined with Fourier transform to meet the requirements of target strength (TS) estimation under broadband signals. This paper includes a series of experiments to prove the rationality and effectiveness of the method. The results demonstrate that the provided calculation model can more accurately reflect the backscattering characteristics of the scatterer, but certain errors remain. This article analyzes the sources of errors and validates the effectiveness of the new TS calculation method. [ABSTRACT FROM AUTHOR]

The utilization of ultraviolet (UV) absorption spectroscopy for monitoring the concentration of specific decomposition gas components in gas-insulated switchgear (GIS) can provide a means to assess its insulation status. Nevertheless, UV optical modules currently deployed in the field are susceptible to external interferences like ambient noise and equipment vibrations. Real-time spectral data acquisition often suffers from significant noise contamination, directly impinging on subsequent feature extraction and detection accuracy. This paper presents an optimized singular value decomposition (SVD) noise reduction method for mitigating noisy spectral signals. First, each singular value within the noisy signal is transformed into a component signal. Next, the highest frequency value in the signal serves as an indicator to characterize the signal. Finally, the primary frequency values are arranged based on the decreasing singular values of the original noisy signal. The singular value corresponding to the first primary frequency value surpassing a preset threshold is selected as the effective order for denoising. Random noise with varying intensities was intentionally introduced to the UV spectral signal of sulfur dioxide (SO2), followed by noise reduction procedures. It is shown that the improved SVD noise reduction algorithm proposed in this paper enhances the signal-to-noise ratio (SNR) by 18.02% and 16.86%, and reduces the root-mean-square error (RMSE) by 15.13% and 14.92%, respectively, compared with the singular value difference spectrum (SVDS) denoising method and wavelet transform denoising method under the condition of low SNR. Furthermore, there exists a linear relationship between the concentration of SO2 samples and the eigenvalues of the UV spectra, demonstrating a higher linear goodness with a coefficient of 0.99735. The denoising method proposed in this paper does not require the manual setting of various types of parameters, and has a better ability to deal with the noise of UV spectral signals in engineering sites with complex environments. [ABSTRACT FROM AUTHOR]

The instrument's sound quality assessment is an arduous task to be performed computationally. This work aims to compare high-quality sound characteristics among instruments. The impulse response of the guitar is recording in the time domain and, by means of the Fourier and Continuous Wavelet Transforms the characteristics among high-quality guitars, in the frequency domain, are shown. To discriminate high quality, through experimentation, the criteria are: 1) tuning of notes, sound projection and definition of notes; 2) sound balance at all frequencies and timbre. Comparison of the frequency response (intensity, density, shape, range) gives a good intuition regarding sound quality of the instruments that shares similar characteristics, according to the criteria defined. Note that, among the high-quality classical guitars, it is not possible to define what is better sound-wise due to the fact that high-quality instruments are hearing-perceptual defined, therefore, depends on the criteria of the musician.

The instrument's sound quality assessment is an arduous task to be performed computationally. This work aims to compare high quality sound characteristics among instruments. The impulse response of the guitar is recording in the time domain and, by means of the Fourier and Continuous Wavelet Transforms the characteristics among high quality guitars, in the frequency domain, are shown. To discriminate high quality, through experimentation, the criteria are: 1) tuning of notes, sound projection and definition of notes; 2) sound balance at all frequencies and timbre. Comparison of the frequency response (intensity, density, shape, range) gives a good intuition regarding sound quality of the instruments that shares similar characteristics, according to the criteria defined. Note that, among the high-quality classical guitars, it is not possible to define what is better sound-wise due to the fact that high quality instruments are hearing-perceptual defined, therefore, depends on the criteria of the musician. [ABSTRACT FROM AUTHOR]

As part of a larger effort to aid in seamless integration of Fourier-based multiplexed ion mobility with a range mass analyzers, we have developed an all-in-one graphical user interface tool for FT-IM-MS data analysis that runs directly within a web browser. This tool, FTflow, accepts mzML files and displays necessary information such as mass spectra and extracted ion chromatograms in order to reconstruct arrival time distributions. It also extracts the corresponding mobility-related information (e.g., Ko and CCS) for each of the target ion populations. Furthermore, input fields for experimental conditions are clearly laid out for users and ease-of-use. With flexibility in mind, the processing scripts and GUI interface are written entirely in Python and allows users the option to modify source code to fit their specific needs. While the intention for this tool is to be a starting point for exploratory analysis of FT-IM-MS data, it has the capability to be adapted for use in more automated data processing pipelines through direct access of core processing routines. [ABSTRACT FROM AUTHOR]

The constant-Q Gabor atom is developed for spectral power, information, and uncertainty quantification from time–frequency representations. Stable multiresolution spectral entropy algorithms are constructed with continuous wavelet and Stockwell transforms. The recommended processing and scaling method will depend on the signature of interest, the desired information, and the acceptable levels of uncertainty of signal and noise features. Selected Lamb wave signatures and information spectra from the 2022 Tonga eruption are presented as representative case studies. Resilient transformations from physical to information metrics are provided for sensor-agnostic signal processing, pattern recognition, and machine learning applications. [ABSTRACT FROM AUTHOR]

Research in AI has proved to be revolutionarily beneficial to humankind from the past few decades. Many supporting techniques have been developed that indirectly evolved AI and directly enhanced various machine learning models; one of them being the feature engineering. It can also be considered as applied-ML. Another is the so-called feature selection which is method in which most contributing feature to final decision making, out of the entire feature space are selected for processing into an ML model. It is not an easy task to precisely calculate the dependency of the output variable onto the candidate features, particularly when the data is high in dimensions. In this regard, this study proposes a novel method named the cisoidal analysis based feature selection (CAFS) which uses both pre-established algorithms as well as a new approach of relating members of feature space to a complex sinusoid (cisoid) mathematically, then using signal processing techniques to eliminate certain elements in the entire feature space for enhanced feature selection and hence to obtain higher classification accuracy. Derived from experiments with five high dimensional datasets, CAFS displays significantly competitive performance than some of the pre-existing algorithms. CAFS is highly advantageous in reducing dimension of feature space in most of the applications. [ABSTRACT FROM AUTHOR]

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Watermarking for identity images printed on a plastic card support is still challenging. In this application, the scheme must be robust against a combination of geometric and signal processing attacks related to the print/scan process. In addition, the scheme must deal with all the possible aggressions that a smart card can encounter during its lifetime. This paper investigates a robust watermarking solution in the Fourier domain in the continuity of our earlier developments reported in this field. It includes both preventive and curative stages that are complementary. The solution is preventive as only a small set of selected bits presenting a small variance are concerned the watermarking process. This property increases the chances of watermark detection rate while maintaining the same level of security. The curative part consists in pre-processing the watermarked image before the detection process. It comprises two specific and accurate counterattacks: the first one deals with blurring correction under a dedicated Wiener filter and the second one focuses on color corrections. The hybrid scheme is highly efficient and has a low computational cost. The new watermarking scheme clearly outperforms competitive approaches and is compatible with industrial constraints. [ABSTRACT FROM AUTHOR]

The utilization of ultraviolet (UV) absorption spectroscopy for monitoring the concentration of specific decomposition gas components in gas-insulated switchgear (GIS) can provide a means to assess its insulation status. Nevertheless, UV optical modules currently deployed in the field are susceptible to external interferences like ambient noise and equipment vibrations. Real-time spectral data acquisition often suffers from significant noise contamination, directly impinging on subsequent feature extraction and detection accuracy. This paper presents an optimized singular value decomposition (SVD) noise reduction method for mitigating noisy spectral signals. First, each singular value within the noisy signal is transformed into a component signal. Next, the highest frequency value in the signal serves as an indicator to characterize the signal. Finally, the primary frequency values are arranged based on the decreasing singular values of the original noisy signal. The singular value corresponding to the first primary frequency value surpassing a preset threshold is selected as the effective order for denoising. Random noise with varying intensities was intentionally introduced to the UV spectral signal of sulfur dioxide (SO2), followed by noise reduction procedures. It is shown that the improved SVD noise reduction algorithm proposed in this paper enhances the signal-to-noise ratio (SNR) by 18.02% and 16.86%, and reduces the root-mean-square error (RMSE) by 15.13% and 14.92%, respectively, compared with the singular value difference spectrum (SVDS) denoising method and wavelet transform denoising method under the condition of low SNR. Furthermore, there exists a linear relationship between the concentration of SO2 samples and the eigenvalues of the UV spectra, demonstrating a higher linear goodness with a coefficient of 0.99735. The denoising method proposed in this paper does not require the manual setting of various types of parameters, and has a better ability to deal with the noise of UV spectral signals in engineering sites with complex environments.

After several decades of continuously optimizing computing systems, the Moore's law is reaching its end. However, there is an increasing demand for fast and efficient processing systems that can handle large streams of data while decreasing system footprints. Neuromorphic computing answers this need by creating decentralized architectures that communicate with binary events over time. Despite its rapid growth in the last few years, novel algorithms are needed that can leverage the potential of this emerging computing paradigm and can stimulate the design of advanced neuromorphic chips. In this work, we propose a time-based spiking neural network that is mathematically equivalent to the Fourier transform. We implemented the network in the neuromorphic chip Loihi and conducted experiments on five different real scenarios with an automotive frequency modulated continuous wave radar. Experimental results validate the algorithm, and we hope they prompt the design of ad hoc neuromorphic chips that can improve the efficiency of state-of-the-art digital signal processors and encourage research on neuromorphic computing for signal processing. [ABSTRACT FROM AUTHOR]

Conveyor belts are used in many industries, from food to mining and power generation. These conveyors consist of many subsystems, and these systems, like any mechanical system, are prone to failure. The failure of these systems can cause the conveyor to be stopped and repaired, and during this downtime, production is lost. Thermal cameras are the most using method to diagnose the faults in bearing that produce the heating than the healthy bearing. Acoustic gear and accelerometer are used to capture the idler sound and vibration to detect the current health of the idler. But, these diagnosis methods need an engineering presence near the belt and monitoring purposes. However, these sensors can be monitored by only a few idlers nearby, and the installation cost of these makes the monitoring system infeasible. A predictive maintenance framework and mathematical modelling of a belt conveyor have been proposed to identify the faults before the severe damage of the belt. The experimental results showed that the in-belt monitor could identify a faulty bearing even before failure occurred. This allows all the defective bearings to be identified and the needed stock can be purchased ahead of a planned outage, and all the faulty bearings can be replaced. One large, scheduled outage can be organized to replace all defective bearings and bearings showing signs of failure soon, reducing the number of short power failures that punctuate the conveyor feed. This form of monitoring is insufficient for estimating the remaining service life of the faulty bearings. [ABSTRACT FROM AUTHOR]

Copyright of Técnica Industrial: Revista Cuatrimestral de Ingeniería, Industria e Innovación is the property of Fundacion Tecnica Industrial and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this paper is a spectrum analysis of signals of various nature, construction of the signal scalogram using Morlet wavelet, modification of the scalogram to obtain a more informative graphic representation of the signal. Spectral analysis of the signal is constructed by means of the Fourier transform. A modification of the graphical representation of the result of the wavelet transform has been developed with the help of the Mathematica system. For this, a wavelet scalogram has been used as a two-dimensional representation of the original signal. A scale has been introduced on it for the value of the signal amplitude depending on the time and period of its constituent components. This graphical representation allows us to obtain additional information about the dynamic properties of the original signal. A modification of the representation of the original signal scalogram has been developed for a more complete spectrum analysis (determination of the period of the constituent components). The paper contains an example using a modified scalogram for the analysis of a signal containing two pulses, an audio signal and white noise. The basic wavelet in this case is the Morlet wavelet. A comparison of the scalogram, obtained using the built-in function, and the modified scalogram has been made in the paper. The disadvantage of the first scalogram is the impossibility of assessing the frequency of the signal; its advantage is the ability to assess the localization of the pulse. For a modified scalogram, the advantage is the estimation of the signal periodicity, and the disadvantage is the inaccuracy in determining the range of pulse localization. For spectrum analysis in Mathematica, it is recommended to use a combination of two approaches (using a standard built-in function to determine the localization of the pulse) and a modified scalogram (to determine the periods of the constituent components).

In recent decades, many tools have been developed and applications have been made possible thanks to graph signal processing (GSP). In this scenario, relationships between elements always occur in pairs. Recently, this theory has been extended to hypergraphs, where elements can be related in groups of two or more elements, the hypergraph signal processing (HGSP). In this context, we propose a novel hypergraph Fourier transform (HGFT) that deals directly with one-dimensional signals. From this Fourier transform, we define the translation and modulation operators for hypergraphs. With the help of these operators, we introduce a methodology for vertex-frequency analysis on hypergraphs based on a novel windowed hypergraph Fourier transform (WHGFT). Finally, in order to illustrate the effectiveness of the proposed tools, we perform experiments with path, cycle, squid, and random geometric hypergraphs. • A novel hypergraph Fourier transform that deals directly with one-dimensional signals is introduced. • A methodology to perform vertex-frequency analysis on hypergraphs is presented. • Experiments with path, cycle, squid, and random geometric hypergraphs are carried out. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is quite important to identify the power quality problems that may occur in the process from generation to distribution of electricity. The voltage obtained from an ideal three-phase alternating current source is a continuous sinusoidal voltage with approximately the same amplitude and 120° phase difference between them. Current or voltage distortions that may occur in power systems such as harmonics and the voltage sag, swell, and interruption cause the low quality of the power. These power quality disturbances may cause the failure of the devices used by the consumer, an increase in maintenance costs and current or voltage unbalance. The causes and types of problems must be well known to improve power quality. Especially if the type of power quality disturbances is classified correctly, the effects of disturbances under load can be identified, the source of the disturbances can be analyzed and thus appropriate solution methods can be developed. It is obtained that the increasing system reliability, reducing line losses, and increasing energy efficiency thanks to improving the power quality. Power quality problems are generally described as waveform distortions in a current and voltage. Sinusoidal distortions in voltage and current waveforms in the electric grid create harmonics. In this study, the purification of harmonics is provided by using signal processing techniques commonly used in different applications in the literature. The optimum signal processing technique to be used in solving power quality problems is determined It is presented comparatively simulation results of signal processing techniques that Fourier Transform (FT), Short-Time Fourier Transform (STFT) and Wavelet Transform (WT). the. The analysis results show that WT is superior to other used transform techniques for signal processing. The signals can be analyzed locally by obtaining low-frequency information in long-time intervals and high-frequency information in a short time interval thanks to the WT.

High Impedance Faults (HIFs) occur by the contact between an energized conductor and a high impedance surface. Due to the low fault current level, HIFs cannot be detected by conventional protection and there is no fully efficient solution to this problem. HIF detection methods often extract metrics using signal processing techniques, such as Fourier Transform, Wavelet Transform, Stockwell Transform, and Mathematical Morphology. However, these techniques are applied under specific conditions, which hinders comparative and critical analyses among them. Therefore, this paper presents a critical review of HIF detection methods based on the aforementioned techniques, and also shows a detailed investigation of the performance of the metrics commonly used with them. To do this efficiently, the authors proposed a set of assessment indices based on the ratio between the metrics’ characteristics and another one based on the repeating of the metrics features. The proposed indices revealed that some of these metrics fail to distinguish HIF from other typical occurrences in power distribution systems, and their performances are negatively affected by the fault location and by the existence of noise in the measurements. Additionally, the results showed a need to specify system conditions in which any HIF detection technique is valid. [ABSTRACT FROM AUTHOR]

In classical information processing, the windowed Fourier transform (WFT), or short-time Fourier transform, which is a variant of the Fourier transform by dividing a longer time signal into shorter segments of equal length and then computing the Fourier transform separately on each shorter segment, is proposed to provide a method of signal processing. Up to now the discrete Fourier transform has been successfully applied to the field of quantum information, but the related short-time discrete Fourier transform of this field has not been developed accordingly. To address this problem, we first introduce the concept of quantum window state, and further prove that the quantum Fourier transform of a quantum window state is also a quantum window state. Based on the definition of the quantum window state the local information of a quantum signal is extracted and the corresponding quantum circuits are also given. And then, by applying the quantum Fourier transform to the windowed quantum superposition states, we propose a novel concept called quantum windowed Fourier transform (QWFT). Finally, an application of quantum signal processing is given where QWFT is employed. [ABSTRACT FROM AUTHOR]

An improved conditioning monitoring technique is provided for rotating components in gearboxes that account for gear system dynamics. A rotation rate for the component is generated from vibration data by estimating the rotation rate based on a tachometer measurement of another shaft and the shaft ratio. This estimated rotation rate is used, together with the known configuration of the component, to estimate a known gear mesh frequency of the component. By filtering for a range of frequencies around the gear mesh frequency based on variation in the shaft rate, the gear mesh frequency can be determined, and from that signal, an actual rotation rate for the component can be calculated. The exact rate can then be used in deriving an analytic vibration spectrum for the component that is not degraded due to gear system dynamics effects. This allows for a more accurate Time Synchronous Average and further, allows the calculation of the gearbox transfer function. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the advantages and drawbacks of four signal processing methods for spectrophotometric quantitation of mixtures of dapagliflozin and saxagliptin. Methods: The methods studied were numerical differentiation (ND), Savitzky-Golay filter (SG), discrete Fourier transform (DFT) and continuous wavelet transform (CWT). The resolution powers of the methods were compared via quantitative determination of dapagliflozin (DAP) and saxagliptin (SAX) in laboratory prepared mixtures. Furthermore, a new approach for validating robustness in spectrophotometric methods was developed, and the methods were compared using their robustness. Results: Continuous wavelet transform (CWT) produced the best results regarding the analysis of the two drugs in different ratios. It also showed a lower limit of quantification (LOQ), when compared to each of the other methods. When the four methods were used for quantitation of pharmaceutical drug formulations, and subjected to validation in line with ICH regulations, they were found to be satisfactorily specific, accurate, precise and robust. Conclusion: These results show that CWT technique is superior to the other three methods for analysis of drug mixtures with regard to sensitivity and resolving power. Thus, CWT can be used in the routine spectrophotometric analysis of pharmaceuticals in quality control laboratories. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ageing of civil engineering infrastructures such as dams, bridges, tunnels and buildings causes many problems with great consequences, both from practical and economical points of view. The main aim of this paper, together with 11], is to describe the state of the art of known methods and how to tackle this important problem. In particular we make a brief description and comparison between the methods. The major challenge for damage detection is the large amount of noise, and presence of harmonic components that leads to erroneous modal identification. New developments in signal processing techniques along with the use of artificial intelligence can play a crucial role in finding solutions for such problems. In this article we also include a new application of OMA, on a high-rise building in Luled, Sweden, where some of the most popular OMA techniques have been applied. [ABSTRACT FROM AUTHOR]

Variational mode decomposition (VMD) is a practical signal decomposition approach, which extracts the modes through bandwidth optimization in the frequency domain. In recent years, many efforts have been made to attenuate the effect of prior parameters, which heavily trouble the traditional VMD. However, as the core step, the bandwidth optimization algorithm including the initialization of center frequencies in VMD is rarely discussed or improved in the existing work. In practical applications, the non-convergence or unreasonable convergence of the bandwidth optimization can lead to the failure of VMD in mode separation. Thus, in this paper, a new signal decomposition method termed adaptive bandwidth Fourier decomposition (ABFD) is proposed to separate the narrowband components from a complicated signal accurately. The proposed ABFD inherits the idea of implementing Fourier spectrum decomposition through bandwidth optimization. In particular, three significant improvements are made in this work. Firstly, in order to reduce the computation complexity, a novel bandwidth optimization algorithm termed Fourier spectrum bandwidth optimization (FSBO) is proposed. Secondly, inspired by the empirical principle proposed in the empirical wavelet transform (EWT), a novel variable initialization method based on spectral energy distribution is introduced. Finally, under the guidance of narrowband characteristics, a method for automatically detecting the appropriate mode number is developed. In order to evaluate the performance of the proposed ABFD, simulation analysis and measured signal analysis are carried out in this paper. The preliminary results indicate that the proposed ABFD can extract the single components more accurately than EMD and VMD.

Set functions are functions (or signals) indexed by the powerset (set of all subsets) of a finite set N. They are fundamental and ubiquitous in many application domains and have been used, for example, to formally describe or quantify loss functions for semantic image segmentation, the informativeness of sensors in sensor networks the utility of sets of items in recommender systems, cooperative games in game theory, or bidders in combinatorial auctions. In particular, the subclass of submodular functions occurs in many optimization and machine learning problems. In this paper, we derive discrete-set signal processing (SP), a novel shift-invariant linear signal processing framework for set functions. Discrete-set SP considers different notions of shift obtained from set union and difference operations. For each shift it provides associated notions of shift-invariant filters, convolution, Fourier transform, and frequency response. We provide intuition for our framework using the concept of generalized coverage function that we define, identify multivariate mutual information as a special case of a discrete-set spectrum, and motivate frequency ordering. Our work brings a new set of tools for analyzing and processing set functions, and, in particular, for dealing with their exponential nature. We show two prototypical applications and experiments: compression in submodular function optimization and sampling for preference elicitation in combinatorial auctions. [ABSTRACT FROM AUTHOR]

Acquisition times and storage requirements have become increasingly important in signal-processing applications, as the sizes of datasets have increased. Hence, compressed sensing (CS) has emerged as an alternative processing technique, as original signals can be reconstructed using fewer data samples collected at frequencies below the Nyquist sampling rate. However, further analysis of CS data in both time and frequency domains requires the reconstruction of the original form of the time-domain data, as traditional signal-processing techniques are designed for uncompressed data. In this paper, we propose a signal-processing framework that extracts spectral properties for frequency-domain analysis directly from under-sampled ultrasound CS data, using an appropriate basis matrix, and efficiently converts this into the envelope of a time-domain signal, avoiding full reconstruction. The technique generates more accurate results than the traditional framework in both time- and frequency-domain analyses, and is simpler and faster in execution than full reconstruction, without any loss of information. Hence, the proposed framework offers a new standard for signal processing using ultrasound CS data, especially for small and portable systems handling large datasets. [ABSTRACT FROM AUTHOR]

In this work, we develop a new design method based on fast Fourier transform (FFT) for implementing zone plates (ZPs) with bifocal focusing profiles. We show that the FFT of the governing binary sequence provides a discrete sequence of the same length, which indicates the location of the main foci at the ZP focusing profile. Then, using reverse engineering and establishing a target focusing profile, we are capable of generating a binary sequence that provides a ZP with the desired focusing profile. We show that this design method, based on the inverse fast Fourier transform (IFFT), is very flexible and powerful and allows to tailor the design of bifocal ZPs to achieve focusing profiles with the desired foci locations and resolutions. The key advantage of our design algorithm, compared to other alternatives presented in previous works, is that our method provides bifocal focusing profiles with an absolute control of the foci locations. Moreover, although we analyze the performance of this novel design algorithm for underwater ultrasonics, it can also be successfully extended to different fields of physics, such as optics or microwaves, where ZPs are widely employed.

Copyright of Albahir Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The processing of sound signals is significantly improved recently. Technique for sound signal processing focusing on music beyond speech area is getting attention due to the development of deep learning techniques. This study is for analysis and process of music signals to generate tow-dimensional tabular data and a new music. For analysis and process part, we represented normalized waveforms for each of input data via frequency domain signals. Then we looked into shorted segment to see the difference wave pattern for different singers. Fourier transform is applied to get spectrogram of the music signals. Filterbank is applied to represent the spectrogram based on the human ear instead of the distance on the frequency dimension, and the final spectrogram has been plotted by Mel scale. For generating part, we created two-dimensional tabular data for data manipulation. With the 2D data, any kind of analysis can be done since it has digit values for the music signals. Then, we generated a new music by applying LSTM toward the song audience preferred more. As the result, it has been proved that the created music showed the similar waveforms with the original music. This study made a step forward for music signal processing. If this study expands further, it can find the pattern that listeners like so music can be generated within favorite singer’s voice in the way that the listener prefers. [ABSTRACT FROM AUTHOR]

Fast Fourier Transform has long been established as an essential tool in signal processing. To address the computational issues while helping the analysis work for multi-dimensional signals in image processing, sparse Fast Fourier Transform model is reviewed here when applied in different applications such as lithography optimization, cancer detection, evolutionary arts and wasterwater treatment. As the demand for higher dimensional signals in various applications especially multimedia appplications, the need for sparse Fast Fourier Transform grows higher. [ABSTRACT FROM AUTHOR]

A primary focus in liquid chromatography analysis of complex samples is high peak capacity separations. Using advanced instrumentation and optimal small, high-efficiency columns, complex multicomponent mixtures can now be analyzed in relatively short times. Despite these advances, chromatographic peak overlap is still observed. Recently, attention has shifted from improvements in chromatographic efficiency and selectivity to enhancing data processing after collection. Curve fitting methods can be used to trace underlying peaks, but do not directly enhance chromatographic resolution. Methods based on the properties of derivatives and power transform were recently shown to enhance chromatographic peak resolution while maintaining critical peak information (peak areas and retention times). These protocols have been extensively investigated for their fundamental properties, advantages, and limitations, but they have not been evaluated with complex chromatograms. Herein, we evaluate the use of deconvolution via Fourier transform (FT), even-derivative peak sharpening, and power law with the fast separation (< 60 s) of a 101-component mixture using ultra-high-pressure liquid chromatography. High noise and peak overlap are present in this gradient separation, which is representative of fast chromatography. Chromatographic resolution enhancement is demonstrated and described. Further, accurate quantitation is maintained and shown with representative examples. Enhancements in peak capacity and peak-to-peak resolutions are discussed. Finally, the statistical theory of overlap is used for 101 peaks and predictions are made for the number of singlet, doublet, and multiplets analyte peaks. The effect of increasing peak capacity by FT even derivative sharpening and power laws leads to a decrease in the number of peak overlaps and an increase in total peak number. [ABSTRACT FROM AUTHOR]

We consider the analogue of the classic Wiener filtering theory to a half-strip of complex domain. In this paper all test signal obtained that solving the filter identification problem for the Hardy spaces in a half-strip. This result can be used for the investigations of electrical, optical, acoustical signals. [ABSTRACT FROM AUTHOR]

The Fourier transform is an important operation in signal processing. However, its exact computation on digital computers can be problematic. In this paper we consider the computability of the Fourier transform and the discrete-time Fourier transform (DTFT). We construct a computable bandlimited absolutely integrable signal that has a continuous Fourier transform, which is, however, not Turing computable. Further, we also construct a computable sequence such that the DTFT is not Turing computable. Turing computability models what is theoretically implementable on a digital computer. Hence, our result shows that the Fourier transform of certain signals cannot be computed on digital hardware of any kind, including CPUs, FPGAs, and DSPs. This also implies that there is no symmetry between the time and frequency domain with respect to computability. Therefore, numerical approaches which employ the frequency domain representation of a signal (like calculating the convolution by performing a multiplication in the frequency domain) can be problematic. Interestingly, an idealized analog machine can compute the Fourier transform. However, it is unclear whether and how this theoretical superiority of the analog machine can be translated into practice. Further, we show that it is not possible to find an algorithm that can always decide for a given signal whether the Fourier transform is computable or not. [ABSTRACT FROM AUTHOR]

It is quite important to identify the power quality problems that may occur in the process from generation to distribution of electricity. The voltage obtained from an ideal three-phase alternating current source is a continuous sinusoidal voltage with approximately the same amplitude and 120° phase difference between them. Current or voltage distortions that may occur in power systems such as harmonics and the voltage sag, swell, and interruption cause the low quality of the power. These power quality disturbances may cause the failure of the devices used by the consumer, an increase in maintenance costs and current or voltage unbalance. The causes and types of problems must be well known to improve power quality. Especially if the type of power quality disturbances is classified correctly, the effects of disturbances under load can be identified, the source of the disturbances can be analyzed and thus appropriate solution methods can be developed. It is obtained that the increasing system reliability, reducing line losses, and increasing energy efficiency thanks to improving the power quality. Power quality problems are generally described as waveform distortions in a current and voltage. Sinusoidal distortions in voltage and current waveforms in the electric grid create harmonics. In this study, the purification of harmonics is provided by using signal processing techniques commonly used in different applications in the literature. The optimum signal processing technique to be used in solving power quality problems is determined It is presented comparatively simulation results of signal processing techniques that Fourier Transform (FT), Short-Time Fourier Transform (STFT) and Wavelet Transform (WT). the. The analysis results show that WT is superior to other used transform techniques for signal processing. The signals can be analyzed locally by obtaining low-frequency information in long-time intervals and high-frequency information in a short time interval thanks to the WT. [ABSTRACT FROM AUTHOR]

The uncertainty principle is a fundamental principle in mathematics and physics, and also plays an important role in signal processing. On the other side, the octonion Fourier transform has got a lot of attentions in recent years. It is the aim of this paper to establish various uncertainty relations for this new hyper-complex Fourier transform. Based on the relations between the octonion Fourier transform and the quaternion Fourier transform, several uncertainty inequalities are derived neatly, including the logarithmic uncertainty inequality, local uncertainty inequalities, generalized global uncertainty inequality and the Benedicks–Amrein–Berthier inequality. In addition, the Mustard convolution associated to the octonion Fourier transform is discussed as well. [ABSTRACT FROM AUTHOR]

Polyharmonic splines of order m satisfy the polyharmonic equation of order m in n variables. Moreover, if employed as basis functions for interpolation they are radial functions. We are concerned with the problem of construction of the smooth interpolation formula presented as the minimizer of suitable functionals subject to interpolation constraints for n ≥ 1. This is the principal motivation of the paper. We show a particular procedure for determining the interpolation formula that in a natural way leads to a linear combination of polyharmonic splines of a fixed order, possibly complemented with lower order polynomial terms. If it is advantageous for the interpolant in the problem solved to be a polyharmonic spline we can construct such an interpolant directly using the multivariate smooth approximation technique. The smoothness of the spline can be a priori chosen. Smooth interpolation can be very useful e.g. in signal processing, computer aided geometric design or construction of geographic information systems. A 1D computational example is presented. [ABSTRACT FROM AUTHOR]

Due to the rapid growth of the smart automotive industry, there has been a strong rise in interest in Internet of Vehicles (IoV) technology recently. Establishing rapid and reliable Vehicle-to-Everything (V2X) connections between several different cars and smart devices are another challenge. This research suggests unique method for autonomous vehicle communication that combines an ensemble of machine learning approaches with sliding window-based fourier transform signal processing. Here, a sliding window-based Fourier transform is used to handle the vehicle communication data together with ensemble reinforcement, spatial gradient, and radial encoder neural networks. Data throughput, energy use, QoS(quality of service), and communication costs are all considered in the experimental study. The outcomes demonstrate the viability of the strategy: applying ML techniques to V2X data allowed for prediction of majority of simulated incidents. Existence of a large number of FPs prevents the use of automated safety systems as a result, drivers must avoid collisions manually. [ABSTRACT FROM AUTHOR]