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Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.

A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.

It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.

The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

The discrete Fourier transformation (DFT) has been proven to be a successful method for determining whether a discrete time series is seasonal and, if so, for detecting the period. This paper deals exclusively with rare data, in which instances occur periodically at a low frequency.

Data based on real-world situations is simulated for analysis.

Cycle number detection is done with spectral analysis, period detection is completed using DFT coefficients and signal shifts in the time domain are found using the convolution theorem. Additionally, a new method for detecting anomalies in binary, rare data is presented: the sum of distances. Using this method, expected events which have not occurred and unexpected events which have occurred at various sampling frequencies can be detected. Anomalies which are not considered outliers to be found.

Aliasing can contribute to extra frequencies which point to extra periods in the time domain. This can be reduced or removed with techniques such as windowing. In future work, this will be explored.

Applications include determining seasonality and thus investigating the underlying causes of hard drive failure, power outages and other undesired events. This work will also lend itself well to finding patterns among missing desired events, such as a scheduled hard drive backup or an employee's regular login to a server.

This paper has shown how seasonality and anomalies are successfully detected in seasonal, discrete, rare and binary data. Previously, the DFT has only been used for non-rare data.

This paper aims to present the Cauchy problem for the Laplace’s equation for profiles of gas turbine blades with one and three cooling channels. The distribution of heat transfer coefficient and temperature on the outer boundary of the blade are known. On this basis, the temperature on inner surfaces of the blade (the walls of cooling channels) is determined.

Such posed inverse problem was solved using the finite element method in the domain of the discrete Fourier transform (DFT).

Calculations indicate that the regularization in the domain of the DFT enables obtaining a stable solution to the inverse problem. In the example under consideration, problems with reconstruction constant temperature, assumed on the outer boundary of the blade, in the vicinity of the trailing and leading edges occurred.

The application of DFT in connection with regularization is an original achievement presented in this study.

This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the Johor River basin during the northeast monsoon season. Time-based clustering is represented by employing dynamic time warping (DTW) dissimilarity measure, whereas non-time-based clustering is represented by employing Euclidean dissimilarity measure in analysing the Johor River discharge data. In addition, we combine each of these clustering methods with a frequency domain representation of the discharge data using Discrete Fourier Transform (DFT) to see if such transformation affects the clustering results. The clustering quality from the hierarchical data structures of the identified river discharge patterns for each of the methods is measured by the Cophenetic Correlation Coefficient (CPCC). The results from the time-based clustering using DTW based on DFT transformation show a higher CPCC value as compared to that of non-time-based clustering methods.

The purpose of this paper is to improve the online monitoring level of low-frequency oscillation in the power system. A modal identification method of discrete Fourier transform (DFT) curve fitting based on ambient data is proposed in this study.

An autoregressive moving average mathematical model of ambient data was established, parameters of low-frequency oscillation were designed and parameters of low-frequency oscillation were estimated via DFT curve fitting. The variational modal decomposition method is used to filter direct current components in ambient data signals to improve the accuracy of identification. Simulation phasor measurement unit data and measured data of the power grid proved the correctness of this method.

Compared with the modified extended Yule-Walker method, the proposed approach demonstrates the advantages of fast calculation speed and high accuracy.

Modal identification method of low-frequency oscillation based on ambient data demonstrated high precision and short running time for small interference patterns. This study provides a new research idea for low-frequency oscillation analysis and early warning of power systems.

The modern missile has low uncertain and wide range vibration frequency. The conventional notch filter with the fixed notch frequency is less effective than that of the adaptive notch filter (ANF) in vibration suppression for the time-varying vibration frequency.

To overcome the drawback, a novel method is based on frequency estimators made by interpolation of three discrete Fourier transform (DFT) spectral lines. The modified frequency estimators based on the interpolation of three DFT spectral lines are presented to identify and track the vibration frequency. Then the notch frequencies of multiple ANFs are real-timely tuned according to estimators.

Finally, taking the second-order flexible missile as an example, the performance of the proposed method is verified. The verified simulation results show that multiple ANFs are effective in vibration suppression.

Cascading multiple ANFs to achieve multi-order vibration suppression is more efficient and feasible than conventional fixed-parameter notch filtering.

The frequency estimation method based on three DFT spectral lines proposed in this paper can effectively identify and track signals in the noise environment. Compared with conventional methods, the method pretended in this paper has high identification accuracy and a stronger ability to track signals. It can meet the fast frequency identification requirements of the actual flexible missile.

Conventional diagnostic techniques, on the other hand, may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify, potentially resulting in misdiagnosis. Meanwhile, early nonmotor signs of Parkinson’s disease (PD) can be mild and may be due to variety of other conditions. As a result, these signs are usually ignored, making early PD diagnosis difficult. Machine learning approaches for PD classification and healthy controls or individuals with similar medical symptoms have been introduced to solve these problems and to enhance the diagnostic and assessment processes of PD (like, movement disorders or other Parkinsonian syndromes).

Medical observations and evaluation of medical symptoms, including characterization of a wide range of motor indications, are commonly used to diagnose PD. The quantity of the data being processed has grown in the last five years; feature selection has become a prerequisite before any classification. This study introduces a feature selection method based on the score-based artificial fish swarm algorithm (SAFSA) to overcome this issue.

This study adds to the accuracy of PD identification by reducing the amount of chosen vocal features while to use the most recent and largest publicly accessible database. Feature subset selection in PD detection techniques starts by eliminating features that are not relevant or redundant. According to a few objective functions, features subset chosen should provide the best performance.

In many situations, this is an Nondeterministic Polynomial Time (NP-Hard) issue. This method enhances the PD detection rate by selecting the most essential features from the database. To begin, the data set's dimensionality is reduced using Singular Value Decomposition dimensionality technique. Next, Biogeography-Based Optimization (BBO) for feature selection; the weight value is a vital parameter for finding the best features in PD classification.

PD classification is done by using ensemble learning classification approaches such as hybrid classifier of fuzzy K-nearest neighbor, kernel support vector machines, fuzzy convolutional neural network and random forest. The suggested classifiers are trained using data from UCI ML repository, and their results are verified using leave-one-person-out cross validation. The measures employed to assess the classifier efficiency include accuracy, F-measure, Matthews correlation coefficient.

Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence.

The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques.

Temporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested.

Denoising of the audio samples for perfect feature extraction was a tedious chore.

The existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.

This paper aims to accurately estimate harmonics/interharmonics in modern power system. There are several high spectral resolution techniques that have been in use for several years like Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Prony methods, etc. but these techniques require prior knowledge of number of modes present in the signal. Model Order (MO) estimation techniques have to make a trade-off between accuracy and their speed i.e., computational burden. Therefore, there is always a requirement of a technique that is fast as well as accurate.

The proposed standard deviation (SD) method eliminates the requirement of energy validation test and analyses the distribution pattern, i.e. standard deviation of eigenvalues to identify the number of modes present in the signal. Signal is reconstructed using estimated modes and reconstruction error is obtained to show accuracy of the proposed estimation.

Six test synthetic signals as well as one practical signal have been taken for validating the proposed method. The paper shows that proposed methodology has a better accuracy compared to modified exact model order (MEMO) method in high noise environment and takes very less computation time compared to the exact model order (EMO) method.

The proposed method has been practically implemented for harmonic/interharmonic analysis at a sewage treatment plant at GIFT City, Gujarat, India. Apart from this the proposed method is modeled in python-based tool and is run into low-cost Raspberry Pi like hardware to create an onsite as well as remote monitoring device.

SD-based approach for model order estimation is novel to this area. Further, the proposed method is compared with EMO and MEMO under varying noise conditions to check for accuracy and estimation time.

Watermarking technique is one of the significant methods in which carrier signal hides digital information in the form of watermark to prevent the authenticity of the stakeholders by manipulating different coefficients as watermark in time and frequency domain to sustain trade-off in performance parameters. One challenging component among others is to maintain the robustness, to limit perceptibility with embedding information. Transform domain is more popular to achieve the required results in color image watermarking. Variants of complex Hadamard transform (CHT) have been applied for gray image watermarking, and it has been proved that it has better performance than other orthogonal transforms. This paper is aimed at analyzing the performance of spatio-chromatic complex Hadamard transform (Sp-CHT) that is proposed as an application of color image watermarking in sequency domain (SD).

In this paper, color image watermarking technique is designed and implemented in SD using spatio-chromatic – conjugate symmetric sequency – ordered CHT. The color of a pixel is represented as complex number a*+jb*, where a* and b* are chromatic components of International Commission on Illumination (CIE) La*b* color space. The embedded watermark is almost transparent to human eye although robust against common signal processing attacks.

Based on the results, bit error rate (BER) and peak signal to noise ratio are measured and discussed in comparison of CIE La*b* and hue, saturation and value color model with spatio-chromatic discrete Fourier transform (Sp-DFT), and results are also analyzed with other discrete orthogonal transforms. It is observed from BER that Sp-CHT has 8%–12% better performance than Sp-DFT. Structural similarity index has been measured at different watermark strength and it is observed that presented transform performs better than other transforms.

This work presents the details and comparative analysis of two orthogonal transforms as color image watermarking application using MATLAB software. A finding from this study demonstrates that the Complex Hadamard transform is the competent candidate that can be replaced with DFT in many signal processing applications.