Short-time Discrete Fourier Transform Research Articles

Identification of exon regions in eukaryotes using fine-tuned variational mode decomposition based on kurtosis and short-time discrete Fourier transform

In genomic research, identifying the exon regions in eukaryotes is the most cumbersome task. This article introduces a new promising model-independent method based on short-time discrete Fourier transform (ST-DFT) and fine-tuned variational mode decomposition (FTVMD) for identifying exon regions. The proposed method uses the N/3 periodicity property of the eukaryotic genes to detect the exon regions using the ST-DFT. However, background noise is present in the spectrum of ST-DFT since the sliding rectangular window produces spectral leakage. To overcome this, FTVMD is proposed in this work. VMD is more resilient to noise and sampling errors than other decomposition techniques because it utilizes the generalization of the Wiener filter into several adaptive bands. The performance of VMD is affected due to the improper selection of the penalty factor (α), and the number of modes (K). Therefore, in fine-tuned VMD, the parameters of VMD (K and α) are optimized by maximum kurtosis value. The main objective of this article is to enhance the accuracy in the identification of exon regions in a DNA sequence. At last, a comparative study demonstrates that the proposed technique is superior to its counterparts.

Denoising multi-view images by soft thresholding: A short-time DFT approach

Short-time discrete Fourier transform (ST-DFT) is known as a promising technique for image and video denoising. The seminal work by Saito and Komatsu hypothesized that natural video sequences can be represented by sparse ST-DFT coefficients and noisy video sequences can be denoised on the basis of statistical modeling and shrinkage of the ST-DFT coefficients. Motivated by their theory, we develop an application of ST-DFT for denoising multi-view images. We first show that multi-view images have sparse ST-DFT coefficients as well and then propose a new statistical model, which we call the multi-block Laplacian model, based on the block-wise sparsity of ST-DFT coefficients. We finally utilize this model to carry out denoising by solving a convex optimization problem, referred to as the least absolute shrinkage and selection operator. A closed-form solution can be computed by soft thresholding, and the optimal threshold value is derived by minimizing the error function in the ST-DFT domain. We demonstrate through experiments the effectiveness of our denoising method compared with several previous denoising techniques. Our method implemented in Python language is available from https://github.com/ctsutake/mviden.

Time-Frequency Sparse Reconstruction of Non-Uniform Sampling for Non-Stationary Signal

We address the problem of the reconstruction of time-frequency characteristics of sparse multi-band signals by using the discrete multi-coset sampling (DMCS) model. In this article, the signal is characterized by complicated time-variable components. According to the feature of the short-time Fourier transform (STFT) analysis method, we obtain the rewritten matrix form of the discrete STFT. Then, an analysis method of the multi-coset sliding window (MCSW) is proposed, and the discrete multi-coset sampling sequence is locally windowed. The sparse signal reconstruction algorithm is used to obtain the optimal time-frequency reconstruction value of the original signal. Numerical simulations show the feasibility of this method. We simulate the effects of measurement noise, sampling rate, and time-frequency analysis parameters on the reconstruction accuracy. Our method can carry out time-frequency reconstruction for undersampled signals obtained from multi-coset sampling to ensure the time-varying feature of the original signals, which can well highlight the characteristics of signals. The method is of great significance to the research and development of the sub-Nyquist sampling technique.

Dropping malware through sound injection: A comparative analysis on Android operating systems

Trojan droppers are regularly ranked among the top 5 worst malware threats; especially for Android. Software for malware detection opts to either identify the payload when executed in memory or detect the connection from which droppers attempt to download malicious payloads. Much effort has been put into securing network channels and stopping droppers from delivering their payloads. This paves the way for alternative routes of infiltration. Our paper extends work on inaudible sound covert channel attacks and audio fingerprinting to use them for close vicinity attacks as potential mass dropper techniques. We demonstrate that malware modules can be dropped over the air to multiple devices using the same sound medium from approximately four to five meters away. Instead of using network connections, we conceal the malware within musics inaudible frequencies. Then we use a seemingly innocent app that implements an algorithm similar to Shazams audio fingerprinting to transmit the payload over the devices microphone and execute it. To accomplish this, we combine Short-Time Discrete Fourier Transform, peak frequency mapping and dilation techniques to a Meterpreter payload as fluctuations in magnitude within inaudible frequencies. To our knowledge, there is currently no technique capable of transmitting payloads to multiple neighboring systems without discrete network connections between the payload remote location and the device. We, therefore, present realistic attack cases and provide solutions for these attack vectors, including modulation and high-peak compression of sound dependent channel frequencies.

DSP techniques for protein coding region identification based on background noise and nonlinear phase delay reduction from period-3 spectrum using zero phased anti-notch filter and Savitzky-Golay (S-G) filter

Short time discrete Fourier transform (STDFT) and anti-notch filters (ANF) are perhaps the most common DSP tools that uses the period-3 feature of protein coding sequences to discriminate between protein-coding regions and noncoding regions. However, the performance of these techniques is still limited due to the background noise present in their period-3 spectrum. The drawback with existing de-noising techniques is that it also distorts the period-3 feature besides noise suppression. In this article, the Savitzky-Golay (SG) filter is used to suppress the background noise from period-3 spectrum. A zero phased anti-notch filter (ZANF) is also used to overcome the effect of nonlinear phase delay from the period-3 spectrum of ANF. It is obtained that the combined use of ZANF and SG filters increases the prediction accuracy of conventional ANF by around 19% -26%.

DSP techniques for protein coding region identification based on background noise and nonlinear phase delay reduction from period-3 spectrum using zero phased anti-notch filter and Savitzky-Golay (S-G) filter

Short time discrete Fourier transform (STDFT) and anti-notch filters (ANF) are perhaps the most common DSP tools that uses the period-3 feature of protein coding sequences to discriminate between protein-coding regions and noncoding regions. However, the performance of these techniques is still limited due to the background noise present in their period-3 spectrum. The drawback with existing de-noising techniques is that it also distorts the period-3 feature besides noise suppression. In this article, the Savitzky-Golay (SG) filter is used to suppress the background noise from period-3 spectrum. A zero phased anti-notch filter (ZANF) is also used to overcome the effect of nonlinear phase delay from the period-3 spectrum of ANF. It is obtained that the combined use of ZANF and SG filters increases the prediction accuracy of conventional ANF by around 19% -26%.

Digital Metering of Electrical Power Components Using Adaptive Non-Uniform Discrete Short Time Fourier Transform

Abstract This paper presents an adaptive Goertzel filter bank based discrete short time Fourier transform (DSTFT) implementation algorithm, called adaptive non-uniform discrete short time Fourier transform (ANDSTFT) for online measurement of electrical power components using IEEE Standard 1459–2010. The proposed ANDSTFT algorithm utilizes effective combination of non-uniform discrete Fourier transform (NDFT) and window method to detect the spectrum of each individual finite short time segment of power signals at distinct, arbitrarily located frequencies. Compared with the well-established technique such as windowed FFT interpreted DSTFT based approaches, the proposed method offers (i) better accuracy (ii) higher degree of immunity and insensitivity to noise, and (iii) reduced computational complexity per sample interval. The simulation results have been given and its response time and accuracy have been compared with the conventional windowed FFT interpreted DSTFT based techniques. Real-time implementation of the proposed approach has also been presented.

Ultra-Wideband Impulse Radar Through-Wall Detection of Vital Signs

This paper presents a new system for the detection of human respiration behind obstacles using impulse ultra-wideband (UWB) radar. In complex environments, low signal-to-noise ratios (SNRs) as they can result in significant errors in the respiration, heartbeat frequency, and range estimates. To improve the performance, the complex signal demodulation (CSD) technique is extended by employing the signal logarithm and derivative. A frequency accumulation (FA) method is proposed to suppress mixed products of the heartbeat and respiration signals and spurious respiration signal harmonics. The respiration frequency is estimated using the phase variations in the received signal, and a discrete short-time Fourier transform (DSFT) is used to estimate the range. The performance of the proposed system is evaluated along with that of several well-known techniques in the literature.

`Through-wall human being detection using UWB impulse radar

Ultra-wideband (UWB) impulse radar plays an important role in contactless vital sign (VS) detection. The VS can be extracted remotely by acquiring the oscillations in the human chest. Unfortunately, it is usually challenging to identify VS due to the low signal-to-noise ratio (SNR) only based on the traditional fast Fourier transform (FFT) especially in complicated conditions. To extract VS accurately, this paper presents a new scheme by analyzing the skewness characteristic of the received UWB impulses, which are modulated by life activities. The distance from the human subject to the radar antenna can be calculated by performing the discrete short-time Fourier transform (DSFT) on skewness. The frequency of human respiratory movement can be estimated based on the developed ensemble empirical mode decomposition (EEMD)-based accumulation technique by canceling out the harmonics effectively. The performance of the developed detection method is tested with several experiments carried out in different environments.

Phase-Sensitive Decision-Directed SNR Estimator for Single-Channel Speech Enhancement

The a priori signal-to-noise ratio (SNR) plays an essential role in many speech enhancement systems. Most of the existing approaches to estimate the a priori SNR only exploit the amplitude spectra while making the phase neglected. Considering the fact that incorporating phase information into a speech processing system can significantly improve the speech quality, this paper proposes a phase-sensitive decision-directed (DD) approach for the a priori SNR estimate. By representing the short-time discrete Fourier transform (STFT) signal spectra geometrically in a complex plane, the proposed approach estimates the a priori SNR using both the magnitude and phase information while making no assumptions about the phase difference between clean speech and noise spectra. Objective evaluations in terms of the spectrograms, segmental SNR, log-spectral distance (LSD) and short-time objective intelligibility (STOI) measures are presented to demonstrate the superiority of the proposed approach compared to several competitive methods at different noise conditions and input SNR levels.

An easily-achieved time-domain beamformer for ultrafast ultrasound imaging based on compressive sensing.

In ultrafast ultrasound imaging technique, how to maintain the high frame rate, and at the same time to improve the image quality as far as possible, has become a significant issue. Several novel beamforming methods based on compressive sensing (CS) theory have been proposed in previous literatures, but all have their own limitations, such as the excessively large memory consumption and the errors caused by the short-time discrete Fourier transform (STDFT). In this study, a novel CS-based time-domain beamformer for plane-wave ultrasound imaging is proposed and its image quality has been verified to be better than the traditional DAS method and even the popular coherent compounding method on several simulated phantoms. Comparing to the existing CS method, the memory consumption of our method is significantly reduced since the encoding matrix can be sparse-expressed. In addition, the time-delay calculations of the echo signals are directly accomplished in time-domain with a dictionary concept, avoiding the errors induced by the short-time Fourier translation calculation in those frequency-domain methods. The proposed method can be easily implemented on some low-cost hardware platforms, and can obtain ultrasound images with both high frame rate and good image quality, which make it has a great potential for clinical application.

Phase Processing for Single-Channel Speech Enhancement: History and recent advances

With the advancement of technology, both assisted listening devices and speech communication devices are becoming more portable and also more frequently used. As a consequence, users of devices such as hearing aids, cochlear implants, and mobile telephones, expect their devices to work robustly anywhere and at any time. This holds in particular for challenging noisy environments like a cafeteria, a restaurant, a subway, a factory, or in traffic. One way to making assisted listening devices robust to noise is to apply speech enhancement algorithms. To improve the corrupted speech, spatial diversity can be exploited by a constructive combination of microphone signals (so-called beamforming), and by exploiting the different spectro?temporal properties of speech and noise. Here, we focus on single-channel speech enhancement algorithms which rely on spectrotemporal properties. On the one hand, these algorithms can be employed when the miniaturization of devices only allows for using a single microphone. On the other hand, when multiple microphones are available, single-channel algorithms can be employed as a postprocessor at the output of a beamformer. To exploit the short-term stationary properties of natural sounds, many of these approaches process the signal in a time-frequency representation, most frequently the short-time discrete Fourier transform (STFT) domain. In this domain, the coefficients of the signal are complex-valued, and can therefore be represented by their absolute value (referred to in the literature both as STFT magnitude and STFT amplitude) and their phase. While the modeling and processing of the STFT magnitude has been the center of interest in the past three decades, phase has been largely ignored.

Online Identification of a Mechanical System in the Frequency Domain with Short-Time DFT

A proper system identication method is of great importance in the process of acquiring an analytical model that adequately represents the characteristics of the monitored system. While the use of dierent time-domain online identication techniques has been widely recognized as a powerful approach for system diagnostics, the frequency domain identication techniques have primarily been considered for oine commissioning purposes. This paper addresses issues in the online frequency domain identication of a exible two-mass mechanical system with varying dynamics, and a particular attention is paid to detect the changes in the system dynamics. An online identication method is presented that is based on a recursive Kalman lter congured to perform like a discrete Fourier transform (DFT) at a selected set of frequencies. The experimental online identication results are compared with the corresponding values obtained from the oine-identied frequency responses. The results show an acceptable agreement and demonstrate the feasibility of the proposed identication method.

A Recursive Least Squares Error Method Aided by Variable-Windowed Short-Time Discrete Fourier Transform for Frequency Tracking in Smart Grid

A Recursive Least Squares Error Method Aided by Variable-Windowed Short-Time Discrete Fourier Transform for Frequency Tracking in Smart Grid

Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform.

A system for epileptic seizure detection in electroencephalography (EEG) is described in this paper. One of the challenges is to distinguish rhythmic discharges from nonstationary patterns occurring during seizures. The proposed approach is based on an adaptive and localized time-frequency representation of EEG signals by means of rational functions. The corresponding rational discrete short-time Fourier transform (DSTFT) is a novel feature extraction technique for epileptic EEG data. A multilayer perceptron classifier is fed by the coefficients of the rational DSTFT in order to separate seizure epochs from seizure-free epochs. The effectiveness of the proposed method is compared with several state-of-art feature extraction algorithms used in offline epileptic seizure detection. The results of the comparative evaluations show that the proposed method outperforms competing techniques in terms of classification accuracy. In addition, it provides a compact representation of EEG time-series.

Voltage harmonic content of long artificially generated electrical arc in out-door experiment at 500 kV towers

This study involves the application of Short Time Discrete Fourier Transform in order to obtain the harmonic content of voltage between the terminals of 647 artificially generated long electrical arcs. Non-confined tests were conducted on three 500 kV towers specially built inside a laboratory facility, in order to represent as close as possible actual transmission line conditions. The 1-s duration arcs were artificially generated along the 4 m insulator string of a 500 kV tower by imposing a sustained 60 Hz current. The arc voltage harmonic content can be used to identify the moment at which the arc is stabilized in the air and only after this instant measured data can be used to derive mathematical arc model. A statistical analysis of the results, considering only the period for which the arc is stabilized, enabled the establishment of the harmonic signature of the electric arcs for a large arc current amplitude range. From the results it can be stated that an accurate arc model should properly reproduce the presented harmonic content for any arc current level.

Objective evaluation of chronic laryngeal dysphonia by spectro- temporal analysis

In this paper we develop system dedicated to the objective characterization of dysphonia chronic laryngeal origin. The purpose of this system is threefold: diagnosis, treatment and monitoring of patient. . For this we proceed initially to the remote recording and archiving of an acoustic speech signal voiced in this case a sustained for three seconds. We then apply at the Otorhinolaryngology (ORL) department of the University Hospital of Tlemcen, different algorithms of objective assessment of three parameters in this case the fundamental frequency, Disturbance of short-term speech signal (jitter) and Short-Time discrete Fourier Transform (ST.DFT) that allow experts to assess the development of chronic dysphonia of tumor or inflammatory origin (larynx cancer, inflammatory polyp of the vocal cords, chronic laryngitis).

Localization Operators and an Uncertainty Principle for the Discrete Short Time Fourier Transform

Localization operators in the discrete setting are used to obtain information on a signalffrom the knowledge on the support of its short time Fourier transform. In particular, the extremal functions of the uncertainty principle for the discrete short time Fourier transform are characterized and their connection with functions that generate a time-frequency basis is studied.

A Punctual Algorithm for Small Gene Prediction in DNA Sequences Using a Time-Frequency Approach based on the Z-Curve

Identification of protein-coding regions in Deoxyribonucleic Acid (DNA) sequences because of their 3-base periodicity has been a challenging issue in bioinformatics. Many DSP (Digital Signal Processing) techniques have been applied for identification task and concentrated on assigning numerical values to the symbolic DNA sequence and then applying spectral analysis tools such as the short-time discrete Fourier transform (ST-DFT) to locate periodicity components. In this paper, we investigate the location of exons in DNA strand using Variable Length Window approach based on z-curve. Z-curve is a unique 3-D curve to illustrate DNA's sequence which presents a complete description of DNA's sequence biological behavior. The proposed algorithm has a high accuracy and resolution due to applying Gaussian window with an adjustable length to identify and estimate exonic areas and non-coding regions are totally eliminated. In order to extract period-3 component we used a narrow-band band-pass filter with a central frequency of . The proposed algorithm was applied on some gene sequences existed in GenBank dataset and its results were compared by other existing methods at the nucleotide level. Simulation results show that our algorithm increases the accuracy of exon detection relative to other methods for exon prediction.

A Novel Fast Algorithm for Exon Prediction in Eukaryotic Genes using Linear Predictive Coding Model and Goertzel Algorithm based on the Z-Curve

identification of protein-coding regions in Deoxyribonucleic Acid (DNA) sequences because of their 3- base periodicity has been a challenging issue in bioinformatics. Many DSP (Digital Signal Processing) techniques have been applied for identification task and concentrated on assigning numerical values to the symbolic DNA sequence and then applying spectral analysis tools such as the short-time discrete Fourier transform (ST-DFT) to locate periodicity components. In this paper, first, the symbolic DNA sequences are converted to digital signal using the Z-curve method, which is a unique 3-D plot to illustrate DNA sequence and presents the biological behavior of DNA sequence. Then a novel fast algorithm is proposed to investigate the location of exons in DNA strand based on the combination of Linear Predictive Coding Model (LPCM) and Goertzel algorithm. The proposed algorithm leads to increase the speed of process and therefor reduce the computational complexity. Detection of small size exons in DNA sequences, exactly, is another advantage of our algorithm. The proposed algorithm ability in exon prediction is compared with several existing methods at the nucleotide level using: (i) specificity - sensitivity values; (ii) Receiver Operating Curves (ROC); and (iii) area under ROC curve. Simulation results show that our algorithm increases the accuracy of exon detection relative to other methods for exon prediction. In this paper, we have also developed a useful user friendly package to analyze DNA sequences. Keywordssequence; Protein coding regions; Signal processing; Exon; Linear predictive coding model; Goertzel algorithm.