Application Of Signal Processing Techniques Research Articles

Application of signal processing techniques for the spectroscopic analysis of dolutegravir and lamivudine: a comparative assessment and greenness appraisal

HIV treatment has greatly improved over the years, with the introduction of antiretroviral drugs that target the virus and suppress its replication. Dolutegravir and lamivudine are two such antiretroviral drugs that are commonly used in HIV treatment regimens. Herein, three spectrophotometric methods manipulating ratio spectra were developed for the simultaneous analysis of dolutegravir and lamivudine in their binary mixtures. These methods include mathematical processing stages like ratio difference method or signal processing approaches such as the first derivative of the ratio spectra, and continuous wavelet transform. The developed spectrophotometric methods exploit the characteristic spectral differences between dolutegravir and lamivudine in order to quantify them simultaneously. These methods have shown promising results in terms of sensitivity and selectivity as validated per the ICH guidelines. Moreover, these methods offer a straightforward and economical alternative to more intricate analytical methodologies like high-performance liquid chromatography. By incorporating the analytical eco-scale and AGREE for greenness evaluation of the proposed methods, we can further ensure that these techniques are effective and environmentally friendly, aligning with the principles of green chemistry. This evaluation will provide a comprehensive understanding of the environmental friendliness of these spectrophotometric methods in pharmaceutical analysis.

Perception of power quality disturbances using Fourier, Short-Time Fourier, continuous and discrete wavelet transforms

Electric power utilities must ensure a consistent and undisturbed supply of power, with the voltage levels adhering to specified ranges. Any deviation from these supply specifications can lead to malfunctions in equipment. Monitoring the quality of supplied power is crucial to minimize the impact of fluctuations in voltage. Variations in voltage or current from their ideal values are referred to as "power quality (PQ) disturbances," highlighting the need for vigilant monitoring and management. Signal processing methods are widely used for power system applications which include understanding of voltage disturbance signals and used for retrieval of signal information from the signals Different signal processing methods are used for extracting information about a signal. The method of Fourier analysis involves application of Fourier transform giving frequency information. The method of Short-Time Fourier analysis involves application of Short-Time Fourier transform (STFT) giving time–frequency information. The method of continuous wavelet analysis involves application of Continuous Wavelet transform (CWT) giving signal information in terms of scale and time where frequency is inversely related to scale. The method of discrete wavelet analysis involves application of Discrete Wavelet transform (DWT) giving signal information in terms of approximations and details where approximations and details are low and high frequency representation of original signal. In this paper, an attempt is made to perceive power quality disturbances in MATLAB using Fourier, Short-Time Fourier, Continuous Wavelet and Discrete Wavelet Transforms. Proper understanding of the signals can be possible by transforming the signals into different domains. An emphasis on application of signal processing techniques can be laid for power quality studies. The paper compares the results of each transform using MATLAB-based visualizations. The discussion covers the advantages and disadvantages of each technique, providing valuable insights into the interpretation of power quality disturbances. As the paper delves into the complexities of each method, it takes the reader on a journey of signal processing complexities, culminating in a nuanced understanding of power quality disturbances and their representations across various domains. The outcomes of this research, elucidated through energy values, 3D plots, and comparative analyses, contribute to a comprehensive understanding of power quality disturbances. The findings not only traverse theoretical domains but also find practical utility in real-world scenarios.

Comparative analysis of harmonic sensitivity for stator fault diagnosis in induction motors

Induction motors play a vital role in various industrial applications due to their commendable efficiency and reliability. However, their susceptibility to faults, especially in challenging industrial environments, highlights the need for vigilant fault detection to prevent unforeseen downtimes and reduce subsequent repair costs. Early fault diagnosis is crucial in this context. A systematic approach to diagnosing faults in asynchronous motors involves the application of signal processing techniques, particularly utilizing the Fast Fourier Transform (FFT). The FFT, as a mathematical tool, enables a comprehensive analysis of signals, facilitating the identification and isolation of their frequency components. Monitoring the frequency components within a motor's signal provides a means to determine the existence and severity of faults. FFT analysis allows for the monitoring of four distinct categories of harmonics: time harmonics (TH), rotor slot harmonics (RSH), rotor bar fault harmonics (RBFH), and eccentricity fault harmonics (EFH). Each type of harmonic offers valuable insights into specific fault categories. Empirical evidence, drawn from experimental results, emphasizes the heightened sensitivity of rotor slot harmonics (RSH) in detecting stator faults. Continuous monitoring of the RSH frequency component enables the prompt detection and localization of stator faults, along with an assessment of their severity. Additionally, this diagnostic methodology proves effective in identifying micro short-circuits between stator coils, allowing for a proactive strategy in predictive maintenance. This proactive approach enables anticipatory part replacement before degradation progresses to the point of causing comprehensive failure in the production chain. The combination of FFT-based signal processing and harmonic analysis establishes a robust framework for the early detection and localization of faults in asynchronous motors within industrial settings. This contributes to enhanced operational reliability and efficiency, ultimately ensuring smoother industrial processes.

Application of signal processing techniques and intelligent classifiers for high-impedance fault detection in ensuring the reliable operation of power distribution systems

High-impedance fault (HIF) is always a threat and the biggest challenge in the power transmission and distribution system (PTDS). For a PTDS to operate effectively, HIF diagnosis is essential. However, given the HIF’s nature and the involved complexity, detection, identification, and fault location are difficult. This will be even more complicated in conventional PTDSs as they are inefficient and highly vulnerable. Given the importance and urgent need for HIF diagnosis in PTDS, this study reviews state-of-the-art HIF phenomenon and detection techniques and proposes the use of “various signal processing techniques for fault feature extraction” and “ different classifiers for identifying HIF.” First, HIF current/voltage signals are analyzed using signal processing techniques, which include the discrete wavelet transform (DWT), pattern recognition, Kalman filtering, TT transform, mathematical morphology (MM), S transform (ST), fast Fourier transform (FFT), principal component analysis (PCA), linear discriminant analysis (LDA), and wavelet transforms, such as dual-tree, maximum overlap discrete wavelet transform (MODWT), and lifting wavelet transform (LWT). Second, the various HIF and non-HIF faults are classified using intelligent classifiers. The intelligent classifiers include artificial neural networks (ANNs), probabilistic neural networks (PNNs), genetic algorithms (GAs), fuzzy logic, adaptive neuro-fuzzy interface system, support vector machine (SVM), extreme learning machine (ELM), adaptive resonance theory, random forests (RFs), decision trees (DTs), and convolution neural networks (CNNs). In addition to the comparative discussion of various classifier techniques, their evaluation criterion and performance are prioritized. Third, this review also studied different test systems, such as radial distribution network, mesh distribution network, IEEE 4 node, IEEE 13 node feeder, IEEE 34 node feeder, IEEE 39 node feeder, IEEE 123 node feeder, Palash feeder, and test microgrid systems, to assess the pertinence of various HIF detection schemes and the behavior along with methods to locate the HIF. Overall, we believe this review would serve as a comprehensive compendium of advanced techniques for HIF diagnosis in different test systems.

Application of signal processing techniques in structural health monitoring of concrete gravity dams

Application of signal processing techniques in structural health monitoring of concrete gravity dams

Text as signal. A tutorial with case studies focusing on social media (Twitter)

Sentiment analysis is the automated coding of emotions expressed in text. Sentiment analysis and other types of analyses focusing on the automatic coding of textual documents are increasingly popular in psychology and computer science. However, the potential of treating automatically coded text collected with regular sampling intervals as a signal is currently overlooked. We use the phrase "text as signal" to refer to the application of signal processing techniques to coded textual documents sampled with regularity. In order to illustrate the potential of treating text as signal, we introduce the reader to a variety of such techniques in a tutorial with two case studies in the realm of social media analysis. First, we apply finite response impulse filtering to emotion-coded tweets posted during the US Election Week of 2020 and discuss the visualization of the resulting variation in the filtered signal. We use changepoint detection to highlight the important changes in the emotional signals. Then we examine data interpolation, analysis of periodicity via the fast Fourier transform (FFT), and FFT filtering to personal value-coded tweets from November 2019 to October 2020 and link the variation in the filtered signal to some of the epoch-defining events occurring during this period. Finally, we use block bootstrapping to estimate the variability/uncertainty in the resulting filtered signals. After working through the tutorial, the readers will understand the basics of signal processing to analyze regularly sampled coded text.

A Novel Approach For Identification Of Exon Locations In DNA Sequences Using GLC Window

The application of signal processing techniques for identification of exons in Deoxyribonucleic acid (DNA) sequence is a challenging task. The objective of this paper is to introduce a combinational window approach for locating exons in DNA sequence. In contrast to the traditional single window function for evaluation of short time Fourier transform (STFT), this work proposes a novel method for evaluating STFT coefficients using a combinational window function comprising of Gaussian, Lanczos and Chebyshev (GLC) windows. The chosen combinational window GLC has the highest relative side lobe attenuation values compared to other window functions introduced by various researchers. The proposed algorithm incorporates GLC window function for evaluating STFT coefficients and in the design of FIR bandpass filter. Simulation results revealed its effectiveness in improving the evaluation parameters like Sensitivity, Specificity, Accuracy, Area under curve (AUC), Discrimination Measure (DM). Furthermore, the proposed algorithm has been applied successfully to some universal benchmark datasets like C. elegans, Homosapiens, etc., The proposed method has shown to be an efficient approach for the prediction of protein coding regions compared to other existing methods. All the simulations are done using the MATLAB 2016a.

Internet of Things for Green Building Management: Disruptive Innovations Through Low-Cost Sensor Technology and Artificial Intelligence

Buildings consume 60% of global electricity. However, current building management systems (BMSs) are highly expensive and difficult to justify for small- to medium-sized buildings. The Internet of Things (IoT), which can collect and monitor a large amount of data on different aspects of a building and feed the data to the BMS's processor, provides a new opportunity to integrate intelligence into the BMS for monitoring and managing a building's energy consumption to reduce costs. Although an extensive literature is available on, separately, IoTbased BMSs and applications of signal processing techniques for some building energy-management tasks, a detailed study of their integration to address the overall BMS is limited. As such, this article will address the current gap by providing an overview of an IoT-based BMS that leverages signal processing and machine-learning techniques. We demonstrate how to extract high-level building occupancy information through simple, low-cost IoT sensors and study how human activities impact a building's energy use-information that can be exploited to design energy conservation measures that reduce the building's energy consumption.

A comprehensive review on soft computing and signal processing techniques in feature extraction and classification of power quality problems

Power quality (PQ) is the interface of electrical power with electrical equipment. The distribution of power supply constantly at high quality to the consumers is predicted by the electrical power systems. The enormous increase in sensitive loads, power electronic devices, nonlinear loads, and incorporation of nonconventional energy sources like solar and wind energy to the electrical power grid is one of the most important sources of PQ events. The identification of PQ events by analyzing distortion of voltage and current waveform is a very important task for power system monitoring. The expansion of novel methods to classify the PQ issues is now a most important concern. This article gives a broad review on the applications of signal processing techniques and soft computing methods used to extract the features and for optimal feature selection of PQ events. This paper helps to motivate and guide the researchers towards the solutions to a problem in the area of PQ assessment.

The application of EMD-based methods for diagnosis of winding faults in a transformer using transient and steady state currents

The application of signal processing techniques is a fundamental step for fault diagnostic methodologies. The application of empirical mode decomposition (EMD)-based methods such as classic EMD, ensemble EMD (EEMD), and complete EEMD (CEEMD) is presented in this work for the analysis of inrush current signals. This analysis leads to the detection of short-circuited turns in transformers. Results show that CEEMD provides the best performance, as it readily extracts the information related to the fault, requiring of acceptable computational resources. Actual inrush current signals of a transformer with short-circuited turns are also considered. The number of short-circuited turns ranges from 5 to 40. Useful indices, such as the Shannon Entropy, Energy, and root-mean-square value, are obtained from the information provided by the CEEMD approach. These indices are analyzed for both the transient state and the steady state of the current signals, providing the proper quantification of the fault severity.

IntroMap: a signal analysis based method for the detection of genomic introgressions

BackgroundBreeding programs often rely on marker-assisted tests or variant calling of next generation sequence (NGS) data to identify regions of genomic introgression arising from the hybridization of two plant species. In this paper we present IntroMap, a bioinformatics pipeline for the screening of candidate plants through the application of signal processing techniques to NGS data, using alignment to a reference genome sequence (annotation is not required) that shares homology with the recurrent parental cultivar, and without the need for de novo assembly of the read data or variant calling.ResultsWe show the accurate identification of introgressed genomic regions using both in silico simulated genomes, and a hybridized cultivar data set using our pipeline. Additionally we show, through targeted marker-based assays, validation of the IntroMap predicted regions for the hybrid cultivar.ConclusionsThis approach can be used to automate the screening of large populations, reducing the time and labor required, and can improve the accuracy of the detection of introgressed regions in comparison to a marker-based approach. In contrast to other approaches that generally rely upon a variant calling step, our method achieves accurate identification of introgressed regions without variant calling, relying solely upon alignment.

Event Detection and Its Signal Characterization in PMU Data Stream

The potential application of signal processing techniques is not only to detect the event but also to characterize them according to physical disturbance. In this paper, event detection and its characterization algorithm is presented. The event detection scheme uses computation of spectral kurtosis on sum of intrinsic mode functions. The algorithm is capable of detecting the event in phasor measurement units data by comparing the maximum energy and root-mean square of energy content of present analysis segment with respect to previous segment. The statistical indices applied are capable to flag specific data and thus the timely detection of events. Further, statistical features extracted from event-related segment suggest that the transient signals from different regions are distinct and thus can be classified. The signal characterization is further represented in terms of short-term energy and group delay. The analysis on event triggered signal demonstrates the related physical phenomenon in each event type. The study suggests the most relevant signal associated with a particular type of event.

Tool wear prediction by using wavelet transform

Tool wear has been a potential problem that costs high for the manufacturing industry. In the past there has been application of signal processing techniques to cutting operation and monitor the tool wear. This paper relates the suitability of application of wavelet transform to tool wear prediction. The suitability of fast Fourier transform (FFT) and discrete wavelet transform (DWT) to process the signals from machining was investigated. To accomplish this objective, experiments were carried out to collect signals from machining experiments and thereafter signals were processed using FFT and wavelet transform. It is observed that at higher cutting speeds, wavelet transform is a more effective signal processing technique compared to FFT as signal gets corrupted by high frequency noise at higher speeds. Effect of wavelet packet decomposition for de-noising and filtering was also studied to remove high frequency noise from the accelerometer signals.

Faults Diagnostics of Railway Axle Bearings Based on IMF's Confidence Index Algorithm for Ensemble EMD.

As train loads and travel speeds have increased over time, railway axle bearings have become critical elements which require more efficient non-destructive inspection and fault diagnostics methods. This paper presents a novel and adaptive procedure based on ensemble empirical mode decomposition (EEMD) and Hilbert marginal spectrum for multi-fault diagnostics of axle bearings. EEMD overcomes the limitations that often hypothesize about data and computational efforts that restrict the application of signal processing techniques. The outputs of this adaptive approach are the intrinsic mode functions that are treated with the Hilbert transform in order to obtain the Hilbert instantaneous frequency spectrum and marginal spectrum. Anyhow, not all the IMFs obtained by the decomposition should be considered into Hilbert marginal spectrum. The IMFs’ confidence index arithmetic proposed in this paper is fully autonomous, overcoming the major limit of selection by user with experience, and allows the development of on-line tools. The effectiveness of the improvement is proven by the successful diagnosis of an axle bearing with a single fault or multiple composite faults, e.g., outer ring fault, cage fault and pin roller fault.

Application of signal processing techniques to the detection of tip vortex cavitation noise in marine propeller

The tip vortex cavitation and its relevant noise has been the subject of extensive researches up to now. In most cases of experimental approaches, the accurate and objective decision of cavitation inception is primary, which is the main topic of this paper. Although the conventional power spectrum is normally adopted as a signal processing tool for the analysis of cavitation noise, a faithful exploration cannot be made especially for the cavitation inception. Alternatively, the periodic occurrence of bursting noise induced from tip vortex cavitation gives a diagnostic proof that the repeating frequency of the bursting contents can be exploited as an indication of the inception. This study, hence, employed the Short-Time Fourier Transform (STFT) analysis and the Detection of Envelope Modulation On Noise (DEMON) spectrum analysis, both which are appropriate for finding such a repeating frequency. Through the acoustical measurement in a water tunnel, the two signal processing techniques show a satisfactory result in detecting the inception of tip vortex cavitation.

Application of signal processing technique for the modification of a fruit sorting machine

The objective of this study involves the optimise process of a sorting machine for spherical fruits, which are currently manually classified with a low level of efficiency in Iran. An instrumentation system is modelled and fabricated for the development of a fruit sorting machine. Mechanical components and control system are assembled on a chassis for fruit transferring and classifying. A gravity duct transfers the fruits toward six ejection points which is manipulated by pneumatic cylinders and magnetic valves. Peak signal to noise ratio (PSNR) criterion is employed to analyse the work quality of the mechatronic system. The PSNR experimental results specify that noise and vibration significantly affect the machine performance. To redesign the fabricated machine, the mechanical system of conveyor and actuators are separated from measuring and control unit. Field trials were conducted to modify the machine characteristics based on the minimum damage for the fruits. To avoid bruising in apple fruits, Golab variety, the duct slope was fixed at 0.02 which produces the maximum fruit velocity of 1 m/s. With no injury on apple fruit the sorting capacity of 130 kg/h is achieved for the machine.

The application of signal processing techniques for real-time monitoring of the dynamic stability of a ship via its motion responses

Significant changes of stability at sea can lead to dangerous situations and eventually stability failure. Despite its importance, the current intact stability criteria do not evaluate the motion responses of a vessel. More recently, the International Maritime Organization has identified phenomena in seaways responsible for stability failures. These phenomena can cause large roll angles and/or accelerations that can endanger ships due to critical stability situations in waves. The measurement of waves while a ship is underway is a major challenge, but ship motion is a good reflection of the wave characteristics and can be captured. Signal processing techniques are used in the detection and estimation of the influential parameters of a wave through the analysis of motion responses. Some variables of the system can be detected by spectral analysis of heave and pitch responses. These variables are the peak wave frequencies and associated magnitudes which can cause a high roll motion when similar to the roll natural frequency. The instantaneous frequency present in the signal is revealed through spectral analysis of short-time Fourier transforms in less than a minute. The instantaneous frequency is a parameter of practical importance which can be used in decision-making processes to avoid high roll motions.

Effect of Group Delay in WCDMA based Pulse Shaping Filter using Discrete-Time Eye Scope

The application of signal processing techniques to wireless communications is an emerging area that has recently achieved dramatic improvement in results and holds the potential for even greater results in the future as an increasing number of researchers from the signal processing and communication areas participate in this expanding field. Today, wireless multimedia is moving from vision to reality. Service providers are deploying third generation (3G) wireless base stations that enable the user to surf the web, check email, conduct videoconferences, and access a wide range of new services from their wireless devices. Underlying 3G innovations, Wideband Code Division Multiple Access (WCDMA) technology is pushing the expectations of wireless networks far beyond the previous limitations of 1G and 2G. To deliver on the promise of 3G, WCDMA system must handle the greater capacity, higher data rates, and support multimedia standards, while at the same time reduces the size, cost and the power consumption. The large expectations through WCDMA are based on its flexibility for multimedia capabilities and the high capacity. However, the demanded traffic grows rapidly and new capacity enhancements are required in order to satisfy the future needs. The present paper deals with Effect of group delay in WCDMA based pulse shaping filter using discrete-time eye scope.

Editorial: engineering approaches to study cardiovascular physiology: modeling, estimation, and signal processing

EDITORIAL article Front. Physiol., 05 November 2012Sec. Computational Physiology and Medicine Volume 3 - 2012 | https://doi.org/10.3389/fphys.2012.00425

A Bragg Wavelength-Insensitive Fiber Bragg Grating Ultrasound Sensing System that Uses a Broadband Light and No Optical Filter

An optical filter is incorporated in a conventional ultrasound detection system that uses a fiber Bragg grating (FBG) and broadband light source, to demodulate the FBG sensor signal. A novel ultrasound sensing system that does not require an optical filter is presented herein. Ultrasound could be detected via the application of signal processing techniques, such as signal averaging and frequency filters, to the photodetector output that corresponds to the intensity of the reflected light from a broadband light-illuminated FBG. Ultrasonic sensitivity was observed to be enhanced when an FBG was installed as a resonant sensor. This FBG ultrasound detection system is small and cheap to fabricate because it does not use a demodulating optical filter. The experimental results demonstrate that this system could be applied to ultrasonic damage inspection and acoustic emission measurements. Furthermore, this system was able to detect ultrasound despite the amount of strain or temperature that was applied to the FBG sensor because the ultrasound detection was not sensitive to the Bragg wavelength of the FBG sensor.