Frequency-based Algorithm Research Articles

Evaluation of cyclic repetition frequency based algorithm for fetal heart rate extraction from fetal phonocardiography

The passive and non-invasive approach of fetal heart rate (FHR) estimation using fetal phonocardiography (fPCG) provides an alternative to the commonly used Doppler ultrasound-based technique. The acoustic signal recorded on the mother’s abdomen is corrupted by noise, making FHR estimation difficult. Thus, an accurate and reliable FHR measurement algorithm from the fPCG signal is needed. This study evaluates a cyclic repetition frequency-based algorithm with additional FHR validation steps on the clinical recordings from three datasets with varying signal quality. The FHR from the algorithms is compared against the reference FHR measurements, and the accuracy and reliability performance measure is determined. The cyclic repetition algorithm significantly improves (p < 0.05) against the Hilbert-envelope autocorrelation algorithm for poor-quality signals. The algorithm's average reliability across three datasets is 93%, and accuracy is 4.5 beats per minute. The algorithm performs well for the gestation week ranging from 26 −40 and for the labor and antenatal ward data.

Featherweight assisted vulnerability discovery

Predicting vulnerable source code helps to focus attention on those parts of the code that need to be examined with more scrutiny. Recent work proposed the use of function names as semantic cues that can be learned by a deep neural network (DNN) to aid in the hunt for vulnerability of functions. Combining identifier splitting, which splits each function name into its constituent words, with a novel frequency-based algorithm, we explore the extent to which the words that make up a function's name can predict potentially vulnerable functions. In contrast to *lightweight* predictions by a DNN that considers only function names, avoiding the use of a DNN provides *featherweight* predictions. The underlying idea is that function names that contain certain "dangerous" words are more likely to accompany vulnerable functions. Of course, this assumes that the frequency-based algorithm can be properly tuned to focus on truly dangerous words. Because it is more transparent than a DNN, the frequency-based algorithm enables us to investigate the inner workings of the DNN. If successful, this investigation into what the DNN does and does not learn will help us train more effective future models. We empirically evaluate our approach on a heterogeneous dataset containing over 73000 functions labeled vulnerable, and over 950000 functions labeled benign. Our analysis shows that words alone account for a significant portion of the DNN's classification ability. We also find that words are of greatest value in the datasets with a more homogeneous vocabulary. Thus, when working within the scope of a given project, where the vocabulary is unavoidably homogeneous, our approach provides a cheaper, potentially complementary, technique to aid in the hunt for source-code vulnerabilities. Finally, this approach has the advantage that it is viable with orders of magnitude less training data.

High frequency-based protection algorithm for meshed MTDC systems

This research presents a high frequency-based algorithm for zonal protection of meshed Multi-Terminal high voltage Direct Current (MTDC) systems. Discrete wavelet transform (DWT) is used to capture high frequency current transients from one end of each line. The faulty line is identified by analyzing two distinct high frequency bands form the captured transients. The characteristics of these current transients during normal condition are different from faulty condition. A four-terminal meshed MTDC model is used to test the performance of the protection algorithm. Various faults with different fault scenarios were simulated using four-terminal meshed MTDC model. The simulation results showed success of the algorithm in identifying internal faults. Moreover, the high frequency algorithm showed robustness against other abnormal conditions such as AC faults and sudden change of wind power.

Science4Fashion: an end-to-end decision support system for fashion designers

Nowadays, the fashion clothing industry is moving towards “fast” fashion, offering a wide variety of products based on different patterns and styles, usually characterized by lower costs and ambiguous quality. The retails markets are trying to present regularly new fashion collections while trying to follow the latest fashion trends at the same time. The main reason is to remain competitive and keep up with ever-changing customer demands. Fashion designers draw inspiration from social media, e-shops, and fashion shows that set the new fashion trends. In this direction, we propose Science4Fashion, an AI end-to-end system that facilitates fashion designers by collecting and analyzing data from many different sources and suggesting products according to their needs. An overview of the system’s modules is presented, emphasizing data collection, data annotation using deep learning models, and product recommendation and user feedback processes. The experiments presented in this paper are twofold: (a) experiments regarding the evaluation of clothing attribute classification, and (b) experiments regarding product recommendation using the baseline kNN enriched by the frequency-based clustering algorithm (FBC), achieving promising results.

KP-Rank: a semantic-based unsupervised approach for keyphrase extraction from text data

Automatic key concept identification from text is the main challenging task in information extraction, information retrieval, digital libraries, ontology learning, and text analysis. The main difficulty lies in the issues with the text data itself, such as noise in text, diversity, scale of data, context dependency and word sense ambiguity. To cope with this challenge, numerous supervised and unsupervised approaches have been devised. The existing topical clustering-based approaches for keyphrase extraction are domain dependent and overlooks semantic similarity between candidate features while extracting the topical phrases. In this paper, a semantic based unsupervised approach (KP-Rank) is proposed for keyphrase extraction. In the proposed approach, we exploited Latent Semantic Analysis (LSA) and clustering techniques and a novel frequency-based algorithm for candidate ranking is introduced which considers locality-based sentence, paragraph and section frequencies. To evaluate the performance of the proposed method, three benchmark datasets (i.e. Inspec, 500N-KPCrowed and SemEval-2010) from different domains are used. The experimental results show that overall, the KP-Rank achieved significant improvements over the existing approaches on the selected performance measures.

Instantaneous Frequency-Based Algorithm for Directional Relays

The looped structure of the transmission and subtransmission networks results in the bidirectional fault current passing through the protection relays. The detection of the current direction is relegated to the directional protection unit. The traditional directional overcurrent relays utilize the reference voltage phasor for the detection of the fault direction, which increases total cost due to the need for both current and voltage measurement units. In this article, a new current only directional algorithm based on the instantaneous frequency is introduced. The proposed method is robust to noise and changes in the grid parameters, such as a change in the power frequency and direction of normal power flow. To evaluate the algorithm, plenty of simulations and actual experiments are carried out in different fault conditions. Also, its performance is compared with some other directional algorithms. The results confirm that the proposed algorithm can detect fault direction with a high degree of accuracy.

Detection and Characterization of Time-variant Non-stationary Voltage Sag Waveforms Using Segmented Hilbert Huang Transform

The automation of distribution systems will need a fast and efficient signal-processing tool in order to extract useful information from the power quality (PQ) monitors, smart meters, or Internet of Things, which are essential for real-time monitoring, detection, and faster decision-making capability of smart grids. This article proposes the application of the Hilbert–Huang transform (HHT) method for decomposition of the PQ data into its individual frequency components in order to separate the nonstationary voltage sag waveform containing the fundamental frequency component. After the decomposition of the data using the HHT method, an innovative fundamental frequency-based algorithm is proposed for the accurate detection of voltage sag starting and ending times, which are useful for accurate calculation of point-on-wave and voltage sag duration. The proposed algorithm is further extended for automatic detection of the transition segments to accurately calculate the time-varying and single event characteristics such as voltage sag magnitude and phase-angle jump. The composite method is referred to as the segmented Hilbert–Huang transform (SHHT). The limitations of the existing methods of voltage sag detection using the rms method, the fast Fourier transform method, and the waveform envelope method are also outlined in this article. Finally, the results of the detection and characterization of time-variant nonstationary voltage sag data using the proposed SHHT method is presented and discussed.

Spectral wave characteristics along the central coast of eastern Red Sea

The Jeddah coast lies in the central eastern Red Sea, which is characterized by the predominant northwest winds and the associated wind waves throughout the year. A detailed investigation on the spectral wave characteristics in the nearshore regions of the Jeddah coast has not been carried out yet, primarily due to the lack of data. In the present study, we have analyzed the available wave spectra measured at two nearshore locations along the Jeddah coast using wave gauges. The wave spectra were separated into wind sea and swell components using a frequency-based algorithm, and the integral wave parameters corresponding to each component were derived. Although the measurements were limited to the summer season, notable features such as the diurnal variability and the superimposition of wind seas and swells have been identified from the spectra. The superimposition is mainly due to the interaction of the young swells propagated from the northern Red Sea and the local breezes prevailing along the coast at certain periods in a diurnal cycle. Based on the wave speed calculations and the estimated time shifts between the wind and waves, the potential swell regions have been backtracked.

Early Seizure Detection by Applying Frequency-Based Algorithm Derived from the Principal Component Analysis

The use of automatic electrical stimulation in response to early seizure detection has been introduced as a new treatment for intractable epilepsy. For the effective application of this method as a successful treatment, improving the accuracy of the early seizure detection is crucial. In this paper, we proposed the application of a frequency-based algorithm derived from principal component analysis (PCA), and demonstrated improved efficacy for early seizure detection in a pilocarpine-induced epilepsy rat model. A total of 100 ictal electroencephalographs (EEG) during spontaneous recurrent seizures from 11 epileptic rats were finally included for the analysis. PCA was applied to the covariance matrix of a conventional EEG frequency band signal. Two PCA results were compared: one from the initial segment of seizures (5 sec of seizure onset) and the other from the whole segment of seizures. In order to compare the accuracy, we obtained the specific threshold satisfying the target performance from the training set, and compared the False Positive (FP), False Negative (FN), and Latency (Lat) of the PCA based feature derived from the initial segment of seizures to the other six features in the testing set. The PCA based feature derived from the initial segment of seizures performed significantly better than other features with a 1.40% FP, zero FN, and 0.14 s Lat. These results demonstrated that the proposed frequency-based feature from PCA that captures the characteristics of the initial phase of seizure was effective for early detection of seizures. Experiments with rat ictal EEGs showed an improved early seizure detection rate with PCA applied to the covariance of the initial 5 s segment of visual seizure onset instead of using the whole seizure segment or other conventional frequency bands.

An efficient code compression for MIPS32 processor using dictionary and bit-mask based static and dynamic frequency algorithm

Purpose Memory plays a vital role in designing embedded systems. A larger memory can accommodate more and larger applications but increases cost area, as well as energy requirements. Hence, the purpose of this paper is to propose code compression techniques to solve this issue by minimizing the code size of the application program by compressing the instructions with higher static frequency. Design/methodology/approach The idea is based on the static and dynamic frequency-based algorithm combined with bit mask and dictionary-based algorithm for MIPS32 processor, in order to minimize the code size and improves compression ratio. Findings The experimental result shows that the proposed system achieves up to 67 percent compression efficiency. Originality/value The paper presents enhanced versions of the code compression technique.

Testing the Sampling-Based NUSS-RF Tool for the Nuclear Data—Related Global Sensitivity Analysis with Monte Carlo Neutronics Calculations

NUSS-RF is a tool for nuclear data uncertainty propagation through neutronics calculations with continuous-energy Monte Carlo codes and ACE-formatted nuclear data libraries. Many existing codes, including the original version of NUSS (Nuclear data Uncertainty Stochastic Sampling), are based on simple random sampling algorithms. The NUSS-RF extension now uses a frequency-based sampling algorithm, called the random balance design (RBD), to analyze individual nuclear data uncertainty contributions in regard to the total output (e.g., keff) uncertainty. The implementation of the RBD method into NUSS-RF is initially verified by comparing the computed individual input variance contributions with analytical solutions for two analytical test cases. As well, it is assessed against the alternative approach based on the use of correlation coefficients. NUSS-RF is then used for an analysis of the Jezebel and Godiva fast-spectrum criticality benchmarks: in a first step, the overall effect of the 239Pu(n,f) and 235U(n,f) cross-section uncertainties on keff is evaluated, while in a second step, the contributions from the individual energy groups are quantified. As an additional verification, the NUSS-RF results are assessed against sensitivity and uncertainty analysis based on perturbation theory, showing good agreement between the two solutions. Finally, the capability of NUSS-RF is demonstrated for ranking the input parameters with respect to their influence on the total uncertainty of the output parameters, taking into account possible correlations between input parameters. Possible future improvements for the current computational scheme are discussed in the conclusions.

Pleural effusion detection system using wideband slot‐loaded loop antenna

Pleural effusion (PE) is the accumulation of fluids around the lungs due to various diseases, such as cancer, liver and heart failure. Given the fact that fluid provides a high contrast in terms of dielectric constant at microwave frequencies, a radar-based microwave imaging system is proposed to utilise that change in properties for the early detection of PE. The system uses a wideband slot-loaded loop antenna to scan the torso, a portable vector network analyser for signal generation and recording, and a laptop for signal processing and image formation using a frequency-based imaging algorithm. The utilised antenna is designed to have a compact size (in wavelengths) of 0.23 × 0.23 that is one-third the size of recent designs. By using proper slots in a conventional slot-loaded loop and adding a staircase-shaped patch to the loop's perimeter, the antenna achieves a fractional bandwidth of 50% (0.72–1.19 GHz), and a peak gain of 4.9 dBi. The practicality of the proposed system is successfully tested by detecting a small amount of fluid (3 mL) around the lungs of a realistic life-size torso phantom.

Traceability of mussel (Mytilus chilensis) in southern Chile using microsatellite molecular markers and assignment algorithms. Exploratory survey

The international seafood trade has adopted the food chain or “from farm to fork” concept in terms of standards and regulations regarding food quality, safety and authenticity, from primary production to the consumer. This has led to an increasing need for traceability, but administrative traceability systems (physical labeling, information recording and automatic data treatment) are not flawless and require validation through analytical procedures. Currently, DNA-based methods used for species identification and population genetics, coupled with allocation algorithms can be used to verify administrative traceability systems. We evaluated the potential of a panel of nine microsatellite markers combined with allocation algorithms for their ability to assign Mytilus individuals from southern Chile to their geographical origin, evaluating the performance of four assignment methods: genetic distance and frequency-based criteria and a Bayesian based method using prior information or not. The reallocation test showed that the Bayesian method with prior information performed best. When tested with a real traceability verification case, the frequency-based algorithm showed the best results, re-allocating individuals to their original population at least 6 times more often than individuals from other locations in a challenging scenario with low genetic differentiation among locations. In order to apply this allocation method for traceability purposes, it would be necessary to strengthen this SSR panel with more informative loci and complement it with SNP markers.

Field Study of Unconsciousness Monitoring in Poultry after Stunning Using Real-Time EEG Interpretation Method

Traditionally, the Muslim’s food must be slaughtered by the Islamic way called Halal. That is, the animals must be alive at the time of slaughter and after stunning they must recover to normal position in 5 minutes. According to the animal welfare point of view, the electrical waterbath stunning is permitted before the poultry being killed in order to make the poultry unconscious during slaughtering period. This is the only condition to satisfy both the Muslim and the animal welfare regulation. During the process of slaughtering until the end of bleeding, approximately 44 second, the poultry should not be conscious during this stage. How to make sure that the poultry will not be conscious during this period according to the various electrical waterbath stunning parameter is a very challenging problem to be solved. As the clinical somatosensory sign, e.g. corneal reflect and wing flapping, may not be reliable to detect the unconsciousness, the electroencephalogram (EEG) becomes one of the most reliable methods to clarify the unconsciousness. However, unlike the laboratory condition, on the field study we cannot measure the prestunning EEG as the baseline to automatically detect the unconscious stage according to the EEG suppression. Without the need of recording the prestunning EEG, frequency-based algorithm is proposed in this paper to detect the unconsciousness. With the proposed algorithm, we can observe that trends of the EEG are shifted from lower to higher frequencies while the poultry is being recovery. Investigations on the suitable current and frequency parameters for the stunner in any animal batches are also explored. According to the preliminary results, the proposed method and criteria can be efficiently used in the real environment (field study).

Wiener Deconvolution for Reconstruction of Pneumatically Attenuated Pressure Signals

Wiener deconvolution is applied to the problem of reconstructing transient signals from pneumatically attenuated pressure measurements. The presented method offers a viable alternative to traditional techniques where pressures are sensed by in situ surface transducers. In situ installations present significant issues, including potential weakening of structural walls, overheating of pressure sensing elements, strain biasing of sensing elements, and resonance within the measurement port. A less complex installation involves a small surface pressure tap with the pressure signal transmitted to the transducer via a significant length of pneumatic tubing. This remote installation allows the transducer to be mounted in a controlled environment, and it virtually eliminates any sensing errors due to surface strain. The presented filter amplifies attenuated pressure signals while selectively rejecting sensor noise. The frequency-based algorithm accounts for both white and colored noise contaminations. The deconvolution method is validated using laboratory-derived step-response data. The examples presented demonstrate the algorithm accuracy for both constant and frequency-weighted signal-to-noise ratios based on the noise threshold of the measured pressure signal. Experiment results demonstrate insensitivity of the Wiener algorithm to weighting schemes used to estimate the signal-to-noise ratio of the measurements. Sensitivity analysis indicates that deconvolution fit errors are relatively insensitive to parameter perturbations near nominal measured values.

DeStripe: frequency-based algorithm for removing stripe noises from AFM images

BackgroundAtomic force microscopy (AFM) is a relatively recently developed technique that shows a promising impact in the field of structural biology and biophysics. It has been used to image the molecular surface of membrane proteins at a lateral resolution of one nanometer or less. An immediate obstacle of characterizing surface features in AFM images is stripe noise. To better interpret structures at a sub-domain level, pre-processing of AFM images for removing stripe noises is necessary. Noise removal can be performed in either spatial or frequency domain. However, denoising processing in the frequency domain is a better solution for preserving edge sharpness.ResultsWe have developed a denoising protocol, called DeStripe, for AFM bio-molecular images that are contaminated with heavy and fine stripes. This program adopts a divide-and-conquer approach by dividing the Fourier spectrum of the image into central and off-center regions for noisy pixels detection and intensity restoration; it is also applicable to other images interfered with high-density stripes such as those acquired by the scanning electron microscope. The denoising effect brought by DeStripe provides better visualization for image objects without introducing additional artifacts into the restored image.ConclusionsThe DeStripe denoising effect on AFM images is illustrated in the present work. It allows extracting extended information from the topographic measurements and implicitly enhances the molecular features in the image. All the presented images were processed by DeStripe with the raw image as the only input without any requirement for other prior information. A web service, http://biodev.cea.fr/destripe, is available for running DeStripe.

Eigen-based clutter filter design for ultrasound color flow imaging: a review

Proper suppression of tissue clutter is a prerequisite for visualizing flow accurately in ultrasound color flow imaging. Among various clutter suppression methods, the eigen-based filter has shown potential because it can theoretically adapt its stopband to the actual clutter characteristics even when tissue motion is present. This paper presents a formative review on how eigen-based filters should be designed to improve their practical efficacy in adaptively suppressing clutter without affecting the blood flow echoes. Our review is centered around a comparative assessment of two eigen-filter design considerations: 1) eigen-component estimation approach (single-ensemble vs. multi-ensemble formulations), and 2) filter order selection mechanism (eigenvalue-based vs. frequencybased algorithms). To evaluate the practical efficacy of existing eigen-filter designs, we analyzed their clutter suppression level in two in vivo scenarios with substantial tissue motion (intra-operative coronary imaging and thyroid imaging). Our analysis shows that, as compared with polynomial regression filters (with or without instantaneous clutter downmixing), eigen-filters that use a frequency-based algorithm for filter order selection generally give Doppler power images with better contrast between blood and tissue regions. Results also suggest that both multi-ensemble and single-ensemble eigen-estimation approaches have their own advantages and weaknesses in different imaging scenarios. It may be beneficial to develop an algorithmic way of defining the eigen-filter formulation so that its performance advantages can be better realized.

Incorporating data from a primary frequency standard into a time scale

It is difficult to combine data from a primary frequency standard with the time-difference measurements that are usually used as input to most time scales because a primary standard usually operates only occasionally on an irregular schedule and because the fundamental output of a primary frequency standard is a frequency rather than a time, and there is often no natural way of inserting this kind of datum into the scale in a manner that is statistically robust. We will present a new time-scale algorithm that seeks to address these problems. We call this frequency-based algorithm AF1, by analogy with the time-based algorithm AT1 that has been used at NIST for many years. Unlike AT1 in which frequency is simply a parameter that specifies how the time of a clock evolves between measurements, however, the frequency of each clock is a fundamental parameter in AF1. This change in focus provides a natural way for incorporating data from a primary frequency standard into the ensemble. We will present the details of the algorithm and results using data from our primary frequency standard, NIST-7.

Contrast Affects Flicker and Speed Perception Differently

We have previously shown that contrast affects speed perception, with lower-contrast, drifting gratings perceived as moving slower. In a recent study, we examined the implications of this result on motion models of speed perception that use the amplitude of the response of linear spatio-temporal filters to determine speed. In this study, we investigate whether the contrast dependence of speed can be understood within the context of models in which speed estimation is made using the temporal frequency of the response of the linear spatio-temporal filters. We measured the effect of contrast on flicker perception and found that contrast manipulations produce opposite effects on perceived drift rate and perceived flicker rate, i.e., reducing contrast increases the apparent temporal frequency of counterphase modulated gratings. This finding argues that, if a temporal frequency-based algorithm underlies speed perception, either flicker and speed perception must not be based on the output of the same mechanism or contrast effects on perceived spatial frequency reconcile the disparate effects observed for perceived temporal frequency and speed.