Achievable simultaneous time and frequency domain energy concentration for finite length sequences

We propose a parametric waveform design approach for improved detection of extended targets embedded in uncorrelated signal-dependent clutter and noise, whose spectral densities are assumed to be known. Unlike canonical waveform design approaches, the transmit waveform is represented as a weighted linear combination of discrete prolate spheroidal sequences. In the optimization problem, the probability of detection is maximized with respect to the weighting factors of the associated discrete prolate spheroidal sequences under the transmit energy constraint. The weighting factors, which are resolved using a numerical method, lead directly to the desired transmit waveform in the time domain. In comparison to the canonical waveform design approaches, the extra step for time sequence synthesis is avoided and the loss in probability of detection produced therein is remedied. Simulation results demonstrate the improvement in the probability of detection for the proposed approach. However, the improvement comes at the cost of higher computational complexity.

PurposePenetration state is one of the most important factors for judging the quality of a gas tungsten arc welding (GTAW) joint. The purpose of this paper is to identify and classify the penetration state and welding quality through the features of arc sound signal during robotic GTAW process.Design/methodology/approachThis paper tried to make a foundation work to achieve on‐line monitoring of penetration state to weld pool through arc sound signal. The statistic features of arc sound under different penetration states like partial penetration, full penetration and excessive penetration were extracted and analysed, and wavelet packet analysis was used to extract frequency energy at different frequency bands. The prediction models were established by artificial neural networks based on different features combination.FindingsThe experiment results demonstrated that each feature in time and frequency domain could react the penetration behaviour, arc sound in different frequency band had different performance at different penetration states and the prediction model established by 23 features in time domain and frequency domain got the best prediction effect to recognize different penetration states and welding quality through arc sound signal.Originality/valueThis paper tried to make a foundation work to achieve identifying penetration state and welding quality through the features of arc sound signal during robotic GTAW process. A total of 23 features in time domain and frequency domain were extracted at different penetration states. And energy at different frequency bands was proved to be an effective factor for identifying different penetration states. Finally, a prediction model built by 23 features was proved to have the best prediction effect of welding quality.

The essential task of a Brain-Computer Interface (BCI) is to extract the motor imagery features from Electro-Encephalogram (EEG) signals for classifying the thought process. It is necessary to analyse these obtained signals in both the time domain and frequency domains. It is observed that the combination of multiple algorithms increases the performance of the feature extraction process. This paper identifies combinations that have not been attempted previously and improves the accuracy of the overall process, although other authors implemented different combinations of the techniques. The focus is given more on the feature extraction process and frequency bands, which are user-specific and subject-specific frequency bands. In both time and frequency domains, after analysing EEG signals with the time domain parameter, we select the frequency band and the timing while using the Fisher ratio of the time domain parameter (TDP). We used Fisher discriminant analysis (FDA)-type F-score to simultaneously select the frequency band and time segment for multi-class classification. We extracted subject-specific TDP features from the training trials to train the classifier when optimal time-frequency areas were selected for each subject. In this paper, various methods are explored for obtaining the features, which are Time Domain Parameters (TDP), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), R2, Fast Correlation Based Filter (FCBF), Empirical Mode Decomposition (EMD), and Intrinsic time-scale decomposition (ITD). After the extraction process, PCA is used for dimensionality reduction. An efficient result was obtained with the combination of TDP, FFT, and PCA. We used the multi-class Fisher’s linear discriminant analysis (LDA) as the classifier, which was in line with the FDA-type F-score. It is observed that the combination of feature extraction techniques to the frequency bands that were selected by the Fisher ratio and FDA type F-score along with Fisher’s LDA classifier had higher accuracy than the results obtained other researches. A kappa coefficient accuracy of 0.64 is obtained for the proposed technique. Our method leads to better classification performance when compared to state-of-the-art methods. The novelty of the approach is based on the combination of frequency bands and two feature extraction methods.

Aiming at the problems of poor resolution and low precision in traditional health diagnosis of Marine engine room equipment, this paper proposes a health diagnosis method of intelligent Marine engine room equipment based on BP neural network and D-S evidence theory. Firstly, the time-domain parameters of the obtained acceleration signal are extracted and the energy in frequency domain after wavelet decomposition is calculated. Then the eigenvectors of time domain and wavelet packet energy were constructed respectively, and the normalized processing was input into two BP neural networks to obtain the classification results. Finally, the fault classification results in time domain and frequency domain are combined with the D-S evidence theory and output diagnosis. Through experimental analysis and verification of rolling bearing data from electrical Engineering Laboratory of Case Western Reserve University, the accuracy of the proposed method is better than that of time domain and frequency domain analysis alone, which improves the accuracy and reliability of fault classification.

In this paper, we propose a novel method for the detection of road accidents by analyzing audio streams for road surveillance application. In the last decade, due to the increase in a number of people and transportation vehicles, traffic accidents have become one of a major public issue worldwide. The vast number of injuries and death due to road traffic accident reveals the story of a global crisis of road safety. A total of 52,160 road traffic accidents (RTA), 1130 injuries and 85 fatalities were registered during the year 2000, in the state of Qatar. An increase in the number of transportation vehicles around cities has risen the need for more security and safety in public environments. The most obvious reason for a person's death during accidents is the absence or prolong response time of the first aid facility, which is due to the delay in the information of the accident being reached to the police, hospital or ambulance team. In the last couple of years, several surveillance systems have been proposed based on image and video processing for automatically detecting road accidents and car crashes to ensure a quick response by emergency teams. However, in some situations such as adverse weather conditions or cluttered environments, the visual information is not sufficient enough, whereas analyzing the audio tracks can significantly improve the overall reliability of surveillance systems. In this paper we propose a novel method that automatically identifies hazardous situations such as tire skidding and car crashes in presence of background noises, by analyzing the audio streams. Previous studies show that methods for the detection, estimation, and classification of nonstationary signals can be enhanced by utilizing the time-frequency (TF) characteristics of such signals. The TF-based techniques have been proved to outperform classical techniques based on either time or frequency domains, in analyzing real-life nonstationary signals. Time-frequency distributions (TFDs) give additional information about signals that cannot be extracted from either the time domain or frequency domain representations, i.e. the instantaneous frequency of the components of a signal. In order to utilize this extra information provided by TF domain representation, the proposed approach extracts TF image features from quadratic time-frequency distributions (QTFDs), for the detection of audio anomalies. The extended modified-B distribution (EMBD) is utilized to transform a 1-dimensional audio signal into 2-dimensional TF representation which is interpreted as an image. The image descriptors based features are then extracted from the TF representation to classify visually the audio signals into background or abnormal activity patterns in the TF domain. The proposed features are based on Haralick's texture features extracted from the TF representation of audio signals considered and processed as a textured image. These features are used to characterize and hence classify audio signals into M classes. This research study demonstrates that a TF image pattern recognition approach offers significant advantages over standard signal classification methods that utilize either t-domain only or f-domain only features. The proposed method has been experimentally validated on a large open source database of sounds, including several kinds of background noise. The events to be recognized are superimposed on different background sounds of roads and traffic jam. The obtained results are compared with a recent study, utilizing the same large and complex data set of audio signals, and the same experimental setup. The overall classification results confirm the superior performance of the proposed approach with accuracy improvement up to 6%.

Vibration signals are obtained by putting three sensors on relevant position on the cylinder head. The technology of wavelet transform is applied to extract feature information under normal and misfire operation, feature in time domain of vibration signal is analyzed and single cylinder relative energy in time domain is computed to judge fault cylinder without the top dead center signal, which can realize diesel misfire diagnosis without disassemble. The experimental results of fault diagnosis in diesel engine show that it can offer a new method in the misfire fault diagnosis of diesel engine.Keywordsdiesel enginefault diagnosismisfirewavelet transform

Third order time and frequency analysis has exhibited great potential for correlation analysis of multi-sensor datasets, but is usually presented as separate time domain and frequency domain approaches. A combined framework of both frequency domain and time domain has rarely been used. This paper proposes a non-parametric third order time and frequency domain framework which used two dimensional Fourier transforms to bridge the gap between time domain and frequency domain. A unified framework offers flexibility and efficiency to apply to data. In this paper we study neural spike train data treated as stochastic point processes. In time domain direct analysis, third order cumulant densities of spike trains are applied, which need all first-, secondand third order product densities to be calculated before constructing the third order cumulant density, which brings additional challenges. The novelty in this study is that a new framework is proposed which can offer an alternative approach without calculating lower order quantities and can reveal nonlinear relationship between neural recordings. The results show that the present framework provides a novel non-parametric method to estimate both time and frequency domain measurements which is applicable to neural spike trains.

Heart rate variability (HRV) has been used to investigate the autonomic modulation of heart rate. Diminished HRV has been observed in diabetic autonomic neuropathy, a condition associated with increased mortality. Uraemia is associated with impaired autonomic function, but reports on the effects of uraemia on HRV are scarce. HRV and its circadian variation were studied in 12 diabetic and 11 non-diabetic renal transplantation and in 12 control patients. HRV in time and frequency domains was determined from 24-hour ECG recordings. In the diabetic group, all time domain and frequency domain measures of HRV were markedly reduced (P < 0.05), when compared with the control group, and the circadian variation of HRV was absent. The mean (SD) amplitudes (ms) in the frequency bands were: high frequency: 3 (1), 6 (3) and 15 (3); low frequency: 9 (7), 16 (10) and 25 (8); very low frequency: 14 (8), 23 (12) and 30 (11) in the diabetic and non-diabetic uraemic and in the control patients, respectively. In non-diabetic uraemic patients, a tendency to reduced HRV was observed, but no statistical differences in HRV measures were found when compared with the control group. The severe impairment of HRV in patients with end-stage diabetic nephropathy is probably due to autonomic neuropathy and partly also to the co-existing heart diseases. It may be a contributing risk factor for ventricular arrhythmias and sudden death in these patients. Uraemia alone causes similar but less severe changes in HRV.

Multi-window spectrum analysis of Búzios oil field PSDM seismic data applied to frequency attenuation study

The iterative method of Gerchberg (GM) for extrapolating the whole spectrum of a finite support object1 has been analyzed and generalized by several authors2-14. In principle, during each iteration of the GM an infinite band of frequencies should be handled. At first sight, it seems that the (obvious) existence of a cut-off frequency in any practical implementation of the GM should simply allow the spectrum extrapolation to be achieved only below such a frequency. This is not the case, as we shall presently show, in that the extrapolated spectrum obtained by this method up to the cut-off frequency does not coincide with the true spectrum. In this paper we present a modified version of the GM that allows to obtain a spectrum extrapolation on a finite band of frequencies. This is of use both to limit the storage and computation time requirements and to reduce the sensitivity to high frequency noise.

This work presents the time domain analysis of a compact Penta notched UWB antenna integrated with band stop resonators. The antenna has the capability to filter out the WiMAX (3.30–3.60 GHz), Lower WLAN (5.150–5.350 GHz), upper WLAN (5.725–5.825 GHz), the downlink of X- and satellite communication (7.0–7.40 GHz) and the uplink of X-band satellite communication (8.10–8.50 GHz) frequency bands from UWB frequency range. The band stop filtering of the antenna has been confirmed using this technique. The time domain study is basically based on pulse response of antenna and fidelity factor of a system. The extraction of the time response has been carried out from the corresponding frequency response of the antenna using the standard technique. The results of the time domain analysis confirm the suitability of the antenna with integrated filtering resonators.

It has become customary to compute pressure transient model solutions in the Laplace domain. This is because it generally simplifies the solution, and allows easy incorporation of wellbore storage, commingled systems or dual-porosity formations. On the other hand, the convolution and parameter estimation are usually done in the time domain. Thus, when the solution is used as the forward model in an optimization scheme, each forward evaluation requires a Laplace inversion of the model (using the Stehfest algorithm) and a time domain convolution. Here we explore the alternative of transforming the data into the Laplace domain (a one-time operation) and then doing the fitting in the Laplace domain. The chief new result is a self-consistent prescription for dealing with the fact that the data are available only over a finite time interval. The technique is illustrated for the case of a conventional well test which reaches radial flow on the time scale of the test, a situation for which the method is particularly efficient.

The vibration signal of transformer body contains abundant fault information. Extracting the effective characteristic parameters from the vibration signal can be used as an important basis for transformer fault diagnosis, which is the main method for non-intrusive monitoring of transformer faults at present. According to the vibration generation and transmission mechanism of transformer noumenon, this paper uses piezoelectric sensors, signal conditioning modules, high-speed data acquisition card and computer set up the transformer vibration monitoring system, and takes graphical language LabVIEW as the system software development platform. Wavelet packet-energy spectrum analysis method is used to analyze the vibration signal on the surface of transformer oil tank in time domain and frequency domain, quantifying the energy distribution of vibration signal in different frequency bands, and taking the percentage of each frequency band energy in the total energy as the characteristic parameter to monitor and diagnose the operation status of transformer. Field test results show that there is a significant difference in the energy distribution of the vibration signals before and after the transformer failure. This method can effectively extract the characteristics of the vibration signal in different states and obtain the percentage of energy distribution of the vibration signal in each frequency band, which can be used as the basis for transformer fault diagnosis.

A key issue in the research and development of next-generation mobile communication systems (Beyond 5G/6G) is the availability of novel frequency bands. Currently, the frequency band below 6 GHz, which is suitable for mobile communications, is already tight, and technologies for highly efficient utilization of finite spectrum resources are required. Therefore, many researches have been conducted on the practical application of dynamic spectrum sharing technology for the effective use of spectrum. For this technology, it is important to precisely estimate the spectrum utilization of the wireless system and the propagation characteristics changes in each environment. Furthermore, in Beyond 5G/6G, non-terrestrial networks (NTNs) such as satellites and drones are expected to be utilized to expand the communication area, which will require spectrum management in the height direction. In this paper, we design and develop a 5-dimensional (5D) spectrum database that extends the spectrum database of radio environment information from the 2-dimensional (2D) plane of latitude and longitude to a 3-dimensional (3D) space that includes height information, as well as time domain and frequency domain. Additionally, we describe the design details for the mobile satellites in the NTNs incorporated into the database. Finally, test results from the measurement campaign and calculated data size in the developed database are discussed.

A fundamental problem for signal processing in wireless communications is the separation multiple signals/users from their mixture. The problem would be simpler if they are non-overlapping in either time domain or frequency domain. Two signals may be overlapping in both time and frequency, but they still can be easily separated if they are uncorrelated (orthogonal), like in Code Division Multiple Access system (CDMA). It may be interesting to know that two signals may be overlapping in both the frequency and time domain and also are not orthogonal to each other, but they can still be separated if each autocorrelation function at the receiver is separable in the domain of time-lags. In this paper, we will show how the Noise-Loop (NL) modulation, originally thought to produce security at physical layer, can be used to effectively separate uncoordinated/unorthogonal users sharing the same time and frequency band. In order to show the potential of noise-loop multiple access (NLMA) scheme, the theoretical solution is derived as well as numerical semi-analytical simulations. It can be shown that the NLMA is able to distinguish the bit stream of the users. This feature together with the intrinsic security make the NL an interesting scheme for wireless technology.