COMPARATIVE STUDY ON ULTRASONIC IMAGING METHODS WITH ARRAY TRANSDUCERS

In this work we propose two hybrid time-and frequency-domain microwave imagingschemes aimed to improve time-to-solution of quantitative time-domain imaging algorithms and image resolution of quantitative frequency-domain imaging algorithms. The proposed hybrid methods combine discontinuous Galerkin method (DGM) implementations of the time-domain (TD) forward-backward time-stepping (FBTS) algorithm and the frequency-domain (FD) contrast source inversion (CSI) or Gauss Newton Inversion (GNI). Simply put, an initial inversion in one domain (time or frequency) is used as prior information for the other. These schemes, referred to as FD-TD when FD prior is used in a TD algorithm, and TD-FD when TD prior is used in a FD algorithm, are applied to experimental and synthetic data. The results of the hybrid imaging approaches manifest an appreciable improvement relative to the stand-alone of FD and TD algorithms. Specifically, this study demonstrates that low-resolution frequency-domain prior information improves TD convergence. Additionally, we show that early-iteration time-domain solutions improves FD algorithm performance. We hope that these hybridization techniques pave the way for future investigations of optimal strategies for combining TD and FD schemes.

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.

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%.

In this work, a solar photovoltaic (PV) array-integrated unified power quality conditioner (PV-UPQC) system is presented for power quality (PQ) improvement. Major task in the control of UPQC is generation of reference signal. The control algorithms in time domain (TD) and frequency domain (FD) do not consider DC offset present in the input signal and this got affected in the reference signal generation. A control algorithm based on modified second-order generalized integrator (MSOGI) with DC offset elimination capability is presented in this work. This MSOGI is integrated with conventional pq-theory-based control for extracting in phase fundamental and quadrature fundamental component of distorted input signal and for eliminating DC offset present in the input signal. The dynamic performance of the system is evaluated under various dynamic conditions like grid voltage sag and swell and change in solar irradiation in MATLAB Simulink. The proposed system improves power quality and generates clean energy from PV array and also enables operation of the system in the absence of grid. The total harmonic distortion (THD) of system is limited according to the IEEE-519 and IEEE-1159 standards.KeywordsPower qualityPV-UPQCSeries VSCShunt VSCDC offsetMSOGI

Conventional time-frequency Transformers primarily focus on the global features of signals in the time-frequency domain while neglecting the local features in both the time domain and frequency domain. This limitation hinders the ability of the model to effectively capture the shared features among time, frequency, and time-frequency representations. To address this issue, a Multi-Branch Joint Time-Frequency Transformer (MBJTF-Transformer) is proposed for domain generalization (DG) fault diagnosis of rotating machinery. Specifically, a time-branch Transformer is designed to extract temporal features, while a frequency-branch Transformer captures frequency-domain information. In addition, a time-frequency Transformer is employed to learn the shared representations across time, frequency, and time-frequency domains. Finally, a multi-decision fusion strategy of MBJTF-Transformer is adopted to enhance the generalization capability of the model. Experimental results on both the SCARA (Selective Compliance Assembly Robot Arm, SCARA) dataset and the PU (Paderborn University) bearing dataset demonstrate that the proposed MBJTF-Transformer achieves superior DG performance compared to multiple state-of-the-art sequential models.

A method for photoacoustic tomography is presented that uses line integrals over the acoustic wave field from a photoacoustic source for the reconstruction of a three-dimensional image. The line integrals are acquired with an optical line sensor based on a Mach–Zehnder interferometer. Image reconstruction is a two-step process. In the first step data from a scan of the line outside the object are used to reconstruct a linear projection of the source distribution. In the second step the inverse linear Radon transform is applied to multiple projections taken at different directions. This study focuses on the first step comparing two different open scan curves of the line detector around the object and corresponding two-dimensional reconstruction algorithms. An open curve, such as a 180° arc or an ‘L’ formed by two lines, establishes a limited view problem for which limitations exist concerning the stability of the reconstruction. Using experimental data from phantoms, time domain direct and iterative reconstruction algorithms as well as frequency domain (FD) algorithms are compared. The results indicate that although the image quality for all algorithms is similar, the FD algorithms offer much faster reconstruction speed, whereas the time domain algorithms allow for a refinement of image quality by including special correction procedures.

Convenience and safety are the key words for the automotive driving and various sensor technologies have been studied for enhanced perception of driving environments. In frequency modulated continuous wave (FMCW) radar systems, single antenna is enough for range and velocity detection of multiple targets. Multiple array antenna is needed for estimating direction of arrival(DoA). Using DoA estimation algorithm in time domain as in the conventional systems, it is difficult to distinguish vehicles lie in the same angle. In order to facilitate the enhanced angle estimation, DoA estimation algorithm is applied in frequency domain. In this paper, the method for applying multiple signal classification(MUSIC) algorithm in frequency domain is suggested and the performance is analyzed.

While fractal boundaries and their ability to describe irregularity have been intensively studied, only a few studies are available on wave propagation in a medium where a fractal or quasi fractal pattern is embedded. Acoustical propagation in 1D or 2D domains can be modeled using Time Domain Finite Differences or in Frequency domain with Finite element methods. The fractal object is then considered as a subwavelength set of scatterers, and the problem becomes a multiple scattering one leading to acoustic localization. So, as some important part of the energy remains trapped inside the fractal pattern, imaging such a medium becomes difficult and imaging such complex media with classical tools as B-scan or comparable methods is not sufficient. The resolution of the inverse problem of wave propagation can then be achieved with the help of the more efficient imaging methods related with Time Reversal. Using the concept of topological derivative as defined in Time Domain Topological Energy and Fast Topological Imaging Method, respectively, in Time domain and Frequency domain is powerful in that case. Examples in 1D and 2D will be presented including the image obtained for a sliced sponge.

Reed-Solomon decoders can be designed using time or frequency domain algorithms. The main advantage of the time domain method is its regular computational structure, an attractive feature for a flexible VLSI implementation. The main disadvantage of this is the relatively high computation count, hence it is important to find means of reducing the computational complexity of the time domain method. Several modifications to the time domain Reed-Solomon decoding algorithm are described for reducing its computational complexity by about 60%, as well as regular VLSI architectures for implementing the time domain algorithm. The paper concludes with a discussion of the advantages and disadvantages of the time domain decoding technique.

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.

In this work, a method is presented to extend the convolutional perfectly matched layer (C-PML) to simulate acoustic wave propagation in elastic media with a second-order equation formulation. This non-physical layer is used at the computational edge of a finite element method algorithm in frequency domain, and a pseudo-spectral algorithm in time domain, as an absorbing boundary condition (ABC) to truncate unbounded media. Numerical results show that the C-PML ABC attenuates the outgoing surface waves more effectively than classical PML ABC as proposed by Berenger [J. Comput. Phys. 114, 195-200 (1994)] for electromagnetic waves in the case of oblique incidence, where the PML method suffers from large spurious reflections. Moreover, a modification of the proposed C-PML formulation is also discussed in order to stabilize the absorbing layer in anisotropic solids where numerical instabilities can appear.

A novel approach of blind constant modulus algorithm (CMA) for single carrier frequency domain equalization is proposed to solve the application of CMA implemented in frequency domain, based on the constant modulus property from frequency domain to time domain. It is illustrated that this method has lower complexity than time-domain subspace algorithm has in broad wireless systems. And compared with the frequency-domain algorithm in [5], the complexity of the new idea is greatly reduced as well. In this research, the transformation of constant modulus algorithm from time domain to frequency domain is obtained under the assumption that the source signal amplitude remains 1. Therefore, the validity of equation (8) results in the conclusion that the performance of symbol error of the method would not be influenced by the transformation from time domain to frequency domain. Simulation results demonstrate it.

In this article, theoretical modeling and simulations are introduced to explore the contact failure characteristics for connectors both in time and frequency domains. The equivalent models are developed to describe the impedance performances of un‐degraded and degraded connectors with the time domain and frequency domain, respectively. The experiments using a Network Analyzer for time and frequency domains analyses are conducted to verify the proposed models. The measured results agree well with the simulation results in the time and frequency domains.

Direct sequence spread spectrum (DSSS) system needs some anti-jam algorithms for communication when strong interference exists. Both Normalized least mean square (NLMS) algorithm in time domain and anti-jam algorithm based on FFT in frequency domain are widely used for the excellent performance and small hardware costs. This paper implemented the two algorithms on FPGA and researched their system performances and hardware costs, finally some useful guides for later system design were given.

Sine-fit algorithms for real signals have many application areas including frequency, harmonics and amplitude estimation in power grids, ADC and DAC characterization. There are numerous methods for the estimation of parameters of sinusoidal signals, due to importance. This paper compares the performance of two outstanding algorithms, in which one of them is implemented in the time domain and the other in the frequency domain. Time domain algorithm is the well-known multi-harmonic sine-fit and frequency domain algorithm is the windowed IpDFT method. Performances of these algorithms are compared, advantages and disadvantages of the methods will be given.