Spatial-temporal Signal Research Articles

Distributed acoustic sensing signal event recognition and localization based on improved YOLOv7

Abstract The distributed acoustic sensing (DAS) system based on phase-sensitive optical time domain reflection ( Φ-OTDR) technology is widely used in pipeline safety monitoring, perimeter security, structure monitoring, etc. Accurate localization and recognition of multi-scene events over long distances has always been a challenge. This paper proposes an improved YOLOv7 algorithm for multi-event real-time detection of DAS system. The algorithm employs space-to-depth Conv(SPD-Conv) to replace the strided convolutions and pooling operations in YOLOv7, reducing fine-grained information loss and learning of inefficient feature representations. In addition, the Convolutional Block Attention Module (CBAM) is introduced in YOLOv7 to improve the model performance. We collected spatial–temporal signal data for six types of pipeline safety events, and passed them into the improved YOLOv7 algorithm in the form of data matrixes for training and evaluation. Experiments have shown that the proposed method achieves an mAP@.5 (mean Average Precision) of 99.7% for the identification of six pipeline safety event types. Positioning loss reduced to 0.2%, and detection speed can reach 70 frames per second(FPS). Our scheme achieves significant improvements in localization and classification accuracy compared to Faster R-CNN, etc. The event recognition localization method proposed in this paper has the advantage of fast speed and high accuracy.

WiVelo: Fine-grained Wi-Fi Walking Velocity Estimation

Passive human tracking using Wi-Fi has been researched broadly in the past decade. Besides straightforward anchor point localization, velocity is another vital sign adopted by the existing approaches to infer user trajectory. However, state-of-the-art Wi-Fi velocity estimation relies on Doppler-Frequency-Shift (DFS), which suffers from the inevitable signal noise incurring unbounded velocity errors, further degrading the tracking accuracy. In this article, we present WiVelo, which explores new spatial-temporal signal correlation features observed from different antennas to achieve accurate velocity estimation. First, we use subcarrier shift distribution (SSD) extracted from channel state information (CSI) to define two correlation features for direction and speed estimation, separately. Then, we design a mesh model calculated by the antennas’ locations to enable a fine-grained velocity estimation with bounded direction error. Finally, with the continuously estimated velocity, we develop an end-to-end trajectory recovery algorithm to mitigate velocity outliers with the property of walking velocity continuity. We implement WiVelo on commodity Wi-Fi hardware and extensively evaluate its tracking accuracy in various environments. The experimental results show our median and 90-percentile tracking errors are 0.47 m and 1.06 m, which are half and a quarter of state-of-the-art. The datasets and source codes are published through Github ( https://github.com/research-source/code ).

Fault detection and isolation for multi-type sensors in nuclear power plants via a knowledge-guided spatial–temporal model

Sensor faults in nuclear power plants (NPPs) have the potential to propagate negative impacts on system stability, leading to false alarms and accident misdiagnosis. Existing methods seldom concurrently consider complex spatial–temporal correlations among multi-type sensors in the primary circuit. This study presents a novel sensor fault detection and isolation scheme named the knowledge-guided spatial–temporal model (KGSTM), using the knowledge-guided recurrent unit (KGRU) and the concurrent detection strategy. To organically express part and whole interdependencies from inherent sensor layout, several graphs are specifically designed with pertinent domain knowledge. KGRU consists of the multi-graph convolutional network (MGCN) for fusing various spatial information and the gate recurrent unit (GRU) for extracting dynamic temporal features, further obtaining precise reconstructed signals and residuals. The concurrent detection strategy can explicitly quantify abnormal behaviors to detect and isolate faulty sensors by characterizing spatial–temporal signal variation. Numerical results on two real-world datasets from a pressurized water reactor (PWR) with simulated faults illustrate that the KGSTM has superior performance over various state-of-the-art methods in terms of signal reconstruction and fault detection.

Seasonal spatial-temporal variability in radar penetration depth

A fully automated processing system for measuring long-term ground deformation time series and deformation rates frame-by-frame using the Differential Interferometric Synthetic Aperture Radar (DInSAR) processing technique was developed and tested. Among the several factors that affect DInSAR’s precision, such as temporal decorrelation and atmospheric noise, it was observed that seasonal spatial-temporal variability in radar penetration depth is the greatest contributor to the loss of precision of the long-term deformation rate measurement. This spatial-temporal signal is described for a specific region in the northern US; however, it is widely observed in multiple regions. In these regions, a gradual penetration depth increase is observed in fall and winter, and an abrupt penetration depth decrease is observed in spring. The latter effect is likely caused by rapid melting of ice and snow that, at the same time, significantly reduces interferometric coherence. Within a specific geographic region, radar penetration depth spatially varies proportionally to the elevation due to an elevation-dependent temperature gradient, producing artifacts that correlate well with the topography. This signal does not extend to high-elevation regions (above ~2500 m) that do not experience seasonal thawing, which allows for distinguishing it from the topography-dependent atmospheric signals. Without accounting for the spatial-temporal variability in radar penetration depth, deformation time series in these areas can contain either an annual periodic signal with an amplitude of over 0.1 m or, if the abrupt penetration depth decrease produced by melting is not resolved due to lower coherence, an erroneous interannual trend. Spatial-temporal variability in radar penetration depth needs to be better understood and corrected to improve the precision of long-term deformation rate products, particularly in regions susceptible to seasonal freezing and thawing.

Airborne Flexible-Array Radar Clutter Characteristics and STAP Performance Analysis

Airborne flexible array radar has increasingly attracted attention due to its promising application potential. As space-time adaptive processing (STAP) plays an important role in moving target indicator (MTI) applications, this paper studies an airborne flexible-array radar framework and its clutter characteristics and STAP performance. Taking deformable structure of the wing flexible-array radar as an example, the corresponding spatial-temporal signal model is formulated with deformation parameters. Next, the spatial-temporal clutter properties of flexible-array radar are analyzed mathematically by exploiting the clutter covariance matrix and Doppler characteristics. Furthermore, one clutter suppression method is proposed by jointly utilizing the compensation algorithm, temporal-dimension reduction filtering, and spatial adaptive processing. Finally, STAP performance analysis for the airborne flexible-array radar is provided. Numerical results verify all the theoretical analyses.

DOA Estimation in B5G/6G: Trends and Challenges.

Direction-of-arrival (DOA) estimation is the preliminary stage of communication, localization, and sensing. Hence, it is a canonical task for next-generation wireless communications, namely beyond 5G (B5G) or 6G communication networks. Both massive multiple-input multiple-output (MIMO) and millimeter wave (mmW) bands are emerging technologies that can be implemented to increase the spectral efficiency of an area, and a number of expectations have been placed on them for future-generation wireless communications. Meanwhile, they also create new challenges for DOA estimation, for instance, through extremely large-scale array data, the coexistence of far-field and near-field sources, mutual coupling effects, and complicated spatial-temporal signal sampling. This article discusses various open issues related to DOA estimation for B5G/6G communication networks. Moreover, some insights on current advances, including arrays, models, sampling, and algorithms, are provided. Finally, directions for future work on the development of DOA estimation are addressed.

Tracking whole-brain connectivity dynamics in the resting-state fMRI with post-facial paralysis synkinesis

BackgroundResting-state functional magnetic resonance imaging (rs-fMRI) is widely applied to explore abnormal functional connectivity (FC) in patients with post-facial paralysis synkinesis (PFPS). However, most studies considered steady spatial-temporal signal interactions between distinct brain regions during the period of scanning. ObjectiveIn this study, we aim to investigate abnormal dynamic functional connectivity (dFC) in PFPS patients. MethodsWe enrolled 31 PFPS patients and 19 healthy controls. All participants underwent rs-fMRI. Sliding windows approach was applied to construct dFC matrices. Next, these matrices were clustered into distinct states using the k-means clustering algorithm. ResultsWe found that it was not the dFC patterns, but rather the temporal properties including the mean dwell time (MDT) and occurrence frequencies, that showed a significant difference between PFPS patients and healthy controls. Two randomly clustered dFC states were recognized for both groups. Among them, State 1 showed significantly lower connectivity compared to State 2 in patients group. Compared to healthy controls, the duration spent by the PFPS patients in the state with lower connectivity significantly increased and is positively correlated with the better facial function. ConclusionsIn conclusion, aberrant dFC is a fundamental feature of brain dysfunction in PFPS patients, which is associated with the facial nerve function. These findings may contribute to a better understanding of the abnormal brain reorganization mechanisms in PFPS patients.

Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (I): Preprocessing and Bipolar Potentials.

During the last years, Electrocardiographic Imaging (ECGI) has emerged as a powerful and promising clinical tool to support cardiologists. Starting from a plurality of potential measurements on the torso, ECGI yields a noninvasive estimation of their causing potentials on the epicardium. This unprecedented amount of measured cardiac signals needs to be conditioned and adapted to current knowledge and methods in cardiac electrophysiology in order to maximize its support to the clinical practice. In this setting, many cardiac indices are defined in terms of the so-called bipolar electrograms, which correspond with differential potentials between two spatially close potential measurements. Our aim was to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology. For this purpose, we first analyzed the basic stages of conventional cardiac signal processing and scrutinized the implications of the spatial-temporal nature of signals in ECGI scenarios. Specifically, the stages of baseline wander removal, low-pass filtering, and beat segmentation and synchronization were considered. We also aimed to establish a mathematical operator to provide suitable bipolar electrograms from the ECGI-estimated epicardium potentials. Results were obtained on data from an infarction patient and from a healthy subject. First, the low-frequency and high-frequency noises are shown to be non-independently distributed in the ECGI-estimated recordings due to their spatial dimension. Second, bipolar electrograms are better estimated when using the criterion of the maximum-amplitude difference between spatial neighbors, but also a temporal delay in discrete time of about 40 samples has to be included to obtain the usual morphology in clinical bipolar electrograms from catheters. We conclude that spatial-temporal digital signal processing and bipolar electrograms can pave the way towards the usefulness of ECGI recordings in the cardiological clinical practice. The companion paper is devoted to analyzing clinical indices obtained from ECGI epicardial electrograms measuring waveform variability and repolarization tissue properties.

A Recursive Algorithm for Estimating the Correlation Matrix of the Interference Based on the QR Decomposition

Many tasks of digital signal processing require the implementation of matrix operations in real time. These are operations of matrix inversion or solving systems of linear algebraic or dif- ferential equations (Kalman filter). The transition to the implementation of digital signal processing on programmable logic device (FPGAs), as a rule, involves calculations based on the representation of numbers with a fixed point. This makes solving spatio-temporal processing problems practically im- possible based on conventional computational methods. The article discusses the implementation of spatial-temporal signal processing algorithms in satellite broadband systems using QR decomposition. The technologies of CORDIC computations required for recurrent QR decomposition when used together in systolic algorithms are presented

Azimuth Estimation for Sectorized Base Station With Improved Soft-Margin Classification

Sector antenna azimuth plays an important role in the operation and maintenance of mobile networks. It is adjusted frequently so as to guarantee the high quality of coverage and low interference among neighboring cells. As one of the key elements of base station almanac (BSA), the azimuth of all the base stations need to be precisely managed by the operator. However, currently it is generally acquired by on-site measurement and updated manually, which is neither timely nor cost-effective. In addition, it is not open for third parties who need the information for network analysis. In view of this, by transforming the problem of azimuth estimation into one of searching for the optimal cell boundaries under the constraint of site location, this paper proposes an azimuth estimation method based on an improved multi-class soft-margin support vector machine (SVM). The explicit expression of the objective function of the problem is deduced through Lagrange duality. A circular one-versus-one strategy is utilized in dealing with the multi-class classification problem. In addition, a so-called average confusion index is designed to evaluate the degree of boundary separability of a base station so as to prejudge whether it is worthwhile to estimate its azimuths. Experiments are undertaken to validate the proposed algorithm by utilizing the spatial-temporal wireless signal dataset crowdsensed from massive user terminals in the 4G network. The experiments show that the proposed algorithm gives a significantly higher accuracy compared to that of two existing methods, namely the Gauss and radial rasterization methods. The impacts of data volume, penalty term and the boundary separability on the azimuth estimation is also discussed. The new algorithm is a promising alternative to the conventional manual solution with a much higher efficiency and lower cost, thus improving the intelligence of mobile network operation.

Estimation of the GNSS signal time-of-arrival in the presence of a Gaussian interference located in space

The problem of low positioning accuracy in the presence of interference is discussed in this work. As a way to solve this problem, spatial-temporal signal processing (STSP) is chosen when maximum likelihood (ML) optimisation criterion is used. The paper assumes that interference is a Gaussian random process with uniform (in the channel band) spectral noise density, and the additive components in each of the receiving channels are independent implementations of additive white Gaussian noise (AWGN). The commonly known form of likelihood function is transformed in the way to exclude interfering parameters of signal of interest. As a theoretical result, ML-estimator of time-of-arrival was obtained. The results are supported by a statistical simulation so that the efficiency of synthesized ML algorithm is shown. As implications for practice, the structure of navigation receiver implementation is proposed at the end of discussion.

Abnormal dynamic properties of functional connectivity in disorders of consciousness.

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to research abnormal functional connectivity (FC) in patients with disorders of consciousness (DOC). However, most studies assumed steady spatial-temporal signal interactions between distinct brain regions during the scan period. The aim of this study was to explore abnormal dynamic functional connectivity (dFC) in DOC patients. After excluding 26 patients' data that failed to meet the requirements of imaging quality, we retained 19 DOC patients (12 with unresponsive wakefulness syndrome and 7 in a minimally conscious state, diagnosed with the Coma Recovery Scale-Revised [CRS-R]) for the dFC analysis. We used the sliding windows approach to construct dFC matrices. Then these matrices were clustered into distinct states using the k-means clustering algorithm. We found that the DOC patients showed decreased dFC in the sensory and somatomotor networks compared with the healthy controls. There were also significant differences in temporal properties, the mean dwell time (MDT) and the number of transitions (NT), between the DOC patients and the healthy controls. In addition, we also used a hidden Markov model (HMM) to test the robustness of the results. With the connectome-based predictive modeling (CPM) approach, we found that the properties of abnormal dynamic network can be used to predict the CRS-R scores of the patients after severe brain injury. These findings may contribute to a better understanding of the abnormal brain networks in DOC patients.

Adaptive algorithm for wireless data transmission (including images) based on SISO system and OFDM technique

The efficiency of wireless systems to transmit information in a complex interference situation is one of the main indicators of the effectiveness of the system, which can be characterized by the probability of an error in the transmitted message. This characteristic depends on the interference situation of the communication channel and the nature of the signals propagation in the medium. If the medium has a multi-path character, the signal interferes with the receiving antenna, causing signal fading. So the developer of wireless communication devices is faced with the task of increasing the noise immunity of the system. It can be increased in various ways, for example, by using adaptive processing algorithms for spatio-temporal signals. In this paper we solve the problem of improving noise immunity in wireless communication systems by applying an algorithm of adaptive spatial-temporal signal processing in the receiver based on an adaptive antenna array, which allows spatial filtering under conditions of a complex pattern of signal propagation in a channel with reflections. Calculation of weights for the adaptation algorithm in the article is based on the theory of eigenvalues and vectors of the spatial correlation matrix.

Повышение эффективности подавления помех за счет коррекции частотных характеристик приемных каналов в навигационной аппаратуре потребителей

Space-time signal processing in adaptive electronic systems, including navigation, remains the subject of intensive research, as it allows to improve the efficiency of receiving useful signals under the influence of natural or intentional interference. The bibliography on this subject is very extensive and includes a large number of articles of scientific and applied nature, dissertations, monographs, patent materials. Much of this work has references to monographs by R. Monzingo and B. Widrow, which set out the basic principles, criteria and algorithms of spatial-temporal signal processing. To date, the development of space-time signal processing is largely constrained by the imperfection of the element base. The use of modern navigation equipment consumers high-speed signal processors that implement advanced methods and algorithms of digital signal processing, provides the ability of digital methods to form the desired beam pattern of the antenna system and to adjust the frequency characteristics of the reception channels, to implement complex algorithms for optimal processing of useful signals, operative to control the main operation modes of equipment. One of the most important applications of space-time signal processing is noise suppression, and its effectiveness depends on the degree of inter-channel correlation of these signals received by antenna elements of the adaptive antenna array. To maximize the coefficient of interference suppression required the adoption of measures on alignment of frequency and phase characteristics of the receiving circuits, and high precision computation of weighting coefficients. The article considers the questions of spatial filtering of the interference when adaptive interference compensator adjusted frequency characteristics of the receiving channels, and means of satellite navigation. Gradient algorithm of spatial compensation of noise, and also influence of adaptive correction of frequency characteristics of receiving channels on quality of suppression of noise is presented. The formation of a directional diagram of a multi-element adaptive antenna array with spatial noise suppression is demonstrated by modeling in the Matlab environment.

Application of synchronous detection for ultrasonic thickness gauging of concrete Articles with a nonuniform Structure

Some problems that are encountered in the ultrasonic pulse-echo thickness gauging of large concrete building structures with high frequency-dependent ultrasound attenuation and a complicated nonuniform structure are considered. It has been shown that ultrasonic signals with complex modulation with further optimal filtering of echo signals should be used in single-channel testing to increase the sensitivity during the testing of concrete articles and to enhance echo signals in the presence of white noise. Synchronous detection of echo signals is advisable to increase the precision of measurements. Multichannel spatial–temporal signal processing via thickness measurements at several neighboring points of an article and further coherent summation of partial measurement results should be used to enhance ultrasonic echo signals in the presence of structural noise. However, it is recommended that the multichannel testing of concrete articles with a nonuniform volumetric distribution of physicomechanical properties be performed with the summation of partial echo-signal envelopes after their synchronous detection due to the broad spread of ultrasonic signal velocities in neighboring regions.

Joint Doa-Frequency Estimation for Coherent Signal Identification

The problem of spatial temporal spectrum technique for coherent signal identification is studied in this paper. Spatial smoothing scheme is firstly introduced to spatial temporal signal processing to eliminate the singularity of array output covariance matrix, and then spatial temporal spectral analysis technique is used to realize the joint DOA and frequency estimation. Simulation results show the proposed method can indentify all the signal sources correctly even when they are full coherent.

Self-Tuning Synthesis Filter against Mutual Coupling and Interferences for GNSS and Its Implementation on Embedded Board

Traditional spatial-temporal adaptive signal processing techniques are often applied to conduct narrowband and wideband interferences. However, its mitigation performance degrades greatly due to mutual coupling. To solve this problem, this paper aims to utilize a spatial-temporal self-tuning synthesis filter capable of mutual coupling compensation and interference mitigation. The spatial filter and temporal filter are to compensate for the effect of mutual coupling and interference mitigation, respectively. Self-tuning mechanism is to adopt least square (LS) and minimum variable distortionless response- (MVDR-) based method to adjust spatial and temporal weights of antenna array. The experiment platform is established by the embedded development board. Simulation and experiment results demonstrate that the proposed method can effectively compensate for mutual coupling, mitigate the cochannel interference up to 30 dB, and enhance the acquisition performance of receivers in global navigation satellite system (GNSS).

Magnetic resonance imaging and analyses of tempering processes in rice kernels

This research involved a magnetic resonance imaging (MRI) technique that was applied to examine water distribution and migration in single rice kernels during the tempering process. The imaging experiments were performed in a Bruker 9.4T MRI system. Three-dimensional spin-echo (SE) imaging sequences were optimized by adjusting the scanning parameters of echo time (TE) and repetition time (TR) to obtain images with maximum contrast. The MR images showed that the moisture distribution in the rice kernel is non-uniform and compartmental. The embryo region exhibited much higher MR signal intensity than the starchy endosperm portion. The tempering process was analyzed with spatial-temporal signal intensities of the endosperm following the drying process of the rice kernel. The transient change of the signal intensities in the endosperm was well fitted with a double exponential function suggesting that both convection and diffusion contributed to the reduction of the moisture gradient within the rice kernel during tempering. This hypothesis was further supported by the experimental data of the insulated rice kernel whose convective mass transfer was excluded. The experimental results revealed that MR imaging of rice kernels could be used as an efficient tool to examine the mechanisms of moisture migration within cereal grains.

A Measurement Fusion Method for Nonlinear System Identification Using a Cooperative Learning Algorithm

Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.

</title> </titles> <publication_date> <month>06</month> <year>2007</year> </publication_date> <pages> <first_page>2</first_page> <last_page>5</last_page> </pages> <publisher_item> <item_number item_number_type='arNumber'>4200698</item_number> </publisher_item> <doi_data> <doi>10.1109/JSTSP.2007.897065</doi> <resource>http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4200698</resource> </doi_data> </journal_article> <journal_article> <titles> <title><![CDATA[Optimal Signal Design for Detection of Gaussian Point Targets in Stationary Gaussian Clutter/Reverberation

In this paper, we address the design of an optimal transmit signal and its corresponding optimal detector for a radar or active sonar system. The focus is on the temporal aspects of the waveform with the spatial aspects to be described in a future paper. The assumptions involved in modeling the clutter/reverberation return are crucial to the development of the optimal detector and its consequent optimal signal design. In particular, the target is assumed to be a Gaussian point target and the clutter/reverberation a stationary Gaussian random process. In practice, therefore, the modeling will need to be assessed and possibly extended, and additionally a means of measuring the in-situ clutter/reverberation spectrum will be required. The advantages of our approach are that a simple analytical result is obtained which is guaranteed to be optimal, and also the extension to spatial-temporal signal design is immediate using ideas of frequency-wavenumber representations. Some examples are given to illustrate the signal design procedure as well as the calculation of the increase in processing gain. Finally, the results are shown to be an extension of the usual procedure which places the signal energy in the noise band having minimum power