Perspective Of Signal Processing Research Articles

Beyond Homophily and Homogeneity Assumption: Relation-Based Frequency Adaptive Graph Neural Networks.

Graph neural networks (GNNs) have been playing important roles in various graph-related tasks. However, most existing GNNs are based on the assumption of homophily, so they cannot be directly generalized to heterophily settings where connected nodes may have different features and class labels. Moreover, real-world graphs often arise from highly entangled latent factors, but the existing GNNs tend to ignore this and simply denote the heterogeneous relations between nodes as binary-valued homogeneous edges. In this article, we propose a novel relation-based frequency adaptive GNN (RFA-GNN) to handle both heterophily and heterogeneity in a unified framework. RFA-GNN first decomposes an input graph into multiple relation graphs, each representing a latent relation. More importantly, we provide detailed theoretical analysis from the perspective of spectral signal processing. Based on this, we propose a relation-based frequency adaptive mechanism that adaptively picks up signals of different frequencies in each corresponding relation space in the message-passing process. Extensive experiments on synthetic and real-world datasets show qualitatively and quantitatively that RFA-GNN yields truly encouraging results for both the heterophily and heterogeneity settings. Codes are publicly available at: https://github.com/LirongWu/RFA-GNN.

Efficient Neural Network Compression Inspired by Compressive Sensing.

Traditional neural network compression (NNC) methods decrease the model size and floating-point operations (FLOPs) in the manner of screening out unimportant weight parameters; however, the intrinsic sparsity characteristics have not been fully exploited. In this article, from the perspective of signal processing and analysis for network parameters, we propose to use a compressive sensing (CS)-based method, namely NNCS, for performance improvements. Our proposed NNCS is inspired by the discovery that sparsity levels of weight parameters in the transform domain are greater than those in the original domain. First, to achieve sparse representations for parameters in the transform domain during training, we incorporate a constrained CS model into loss function. Second, the proposed effective training process consists of two steps, where the first step trains raw weight parameters and induces and reconstructs their sparse representations and the second step trains transform coefficients to improve network performances. Finally, we transform the entire neural network into another new domain-based representation, and a sparser parameter distribution can be obtained to facilitate inference acceleration. Experimental results demonstrate that NNCS can significantly outperform the other existing state-of-the-art methods in terms of parameter reductions and FLOPs. With VGGNet on CIFAR-10, we decrease 94.8% parameters and achieve a 76.8% reduction of FLOPs, with 0.13% drop in Top-1 accuracy. With ResNet-50 on ImageNet, we decrease 75.6% parameters and achieve a 78.9% reduction of FLOPs, with 1.24% drop in Top-1 accuracy.

Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)

The future power grid will have more distributed energy sources, and the widespread access of distributed energy sources has the potential to improve the energy efficiency, resilience, and sustainability of the system. However, distributed energy, mainly wind power generation and photovoltaic power generation, has the characteristics of intermittency and strong randomness, which will bring challenges to the safe operation of the power grid. Accurate prediction of solar power generation with high spatial and temporal resolution is very important for the normal operation of the power grid. In order to improve the accuracy of distributed photovoltaic power generation prediction, this paper proposes a new distributed photovoltaic power generation prediction model: ROLL-GNN, which is defined as a prediction model based on rolling prediction of the graph neural network. The ROLL-GNN uses the perspective of graph signal processing to model distributed generation production timeseries data as signals on graphs. In the model, the similarity of data is used to capture their spatio-temporal dependencies to achieve improved prediction accuracy.

Editorial Introduction to the Special Issue on Recent Advances in Wideband Signal Processing for Classical and Quantum Synthetic Apertures

A key aphorism pertaining to the nature of instrumentation comes from (although the attribution is sometimes disputed) the Italian scientist Galileo Galilei: “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Measure what can be measured, and make measurable what cannot be measured</i> .” Clearly, making things measurable is akin to choosing appropriate methods to measure them. From the perspective of signal processing, such techniques involve designing efficient signal acquisition systems and processing the acquired signals to extract relevant information.

Signal Modeling of Permanent Magnet Sodium Flowmeter With Embedded Vortex Generator Based on Transit Time Series

The permanent magnet sodium flowmeter with embedded vortex generator (PMSF-VG) monitors the liquid metal sodium flow rate in real time in order to ensure the safe operation of the fast neutron reactor. The previous literature studied how to improve the in-situ calibration accuracy of the flowmeter from the perspective of signal processing. This paper studies this issue from the perspective of signal source. According to the experimental data, the transit time series is calculated by using the generalized cross-correlation method, its characteristics are analyzed, and a moving average (MA) model is established for predicting and improving the accuracy of the in-situ calibration. This model is composed of a steady-state component and a fluctuation component. The former reflects the ideal value of the cross-correlation flow rate in the in-situ calibration, and is related to the meter linearity and indication error, which determines the upper limit of the measurement accuracy. The latter determines the repeatability error of the in-situ calibration. Aiming at its feature, a two-stage moving average filtering method is adopted to reduce the repeatability error of measuring the transit time and improve the accuracy of the in-situ calibration.

Milling chatter recognition based on dynamic and wavelet packet decomposition

Abstract. In metal milling, especially in the machining of low-stiffness workpieces, chatter is a key factor affecting many aspects such as surface quality, machining efficiency, and tool life. In order to avoid chatter, a milling chatter identification method based on dynamic wavelet packet decomposition (WPD) is proposed from the perspective of signal processing. The dynamic characteristics of the system are obtained by a hammer test. Based on the principle that the chatter frequency will reach a peak value near the natural frequency of the system, the original milling force signal is decomposed by WPD, and the sub-signals containing rich chatter information are selected for signal reconstruction. After numerical analysis and spectrum comparison, the reconstruction scheme is proved to be robust. Then, the time–frequency domain image of the reconstructed signal and the Hilbert spectrum feature are compared and analyzed to identify the chatter. Finally, the validity and reliability of the proposed method for chatter recognition are verified by experiments.

A survey of Lisa Zurk's contributions to physics-based signal processing in underwater acoustics

The incorporation and exploitation of the physics of underwater acoustic propagation (including reflection and scattering) in signal and information processing can improve the performance of detection, classification, localization and tracking (DCLT) in sonar systems. This presentation will survey several areas of Prof. Lisa Zurk's research from this perspective of physics-based signal processing. Contributions are found in the areas of (i) assessing the impact of propagation through a non-stationary underwater environment on the performance of signal processing algorithms, (ii) adapting signal processing algorithms to be more robust to these environmental effects, and (iii) the exploitation of specific characteristics of the propagation channel in novel approaches to solving the above inferential objectives. Of these areas, identifying consistently observable propagation phenomena that can be exploited to improve DCLT performance is the most essential and yet the most difficult because it requires balancing just enough accuracy in the physical model with a simple enough representation to develop robust signal and information processing algorithms. Impressively, Prof. Zurk was quite successful in this crucial area of physics-based signal processing.

Study of high-precision velocimetry technique based on absorption spectrum for deep space exploration

Asymmetric spatial heterodyne spectroscopy is a new ultra-high resolution remote sensing technique. Due to the advantages of large luminous flux, small size, and high precision, it is suitable for high-precision detection of detector motion speed in a deep-space exploration environment. However, even after decades of development, this technique has still not been used as a mature scientific tool for space detection. This paper deeply analyzes the Doppler Asymmetric Spatial Heterodyne (DASH) spectral velocimetry technique from the perspective of digital signal processing, establishes the corresponding mathematical model, and deduces the measurement constraint relationship. To overcome the accuracy defect of direct measurement based on the absorption spectral signal, the Background Light Synchronous Elimination (BLSE) method has been introduced as a feasible indirect measurement scheme. Through data simulation experiments, the resolution accuracy of direct and indirect measurements was verified, and it is proven that the indirect measurement method is necessary for high-precision spectral velocimetry in a deep-space environment.

Research on Dual-Frequency Electromagnetic False Alarm Interference Effect of a Typical Radar

In order to master the position variation rule of radar false alarm signal under continuous wave (CW) electromagnetic interference and reveal the mechanism of CW on radar, taking a certain type of stepping frequency radar as the research object, theoretical analysis of the imaging mechanism of radar CW electromagnetic interference false alarm signals from the perspective of time-frequency decoupling and receiver signal processing. Secondly, electromagnetic interference injection method is used to test the single-frequency and dual-frequency electromagnetic interference effect of the tested equipment. The results show that under the single frequency CW electromagnetic interference, the sensitive bandwidth of false alarm signal is about ±75 MHz, and the position of false alarm signal irregularity changes. Under the in-band dual-frequency CW electromagnetic interference, the position of non-intermodulation false alarm signal is similar to that of single frequency. However, the distance difference of two non-intermodulation false alarm signals is regular. In addition, the positions of the second-order intermodulation false alarm signals of the tested radar are also regular, and its position changes with the change of the second-order intermodulation frequency difference.

An Adaptive Three-Axis Attitude Estimation Method Based on Multi-Sensor Fusion for Optoelectronic Platform

Optoelectronic platform is an important payload in the field of aerospace and widely used in geographic mapping, measurement, and positioning. In order to obtain high-precision attitude measurement, gyro and accelerometer are applied in the feedback loop of light of sight (LOS) control system of optoelectronic platform. Aiming at compensating for gyro drift and maneuvering acceleration disturbance, an adaptive 3-axis attitude estimation method is proposed in this paper. An adaptive threshold criterion is designed by applying the accelerometer data in the sliding window. The threshold is determined in real time to judge whether the maneuvering acceleration exists. If it exists, the angular attitude error is compensated for by the gyro drift model. Otherwise, the angular attitude error is compensated by multi-sensor fusion. Furthermore, a phase-lag-free low pass filter (LPF) is applied to compensate for the phase lag error introduced in the above attitude estimation process. Compared with the angular attitude calculated by gyro, the root mean square error (RMSE) of the proposed method in roll, pitch, and yaw attitude decreased 44.23%, 49.91%, and 46.21%, respectively. In addition, the proposed method can estimate the attitude accurately without obvious phase lag when the maneuvering acceleration disturbance exists. The focus of this paper is to improve the performance of LOS motion control system of optoelectronic platform from the perspective of sensor signal processing. This method is suitable for aerospace applications with high-precision measurement and positioning requirements without maneuver interference, drift error and phase lag.

A Novel Feature Extraction Approach for Mechanical Fault Diagnosis Based on ESAX and BoW Model

Condition monitoring and fault diagnosis are of great significance to the development of modern industry, for they enable enterprises to avoid unexpected interruptions or severe accidents, and extracting the fault-related features from vibration signals is a critical step to achieve accurate diagnosis. Among diverse of feature extraction approaches, symbolic aggregate approximation is a promising one that has been introduced into fault diagnosis recently. Nevertheless, when dealing with the sampled vibration signals, the symbolic aggregate approximation ignores the change of signal frequency characteristics, which eventually leads to information aliasing and cannot ensure the information validity. In this work, the information aliasing is analyzed from the perspective of signal processing, and the extremum symbolic aggregate approximation is developed as a substitution on the premise of maintaining the validity of the information. Subsequently, to convert the symbol strings generated by the extremum symbolic aggregate approximation into usable digital feature vectors, the bag-of-words model in natural language processing is employed to perform the counting statistics of the fault-related words, and the laplacian score algorithm is then utilized to re-rank the statistical results, thereby realizing the extraction of mechanical fault feature. The superiority of the developed method is verified by experiments.

System Error Calibration in Large Datasets of Wireless Channel Sounding for Industrial Applications.

In industrial applications, the large comprehensive wireless channel impulse response (CIR) reference dataset, measured by National Institute of Standards and Technology (NIST), has been a useful tool for understanding propagation within factory environments. The NIST CIR reference dataset is obtained using a precision channel sounder instrument where transmitter and receiver are time-synchronized by two rubidium clocks. While the accuracy of the NIST CIRs is much higher than the CIRs measured by general commercial digital receiver, two types of system errors have been discovered within the dataset from the perspective of signal processing. These errors are significant for wireless localization, physical layer security, and related applications. To calibrate the CIR, two channel sounder error calibration methods (CSEC) is proposed: the CSEC based on phase compensation and carrier frequency offset recovery. Our results reveal that the CSEC method can improve the accuracy of the CIR to the accuracy that precise instruments cannot achieve. To demonstrate the consequence of these systemic errors, a case study involving physical layer authentication is investigated showing a marked improvement in authentication accuracy after the systemic errors in the dataset are removed. Moreover, the CSEC method may be used to correct other CIR datasets with similar systemic errors.

Graph Signal Processing for Heterogeneous Change Detection

This paper provides a new strategy for the heterogeneous change detection (HCD) problem: solving HCD from the perspective of graph signal processing (GSP). We construct a graph to represent the structure of each image, and treat each image as a graph signal defined on the graph. In this way, we convert the HCD into a GSP problem: a comparison of the responses of signals on systems defined on the graphs, which attempts to find structural differences and signal differences due to the changes between heterogeneous images. Firstly, we analyze the GSP for HCD from the vertex domain. We show that once a region has changed, the local structure of image changes, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> . the connectivity of the vertex containing this region changes. Therefore, we can compare the output signals of the same input graph signal passing through filters defined on the two graphs to detect changes. We analyze the negative effects of changing regions on the change detection results from the viewpoint of signal propagation, and we also design different filters from the vertex domain to explore the high-order neighborhood information hidden in original graphs. Secondly, we analyze the GSP for HCD from the spectral domain. We explore the spectral properties of different images on the same graph, and show that their spectra exhibit commonalities and dissimilarities. Specifically, it is the change that leads to the dissimilarities of their spectra. With the help of graph spectral analysis, we propose a regression model for the HCD, which decomposes the source signal into the regressed signal and changed signal, and constrains the spectral property of the regressed signal. Experiments conducted on seven real data sets show the effectiveness of the vertex domain filtering based and spectral domain analysis based HCD methods. Source code will be made available at https://github.com/yulisun/HCD-GSP.

Guest Editorial Advanced Signal Processing for Local and Private 5G Networks

The papers in this special section focus on advanced signal procesing for local and private 5G mobile communication netwworks. The papers describe the latest advances in emerging private 5G networks from the perspective of signal processing to advance its theoretical underpinnings and practical applications. Some enterprises, factories and other potential users have ultra-stringent communications performance requirements in terms of throughput, latency, reliability, availability, and device density, which cannot be met by 4G long term evolution (LTE) radio features. Instead, 5G new radio (NR) has the potential to deliver on such requirements, and shape both the industrial world as well as our daily lives, by providing spectrum exibility, multi-Gbps peak data rates, ultra-low latencies, high reliability, and massive connectivity. By building dedicated networks with complete control over every aspect of the network.

Positioning and perception in LIDAR point clouds

In the last decade, Light Detection and Ranging (LIDAR) became a leading technology of detailed and reliable 3D environment perception. This paper gives an overview of the wide applicability of LIDAR sensors from the perspective of signal processing for autonomous driving, including dynamic and static scene analysis, mapping, situation awareness which functions significantly point beyond the role of a safe obstacle detector, which was the sole typical function for LIDARs in the pioneer years of driver-less vehicles. The paper focuses on a wide range of LIDAR data analysis applications of the last decade, and in addition to the presentation of a state-of-the-art survey, the article also summarizes some issues and expected directions of the development in this field, and the future perspectives of LIDAR systems and intelligent LIDAR based information processing.

Robust and label efficient bi-filtering graph convolutional networks for node classification

Due to their success at node classification, Graph Convolutional Networks (GCN) have raised a research upsurge of deep learning on graph-structured data. For the semi-supervised classification, graph convolution essentially acts as a low-pass filter on graph spectral domain. According to Graph Signal Processing theory, the low-pass filter in GCN is a finite impulse response (FIR) graph filter. However, compared with FIR graph filters, infinite impulse response (IIR) graph filters exhibit more powerful representation ability and flexibility. Intuitively, it is feasible to replace FIR filter in GCN with IIR graph filter to improve GCN. Therefore, inspired by the direct implementation of IIR graph filters, we propose a Bi-filtering Graph Convolutional Network (BGCN) which can be realized by simply cascading two sub filtering modules. Experimental results demonstrate that BGCN works well in node classification task and achieves comparable performance to GCN and its variants. The improvement of BGCN, however, is at the expense of a time-complexity increase. To simplify the proposed BGCN, we construct a Simple Bi-filtering Graph Convolution framework (SBGC) from the perspective of Graph Signal Processing. Furthermore, for the implementations of BGCN and SBGC, we design a novel low-pass graph filter to capture the low-frequency components that are beneficial to data representation for the task of node classification. Extensive experiments show that SBGC not only outperforms other baseline methods in performance, but also keeps a high level in computational efficiency. Moreover, it is particularly worth noting that both BGCN and SBGC are robust to feature noise and exhibit high label efficiency.

Radar networks: A review of features and challenges

Networks of multiple radars are typically used for improving the coverage and tracking accuracy. Recently, such networks have facilitated deployment of commercial radars for civilian applications such as healthcare, gesture recognition, home security, and autonomous automobiles. They exploit advanced signal processing techniques together with efficient data fusion methods in order to yield high performance of event detection and tracking. This paper reviews outstanding features of radar networks, their challenges, and their state-of-the-art solutions from the perspective of signal processing. Each discussed subject can be evolved as a hot research topic.

FMCW ISAR Autofocus Imaging Algorithm for High-Speed Maneuvering Targets Based on Image Contrast-Based Autofocus and Phase Retrieval

From the perspective of radar signal processing, the pulse-Doppler radar signal model is mostly based on the stop-and-go model due to the short pulse propagation time. Unlike the pulse-Doppler radar, the frequency modulated continuous wave (FMCW) radar takes a long time in the process of signal transmission to echo reception. During this period, the target cannot be considered to be stationary during the acquisition of the entire sweep echo, so the stop-and-go assumption in the FMCW system is no longer valid, which means that the target's motion related to the fast time must be taken into account. In the inverse synthetic aperture radar (ISAR) imaging scenario of high-speed maneuvering targets, the frequency offset correction introduced by the target's motion becomes reasonably complicated that it is difficult to restore the usual ISAR signal processing. In this work, a new method based on phase retrieval and image contrast-based autofocus (ICBA) is proposed to address the complex problem of motion compensation for FMCW ISAR systems. To cope with the fast time-varying characteristics of the phase error and the higher number of phase terms relative to pulsed radars, the proposed algorithm performs fine compensation with higher accuracy, resulting in high-resolution and autofocus imaging. Experimental results demonstrate the effectiveness of the proposed method.

Removal of Muscle Artifacts From the EEG: A Review and Recommendations

Electroencephalography (EEG) has been widely used for studying brain function. As cortical signals recorded by the EEG are very weak, they are often obscured by motion artifacts and various non-brain physiological activities, such as eye movement, heart rhythms, and muscle activity, adversely affecting subsequent analysis and interpretation. Over the past decades, a number of techniques have been developed for preprocessing EEG recordings to improve the signal-to-noise ratio. However, based on our extensive literature survey, despite an increasing trend recently, only 14.72% of published studies on EEG preprocessing were involved with the removal of electromygraphic (EMG) artifacts. Given that ambulatory healthcare systems are continuously emerging, artifacts induced by muscle contraction become unavoidable, whereas in the past data tended to be collected in well-controlled clinical/laboratory settings. Motivated by the fact that EMG artifact removal is becoming an important issue to be addressed, we investigated the state-of-the-art muscle artifact removal methods systematically and comparatively, from the perspective of signal processing. In this review, we first present the signal characteristics from brain and muscle activity, and highlight the importance of this issue for subsequent artifact removal. We then provide an overview and taxonomy of representative methods. Based on the results in reported studies, we describe the pros and cons of different methods and give suggestions on selecting a suitable technique in different scenarios (e.g., single-channel, few-channel, and multichannel). Finally, we discuss remaining challenges and provide feasible recommendations for further exploration in this field.

Helitron’s Periodicities Identification in C.Elegans based on the Smoothed Spectral Analysis and the Frequency Chaos Game Signal Coding

Helitrons are typical rolling circle transposons which make up the bulk of eukaryotic genomes. Unlike of other DNA transposons, these transposable elements (TEs), don’t create target site duplications or end in inverted repeats, which make them particular challenge to identify and more difficult to annotate. To date, these elements are not well studied; they only attracted the interest of researchers in biology. The focus of this paper is oriented towards identifying the helitrons in C.elegans genome in the perspective of signal processing. Aiming at the helitron's identification, a novel methodology including two steps is proposed: the coding and the spectral analysis. The first step consists in converting DNA into a 1-D signal based on the statistical features of the sequence (FCGS coding). As for the second step, it aims to identify the global periodicities in helitrons using the Smoothed Fourier Transform. The resulting spectrum and spectrogram are shown to present a specific signature of each helitron’s type.