Signal Space Research Articles

Line spectrum tracking method in transformed signal space for underwater moving targets in low signal-to-noise ratio environment

Line spectrum tracking is an essential signal-processing method for underwater passive detection. However, its performance is often seriously degraded due to signal fluctuation, especially in low signal-to-noise ratio scenarios. In this paper, based on signal space transformation and hidden Markov model, a signal trajectory tracking method is proposed for underwater moving target detection and parameter estimation. With this method, tracking the varying line spectrum trajectory in three-dimensional frequency-azimuth-time signal space is constrained onto a two-dimensional plane in the transformed signal space. Not only is the computation complexity reduced, but the ability to track weak line spectrums and estimate target parameters is improved. The performance of this method is verified with numerical simulations and experimental data processing.

Selected applications of satellite technologies in rail transport

Global Navigation Satellite Systems (GNSS) are increasingly being used in various modes of transport, including rail transport. When this technology is applied to railway traffic control, it is essential to ensure a high level of safety and reliability. The approval of a railway traffic control system requires a safety analysis, which includes hazard analysis and risk analysis. This also includes GNSS-based solutions in terms of their compliance with safety integrity requirements, i.e. THR (Tolerable Hazard Rate) and SIL (Safety Integrity Level) parameters, as defined in normative documents. In the case of railway traffic control systems, the level of dependability of the determined train position, referred to as position integrity, is very important in ensuring safety. Position integrity is affected by many factors, including: errors due to SIS (Signal-In-Space) propagation, multipath errors, signal interference or GNSS receiver errors. In order to improve position integrity, among other things, new data processing methods can be used to improve the accuracy and reliability of measurements. The paper presents the concept of satellite signal processing for precise determination of the position of objects in selected railway systems. The Kalman filter based model of satellite signal filtration and its selected applications, which were tested in the real condition, was presented. The application of Kalman filtration indicated in the paper is a universal method that improves the estimated measurement parameters and can be used in many applications of satellite systems for railway tasks. The applicability of satellite systems to automatic train operation, defect positioning in automatic and manual flaw detection tests, determining track spatial orientation and train integrity control have been considered. The conducted tests confirmed the correctness of the adopted concept and the model of satellite signals filtration developed for this purpose. According to the authors, the described methods can also be used in many other tasks related to rail transport.

Study on Self-Learning System for Milling Chatter Feature Based on Hybrid Preprocessing Model and Improved Convolutional Clustering

Milling chatter is a self-excited vibration phenomenon during the cutting process, typically occurring in machining operations with lower stiffness. It significantly impacts the machining precision and surface morphology of the workpiece. To summarize the vibration signal features under operating conditions and enhance the precision of the intelligent recognition model for milling chatter features, this paper proposes an adaptive learning model for chatter feature recognition based on the improved convolutional clustering algorithm. This study uses wavelet analysis to extract chatter features and improved convolutional clustering to identify chatter and stable cutting signals. This method selected appropriate mapping techniques based on differences in chatter signals under various conditions and uses convolution for feature extraction. Improve convolutional clustering evaluates sample similarity through energy transfer, less influenced by signal space structure compared to Euclidean or Mahalanobis distances. This method provides a unified criterion for evaluating different signal feature representation spaces, thereby improving the accuracy and efficiency of chatter sample recognition. The recognition accuracy of chatter phenomena at different spindle speed ranges reaches $95$ % and $96.3$ % respectively. The results show that this method can effectively distinguish between chatter signals and stable cutting signals.

Optimizing magnetometers arrays and analysis pipelines for multivariate pattern analysis

BackgroundMultivariate pattern analysis (MVPA) has proven an excellent tool in cognitive neuroscience. It also holds a strong promise when applied to optically-pumped magnetometer-based magnetoencephalography. New methodTo optimize OPM-MEG systems for MVPA experiments this study examines data from a conventional MEG magnetometer array, focusing on appropriate noise reduction techniques for magnetometers. We determined the least required number of sensors needed for robust MVPA for image categorization experiments. ResultsWe found that the use of signal space separation (SSS) without a proper regularization significantly lowered the classification accuracy considering a sub-array of 102 magnetometers or a sub-array of 204 gradiometers. We also found that classification accuracy did not improve when going beyond 30 sensors irrespective of whether SSS has been applied. Comparison with existing methodsThe power spectra of data filtered with SSS has a substantially higher noise floor that data cleaned with SSP or HFC. Consequently, MVPA decoding results obtained from the SSS-filtered data are significantly lower compared to all other methods employed. ConclusionsWhen designing MEG system based on SQUID magnetometers optimized for multivariate analysis for image categorization experiments, about 30 magnetometers are sufficient. We advise against applying SSS filters without a proper regularization to data from MEG and OPM systems prior to performing MVPA as this method, albeit reducing low-frequency external noise contributions, also introduces an increase in broadband noise. We recommend employing noise reduction techniques that either decrease or maintain the noise floor of the data like signal-space projection, homogeneous field correction and gradient noise reduction.

Bimodal distribution of inter-individual distance in free-ranging narrow-ridged finless porpoises

Inter-individual distance (IID) is an important indicator of social organisation because solitary species are spatially intolerant towards conspecifics, whereas group-living species are usually gregarious with collective behaviour. Therefore, by examining the relationship between the distribution of IIDs and the active space of cues or signals, and behaviours, we can predict their social organisation. The narrow-ridged finless porpoises (NRFPs) have been described as a solitary species; however, recent studies described NRFPs tend to live in groups more than alone. To resolve the inconsistency, the present study investigated IIDs, the active spaces of sounds, and behaviours. The distribution of IIDs measured using drone was classified into three distributions. The close and intermediate distributions were significantly shorter than the distribution predicted by the angle of drone camera, whereas the far distributions were not. The far distributions were thus a random distribution within the limited angle of the camera. The close distributions were shorter than the active space, exhibiting a high proportion of collective behaviours, while intermediate distributions did not. These results suggest that NRFPs have both solitary- and group-living characteristics. Specifically, the intermediate distribution suggests a solitary aspect to maintain IIDs from others, while the close distribution indicates a group-living aspect with social interactions.

Neural Gaussian Scale-Space Fields

Gaussian scale spaces are a cornerstone of signal representation and processing, with applications in filtering, multiscale analysis, anti-aliasing, and many more. However, obtaining such a scale space is costly and cumbersome, in particular for continuous representations such as neural fields. We present an efficient and lightweight method to learn the fully continuous, anisotropic Gaussian scale space of an arbitrary signal. Based on Fourier feature modulation and Lipschitz bounding, our approach is trained self-supervised, i.e., training does not require any manual filtering. Our neural Gaussian scale-space fields faithfully capture multiscale representations across a broad range of modalities, and support a diverse set of applications. These include images, geometry, light-stage data, texture anti-aliasing, and multiscale optimization.

Autonomous and reliable fingerprint map maintenance for indoor positioning system

In WiFi-based crowdsourced fingerprinting indoor positioning systems (IPS), the signal space of WiFi APs changes over time. To autonomously maintain a highly reliable localization accuracy, the fingerprint database needs regular maintenance and updates. However, this process is labor-intensive and time-consuming due to the appearance of faulty access points (APs) in crowdsourced samples received from target clients, which are mainly used for autonomous maintenance and updates. Additionally, if some APs are relocated, removed, or replaced in the indoor environment after fingerprint map construction, it can severely impact IPS accuracy. In this paper, we present Dynamic Online Fingerprint Learning with Altered APs status-aware and updating (DOFLAAU) for IPS. Our systematic methodology involves several key steps. First, we employ the Modified Group Method of Data Handling (GMDH) neural network regression for autonomous AP status monitoring and alteration detection. Next, we perform autonomous APs subset sample selection using a modified Thomson model, followed by outlier feature detection and removal based on the weighted sample density rate. Thirdly, we propose a dynamic online fingerprint map updating model using the cosine similarity distance metric. Lastly, we adopted an effective signal distance-based weighted k-nearest neighbor algorithm to improve IPS accuracy. The experiment results validate the efficiency and effectiveness of our proposed algorithms in terms of better localization accuracy compared to peer algorithms.

Changes in Spatiotemporal Dynamics of Default Network Oscillations between 19 and 29 Years of Age.

The exploration of functional resting-state brain developmental parameters and measures can help to improve scientific, psychological, and medical applications. The present work focussed on both traditional approaches, such as topographical power analyses at the signal space level, and advanced approaches, such as the exploration of age-related dynamics of source space data. The results confirmed the expectation that the third life decade would show a kind of stability in oscillatory signal and source-space-related parameters. However, from a source dynamics perspective, different frequency ranges appear to develop quite differently, as reflected in age-related sequential network communication profiles. Among other discoveries, the left anterior cingulate source location could be shown to reduce bi-directional network communication in the lower alpha band, whereas it differentiated its uni- and bidirectional communication dynamics to sub-cortical and posterior brain locations. Higher alpha oscillations enhanced communication dynamics between the thalamus and particularly frontal areas. In conclusion, resting-state data appear to be, at least in part, functionally reorganized in the default mode network, while quantitative measures, such as topographical power and regional source activity, did not correlate with age in the third life decade. In line with other authors, we suggest the further development of a multi-perspective approach in biosignal analyses.

A note on matrix-valued orthonormal bases over LCA groups

It is well known that orthonormal bases for a separable Hilbert space [Formula: see text] are precisely collections of the form [Formula: see text], where [Formula: see text] is a linear unitary operator acting on [Formula: see text] and [Formula: see text] is a given orthonormal basis for [Formula: see text]. We show that this is not true for the matrix-valued signal space [Formula: see text], [Formula: see text] is a locally compact abelian group which is [Formula: see text]-compact and metrizable, and [Formula: see text] and [Formula: see text] are positive integers. This problem is related to the adjointability of bounded linear operators on [Formula: see text]. We show that any orthonormal basis of the space [Formula: see text] is precisely of the form [Formula: see text], where [Formula: see text] is a linear unitary operator acting on [Formula: see text] which is adjointable with respect to the matrix-valued inner product and [Formula: see text] is a matrix-valued orthonormal basis for [Formula: see text].

An Augmented Space Smoothing Method based on the Signal Space in Coherent Scenarios

An Augmented Space Smoothing Method based on the Signal Space in Coherent Scenarios

Signal points allocation for generalized enhanced spatial modulation

AbstractIn this paper, making full use of the spatial domain of transmit antennas (TAs) and keeping the characteristic of the squared minimum Euclidean distance (MED) between the transmitted spatial vectors (TSVs), generalized enhanced spatial modulation with signal points allocation (GESM‐SPA) is proposed to expand the size of signal spaces for enhancing the spectral efficiency and the reliability of communications. In the GESM‐SPA, according to the number of active TAs, signal constellation points (CPs) from the QAM or secondary QAM constellations are allocated and then modulated on the corresponding active TAs with the selected antenna index (AI) vector. Through this design, which further exploits the spatial domain with the variability of active TAs, the squared MED between the TSVs is increased in comparison with the existing traditional systems. More specifically, in view of the disadvantage of the classic ESM system, the constellation groups (CGs) with priority given to the QAM CPs are constructed to further maximize the squared MED. Then, the AI vector subsets corresponding to the obtained CGs are designed to be candidate for the specified AI vector set with the AI information. The squared MED and the average bit error probability (BEP) are analyzed. In simulation results using Monte Carlo, the GESM‐SPA outperforms the existing classic systems in terms of the bit error rate performance.

Antijamming Schemes for the Generalized MIMO Y Channel.

Signal space alignment (SSA) is a promising technique for interference management in wireless networks. However, despite the excellent work done on SSA, its robustness against jamming attacks has not been considered in the literature. In this paper, we propose two antijamming strategies for the SSA scheme applied in the multiple-input-multiple-output (MIMO) Y channel. The first scheme involves projecting the jamming signal into the null space of each source's precoding vectors, effectively eliminating it entirely. The second scheme removes interference originating from the jammer by subtracting the disturbance estimate from the incoming signal. The estimate is derived on the basis of the criterion of minimizing the received signal energy. The block error rate (BLER) performance of the proposed strategies in various channel configurations is verified by link level simulations and is presented to show the efficiency in mitigating jamming signals within the SSA-based MIMO Y channel.

Research on Dual-Grating Spacing Calibration Method Based on Multiple Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Combined with Hilbert Transform

The paper proposes a method for the calibration of spacing in dual-grating based on Multiple Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with Hilbert Transform (HT), referred to as Multiple ICEEMDAN-HT. This method addresses the potential impact of nonlinear factors on phase extraction accuracy, consequently on ranging precision in the homodyne interference of the dual-grating. Building upon the ICEEMDAN algorithm, the signal undergoes iterative decomposition and reconstruction using the sample entropy criterion. The intrinsic mode functions (IMFs) obtained from multiple iterations are then reconstructed to obtain the complete signal. Through a simulation and comparison with other signal decomposition methods, the repeatability and completeness of signal reconstruction by Multiple ICEEMDAN are verified. Finally, an actual dual-grating ranging system is utilized to calibrate the spacing of the planar grating. Experimental results demonstrate that the calibration relative error of the Multiple ICEEMDAN-HT phase unwrapping method can be reduced to as low as 0.07%, effectively enhancing the signal robustness and spacing calibration precision.

Origin coordinate influence on performance of temporally extended signal space separation in magnetoencephalography

ObjectiveTemporally extended signal space separation (tSSS) is a powerful method for artifact suppression in magnetoencephalography (MEG). Because tSSS first separates MEG signals coming from inside and outside a certain sphere, definition of the sphere origin is important. For this study, we explored the influence of origin choice on tSSS performance in spontaneous and evoked activity from epilepsy patients. MethodsInterictal epileptiform discharges (IEDs) and somatosensory evoked fields (SEFs) were processed with two tSSSs: one with the default origin of (0, 0, 40 mm) in the head coordinate, and the other with an individual origin estimated using each patient’s anatomical magnetic resonance imaging (MRI). Equivalent current dipoles (ECDs) were calculated for the data. The ECD location and quality of estimation were compared across conditions. ResultsMEG data from 21 patients revealed marginal differences in ECD location, but the estimation quality inferred from goodness of fit (GOF) and confidence volume (CV) was better for the tSSS with individual origins. This choice affected IEDs more than it affected SEFs. ConclusionsIndividual sphere model resulted in better GOF and CV. SignificanceApplication of tSSS using an individual origin would be more desirable when available. This parameter might influence spontaneous activity more strongly.

Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG.

Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain-computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain-computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III-IV(A) and BCI competition IV-I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.

Parameter identification based on iterative signal space in blade tip timing

Blade tip timing (BTT), as an effective non-contact measurement technology, is widely used for health monitoring of rotating blades. Due to the installed number of probes is limited by the engine structure, the requirement of BTT sampling frequency is difficult to meet the Nyquist sampling theorem, which leads the BTT signal is severely under-sampled. Therefore, it is difficult to extract vibration parameters, such as frequency, amplitude. Many algorithms was proposed to solve the undersampling problem. In this paper, a new BTT signal processing algorithm is proposed based iterative signal space, which expands individual data into subspace level data geometrically. Compared to some previous methods, the iterative signal space algorithm can identify the blade vibration parameters more accurately, which is verified in both numerical simulation and experiment. Furthermore, different degrees of noise is used to show the feasibility and robustness. The results are very significant for online blade fault diagnosis and health monitoring of turbines.

Cosmological Background Interpretation of Pulsar Timing Array Data.

We discuss the interpretation of the detected signal by pulsar timing array (PTA) observations as a gravitational wave background of cosmological origin. We combine NANOGrav 15-years and EPTA-DR2new datasets and confront them against backgrounds from supermassive black hole binaries (SMBHBs), and cosmological signals from inflation, cosmic (super)strings, first-order phase transitions, Gaussian and non-Gaussian large scalar fluctuations, and audible axions. We find that scalar-induced, and to a lesser extent audible axion and cosmic superstring signals, provide a better fit than SMBHBs. These results depend, however, on modeling assumptions, so further data and analysis are needed to reach robust conclusions. Independently of the signal origin, the data strongly constrain the parameter space of cosmological signals, for example, setting an upper bound on primordial non-Gaussianity at PTA scales as |f_{nl}|≲2.34 at 95%C.L.

Relevant sampling in a reproducing kernel subspace of Orlicz space

In this paper, we aim to provide a general paradigm for dealing with the sampling and random sampling problem in a reproducing kernel subspace of Orlicz space [Formula: see text]. We consider the function space [Formula: see text] as the image of an idempotent integral operator on [Formula: see text], where the integral kernel satisfies certain off-diagonal decay and regularity conditions. The model example of such reproducing kernel subspace of [Formula: see text] includes the finitely generated shift-invariant space and signal space with a finite rate of innovation. We show that a signal in [Formula: see text] can be stably reconstructed from its samples at distinct points separated by a sufficiently small gap. Next, we deduce that the random sampling inequality holds with a high probability for the class of functions in [Formula: see text] concentrated on a cube [Formula: see text], when the samples collected at i.i.d. random points are drawn on [Formula: see text] of order [Formula: see text].

Direction of Arrival Estimation of Coherent Sources via a Signal Space Deep Convolution Network

In the field of direction of arrival (DOA) estimation for coherent sources, subspace-based model-driven methods exhibit increased computational complexity due to the requirement for eigenvalue decomposition. In this paper, we propose a new neural network, i.e., the signal space deep convolution (SSDC) network, which employs the signal space covariance matrix as the input and performs independent two-dimensional convolution operations on the symmetric real and imaginary parts of the input signal space covariance matrix. The proposed SSDC network is designed to address the challenging task of DOA estimation for coherent sources. Furthermore, we leverage the spatial sparsity of the output from the proposed SSDC network to conduct a spectral peak search for obtaining the associated DOAs. Simulations demonstrate that, compared to existing state-of-the-art deep learning-based DOA estimation methods for coherent sources, the proposed SSDC network achieves excellent results in both matching and mismatching scenarios between the training and test sets.

Progressive feature fusion for SNR-aware low-light image enhancement

Restoring image quality in low-light environments is an intriguing topic. While deep learning models have made significant strides in low-light enhancement, most models do not take into account the inherent characteristics of objects themselves. In this paper, we use the characteristics of the image itself to construct a Signal-to-Noise Ratio (SNR) map that guides the signal space variation to dynamically stretch the pixel values. Specifically, we propose a novel signal-to-noise ratio image-guided enhancement framework that uses the feature information of the original image to guide spatial variations in the image. It involves step-wise guidance for image feature fusion, gradually emphasizing high-frequency feature information within the image. Meanwhile, we introduced a texture optimization module that utilizes the feature information extracted by the feature fusion module to address the issues of overexposure and detail loss. We performed qualitative and quantitative evaluations on synthetic and real low-light image datasets to demonstrate the performance of our method. The experimental results show that our model outperforms other state-of-the-art methods (SOTA) in robust low-light enhancement, especially in processing images captured in complex scenes.