Spectrum Domain Research Articles

VOLUME SINGULAR INTEGRAL EQUATIONS FOR PROBLEMS OF LOW-FREQUENCY SCATTERING OF ELECTROMAGNETIC WAVES IN ANISOTROPIC STRUCTURES

This paper deals with volume singular integral equations describing the problems of low-frequency scattering of electromagnetic waves in bounded three-dimensional anisotropic structures. The spectrum of integral operators is studied. The domain of the operator spectrum on the complex plane for the low-frequency case is presented explicitly. The generalized method of simple iteration is described, for application of which it is necessary to know the area of the operator spectrum on the complex plane. The collocation method on a uniform grid is used to discretize the integral equations. This allows, using a fast discrete Fourier transform, to construct an algorithm for fast multiplication of the matrix of a system of linear equations by a vector. The results of numerical solution of the considered problems are given.

Application of multiple spectrum domain fault probability fusion model in high-speed rail bearings

Application of multiple spectrum domain fault probability fusion model in high-speed rail bearings

Wind vector retrieval from Gaofen-3 SAR imagery using the method of iterative line fitting in image power spectrum domain

Wind vector retrieval from Gaofen-3 SAR imagery using the method of iterative line fitting in image power spectrum domain

Major Depressive Disorder with Catatonia: A Phenotype Related to Autistic Traits and High Suicidality.

Background: Major Depressive Disorder (MDD) represents a significant global health concern, often complicated by comorbidities such as catatonia and autism spectrum disorder (ASD). Recognizing the interplay among these conditions and their impact on suicidal tendencies is crucial for effective clinical management. Methods: A total sample of 147 subjects with MDD was divided into Significant Catatonia (SC) and Non-Significant Catatonia (NSC) groups based on Catatonia Spectrum (CS) scores. Participants were evaluated through the Structured Clinical Interview for DSM-5, Research Version (SCID-5-RV), the Adult Autism Subtreshold Spectrum (AdAS Spectrum), and the Mood Spectrum-Self Report questionnaires. Statistical analyses included Mann-Whitney U test, Chi-square test, logistic regression analyses, and a decision tree model. Results: The SC group exhibited higher CS, AdAS Spectrum, and MOODS-SR total and domain scores compared to the NSC group. Individuals with significant autistic traits were over-represented in the SC group, as well as participants with higher suicidality, suicidal ideation, and a history of suicide attempts. The total AdAS Spectrum and MOOD-SR score, the AdAS domain "Hyper-hypo reactivity to sensory input", and the "Cognitive depressive" MOOD-SR domain were predictive of belonging to the SC group. Suicidality levels appeared to be higher in clinically significant ASD, intermediate in subjects with autistic traits (AT), and low in the absence of AT. Conclusions: the study suggests the existence of a specific phenotype of MDD associated with catatonia, characterized by elevated autistic traits and suicide risk.

Efficient single-frequency millimeter-wave three-dimensional imaging for rapid target identification

Compared to broadband systems, single-frequency millimeter-wave (SFQ-MMW) imaging systems can significantly simplify system complexity, but can only obtain two-dimensional information of the target. Moreover, the position of the reference plane must be adjusted in real time during the imaging process to obtain a high-quality focusing effects. Therefore, this paper proposes a SFQ-MMW three-dimensional imaging algorithm. In the plane wave spectrum domain, the propagation operator is used as a focusing term to accurately locate the target focus. Only single-frequency echo data is required to retrieve information and reconstruct high-quality images. This method can significantly improve the efficiency of near-field millimeter wave measurement and imaging. This work facilitates applications in rapid target identification and characterization. The performance of this method was tested through numerical simulation. Finally, validation was performed on real data in an independently developed system. The results show that the test results are in line with expectations, and the imaging efficiency has been significantly improved.

Panic-agoraphobic symptoms in adults with ASD: the role of ruminative thinking and inflexibility.

Scientific literature has highlighted the link between autism spectrum disorder (ASD) and anxiety disorders, but few studies have delved into the relationship between ASD and panic-agoraphobic disorders. The aim of this study is to investigate the relationship between autism spectrum and panic-agoraphobic symptoms, examining whether and which autistic domains are predictive of the presence of specific panic-agoraphobic symptoms. Forty-five adult subjects with ASD and 50 healthy controls (HCs) were evaluated through the Structured Clinical Interview for DSM-5, Research Version and assessed with the Adult Autism Subthreshold Spectrum (AdAS Spectrum) and the Panic-Agoraphobic - Short Version (PAS-SV) questionnaires. Statistical analyses included Mann-Whitney U test, chi-square test, and a set of linear and logistic regression analyses. The PAS-SV total and domain scores were significantly higher in the ASD group than in the HC group. A higher AdAS total score appeared to be predictive of a higher PAS-SV total score. The AdAS domain Restricted Interests and Rumination would increase the risk of obtaining higher PAS-SV total and domain scores. Conversely, the AdAS Spectrum domain Inflexibility and Adherence to Routine would predict lower total PAS-SV score. This study revealed a greater representation of panic-agoraphobic symptoms in adults with ASD, as well as an increased risk of showing such symptoms in the presence of significant autistic traits. Restricted interests and ruminative thinking emerged as predominant risk factors for panic-agoraphobic manifestations.

Presence and correlates of autistic traits among patients with social anxiety disorder.

Due to their similar behavioral presentation, it can sometimes be challenging to distinguish between a social anxiety disorder (SAD) and the social avoidance that is frequently described in autism spectrum disorder (ASD). Moreover, a growing body of evidences is reporting that a significant proportion of subjects with ASD also meet the requirements for SAD and, vice versa, subjects with SAD tend to exhibit a higher prevalence of autistic traits. In this framework, the current study aims to evaluate prevalence and correlates of autistic traits in a sample of adult subjects diagnosed with SAD and healthy controls (HC), also evaluating which autism spectrum dimensions may statistically predict higher SAD symptoms. 56 subjects with a clinical diagnosis of SAD and 56 gender and age matched HC were recruited from the Psychiatric Clinic of the University of Pisa. Subjects were assessed with the SCID-5, the Social Anxiety Spectrum - Short Version (SHY- SV) and the Adult Autism Subthreshold Spectrum (AdAS Spectrum). SAD group scored significantly higher in all AdAS Spectrum and SHY-SV domains and total score compared to the HC group with no significant gender difference. SHY-SV total and domain scores, were strongly and positively and strongly correlated with all AdAS Spectrum domains and total score. AdAS Spectrum total score and Childhood/Adolescence, Non-Verbal Communication, Empathy and Restricted interests and Rumination domain scores score were significant predictors of higher SHY-SV score. Our results confirm the link between SAD and autistic traits also in adult population, describing not only high levels of autistic traits in SAD adults, but also significant correlations between many core features of the two disorders and a predictive role of autistic traits on higher SAD symptoms.

Unsupervised Wavelet-Feature Correlation Ratio Markov Clustering Algorithm for Remotely Sensed Images

The spectrums of one type of object under different conditions have the same features (up, down, protruding, concave) at the same spectral positions, which can be used as primary parameters to evaluate the difference among remotely sensed pixels. The wavelet-feature correlation ratio Markov clustering algorithm (WFCRMCA) for remotely sensed data is proposed based on an accurate description of abrupt spectral features and an optimized Markov clustering in the wavelet feather space. The peak points can be captured and identified by applying a wavelet transform to spectral data. The correlation ratio between two samples is a statistical calculation of the matched peak point positions on the wavelet feature within an adjustable spectrum domain or a range of wavelet scales. The evenly sampled data can be used to create class centers, depending on the correlation ratio threshold at each Markov step, accelerating the clustering speed by avoiding the computation of Euclidean distance for traditional clustering algorithms, such as K-means and ISODATA. Markov clustering applies several strategies, such as a simulated annealing method and gradually shrinking the clustering size, to control the clustering convergence. It can quickly obtain the best class centers at each clustering temperature. The experimental results of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Thermal Mapping (TM) data have verified its acceptable clustering accuracy and high convergence velocity.

Autistic Traits as Predictors of Increased Obsessive-Compulsive Disorder Severity: The Role of Inflexibility and Communication Impairment.

Due to similar manifestations, some authors have proposed a potential correlation between autism spectrum disorder (ASD) and obsessive-compulsive disorder (OCD). This link has long been recognized and debated, with some authors arguing that these disorders frequently occur comorbid but distinct while others believe they are part of the same spectrum. The aim of our study was to explore the prevalence and correlates of autistic traits in 55 OCD patients and 55 matched controls and to assess possible autistic dimensions predictive of higher OCD symptoms. All participants were assessed with the Obsessive-Compulsive Spectrum-Short Version (OBS-SV) and the Adult Autism Subthreshold Spectrum (AdAS Spectrum). The OCD group scored significantly higher in both questionnaires. Total OBS-SV scores and domains were significantly correlated with all AdAS Spectrum domains and total score. The AdAS Spectrum total, Verbal Communication and Inflexibility and adherence to routine domain scores were significant positive predictors of higher OBS-SV scores. Lastly, when two clusters of subjects (high and low autism) were determined, Inflexibility and adherence to routine domain presented the greatest influence in forming the clusters. Our findings support the association between OCD and autistic traits in the adult population, supporting the hypothesis of a neurodevelopmental basis for these psychiatric conditions.

Conditional diffusion model-based generation of speckle patterns for digital image correlation

Generating speckle images is crucial for calibrating errors in digital image correlation (DIC) algorithms, alleviating the scarcity of training data for deep learning-based DIC algorithms, and even for the broader application of digital speckle methods. Traditional methods for speckle image synthesis struggle to accurately replicate the brightness variations in the foreground and background of realistic speckle images, and these methods come with high computational costs. Additionally, there needs to be more scientific and systematic evaluation methods for the quality of generated DIC speckle images. Our study presents a high-fidelity method for generating DIC speckle images with cracks based on a conditional diffusion model. We established evaluation metrics for generated DIC speckle images based on multiscale spatial and spectrum domains. We verified them on an open-source dataset of DIC speckle images depicting cracks in stone masonry walls. The conditional diffusion model excels at forward tasks (DIC crack image segmentation) and inverse tasks (DIC speckle image generation), with particular strength in the latter. The proposed spatial and multiscale spectrum domain metrics allow for the comprehensive and accurate evaluation of the alignment between the synthetic and natural speckle images. The conditional diffusion model for DIC speckle image generation and evaluation proposed in this paper has the potential to enhance further the accuracy and robustness of DIC technology in experimental mechanics and engineering applications.

Hermitian and pseudo-Hermitian Hamiltonians of [formula omitted] system—Spectrum, exceptional point, quantum–classical correspondence

We in this paper study the relation of hermiticity and energy spectrum for Hamiltonians consisting of SU(1,1) generators. In contrast with the common belief, the transition from real to imaginary spectra can appear at an exceptional point for the Hermitian Hamiltonian of a conservative system. The imaginary domain of spectrum resulted from an inverted potential well unbounded from below. An outward force applies on the particle, which moves acceleratingly away from the central point similar to the minisuperspace model of expanding universe. The Hamiltonian remains Hermitian beyond the exceptional point in a price that the boson-operator realization of SU(1,1) generator Ŝz becomes non-Hermitian. Oppositely a non-Hermitian Hamiltonian (called pseudo-Hermitian) possesses real eigenvalues in the entire region of interaction constant, which increases the gradient of potential well. While the Hermitian interaction decreases the gradient continuously to zero, namely the exceptional point, where eigenstates are degenerate. We extend the non-Hermitian Hamiltonian to the periodically time-dependent and two-dimensional system. The probability density of wave function coincides with classical orbits, which are derived from the corresponding classical-counterpart of the non-Hermitian quantum Hamiltonian.

3D Synthetic Aperture Imaging Method in Spectrum Domain for Low-Cost Portable Ultrasound Systems

3D Synthetic Aperture Imaging Method in Spectrum Domain for Low-Cost Portable Ultrasound Systems

Cumulative spectrum distribution entropy for rotating machinery fault diagnosis

Entropy-based methods have shown promise in detecting dynamic changes in non-linear signals and have been widely applied in fault diagnosis for rotating machinery. However, these methods have limitations when it comes to capturing frequency-domain information of fault features, as they are primarily based on time-domain signals. To address this issue, this paper proposes a new entropy measure called cumulative spectrum distribution entropy (CSDEn), which is based on the cumulative distribution of the spectrum and considers both frequency probability and frequency values in the spectrum domain. The proposed method is evaluated using synthetic signals and experimental data from different bearing and gear working states. The results show that CSDEn outperforms other widely used entropy measures in detecting dynamic changes and measuring signal complexity with low noise sensitivity and high computing efficiency. Nonparametric Mann–Whitney U tests reveal significant differences between different working states for proposed CSDEn method, and compared with other entropy methods, CSDEn achieves the highest recognition rates in diagnosing different bearing and gear working states. Moreover, proposed CSDEn method demonstrates its effectiveness in addressing the challenges of small sample datasets and strong noise interference, making it highly competitive in real industrial applications.

Variance characteristic preserving common spatial pattern for motor imagery BCI.

The common spatial patterns (CSP) algorithm is the most popular technique for extracting electroencephalogram (EEG) features in motor imagery based brain-computer interface (BCI) systems. CSP algorithm embeds the dimensionality of multichannel EEG data to extract features of motor imagery tasks. Most previous studies focused on the optimization of the time domain and the spectrum domain of EEG signal to improve the effectiveness of CSP, whereas ignoring the constraint on the projected feature space. This study proposed a variance characteristic preserving CSP (VPCSP) that is modified by a regularization item based on graph theory. Specifically, we calculated the loss of abnormalities of the projected data while preserving the variance characteristic locally. Then the loss could be rewritten as a matrix with the introduction of the Laplace matrix, which turned it into a generalized eigenvalue problem equivalent to CSP. This study evaluated the proposed method on two public EEG datasets from the BCI competition. The modified method could extract robust and distinguishable features that provided higher classification performance. Experimental results showed that the proposed regularization improved the effectiveness of CSP significantly and achieved superior performance compared with reported modified CSP algorithms significantly. The classification accuracy of the proposed method achieved 87.88 %, 90.07 %, and 76.06 % on public dataset IV part I, III part IVa and the self-collected dataset, respectively. Comparative experiments are conducted on two public datasets and one self-collected dataset. Results showed that the proposed method outperformed the reported algorithm. The proposed method can extract robust features to increase the performance of BCI systems. And the proposal still has expandability. These results show that our proposal is a promising candidate for the performance improvement of MI-BCI.

Implications of Social Anxiety Symptoms in Adults with Autism Spectrum Disorder: Is There a Predictive Role of Interpersonal Sensitivity and Substance Abuse?

Social anxiety disorder (SAD) has been frequently reported by subjects with Autism Spectrum Disorder (ASD). However, interestingly, the overlap between social anxiety and autistic traits may sometimes impede ASD diagnosis in subjects without intellectual or language impairment. The aim of the present work was to evaluate the presence and correlates of social phobic features among subjects with ASD, with a specific focus on evaluating which social anxiety symptoms may be statistically predictive of an ASD diagnosis. With this purpose, 48 subjects with ASD and 48 gender- and age- matched healthy controls (HCs) were recruited and assessed with the SHY-SV and the AdAS Spectrum questionnaires. Results highlighted higher scores in all SHY-SV Spectrum domains and total scores for the ASD group. Moreover, AdAS Spectrum scores were significantly correlated with all SHY-SV domain and total scores. A logistic regression analysis highlighted the SHY-SV Interpersonal sensitivity and Substance Abuse domains scores as significant positive predictors of an ASD diagnosis. These results confirm the link between ASD and SAD. Because of this association, particular attention should be paid to subjects with high interpersonal sensitivity traits and substance abuse problems.

Local spectral attention for full-band speech enhancement.

Attention mechanism has been widely used in speech enhancement (SE) because, theoretically, it can effectively model the inherent connection of signal in time domain and spectrum domain. In this Letter, it is found that the attention over the entire frequency range hampers the inference for full-band SE and possibly leads to excessive residual noise and degradation of speech. To alleviate this problem, the local spectral attention is introduced into full-band SE model by limiting the span of attention. The ablation tests on three full-band SE models reveal that the local frequency attention can effectively improve overall performance.

Simulations of optical properties of gallium alloy nanorods based on aspect ratio and shell thickness

Liquid plasmonics in contemporary research heavily relies on gallium-based alloy nanoparticles (NPs). The focus of this study is to explore the plasmonic properties of rod-shaped gallium alloy nanoparticles. The primary aim is to examine how variations in Ag shell thickness and aspect ratio affect the optical properties based on localized surface plasmon resonance (LSPR). The outcomes of this study will provide invaluable insights into improving the performance of plasmonic nanostructures. Notably, the focus is solely on analyzing the impact of size and material on the plasmon resonance of gallium-based alloy (GaAg), distinguishing it from the more widely known eutectic gallium-indium (EGaIn) alloy and Galinstan. To investigate the influence of varying aspect ratios and noble metal shell thickness under three different light polarization conditions, the boundary element method (BEM) was utilized. MATLAB-based simulations were employed to generate near-field and far-field plots of the extinction profiles of these nanorods. A large amount of tuning of the extinction spectra observed in the visible domain (380–780 nm) of the electromagnetic spectrum can be achieved with the size variation parameters. The gallium alloy (GaAg) examined in this study exhibits superior plasmonic properties and can be effectively utilized in soft electronic components, plasmonic energy storage devices, and as an alternative to the widely promoted GaIn alloy.

Automated diagnosis of EEG abnormalities with different classification techniques.

Automatic seizure detection and prediction using clinical Electroencephalograms (EEGs) are challenging tasks due to factors such as low Signal-to-Noise Ratios (SNRs), high variance in epileptic seizures among patients, and limited clinical data constraints. To overcome these challenges, this paper presents two approaches for EEG signal classification. One of these approaches depends on Machine Learning (ML) tools. The used features are different types of entropy, higher-order statistics, and sub-band energies in the Hilbert Marginal Spectrum (HMS) domain. The classification is performed using Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbor (KNN) classifiers. Both seizure detection and prediction scenarios are considered. The second approach depends on spectrograms of EEG signal segments and a Convolutional Neural Network (CNN)-based residual learning model. We use 10000 spectrogram images for each class. In this approach, it is possible to perform both seizure detection and prediction in addition to a 3-state classification scenario. Both approaches are evaluated on the Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) dataset, which contains 24 EEG recordings for 6 males and 18 females. The results obtained for the HMS-based model showed an accuracy of 100%. The CNN-based model achieved accuracies of 97.66%, 95.59%, and 94.51% for Seizure (S) versus Pre-Seizure (PS), Non-Seizure (NS) versus S, and NS versus S versus PS classes, respectively. These results demonstrate that the proposed approaches can be effectively used for seizure detection and prediction. They outperform the state-of-the-art techniques for automatic seizure detection and prediction. Block diagram of proposed epileptic seizure detection method using HMS with different classifiers.

Rolling bearing fault diagnosis under time-varying speeds based on time-characteristic order spectrum and multi-scale domain adaptation network

In practical industrial applications, rolling bearing generally operates under variable conditions and its vibration signal significantly fluctuates in amplitude and frequency. This increases the feature distribution differences of the bearing fault samples and makes the health status identification of the bearing more difficult. To this end, a new intelligent fault diagnosis method for bearing under time-varying speed conditions is proposed based on time-characteristic order (TCO) spectrum and multi-scale domain adaptation network (MSDAN). Firstly, by using the good noise robustness and high time–frequency aggregation properties of the synchrosqueezed wave packet transform (SSWPT), the TCO spectrum method based on SSWPT is proposed to eliminate the impact of speed fluctuation, reducing the distribution shift of bearing data under time-varying speeds. Secondly, an MSDAN model based on global-local feature fusion is established to extract the domain-invariant features closely related to the bearing fault state from the TCO spectrum. Finally, the local maximum distribution discrepancy is introduced to capture the discriminative fine-grained features. The feasibility of the proposed method is verified in various transfer tasks on two different bearing datasets with time-varying speeds. Compared with some state-of-art methods, the proposed method can eliminate the sample distribution differences under time-varying speeds, significantly improving its accuracy and generalization performance in cross-domain fault diagnosis of rolling bearing.

Autistic and Catatonic Spectrum Symptoms in Patients with Borderline Personality Disorder.

Recent literature has shown that a considerable percentage of patients with severe mental disorders can develop, over time, full-blown or subthreshold catatonia. Some studies corroborate the model of an illness trajectory in which different mental disorders would be arranged along a continuum of severity until the development of catatonia. In such an illness pathway, autistic traits (AT) and borderline personality disorder (BPD) may represent important steps. In order to further explore the association between AT, BPD, and catatonia, the aim of this study was to compare catatonic spectrum symptoms and AT among patients with major depressive disorder (MDD), BPD, and healthy controls (CTL), also evaluating possible predictive dimensions of the different diagnoses. A total of 90 adults affected by BPD, 90 adults with a diagnosis of MDD, and 90 CTL, homogeneous in terms of gender and age, were recruited from six Italian university departments of psychiatry and assessed with the SCID-5-RV, the Catatonia Spectrum (CS), and the Adult Autism Subthreshold Autism Spectrum (AdAS Spectrum). The total CS score was significantly higher in the BPD and MDD groups than in the CTL group, while the majority of CS domain scores were significantly higher in the BPD group than in the MDD group, which scored significantly higher than the CTL group. The total AdAS Spectrum score and the AdAS Spectrum domain scores were significantly higher in the BPD group than in the MDD group, which in turn scored significantly higher than the CTL group. The CS domains "psychomotor activity" and "impulsivity", and AdAS Spectrum domains "verbal communication", "empathy", and "hyper-/hyporeactivity to sensory input" were associated with the risk of presenting a diagnosis of BPD.