Altered white matter connectivity of ventral language networks in autism spectrum disorder: An automated fiber quantification analysis with multi-site datasets.

Comprehension and pragmatic deficits are prevalent in autism spectrum disorder (ASD) and are potentially linked to altered connectivity in the ventral language networks. However, previous magnetic resonance imaging studies have not sufficiently explored the microstructural abnormalities in the ventral fiber tracts underlying comprehension dysfunction in ASD. Additionally, the precise locations of white matter (WM) changes in the long tracts of patients with ASD remain poorly understood. In the current study, we applied the automated fiber-tract quantification (AFQ) method to investigate the fine-grained WM properties of the ventral language pathway and their relationships with comprehension and symptom manifestation in ASD. The analysis included diffusion/T1 weighted imaging data of 83 individuals with ASD and 83 age-matched typically developing (TD) controls. Case-control comparisons were performed on the diffusion metrics of the ventral tracts at both the global and point-wise levels. We also explored correlations between diffusion metrics, comprehension performance, and ASD traits, and conducted subgroup analyses based on age range to examine developmental moderating effects. Individuals with ASD exhibited remarkable hypoconnectivity in the ventral tracts, particularly in the temporal portions of the left inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF). These WM abnormalities were associated with poor comprehension and more severe ASD symptoms. Furthermore, WM alterations in the ventral tract and their correlation with comprehension dysfunction were more prominent in younger children with ASD than in adolescents. These findings indicate that WM disruptions in the temporal portions of the left ILF/IFOF are most notable in ASD, potentially constituting the core neurological underpinnings of comprehension and communication deficits in autism. Moreover, impaired WM connectivity and comprehension ability in patients with ASD appear to improve with age.

High-precision all-in-one dual robotic arm strategy in oral implant surgery.

INTRODUCTION: Dental implantation has emerged as an efficient substitute for missing teeth, which is essential for restoring oral function and aesthetics. Compared to traditional denture repair approaches, dental implants offer better stability and sustainability. The position, angle, and depth of dental implants are crucial factors for their long-term success and necessitate high-precision operation and technical support. METHOD: We propose an integrated dual-arm high-precision oral implant surgery navigation positioning system and a corresponding control strategy. Compared with traditional implant robots, the integrated dual-arm design greatly shortens the preparation time before surgery and simplifies the operation process. We propose a novel control flow and module for the proposed structure, including an Occluded Target Tracking Module (OTTM) for occlusion tracking, a Planting Plan Development Module (PPDM) for generating implant plans, and a Path Formulation Module (PFM) for controlling the movement path of the two robot arms. RESULT: Under the coordinated control of the aforementioned modules, the robot achieved excellent accuracy in clinical trials. The average angular error and entry point error for five patients who underwent implant surgery using the proposed robot were 2.1° and 0.39 mm, respectively. CONCLUSION: In essence, our study introduces an integrated dual-arm high-precision navigation system for oral implant surgery, resolving issues like lengthy preoperative preparation and static surgical planning. Clinical results confirm its efficacy, emphasizing its accuracy and precision in guiding oral implant procedures.

Effect of Novel Inflammatory Biomarkers on Adverse Outcomes in Patients with Interstitial Lung Disease and Pneumonia: A Multicenter Retrospective Cohort Study.

BACKGROUND: Interstitial lung disease frequently coincides with pneumonia in clinical settings, and both conditions are closely associated with immunoinflammation. The Systemic Immune Inflammatory Index (SII) is a recently identified marker, and its connection to the prognosis of individuals suffering from interstitial lung disease and concurrent pneumonia remains unclear. The objective of this study was to scrutinize the correlation between varying SII levels and unfavorable outcomes in patients grappling with interstitial lung disease complicated by pneumonia. METHODS: This study encompassed a retrospective multicenter cohort of 324 patients diagnosed with interstitial lung disease and pneumonia, all receiving glucocorticoid treatment during their hospitalization. We initially conducted ROC analysis to determine the optimal SII threshold. Subsequently, we examined disparities in clinical symptoms, physical signs, clinical test data, and other clinical attributes among patients with differing SII levels. Later, we employed the Kaplan-Meier survival curve method to assess the association between distinct SII levels and the 30-day and 90-day mortality rates in patients dealing with interstitial lung disease complicated by pneumonia. Finally, a Cox regression model was employed to identify factors influencing adverse prognosis in these patients. RESULTS AND DISCUSSION: The findings demonstrated that the optimal SII threshold for predicting 30-day mortality was 1416.97, with an AUC of 0.633 (95% CI: 0.559-0.708) and a P value of <0.001. For 90-day mortality, the optimal SII threshold was 994.59, yielding an AUC of 0.628 (95% CI: 0.56-0.697) and a P value of <0.001. Noteworthy statistical distinctions emerged in dyspnea, cyanosis, and oxygenation index among patients with varying SII levels. Additionally, invasive mechanical ventilation, non-invasive ventilation, and extended infection duration independently constituted 30-day and 90-day mortality risk factors. Elevated heart rate and higher SII levels emerged as independent risk factors for 90-day mortality. CONCLUSION: To some extent, SII levels exhibit correlations with the clinical manifestations in patients grappling with interstitial lung disease complicated by pneumonia. Notably, a high SII level is an independent predictor for an unfavorable prognosis in these patients. Nevertheless, these findings warrant further validation through prospective cohort studies.

Differential miRNA Profiling Reveals miR-4433a-5p as a Key Regulator of Chronic Obstructive Pulmonary Disease Progression via PIK3R2- mediated Phenotypic Modulation.

OBJECTIVE: In this study, a high-throughput sequencing technology was used to screen the differentially expressed miRNA in the patients with "fast" and "slow" progression of chronic obstructive pulmonary disease (COPD). Moreover, the possible mechanism affecting the progression of COPD was preliminarily analyzed based on the target genes of candidate miRNAs. METHODS: The "fast" progressive COPD group included 6 cases, "slow" and Normal progressive COPD groups included 5 cases each, and COPD group included 3 cases. The peripheral blood samples were taken from the participants, followed by total RNA extraction and high throughput miRNA sequencing. The differentially expressed miRNAs among the progressive COPD groups were identified using bioinformatics analysis. Then, the candidate miRNAs were externally verified. In addition, the target gene of this miRNA was identified, and its effects on cell activity, cell cycle, apoptosis, and other biological phenotypes of COPD were analyzed. RESULTS: Compared to the Normal group, a total of 35, 16, and 7 differentially expressed miRNAs were identified in the "fast" progressive COPD, "slow" progressive COPD group, and COPD group, respectively. The results were further confirmed using dual-luciferase reporter assay and transfection tests with phosphoinositide- 3-kinase, regulatory subunit 2 (PIK3R2) as a target gene of miR-4433a-5p; the result showed a negative regulatory correlation between the miRNA and its target gene. The phenotype detection showed that the activation of the phosphatidylinositol 3 kinase (PI3K)/protein kinase B (AKT) signaling pathway might participate in the progression of COPD by promoting the proliferation of inflammatory A549 cells and inhibiting cellular apoptosis. CONCLUSIONS: MiR-4433a-5p can be used as a marker and potential therapeutic target for the progression of COPD. As a target gene of miR-4433a-5p, PIK3R2 can affect the progression of COPD by regulating phenotypes, such as cellular proliferation and apoptosis.

Comparison of Imaging Radar Configurations for Roadway Inspection and Characterization.

This paper investigates the performance of a wide variety of radar imaging modes, such as nadir-looking B-scan, or side-looking synthetic aperture radar tomographic acquisitions, performed in both back- and forward-scattering geometries, for the inspection and characterization of roadways. Nadir-looking B-scan corresponds to a low-complexity mode exploiting the direct return from the response, whereas side-looking configurations allow the utilization of angular and polarimetric diversity in order to analyze advanced features. The main objective of this paper is to evaluate the ability of each configuration, independently of aspects related to operational implementation, to discriminate and localize shallow underground defects in the wearing course of roadways, and to estimate key geophysical parameters, such as roughness and dielectric permittivity. Campaign measurements are conducted using short-range radar stepped-frequency continuous-waveform (SFCW) devices operated in the C and X bands, at the pavement fatigue carousel of Université Gustave Eiffel, over debonded areas with artificial defects. The results indicate the great potential of the newly proposed forward-scattering tomographic configuration for detecting slight defects and characterizing roadways. Case studies, performed in the presence of narrow horizontal heterogeneities which cannot be detected using classical B-scan, show that both the coherent integration along an aperture using the back-projection algorithm, and the exploitation of scattering mechanisms specific to the forward-looking bistatic geometry, allows anomalous echoes to be detected and further characterized, confirming the efficacy of radar imaging techniques in such applications.

Double voltage vector model predictive control for grid-connected cascade H-bridge multilevel inverter with fixed switching frequency.

Model predictive control (MPC) is an outstanding control method which can achieve superior dynamic response, nonlinear control and multi-objective collaborative control. However, because of unfixed switching frequency, the harmonic spectrum of the output current is dispersed, which would make it difficult to design the output filter. In this work, a double voltage vector model predictive control (DVV-MPC) algorithm for grid-connected cascade H-bridge (CHB) multilevel inverter is presented. The algorithm not only has the advantages of MPC but also produces fixed the switching frequency of the inverter. It is realized by a modulation stage with two adjacent voltage vectors in one switching cycle. And the duty cycle of the associated voltage level is obtained by minimizing an additional cost function. This method is suitable for the cascades of n cells inverter. For reducing the computation burden and eliminating the computation delay, adjacent regions prediction method and time-delay compensation method are applied to the inverter. Finally, in this paper, the proposed strategy is applied to a single-phase grid-connected 7-level CHB inverter. Simulation and experiment are executed to show the advantages of the proposed control algorithm in dynamic state and steady state.

Impact of Previous Pulmonary Tuberculosis on Chronic Obstructive Pulmonary Disease: Baseline Results from a Prospective Cohort Study.

OBJECTIVE: Pulmonary tuberculosis (PTB) is a significant risk factor for COPD, and Xinjiang, China, has a high incidence of pulmonary tuberculosis. The effects of tuberculosis history on airflow restriction, clinical symptoms, and acute episodes in COPD patients have not been reported in the local population. Besides, the exact relationship between lung function changes in people with a history of tuberculosis and COPD risk is not clear. METHODS: This study is based on the Xinjiang baseline survey data included in the Natural Population Cohort Study in Northwest China from June to December, 2018. Subjects' questionnaires, physical examination, and lung function tests were performed through a face-to-face field survey to analyze the impact of previous pulmonary tuberculosis on local COPD. Furthermore, we clarified the specific relationship between pulmonary function decline and the probability of developing COPD in people with a history of tuberculosis. RESULTS: A total of 3249 subjects were eventually enrolled in this study, including 87 with a history of tuberculosis and 3162 non-TB. The prevalence of COPD in the prior TB group was significantly higher than that in the control group (p-value = 0.005). First, previous pulmonary tuberculosis is an essential contributor to airflow limitation in the general population and patients with COPD. In all subjects included, pulmonary function, FEV1% predicted (p-value < 0.001), and FEV1/FVC (%) (p-value < 0.001) were significantly lower in the prior TB group than in the control group. Compared to non-TB group, FEV1% prediction (p-value = 0.019) and FEV1/FVC (%) (p-value = 0.016) were found to be significantly reduced, and airflow restriction (p-value = 0.004) was more severe in prior TB group among COPD patients. Second, COPD patients in the prior TB group had more severe clinical symptoms. Compared with no history of tuberculosis, mMRC (p-value = 0.001) and CAT (p-value = 0.002) scores were higher in the group with a history of tuberculosis among COPD patients. Third, compared with the non-TB group, the number of acute exacerbations per year (p-values=0.008), the duration of each acute exacerbation (p-values=0.004), and hospitalization/ patient/year (p-values<0.001) were higher in the group with a history of tuberculosis among COPD patients. Finally, a dose-response relationship between FEV1/FVC (%) and the probability of developing COPD in people with previous pulmonary TB was observed; when FEV1/FVC (%) was < 80.8, the risk of COPD increased by 13.5% per unit decrease in lung function [0.865(0.805, 0.930)]. CONCLUSION: COPD patients with previous pulmonary tuberculosis have more severe airflow limitations and clinical symptoms and are at higher risk for acute exacerbations. Furthermore, lung function changes in people with a history of tuberculosis were associated with a dose-response relationship with the probability of developing COPD.

Identification of Genes Related to Endoplasmic Reticulum Stress (ERS) in Chronic Obstructive Pulmonary Disease (COPD) and Clinical Validation.

Objective: Endoplasmic reticulum stress (ERS) is key in chronic obstructive pulmonary disease (COPD) incidence and progression. This study aims to identify potential ERS-related genes in COPD through bioinformatics analysis and clinical experiments. Methods: We first obtained a COPD-related mRNA expression dataset (GSE38974) from the Gene Expression Omnibus (GEO) database. The R software was then used to identify potential differentially expressed genes (DEGs) of COPD-related ERS (COPDERS). Subsequently, the identified DEGs were subjected to protein-protein interaction (PPI), correlation, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Following that, qRT-PCR was used to examine the RNA expression of six ERS-related DEGs in blood samples obtained from the COPD and control groups. The genes were also subjected to microRNA analysis. Finally, a correlation analysis was performed between the DEGs and key clinical indicators. Results: Six ERS-related DEGs (five upregulated and one downregulated) were identified based on samples drawn from 23 COPD patients and nine healthy individuals enrolled in the study. Enrichment analysis revealed multiple ERS-related pathways. The qRT-PCR and mRNA microarray bioinformatics analysis results showed consistent STC2, APAF1, BAX, and PTPN1 expressions in the COPD and control groups. Additionally, hsa-miR-485-5p was identified through microRNA prediction and DEG analysis. A correlation analysis between key genes and clinical indicators in COPD patients demonstrated that STC2 was positively and negatively correlated with eosinophil count (EOS) and lymphocyte count (LYM), respectively. On the other hand, PTPN1 showed a strong correlation with pulmonary function indicators. Conclusion: Four COPDERS-related key genes (STC2, APAF1, BAX, and PTPN1) were identified through bioinformatics analysis and clinical validation, and the expressions of some genes exhibited a significant correlation with the selected clinical indicators. Furthermore, hsa-miR-485-5p was identified as a potential key target in COPDERS, but its precise mechanism remains unclear.

Atypical structural connectivity of language networks in autism spectrum disorder: A meta-analysis of diffusion tensor imaging studies.

Patients with autism spectrum disorder (ASD) often show pervasive and complex language impairments that are closely associated with aberrant structural connectivity of language networks. However, the characteristics of white matter connectivity in ASD have remained inconclusive in previous diffusion tensor imaging (DTI) studies. The current meta-analysis aimed to comprehensively elucidate the abnormality in language-related white matter connectivity in individuals with ASD. We searched PubMed, Web of Science, Scopus, and Medline databases to identify relevant studies. The standardized mean difference was calculated to measure the pooled difference in DTI metrics in each tract between the ASD and typically developing (TD) groups. The moderating effects of age, sex, language ability, and symptom severity were investigated using subgroup and meta-regression analysis. Thirty-three DTI studies involving 831 individuals with ASD and 836 TD controls were included in the meta-analysis. ASD subjects showed significantly lower fractional anisotropy or higher mean diffusivity across language-associated tracts than TD controls. These abnormalities tended to be more prominent in the left language networks than in the right. In addition, children with ASD exhibit more pronounced and pervasive disturbances in white matter connectivity than adults. These results support the under-connectivity hypothesis and demonstrate the widespread abnormal microstructure of language-related tracts in patients with ASD. Otherwise, white matter abnormalities in the autistic brain could vary depending on the developmental stage and hemisphere. LAY SUMMARY: This meta-analysis explored abnormalities in white matter connectivity in language networks of individuals with ASD. Significantly reduced white matter integrity was found in all language-associated tracts in subjects with ASD compared with TD controls. In addition, structural disturbances of language networks in the autistic brain exhibit a leftward tendency, and more prominent abnormalities are observed in younger people with ASD than in adults.

Structural and functional characterizations of infectivity and immune evasion of SARS-CoV-2 Omicron.

The SARS-CoV-2 Omicron variant with increased fitness is spreading rapidly worldwide. Analysis of cryo-EM structures of the spike (S) from Omicron reveals amino acid substitutions forging interactions that stably maintain an active conformation for receptor recognition. The relatively more compact domain organization confers improved stability and enhances attachment but compromises the efficiency of the viral fusion step. Alterations in local conformation, charge, and hydrophobic microenvironments underpin the modulation of the epitopes such that they are not recognized by most NTD- and RBD-antibodies, facilitating viral immune escape. Structure of the Omicron S bound with human ACE2, together with the analysis of sequence conservation in ACE2 binding region of 25 sarbecovirus members, as well as heatmaps of the immunogenic sites and their corresponding mutational frequencies, sheds light on conserved and structurally restrained regions that can be used for the development of broad-spectrum vaccines and therapeutics.

Nonlinear relationship between visceral adiposity index and lung function: a population-based study.

BACKGROUND: As one of the critical indicators of obesity, the interaction between visceral fat content and lung disease is the focus of current research. However, the exact relationship between Visceral adipose index (VAI) and lung function is not fully understood. The purpose of this study was to evaluate the relationship between VAI and lung function, METHODS: Our study included all participants from the baseline survey population in Xinjiang in the Natural Population Cohort Study in Northwest China. A field survey was conducted in rural areas of Moyu County, Xinjiang, China, between 35 and 74 years old from June to December 2018. We collected standard questionnaires and completed physical examinations, visceral fat tests, and lung function measurements. RESULTS: The study included 2367 participants with a mean VAI of 10.35 ± 4.35, with males having a significantly higher VAI than females: 13.17 ± 3.91 vs. 7.58 ± 2.65. The piecewise linear spline models indicated a significant threshold effect between lung function and VAI in the general population and the males population, showing an inverted U-shaped curve. But there was no significant association between VAI and lung function in females. FEV1% predicted and FVC% predicted increased with the increase of VAI (ß 0.76; 95% CI 0.30, 1.21) and (ß 0.50; 95% CI 0.06, 0.94) in males with VAI ≤ 14, while FEV1% predicted and FVC% predicted decreased with the increase of VAI (ß - 1.17; 95% CI - 1.90, - 0.45) and (ß - 1.36; 95% CI - 2.08, - 0.64) in males with VAI ≥ 15. CONCLUSIONS: The relationship between lung function and VAI in male participants showed an inverted U-shaped curve, with the turning point of VAI between 14 and 15. The association between visceral fat and lung function was more robust in males than in females.

Development and Assessment of Prediction Models for the Development of COPD in a Typical Rural Area in Northwest China.

OBJECTIVE: This study aimed to construct and evaluate a clinical predictive model for the development of COPD in northwest China's rural areas. METHODS: A cross-sectional study of a natural population was performed in rural northwest China. After assessing demographic and disease characteristics, a clinical prediction model was developed. First, we used the least absolute shrinkage and selection operator regression model to screen possible factors influencing COPD. Then construct a logistic regression model and draw a nomogram. The discriminability of the model was further evaluated by the calibration diagram, C-index and ROC curve system. Clinical benefit was analyzed using the decision curve. Finally, the 1000 bootstrap resamples and Harrell's C-index was used for internal verification of the nomogram. RESULTS: Among 3249 patients in the local rural natural population, 394 (12.13%) were diagnosed with COPD. The LASSO regression model was used to find the optimal combination of parameters, and the screened influencing factors included age, gender, barbeque, smoking, passive smoking, energy type, ventilation system and Post-Bronchodilator FEV1. These predictors are used to construct a nomogram. C index is 0.81 (95% confidence interval:0.79-0.83). The combination of the calibration curve and ROC curve indicates that the model has high discriminability. The decision curve shows benefits in clinical practice when the threshold probability is >6% and <58%, respectively. The internal verification results using Harrell's C-Index were 0.80 (95% confidence interval: 0.78-0.83). CONCLUSION: Combining information such as age, sex, barbeque, smoking, passive smoking, type of energy, ventilation systems, and Post-Bronchodilator FEV1 can be easily used to predict the risk of COPD in local rural areas.

Differences in key genes in human alveolar macrophages between phenotypically normal smokers and nonsmokers: diagnostic and prognostic value in lung cancer.

OBJECTIVE: To explore the effect of smoking on gene expression in human alveolar macrophages and the value of identified key genes in the early diagnosis and prognosis of lung cancers. METHODS: We downloaded three data sets (GSE8823, GSE2125, and GSE3212) from the Gene Expression Omnibus (GEO) database, including 31 non-smoking and 33 smoking human alveolar macrophage samples. We identified common differentially expressed genes (DEGs), from which we obtained module genes and hub genes by using STRING and Cytoscape. Then we analyzed the protein-protein interaction (PPI) network of DEGs, hub genes, and module genes and used David online analysis tool to carry out functional enrichment analysis of DEGs and module genes. RESULTS: A total of 85 differentially expressed genes was obtained, including 42 up-regulated genes and 43 down-regulated genes. The Human Protein Atlas and Survival analysis showed that GBP1, ITGAM, CSF1, SPP1, COL1A1, LAMB1 and THBS1 may be closely associated with the carcinogenesis and prognosis of lung cancer. CONCLUSION: DEGs, module, and hub genes identified in the present study help explain the effects of smoking on human alveolar macrophages and provide candidate targets for diagnosis and treatment of smoking-related lung cancer.

A residual-based deep learning approach for ghost imaging.

Ghost imaging using deep learning (GIDL) is a kind of computational quantum imaging method devised to improve the imaging efficiency. However, among most proposals of GIDL so far, the same set of random patterns were used in both the training and test set, leading to a decrease of the generalization ability of networks. Thus, the GIDL technique can only reconstruct the profile of the image of the object, losing most of the details. Here we optimize the simulation algorithm of ghost imaging (GI) by introducing the concept of "batch" into the pre-processing stage. It can significantly reduce the data acquisition time and create reliable simulation data. The generalization ability of GIDL has been appreciably enhanced. Furthermore, we develop a residual-based framework for the GI system, namely the double residual U-Net (DRU-Net). The imaging quality of GI has been tripled in the evaluation of the structural similarity index by our proposed DRU-Net.

Transfer Clustering Ensemble Selection.

Clustering ensemble (CE) takes multiple clustering solutions into consideration in order to effectively improve the accuracy and robustness of the final result. To reduce redundancy as well as noise, a CE selection (CES) step is added to further enhance performance. Quality and diversity are two important metrics of CES. However, most of the CES strategies adopt heuristic selection methods or a threshold parameter setting to achieve tradeoff between quality and diversity. In this paper, we propose a transfer CES (TCES) algorithm which makes use of the relationship between quality and diversity in a source dataset, and transfers it into a target dataset based on three objective functions. Furthermore, a multiobjective self-evolutionary process is designed to optimize these three objective functions. Finally, we construct a transfer CE framework (TCE-TCES) based on TCES to obtain better clustering results. The experimental results on 12 transfer clustering tasks obtained from the 20newsgroups dataset show that TCE-TCES can find a better tradeoff between quality and diversity, as well as obtaining more desirable clustering results.

A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine.

The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is chosen as the fault-diagnosing signal, after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with other methods, the experiment results show that the proposed method can diagnose the blade attachment with higher accuracy.

A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays.

The direction-of-arrivals (DOA) estimation with an unfolded coprime linear array (UCLA) has been investigated because of its large aperture and full degrees of freedom (DOFs). The existing method suffers from low resolution and high computational complexity due to the loss of the uniform property and the step of exhaustive peak searching. In this paper, an improved DOA estimation method for a UCLA is proposed. To exploit the uniform property of the subarrays, the diagonal elements of the two self-covariance matrices are averaged to enhance the accuracy of the estimated covariance matrices and therefore the estimation performance. Besides, instead of the exhaustive peak searching, the polynomial roots finding method is used to reduce the complexity. Compared with the existing method, the proposed method can achieve higher resolution and better estimation performance with lower computational complexity.

Low-Complexity High-Order Propagator Method for Near-Field Source Localization.

In this paper, an efficient high-order propagator method is proposed to localize near-field sources. We construct a specific non-Hermitian matrix based on the high-order cumulant of the received signals. With its columns and rows, we can obtain two subspaces orthogonal to all the columns of two steering matrices, respectively, with which the estimation of the directions of arrival (DOA) and ranges of near-field sources can be achieved. Different from other methods, the proposed method needs only one matrix for estimating two parameters separately, therefore leading to a smaller computational burden. Simulation results show that the proposed method achieves the same performance as the other high order statistics-based methods with a lower complexity.

Spatially Correlated Sparse MIMO Channel Path Delay Estimation in Scattering Environments Based on Signal Subspace Tracking.

A parametric scheme for spatially correlated sparse multiple-input multiple-output (MIMO) channel path delay estimation in scattering environments is presented in this paper. In MIMO outdoor communication scenarios, channel impulse responses (CIRs) of different transmit⁻receive antenna pairs are often supposed to be sparse due to a few significant scatterers, and share a common sparse pattern, such that path delays are assumed to be equal for every transmit⁻receive antenna pair. In some existing works, an exact common support condition is exploited, where the path delays are considered equal for every transmit⁻receive antenna pair, meanwhile ignoring the influence of scattering. A more realistic channel model is proposed in this paper, where due to scatterers in the environment, the received signals are modeled as clusters of multi-rays around a nominal or mean time delay at different antenna elements, resulting in a non-strictly exact common support phenomenon. A method for estimating the channel mean path delays is then derived based on the subspace approach, and the tracking of the effective dimension of the signal subspace that changes due to the wireless environment. The proposed method shows an improved channel mean path delays estimation performance in comparison with the conventional estimation methods.