With the development of multi-model neuroimaging technology and gene detection technology, the efforts of integrating multi-model imaging genetics data to explore the virulence factors of schizophrenia (SZ) are still limited. To address this issue, we propose a novel algorithm called group sparse of joint non-negative matrix factorization on orthogonal subspace (GJNMFO). Our algorithm fuses single nucleotide polymorphism (SNP) data, function magnetic resonance imaging (fMRI) data and epigenetic factors (DNA methylation) by projecting three-model data into a common basis matrix and three different coefficient matrices to identify risk genes, epigenetic factors and abnormal brain regions associated with SZ. Specifically, we introduce orthogonal constraints on the basis matrix to discard unimportant features in the row of coefficient matrices. Since imaging genetics data have rich group information, we draw into group sparse on three coefficient matrices to make the extracted features more accurate. Both the simulated and real Mind Clinical Imaging Consortium (MCIC) datasets are performed to validate our approach. Simulation results show that our algorithm works better than other competing methods. Through the experiments of MCIC datasets, GJNMFO reveals a set of risk genes, epigenetic factors and abnormal brain functional regions, which have been verified to be both statistically and biologically significant.

Real-time, accurate, and robust positioning system plays a crucial role in many vehicular applications for automatic driving system and Vehicular Ad-hoc Network (VANET). In the tunnel, the positioning accuracy of Global Navigation Satellite System (GNSS) decreases due to blocked satellite signals. In order to estimate the exact location of vehicles in tunnel environments, many positioning systems have been presented. However, there is a lack of effort in systematically comparing, organizing and analyzing these existing positioning systems, and identifying the strengths and weaknesses of different technologies and applicable scenarios. Therefore, this paper undertakes a thorough investigation into current vehicle localization technologies and methods for tunnel scenarios. The analysis starts with discussing various application scenarios for vehicle positioning system. Then, various vehicle positioning technologies are investigated, the advantages and drawbacks of each technology are illustrated. Thereafter, we discuss some widely used positioning methods in terms of range-based localization method, range-free localization method, multi-sensor fusion localization method, and cooperative positioning (CP) method. Finally, we discuss some challenges faced in vehicle positioning for tunnel environments, and propose some potential research topics for future research work.

In terms of complex diseases like schizophrenia, more and more studies are beginning to treat genetic variants and brain imaging phenotypes as an important factor. In this paper, a competent optimization model is exploited to overcome the weakness of deep canonical correlation analysis (DCCA). The model consists of principal component analysis (PCA) on the multi-modality linear features learning and multi-layer belief networks on multi-modality nonlinear features learning. In order to complete a better result of correlation analysis and classification, the output nodes of multi-layer belief network are used for back propagation (BP) network training. Previous works on solving canonical correlation analysis (CCA) had proposed several models based on deep neural network or regularization, typically involving either some form of norm or auto-encoders with a reconstruction objective. Many existing advanced models had been developed to find the maximal correlation in multi-modality data. However, these multi-modality data tend to have the number of feature dimensions which more than that of samples. Differ from these advanced models, our proposed model is applied to analyze the real set of multi-modality data and test several previous models, then comparing them experimentally on fMRI imaging and SNPs genomics. In experiments, the results show that our model, deep principal correlated auto-encoders (DPCAE), learns features with effectively higher correlation and better performance of classification than those previous models. In terms of classification accuracy, the classification accuracy of the datasets exceeds 90%, but that of the CCA-based model are about 65%, and that of the DNN-based model are about 80%, the classification accuracy of the DPCAE is significantly improved obviously. In the experiment of clustering performance evaluation, the DPCAE further verified its superior classification performance with an average normalized mutual information index of 93.75% and an average classification error rate index of 3.8%. In terms of maximal correlation analysis, the model outperforms other advanced models with a maximal correlation of 0.926, showing excellent performance in high-dimensional data analysis.

Range-based positioning using wireless signal has gained remarkable attention in past decades. Accurately extracting the distance information, i.e., estimating parameters of individual multipath, is of interest. It has been shown that diffuse multipath component (DMC) in wireless propagation channel has significant influence on channel estimation and, thus, the ranging performance. In addition to specular component (SC), DMC should be considered in order to improve the estimated accuracy of channel paths’ parameters and, thus, the ranging performance. In this paper, we extend the quasi-maximum likelihood space-alternating generalized expectation-maximization (SAGE) algorithm by incorporating an autoregressive moving average (ARMA) filter to estimate the channel parameters under the presence of DMC. The parameters of both DMC and SC are estimated in an iterative approach, where the DMC is assumed as colored noise and the ARMA-filter is used to estimate the contributions from DMC. The validation of the algorithm based on measured data shows that both SC and DMC can be accurately estimated by the proposed approach. A comparison with standard SAGE and AR-SAGE is conducted, where the results reveal that the proposed method outperforms other two approaches in terms of ranging accuracy.

Due to the satellite signals are blocked, it is difficult to obtain the vehicle position in the tunnels. We propose a single-site vehicle localization scheme for the rectangular tunnel environment, where most satellite-based positioning methods can not provide the required localization accuracy. In the non-line-of-sight (NLOS) scenarios, we make use of the reflection paths as assistants for vehicle positioning. Specifically, first, the virtual stations are established based on the actual geometrical structure of the tunnel. Second, we use the direction-of-arrival (DOA) and time-of-arrival (TOA) information of reflection paths from two tunnel walls to achieve vehicle positioning. Especially, the Cramer-Rao lower bound (CRLB) of the joint TOA and DOA localization for NLOS propagations in a two-dimensional (2D) space is derived. In addition, based on the localization algorithms with and without filters, we assess the localization performance. In the line-of-sight (LOS) scenarios, we use the LOS path and two reflection paths from the tunnel walls to estimate the vehicle location. First, virtual base stations are established. Second, based on the obtained TOA information, different positioning algorithms are used to estimate the vehicle location. Simulation results illustrate that the proposed positioning approach can provide a small root mean square error. The localization algorithms using filters improve the localization accuracy, compared with the positioning algorithm without using filters, namely, the two-stage weighted least squares (TSWLS) algorithm. Moreover, the Unscented Particle Filter (UPF) algorithm achieves better positioning accuracy than other methods (i.e., Unscented Kalman Filter (UKF), Extended Kalman Filter (EKF), TSWLS algorithms).

Rockfalls on mountainous roads pose significant safety risks to pedestrians and vehicles, particularly in remote areas with underdeveloped communication infrastructure. To enable efficient detection, this study proposes a rockfall detection system based on embedded technology and an improved Yolov8 algorithm, termed Yolov8-GCB. The algorithm enhances detection performance through the following optimizations: (1) integrating a lightweight DeepLabv3+ road segmentation module at the input stage to generate mask images, which effectively exclude non-road regions from interference; (2) replacing Conv convolution units in the backbone network with Ghost convolution units, significantly reducing model parameters and computational cost while improving inference speed; (3) introducing the CPCA (Channel Priori Convolution Attention) mechanism to strengthen the feature extraction capability for targets with diverse shapes; and (4) incorporating skip connections and weighted fusion in the Neck feature extraction network to enhance multi-scale object detection. Experimental results demonstrate that Yolov8-GCB improves AP@0.5 and AP@0.75 by 1.2% and 1%, respectively, while reducing the number of parameters by 14.1% and the GFLOPs by 16.1% and increasing inference speed by 20.65%. This method provides an effective technological solution for real-time rockfall detection on embedded devices and can be extended to other disaster scenarios, such as landslides and debris flows, in regions with limited infrastructure.