ABSTRACT The choroid, a critical vascular structure in the eye, is closely associated with a wide range of ocular diseases, and its early and accurate evaluation is fundamental to effective diagnosis and management. Advances in imaging modalities like optical coherence tomography (OCT) have revolutionised choroidal visualisation, yet manual analysis remains labour‐intensive and subjective. This review synthesizes recent developments in deep learning (DL)‐driven solutions for choroidal imaging. DL architectures, including convolutional neural networks (CNNs), U‐Net variants and vision transformers, demonstrate high accuracy in automating choroidal sublayer segmentation, 3D vascular network reconstruction and biomarker extraction, outperforming traditional methods in speed and reproducibility. Innovations such as hybrid loss functions, attention mechanisms, and generative adversarial networks address challenges like class imbalance and boundary ambiguity. At the same time, multimodal DL models integrating OCT, fundus photography and angiography enhance diagnostic precision. However, limitations persist, including data dependency, annotation variability and the lack of standardised protocols for sublayer demarcation. Future directions emphasise image enhancement techniques, self‐supervised learning to reduce annotation burdens and ethical frameworks for clinical translation.

With the rapid development of intelligent surveillance technology, the massive amount of multimodal data (e.g., videos, images, and text) has imposed higher demands on efficient information retrieval and security. Traditional single-modal retrieval methods struggle to meet practical requirements, making multimodal image-text retrieval a research hotspot in this field. Existing approaches, however, still face challenges in fine-grained semantic alignment and suffer from rigid matching mechanisms. To address these issues, this paper introduces SeaNcr, a novel framework that integrates cross-modal semantic entity alignment with non-correspondence reasoning. Our method constructs class-level entity representations enhanced by saliency-guided masking to capture discriminative semantic features. A pseudo-frozen asynchronous optimization strategy is introduced to maintain semantic consistency across modalities by associating stable entity representations with dynamically updated encoder features. Moreover, to overcome rigid matching, we design a non-correspondence reasoning module that jointly leverages intra-modal similarity and cross-modal mutual nearest neighbor constraints, optimizing matching flexibility and generalization. Extensive experiments validate that SeaNcr significantly enhances cross-modal feature representation and retrieval robustness, achieving state-of-the-art performance on multiple person re-identification benchmarks.

Multivariate time series forecasting (MTSF) is of immense significance in extensive domains, such as traffic analysis and weather forecasting. Despite the accuracy of MTSF has made significant progress in recent years, most research efforts typically require prohibitive spatio-temporal overhead, especially when dealing with higher-dimensional data. This limitation hinders the scalability of the model for real-world applications. To address this issue, we propose LSSM, a Low-Rank State Space Model that conducts multivariate time series forecasting at lower computation costs while effectively capturing intricate long-range dependencies and variable correlations within data. We apply the KL-constraint during training which enhances both the generalization of the model and the association between subsequences. Experiment results on four benchmark datasets verify the superiority of our approach compared with the state-of-the-art baselines.

Medical image segmentation is critical for clinical diagnosis, but the scarcity of annotated data limits robust model training, making few-shot learning indispensable. Existing methods often suffer from two issues—performance degradation due to significant inter-class variations in pathological structures, and overreliance on attention mechanisms with high computational complexity ( O ( n 2 )), which hinders the efficient modeling of long-range dependencies. In contrast, the state space model (SSM) offers linear complexity ( O ( n )) and superior efficiency, making it a key solution. To address these challenges, we propose PPFFR (parallel prototype filter and feature refinement) for few-shot medical image segmentation. The proposed framework comprises three key modules. First, we propose the prototype refinement (PR) module to construct refined class subgraphs from encoder-extracted features of both support and query images, which generates support prototypes with minimized inter-class variation. We then propose the parallel prototype filter (PPF) module to suppress background interference and enhance the correlation between support and query prototypes. Finally, we implement the feature refinement (FR) module to further enhance segmentation accuracy and accelerate model convergence with SSM’s robust long-range dependency modeling capability, integrated with multi-head attention (MHA) to preserve spatial details. Experimental results on the Abd-MRI dataset demonstrate that FR with MHA outperforms FR alone in segmenting the left kidney, right kidney, liver, and spleen, and in terms of mean accuracy, confirming MHA’ s role in improving precision. In extensive experiments conducted on three public datasets under the 1-way 1-shot setting, PPFFR achieves Dice scores of 87.62%, 86.74%, and 79.71% separately, consistently surpassing state-of-the-art few-shot medical image segmentation methods. As the critical component, SSM ensures that PPFFR balances performance with efficiency. Ablation studies validate the effectiveness of the PR, PPF, and FR modules. The results indicate that explicit inter-class variation reduction and SSM-based feature refinement can enhance accuracy without heavy computational overhead. In conclusion, PPFFR effectively enhances inter-class consistency and computational efficiency for few-shot medical image segmentation. This work provides insights for few-shot learning in medical imaging and inspires lightweight architecture designs for clinical deployment.

Person re-identification (re-ID) is a research hotspot in the field of intelligent monitoring and security. Domain generalizable (DG) person re-identification transfers the trained model directly to the unseen target domain for testing, which is closer to the practical application than supervised or unsupervised person re-ID. Meta-learning strategy is an effective way to solve the DG problem, nevertheless, existing meta-learning-based DG re-ID methods mainly simulates the test process in a single aspect such as identity or style, while ignoring the completely different person identities and styles in the unseen target domain. As to this problem, we consider a double disentangling from two levels of training strategy and feature learning, and propose a novel dualistic disentangled meta-learning (D$^{\mathbf {2}}$ML) model. D$^{\mathbf {2}}$ML is composed of two disentangling stages, one is for learning strategy, which spreads one-stage meta-test into two-stage, including an identity meta-test stage and a style meta-test stage. The other is for feature representation, which decouples the shallow layer features into identity-related features and style-related features. Specifically, we first conduct identity meta-test stage on different person identities of the images, and then employ a feature-level style perturbation module (SPM) based on Fourier spectrum transformation to conduct the style meta-test stage on the image with diversified styles. With these two stages, abundant changes in the unseen domain can be simulated during the meta-test phase. Besides, to learn more identity-related features, a feature disentangling module (FDM) is inserted at each stage of meta-learning and a disentangled triplet loss is developed. Through constraining the relationship between identity-related features and style-related features, the generalization ability of the model can be further improved. Experimental results on four public datasets show that our D$^{\mathbf {2}}$ML model achieves superior generalization performance compared to the state-of-the-art methods.

Amidst the rapid advancement of Internet of Things (IoT) technology, achieving precise indoor localization has emerged as a pivotal research area. Localization algorithms relying on Radio Frequency Identification (RFID) received signal strength indicator (RSSI) have gained widespread adoption in numerous indoor positioning systems due to their straightforward implementation and cost-effectiveness. However, in indoor settings, challenges like building obstructions and multipath effects often lead to signal reception failures by RFID antennas, consequently compromising the reliability of positioning outcomes. Recent research has approached indoor localization as a regression problem, employing deep learning models for analysis and prediction. But most current indoor localization models primarily focus on either spatial or temporal features within RSSI data, leading to suboptimal localization outcomes. To tackle these challenges, this paper proposes an enhanced methodology that leverages Generative Adversarial Networks (GAN) to impute missing RSSI data. Additionally, Convolutional Neural Networks (CNN) are utilized to extract spatial domain features, while Long Short-Term Memory Networks (LSTM) are employed for extracting temporal domain features. Ultimately, this paper designs a novel model, GCLA, which integrates an Attention mechanism with a location coding strategy to fuse features for precise location prediction. Experimental results show that the proposed GCLA model can obtain stable localization results after a short training on a small number of datasets.

Infrared and visible image fusion aims to generate one image with comprehensive information. It can maintain rich texture characteristics and thermal information. However, for existing image fusion methods, the fused images either sacrifice the salience of thermal targets and the richness of textures or introduce the interference of useless information like artifacts. To alleviate these problems, an effective cross-modal coordinate attention network for infrared and visible image fusion called CCAFusion is proposed in this paper. To fully integrate complementary features, the cross-modal image fusion strategy based on coordinate attention is designed, which consists of the feature-awareness fusion module and the feature-enhancement fusion module. Moreover, a multiscale skip connection-based network is employed to obtain multiscale features in the infrared image and the visible image, which can fully utilize the multi-level information in the fusion process. To reduce the discrepancy between the fused image and the input images, a multiple constrained loss function including the base loss and the auxiliary loss is developed to adjust the gray-level distribution and ensure the harmonious coexistence of structure and intensity in fused images, thereby preventing the pollution of useless information like artifacts. Extensive experiments conducted on widely used datasets demonstrate that our CCAFusion achieves superior performance over state-of-the-art image fusion methods in both qualitative evaluation and quantitative measurement. Furthermore, the application to salient object detection reveals the potential of our CCAFusion for high-level vision tasks, which can effectively boost the detection performance.

Abstract As a basic component in the field of computer vision, the pedestrian detection plays an essential role in several real‐world applications such as video surveillance. The promising performance has been achieved in pedestrian detection relying on deep learning, but large‐scale variance and small‐scale pedestrian detection remain inherently hard as before. In order to deal with the aforementioned problems, this paper proposes a multi‐scale pedestrian detection method with global–local attention and multi‐scale receptive field context (MRFC). To make the network focus on small‐scale pedestrians, we add a high‐resolution detection branch on the original detector. To better integrate the incongruous semantic feature, the global–local attention module is embedded to highlight the feature representation of pedestrians so as to implement the feature fusion effectively. In order to adapt the receptive field of the network to achieve scale‐variance detection, the MRFC is applied. Based on integrating the above structures, the proposed method achieves competitive results on Caltech and CityPersons datasets. The source code is released in https://github.com/xiaopan999/yolov5‐pedestrian_detection .

Multimodal magnetic resonance imaging (MRI) contains complementary information in anatomical and functional images that help the accurate diagnosis and treatment evaluation of lung cancers. However, effectively exploiting the complementary information in chest MRI images remains challenging due to the lack of rigorous registration. In this paper, a novel method is proposed that can effectively exploit the complementary information in weakly paired images for accurate tumor segmentation, namely coco-attention mechanism. Coco-attention module consists of two parts: the multi-modal co-attention (MultiCo-attn) and the multi-level coordinate attention (MultiCord-attn). The former aims to obtain tumor-aware deep features for accurate tumor localization, and the latter aims to highlight tumor area for more precise segmentation. Specifically, the MultiCo-attn extracts complementary information from multimodal high-dimensional semantic features using a bidirectional algorithm to generate attention maps focused on tumor region, and then uses the attention maps to enhance the feature representations. The MultiCord-attn leverages multi-level feature information to highlight tumor regions by adjusting the weight of each point in the feature. We evaluate the proposed method on lung tumor segmentation with a clinical dataset of 90 chest MRI scans of non-small cell lung cancer (NSCLC). The results show that the proposed method is effective for tumor segmentation in weakly paired images and achieves significant improvement (p < 0.005) over several commonly used multimodal segmentation methods. Furthermore, the ablation experiment results confirm the effectiveness and interpretability of the proposed coco-attention module.

Person re-identification (re-ID) aims to match images of the same person in different camera views. The general convolutional neural network has the problem of insufficient ability to discriminate specific targets, resulting in limited feature representation when directly applied in person re-ID task. In this paper, a person re-ID baseline model based on attention block neural architecture search is proposed. Since the attention mechanism is helpful to improve the feature expression ability of the network, attention blocks are automatically searched and added to ResNet-50 model, which aims to find the most suitable model structure for person re-ID task. Besides, in order to integrate information between the samples of same identity, an intra-class self-distillation loss is introduced according to the idea of knowledge integration. Experiments on two popular datasets confirm the effectiveness of our baseline. The code has been released in https://github.com/Nicholasxin/Attention-NASReID.

Tumor segmentation in 3D automated breast ultrasound (ABUS) plays an important role in breast disease diagnosis and surgical planning. However, automatic segmentation of tumors in 3D ABUS images is still challenging, due to the large tumor shape and size variations, and uncertain tumor locations among patients. In this paper, we develop a novel cross-model attention-guided tumor segmentation network with a hybrid loss for 3D ABUS images. Specifically, we incorporate the tumor location into a segmentation network by combining an improved 3D Mask R-CNN head into V-Net as an end-to-end architecture. Furthermore, we introduce a cross-model attention mechanism that is able to aggregate the segmentation probability map from the improved 3D Mask R-CNN to each feature extraction level in the V-Net. Then, we design a hybrid loss to balance the contribution of each part in the proposed cross-model segmentation network. We conduct extensive experiments on 170 3D ABUS from 107 patients. Experimental results show that our method outperforms other state-of-the-art methods, by achieving the Dice similarity coefficient (DSC) of 64.57%, Jaccard coefficient (JC) of 53.39%, recall (REC) of 64.43%, precision (PRE) of 74.51%, 95th Hausdorff distance (95HD) of 11.91 mm, and average surface distance (ASD) of 4.63 mm. Our code will be available online (https://github.com/zhouyuegithub/CMVNet).

Unsupervised cross domain (UCD) person re-identification (re-ID) aims to apply a model trained on a labeled source domain to an unlabeled target domain. It faces huge challenges as the identities have no overlap between these two domains. At present, most UCD person re-ID methods perform "supervised learning" by assigning pseudo labels to the target domain, which leads to poor re-ID performance due to the pseudo label noise. To address this problem, a multi-loss optimization learning (MLOL) model is proposed for UCD person re-ID. In addition to using the information of clustering pseudo labels from the perspective of supervised learning, two losses are designed from the view of similarity exploration and adversarial learning to optimize the model. Specifically, in order to alleviate the erroneous guidance brought by the clustering error to the model, a ranking-average-based triplet loss learning and a neighbor-consistency-based loss learning are developed. Combining these losses to optimize the model results in a deep exploration of the intra-domain relation within the target domain. The proposed model is evaluated on three popular person re-ID datasets, Market-1501, DukeMTMC-reID, and MSMT17. Experimental results show that our model outperforms the state-of-the-art UCD re-ID methods with a clear advantage.

Image registration of lung dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is challenging because the rapid changes in intensity lead to non-realistic deformations of intensity-based registration methods. To address this problem, we propose a novel landmark-based registration framework by incorporating landmark information into a group-wise registration. Robust principal component analysis is used to separate motion from intensity changes caused by a contrast agent. Landmark pairs are detected on the resulting motion components and then incorporated into an intensity-based registration through a constraint term. To reduce the negative effect of inaccurate landmark pairs on registration, an adaptive weighting landmark constraint is proposed. The method for calculating landmark weights is based on an assumption that the displacement of a good matched landmark is consistent with those of its neighbors. The proposed method was tested on 20 clinical lung DCE-MRI image series. Both visual inspection and quantitative assessment are used for the evaluation. Experimental results show that the proposed method effectively reduces the non-realistic deformations in registration and improves the registration performance compared with several state-of-the-art registration methods.

Accurate breast mass segmentation of automated breast ultrasound (ABUS) images plays a crucial role in 3D breast reconstruction which can assist radiologists in surgery planning. Although the convolutional neural network has great potential for breast mass segmentation due to the remarkable progress of deep learning, the lack of annotated data limits the performance of deep CNNs. In this article, we present an uncertainty aware temporal ensembling (UATE) model for semi-supervised ABUS mass segmentation. Specifically, a temporal ensembling segmentation (TEs) model is designed to segment breast mass using a few labeled images and a large number of unlabeled images. Considering the network output contains correct predictions and unreliable predictions, equally treating each prediction in pseudo label update and loss calculation may degrade the network performance. To alleviate this problem, the uncertainty map is estimated for each image. Then an adaptive ensembling momentum map and an uncertainty aware unsupervised loss are designed and integrated with TEs model. The effectiveness of the proposed UATE model is mainly verified on an ABUS dataset of 107 patients with 170 volumes, including 13382 2D labeled slices. The Jaccard index (JI), Dice similarity coefficient (DSC), pixel-wise accuracy (AC) and Hausdorff distance (HD) of the proposed method on testing set are 63.65%, 74.25%, 99.21% and 3.81mm respectively. Experimental results demonstrate that our semi-supervised method outperforms the fully supervised method, and get a promising result compared with existing semi-supervised methods.

In this paper, a bilateral mass detection method is proposed for mammogram combining self-supervised learning network and Siamese-Faster-RCNN. The breast region is first identified by threshold segmentation and morphological filter. Then self-supervised learning network is built to learn the spatial transformation between the bilateral breast regions. Following bilateral mammograms are registered, a Siamese-Faster-RCNN consisting of the Region Proposal Network (RPN) and a Siamese fully connected (Siamese-FC) network is designed and employed for mass detection. The proposed method is estimated on two datasets (publicly available dataset INbreast and private dataset BCPKUPH). Experimental results show that the proposed method performs better than the previous state of art methods, which demonstrates the promise of the proposed method.

Surface nodularity is an important image biomarker for cirrhosis. In this study, we explore how the location of the liver boundary curve may influence the liver surface nodularity (LSN). Based on computed tomography images of 7 patients with a normal liver and 9 patients with cirrhosis, we quantitatively estimate the LSN of the boundary curves selected from different locations of the liver. By repeating the estimation for different boundary curves, the difference in the variation of the LSN between the normal liver and cirrhosis is investigated. Receiver operating characteristic (ROC) analysis is used to assess the classification performance between the normal and cirrhotic livers. Results show that the LSN of the normal liver is significantly different from that of the cirrhosis, that the variation pattern of the LSN is different if the boundary curve is selected from different locations of the liver, and that the LSN leads to accurate classification between the normal and cirrhotic livers with the area under the ROC curve of 0.97- 0.98. We conclude that the LSN can be used to classify cirrhosis from the normal liver and studies are on-going to validate the conclusion.

Mammography is one of the broadly used imaging modality for breast cancer screening and detection. Locating mass from the whole breast is an important work in computer-aided detection. Traditionally, handcrafted features are employed to capture the difference between a mass region and a normal region. Recently convolution neural network (CNN) which automatically discovers features from the images shows promising results in many pattern recognition tasks. In this paper, three mass detection schemes based on CNN are evaluated. First, a suspicious region locating method based on heuristic knowledge is employed. Then three different CNN schemes are designed to classify the suspicious region as mass or normal. The proposed schemes are evaluated on a dataset of 352 mammograms. Compared with several handcrafted features, CNN-based methods shows better mass detection performance in terms of free receiver operating characteristic (FROC) curve.

The majority of the existing appearance-based vehicle-detection systems make use of a sliding-window paradigm for vehicle-candidate regions location. In order to locate all vehicle regions with various sizes and shapes, a large number of search windows are generated by a sliding-window paradigm in most vehicle-detection systems. It is desirable to obtain fewer and more precisely located vehicle candidate regions for further detection. For this purpose, a novel graph-based algorithm is proposed to locate the vehicle proposal regions, which estimates the possibility of a vehicle contained in a bounding box. Experimental results on the public traffic analysis data set (KITTI) and PASCAL VOC 2007 show that the proposed region proposal approach leads to better performances compared with popular bottom-up region proposal methods. Moreover, the proposed vehicle-detection system is evaluated on the KITTI data set, which are determined to be satisfactory, even for the images containing vehicles that have undergone scale variations and camera viewpoint changes, as well as for images that were photographed with complex backgrounds.

Classification of traffic signs with partial occlusions is important for traffic sign maintenance and inventory systems. It is also important to help drivers identify possible traffic signs in time. Motivated by human cognitive processes in identifying an occluded sign, a novel structure is designed to explicitly handle occluded samples in this paper. Occlusion maps are analyzed for possible occluded signs, and a new occlusion descriptor is proposed to distinguish occluded signs from negative samples. A series of tests shows that the developed method could effectively handle samples with partial occlusions and thus reduce the missed detections caused by occlusions. The developed method could also be easily used for any other object detection.

The majority of the existing appearance-based vehicle-detection systems make use of local features for detection purposes, such as Haar-like, histograms of oriented gradients, scale-invariant feature transform, and so forth. However, these local features have limitations when dealing with illuminations, scale, shape variations, and complex background situations. It is desirable for a vehicle to have discriminative and robust features. For this purpose, a novel context-aware multichannel feature pyramid has been proposed in this paper. The main contribution of this paper is proposing two context-aware structural descriptors, termed as a context-aware difference sign transform feature and context-aware difference magnitude transform feature. An image has been tiled with a dense grid of the cells, and each cell is described by both local details and context-aware structural descriptors. The context-aware structural descriptors have the ability to capture the context-aware structural information of cells. The proposed context-aware multichannel feature pyramid is able to provide more effective features for vehicle detection. The results of the two public traffic analysis datasets show that the proposed approach leads to better performances when compared with the current state-of-the-art methods. Moreover, the experimental results are determined to be satisfactory, even for the images containing vehicles that have undergone scale variations and camera viewpoint changes, as well as for images that were photographed with complex backgrounds.

The recent hot research topic of a massive multiple-output (MIMO) has been primarily studied based on the assumption that channel vectors are asymptotically pairwise orthogonal. This condition is not exactly satisfied in practice. Correlation will occur among antenna elements at the base- station antenna array in a dense scattering environment, or in a LOS propagation condition. In this paper, by using the Mellin transform of the eigenvalue distribution, we derive the semi- correlated sum-rate capacity of multi-antenna channels with an arbitrary number of antennas in closed form. Afterwards, we employ two commonly- used correlation models to compare the theoretical closed-form results and the simulated results and the final results show that they match well.

Massive multiple-input multiple-output (MIMO) is a potential candidate key technology for the fifth generation of wireless communication systems. In previous research, different power loss and shadowing effects on different individual antenna elements have been neglected. When characterizing the channel of a massive MIMO system and investigating the system performance, the large scale attenuation (LSA) over the antenna array has not been previously considered. In this paper, based on our proposed accurate geometrical propagation model, the spectral efficiency (in terms of bits/s/Hz sum-rate) result of maximal ratio combining (MRC) detection is investigated. From the simulation results, we find that the sum-rate performance for the MRC detection of our proposed more realistic channel model exceeds the results using the conventional model (where the LSA effect is not included). This proposed model LSA model is beneficial and informative for the research, design, and evaluation of the next generation of wireless communication systems employing massive MIMO configurations.

The plane wave assumption has been used extensively in wireless channel modeling for simplicity. However, when the plane wave model is applied to the massive multiple input and multiple output (MIMO) channel characterization, it is no longer suitable. In this paper, by using the geometrical channel parameterizations, the phase shift difference caused by the spherical wave for a large linear antenna array is investigated, and the Far Region Boundary of a Linear Massive Antenna has been proposed as a criterion to determine whether the user terminal is within the near field of this large antenna structure. Finally, by using ray-tracing method, the proposed boundary within which the propagation caused phase difference exceeds 22.5 is verified. The spherical wave model is necessary for the more accurate channel characterization.

Massive multiple input and multiple output (MIMO) systems can increase the spectrum and energy efficiency of existing cells, and because of this, massive MIMO has been considered as a potential technique for next generation wireless communication networks. Since a thorough knowledge of the propagation channel is a prerequisite of reliable communication systems, massive MIMO channels are of great current interest. In this paper, based on realistic measurements in a stadium scenario in two frequency bands, the stationarity of three basic channel parameters is investigated by using the reverse arrangements test. The results show that channel behaviors in our higher frequency band are stationary over the linear antenna array, whereas this appears untrue at the low frequency band. This non-stationarity phenomenon in the line of sight propagation environment is mainly caused by the stronger reflection and smaller path loss at the low frequency band, which allows more and stronger multipath components, and this leads to substantial channel changes over the large size antenna array.

The majority of existing traffic sign detection systems utilize color or shape information, but the methods remain limited in regard to detecting and segmenting traffic signs from a complex background. In this paper, we propose a novel graph-based traffic sign detection approach that consists of a saliency measure stage, a graph-based ranking stage, and a multithreshold segmentation stage. Because the graph-based ranking algorithm with specified color and saliency combines the information of color, saliency, spatial, and contextual relationship of nodes, it is more discriminative and robust than the other systems in terms of handling various illumination conditions, shape rotations, and scale changes from traffic sign images. Furthermore, the proposed multithreshold segmentation algorithm focuses on all the nodes with a nonzero ranking score, which can effectively solve problems such as complex background, occlusion, various illumination conditions, and so on. The results for three public traffic sign sets show that our proposed approach leads to better performance than the current state-of-the-art methods. Moreover, the results are satisfactory even for images containing traffic signs that have been rotated or undergone occlusion, as well as for images that were photographed under different weather and illumination conditions.

The non-stationary properties based on Markov chains are proposed to describe the wireless propagation mechanism of High Speed Railway (HSR) under viaduct scenarios. This Markov modeling method reveals the statistical behaviors of the persistence process corresponding to a resolvable multipath component. Based upon the channel measurement on Beijing-Tianjin HSR at 2.35 GHz, the transition probability matrix and the steady-state probability matrix of Markov chains are specified. These proposed model parameters are informative for link-level simulation and prototype verification for HSR communication systems. In addition, the non-stationary properties are first investigated by medium-scale fading entropy and run length to evaluate the degrees of activity and persistence, respectively. Finally, our Markov models are compared with the experimental results by the Kullback-Leibler (KL) distance to obtain the degree of approximation, which show that the second order model provides a good match to the measured data.

Most of the existing traffic sign recognition (TSR) systems make use of the inner region of the signs or the local features such as Haar, histograms of oriented gradients (HOG), and scale-invariant feature transform for recognition, whereas these features are still limited to deal with the rotation, illumination, and scale variations situations. A good feature of a traffic sign is desired to be discriminative and robust. In this paper, a novel Color Global and Local Oriented Edge Magnitude Pattern (Color Global LOEMP) is proposed. The Color Global LOEMP is a framework that is able to effectively combine color, global spatial structure, global direction structure, and local shape information and balance the two concerns of distinctiveness and robustness. The contributions of this paper are as follows: 1) color angular patterns are proposed to provide the color distinguishing information; 2) a context frame is established to provide global spatial information, due to the fact that the context frame is established by the shape of the traffic sign, thus allowing the cells to be aligned well with the inside part of the traffic sign even when rotation and scale variations occur; and 3) a LOEMP is proposed to represent each cell. In each cell, the distribution of the orientation patterns is described by the HOG feature, and then, each direction of HOG is represented in detail by the occurrence of local binary pattern histogram in this direction. Experiments are performed to validate the effectiveness of the proposed approach with TSR systems, and the experimental results are satisfying, even for images containing traffic signs that have been rotated, damaged, altered in color, or undergone affine transformations or images that were photographed under different weather or illumination conditions.

Based on the realistic channel measurement on High-Speed Railway (HSR) in viaduct scenarios at 2.35 GHz, the dynamic evolution of multipath components is investigated from the birth-death process point of view. Due to the distinction in the amount of resolvable multipath signals, the channel is divided into five segments and can be completely parameterized by several sets of statistical parameters associated with the type of environment and scenario. Then the four-state Markov chain, describing the birth-death number variation of the detected propagation waves, is employed to specialize the temporal stochastic properties. Furthermore, the steady probabilities and transition probabilities are provided which will facilitate the development and evaluation of wireless communication systems under HSR.

This paper presents a novel and practical study on the position-based radio propagation channel for High-Speed Railway by performing extensive measurements at 2.35 GHz in China. The specification on the path loss model is developed. In particular, small scale fading properties such as K-factor, Doppler frequency feature and time delay spread are parameterized, which show dynamic variances depending on the train location and the transceiver separation. Finally, the statistical position-based channel models are firstly established to characterize the High-Speed Railway channel, which significantly promotes the evaluation and verification of wireless communications in relative scenarios.

In this paper, a new nonlinear Refined Extended Chirp Scaling (RECS) imaging algorithm is proposed for spaceborne ScanSAR with large cell migration to resolve the effects of residual cubic phase error in the deducing of the traditional RECS algorithm. The algorithm achieves cubic phase error correction of RECS by nonlinear filter to improve the spaceborne ScanSAR image qualities. The full derivation and the realizing approach of the algorithm are presented. And the algorithm is verified with simulations.