In this paper, we study the challenging problem of simultaneously removing haze and estimating depth from real monocular hazy videos. These tasks are inherently complementary: enhanced depth estimation improves dehazing via the atmospheric scattering model (ASM), while superior dehazing contributes to more accurate depth estimation through the brightness consistency constraint (BCC). To tackle these intertwined tasks, we propose a novel depth-centric learning framework that integrates the ASM model with the BCC constraint. Our key idea is that both ASM and BCC rely on a shared depth estimation network. This network simultaneously exploits adjacent dehazed frames to enhance depth estimation via BCC and uses the refined depth cues to more effectively remove haze through ASM. Additionally, we leverage a non-aligned clear video and its estimated depth to independently regularize the dehazing and depth estimation networks. This is achieved by designing two discriminator networks: D_MFIR enhances high-frequency details in dehazed videos, and D_MDR reduces the occurrence of black holes in low-texture regions. Extensive experiments demonstrate that the proposed method outperforms current state-of-the-art techniques in both video dehazing and depth estimation tasks, especially in real-world hazy scenes.

Accurate segmentation of medical ultrasound images is crucial for guiding treatment decisions and assessing intervention effectiveness. The challenge of segmenting lesions in ultrasound images arises from factors such as low contrast, high speckle noise, artifacts, and blurred boundaries. Furthermore, this complexity varies significantly among lesions in different cases. While methods based on Convolutional Neural Networks (CNNs) and Transformers have shown promising results in this field, each approach possesses distinct advantages and limitations. To address these challenges, we propose a novel Frequency-aware Interaction Network (FINet). At the core of our FINet lies the proposed Multi-scale Frequency-aware Self-attention (MFS) module, which effectively captures multi-scale feature information within the self-attention layer. This enables our network to model both local and global features, capitalizing on the strengths of both CNNs and Transformers. Additionally, a frequency-aware network is introduced to learn the interactions between spatial locations in the frequency domain to enhance detailed feature representation such as edges. Furthermore, we present a collaborative interactive decoder network, in which a Selective Feature Interaction (SFI) module is proposed to facilitate the semantic and boundary feature interaction, resulting in more precise segmentation outcomes. Experimental results on four medical ultrasound image datasets show the superiority of our FINet over other state-of-the-art segmentation methods. More importantly, our model achieves an excellent trade-off between performance and computational efficiency.

Learning robust feature matching between the template and search area is crucial for 3-D Siamese tracking. The core of Siamese feature matching is how to assign high feature similarity to the corresponding points between the template and the search area for precise object localization. In this article, we propose a novel point cloud registration-driven Siamese tracking framework, with the intuition that spatially aligned corresponding points (via 3-D registration) tend to achieve consistent feature representations. Specifically, our method consists of two modules, including a tracking-specific nonlocal registration (TSNR) module and a registration-aided Sinkhorn template-feature aggregation module. The registration module targets the precise spatial alignment between the template and the search area. The tracking-specific spatial distance constraint is proposed to refine the cross-attention weights in the nonlocal module for discriminative feature learning. Then, we use the weighted singular value decomposition (SVD) to compute the rigid transformation between the template and the search area and align them to achieve the desired spatially aligned corresponding points. For the feature aggregation model, we formulate the feature matching between the transformed template and the search area as an optimal transport problem and utilize the Sinkhorn optimization to search for the outlier-robust matching solution. Also, a registration-aided spatial distance map is built to improve the matching robustness in indistinguishable regions (e.g., smooth surfaces). Finally, guided by the obtained feature matching map, we aggregate the target information from the template into the search area to construct the target-specific feature, which is then fed into a CenterPoint-like detection head for object localization. Extensive experiments on KITTI, NuScenes, and Waymo datasets verify the effectiveness of our proposed method.

Remote noncontact respiratory rate estimation by facial visual information has great research significance, providing valuable priors for health monitoring, clinical diagnosis, and anti-fraud. However, existing studies suffer from disturbances in epidermal specular reflections induced by head movements and facial expressions. Furthermore, diffuse reflections of light in the skin-colored subcutaneous tissue caused by multiple time-varying physiological signals independent of breathing are entangled with the intention of the respiratory process, leading to confusion in current research. To address these issues, this article proposes a novel network for natural light video-based remote respiration estimation. Specifically, our model consists of a two-stage architecture that progressively implements vital measurements. The first stage adopts an encoder-decoder structure to recharacterize the facial motion frame differences of the input video based on the gradient binary state of the respiratory signal during inspiration and expiration. Then, the obtained generative mapping, which is disentangled from various time-varying interferences and is only linearly related to the respiratory state, is combined with the facial appearance in the second stage. To further improve the robustness of our algorithm, we design a targeted long-term temporal attention module and embed it between the two stages to enhance the network's ability to model the breathing cycle that occupies ultra many frames and to mine hidden timing change clues. We train and validate the proposed network on a series of publicly available respiration estimation datasets, and the experimental results demonstrate its competitiveness against the state-of-the-art breathing and physiological prediction frameworks.

Periodically conducting land cover mapping plays a vital role in monitoring the status and changes of the land use. The up-to-date and accurate land use database serves importantly for a wide range of applications. This letter constructs an efficient self-attention capsule network (ESA-CapsNet) for land cover classification of multispectral light detection and ranging (LiDAR) data. First, formulated with a novel capsule encoder–decoder architecture, the ESA-CapsNet performs promisingly in extracting high-level, informative, and strong feature semantics for pixel-wise land cover classification by using the five types of rasterized feature images. Furthermore, designed with a novel capsule-based attention module, the channel and spatial feature encodings are comprehensively exploited to boost the feature saliency and robustness. The ESA-CapsNet is evaluated on two multispectral LiDAR data sets and achieves an advantageous performance with the overall accuracy, average accuracy, and kappa coefficient of over 98.42%, 95.15%, and 0.9776, respectively. Comparative experiments with the existing methods also demonstrate the effectiveness and applicability of the ESA-CapsNet in land cover classification tasks.

For the traditional model based on the deep learning method most used CNN(convolutional neural networks) or RNN(Recurrent neural Network) model and is based on the dynamic character-level embedding or word-level embedding as input, so there is a problem that the text feature extraction is not comprehensive. In the development environment of the Internet of Things, A method of Automatic text classification based on BERT(Bidirectional Encoder Representations from Transformers) and Feature Fusion was proposed in this paper. Firstly, the text-to-dynamic character-level embedding is transformed by the BERT model, and the BiLSTM(Bi-directional Long-Short Term Memory) and CNN output features are combined and merged to make full use of CNN to extract the advantages of local features and to use BiLSTM to have the advantage of memory to link the extracted context features to better represent the text, so as to improve the accuracy of text classification task. A comparative study with state-of-the-art approaches manifests the proposed method outperforms the state-of-the-art methods in accuracy. It can effectively improve the accuracy of tag prediction for text data with sequence features and obvious local features.

In the original compression tracking algorithm, the size of the tracking box is fixed. There should be better tracking results for scale-invariant objects, but worse tracking results for scale-variant objects. To overcome this defect, a scale-adaptive compressive tracking (CT) algorithm is proposed. First of all, the imbalance of the gray and texture features in the original CT algorithm is balanced by the multi-feature method, which makes the algorithm more robust. Then, searching different candidate regions by using the method of multi-scale search along with feature normalization makes the features extracted from different scales comparable. Finally, the candidate region with the maximum discriminate degree is selected as the object region. Thus, the tracking-box size is adaptive. The experimental results show that when the object scale changes, the improving CT algorithm has higher accuracy and robustness than the original CT algorithm.

Contextual information can accurately describe the user's time, location, environment, activities, the use of devices, and so on. The recommender systems with contextual information have better prediction accuracy, coverage, and user satisfaction. However, the application of conventional context-aware framework in some special fields has some drawbacks. For example, in the field of cold chain logistics distribution, the conventional context-aware framework cannot adapt to the multi-dimensional, complex and dynamic contexts of cold chain logistics, the lack of context-aware risk management, and the lack of traceability of cargo contexts. In this paper, we propose an intelligent context-aware management framework for cold chain logistics (CMFCCL) distribution that contains acquisition framework, recommender systems framework, risk management framework, tracing back framework, and user portrait framework of cold chain logistics distribution. Cold chain info data set is used for simulation, and three other recommendation approaches are considered in comparison. The experimental results indicate that CMFCCL can reduce the mean absolute error and root mean squared error of the data set by about 8.37%-19.99%. Thus, the evaluation presents encouraging results, indicating that CMFCCL would be useful in the context-aware management of cold chain logistics distribution.

Biclustering analysis of gene expression data can reveal a large number of biologically significant local gene expression patterns. Therefore, a large number of biclustering algorithms apply meta-heuristic algorithms such as genetic algorithm (GA) and cuckoo search (CS) to analyze the biclusters. However, different meta-heuristic algorithms have different applicability and characteristics. For example, the CS algorithm can obtain high-quality bicluster and strong global search ability, but its local search ability is relatively poor. In contrast to the CS algorithm, the GA has strong local search ability, but its global search ability is poor. In order to not only improve the global search ability of a bicluster and its coverage, but also improve the local search ability of the bicluster and its quality, this paper proposed a meta-heuristic algorithm based on GA and CS algorithm (GA-CS Biclustering, Georgia Association of Community Service Boards (GACSB)) to solve the problem of gene expression data clustering. The algorithm uses the CS algorithm as the main framework, and uses the tournament strategy and the elite retention strategy based on the GA to generate the next generation of the population. Compared with the experimental results of common biclustering analysis algorithms such as correlated correspondence (CC), fast, local clustering (FLOC), interior search algorithm (ISA), Securities Exchange Board of India (SEBI), sum of squares between (SSB) and coordinated scheduling/beamforming (CSB), the GACSB algorithm can not only obtain biclusters of high quality, but also obtain biclusters of high-biologic significance. In addition, we also use different bicluster evaluation indicators, such as Average Correlation Value (ACV), Mean-Squared Residue (MSR) and Virtual Error (VE), and verify that the GACSB algorithm has a strong scalability.

To solve the essential objective of LDA-L1, NLDA-L1 proposes a nongreedy algorithm by constructing an auxiliary function. In this correspondence, we show that essentially, this algorithm directly solves the objective using a gradient ascending procedure, meaning that the auxiliary function may be not necessary. Then, we further show that NLDA-L1 is a special case of ILDA-L1, which applies the same iterative procedure of ILDA-L1.

Twin support vector clustering (TWSVC) is a recently proposed powerful k-plane clustering method. It, however, is prone to outliers due to the utilization of squared L2-norm distance. Besides, TWSVC is computationally expensive, attributing to the need of solving a series of constrained quadratic programming problems (CQPPs) in learning each clustering plane. To address these problems, this brief first develops a new k-plane clustering method called L1-norm distance minimization-based robust TWSVC by using robust L1-norm distance. To achieve this objective, we propose a novel iterative algorithm. In each iteration of the algorithm, one CQPP is solved. To speed up the computation of TWSVC and simultaneously inherit the merit of robustness, we further propose Fast RTWSVC and design an effective iterative algorithm to optimize it. Only a system of linear equations needs to be computed in each iteration. These characteristics make our methods more powerful and efficient than TWSVC. We also conduct some insightful analysis on the existence of local minimum and the convergence of the proposed algorithms. Theoretical insights and effectiveness of our methods are further supported by promising experimental results.

Motion is a critical indicator of human presence and activities. Recent developments in the field of indoor motion detection have revealed their potentials in enhancing our living experiences through applications like intrusion detection and sleep monitoring. However, existing solutions still face several critical downsides such as the availability (specialized hardware), reliability (illumination and line-of-sight constraints), and privacy issues (being watched). To overcome such shortages, a radio frequency (RF) based device-free motion detection system (MoSense) is designed via leveraging the attenuation of ubiquitous WiFi signals induced by motions to deliver a reliable and transparent detection service in realtime. The design and implementation of MoSense face two challenges: 1) characterizing stationary states and 2) the noisy subcarriers. For the first challenge, a silence analysis model is proposed to characterize stationary states for distinguishing motions. For the second challenge, we design a distance-based mechanism to select certain subcarriers that better capture the impact of motions from the noisy channel through measuring the similarity between subcarriers. A prototype of MoSense is realized and evaluated in real environments. By comparing MoSense with other two state-of-the-art systems, i.e., FIMD and FRID, we have shown that MoSense is superior in terms of precision, false negative rate and computational complexity. Considering that MoSense is compatible with existing WiFi infrastructure, it constitutes a low-cost yet promising solution for motion detection.

In this study, a novel saliency detection algorithm based on linear neighbourhood propagation is proposed. The proposed algorithm is divided into three steps. First, the authors segment an input image into superpixels which are represented as the nodes in a graph. The weight matrix of the graph, which indicates the similarities between the nodes, is calculated by linear neighbourhood reconstruction. Second, the nodes, which are located at top, bottom, left and right of image boundary, are labelled as boundary priors. Then, based on weight matrix, label propagation is used to propagate the labels to unlabelled nodes. They rank the nodes according to the label information and select the nodes with minor information as saliency priors. Last, based on saliency priors, saliency detection is carried out by label propagation again. The nodes with more information are considered as saliency regions. Experimental results on three benchmark databases demonstrate the proposed method performs well when it is against the state‐of‐the‐art methods in terms of accuracy and robustness.