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In response to the challenge of spectral aliasing in target signals, which arose from constraints on the temporal modulation rate in existing time-domain direction of arrival (DOA) estimation methods, a novel asynchronous control-based DOA estimation technique was proposed. This approach significantly increased the modulation rate, effectively mitigating the problem of spectral aliasing and consequently broadening the signal processing bandwidth. This novel approach capitalized on the inherent persistence of unit states over a duration, without necessitating any alterations to the hardware constraints of the time-varying metasurface (TVM). By staggering the initiation of state changes across various column units, the method enabled the acquisition of multiple distinct responses within a single state’s temporal window. The asynchronous modulation strategy effectively augmented the virtual channel count under the limitations imposed by material properties, thereby enhancing the precision of DOA estimation. Empirical simulation outcomes substantiated the efficacy of this approach, demonstrating a significant enhancement in DOA estimation performance compared to existing synchronous modulation techniques, closely approximating the theoretical optimum for DOA estimation outcomes.

Recovering structures from images with 180∘ or 360∘ FoV is pivotal in computer vision and computational photography, particularly for VR/AR/MR and autonomous robotics applications. Due to varying distortions and the complexity of indoor scenes, recovering flexible structures from a single image is challenging. We introduce OmniSRNet, a comprehensive deep learning framework that merges distortion-aware learning with bidirectional LSTM. Utilizing a curated dataset with optimized panorama and expanded fisheye images, our framework features a distortion-aware module (DAM) for extracting features and a horizontal and vertical step module (HVSM) of LSTM for contextual predictions. OmniSRNet excels in applications such as VR-based house viewing and MR-based video surveillance, achieving leading results on cuboid and non-cuboid datasets. The code and dataset can be accessed at https://github.com/mmlph/OmniSRNet/.

Abstract Background With potential of deep learning in musculoskeletal image interpretation being explored, this paper focuses on the common site of rotator cuff tears, the supraspinatus. It aims to propose and validate a deep learning model to automatically extract the supraspinatus, verifying its superiority through comparison with several classical image segmentation models. Method Imaging data were retrospectively collected from 60 patients who underwent inpatient treatment for rotator cuff tears at a hospital between March 2021 and May 2023. A dataset of the supraspinatus from MRI was constructed after collecting, filtering, and manually annotating at the pixel level. This paper proposes a novel A-DAsppUnet network that can automatically extract the supraspinatus after training and optimization. The analysis of model performance is based on three evaluation metrics: precision, intersection over union, and Dice coefficient. Results The experimental results demonstrate that the precision, intersection over union, and Dice coefficients of the proposed model are 99.20%, 83.38%, and 90.94%, respectively. Furthermore, the proposed model exhibited significant advantages over the compared models. Conclusion The designed model in this paper accurately extracts the supraspinatus from MRI, and the extraction results are complete and continuous with clear boundaries. The feasibility of using deep learning methods for musculoskeletal extraction and assisting in clinical decision-making was verified. This research holds practical significance and application value in the field of utilizing artificial intelligence for assisting medical decision-making.

Mixed Reality (MR) video fusion system fuses video imagery with 3D scenes. It makes the scene much more realistic and helps the users understand the video contents and temporalspatial correlation between them, thus reducing the user’s cognitive load. Nowadays, MR video fusion has been used in various applications. However, video fusion systems require powerful client machines because video streaming delivery, stitching, and rendering are computation-intensive. Moreover, huge bandwidth usage is also another critical factor that affects the scalability of video fusion systems. The framework proposed in this paper overcomes this client limitation by utilizing remote rendering. Furthermore, the framework we built is based on browsers. Therefore, the user could try the MR video fusion system with a laptop or even pad, no extra plug-ins or application programs need to be installed. Several experiments on diverse metrics demonstrate the effectiveness of the proposed framework.

Aiming at solving the challenges of insufficient data samples and low detection efficiency in tunnel lining crack detection methods based on deep learning, a novel detection approach for tunnel lining crack was proposed, which is based on pruned You Look Only Once v4 (YOLOv4) and Wasserstein Generative Adversarial Network enhanced by Residual Block and Efficient Channel Attention Module (WGAN-RE). In this study, a data augmentation method named WGAN-RE was proposed, which can achieve the automatic generation of crack images to enrich data set. Furthermore, YOLOv4 was selected as the basic model for training, and a pruning algorithm was introduced to lighten the model size, thereby effectively improving the detection speed. Average Precision (AP), F1 Score (F1), model size, and Frames Per Second (FPS) were selected as evaluation indexes of the model performance. Results indicate that the storage space of the pruned YOLOv4 model is only 49.16 MB, which is 80% compressed compared with the model before pruning. In addition, the FPS of the model reaches 40.58f/s, which provides a basis for the real-time detection of tunnel lining cracks. Findings also demonstrate that the F1 score and AP of the pruned YOLOv4 are only 0.77% and 0.50% lower than that before pruning, respectively. Besides, the pruned YOLOv4 is superior in both model accuracy and efficiency compared with YOLOv3, SSD, and Faster RCNN, which indicated that the pruned YOLOv4 model can realize the accurate, fast and intelligent detection of tunnel lining cracks in practical tunnel engineering.

The dual-credit policy promotes green transition in automobile companies. This paper investigates the dual-credit policy framework in the Chinese automotive industry, with a focus on the phenomenon of free riding. This occurs when traditional vehicle manufacturers within an alliance benefit from the excess credits generated by a transitioning vehicle company without fully committing to their own green transitioning. The focus of this study lies on an alliance constituted by a transitioning vehicle company in partnership with two traditional vehicle manufacturers, all interconnected via equity ties. Utilizing an agent-based system dynamics model, this study explores the strategic behaviors emerging from such credit collaborations and their consequent effects on operational efficiency and financial performance. The findings reveal that 1. free riding negatively impacts the transitioning company’s revenue but benefits the alliance by easing transition pressures and boosting collective performance; 2. stricter policies increase intra-alliance credit transfers and performance, while lower credit prices reduce transfer value and harm the transitioning company’s earnings. This study implies that transitioning vehicle companies with equity-linked partners can benefit from a nuanced understanding of how policy mechanisms interact with alliance dynamics under free riding. By adjusting credit transfer strategies in line with market conditions and policy trends, they can better navigate the dual-credit policy landscape, balancing individual profitability with the needs of the broader alliance and long-term sustainability goals.

The evaluation of coronary morphology provides important guidance for the treatment of coronary heart disease (CHD). A chaotic Gaussian mutation antlion optimizer algorithm (CGALO) is proposed in the paper, and it is combined with SVM to construct a classification prediction model for Fractional flow reserve (FFR). To overcome the limitations of the original antlion optimizer (ALO) algorithm, the chaotic Gaussian mutation strategy is introduced, which leads to an improvement in its convergence speed and accuracy. To evaluate the proposed algorithm's performance, comparative experiments were conducted on 23 benchmark functions alongside 12 other cutting-edge optimization algorithms. The experimental outcomes demonstrate that the proposed algorithm achieves superior convergence accuracy and speed compared to the alternative comparison algorithms. Additionally, it is combined with SVM and FS to construct a hierarchical FFR classification model, which is utilized to make effective predictions for 84 patients at the affiliated hospital of medical school, Ningbo university. The experimental results demonstrate that the proposed model achieves an average accuracy of 92%. Moreover, it concludes that smoking history, number of lesion vessels, lesion location, diffuse lesions and ST segment changes, and other factors are the most critical indicators for FFR. Therefore, the model that has been established is a new FFR intelligent classification prediction technology that can effectively assist doctors in making corresponding decisions and evaluation plans.

Secret key generation based on a wireless channel (WC-SKG) is a promising solution to address the security issues in wireless communication. However, the consistency of channel estimation between two legal communication nodes in WC-SKG is often poor due to the receiver noise, signal power, etc., leading to a low secret key generation rate (SKGR). Although there are several denoising algorithms such as orthogonal transformation to address this issue, existing schemes overlook the fact that data symbols are also affected by the channel. This results in existing schemes only using the pilot symbols for channel estimation and not fully utilizing the received signal power of the WC-SKG. To address this issue, we propose a consistency enhancement algorithm based on constellation decision information (CEA-CDI), which utilizes both pilot symbols and soft decision information of data symbols to improve SKGR. Monte Carlo simulation and numerical results demonstrate that our proposed scheme can improve performance by approximately 16 dB compared to initial channel estimation.

Background: In this study, we propose a novel 3D scene graph prediction approach for scene understanding from point clouds. Methods: It can automatically organize the entities of a scene in a graph, where objects are nodes and their relationships are modeled as edges. More specifically, we employ the DGCNN to capture the features of objects and their relationships in the scene. A Graph Attention Network (GAT) is introduced to exploit latent features obtained from the initial estimation to further refine the object arrangement in the graph structure. A one loss function modified from cross entropy with a variable weight is proposed to solve the multi-category problem in the prediction of object and predicate. Results: Experiments reveal that the proposed approach performs favorably against the state-of-the-art methods in terms of predicate classification and relationship prediction and achieves comparable performance on object classification prediction. Conclusions: The 3D scene graph prediction approach can form an abstract description of the scene space from point clouds.

Automatic medical report generation based on deep learning can improve the efficiency of diagnosis and reduce costs. Although several automatic report generation algorithms have been proposed, there are still two main challenges in generating more detailed and accurate diagnostic reports: using multi-view images reasonably and integrating visual and semantic features of key lesions effectively. To overcome these challenges, we propose a novel automatic report generation approach. We first propose the Cross-View Attention Module to process and strengthen the multi-perspective features of medical images, using mean square error loss to unify the learning effect of fusing single-view and multi-view images. Then, we design the module Medical Visual-Semantic Long Short Term Memorys to integrate and record the visual and semantic temporal information of each diagnostic sentence, which enhances the multi-modal features to generate more accurate diagnostic sentences. Applied to the open-source Indiana University X-ray dataset, our model achieved an average improvement of 0.8% over the state-of-the-art (SOTA) model on six evaluation metrics. This demonstrates that our model is capable of generating more detailed and accurate diagnostic reports.

A polymer network with a memory effect based on a polymer-stabilized narrow-bandwidth cholesteric liquid crystal (CLC) was prepared using the washing-out/refill method. The effects of different polymerization conditions on the reflection properties of CLC films were investigated. Meanwhile, the selective reflection property and narrow-bandwidth reflection memory effect of the polymer network were proved, and the response mechanism was provided. Furthermore, different materials from liquid crystals, with an anisotropic refractive index, to toluene, with an isotropic refractive index, were refilled to polymer scaffolds with helical structures, which originated from the periodic arrangement of CLCs. It was confirmed that the reflection bandwidth of these films can be dramatically narrowed by the reduced birefringence (Δn) of the refilled materials. The narrowest bandwidth of 22.5 nm refilling toluene with an isotropic refractive index (Δn = 0) was obtained. These results may provide a novel idea for flexible reflective displays, color filters, printing, and colored cladding of a variety of objects.

This work aims to establish visual correspondences between a pair of images depicting objects of the same semantic category. It encounters many challenges such as non-overlapping of scenes or objects, background clutter, and large intra-class variation. Existing methods handle this task with handcrafted features, which cannot effectively fit the correlations between non-overlapping images. Besides, additional training or information may be implemented into the learned features. In this paper, we propose a novel approach for semantic correspondence, which is based on deep feature representation, geometric and semantic associations between intra-class objects, and hierarchical matching selection according to the convolutional feature pyramid. Firstly, we construct the initial correspondence by developing a sparse feature matching model on the coarsest feature level, which enforces the nearest-neighbor searching under semantic and geometric consistency constraints. Further, a narrowing strategy is proposed and employed from the coarsest to the finest feature level, which hierarchically refine and optimize the correspondence. The results illustrate that this approach achieves competitive performance on the public datasets for semantic correspondence.

Image/video stitching is a technology for solving the field of view (FOV) limitation of images/ videos. It stitches multiple overlapping images/videos to generate a wide-FOV image/video, and has been used in various fields such as sports broadcasting, video surveillance, street view, and entertainment. This survey reviews image/video stitching algorithms, with a particular focus on those developed in recent years. Image stitching first calculates the corresponding relationships between multiple overlapping images, deforms and aligns the matched images, and then blends the aligned images to generate a wide-FOV image. A seamless method is always adopted to eliminate such potential flaws as ghosting and blurring caused by parallax or objects moving across the overlapping regions. Video stitching is the further extension of image stitching. It usually stitches selected frames of original videos to generate a stitching template by performing image stitching algorithms, and the subsequent frames can then be stitched according to the template. Video stitching is more complicated with moving objects or violent camera movement, because these factors introduce jitter, shakiness, ghosting, and blurring. Foreground detection technique is usually combined into stitching to eliminate ghosting and blurring, while video stabilization algorithms are adopted to solve the jitter and shakiness. This paper further discusses panoramic stitching as a special-extension of image / video stitching. Panoramic stitching is currently the most widely used application in stitching. This survey reviews the latest image/video stitching methods, and introduces the fundamental principles/advantages/weaknesses of image/video stitching algorithms. Image/video stitching faces long-term challenges such as wide baseline, large parallax, and low-texture problem in the overlapping region. New technologies may present new opportunities to address these issues, such as deep learning-based semantic correspondence, and 3D image stitching. Finally, this survey discusses the challenges of image/video stitching and proposes potential solutions. Keywords: Image stitching, Video stitching, Panoramic stitching, Registration, Alignment, Mesh optimization, Deep learning, 3D stitching

Foamed concrete consists of cement matrix and air-foam and has more complicated structure characteristics than normal concrete. However, current research on the elastic modulus of foamed concrete is still limited to empirical equations. In this work, a new theoretical prediction model was proposed for calculating the elastic modulus of foamed concrete. The elastic modulus calculation model for the spherical shell element of foamed concrete is constructed based on the Walsh formula and the assumption of spherical pores. Moreover, the theoretical prediction model is established by introducing the two-layer embedded model for the elastic modulus of foamed concrete. Then, the compressive test is employed to verify the accuracy of the model. The results show that the elastic modulus of foamed concrete decreases with the increase in porosity and matrix Poisson’s ratio, and increases with the increase in the matrix elastic modulus. The research results can improve the mechanical theories of foamed concrete materials and have good engineering application values.

Background/Aims: MicroRNAs (miRNAs) can be used as biomarkers for cardiovascular diseases, especially for heart failure. However, there are few reports on serum exosomal miRNA biomarkers in patients with acute heart failure (AHF) due to Dilated cardiomyopathy (DCM). Methods: We analyzed 3 different serum exosomal miRNAs (exo-miR-92b-5p, exo-miR-192-5p, and exo-miR-320a) in 43 patients with DCM-AHF and 34 healthy volunteers as a control group (CG) by using exosome separation followed by a quantitative reverse-transcript PCR assay. Exosomes were identified by electron microscopy, NaNOZS-90, and western blot analyses (CD63 and Hsp70). Results: Serum exo-miR-92b-5p expression was increased in DCM-AHF patients compared to the CG (Mann–Whitney U-test: P < 0.001). Exo-miR-92b-5p was positively related to age and some ultrasound data (Spearman’s correlation: exo-miR-92b-5p vs. age, r = 0.297, P = 0.014; exo-miR-92b-5p vs. left atrial diameter, r = 0.431, P < 0.001; exo-miR-92b-5p vs. left ventricular diastolic diameter, r = 0.419, P < 0.001; exo-miR-92b-5p vs. left ventricular systolic diameter, r = 0.446, P < 0.001). Exo-miR-92b-5p was also negatively related to other ultrasound data (Spearman’s correlation: exo-miR-92b-5p vs. left ventricular fraction shortening, r = -0.497, P < 0.001; exo-miR-92b-5p vs. left ventricular ejection fraction, r = -0.482, P < 0.001). The discrimination of DCM-AHF patients from the CG by exo-miR-92b-5p was demonstrated by a receiver operating characteristic curve (exo-miR-92b-5p: cutoff value = 0.0023, area under the curve = 0.808, P < 0.001, sensitivity = 62.8%, specificity = 85.3%). Conclusion: Serum exo-miR-92b-5p is a potential biomarker for the diagnosis of DCM-AHF.

Based on the analyses of data obtained from the underground powerhouse at Jinping I hydropower station, a comprehensive review of engineering rock mechanics practice in the underground powerhouse is first conducted. The distribution of strata, lithology, and initial geo-stress, the excavation process and corresponding rock mass support measures, the deformation and failure characteristics of the surrounding rock mass, the stress characteristics of anchorage structures in the cavern complex, and numerical simulations of surrounding rock mass stability and anchor support performance are presented. The results indicate that the underground powerhouse of Jinping I hydropower station is characterized by high to extremely high geo-stresses during rock excavation. Excessive surrounding rock mass deformation and high stress of anchorage structures, surrounding rock mass unloading damage, and local cracking failure of surrounding rock masses, etc., are mainly caused by rock mass excavation. Deformations of surrounding rock masses and stresses in anchorage structures here are larger than those found elsewhere: 20% of extensometers in the main powerhouse record more than 50 mm with the maximum at around 250 mm observed in the downstream sidewall of the transformer hall. There are about 25% of the anchor bolts having recorded stresses of more than 200 MPa. Jinping I hydropower plant is the first to have an underground powerhouse construction conducted in host rocks under extremely high geo-stress conditions, with the ratio of rock mass strength to geo-stress of less than 2.0. The results can provide a reference to underground powerhouse construction in similar geological conditions.

Unmanned aerial vehicles (UAVs) can capture high-quality aerial photos and have been widely used for large-scale urban 3D reconstruction. However, even with the help of commercial flight control software, it is still a challenging task for non-professional users to capture full-coverage aerial photos in complex urban environments, which normally leads to incomplete 3D reconstruction. In this paper, we propose a novel path planning method for the high-quality aerial 3D reconstruction of urban scenes. The proposed approach first captures aerial photos, following an initial path to generate a coarse 3D model as prior knowledge. Then, 3D viewpoints with constrained location and orientation are generated and evaluated, according to the completeness and accuracy of the corresponding visible regions of the prior model. Finally, an optimized path is produced by smoothly connecting the optimal viewpoints. We perform an extensive evaluation of our method on real and simulated data sets, in comparison with a state-of-the-art method. The experimental results indicate that the optimized trajectory generated by our method can lead to a significant boost in the performance of aerial 3D urban reconstruction.

In recent years, the amount of remote sensing imagery data has increased exponentially. The ability to quickly and effectively find the required images from massive remote sensing archives is the key to the organization, management, and sharing of remote sensing image information. This paper proposes a high-resolution remote sensing image retrieval method with Gabor-CA-ResNet and a split-based deep feature transform network. The main contributions include two points. (1) For the complex texture, diverse scales, and special viewing angles of remote sensing images, A Gabor-CA-ResNet network taking ResNet as the backbone network is proposed by using Gabor to represent the spatial-frequency structure of images, channel attention (CA) mechanism to obtain stronger representative and discriminative deep features. (2) A split-based deep feature transform network is designed to divide the features extracted by the Gabor-CA-ResNet network into several segments and transform them separately for reducing the dimensionality and the storage space of deep features significantly. The experimental results on UCM, WHU-RS, RSSCN7, and AID datasets show that, compared with the state-of-the-art methods, our method can obtain competitive performance, especially for remote sensing images with rare targets and complex textures.

Recently, there have been rapid advances in high-resolution remote sensing image retrieval, which plays an important role in remote sensing data management and utilization. For content-based remote sensing image retrieval, low-dimensional, representative and discriminative features are essential to ensure good retrieval accuracy and speed. Dimensionality reduction is one of the important solutions to improve the quality of features in image retrieval, in which LargeVis is an effective algorithm specifically designed for Big Data visualization. Here, an extended LargeVis (E-LargeVis) dimensionality reduction method for high-resolution remote sensing image retrieval is proposed. This can realize the dimensionality reduction of single high-dimensional data by modeling the implicit mapping relationship between LargeVis high-dimensional data and low-dimensional data with support vector regression. An effective high-resolution remote sensing image retrieval method is proposed to obtain stronger representative and discriminative deep features. First, the fully connected layer features are extracted using a channel attention-based ResNet50 as a backbone network. Then, E-LargeVis is used to reduce the dimensionality of the fully connected features to obtain a low-dimensional discriminative representation. Finally, L2 distance is computed for similarity measurement to realize the retrieval of high-resolution remote sensing images. The experimental results on four high-resolution remote sensing image datasets, including UCM, RS19, RSSCN7, and AID, show that for various convolutional neural network architectures, the proposed E-LargeVis can effectively improve retrieval performance, far exceeding other dimensionality reduction methods.

Background: Fractional flow reserve (FFR) has become an increasingly important index when making decisions with respect to revascularization of coronary artery stenosis. However, the pressure guidewire used in obtaining FFR measurements is difficult to control and manipulate in certain complex coronary artery lesions, resulting in increased fluoroscopy time and contrast dye usage. This study examined a novel (NOV) technique for obtaining FFR measurements in hope of easing the difficulties associated with evaluating and treating complex coronary artery lesions. Methods: Fifty-six patients with complex coronary artery lesions were assigned to a conventional (CON) FFR technique group or a NOV FFR technique group. The NOV technique involved the use of a balloon and wire exchange within the coronary artery. The fluoroscopy time, contrast dye usage, and FFR-related complications were assessed after completing the FFR measurement procedure for each patient. Results: The median time required for fluoroscopy in the NOV technique group was significantly less than that in the CON technique group; additionally, lesser amounts of contrast dye were used in the NOV technique group (both P < 0.05). The NOV technique was successfully performed in thirty patients, without any FFR-related complications. However, the CON technique failed in three patients, including two who experienced coronary artery spasms (P > 0.05). Conclusions: Compared to the CON technique used for measuring FFR, the new technique reduced the fluoroscopy time and amount of contrast dye used when evaluating complex coronary artery lesions. The new technique did not increase the risk of operation or decrease the success rate.

In this paper, we propose an efficient geo-routing aware MAC protocol (GOAL) for underwater acoustic networks. It smoothly integrates self-adaptation based REQ/REP handshake, geographic cyber carrier sensing, and implicit ACK to perform combined channel reservation and next-hop selection. As a result, it incorporates the advantages of both a geo-routing protocol and a reservation-based medium access control (MAC) protocol. Specifically, with its self-adaptation based REQ/REP, nodes can dynamically detect the best next-hop with low route discovery cost. In addition, through geographic cyber carrier sensing, a node can map its neighbors’ time slots for sending/receiving DATA packets to its own time line, which allows the collision among data packets to be greatly reduced. With these features, GOAL outperforms geo-routing protocols coupling with broadcast MAC. Simulation results show that GOAL provides much higher end-to-end reliability with lower energy consumptions than existing Vector-Based Forwarding (VBF) routing with use of a broadcast MAC protocol. Moreover, we develop a theoretical model for the probability of a successful handshake, which coincides well with the simulation results.

Abscisic acid (ABA) plays a key role in many developmental processes and responses to adaptive stresses in plants. Recently, a new family of nucleocytoplasmic PYR/PYL/RCAR (PYLs) has been identified as bona fide ABA receptors. PYLs together with protein phosphatases type-2C (PP2Cs), Snf1 (Sucrose-non-fermentation 1)-related kinases subfamily 2 (SnRK2s) and downstream substrates constitute the core ABA signaling network. Generally, PP2Cs inactivate SnRK2s kinases by physical interaction and direct dephosphorylation. Upon ABA binding, PYLs change their conformations and then contact and inhibit PP2Cs, thus activating SnRK2s. Here, we reviewed the recent progress in research regarding the structures of the core signaling pathways of ABA, including the (+)-ABA, (−)-ABA and ABA analogs pyrabactin as well as 6AS perception by PYLs, SnRK2s mimicking PYLs in binding PP2Cs. PYLs inhibited PP2Cs in both the presence and absence of ABA and activated SnRK2s. The present review elucidates multiple ABA signal perception and transduction by PYLs, which might shed light on how to design small chemical compounds for improving plant performance in the future.

Considering rock wrapped by soil in the mesoscopic structure of soil-rock mixture at normal temperature, a two-layer embedded model of single inclusion composite material was established to obtain the elastic modulus of soil-rock mixture. Given an interface ice interlayer attached between the soil and rock interface in the mesoscopic structure of soil-rock mixture at freezing temperature, a three-layer embedded model of double inclusion composite material and multistep multiphase micromechanics model was established to obtain the elastic modulus of a frozen soil-rock mixture. With the effect of structure pore with soil-rock mixture at normal temperature taken into consideration, its elastic modulus was calculated with the three-layer embedded model. An experimental comparison found that the predicted effect of the three-layer embedded model on the soil-rock mixture was better than that of the two-layer embedded model. The elastic modulus of soil-rock mixture gradually increased with the increase in rock content regardless of temperature. The increase rate of the elastic modulus of the soil-rock mixture increased quickly especially when the rock content is between 50% and 70%. The elastic modulus of the frozen soil-rock mixture is close to four times higher than that of the soil-rock mixture at a normal temperature.

Smoke detection is a very key part of fire recognition in a forest fire surveillance video since the smoke produced by forest fires is visible much before the flames. The performance of smoke video detection algorithm is often influenced by some smoke-like objects such as heavy fog. This paper presents a novel forest fire smoke video detection based on spatiotemporal features and dynamic texture features. At first, Kalman filtering is used to segment candidate smoke regions. Then, candidate smoke region is divided into small blocks. Spatiotemporal energy feature of each block is extracted by computing the energy features of its 8-neighboring blocks in the current frame and its two adjacent frames. Flutter direction angle is computed by analyzing the centroid motion of the segmented regions in one candidate smoke video clip. Local Binary Motion Pattern (LBMP) is used to define dynamic texture features of smoke videos. Finally, smoke video is recognized by Adaboost algorithm. The experimental results show that the proposed method can effectively detect smoke image recorded from different scenes.

This paper investigates the traffic pattern prediction based on seasonal deviation and spectrum reallocation with multiple channel width in cognitive cellular networks. Compared to the existing approaches based on time series or classical statistic method, the binary exponential deviation offset prediction proposed in this paper focuses on the increment or decrement on every sampling point during an exponential offset period. Then the deviations will be revised at different levels in the next prediction process. The proposed approach is validated with some real end-user data from a WiFi network and simulation experiments. Based on such a precise prediction, we allocate the channels with different bandwidth to end-users according to diverse quality-of-service (QoS), which increases both the system's profits and actual spectrum utilization. The multidimensional bounded knapsack problem is introduced to divide channels, to which the proposed balance between value density and request probability strategy gets the approximate solution. The simulation experiment results show its good performance in not only utility but also spectrum utilization of the base-stations, especially when the resources are deficient.

The effect of bevel angle of three-layer parallel plate of otter-board on hydrodynamic performances is investigated by model wind tunnel test. Four otter-board models are designed with four different bevel angles (8°, 10°, 12° and 14°) , and tested under the wind speed 28 m / s in wind tunnel. Model experiment is conducted to obtain the drag coefficients Cx, the lift coefficient Cy, the pitch moment coefficient Cm, the center of pressure coefficient Cp and calculated the lift-drag ratio Cy/Cx. The result shows that the maximum lift coefficient and the maximum lift-drag ratio of the otter-board model with a bevel angle 12 degree is higher, there is 2.598(a=65°) and 2.607(a=37.5°); For comparison in stability of otter-board, the stability of the otter-board model with the bevel angle 8 degree is better by the comparative analysis of Cm and Cp, the absolute value of Cm is 0.174 and the minimum variation coefficient of Cp is 4.37%. The results can offer reference for the structural optimization design of trawl otter-board.

It has long been established that universities have three main functions: to teach, to research and supervise research, and to benefit the communities and regions in which they are located. In reality, the third main function permeates the other two. There are clearly social responsibilities to the students and to the staff who teach them and/or maintain their well-being through administration and other services such as counselling. Including issues of physical, social and economic well-being in their research portfolios is also common, though not universal. In this paper, we take a spatial scale approach to social responsibilities, which can take us from social responsibilities within the university, through what might be termed 'civic responsibilities', on to the immediate populations of a town or city, and then to 'regional and international responsibilities'. The impact of the COVID-19 pandemic in line with the sector's social responsibility dynamic is also examined.

Underwater Sensor Networks (UWSNs) are significantly different from terrestrial sensor networks in the following aspects: low bandwidth, high latency, node mobility, high error probability, and 3-dimensional space. These new features bring many challenges to the network protocol design of UWSNs. In this paper, we tackle one fundamental problem in UWSNs: robust, scalable, and energy efficient routing. We propose vector-based forwarding (VBF), a geographic routing protocol. In VBF, the forwarding path is guided by a vector from the source to the target, no state information is required on the sensor nodes, and only a small fraction of the nodes is involved in routing. To improve the robustness, packets are forwarded in redundant and interleaved paths. Further, a localized and distributed self-adaptation algorithm allows the nodes to reduce energy consumption by discarding redundant packets. VBF performs well in dense networks. For sparse networks, we propose a hop-by-hop vector-based forwarding (HH-VBF) protocol, which adapts the vector-based approach at every hop. We evaluate the performance of VBF and HH-VBF through extensive simulations. The simulation results show that VBF achieves high packet delivery ratio and energy efficiency in dense networks and HH-VBF has high packet delivery ratio even in sparse networks.