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Lesion segmentation is a fundamental task in medical image processing, often facing the challenge of subtle lesions. It is important to detect these lesions, even though they can be difficult to identify. Convolutional neural networks, an effective method in medical image processing, often ignore the relationship between lesions, leading to topological errors during training. To tackle topological errors, move is made from pixel‐level to hypergraph representations. Hypergraphs can model lesions as vertices connected by hyperedges, capturing the topology between lesions. This paper introduces a novel dynamic hypergraph learning strategy called DHLS. DHLS allows for the dynamic construction of hypergraphs contingent upon input vertex variations. A hypergraph global‐aware segmentation network, termed HGSNet, is further proposed. HGSNet can capture the key high‐order structure information, which is able to enhance global topology expression. Additionally, a composite loss function is introduced. The function emphasizes the global aspect and the boundary of segmentation regions. The experimental setup compared HGSNet with other advanced models on medical image datasets from various organs. The results demonstrate that HGSNet outperforms other models and achieves state‐of‐the‐art performance on three public datasets. [ABSTRACT FROM AUTHOR]

In recent times, airborne and terrestrial laser scanning have been utilized to collect point cloud data for forest resource surveys, aiding in the estimation of tree and stand attributes over hectare-scale plots. In this study, an automated approach was devised to estimate the diameter at breast height (DBH) and tree height across the entire sample area, utilizing information acquired from terrestrial laser scanning (TLS) and airborne laser scanning (ULS). Centered around a meticulously managed artificial forest in Northern China, where Mongolian oak and Chinese Scots pine are the predominant species, both TLS and ULS operations were conducted concurrently on each plot. Subsequent to data collection, a detailed processing of the point cloud data was carried out, introducing an innovative algorithm to facilitate the matching of individual tree point clouds from ULS and TLS sources. To enhance the accuracy of DBH estimation, a weighted regression correction equation based on TLS data was introduced. The estimations obtained for the Chinese Scots pine plots showed a correlation of R2 = 0.789 and a root mean square error (RMSE) of 3.2 cm, while for the Mongolian oak plots, an improved correlation of R2 = 0.761 and a RMSE of 3.1 cm was observed between predicted and measured values. This research significantly augments the potential for non-destructive estimations of tree structural parameters on a hectare scale by integrating TLS and ULS technologies. The advancements hold paramount importance in the domain of large-scale forest surveys, particularly in the calibration and validation of aboveground biomass (AGB) estimations. [ABSTRACT FROM AUTHOR]

For the multi-label classification task of remote sensing images (RSIs), it is difficult to accurately extract feature information from complex land covers, and it is easy to generate redundant features by ordinary convolution extraction features. This paper proposes a multi-label classification model for multi-source RSIs that combines dense convolution and an attention mechanism. This method adds fusion channel attention and a spatial attention mechanism to each dense block module of the DenseNet, and the sigmoid activation function replaces the softmax activation function in multi-label classification. The improved model retains the main features of RSIs to the greatest extent and enhances the feature extraction of the images. The model can integrate local features, capture global dependencies, and aggregate contextual information to improve the multi-label land cover classification accuracy of RSIs. We conducted comparative experiments on the SEN12-MS and UC-Merced land cover dataset and analyzed the evaluation indicators. The experimental results show that this method effectively improves the multi-label classification accuracy of RSIs. [ABSTRACT FROM AUTHOR]

The accuracy of current deep learning algorithms has certainly increased. However, deploying deep learning networks on edge devices with limited resources is challenging due to their inherent depth and high parameter count. Here, we proposed an improved YOLO model based on an attention mechanism and receptive field (RFA-YOLO) model, applying the MobileNeXt network as the backbone to reduce parameters and complexity, adopting the Receptive Field Block (RFB) and Efficient Channel Attention (ECA) modules to improve the detection accuracy of multi-scale and small objects. Meanwhile, an FPGA-based model deployment solution was proposed to implement parallel acceleration and low-power deployment of the detection algorithm model, which achieved real-time object detection for optical remote sensing images. We implement the proposed DPU and Vitis AI-based object detection algorithms with FPGA deployment to achieve low power consumption and real-time performance requirements. Experimental results on DIOR dataset demonstrate the effectiveness and superiority of our RFA-YOLO model for object detection algorithms. Moreover, to evaluate the performance of the proposed hardware implementation, it was implemented on a Xilinx ZCU104 board. Results of the experiments for hardware and software simulation show that our DPU-based hardware implementation are more power efficient than central processing units (CPUs) and graphics processing units (GPUs), and have the potential to be applied to onboard processing systems with limited resources and power consumption. [ABSTRACT FROM AUTHOR]

With the continuous development of digital agriculture and intelligent forestry, the demand for three-dimensional modeling of trees or plants using agricultural robots is also increasing. Laser radar technology has gradually become an important technical means for agricultural robots to obtain three-dimensional information about trees. When using laser radar to scan trees, incomplete point cloud data are often obtained due to leaf occlusion, visual angle limitation, or operation error, which leads to quality degradation of the subsequent 3D modeling and quantitative analysis of trees. At present, a lot of research work has been carried out in the direction of point cloud completion, in which the deep learning model is the mainstream solution. However, the existing deep learning models have mainly been applied to urban scene completion or the point cloud completion of indoor regularized objects, and the research objects generally have obvious continuity and symmetry characteristics. There has been no relevant research on the point cloud completion method for objects with obvious individual morphological differences, such as trees. Therefore, this paper proposes a single-tree point cloud completion method based on feature fusion. This method uses PointNet, based on point structure, to extract the global features of trees, and EdgeConv, based on graph structure, to extract the local features of trees. After integrating global and local features, FoldingNet is used to realize the generation of a complete point cloud. Compared to other deep learning methods on the open source data set, the CD index using this method increased by 21.772% on average, and the EMD index increased by 15.672% on average, which proves the effectiveness of the method in this paper and provides a new solution for agricultural robots to obtain three-dimensional information about trees. [ABSTRACT FROM AUTHOR]

In this paper, we study the semi-supervised semantic segmentation problem via limited labeled samples and a large number of unlabeled samples. We propose a self-learning semi-supervised approach for the semantic segmentation of high-resolution remote sensing images. Our approach uses two networks (UNet and DeepLabV3) to predict the labels of the same unlabeled sample, and the pseudo labels samples with high prediction consistency are added to the training samples to improve the accuracy of semantic segmentation under the condition of limited labeled samples. Our method expands training data samples by using unlabeled data samples with pseudo labels. In order to verify the effectiveness of the proposed method, some experiments were conducted on the improved ISPRS Vaihingen 2D Semantic Labeling dataset using the method that we proposed. We focus on the extraction of forest and vegetation information and focus on the impact of a large number of unlabeled samples on the precision of semantic segmentation, we combine water, surface, buildings, cars, and background into one category and named others, and we call the changed dataset the improved ISPRS Vaihingen dataset. The experimental results show that the proposed method can effectively improve the semantic segmentation accuracy of high-scoring remote sensing images with limited samples than common deep semi-supervised learning. [ABSTRACT FROM AUTHOR]

Remote-sensing (RS) images with high spatial and temporal resolutions play a significant role in monitoring periodic landscape changes for earth observation science. To enrich RS images, spatiotemporal fusion (STF) is considered a promising approach. The key challenge in the current STF-based methods is the requirement for large-scale data. In this work, we propose a deep-learning-based method called spatiotemporal fusion multilayer perceptron (StfMLP) to tackle this challenge. First, our method focuses on the given data in the manner of transductive learning. Second, we propose a designed multilayer perceptron (MLP) model to capture the time dependency and consistency among the input images. Consequently, StfMLP is capable of simultaneously achieving more accurate fusion and requiring a small-scale of data. We conduct extensive experiments on two widely adopted public datasets, namely Coleambally irrigation area (CIA) and the lower Gwydir catchment (LGC). The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods effectively. Code, trained model, and cropped images are available online (https://github.com/luhailaing-max/StfMLP-master). [ABSTRACT FROM AUTHOR]

The smart city system, which is a type of enterprise management system (EMS), automatically manages cities and schedules resources efficiently based on spatial data generated by devices, such as the Internet of Things and mobile. However, with the increasing deployment of technologies, including sensor and location-based services, their ever-growing spatial data are no longer managed efficiently by traditional EMS. To overcome this issue, we present SeFrame, which is a spatially enabled framework for improving the efficiency of smart city EMS based on a distributed architecture. The framework supports a set of spatial queries, including: The range query, k-nearest neighbors query, and spatial join query. It benefits greatly from using the buffer-enabled partition method to eliminate duplicate results. In each partition, the local index based on combination of the quad-tree and grid index (CQG) significantly improves the spatial query efficiency in memory. CQG manages complex spatial objects, including a point, polygon, and polyline. By taking full advantage of the local index, SeFrame accesses skewed spatial data in constant time. In experiments, we demonstrated that the proposed method delivered superior performance in terms of scalability and query efficiency, in most cases. [ABSTRACT FROM AUTHOR]

The semantic segmentation of high-resolution remote sensing images (HRRSIs) is a basic task for remote sensing image processing and has a wide range of applications. However, the abundant texture information and wide imaging range of HRRSIs lead to the complex distribution of ground objects and unclear boundaries, which bring huge challenges to the segmentation of HRRSIs. To solve this problem, in this paper we propose an improved squeeze and excitation residual network (SERNet), which integrates several squeeze and excitation residual modules (SERMs) and a refine attention module (RAM). The SERM can recalibrate feature responses adaptively by modeling the long-range dependencies in the channel and spatial dimensions, which enables effective information to be transmitted between the shallow and deep layers. The RAM pays attention to global features that are beneficial to segmentation results. Furthermore, the ISPRS datasets were processed to focus on the segmentation of vegetation categories and introduce Digital Surface Model (DSM) images to learn and integrate features to improve the segmentation accuracy of surface vegetation, which has certain prospects in the field of forestry applications. We conduct a set of comparative experiments on ISPRS Vaihingen and Potsdam datasets. The results verify the superior performance of the proposed SERNet. [ABSTRACT FROM AUTHOR]

Abiotic stress is one of the environmental conditions that effects plant survival and growth. As a vital model plant and papermaking raw material, it is very important to identify the differentially expressed genes of Populus trichocarpa Torr. & A.Gray ex Hook under abiotic stress in order to cultivate stress-tolerant plants. We analyzed the whole transcription spectrum and potential differentially expressed genes of 54 groups of roots and stem-xylem sequencing sample data under cold, drought, heat and salt stress for different durations. Gene Ontology (GO) enrichment analysis showed that molecular function played a stronger role in stems' response to abiotic stress, and genes in roots could barely respond to both cold stress and heat stress. Degree and betweenness centrality were used to identify transcription factors. It was considered that intermediate centrality is more suitable to determine whether the transcription factor is a hub gene. DNA binding was the biggest enrichment, while transcription factors responded to the abiotic stress. The multipronged approach identified in the roots and stems provides a genetic basis for resistance and more targeted genetic improvement of Populus trichocarpa. The comparison of two centralities can more effectively analyze the importance of complex gene network nodes in plants under corresponding abiotic stresses. [ABSTRACT FROM AUTHOR]

With the development of big data, analyzing the environmental benefits of transportation systems by artificial intelligence has become a hot issue in recent years. The ground traffic changes can be overlooked from a high-altitude perspective, using the technology of multi-temporal remote sensing change detection. We proposed a novel unsupervised algorithm by combining the image transformation and deep learning method. The new algorithm for remote sensing images is named multi-attention slow feature analysis (ASFA). In this model, three parts perform different functions respectively. The first part records to the K-BoVW to classify the categories of the ground objects as a channel parameter. The second part is a residual convolution with multiple attention mechanisms including temporal, spatial, and channel attention. Feature extraction and updating are completed at this link. Finally, we put the updated features in the slow feature analysis to highlight the variant components which we want and then generate the change map visually. Experiments on three very high-resolution datasets verified that the ASFA has a better performance than four basic change detection algorithms and an improved SFA algorithm. More importantly, this model works well for traffic road detection and helps us analyze the environmental benefits of traffic changes. [ABSTRACT FROM AUTHOR]

Radiotherapy is one of the important treatments for malignant tumors. The precision of radiotherapy is affected by the respiratory motion of human body, so real-time motion tracking for thoracoabdominal tumors is of great significance to improve the efficacy of radiotherapy. This paper aims to establish a highly precise and efficient prediction model, thus proposing to apply a depth prediction model composed of multi-scale enhanced convolution neural network and temporal convolutional network based on empirical mode decomposition (EMD) in respiratory prediction with different delay times. First, to enhance the precision, the unstable original sequence is decomposed into several intrinsic mode functions (IMFs) by EMD, and then, a depth prediction model of parallel enhanced convolution structure and temporal convolutional network with the characteristics specific to IMFs is built, and finally training on the respiratory motion dataset of 103 patients with malignant tumors is conducted. The prediction precision and time efficiency of the model are compared at different levels with those of the other three depth prediction models so as to evaluate the performance of the model. The result shows that the respiratory motion prediction model determined in this paper has superior prediction performance under different lengths of input data and delay time, and, furthermore, the network update time is shortened by about 60%. The method proposed in this paper will greatly improve the precision of radiotherapy and shorten the radiotherapy time, which is of great application value. [ABSTRACT FROM AUTHOR]

The increasing demand for forestry resources is driving the need for smarter systems capable of saving and protecting forests that can optimize agile forestry production. This study uses the continuous hot-pressing process of wooden medium-density fiberboard (MDF) to investigate the possibility of automatic quality control of the continuous flat pressing process. For this purpose, conceptual digital twin modeling for mechanism and sequence parameter control was conducted based on the cellular automata (CA) theory. A distributed coordination mode framework was constructed, and a craft control programming method was proposed for the quality control of MDF continuous flat pressing. Based on the MDF continuous flat press craft mechanism and control standards, a framework of five distributed flat press cooperative control mode elements for the cylinder array of the continuous panel system (CPS) was defined. To satisfy the distributed distance servo and pressure servo demands of the multi-stage hot pressing craft design, five kinds of synergy collaborative control modes of multiple rack groups were constructed using mode elements: For the four types of typical deviations in slab production, i.e., thickness, slope, depression, and bulge, a multi-zone mutual cooperative mode craft control sequence was programmed. According to the type and intensity of real-time deviation, the corresponding regulation sequence was applied. This effectively counteracts the deviation caused by the uncertainty interference due to the multi-field coupling effect in actual production. The application tests demonstrate that the adjustment and response time of the continuous flat press were greatly improved, and the quality superiority rate is controlled above 95%, thereby confirming the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

Deep unsupervised hashing methods are gaining attention in the field of remote sensing (RS) image retrieval due to the rapid growth in the volume of unlabeled RS data. Most previous unsupervised hashing research used only natural image-based pre-trained models to generate label matrices; however, this method cannot capture the semantic information of RS images well and limits the accuracy of retrieval. To solve this problem, the authors propose a deep unsupervised weighted hashing (DUWH) model that uses a similarity matrix updating strategy based on a weighted similarity structure to achieve the mutual optimization of the similarity matrix and hash network. The authors devise a novel combinatorial loss function to improve the hash performance that can be used to obtain higher quality hash codes by assigning different weights to the sample pairs with different difficulties. Experiments were conducted on two RS datasets to verify the excellent performance of the proposed method. [ABSTRACT FROM AUTHOR]

The ubiquitous deployment of 5G New Radio (5G NR) stimulates Unmanned Aircraft Systems (UAS) swarm networking to evolve to achieve more imminent progress. The heterogeneous collaboration between UAS swarm enhances the complexity and the efficiency of mission complement that requires robustness, flexibility, and sustainability of throughput in UAS swarm networking. The conventional approaches mainly are based on the hierarchical architectures that are limited to satisfy the challenges of UAS swarm with high dynamics on a large scale. In this paper, we propose an optimal cell wall paradigm to enhance the throughput in heterogeneous UAS swarm networking. With the weight adjustment of each link, we map the optimization into a polyhedron scheduling problem and formula the problem into Max-min Throughput Fair Scheduling (MTFS). Further, we propose a max-min throughput algorithm to optimize the minimum throughput of cell wall paradigm. With the optimal max-min throughput, we optimize the schedule with edge-coloring to achieve global MTFS solving. The normalized MTFS shows our algorithm can achieve over 40% improvement of MTFS globally. In terms of MTFS solving, our algorithms have promising potential to improve the throughput and mitigate the incidents for multiple beams enabling of UAS in cell wall communication. With the throughput enhancement, the advanced aerial mobility of UAS swarm networking can be escalated on a large scale. [ABSTRACT FROM AUTHOR]

High resolution remote sensing image cloud removal can bring a lot of convenience for human activities. However, the existing cloud removal algorithms have a variety of disadvantages. First of all, they have the disadvantages of long computing time and large consumption of computing resources. Secondly, the effect of recovery needs to be improved. In order to improve the above two points, a near real‐time effective algorithm is proposed, namely MSAR‐Defognet (multiple scale attention residual network using for cloud remove), which consumes less computing power and space and has superior cloud removal effect. On the one hand, several different large‐scale filters are chosen to extract the weak information effectively, while can save the computing power and shorten the image processing time. On the other hand, the fine‐grained convolution residual block with channel attention mechanism is used to enhance the network's ability to extract cloud features. In addition, a data set which is closer to the real cloud shape and has higher richness to train the cloud removal network, so that the parameters obtained by training have stronger robustness and can adaptively remove clouds with different thickness. Experiments show that, compared with other advanced network models, the network not only has the advantage of fast processing speed, but also has better image restoration effect in high‐resolution remote sensing image restoration. It can meet the requirements of many hard real‐time tasks, so that remote sensing images can play a greater value for human activities. [ABSTRACT FROM AUTHOR]

Epilepsy is a common mental disorder that affects about 70 million people worldwide. Epileptic electroencephalogram (EEG) signal, an important means to judge epileptic seizure, needs neurologists' prior knowledge to mark manually. This marking method is time‐consuming and laborious. Currently, the existing automated diagnosis methods have achieved good results on one benchmark EEG dataset, most of which can achieve accuracy of more than 0.95. However, the method has limitations on the dataset, and the accuracy of the diagnosis results on another new dataset drops sharply to nearly 0.5. Aiming at the existing EEG signal diagnosis lacks stability and generalization ability, this paper proposed a multilayer‐weighted integrated self‐learning algorithm for different classifiers. For this algorithm, weighted voting was first conducted on the the diagnostic results by different classifiers to obtain a result, which was weighted again to produce the final diagnostic results. This algorithm improves the problem that the traditional self‐learning algorithm is greatly affected by data noise, which shows a strong stability in different data sets and in clinical epileptic EEG signal data detection, so as to reduce the workload of neurologists and provide support and assistance for the diagnosis and treatment of epilepsy. The experiment result shows that the algorithm can improve the stability and reliability of EEG automatic diagnosis of epilepsy. The accuracy and AUC area of its classification in two different public data sets and clinical data can reach 0.80 to 0.95. [ABSTRACT FROM AUTHOR]

Summary: With the development of smart Internet of things devices, intelligent applications are expected to lead further innovation in smart city. However, although cloud computing infrastructure can be used to meet traditional challenges, the scheduling model for new big data intelligent application has still not matured. In this work, we proposed a two‐stage scheduling framework for smart city intelligent application. In the first stage, we propose a virtual machine selection algorithm for edge computing to enhance relative migration benefits. The algorithm defines the invalid virtual machine migration and relative migration benefits from the change in the overall computing resources of the cloud data center after the virtual machine migration. In the second stage, we proposed an energy efficient and resource‐constrained scheduling framework for edge computing. The historical data of the cloud and edge computing workload of the computing node are processed in a sliding window manner, and the median absolute deviation of the historical data is used as the base of the physical reserved resource constraint when the base also changes as the workload changes. The experimental results show that energy‐efficient and resource‐constrained can make the computer resource provide high‐quality services for users in a low‐energy state. [ABSTRACT FROM AUTHOR]

The efficient and accurate extraction of building feature information in remote-sensing images has become one of the most important elements of satellite remote-sensing image research. The paper proposes a convolutional neural network with a symmetric encoding-decoding structure. Alternating convolutional blocks and maximum pooled under-sampling at the encoder end are used to complete the relevant operations. The convolutional blocks are operated by linear residual blocks, and complementary zeros are added after 3 × 3 convolutional layers to ensure consistency in feature-map dimensions. A traditional ReLU activation function is replaced with a SELU activation function in order to retain more feature information during training and to solve the problem of dead neurons. A 1 × 1 convolutional layer and a Sigmoid function are finally introduced to complete the final building extraction. The experimental results show that the model is more effective in denselypopulated urban areas than in Alpine towns, but the overcrowding of buildings also causes difficulties in accurate edge segmentation. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy failure mode and effects analysis (FMEA) method is proposed by combining fault theory and a failure analysis method. The method addresses the problem of board thickness control failure and the problem of thickness deviation defect blanking, which can occur during continuous hot pressing (CHP) process, which is one of the most important processes in the production of medium-density fiberboard (MDF). The method combines the fault analysis with the Hamming code method and using the Hamming code to calculate and represent the cylinder array of the continuous hot-pressed thickness control execution unit to analyze and process the potential fixed thickness failure modes in MDF hot press production, and then summarizes the decision rules for controlling the board thickness and the level of sheet deviation. By combining the fuzzy FMEA method of the Hamming code and the logical OR operation of the experimental analysis, the method of thickness deviation and recognition control fault information for the CHP of MDF, which is proposed in this paper, permits the increase of the number of error levels, which makes optimization for controller more convenient and improves the efficiency to recognize errors. [ABSTRACT FROM AUTHOR]

The traditional method is used to deal with massive remote sensing data stored in low efficiency and poor scalability. This article presents a parallel processing method based on MapReduce and HBase. The filling of remote sensing images by the Hilbert curve makes the MapReduce method construct pyramids in parallel to reduce network communication between nodes. Then, the authors design a massive remote sensing data storage model composed of metadata storage model, index structure and filter column family. Finally, this article uses MapReduce frameworks to realize pyramid construction, storage and query of remote sensing data. The experimental results show that this method can effectively improve the speed of data writing and querying, and has good scalability. [ABSTRACT FROM AUTHOR]

With the development of network communication, a 1000-fold increase in traffic demand from 4G to 5G, it is critical to provide efficient and fast spatial data access interface for applications in mobile environment. In view of the low I/O efficiency and high latency of existing methods, this paper presents a memory-based spatial data query method that uses the distributed memory file system Alluxio to store data and build a two-level index based on the Alluxio key-value structure; moreover, it aims to solve the problem of low efficiency of traditional method; according to the characteristics of Spark computing framework, a data input format for spatial data query is proposed, which can selectively read the file data and reduce the data I/O. The comparative experiments show that the memory-based file system Alluxio has better I/O performance than the disk file system; compared with the traditional distributed query method, the method we proposed reduces the retrieval time greatly. [ABSTRACT FROM AUTHOR]

Classification and segmentation of point clouds have attracted increasing attention in recent years. On the one hand, it is difficult to extract local features with geometric information. On the other hand, how to select more important features correctly also brings challenges to the research. Therefore, the main challenge in classifying and segmenting the point clouds is how to locate the attentional region. To tackle this challenge, we propose a graph-based neural network with an attention pooling strategy (AGNet). In particular, local feature information can be extracted by constructing a topological structure. Compared to existing methods, AGNet can better extract the spatial information with different distances, and the attentional pooling strategy is capable of selecting the most important features of the topological structure. Therefore, our model can aggregate more information to better represent different point cloud features. We conducted extensive experiments on challenging benchmark datasets including ModelNet40 for object classification, as well as ShapeNet Part and S3DIS for segmentation. Both the quantitative and qualitative experiments demonstrated a consistent advantage for the tasks of point set classification and segmentation. [ABSTRACT FROM AUTHOR]

The segmentation of high-resolution (HR) remote sensing images is very important in modern society, especially in the fields of industry, agriculture and urban modelling. Through the neural network, the machine can effectively and accurately extract the surface feature information. However, using the traditional deep learning methods requires plentiful efforts in order to find a robust architecture. In this paper, we introduce a neural network architecture search (NAS) method, called NAS-HRIS, which can automatically search neural network architecture on the dataset. The proposed method embeds a directed acyclic graph (DAG) into the search space and designs the differentiable searching process, which enables it to learn an end-to-end searching rule by using gradient descent optimization. It uses the Gumbel-Max trick to provide an efficient way when drawing samples from a non-continuous probability distribution, and it improves the efficiency of searching and reduces the memory consumption. Compared with other NAS, NAS-HRIS consumes less GPU memory without reducing the accuracy, which corresponds to a large amount of HR remote sensing imagery data. We have carried out experiments on the WHUBuilding dataset and achieved 90.44% MIoU. In order to fully demonstrate the feasibility of the method, we made a new urban Beijing Building dataset, and conducted experiments on satellite images and non-single source images, achieving better results than SegNet, U-Net and Deeplab v3+ models, while the computational complexity of our network architecture is much smaller. [ABSTRACT FROM AUTHOR]

Deforestation in the Amazon rainforest results in reduced biodiversity, habitat loss, climate change, and other destructive impacts. Hence obtaining location information on human activities is essential for scientists and governments working to protect the Amazon rainforest. We propose a novel remote sensing image classification framework that provides us with the key data needed to more effectively manage deforestation and its consequences. We introduce the attention module to separate the features which are extracted from CNN(Convolutional Neural Network) by channel, then further send the separated features to the LSTM(Long-Short Term Memory) network to predict labels sequentially. Moreover, we propose a loss function by calculating the co-occurrence matrix of all labels in the dataset and assigning different weights to each label. Experimental results on the satellite image dataset of the Amazon rainforest show that our model obtains a better F 2 score compared to other methods, which indicates that our model is effective in utilizing label dependencies to improve the performance of multi-label image classification. [ABSTRACT FROM AUTHOR]

Summary: In the era of edge computing, real‐time data preprocessing on the edge node has the potential to improve computational efficiency and data accuracy. However, a significant challenge is private data disclosure, particularly in the case of location‐based services. To address this challenge, in this paper, by leveraging differential privacy, we propose a privacy‐aware framework for mobile edge computing called MEPA to protect the location privacy in which the edge node is regarded as an anonymous central server. The proposed framework can provide computing services without deploying special infrastructure. To be specific, in order to solve the problem of constrained computing resources in the edge nodes, the algorithm of Quadtree Differential Privacy based on Hilbert curve division (QTDP‐H) two‐dimensional spatial data query transmission is proposed. First, a noise quadtree is established and the privacy budget is divided according to the tree level. Then, the constructed quadtree is represented by quanternary, so that the partition based on Hilbert curve can be established and the two‐dimensional data in the area can be converted into one‐dimensional, which can greatly improve the retrieval efficiency. The effectiveness of the proposed algorithm in terms of time complexity and retrieval accuracy has been verified by extensive experimental results. Compared with traditional methods of (D,ε) − LP, the average runtime can be reduced by 15%‐20%, and the average relative error is reduced by 20%. [ABSTRACT FROM AUTHOR]

This article focuses on data aggregation scheduling problem with delay constraint in wireless sensor networks. Prior works on this problem have dealt with a tree topology wireless sensor network. However, in fact it is more common that the topology of wireless sensor network is graph topology. Delay-constrained data aggregation problem is formulated. To solve this problem, we propose an algorithm based on dynamic programming in the shortest path tree of the wireless sensor network. This approach classifies conflicts into two types, tree-inside conflicts and tree-outside conflicts with the aggregating tree. First, scheduling transmission time utilizes a dynamic programming algorithm. Then, transmissions with tree-outside conflicts are removed with maximum weight independent set in tree-outside conflict graph. As the scheduling performance depends on the aggregation tree, we propose another idea, simultaneous execution of aggregation tree construction and scheduling. We propose a greedy algorithm in wireless sensor networks. This approach is to maximize the number of scheduled nodes in every time slot from deadline to time slot 1. Simulation results show that greedy algorithm in wireless sensor networks outperforms dynamic programming in the shortest path tree and naive algorithm in terms of the effectiveness and the average delay. [ABSTRACT FROM AUTHOR]

Maize is widely cultivated and planted all over the world, which is one of the main food resources. Accurately identifying the defect of maize seeds is of great significance in both food safety and agricultural production. In recent years, methods based on deep learning have performed well in image processing, but their potential in the identification of maize seed defects has not been fully realized. Therefore, in this paper, a lightweight and effective network for maize seed defect identification is proposed. In the proposed network, the Convolutional Block Attention Module (CBAM) was integrated into the pretrained MobileNetv3 network for extracting important features in the channel and spatial domain. In this way, the network can be focused on useful feature information, and making it easier to converge. To verify the effectiveness of the proposed network, a total of 12784 images was collected, and 7 defect types were defined. Compared with other popular pretrained models, the proposed network converges with the least number of iterations and achieves the true positive rate is 93.14% and the false positive rate is 1.14%., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Chen, Jing, Wu and Zhao.)