Search

This study is concerned with path planning in a structured greenhouse, in contrast to much of the previous research addressing applications in outdoor fields. The prototype mainly comprises an independently driven Mecanum wheel, a lidar measuring module, a single-chip microcomputer control board, and a laptop computer. Environmental information collection and mapping were completed on the basis of lidar and laptop computer connection. The path planning algorithm used in this paper expanded the 8-search-neighborhood of the traditional A* algorithm to a 48-search-neighborhood, increasing the search direction and improving the efficiency of path planning. The Floyd algorithm was integrated to smooth the planned path and reduced the turning points in the path. In this way, the problems of the traditional A* algorithm could be solved (i.e., slow the path planning speed and high numbers of redundant points). Tests showed that the turning points, planning path time, and distance of the improved algorithm were the lowest. Compared with the traditional 8-search-neighborhood A* algorithm, the turning point was reduced by 50%, the planning time was reduced by 13.53%, and the planning distance was reduced by 13.96%. Therefore, the improved method of the A* algorithm proposed in this paper improves the precision of the planning path and reduces the planning time, providing a theoretical basis for the navigation, inspection, and standardization construction of greenhouses in the future. [ABSTRACT FROM AUTHOR]

An algorithm to improve the resolution of the frequency spectrum by detecting the number of complete cycles, removing any fractional components of the signal, signal discontinuities, and interpolating the signal for fault diagnostics of electrical machines using low-power data acquisition cards is proposed in this paper. Smart sensor-based low-power data acquisition and processing devices such as Arduino cards are becoming common due to the growing trend of the Internet of Things (IoT), cloud computation, and other Industry 4.0 standards. For predictive maintenance, the fault representing frequencies at the incipient stage are very difficult to detect due to their small amplitude and the leakage of powerful frequency components into other parts of the spectrum. For this purpose, offline advanced signal processing techniques are used that cannot be performed in small signal processing devices due to the required computational time, complexity, and memory. Hence, in this paper, an algorithm is proposed that can improve the spectrum resolution without complex advanced signal processing techniques and is suitable for low-power signal processing devices. The results both from the simulation and practical environment are presented. [ABSTRACT FROM AUTHOR]

The unmanned aerial vehicles (UAVs) network is vulnerable due to the high mobility and energy-constrained characteristics of UAVs. Nonetheless, as a UAV-based communication network, a stable network topology is crucial for efficient communication. To this end, in this paper, we propose a dynamic weighted clustering algorithm with dual cluster heads (DWCA-DCH) in this paper to deploy the UAV network. To trade off communication efficiency and lifetime, the selection of prime and backup cluster heads is designed by synthetically considering communication quality and remaining energy of the UAV. Furthermore, a survivability analysis method based on Markov process (SAM-MP) is constructed to analyze the survivability performance of the proposed UAV network based on DWCA-DCH when the UAV node suffers from energy exhausting or accidents. The simulation results show that the survivability and stability of the UAV cluster ad hoc network based on DWCA-DCH proposed in this paper is improved by about 35% compared with the single cluster head network. [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) positioning systems often operate in a non-line-of-sight (NLOS) environment. NLOS propagation has become the main source of ultra-wideband indoor positioning errors. As such, how to identify and correct NLOS errors has become a key problem that must be solved in high-accuracy indoor positioning technology. This paper firstly describes the influence of the NLOS propagation path on localization accuracy and the generation method of ultra-wideband signals, and secondly classifies and analyzes the currently available algorithms for ultra-wideband non-line-of-sight (NLOS) identification and error suppression. For the identification of NLOS, the residual analysis judgement method, statistical feature class identification method, machine learning method and geometric feature judgement method are discussed. For the suppression of NLOS propagation errors, weighting-based methods, filtering-based methods, line-of-sight reconstruction algorithms, neural network algorithms, optimization methods with constraints, and path tracing methods are discussed. Finally, we conclude the paper and point out the problems that need to be solved in NLOS indoor positioning. [ABSTRACT FROM AUTHOR]

Traditional anti-jamming techniques such as frequency hopping (FH) and direction-sequence spread spectrum (DSSS) are easily targeted by jammers. Inspired by the significant advantages of reconfigurable intelligent surfaces (RIS), and in order to overcome "double fading", controllable multiple active RISs are proposed to explore anti-jamming communication in this paper. To verify the feasibility of active RIS, the anti-jamming performance of active RIS is analyzed through theoretical derivation and simulation and compared with passive RIS. Furthermore, to solve the optimization problem of active RIS, a controllable multi-active RIS assisted anti-jamming algorithm based on BCD is proposed. Theoretical analysis and simulation results show that in small-scale deployment scenarios of RIS, the anti-jamming performance of active RIS is better than that of passive RIS, and the complexity and optimization performance of the proposed algorithm are better than those of semidefinite relaxation (SDR) algorithms. [ABSTRACT FROM AUTHOR]

Phosphor in Glass (PiG) is easy to be stirred unevenly during production and processing, and improper use of instruments and other factors lead to defective products. In this paper, we propose an improved YOLOv5 target detection algorithm. Firstly, the Coordinate Attention (CA) is introduced into the backbone network to enable the network to notice detect targets in a larger range. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) is used to fuse different scale information in the neck part to obtain the output feature map with rich semantic information. At the same time, the weighted bidirectional feature fusion pyramid structure adjusts the contribution of different scale input feature maps to the output by introducing weights. This optimization enhances the feature fusion effect, reduces the loss of feature information in the convolution process, and improves detection accuracy. Then, the GIOU_Loss function is replaced with the EIOU_Loss function to speed up the convergence. Finally, the comparative experiment is carried out with the self-made PiG dataset. The experimental results show that the average accuracy mAP of this method is 12.35% higher than that of the original method (YOLOv5s), with a detection speed is 53.92 FPS, aligning with the actual needs of industrial detection. [ABSTRACT FROM AUTHOR]

The transferability of adversarial examples has been proven to be a potent tool for successful attacks on target models, even in challenging black-box environments. However, the majority of current research focuses on non-targeted attacks, making it arduous to enhance the transferability of targeted attacks using traditional methods. This paper identifies a crucial issue in existing gradient iteration algorithms that generate adversarial perturbations in a fixed manner. These perturbations have a detrimental impact on subsequent gradient computations, resulting in instability of the update direction after momentum accumulation. Consequently, the transferability of adversarial examples is negatively affected. To overcome this issue, we propose an approach called Adversarial Perturbation Transform (APT) that introduces a transformation to the perturbations at each iteration. APT randomly samples clean patches from the original image and replaces the corresponding patches in the iterative output image. This transformed image is then used to compute the next momentum. In addition, APT could seamlessly integrate with other iterative gradient-based algorithms, incurring minimal additional computational overhead. Experimental results demonstrate that APT significantly enhances the transferability of targeted attacks when combined with traditional methods. Our approach achieves this improvement while maintaining computational efficiency. [ABSTRACT FROM AUTHOR]

The emergence of numerous consensus algorithms for distributed systems has resulted from the swift advancement of blockchain and its related technologies. Consensus algorithms play a key role in decentralized distributed systems, because all nodes in the system need to reach a consensus on requests or commands through consensus algorithms. In a distributed system where nodes work together to reach consensus, there may be Byzantine nodes present. The emergence of Byzantine nodes will affect the consensus of nodes in the distributed system. Therefore, tolerating Byzantine nodes in a distributed system and then reaching a consensus is an essential function of a consensus algorithm. So far, many Byzantine fault-tolerant (BFT) consensus algorithms have emerged, and there are correspondingly many methods to improve the performance of these algorithms. In order to allow researchers to have a clearer understanding of the existing methods, this paper systematically investigated and studied the research progress of the current Byzantine fault-tolerant consensus algorithm. The scope of the research ranged from the classic Byzantine consensus algorithm to some of the latest Byzantine consensus algorithms. The articles were classified according to the methods used to improve the Byzantine consensus algorithm. Through classification and centralized analysis and discussion, we achieved a clearer understanding of the development of Byzantine consensus algorithms and, at the same time, clarified the advantages and disadvantages of this type of method and the latest research progress using this method. At the end of this article, an in-depth discussion and analysis is also presented. By analyzing the impact of the use of these methods on the performance of the BFT consensus algorithm, it is proposed that future research can be improved. [ABSTRACT FROM AUTHOR]

An environment-aware millimeter wave radar (EMWR) is one of the most important sensors in an autonomous driving system. With the increasing penetration of an EMWR on an autonomous vehicle, the possibility of radar interference increases accordingly. Interference may seriously affect the detection performance of an EMWR. When the transmitted signals from other EMWRs are received by the EMWR on an autonomous vehicle, the accuracy of target detection will be affected, which may affect the environment-aware performance. Therefore, more and more attention is paid to how to restrain the interference between EMWR signals. Radar interference can be divided into two types: same-frequency interference (SFI) and different-frequency interference (DFI). In this paper, an anti-DFI algorithm of an EMWR is proposed. Firstly, the causes and signal characteristics of DFI are analyzed. Secondly, the signal amplitude is obtained via Hilbert transform to locate the interference according to the signal characteristics. Then, the Lagrange interpolation based on empirical-mode decomposition (EMD) is used to reconstruct the interference signal to reduce the influence of DFI. Finally, the feasibility of the proposed algorithm is verified by the simulation results. The simulation results validate that the interference signal after EMD filtering and Lagrange interpolation can repair the interference region and achieve the purpose of anti-DFI. [ABSTRACT FROM AUTHOR]

With the popularization of the network and the expansion of its application scope, the problem of abnormal network traffic caused by network attacks, malicious software, traffic peaks, or network device failures is becoming increasingly prominent. This problem not only leads to a decline in network performance and service quality but also may pose a serious threat to network security. This paper proposes a hybrid data processing model based on deep learning for network anomaly detection to improve anomaly detection performance. First, the Grey Wolf optimization algorithm is improved to select high-quality data features, which are then converted to RGB images and input into an anomaly detection model. An anomaly detection model of network dataflow based on a convolutional neural network is designed to recognize network anomalies, including DoS (Denial of Service), R2L (Remote to Local), U2R (User to Root), and Probe (Probing). To verify the effectiveness of the improved Grey Wolf algorithm and the anomaly detection model, we conducted experiments on the KDD99 and UNSW-NB15 datasets. The proposed method achieves an average detection rate of 0.986, which is much higher than all the counterparts. Experimental results show that the accuracy and the detection rates of our method were improved, while the false alarm rate has been reduced, proving the effectiveness of our approach in network anomaly classification tasks. [ABSTRACT FROM AUTHOR]

When light propagates in foggy weather, it is affected and scattered by suspended particles in the air. As a result, images taken in this environment often suffer from blurring, reduced contrast, loss of details, and other issues. The primary challenge in dehazing images is to estimate the transmission coefficient map in the atmospheric degradation model. In this paper, we propose a dehazing algorithm based on the optimization of the "haze-line" prior and non-local self-similarity prior. First, we divided the input haze image into small blocks and used the nearest neighbor classification algorithm to cluster the small patches, which were referred to as "patch-lines". Based on the characteristics of these "patch-lines", we could estimate the transmission coefficient map for the image. We then applied the transmission map to a weighted least squares filter to smooth it. Finally, we calculated the clear image using the haze degradation model. The experimental results demonstrate that our algorithm enhanced the image contrast and preserved the fine details, both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Datasets play an important role in the field of object detection. However, the production of the dataset is influenced by objective environment and human subjectivity, resulting in class imbalanced or even long-tailed distribution in the datasets. At present, the optimization methods based on data augmentation still rely on subjective parameter adjustments, which is tedious. In this paper, we propose a multi-stage adaptive Copy-Paste augmentation (MSACP) algorithm. This algorithm divides model training into multiple training stages, each stage forming unique training preferences for that stage. Based on these training preferences, the class information of the training set is adaptively adjusted, which not only alleviates the problem of class imbalance in training, but also expands different sample sizes for categories with insufficient information at different training stages. Experimental verification of the traffic sign dataset Tsinghua–Tencent 100K (TT100K) was carried out and showed that the proposed method not only can improve the class imbalance in the dataset, but can also improve the detection performance of models. By using MSACP to transplant the trained optimal weights to an embedded platform, and combining YOLOv3-tiny, the model's accuracy in detecting traffic signs in autonomous driving scenarios was improved, verifying the effectiveness of the MSACP algorithm in practical applications. [ABSTRACT FROM AUTHOR]

In this article, we design a new lightweight infrared optical system that fully meets airborne settings and greatly reduces the collection of invalid information. This new system targets the technical problems of stray light, strong invalid information, weak texture information of small targets, and low intensity of valid information under a complex background, which lead to difficult identification of small targets. Image enhancement of weak, small targets against complex backgrounds has been the key to improving small-target search and tracking technology. For the complex information that is still collected, an improved two-channel image enhancement processing algorithm is proposed: the A-channel adopts an improved nonlinear diffusion method and improved curvature filtering, and the B-channel adopts bootstrap filtering and a local contrast enhancement algorithm. The weak target is then extracted by the algorithm of weighted superposition. The false alarm rate is effectively weakened, and robustness is improved. As a result of the experimental data analysis, the method can effectively extract the weak targets in complex backgrounds, such as artificial backgrounds, surface vegetation, etc., enlarge the target gray value, and reduce Fa by 56%, compared with other advanced methods, while increasing Pd by 17%. The algorithm proposed in this paper is of great significance and value for weak target identification and tracking, and it has been successfully applied to industrial detection, medical detection, and in the military field. [ABSTRACT FROM AUTHOR]

Segmentation of high-resolution remote sensing images is one of the hottest topics in deep learning. Compared to ordinary images, high-resolution remote sensing images possess characteristics such as higher intra-class diversity and lower inter-class separability. Additionally, the objects in these images are complex and have smaller sizes. Aiming at the classical segmentation network in remote sensing images, there are some problems, such as inaccurate edge object segmentation, inconsistent segmentation of different types of objects, low detection accuracy, and a high false detection rate. This paper proposes a new hybrid attention model (S-CA), a new coordinate efficient channel attention module (C-ECA), and a new small-target feature extraction network (S-FE). The S-CA model enhances important spatial and channel features in shallow layers, allowing for more detailed feature extraction. The C-ECA model utilizes convolutional layers to capture complex dependencies between variations, thereby better capturing feature information at each position and reducing redundancy in feature channels. The S-FE network can capture the local feature information of different targets more effectively. It enhances the recognition and classification capabilities of various targets and improves the detection rate of small targets. The algorithm is used for segmentation in high-resolution remote sensing images. Experiments were conducted on the public dataset GID-15 based on Gaofen-2 satellite remote sensing images. The experimental results demonstrate that the improved DeepLabV3+ segmentation algorithm for remote sensing images achieved a mean intersection over union (mIoU), mean pixel accuracy (mPA), and mean precision (mP) of 91.6%, 96.1%, and 95.5%, respectively. The improved algorithm is more effective than current mainstream segmentation networks. [ABSTRACT FROM AUTHOR]

Recently, complicated spatial search algorithms have emerged as spatial-information-based applications, such as location-based services (LBS), and have become very diverse and frequent. The aggregate nearest neighbor (ANN) search is an extension of the existing nearest neighbor (NN) search; it finds the object p * that minimizes G { d (p * , q i) , q i ∈ Q } from a set Q of M (≥1) query objects, where G is an aggregate function and d () is the distance between two objects. The flexible aggregate nearest neighbor (FANN) search is an extension of the ANN search by introducing flexibility factor  ϕ (0 < ϕ ≤ 1) ; it finds the object p * that minimizes G { d (p * , q i) , q i ∈ Q ϕ } from Q ϕ , a subset of Q with | Q ϕ | = ϕ | Q | . This paper proposes an efficient ϵ -approximate k-FANN (k ≥ 1) search algorithm for an arbitrary approximation ratio ϵ (≥1) in road networks. In general, ϵ -approximate algorithms are expected to give an improved search performance at the cost of allowing an error ratio of up to the given ϵ. Since the optimal value of ϵ varies greatly depending on applications and cases, the approximate algorithm for an arbitrary ϵ is essential. We prove that the error ratios of the approximate FANN objects returned by our algorithm do not exceed the given ϵ. To the best of our knowledge, our algorithm is the first ϵ -approximate k-FANN search algorithm in road networks for an arbitrary ϵ. Through a series of experiments using various real-world road network datasets, we demonstrated that our approximate algorithm always outperformed the previous state-of-the-art exact algorithm and that the error ratios of the approximate FANN objects were significantly lower than the given ϵ value. [ABSTRACT FROM AUTHOR]

Aiming to solve the problems of large-scale changes, the dense occlusion of ship targets, and a low detection accuracy caused by challenges in the localization and identification of small targets, this paper proposes a ship target-detection algorithm based on the improved YOLOv5s model. First, in the neck part, a weighted bidirectional feature pyramid network is used from top to bottom and from bottom to top to solve the problem of a large target scale variation. Second, the CNeB2 module is designed to enhance the correlation of coded spatial space, reduce interference from redundant information, and enhance the model's ability to distinguish dense targets. Finally, the Separated and Enhancement Attention Module attention mechanism is introduced to enhance the proposed model's ability to identify and locate small targets. The proposed model is verified by extensive experiments on the sea trial dataset. The experimental results show that compared to the YOLOv5 algorithm, the accuracy, recall rate, and mean average precision of the proposed algorithm are increased by 1.3%, 1.2%, and 2%, respectively; meanwhile, the average precision value of the proposed algorithm for the dense occlusion category is increased by 4.5%. In addition, the average precision value of the proposed algorithm for the small target category is increased by 5% compared to the original YOLOv5 algorithm. Moreover, the detection speed of the proposed algorithm is 66.23 f/s, which can meet the requirements for detection speed and ensure high detection accuracy and, thus, realize high-speed and high-precision ship detection. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges of wideband signal beamforming in radar systems and proposes a new calibration method. Due to operating conditions, the frequency-dependent characteristics of the system can be changed, and the amplitude, phase, and time delay error can be generated. The proposed method is based on the concept of the sliding window algorithm for linear frequency modulated (LFM) signals. To calibrate the frequency-dependent errors from the transceiver and the time delay error from the true time delay elements, the proposed method utilizes the characteristic of the LFM signal. The LFM signal changes its frequency linearly with time, and the frequency domain characteristics of the hardware are presented in time. Therefore, by applying a matched filter to a part of the LFM signal, the frequency-dependent characteristics can be monitored and calibrated. The proposed method is compared with the conventional matched filter-based calibration results and verified by simulation results and beampatterns. Since the proposed method utilizes LFM signal as the calibration tone, the proposed method can be applied to any beamforming systems, not limited to LFM radars. [ABSTRACT FROM AUTHOR]

Path planning is crucial in the scheduling and motion planning of multiple robots. However, solving multi-robot path-planning problems efficiently and quickly is challenging due to their high complexity and long computational time, especially when dealing with many robots. This paper presents a unified mathematical model and algorithm for the path planning of multiple robots moving from one formation to another in an area with obstacles. The problem was initially simplified by constructing a cost matrix, and then the route planning was achieved by integrating an elite enhanced multi-population genetic algorithm and an ant colony algorithm. The performance of the proposed planning method was verified through numerical simulations in various scenarios. The findings indicate that this method exhibits high computational efficiency and yields a minimal overall path distance when addressing the path-planning problem of a multi-robot formation reconstruction. As a result, it holds promising potential for the path-planning problem of a multi-robot formation reconstruction. [ABSTRACT FROM AUTHOR]

The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing, computing, and communication capabilities have motivated intelligent applications empowered by artificial intelligence. Classical artificial intelligence algorithms require centralized data collection and processing, which are challenging in realistic intelligent IoT applications due to growing data privacy concerns and distributed datasets. Federated Learning (FL) has emerged as a privacy-preserving distributed learning framework, which enables IoT devices to train global models through sharing model parameters. However, inefficiency due to frequent parameter transmissions significantly reduces FL performance. Existing acceleration algorithms consist of two main types including local update and parameter compression, which considers the trade-offs between communication and computation/precision, respectively. Jointly considering these two trade-offs and adaptively balancing their impacts on convergence have remained unresolved. To solve the problem, this paper proposes a novel efficient adaptive federated optimization (FedEAFO) algorithm to improve the efficiency of FL, which minimizes the learning error via jointly considering two variables including local update and parameter compression. The FedEAFO enables FL to adaptively adjust two variables and balance trade-offs among computation, communication, and precision. The experiment results illustrate that compared with state-of-the-art algorithms, the FedEAFO can achieve higher accuracies faster. [ABSTRACT FROM AUTHOR]

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04. [ABSTRACT FROM AUTHOR]

In the clock network design, the trade-off between power consumption and timing closure is an important and difficult issue. The clock tree architecture has a shorter wire length and better power consumption, but it is more difficult to achieve timing closure with it. On the other hand, clock mesh architecture is easier to satisfy the clock skew constraint, but it usually has much more power consumption. Therefore, a hybrid clock network architecture that combines both the clock tree and clock mesh seems to be a promising solution. In a normal hybrid mesh/tree structure, a driving buffer is placed in the intersection of mesh lines. In this paper, we propose a novel cross-mesh architecture, and we distribute the buffers to balance the overall switching capacitance, reducing the number of registers connected to a subtree, and the load capacitance of a buffer. With the average dispersion of the overall driving force, our methodology creates small non-zero skew clock trees. In addition, we integrate clock gating, register clustering, and load balancing techniques to optimize clock skew and load capacitance simultaneously. The proposed methodology has four stages: cross-mesh planning, register clustering, mesh line connecting, and load balancing. Experimental results show that our cross-mesh architecture has high tolerance for process variation, and is robust in all the operation modes. Comparing it to the uniform mesh architecture, our methodology and algorithms reduce 28.9% of load capacitance and 80.4% of clock skew on average. Compared to the non-uniform mesh architecture, we also reduce capacitance by 22.4% and skew by 76.7% on average. This illustrates that we can obtain a feasible solution effectively and improve both power consumption and clock skew simultaneously. [ABSTRACT FROM AUTHOR]

This study proposes a side-scan sonar target detection technique for CPU or low-performance GPU to meet the requirement of underwater target detection. To rectify the gray distribution of the original side scan sonar data, enhance picture segmentation, and supply the data distribution probability for the clustering algorithm, the methodology uses a classic image processing technique that is GPU-friendly. The modified adaptive Finch clustering technique is used to segment the image and remove image voids after assessing the processed image attributes. The posterior information is then used to apply a classification label to each pixel. The characteristics of the connected region are analyzed in the data playback of the Tuandao experiment in accordance with the imaging principle of side-scan sonar and the original shape and size characteristics of the target. The predicted target results are combined with the AUV navigation information to obtain the predicted target longitude and latitude information, which is then sent to the AUV master control system to guide the next plan. The Jiaozhou Bay sea test results demonstrate that the traditional target detection algorithm put forth in this paper can be integrated into a low-performance GPU to detect targets and locate them. The detection accuracy and speed exhibit strong performance, and real-time autonomous sonar detection is made possible. [ABSTRACT FROM AUTHOR]

Edge computing has emerged as a promising solution to reduce communication delays and enhance the performance of real-time applications. However, due to the limited processing power of edge servers, it is challenging to achieve efficient task processing. In this paper, we propose a Q-learning-based load-balancing method that optimizes the distribution of real-time tasks between edge servers and cloud servers to reduce processing time. The proposed method is dynamic and adaptive, taking into account the constantly changing network status and server usage. To evaluate the effectiveness of the proposed load-balancing method, extensive simulations are conducted in an Edge-Cloud network environment. The simulation results demonstrate that the proposed method significantly reduces processing time compared to traditional static load-balancing methods. The Q-learning algorithm enables the load-balancing system to dynamically learn the optimal decision-making strategy to allocate tasks to the most appropriate server. Overall, the proposed Q-learning-based load-balancing method provides a dynamic and efficient solution to balance the workload between edge servers and cloud servers. The proposed method effectively achieves real-time task processing in edge computing environments and can contribute to the development of high-performance edge computing systems. [ABSTRACT FROM AUTHOR]

To keep the balance of precision and speed of unmanned aerial vehicles (UAVs) in detecting insulator defects during power inspection, an improved insulator defect identification algorithm, Insu-YOLO, which is based on the latest YOLOv8 network, is proposed in this paper. Firstly, to lower the computational complexity of the network, the GSConv module is introduced in the backbone and neck network. In the neck network, a lightweight content-aware reassembly of features (CARAFE) structure is adopted to better utilize the feature information for upsampling, which enhances the feature fusion capability of Insu-YOLO. Additionally, Insu-YOLO enhances the fusion between shallow and deep feature maps by adding an extra object detection layer, thereby increasing the accuracy for detecting small targets. The experimental results indicate that the mean average precision of Insu-YOLO reaches 95.9%, which is 3.95% higher than the YOLOv8n baseline model, with a memory usage of 9.2 MB. Moreover, the detection speed of Insu-YOLO is 87 frames/s which achieves the purpose of real-time identification of insulator defects. [ABSTRACT FROM AUTHOR]

For the shortcomings of an unstable clustering effect and low accuracy caused by the manual setting of the two parameters Eps and MinPts of the DBSCAN (density-based spatial clustering of applications with noise) algorithm, this paper proposes an adaptive determination method for DBSCAN algorithm parameters based on the K-dist graph, noted as X-DBSCAN. The algorithm uses the least squares polynomial curve fitting method to fit the curve in the K-dist graph to generate a list of candidate Eps parameters and uses the mathematical expectation method and noise reduction threshold to generate the corresponding MinPts parameter list. According to the clustering results of each group of parameters in the Eps and MinPts parameter lists, a stable range of cluster number changes is found, and the MinPts and Eps corresponding to the maximum K value in the stable range are selected as the optimal algorithm parameters. The optimality of this parameter was verified using silhouette coefficients. A variety of experiments were designed from multiple angles on the artificial dataset and the UCI real dataset. The experimental results show that the clustering accuracy of X-DBSCAN was 21.83% and 15.52% higher than that of DBSCAN on the artificial and real datasets, respectively. The X-DBSCAN algorithm was also superior to other algorithms through comprehensive evaluation and analysis of various clustering indicators. In addition, experiments on four synthetic Gaussian datasets of different dimensions showed that the average clustering indices of the proposed algorithm were above 0.999. The X-DBSCAN algorithm can select parameters adaptively in combination with the characteristics of the dataset; the clustering effect is better, and clustering process automation is realized. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel normalized least mean squares (NLMS) algorithm for system identification applications. Our approach involves analyzing the mean squared deviation performance of the NLMS algorithm using a random walk model to select two optimal parameters, the step size and regularization parameters, for the rapid convergence of the colored input signals. We verified that the proposed algorithm exhibited faster convergence than existing algorithms, even in scenarios of sudden system changes. [ABSTRACT FROM AUTHOR]

Detecting small objects in aerial images captured by unmanned aerial vehicles (UAVs) is challenging due to their complex backgrounds and the presence of densely arranged yet sparsely distributed small targets. In this paper, we propose a real-time small object detection algorithm called YOLOv7-UAV, which is specifically designed for UAV-captured aerial images. Our approach builds upon the YOLOv7 algorithm and introduces several improvements: (i) removal of the second downsampling layer and the deepest detection head to reduce the model's receptive field and preserve fine-grained feature information; (ii) introduction of the DpSPPF module, a spatial pyramid network that utilizes concatenated small-sized max-pooling layers and depth-wise separable convolutions to extract feature information across different scales more effectively; (iii) optimization of the K-means algorithm, leading to the development of the binary K-means anchor generation algorithm for anchor allocation; and (iv) utilization of the weighted normalized Gaussian Wasserstein distance (nwd) and intersection over union (IoU) as indicators for positive and negative sample assignments. The experimental results demonstrate that YOLOv7-UAV achieves a real-time detection speed that surpasses YOLOv7 by at least 27% while significantly reducing the number of parameters and GFLOPs to 8.3% and 73.3% of YOLOv7, respectively. Additionally, YOLOv7-UAV outperforms YOLOv7 with improvements in the mean average precision (map (0.5:0.95)) of 2.89% and 4.30% on the VisDrone2019 and TinyPerson datasets, respectively. [ABSTRACT FROM AUTHOR]

As many new coding techniques and coding structures have been introduced to further improve the coding efficiency of depth maps in 3D video extensions, the coding complexity has been greatly increased. Fast algorithms are now needed to improve coding unit (CU) depth decisions as well as the coding pattern decision based on the coding. This paper presents an innovative machine learning-based approach aimed at mitigating the complexity associated with in-frame coding algorithms. We build different clustering models for different CU sizes to cluster CUs of the same size to decide their CU sizes. This is achieved by augmenting ensemble clustering through the expedited propagation of clustering similarities, considering CU with the same or similar texture complexity the same as for CU depth selection, which is informed by a comprehensive analysis of the original texture and its neighboring elements. The experimental findings demonstrate that the proposed scheme yields a substantial average reduction of 44.24% in the coding time. Remarkably, the corresponding Bjøntegaard delta bit rate (BDBR) increment observed for the synthetic view is a mere 0.26%. [ABSTRACT FROM AUTHOR]

A novel content-aware image resizing mechanism based on composition detection and composition rules is proposed to address the lack of esthetic perception in current content-aware resizing algorithms. A composition detection module is introduced for the detection of the composition of the input image types in the proposed algorithm. According to the classification results, the corresponding composition rules in computational esthetics are selected. Finally, the algorithm performs the operations of seam carving using the corresponding esthetic rules. The resized image not only protects the important content of the image, but also meets the composition rules to optimize the overall visual effect of the image. The simulation results show that the proposed algorithm achieves a better visual effect. Compared with the existing algorithms, the proposed algorithm not only effectively protects important image content, but also protects important structures and improves the overall beauty of the image. [ABSTRACT FROM AUTHOR]

Network clustering for mining protein complexes from protein–protein interaction (PPI) networks has emerged as a prominent research area in data mining and bioinformatics. Accurately identifying complexes plays a crucial role in comprehending cellular organization and functionality. Network characteristics are often useful in enhancing the performance of protein complex detection methods. Many protein complex detection algorithms have been proposed, primarily focusing on local micro-topological structure metrics while overlooking the potential power-law distribution characteristic of community sizes at the macro global level. The effective use of this distribution characteristic information may be beneficial for mining protein complexes. This paper proposes a network clustering algorithm for protein complex detection fused with power-law distribution characteristic. The clustering algorithm constructs a cluster generation model based on scale-free power-law distribution to generate a cluster with a dense center and relatively sparse periphery. Following the cluster generation model, a candidate cluster is obtained. From a global perspective, the number distribution of clusters of varying sizes is taken into account. If the candidate cluster aligns with the constraints defined by the power-law distribution function of community sizes, it is designated as the final cluster; otherwise, it is discarded. To assess the prediction performance of the proposed algorithm, the gold standard complex sets CYC2008 and MIPS are employed as benchmarks. The algorithm is compared to DPClus, IPCA, SEGC, Core, SR-MCL, and ELF-DPC in terms of F-measure and Accuracy on several widely used protein–protein interaction networks. The experimental results show that the algorithm can effectively detect protein complexes and is superior to other comparative algorithms. This study further enriches the connection between analyzing complex network topology features and mining network function modules, thereby significantly contributing to the improvement of protein complex detection performance. [ABSTRACT FROM AUTHOR]

Motion drive algorithms are a set of filters designed to simulate realistic motion and are an integral part of contemporary vehicle simulators. This paper presents the design of a novel intelligent interval type 2 fuzzy adaptive motion drive algorithm for an off-road uphill vehicle simulator. The off-road, uphill vehicle simulator is used to train and assess the driver's behavior under varying operational and environmental conditions in mountainous terrain. The proposed algorithm is the first of its kind to be proposed for off-road uphill vehicle simulators, and it offers numerous benefits over other motion drive algorithms. The proposed algorithm enables the simulator to adapt to changes in the uphill road surface, vehicle weight distribution, and other factors that influence off-road driving in mountainous terrain. The proposed algorithm simulates driving on hilly terrain more realistically than existing algorithms, allowing drivers to learn and practice in a safe and controlled environment. Additionally, the proposed algorithm overcomes limitations present in existing algorithms. The performance of the proposed algorithm is evaluated via test drives and compared to the performance of the conventional motion drive algorithm. The results demonstrate that the proposed algorithm is more effective than the conventional motion drive algorithm for the ground vehicle simulator. The pitch and roll responses demonstrate that the proposed algorithm has enabled the driver to experience abrupt changes in terrain while maintaining the driver's safety. The surge response demonstrated that the proposed MDA handled the acceleration and deceleration of the vehicle very effectively. In addition, the results demonstrated that the proposed algorithm resulted in a smoother drive, prevented false motion cues, and offered a more immersive and realistic driving experience. [ABSTRACT FROM AUTHOR]

Stand-alone microgrids have become reliable and efficient solutions for remote areas and critical infrastructures. However, the converters within these microgrids experience long-term complex power fluctuations caused by random variations in micro sources and loads. These power fluctuations induce thermal cycling in semiconductor chips, leading to thermal fatigue failure and compromising the safety and reliability of both the converter and microgrid operation. To address this issue, this paper proposes a reactive power injection algorithm aimed at reducing the output power fluctuation of the converter. The algorithm implements reactive power injection at the converter control level, thereby restructuring the output power profile and resulting in reduced junction temperature fluctuations in IGBTs. This approach effectively mitigates thermal stress within the material layers of the module, extending the lifetime of power devices and improving the operational reliability of the microgrid. The algorithm implementation is based on the PQ control strategy, integrating the power triangle with the envelope detection technique. Furthermore, the lifetime prediction process utilizes the electro-thermal coupling model, the rainflow counting algorithm, and the Lesit model. Simulation results demonstrate that, for an active power fluctuation range of 10 kW to 80 kW and an equivalent RC time constant of 22.5 s, the algorithm achieves a significant reduction of 62.64% in the amplitude of output power fluctuation and extends the lifetime of power devices by 74.13%. The obtained data showcase the effectiveness of the algorithm in enhancing the lifetime of power devices and further improving the microgrid operational reliability under specific parameter conditions. [ABSTRACT FROM AUTHOR]

Single instruction multiple data (SIMD) vector extension has become an essential feature of high-performance processors. Architectures such as x86, ARM, MIPS, and PowerPC have specific vector extension instruction sets and SIMD micro-architectures. Using SIMD vectorization programming can significantly improve the performance of application algorithms while keeping the hardware overhead low. In addition, other methods can enhance algorithm performance, such as selecting the best SIMD vectorization model for algorithms, ensuring sufficient instruction streams, implementing reasonable and effective cache data prefetching, and aligning data access and storage addresses according to instruction characteristics. The goal of this paper is three-fold. First, we introduce the basic structural characteristics of a general RISC processor, Hua Rui (HR) DSP, with a custom vector instruction set based on compatibility with an MIPS64 fixed-point and floating-point instruction set, as well as a Fei Teng (FT) processor compatible with an ARMv8 instruction set. Second, we summarize the fundamental principles of SIMD vectorization programming design for the HR DSP, which provides ideas for other scholars or engineering and technical personnel to study the algorithm performance using SIMD vectorization optimization. Third, we implement representative typical algorithms based on the HR and FT platforms and obtain experimental results that show improvement in algorithm SIMD vectorization optimization according to the vector programming design principles summarized in this article can improve the single-core performance of scalar implementation without vectorization, instruction streams, and cache data prefetching by 4–22 times for mean filter, accumulation, and matrix–matrix multiplication, which is significantly better than the performance improvement of 3–13 times for the FT platform. Moreover, the performance of matrix–matrix multiplication using the best vectorization model on the HR platform is about 84% higher than that of the common SIMD vectorization model. [ABSTRACT FROM AUTHOR]

In intelligent surveillance of construction sites, safety helmet detection is of great significance. However, due to the small size of safety helmets and the presence of high levels of noise in construction scenarios, existing detection methods often encounter issues related to insufficient accuracy and robustness. To address this challenge, this paper introduces a new safety helmet detection algorithm, FEFD-YOLOV5. The FEFD-YOLOV5 algorithm enhances detection performance by adding a shallow detection head specifically for small target detection and incorporating an SENet channel attention module to compress global spatial information, thus improving the model's mean average precision (mAP) in corresponding scenarios. Additionally, a novel denoise module is introduced in this algorithm, ensuring the model maintains high accuracy and robustness under various noise conditions, thereby enhancing the model's generalization capability to meet real-world scenario demands. Experimental results show that the proposed improved algorithm achieves a detection accuracy of 94.89% in noiseless environments, and still reaches 91.55% in high-noise environments, demonstrating superior detection efficacy compared to the original algorithm. [ABSTRACT FROM AUTHOR]

To address the problems of poor adaptability to pulse loss, susceptibility to interfered pulses, and the need of sub-PRI (Pulse Repetition Interval) ranking in the existing signal sorting algorithms, this paper proposes an algorithm for sorting staggered PRI signals based on the congruence transform. According to the analysis of the congruence characteristics of the staggered PRI signal, the proposed algorithm transforms the arrival time of the pulse to a fixed value, based on which the staggered PRI signal sorting and the sub-PRI sequence extraction can be achieved. Simulation results show that the proposed algorithm can effectively sort the staggered PRI signals and obtain the sub-PRI sequence directly without sub-PRI ranking, and, compared to some typical algorithms, it is less affected by the interfered pulses and the pulse loss. [ABSTRACT FROM AUTHOR]

As an essential protection mechanism of information security, hash algorithms are extensively used in various security mechanisms. The diverse application scenarios make the implementation of hash algorithms more challenging regarding flexibility, performance, and resources. Since the existing studies have such issues as wasted resources and few algorithms are supported when implementing hash algorithms, we proposed a new reconfigurable hardware architecture for common hash algorithms in this paper. First, we used the characteristics of symmetry of SM3 (Shang Mi 3) and SHA2 (Secure Hash Algorithm 2) to design an architecture that also supports MD5 (Message Digest 5) and SHA1 (Secure Hash Algorithm 1) on both sides. Then we split this architecture into two layers and eliminated the resource wastes introduced by different word widths through exploiting greater parallelism. Last, we further divided the architecture into four operators and designed an array. The experimental results showed that our architecture can support four types of hash algorithms successfully, and supports 32-bit and 64-bit word widths without wasting resources. Compared with existing designs, our design has a throughput rate improvement of about 56.87–226% and a throughput rate per resource improvement of up to 5.5 times. Furthermore, the resource utilization rose to 80% or above when executing algorithms. [ABSTRACT FROM AUTHOR]

The aircraft four-dimensional trajectory planning is an important technology for multiple aircraft to achieve cooperation. However, the current four-dimensional trajectory planning technology is mainly used for civil aviation and helicopters and is difficult to meet the requirements of fixed-wing aircraft. This paper proposed a four-dimensional trajectory planning algorithm for a fixed-wing aircraft formation, considering the speed range, turning radius and maximum overload. The improved tau-J strategy (ITJS) is used to generate the four-dimensional trajectory of the aircraft. This strategy is a bio-inspired trajectory planning algorithm that can generate a four-dimensional trajectory with continuous acceleration. Furthermore, the improved hunter–prey optimization (IHPO) algorithm is used to optimize the trajectory to make the generated trajectory meet the constraints and speed up the algorithm convergence. This algorithm improves the updated strategy and initialization strategy based on the hunter–prey optimization (HPO) algorithm, which prevents the algorithm from falling into local optima. The results of the benchmark test function show that the optimization result of the algorithm is improved by more than 10% compared with the original HPO algorithm. The simulation results show that the proposed algorithm jumps out of local optima and generates a trajectory that meets the constraints. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm for adaptive deep image compression (DIC) that can compress images for different purposes or contexts at different rates. The algorithm can compress images with semantic awareness, which means classification-related semantic features are better protected in lossy image compression. It builds on the existing conditional encoder-based DIC method and adds two features: a model-based rate-distortion-classification-perception (RDCP) framework to control the trade-off between rate and performance for different contexts, and a mechanism to generate coding conditions based on image complexity and semantic importance. The algorithm outperforms the QMAP2021 benchmark on the ImageNet dataset. Over the tested rate range, it improves the classification accuracy by 11 % and the perceptual quality by 12.4 % , 32 % , and 1.3 % on average for NIQE, LPIPS, and FSIM metrics, respectively. [ABSTRACT FROM AUTHOR]

In direct current optical orthogonal frequency division multiplexing (DCO-OFDM) systems, the high peak-to-average power ratio (PAPR) has been a significant challenge. Recently, lexicographical symbol position permutation (LSPP) using random permutations has been introduced as an efficient solution to reduce high PAPR. In this paper, we aim to evaluate the effectiveness of LSPP by comparing both adjacent and interleaved lexicographical permutation sequences with random lexicographical permutation sequences. Our findings demonstrate that random permutation yields superior PAPR reduction performance results when compared to adjacent and interleaved permutation. However, in scenarios with a limited number of sub-blocks, the use of adjacent and interleaved permutation becomes more favorable, as they can eliminate the possibility of generating identical permutation sequences, a drawback of random permutation. Additionally, we propose a novel algorithm to determine the optimal number of candidate permutation sequences that can achieve acceptable PAPR reduction performance while adhering to computational complexity constraints defined by the system requirements. [ABSTRACT FROM AUTHOR]

This paper proposes a parameter coordination optimization design of a power system stabilizer (PSS) based on an improved quasi-affine transformation evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power systems. To begin, the simulated annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to the power system. Since the damping ratio is one of the criteria with which to measure the dynamic stability of the power system, this paper sets the objective function according to the principle of maximization of the damping coefficient of the electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the safety factor of the system as much as possible. Finally, the method's rationality and validity were validated by applying it to the simulation examples of the IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, can restrain the low-frequency oscillation of the power system more effectively and can enhance the system's stability. [ABSTRACT FROM AUTHOR]

The trajectory data of aircraft, ships, and so on, can be analyzed to obtain valuable information. Clustering is the basic technology of trajectory analysis, and the feature extraction process is one of the decisive factors for clustering performance. Trajectory features can be divided into two categories: spatial features and temporal features. In mainstream algorithms, spatial features are represented by latitude and longitude coordinates. However, such algorithms are only suitable for trajectories where spatial features are tightly coupled with latitude and longitude. When the same types of trajectories are in different latitude and longitude ranges or there are transformations such as rotation, scaling, and so on, this kind of algorithm is infeasible. Therefore, this paper proposes a spatio-temporal feature trajectory clustering algorithm based on deep learning. In this algorithm, the extraction process of the trajectory spatial shape feature is designed based on image matching technology, and the extracted spatial features are combined with the trajectory temporal features to improve the clustering performance. The experimental results on simulated and real datasets show that the algorithm can effectively extract the trajectory spatial shape features and that the clustering effect of the fused spatio-temporal feature is better than that of a single feature. [ABSTRACT FROM AUTHOR]

In this paper, we present a method to identify paramaters for controlling electro-hydraulic servo systems in a real-time environment. With the aim of addressing the problem that it is difficult to accurately obtain the state space equation parameters of the physical entity of the electro-hydraulic servo system, we introduce an online identification theory (recursive least squares method) for identifying said parameters of the state space model in a valve-controlled symmetrical cylinder system. After accurately obtaining the parameters of the system, nonlinear control of the valve-controlled symmetrical cylinder system is carried out using a backstepping algorithm. In order to verify the actual effect of the online identification algorithm and backstepping algorithm, a hardware-in-the-loop (HIL) simulation platform for the valve-controlled symmetrical cylinder system is built in a Linux real-time system, and the real-time performance of the system is evaluated, which demonstrates that the platform can be reliably applied for subsequent system identification and backstepping verification. The results of the HIL simulation test demonstrate that the online identification algorithm and backstepping control method developed in this paper are effective and reliable. [ABSTRACT FROM AUTHOR]

Object detection algorithms play a crucial role in other vision tasks. This paper finds that the action recognition algorithm SlowFast's detection algorithm FasterRCNN (Region Convolutional Neural Network) has disadvantages in terms of both detection accuracy and speed and the traditional IOU (Intersection over Union) localization loss is difficult to make the detection model converge to the minimum stability point. To solve the above problems, the article uses YOLOv3 (You Only Look Once), YOLOX, and CascadeRCNN to improve the detection accuracy and speed of the SlowFast. This paper proposes a new localization loss function that adopts the Lance and Williams distance as a new penalty term. The new loss function is more sensitive when the distance difference is smaller, and this property is very suitable for the late convergence of the detection model. The experiments were conducted on the VOC (Visual Object Classes) dataset and the COCO dataset. In the final videos test, YOLOv3 improved the detection speed by 10.5 s. CascadeRCNN improved by 3.1%AP compared to FasterRCNN in the COCO dataset. YOLOX's performance on the COCO dataset is also mostly better than that of FasterRCNN. The new LIOU (Lance and Williams Distance Intersection over Union) localization loss function performs better than other loss functions in the VOC dataset. It can be seen that improving the detection algorithm of the SlowFast seems to be crucial and the proposed loss function is indeed effective. [ABSTRACT FROM AUTHOR]

A simplified bubble model and its solver optimization method are proposed to solve the problem of poor realistic simulation and complex solutions for bubble-motion behavior in water. Firstly, the internal velocity of the bubble was avoided, and the bubble model was established by only considering the net flux of the inlet and outlet bubbles, which reduced the computational complexity. The bubble constraint was then introduced into the motion equation of water, and the mixed Euler–Lagrangian method was used to solve it. FLIP particles tracked the bubble position, velocity, and deformation, and the mesh updated the vector field. At the same time, the viscosity term was simplified. Finally, it was combined with implicit incompressible SPH particles to achieve the purpose of volume correction. The experimental results show that the method in this paper can present a simulation effect of bubbles in water with rich detail and a realistic sense, whether compared with actual pictures or with existing methods. [ABSTRACT FROM AUTHOR]

In the 5G era, the amount of network data has grown explosively. A large number of new computation-intensive applications have created demand for edge computing in mobile networks. Traditional optimization methods are difficult to adapt to the dynamic wireless network environment because they solve the problem online, which is not suitable in edge computing scenarios. Therefore, in order to obtain a mobile network with better performance, we propose a network frame with a resource allocation algorithm based on power consumption, delay and user cooperation. This algorithm can quickly realize the optimization of a network to improve performance. Specifically, compared with heuristic algorithms, such as particle swarm optimization, ant colony algorithm, etc., commonly used to solve such problems, the algorithm proposed in this paper can reduce some aspects of network performance (including delay and user energy consumption) by about 10% in a network dominated by downlink tasks. The performance of the algorithm under certain network conditions was demonstrated through simulations. [ABSTRACT FROM AUTHOR]

Ground-based multiple-input multiple-output synthetic aperture radar (MIMO-SAR) is a new type of deformation monitoring sensor that has the advantages of no mechanical motion and fast echo acquisition. Ground-based MIMO-SAR can significantly improve the data rate of deformation monitoring. In this paper, a fast imaging algorithm tailored for ground-based MIMO-SAR data is proposed, which can be applied in both far-field and near-field scenarios. First, the phase center approximation error of the non-collinear array in the near field is analyzed. Then, a fast imaging algorithm based on geometric transformation for the coherent synthesis of subimages is put forward. The algorithm uses the geometric transformation to convert the subaperture imaging results into the full aperture coordinate system, which avoids the point-by-point interpolation calculation and further reduces the computational cost of the subimage coherent synthesis algorithm. Simulations and experiments show that the algorithm can achieve high-precision focusing imaging, and its operation efficiency is significantly improved compared with the algorithm based on interpolation. [ABSTRACT FROM AUTHOR]

With services in cloud manufacturing expanding, cloud manufacturers increasingly use service level agreements (SLAs) to guarantee business processing cooperation between CSPs and CSCs (cloud service providers and cloud service consumers). Although blockchain and smart contract technologies are critical innovations in cloud computing, consensus algorithms in Blockchain as a Service (BaaS) systems often overlook the importance of SLAs. In fact, SLAs play a crucial role in establishing clear commitments between a service provider and a customer. There are currently no effective consensus algorithms that can monitor the SLA and provide service level priority. To address this issue, we propose a novel KNN-based consensus algorithm that classifies transactions based on their priority. Any factor that impacts the priority of the transaction can be used to calculate the distance in the KNN algorithm, including the SLA definition, the smart contract type, the CSC type, and the account type. This paper demonstrates the full functionality of the enhanced consensus algorithm. With this new method, the CSP in BaaS systems can provide improved services to the CSC. Experimental results obtained by adopting the enhanced consensus algorithm show that the SLA is better satisfied in the BaaS systems. [ABSTRACT FROM AUTHOR]

At present, the core of lidar data registration algorithms depends on search correspondence, which has become the core factor limiting the performance of this kind of algorithm. For point-based algorithms, the data coincidence rate is too low, and for line-based algorithms, the method of searching the correspondence is too complex and unstable. In this paper, a laser radar data registration algorithm based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering is proposed, which avoids the search and establishment of the corresponding relationship. Firstly, a ring band filter is designed to process the outliers with noise in a point cloud. Then, the adaptive threshold is used to extract the line segment features in the laser radar point cloud. For the point cloud to be registered, a DBSCAN density clustering algorithm is used to obtain the key clusters of the rotation angle and translation matrix. In order to evaluate the similarity of the two frames of the point cloud in the key clusters after data registration, a kernel density estimation method is proposed to describe the registered point cloud, and K-L divergence is used to find the optimal value in the key clusters. The experimental results show that the proposed algorithm avoids the direct search of the correspondence between points or lines in complex scenes with many outliers in laser point clouds, which can effectively improve the robustness of the algorithm and suppress the influence of outliers on the algorithm. The relative error between the registration result and the actual value is within 10%, and the accuracy is better than the ICP algorithm. [ABSTRACT FROM AUTHOR]

Video splicing forgery is a common object-based intra-frame forgery operation. It refers to copying some regions, usually moving foreground objects, from one video to another. The splicing video usually contains two different modes of camera sensor pattern noise (SPN). Therefore, the SPN, which is called a camera fingerprint, can be used to detect video splicing operations. The paper proposes a video splicing detection and localization scheme based on SPN, which consists of detecting moving objects, estimating reference SPN, and calculating signed peak-to-correlation energy (SPCE). Firstly, foreground objects of the frame are extracted, and then, reference SPN are trained using frames without foreground objects. Finally, the SPCE is calculated at the block level to distinguish forged objects from normal objects. Experimental results demonstrate that the method can accurately locate the tampered area and has higher detection accuracy. In terms of accuracy and F1-score, our method achieves 0.914 and 0.912, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a solution to the problem of providing an autonomous vehicle with a safe control task when moving around many other autonomous vehicles. This is achieved by developing an appropriate computer control algorithm that takes into account the possible risk of a collision resulting from both the impact of environmental disturbances and the imperfection of the rules of maneuvering in situations where many vehicles pass each other, giving the control process a decisive character. For this purpose, three types of algorithms were synthesized: kinematic and dynamic optimization with neural domains, as well as sequential game control of an autonomous vehicle. The control algorithms determine a safe trajectory, which is implemented by the actuators of the autonomous vehicle. Computer simulations of the control algorithms in the Matlab/Simulink software allow for their comparative analysis in terms of meeting the criteria for the optimality and safety of an autonomous vehicle when passing a larger number of other autonomous vehicles. For this purpose, scenarios of multidirectional and one-way traffic of autonomous vehicles were used. [ABSTRACT FROM AUTHOR]