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License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M. [ABSTRACT FROM AUTHOR]

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To address the challenges of high complexity, poor real-time performance, and low detection rates for small target vehicles in existing vehicle object detection algorithms, this paper proposes a real-time lightweight architecture based on You Only Look Once (YOLO) v5m. Firstly, a lightweight upsampling operator called Content-Aware Reassembly of Features (CARAFE) is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles, reducing the missed detection rate and false detection rate. Secondly, a new prediction layer for tiny targets is added, and the feature fusion network is redesigned to enhance the detection capability for small targets. Finally, this paper applies L1 regularization to train the improved network, followed by pruning and fine-tuning operations to remove redundant channels, reducing computational and parameter complexity and enhancing the detection efficiency of the network. Training is conducted on the VisDrone2019-DET dataset. The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%, respectively. The average detection accuracy improves by 5.15%, and the detection speed reaches 47 images per second, satisfying real-time requirements. Compared with existing approaches, including YOLOv5m and classical vehicle detection algorithms, our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing. [ABSTRACT FROM AUTHOR]

Privacy preserving utility mining (PPUM) aims to solve the problem of sensitive information leakage in utility pattern mining. In recent years, researchers have proposed algorithms to solve the privacy-preserving problem. However, these algorithms have high side effects, long sanitization time, and computational complexity. Although the FPUTT algorithm reduces the number of database scans, tree construction and traversal still take much time. The paper proposes a fast utility-list dictionary algorithm (FULD). The utility-list dictionary consists of all sensitive items. Through dictionary lookup, sensitive items can be found and modified. In addition, the novel concepts of SINS and tns are proposed to reduce the side effects of the algorithm. In this paper, the experiments show that the FULD algorithm has good performance, such as running time and side effects. The running time of the FULD is 15–20 times shorter than the FPUTT algorithm. It performs well both on sparse and dense datasets. [ABSTRACT FROM AUTHOR]

With the advancement of network technology, multimedia videos have emerged as a crucial channel for individuals to access external information, owing to their realistic and intuitive effects. In the presence of high frame rate and high dynamic range videos, the coding efficiency of high-efficiency video coding (HEVC) falls short of meeting the storage and transmission demands of the video content. Therefore, versatile video coding (VVC) introduces a nested quadtree plus multi-type tree (QTMT) segmentation structure based on the HEVC standard, while also expanding the intra-prediction modes from 35 to 67. While the new technology introduced by VVC has enhanced compression performance, it concurrently introduces a higher level of computational complexity. To enhance coding efficiency and diminish computational complexity, this paper explores two key aspects: coding unit (CU) partition decision-making and intra-frame mode selection. Firstly, to address the flexible partitioning structure of QTMT, we propose a decision-tree-based series partitioning decision algorithm for partitioning decisions. Through concatenating the quadtree (QT) partition division decision with the multi-type tree (MT) division decision, a strategy is implemented to determine whether to skip the MT division decision based on texture characteristics. If the MT partition decision is used, four decision tree classifiers are used to judge different partition types. Secondly, for intra-frame mode selection, this paper proposes an ensemble-learning-based algorithm for mode prediction termination. Through the reordering of complete candidate modes and the assessment of prediction accuracy, the termination of redundant candidate modes is accomplished. Experimental results show that compared with the VVC test model (VTM), the algorithm proposed in this paper achieves an average time saving of 54.74%, while the BDBR only increases by 1.61%. [ABSTRACT FROM AUTHOR]

This paper delves into the problem of direct position determination (DPD) for non-Gaussian sources. Existing DPD algorithms are hindered by their high computational complexity from exhaustive grid searches and a disregard for the received signal characteristics by multiple nested arrays (MNAs). To address these issues, the paper proposes a novel DPD algorithm for non-Gaussian sources with MNAs: the Discrete Fourier Transform (DFT) and Taylor compensation algorithm. Initially, the fourth-order cumulant matrix of the received signal is computed, and the vectorizing method is applied. Subsequently, a computationally efficient DPD cost function is proposed by leveraging a normalized DFT matrix to reduce complexity. Finally, first-order Taylor compensation is utilized to enhance the accuracy of the localization results. The superiority of the proposed algorithm is demonstrated through numerical simulation results. [ABSTRACT FROM AUTHOR]

Digital fringe projection profilometry based on phase-shifting technology is a reliable method for complex shape measurement, and the phase is one of the most important factors affecting measurement accuracy. The calculation of the absolute phase depends on the calculation of the wrapped phase and encoding technology. In this paper, a technique of obtaining the absolute phase of multi-frequency heterodyne fringe images using the Hilbert transform is presented. Since the wrapped phase can be calculated from only one fringe image of each frequency, the method does not need phase-shifting. The absolute phase can be obtained from the wrapped phase by applying the heterodyne method. The measurement time and computational complexity are dramatically reduced, the measurement efficiency is greatly improved, and this benefit from the number of images is greatly reduced. The experimental results show that the method presented in this paper performs well in the application, and the accuracy is no different from that of the phase-shifting method while the efficiency is greatly improved. [ABSTRACT FROM AUTHOR]

Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of the feature descriptors. However, the existing feature descriptors face several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposes a set of feature descriptors for image stitching named Lightweight Multi-Feature Descriptors (LMFD). Based on the extensive extraction of gradients, means, and global information surrounding the feature points, feature descriptors are generated through various combinations to enhance the image stitching process. This endows the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0s and 1s, not only facilitating more efficient hardware deployment but also enhancing computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrate that LMFD outperforms state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of LMFD and substantiates its potential for practical applications in various domains. [ABSTRACT FROM AUTHOR]

In this paper, a new timing synchronization algorithm for the main synchronous signal (PSS) is proposed for the risk identification of cross-border e-commerce in the Internet of things, aiming at the problems of poor performance of anti frequency bias and high computational complexity of the improved PSS timing synchronization algorithm. Based on the piecewise correlation algorithm, the normalized frequency deviation of PSS sequence is preset. The segmented ones are pre stored at the terminal by using the conjugate symmetry of PSS sequence. The fast correlation of each segment correlation window is realized by combining convolution and overlapping reservation block method. Then, the threshold judgment is made after the time delay accumulation of the correlation values of the segments is made, so as to complete the joint detection of timing synchronization and coarse frequency deviation. The simulation results show that the algorithm can improve the performance of the system anti frequency offset effectively, reduce the complexity of the calculation and show that the timing synchronization conditions of the Internet can be satisfied. At the same time, under the background of the current development of cross-border logistics, this paper reviews the current research status of Transnational E-commerce logistics and Transnational E-commerce logistics risk. By comparing the advantages and disadvantages of various risk assessment methods, neural network and genetic algorithm are selected as the basic risk assessment methods in this paper. Based on the improved PSS timing synchronization algorithm and the Internet of things, the risk indicators of e-commerce logistics transnational will be selected from five risk dimensions: platform risk, customs clearance risk, organizational risk, process risk and environmental risk. Through the comprehensive literature and expert's opinion, the logistics risk assessment index system of cross-border e-commerce is established. [ABSTRACT FROM AUTHOR]

This paper presents an effective algorithm for globally solving the sum of linear ratios problem (SLRP), which has broad applications in government planning, finance and investment, cluster analysis, game theory and so on. In this paper, by using a new linearization technique, the linear relaxation problem of the equivalent problem is constructed. Next, based on the linear relaxation problem and the branch-and-bound framework, an effective branch-and-bound algorithm for globally solving the problem (SLRP) is proposed. By analyzing the computational complexity of the proposed algorithm, the maximum number of iterations of the algorithm is derived. Numerical experiments are reported to verify the effectiveness and feasibility of the proposed algorithm. Finally, two practical application problems from power transportation and production planning are solved to verify the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

The detection of road damage is highly important for traffic safety and road maintenance. Conventional detection approaches frequently require significant time and expenditure, the accuracy of detection cannot be guaranteed, and they are prone to misdetection or omission problems. Therefore, this paper introduces an enhanced version of the You Only Look Once version 8 (YOLOv8) road damage detection algorithm called RDD-YOLO. First, the simple attention mechanism (SimAM) is integrated into the backbone, which successfully improves the model's focus on crucial details within the input image, enabling the model to capture features of road damage more accurately, thus enhancing the model's precision. Second, the neck structure is optimized by replacing traditional convolution modules with GhostConv. This reduces redundant information, lowers the number of parameters, and decreases computational complexity while maintaining the model's excellent performance in damage recognition. Last, the upsampling algorithm in the neck is improved by replacing the nearest interpolation with more accurate bilinear interpolation. This enhances the model's capacity to maintain visual details, providing clearer and more accurate outputs for road damage detection tasks. Experimental findings on the RDD2022 dataset show that the proposed RDD-YOLO model achieves an mAP50 and mAP50-95 of 62.5% and 36.4% on the validation set, respectively. Compared to baseline, this represents an improvement of 2.5% and 5.2%. The F1 score on the test set reaches 69.6%, a 2.8% improvement over the baseline. The proposed method can accurately locate and detect road damage, save labor and material resources, and offer guidance for the assessment and upkeep of road damage. [ABSTRACT FROM AUTHOR]

For the characteristics of channel instability in wireless sensor networks, this paper proposes an intrusion detection algorithm based on FedAvg (federated averaging) and XGBoost (extreme gradient boosting) wireless sensor networks using fog computing architecture. First, the network edge is extended by introducing fog computing nodes to reduce the communication delay. It reduces the transmission bandwidth and privacy leakage risk while improving the accuracy of jointly learned global and local models. Then, the histogram-based approximation calculation method is improved to adapt to the unbalanced data characteristics of wireless sensor networks. Finally, by introducing TOP-K gradient selection, the number of model parameter uploads is minimized, and the efficiency of model parameter interaction is improved. The experimental results show that this algorithm has superior detection performance and low energy consumption. It is also compared with other algorithms to demonstrate the high detection rate and low computational complexity of this algorithm. [ABSTRACT FROM AUTHOR]

It is well known that accurate estimation of the loop delay between the input and output feedback signals is the key to behavioral modeling and digital predistortion of power amplifier. Nevertheless, most of the traditional loop‐delay compensation algorithms have the disadvantage of high computational complexity. For the purpose of solving this problem, a loop‐delay estimation algorithm based on sliding window has been proposed in this paper, where the first derivative characteristic of the signal is employed to evaluate the correlation between the input and output feedback signals. The simulation and experiment are carried out with different bandwidth signals in this paper. The results show that the proposed algorithm and the traditional cross‐correlation‐based method could align the input and output feedback signals successfully. But in terms of resource consumption, the proposed algorithm could reduce the multiplier nearly by 100% and the adder by approximately 93.2%. [ABSTRACT FROM AUTHOR]

In the paper we investigate Two Sets Intersection problem. Assume that we have two sets that are subsets of n objects. Sets are presented by two predicates that show which of n objects belong to these sets. We present a quantum algorithm that finds an element from the two sets intersection. It is a modification of the well-known Grover's search algorithm that uses two Oracles with access to the predicates. The algorithm is faster than the naive application of Grover's search. [ABSTRACT FROM AUTHOR]

In order to solve the existing distracted driving behaviour detection algorithms' problems such as low recognition accuracy, high leakage rate, high false recognition rate, poor real-time performance, etc., and to achieve high-precision real-time detection of common distracted driving behaviours (mobile phone use, smoking, drinking), this paper proposes a driver distracted driving behaviour recognition algorithm based on YOLOv5. Firstly, to address the problem of poor real-time identification, the computational and parametric quantities of the network are reduced by introducing a lightweight network, Ghostnet. Secondly, the use of GSConv reduces the complexity of the algorithm and ensures that there is a balance between the recognition speed and accuracy of the algorithm. Then, for the problem of missed and misidentified cigarettes during the detection process, the Soft-NMS algorithm is used to reduce the problems of missed and false detection of cigarettes without changing the computational complexity. Finally, in order to better detect the target of interest, the CBAM is utilised to enhance the algorithm's attention to the target of interest. The experiments show that on the homemade distracted driving behaviour dataset, the improved YOLOv5 model improves the mAP@0.5 of the YOLOv5s by 1.5 percentage points, while the computational volume is reduced by 7.6 GFLOPs, which improves the accuracy of distracted driving behaviour recognition and ensures the real-time performance of the detection speed. [ABSTRACT FROM AUTHOR]

The parallel computing power of quantum computing and the special properties of qubits provide an effective solution for image processing tasks. This paper presents a quantum image encryption algorithm based on Fisher–Yates algorithm and Logistic mapping. Firstly, the Fisher–Yates algorithm is used to generate three key sequences, one of which is used to encode the coordinate qubits of the image. Using the other two keys and the preset rules, the quantum coordinate scrambling operation is designed based on the encoded coordinate qubit, which effectively scrambles the spatial information of the plaintext image. Next, another set of key sequences is generated, one of which is used to encode the color qubits of the image. Using two other key sequences and different rules, a qubit plane scrambling operation based on coded color qubits is designed, and the color information of image is scrambled successfully. Finally, the quantum key image is generated based on Logistic mapping, and the key image is scrambled based on Fisher–Yates algorithm to improve the key complexity. The final ciphertext image is obtained by performing XOR operation between the original image and the scrambled key image. The complete quantum circuit diagram of the scheme is given in this paper. The experimental results and security analysis prove the effectiveness of the scheme, which provides a large key space and the computational complexity is only O(n). [ABSTRACT FROM AUTHOR]

Matrix triangular factorizations, as an intermediate step of some algorithms, are widely employed to solve scientific and engineering problems. However, there is no exact and explicit expressions on the computational complexity of triangular factorizations for general banded matrices in literatures so far. In this paper, specific and detailed descriptions on triangular factorization algorithms are presented for general banded matrices, and then by carefully dividing matrix into special blocks and with the help of the mathematical software 'Maple', exact and explicit expressions on the computational complexity of these algorithms are rigorously derived. These theoretical results are helpful for calculating the computational complexity of numerical algorithms that employ triangular factorizations, and also provide guidance for choosing appropriate algorithms for specific problems. Numerical experiments validate the obtained theoretical results. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm based on sensitivity analysis and the Wolfe method to solve a sequence of parametric quadratic programming (QP) problems such as those that arise in quadratic model predictive control (QMPC). The Wolfe method, based on Karush–Kuhn–Tucker conditions, has been used to convert parametric QP problems to parametric linear programming (LP) problems, and then the sensitivity analysis is applied to solve the sequence of the parametric LP problems. This strategy obtains sensitivity analysis‐based QMPC (SA‐QMPC) algorithm. It is proved that the computational complexity of SA‐QMPC is O(Nn2)$O(Nn^2)$ for a region of the initial conditions and O(N2n2)$O(N^2n^2)$ for sufficiently small sampling time and all initial conditions, where N$N$ and n$n$ are the horizon time and dimension of the state vector, respectively. Numerical results indicate the potential and properties of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Robust face clustering enjoys a wide range of applications for gate passes, surveillance systems and security analysis in embedded sensors. Nevertheless, existing algorithms have limitations in finding accurate clusters when data contain noise (e.g., occluded face clustering and recognition). It is known that in subspace clustering, the ℓ 1 - and ℓ 2 -norm regularizers can improve subspace preservation and connectivity, respectively, and the elastic net regularizer (i.e., the mixture of the ℓ 1 - and ℓ 2 -norms) provides a balance between the two properties. However, existing deterministic methods have high per iteration computational complexities, making them inapplicable to large-scale problems. To address this issue, this paper proposes the first accelerated stochastic variance reduction gradient (RASVRG) algorithm for robust subspace clustering. We also introduce a new momentum acceleration technique for the RASVRG algorithm. As a result of the involvement of this momentum, the RASVRG algorithm achieves both the best oracle complexity and the fastest convergence rate, and it reaches higher efficiency in practice for both strongly convex and not strongly convex models. Various experimental results show that the RASVRG algorithm outperformed existing state-of-the-art methods with elastic net and ℓ 1 -norm regularizers in terms of accuracy in most cases. As demonstrated on real-world face datasets with different manually added levels of pixel corruption and occlusion situations, the RASVRG algorithm achieved much better performance in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Many networked embedded sensing and control systems can be modeled as hybrid systems with interacting continuous and discrete dynamics. These systems present significant challenges for monitoring and diagnosis. Many existing model-based approaches focus on diagnostic reasoning assuming appropriate fault signatures have been generated. However, an important missing piece is the integration of model-based techniques with the acquisition and processing of sensor signals and the modeling of faults to support diagnostic reasoning. This paper addresses key modeling and computational problems at the interface between model-based diagnosis techniques and signature analysis to enable the efficient detection and isolation of incipient and abrupt faults in hybrid systems. A hybrid automata model that parameterizes abrupt and incipient faults is introduced. Based on this model, an approach for diagnoser design is presented. The paper also develops a novel mode estimation algorithm that uses model-based prediction to focus distributed processing signal algorithms. Finally. the paper describes a diagnostic system architecture that integrates the modeling, prediction, and diagnosis components. The implemented architecture is applied to fault diagnosis of a complex electro-mechanical machine, the Xerox DC265 printer, and the experimental results presented validate the approach. A number of design trade-otis that were made to support implementation of the algorithms for online applications are also described. [ABSTRACT FROM AUTHOR]

In this paper, we address the issue of solving problems with multiple components, multiple objectives, and target values for each objective. There are limitations in managing these multi-component, multi-goal problems such as the need for domain expertise to combine or prioritize the goals. In this paper, we propose a domain-independent method, Adaptive Leveling-Weighting-Clustering (ALWC), to manage the exploration of design scenarios of multi-goal, engineering-design problems. Using ALWC, designers explore combinations and priorities of the goals based on their interrelationships. Through iteration, design scenarios are obtained with higher goal achievements and an improved understanding of the relationship among subsystems. This is achieved without increasing computational complexity. This knowledge is helpful for multi-component design. The ALWC method is demonstrated using a thermal-system design problem. [ABSTRACT FROM AUTHOR]

The goal of the influence maximization problem is to identify a seed set that maximizes the number of users who will be impacted. This issue is critical to the spread of information in social networks. The greedy and heuristic algorithms are two prominent algorithms for this problem. The greedy algorithms are usually effective but they have high computational complexity. The heuristic algorithms are tendly efficient while the accuracy is low. The major goal of this study is to strike a compromise between efficacy and efficiency in selecting k influential nodes to maximize influence propagation while keeping the expense of doing so to a minimal. This paper proposes an improved heuristic algorithm, called the Best Hop for Independent Cascade Model (BHICM). This approach presents a dynamic relationship strategy between propagation probability and hop count that avoids processing the seed node's neighbors. Afterwards, the proposed algorithm defines the diffusion score as the criterion for selecting seed nodes, which ensures its accuracy and efficiency. According to experimental results on four genuine social networks, the proposed approach exceeds all comparison algorithms in terms of accuracy while maintaining an acceptable running time. [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]

Summary: Multi‐user multiple‐input multiple‐output (MU‐MIMO) system has the capability of delivering optimal system capacity that provides the simultaneous service to a large number of users using dirty paper coding (DPC) scheme. However, the DPC scheme is quite difficult to be implemented in the real‐time scenario as the computational complexity is very high and the process cannot be accomplished within the duration of few coherence periods. In this paper, we have adopted a newly developed binary spider monkey optimization (binary SMO) algorithm for the joint user and antenna scheduling (JUAS) problem to maximize the achievable system sum‐rate performance. It has been shown that JUAS with binary SMO achieves nearly 99% of system throughput achieved by the extensive search algorithm (ESA) using DPC. Also it is compared with binary flower pollination algorithm (binary FPA) to check which of the two gives better result. It is observed that the binary SMO in JUAS achieves a globally optimal solution quite rapidly, to remain well within the modern‐day packet data communication interval. The proposed binary SMO shows low computational complexity and computation time as compared to ESA (i.e., DPC). Also when it is compared with binary FPA, it shows quite a significant improvement in system throughput. Moreover, the effectiveness of binary SMO in the MU‐MIMO broadcasting scenario is verified through extensive simulation results. [ABSTRACT FROM AUTHOR]

Sidelobe cancellation (SLC) is a well-established beamforming technique for mitigating interference, particularly in the context of satellite communication (SATCOM). However, traditional SLC suffers from the issue of partially canceling the desired signal at high signal-to-noise ratio (SNR), primarily due to unconstrained beamforming processing. Extensive research has been conducted to address this problem; however, existing algorithms have limitations such as dependence on knowledge of signal array vectors or number of interferers and involve high computational complexity. In this paper, we propose a robust SLC algorithm based on beamforming vector norm constraint. Our proposal offers a practical solution by only requiring knowledge of the earth station antenna gain and maximum auxiliary array gain to the desired signal, both of which are fully known. Furthermore, compared to traditional SLC, our proposed method introduces additional computational complexity that only scales linearly with the size of the auxiliary array. Simulation results demonstrate comparable performance between our proposed method and existing techniques such as diagonal loading and spatial degrees-of-freedom control-based algorithms. [ABSTRACT FROM AUTHOR]

In applications where large-order filters are needed, the computational load of adaptive filtering algorithms can become prohibitively expensive. In this paper, a comprehensive analysis of a selective partial-update least mean squares, named SPU-LMS-M-min, is developed. By employing the partial-update strategy for a non-normalized adaptive scheme, the designer can choose an appropriate number of update blocks considering a trade-off between convergence rate and computational complexity, which can result in a more than 40% reduction in the number of multiplications in some configurations compared to the traditional LMS algorithm. Based on the principle of minimum distortion, a selection criterion is proposed that is based on the input signal's blocks with the lowest energy, whereas typical Selective Partial Update (SPU) algorithms use a selection criterion based on blocks with highest energy. Stochastic models are developed for the mean weights and mean and mean squared behaviour of the proposed algorithm, which are further extended to accommodate scenarios involving time-varying dynamics and suboptimal filter lengths. Simulation results show that the theoretical predictions are in good agreement with the experimental outcomes. Furthermore, it is demonstrated that the proposed selection criterion can be easily extended to active noise cancellation algorithms as well as algorithms utilizing variable filter length. This allows for the reduction of computational costs for these algorithms without compromising their asymptotic performance. [ABSTRACT FROM AUTHOR]

Recently, there has been renewed interest in signed distance bound representations due to their unique properties for 3D shape modelling. This is especially the case for deep learning-based bounds. However, it is beneficial to work with polygons in most computer graphics applications. Thus, in this paper, we introduce and investigate an asymptotically fast method for transforming signed distance bounds into polygon meshes. This is achieved by combining the principles of sphere tracing (or ray marching) with traditional polygonization techniques, such as marching cubes. We provide theoretical and experimental evidence that this approach is of the O (N 2 log N) computational complexity for a polygonization grid with N 3 cells. The algorithm is tested on both a set of primitive shapes and signed distance bounds generated from point clouds by machine learning (and represented as neural networks). Given its speed, implementation simplicity, and portability, we argue that it could prove useful during the modelling stage as well as in shape compression for storage. [ABSTRACT FROM AUTHOR]

The mathematical side of applied problems in multiple subject areas (biology, pattern recognition, etc.) is reduced to the problem of discrete optimization in the following mathematical method. We were provided a network and graphs in its leaves, for which we needed to find a rearrangement of graphs by non-leaf nodes, in which the given functional reached its minimum. Such a problem, even in the simplest case, is NP-hard, which means unavoidable restrictions on the network, on graphs, or on the functional. In this publication, this problem is addressed in the case of all graphs being so-called "structures", meaning directed-loaded graphs consisting of paths and cycles, and the functional as the sum (over all edges in the network) of distances between structures at the endpoints of every edge. The distance itself is equal to the minimal length of sequence from the fixed list of operations, the composition of which transforms the structure at one endpoint of the edge into the structure at its other endpoint. The list of operations (and their costs) on such a graph is fixed. Under these conditions, the given discrete optimization problem is called the reconstruction problem. This paper presents novel algorithms for solving the reconstruction problem, along with full proofs of their low error and low polynomial complexity. For example, for the network, the problem is solved with a zero error algorithm that has a linear polynomial computational complexity; and for the tree the problem is solved using an algorithm with a multiplicative error of at most two, which has a second order polynomial computational complexity. [ABSTRACT FROM AUTHOR]

Arc faults are the main cause of electrical fires according to national fire data statistics. Intensive studies of artificial intelligence-based arc fault detection methods have been carried out and achieved a high detection accuracy. However, the computational complexity of the artificial intelligence-based methods hinders their application for arc fault detection devices. This paper proposes a lightweight arc fault detection method based on the discrimination of a novel feature for lower current distortion conditions and the Adam-optimized BP neural network for higher distortion conditions. The novel feature is the pulse signal number per unit cycle, reflecting the zero-off phenomena of the arc current. Six features, containing the novel feature, are chosen as the inputs of the neural network, reducing the computational complexity. The model achieves a high detection accuracy of 99.27% under various load types recommended by the IEC 62606 standard. Finally, the proposed lightweight method is implemented on hardware based on the STM32 series microcontroller unit. The experimental results show that the average detection accuracy is 98.33%, while the average detection time is 45 ms and the average tripping time is 72–201 ms under six types of loads, which can fulfill the requirements of real-time detection for commercial arc fault detection devices. [ABSTRACT FROM AUTHOR]

A novel compressive sensing (CS) synthetic-aperture radar (SAR) called AgileSAR has been proposed to increase swath width for sparse scenes while preserving azimuthal resolution. AgileSAR overcomes the limitation of the Nyquist sampling theorem so that it has a small amount of data and low system complexity. However, traditional CS optimization-based algorithms suffer from manual tuning and pre-definition of optimization parameters, and they generally involve high time and computational complexity for AgileSAR imaging. To address these issues, a pseudo- L 0 -norm fast iterative shrinkage algorithm network (pseudo- L 0 -norm FISTA-net) is proposed for AgileSAR imaging via the deep unfolding network in this paper. Firstly, a pseudo- L 0 -norm regularization model is built by taking an approximately fair penalization rule based on Bayesian estimation. Then, we unfold the operation process of FISTA into a data-driven deep network to solve the pseudo- L 0 -norm regularization model. The network's parameters are automatically learned, and the learned network significantly increases imaging speed, so that it can improve the accuracy and efficiency of AgileSAR imaging. In addition, the nonlinearly sparsifying transform can learn more target details than the traditional sparsifying transform. Finally, the simulated and data experiments demonstrate the superiority and efficiency of the pseudo- L 0 -norm FISTA-net for AgileSAR imaging. [ABSTRACT FROM AUTHOR]

This paper constructs an interval partition linearization algorithm for solving minimax linear fractional programming (MLFP) problem. In this algorithm, MLFP is converted and decomposed into a series of linear programs by dividing the outer 1-dimensional space of the equivalent problem (EP) into polynomially countable intervals. To improve the computational efficiency of the algorithm, two new acceleration techniques are introduced, in which the regions in outer space where the optimal solution of EP does not exist are largely deleted. In addition, the global convergence of the proposed algorithm is summarized, and its computational complexity is illustrated to reveal that it is a fully polynomial time approximation scheme. Finally, the numerical results demonstrate that the proposed algorithm is feasible and effective. [ABSTRACT FROM AUTHOR]

Ship fire may result in significant damage to its structure and large economic loss. Hence, the prompt identification of fires is essential in order to provide prompt reactions and effective mitigation strategies. However, conventional detection systems exhibit limited efficacy and accuracy in detecting targets, which has been mostly attributed to limitations imposed by distance constraints and the motion of ships. Although the development of deep learning algorithms provides a potential solution, the computational complexity of ship fire detection algorithm pose significant challenges. To solve this, this paper proposes a lightweight ship fire detection algorithm based on YOLOv8n. Initially, a dataset, including more than 4000 unduplicated images and their labels, is established before training. In order to ensure the performance of algorithms, both fire inside ship rooms and also fire on board are considered. Then after tests, YOLOv8n is selected as the model with the best performance and fastest speed from among several advanced object detection algorithms. GhostnetV2-C2F is then inserted in the backbone of the algorithm for long-range attention with inexpensive operation. In addition, spatial and channel reconstruction convolution (SCConv) is used to reduce redundant features with significantly lower complexity and computational costs for real-time ship fire detection. For the neck part, omni-dimensional dynamic convolution is used for the multi-dimensional attention mechanism, which also lowers the parameters. After these improvements, a lighter and more accurate YOLOv8n algorithm, called Ship-Fire Net, was proposed. The proposed method exceeds 0.93, both in precision and recall for fire and smoke detection in ships. In addition, the mAP@0.5 reaches about 0.9. Despite the improvement in accuracy, Ship-Fire Net also has fewer parameters and lower FLOPs compared to the original, which accelerates its detection speed. The FPS of Ship-Fire Net also reaches 286, which is helpful for real-time ship fire monitoring. [ABSTRACT FROM AUTHOR]

With the advent of the information security era, it is necessary to guarantee the privacy, accuracy, and dependable transfer of pictures. This study presents a new approach to the encryption and compression of color images. It is predicated on 2D compressed sensing (CS) and the hyperchaotic system. First, an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security. Then, the processed images are concurrently encrypted and compressed using 2D CS. Among them, chaotic sequences replace traditional random measurement matrices to increase the system's security. Third, the processed images are re-encrypted using a combination of permutation and diffusion algorithms. In addition, the 2D projected gradient with an embedding decryption (2DPG-ED) algorithm is used to reconstruct images. Compared with the traditional reconstruction algorithm, the 2DPG-ED algorithm can improve security and reduce computational complexity. Furthermore, it has better robustness. The experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective. [ABSTRACT FROM AUTHOR]

The cryptographic system is the foundation of blockchain security mechanisms. This paper constructs a blockchain security mechanism based on the Chinese national cryptographic SM2 algorithm, involving the generation of public–private key pairs and the signing of block information using key pairs. However, the original SM2 algorithm has a high computational complexity, which cannot meet the requirements of low on-chain delay in blockchain applications. To solve this problem, this paper optimizes the SM2 digital signature algorithm. The scalar multiplication operation in the digital signature algorithm has a large computational complexity, which affects the efficiency of a digital signature. To reduce its computational complexity, this paper improves the traditional window non-adjacent form (w-NAF) algorithm for the scalar multiplication operation and proposes a low computational complexity Fw-NAF algorithm based on the prime number precomputation window, which greatly reduces its computational complexity. [ABSTRACT FROM AUTHOR]

A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The proposed method has three key components. Firstly, the depthwise separable convolution (DSConv) technique replaces the ordinary convolution, which reduces the computational complexity by generating a large number of feature maps with a small amount of calculation. Secondly, the dual-path attention gate module (DPAG) is designed to improve the model's detection precision in complex environments by enhancing the network's ability to distinguish between tomatoes and the background. Thirdly, the feature enhancement module (FEM) is added to highlight the target details, prevent the loss of effective features, and improve detection precision. We built, trained, and tested the tomato dataset, which included 3098 images and 3 classes. The proposed algorithm's performance was evaluated by comparison with the SSD, faster R-CNN, YOLOv4, YOLOv5, and YOLOv7 algorithms. Precision, recall rate, and mAP (mean average precision) were used for evaluation. The test results show that the improved YOLOv8s network has a lower loss and 93.4% mAP on this dataset. This improvement is a 1.5% increase compared to before the improvement. The precision increased by 2%, and the recall rate increased by 0.8%. Moreover, the proposed algorithm significantly reduced the model size from 22 M to 16 M, while achieving a detection speed of 138.8 FPS, which satisfies the real-time detection requirement. The proposed method strikes a balance between model size and detection precision, enabling it to meet agriculture's tomato detection requirements. The research model in this paper will provide technical support for a tomato picking robot to ensure the fast and accurate operation of the picking robot. [ABSTRACT FROM AUTHOR]

Lightweight detection methods are frequently utilized for unmanned system sensing; however, when put in complicated water surface environments, they suffer from insufficient feature fusion and decreased accuracy. This paper proposes a lightweight surface target detection algorithm with multi-scale feature fusion augmentation in an effort to improve the poor detection accuracy of lightweight detection algorithms in the mission environment of unmanned surface vehicles (USVs). Based on the popular one-stage lightweight YOLOv7-Tiny target detection algorithms, a lightweight extraction module is designed first by introducing the multi-scale residual module to reduce the number of parameters and computational complexity while improving accuracy. The Mish and SiLU activation functions are used to enhance network feature extraction. Second, the path aggregation network employs coordinate convolution to strengthen spatial information perception. Finally, the dynamic head, which is based on the attention mechanism, improves the representation ability of object detection heads without any computational overhead. According to the experimental findings, the proposed model has 22.1% fewer parameters than the original model, 15% fewer GFLOPs, a 6.2% improvement in mAP@0.5, a 4.3% rise in mAP@0.5:0.95, and satisfies the real-time criteria. According to the research, the suggested lightweight water surface detection approach includes a lighter model, a simpler computational architecture, more accuracy, and a wide range of generalizability. It performs better in a variety of difficult water surface circumstances. [ABSTRACT FROM AUTHOR]

Introduction: Real-time fruit detection is a prerequisite for using the Xiaomila pepper harvesting robot in the harvesting process. Methods: To reduce the computational cost of the model and improve its accuracy in detecting dense distributions and occluded Xiaomila objects, this paper adopts YOLOv7-tiny as the transfer learning model for the field detection of Xiaomila, collects images of immature and mature Xiaomila fruits under different lighting conditions, and proposes an effective model called YOLOv7- PD. Firstly, the main feature extraction network is fused with deformable convolution by replacing the traditional convolution module in the YOLOv7- tiny main network and the ELAN module with deformable convolution, which reduces network parameters while improving the detection accuracy of multiscale Xiaomila targets. Secondly, the SE (Squeeze-and-Excitation) attention mechanism is introduced into the reconstructed main feature extraction network to improve its ability to extract key features of Xiaomila in complex environments, realizing multi-scale Xiaomila fruit detection. The effectiveness of the proposed method is verified through ablation experiments under different lighting conditions and model comparison experiments. Results: The experimental results indicate that YOLOv7-PD achieves higher detection performance than other single-stage detection models. Through these improvements, YOLOv7-PD achieves a mAP (mean Average Precision) of 90.3%, which is 2.2%, 3.6%, and 5.5% higher than that of the original YOLOv7-tiny, YOLOv5s, and Mobilenetv3 models, respectively, the model size is reduced from 12.7 MB to 12.1 MB, and the model’s unit time computation is reduced from 13.1 GFlops to 10.3 GFlops. Discussion: The results shows that compared to existing models, this model is more effective in detecting Xiaomila fruits in images, and the computational complexity of the model is smaller. [ABSTRACT FROM AUTHOR]

360-degree videos have become increasingly popular with the application of virtual reality (VR) technology. To encode such kind of videos with ultra-high resolution, an efficient and real-time video encoder becomes a key requirement. The Versatile Video Coding (VVC) standard has good coding performance. However, it has pretty high computational complexity which increasing the application cost of 360-degree videos. Among them, the decision of the quadtree with nested multi-type tree (QTMT) partitioning structure is one of the time-consuming procedures. In this paper, based on the characteristics of 360-degree video with Equirectangular projection (ERP) format, the empirical variogram combined with Mahalanobis distance is introduced to measure the difference between the horizontal and vertical directions of the CU, and a fast partition algorithm is proposed. The experimental results show that the algorithm saves 32.13% of the coding time with only an increase of 0.66% in BDBR. [ABSTRACT FROM AUTHOR]

In this paper, the performance of the millimeter-wave (mmWave) massive multiple-input multiple-output (mMIMO) non-orthogonal multiple access (NOMA) systems is investigated under multiple user scenarios. The performance of the system has been analyzed in terms of spectral efficiency (SE), energy efficiency (EE), and computational complexity. In the case of the mMIMO system, the linear precoder with matrix inversion becomes less efficient due to its high computational complexity. Therefore, the design of a low-complex hybrid precoder (HP) is the main aim of this paper. Here, the authors have proposed a symmetric successive over-relaxation (SSOR) complex regularized zero-forcing (CRZF) linear precoder. Through simulation, this paper demonstrates that the proposed SSOR-CRZF-HP performs better than the conventional linear precoder with reduced complexity. [ABSTRACT FROM AUTHOR]

In this paper, a novel algorithm based on logistic regression digital backpropagation (LR‐DBP) is proposed to mitigate the effect of dispersion and nonlinearity in a long‐distance optical transmission system. The LR‐DBP can evaluate nonlinear effects by estimating the cross‐entropy error and adjusting the structure of DBP. The experiment is setup to demonstrate an 80 Gbit/s 16QAM signal transmitted across 1000 km single‐mode fiber. The results show that LR‐DBP achieves a 1.8 dB launch power improvement over the dispersion compensation algorithm and a 0.6 dB launch power gain with DBP. The proposed algorithm could obtain with 19.2% computational complexity decrease and 19.6% central processing unit (CPU) running time decrease by calculating the average numbers of real multiplications and CPU running times. [ABSTRACT FROM AUTHOR]

In this article, a multiuser (MU) multiinput multioutput (MIMO) system is considered, which is essential to support a huge number of subscribers without consuming extra bandwidth or power. Dirty paper coding (DPC) for MU MIMO channel achieves the peak sum-rate for the MU multiple antenna system at the cost of high computational complexity. Both user and antenna scheduling with a population based meta-heuristic algorithm, i.e. binary flower pollination algorithm (binary FPA) has been demonstrated in this article to achieve system sum-rate comparable to DPC with very less computational complexity and time complexity. Moreover, binary FPA shows a significant improvement in system throughput/sum-rate performance as compared to other population based meta-heuristic algorithms like binary bat algorithm (binary BA) and binary genetic algorithm (binary GA). Furthermore, the proposed binary FPA algorithm successfully achieves higher system sum-rate as compared to random search scheme and different existing suboptimal scheduling algorithms from literature as well. The binary FPA has also better convergence rate and searching ability than both binary BA and binary GA techniques. The percentage deviation achieved by the proposed binary FPA algorithm is quite less than that of binary BA, binary GA, random search method, and existing suboptimal scheduling algorithms from the literature. The efficiency of binary FPA in all these fronts is verified using exhaustive simulation studies. [ABSTRACT FROM AUTHOR]

The emergence of portable devices provides great convenience for image acquisition, but the image resolution is too low, which affects the identification and use of the image. Multiframe super-resolution algorithm makes a great contribution to extracting image features, but there is a problem with too much computation. Based on this, this paper proposes a low-resolution image fusion algorithm based on deep learning. Based on the introduction of extracting image features from low-resolution images, this paper proposes to fuse multiple low-resolution images, reduce the time of calculation error per frame, reduce the amount of calculation, extract feature information, and improve the alexnet network model to realize image extraction. The convolutional self-coding fusion network is used to realize image fusion and complete the reconstruction and fusion of low-resolution images. In the simulation analysis of the fusion algorithm, objective indexes such as network structure and loss function are selected to evaluate the effectiveness of the algorithm. This paper reduces the computational complexity of image acquisition and ensures that the amount of calculation can be reduced in each iteration. Analyze the effectiveness of the fusion algorithm, comprehensively evaluate the objective indicators, and verify the advantages of the algorithm through indicators such as gradient change and information entropy. [ABSTRACT FROM AUTHOR]

To improve the resistance to geometric rotation attacks and to solve the problem of high computational complexity of existing watermarking algorithms, a novel efficient and robust zero watermarking algorithm in spatial domain is proposed. First, the central pixels of different channels of colorful host image are taken as the center of the circle, and the features of the image are constructed by the pixels covered by rings with different radius and width. The binary feature image is obtained by binary operation on the constructed image features. Then, the zero watermark image is constructed by XOR operation for the scrambled binary feature image and copyright watermark image encrypted Logistic chaotic algorithm. Finally, the zero watermark image is stored in the intellectual property database for copyright certification in possible copyright disputes. Experimental results show that the proposed algorithm is robust to geometric rotation attacks and common image processing attacks. Compared with similar zero watermarking schemes, the proposed algorithm has lower computational complexity and better robustness. [ABSTRACT FROM AUTHOR]

Vehicle tracking in the field of intelligent transportation has received extensive attention in recent years. Multi-sensor-based vehicle tracking system is widely used in some critical environments. However, in the actual scenes, the observation error of each sensor is often different and time varying because of the environmental change and the channel difference. Therefore, in this paper, we propose a multi-sensor interacted vehicle-tracking algorithm with time-varying observation error (MI-TVOE). The algorithm establishes a jointed and time-varying observation error model for each sensor to indicate the variation of observation noise. Then, we develop a multi-sensor interacted vehicle-tracking algorithm which can predict the statistical information of a time-varying observation error and fuse the tracking result of each sensor to provide a global estimation. Simulation results show that the proposed MI-TVOE algorithm can significantly improve the tracking performance compared to the single-sensor-based tracking method, the traditional unscented Kalman filter (UKF), the apdative UKF method (AUKF) and the multi-error fused UKF method (MEF-UKF), which will be well applied to the complex tracking scenes and will reduce the computational complexity with time-varying observation error. The experiments in this paper also prove the superiority of the proposed MI-TVOE algorithm in complex environments. [ABSTRACT FROM AUTHOR]

In the application of Compressive Sensing (CS) theory for sidelobe suppression in Random Frequency and Pulse Repetition Interval Agile (RFPA) radar, the off−grid issues affect the performance of target parameter estimation in RFPA radar. Therefore, to address this issue, this paper presents an off−grid CS algorithm named Refinement and Generalized Double Pareto (GDP) distribution based on Sparse Bayesian Learning (RGDP−SBL) for RFPA radar that utilizes a coarse−to−fine grid refinement approach, allowing precise and cost−effective signal recovery while mitigating the impact of off−grid issues on target parameter estimation. To obtain a high-precision signal recovery, especially in scenarios involving closely spaced targets, the RGDP−SBL algorithm makes use of a three−level hierarchical prior model. Furthermore, the RGDP−SBL algorithm efficiently utilizes diagonal elements during the coarse search and exploits the convexity of the grid energy curve during the fine search, therefore significantly reducing computational complexity. Simulation results demonstrate that the RGDP−SBL algorithm significantly improves signal recovery performance while maintaining low computational complexity in multiple scenarios for RFPA radar. [ABSTRACT FROM AUTHOR]