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This paper proposes an image encryption algorithm based on combined 1D chaotic maps. First, a permutation technique was applied. It was then reorganized into 1D matrices along the rows and columns respectively, which were then shuffled by computing the substituted position indices to obtain the scrambled image. Subsequently, a method of confusion of the scrambled image was used through another generated data map, combined with random sub‐matrices for diffusion, then resulting in an encrypted image. Finally, the proposed cryptosystem was implemented in a single kernel platform developed using the Nios II Software Build Tools processor for Eclipse. A hardware architecture was designed using the Qsys‐built tool which is available in the Quartus II 13.0sp1 environment. The developed single‐core system was implemented using the Cyclone IV EP4CE6E22C8. Robustness evaluation of the cryptosystem was performed through security analysis tests such as histogram analysis, correlation coefficient, differential analysis, and key space analysis to prove that it is of good quality, efficient, fast, and successfully resisting brute force attacks. The hardware performance analysis was also carried out. Then the cryptosystem is compared with those in the literature both in the hardware and security performance aspects. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) can provide unprecedented spectral efficiency gains and excellent ability to manipulate electromagnetic waves. This article considered a RIS‐assisted multiuser multiple‐input multiple‐output (MIMO) downlink system, where the beamforming at the base station and RIS are jointly designed to maximize the sum‐rate. For the large dimension scenario and high‐rank beamforming matrix, the accurate deterministic approximations from random matrix theory are then utilized to simplify the RIS‐assisted MIMO systems. The asymptotical signal‐to‐interference‐plus‐noise ratio values obtained through random matrix theory is infinitely close to the theoretical limits calculated by accurately iteration. And the performance of the proposed algorithm computed via the sharing second‐order channel statistics matches that of the RIS algorithm with sharing full channel state information asymptotically. The deterministic approximations are instrumental to get improvement into the structure of the optimal beamforming and to reduce the implementation complexity in large‐scale MIMO system. Numerical simulations results are provided to evaluate and verify the accuracy of the asymptotic results obtained from the proposed algorithm in the finite system regime. With the complex operation process of large dimension matrix reducing to the deterministic approximations, a lower computational complexity can be obtained compared with other methods. [ABSTRACT FROM AUTHOR]

This study introduces a conceptually novel polynomial encoding algorithm for simulating potential energy operators encoded in diagonal unitary forms in a quantum computing machine. The current trend in quantum computational chemistry is effective experimentation to achieve high-precision quantum computational advantage. However, high computational gate complexity and fidelity loss are some of the impediments to the realization of this advantage in a real quantum hardware. In this study, we address the challenges of building a diagonal Hamiltonian operator having exponential functional form, and its implementation in the context of the time evolution problem (Hamiltonian simulation and encoding). Potential energy operators when represented in the first quantization form is an example of such types of operators. Through systematic decomposition and construction, we demonstrate the efficacy of the proposed polynomial encoding method in reducing gate complexity from O (2 n) to O ∑ i = 1 r n C r (for some r ≪ n ). This offers a solution with lower complexity in comparison to the conventional Hadamard basis encoding approach. The effectiveness of the proposed algorithm was validated with its implementation in the IBM quantum simulator and IBM quantum hardware. This study demonstrates the proposed approach by taking the example of the potential energy operator of the sodium iodide molecule (NaI) in the first quantization form. The numerical results demonstrate the potential applicability of the proposed method in quantum chemistry problems, while the analytical bound for error analysis and computational gate complexity discussed, throw light on issues regarding its implementation. [ABSTRACT FROM AUTHOR]

Automotive radar is one of the key sensors for intelligent driving. Radar image sequences contain abundant spatial and temporal information, enabling target classification. For existing radar spatiotemporal classifiers, multi-view radar images are usually employed to enhance the information of the target and 3D convolution is employed for spatiotemporal feature extraction. These models consume significant hardware resources and are not applicable to real-time applications. In this paper, RadarTCN, a novel lightweight network, is proposed that achieves high-accuracy online target classification using single-view radar image sequences only. In RadarTCN, 2D convolution and 3D-TCN are employed to extract spatiotemporal features sequentially. To reduce data dimensionality and computational complexity, a multi-layer max pooling down-sampling method is designed in a 2D convolution module. Meanwhile, the 3D-TCN module is improved through residual pruning and causal convolution is introduced for leveraging the performance of online target classification. The experimental results demonstrate that RadarTCN can achieve high-precision online target recognition for both range-angle and range-Doppler map sequences. Compared to the reference models on the CARRADA dataset, RadarTCN exhibits better classification performance, with fewer parameters and lower computational complexity. [ABSTRACT FROM AUTHOR]

This paper presents a new technique for estimating the two-dimensional direction of departure (2D-DOD) and direction of arrival (2D-DOA) in bistatic uniform planar array Multiple-Input Multiple-Output (MIMO) radar systems. The method is based on the reduced-dimension (RD) MUSIC algorithm, aiming to achieve improved precision and computational efficiency. Primarily, this pioneering approach efficiently transforms the four-dimensional (4D) estimation problem into two-dimensional (2D) searches, thus reducing the computational complexity typically associated with conventional MUSIC algorithms. Then, exploits the spatial diversity of array response vectors to construct a 4D spatial spectrum function, which is crucial in resolving the complex angular parameters of multiple simultaneous targets. Finally, the objective is to simplify the spatial spectrum to a 2D search within a 4D measurement space to achieve an optimal balance between efficiency and accuracy. Simulation results validate the effectiveness of our proposed algorithm compared to several existing approaches, demonstrating its robustness in accurately estimating 2D-DOD and 2D-DOA across various scenarios. The proposed technique shows significant computational savings and high-resolution estimations and maintains high precision, setting a new benchmark for future explorations in the field. [ABSTRACT FROM AUTHOR]

There are two standard methods in instance segmentation: top-down and bottom-up. The top-down approach performs object detection to generate candidate proposals and then performs pixel-level segmentation for each proposal. It is accurate and flexible, capable of handling objects of different sizes and shapes. However, it is computationally complex and relies on object detection accuracy. The bottom-up approach first performs pixel-level clustering or segmentation and then combines candidate instances to obtain the final segmentation result. It can handle overlapping cases and has lower computational complexity, but it may need to localize accurately, and segment instances, and the segmentation granularity is coarser. In this paper, the Urban Street Scene Instance Segmentation (UISNet) algorithm is proposed. Firstly, the feature extraction network is the foundation of UISNet, which uses EfficientNet as the backbone network. Secondly, MPAFPN is the feature pyramid network part of UISNet, used for multi-scale feature fusion. By using EfficientNet and MPAFPN as the backbone network and bottleneck layers, the accuracy of UISNet is improved by 4% compared to ResNet and FPN. In the inference phase, this paper introduces an innovative dual-branch design that combines top-down and bottom-up strategies. One branch is the bounding box aggregation branch, which generates highdimensional information such as the shape and orientation of bounding boxes based on the FCOS Head. The other branch is the mask decoding branch, which creates mask prediction results. These two branches are fused using the Mask FCN Header to obtain the final instance segmentation result. With this dual-branch design, the model can effectively utilize the information from both top-down and bottom-up approaches, thereby improving the accuracy and robustness of instance segmentation. Through experimental comparisons, the proposed network model in this paper achieves the best performance in terms of accuracy compared to other instance segmentation networks, with an accuracy of 36.28%. Moreover, the proposed model performs better in urban street scenes, enhancing object detection and segmentation and offering more reliable and efficient solutions for applications such as autonomous driving and intelligent transportation. [ABSTRACT FROM AUTHOR]

Chemical mechanical polishing/planarization (CMP) combines physical grinding and chemical reactions to planarize the wafer surface. The complex mechanism of CMP brings great challenges to the mechanism-based modeling process. The data-driven CMP modeling process is limited by insufficient datasets. At the same time, these two types of models generally have high computational complexity. In this paper, we introduce the group method of data handling (GMDH)-type polynomial network to build the CMP model to address the above challenges. We designed and manufactured the test chip using a 28nm process. The measurement data from the test chip shows that compared with the mechanism-based CMP model, the trained CMP model based on GMDH-type polynomial network has higher accuracy and lower computational complexity, with the average simulation speed being 115x faster. Experiments based on silicon data show that this modeling method has a small demand for data, and 20 randomly selected sets of data can meet the needs for modeling the current CMP process. [ABSTRACT FROM AUTHOR]

In this article, we present two resilient algorithms, the improved alternative hard c‐means (IAHCM) and the improved alternative fuzzy c‐means (IAFCM). We implement the Gaussian distance‐dependent function proposed by Zhang and Chen (D.‐Q. Zhang and Chen, 2004). In some cases, Zhang and Chen's metric distance does not account for the clustering centroid effect predicted by the large value. R* is employed in IAHCM and IAFCM to discover robust results while minimizing its sensitivity. Experiments are conducted using two‐and three‐dimensional data, including Diamond and Iris real‐world data. The results are based on demonstrating the robust simplicity and applicability of the offered algorithms. Similarly, computational complexity is assessed. [ABSTRACT FROM AUTHOR]

INTRODUCTION: With the continuous development of video analysis technology, sports video object tracking has become one of the research hotspots. Particle filter, as an effective object tracking algorithm, has been widely used in sports video object tracking. However, traditional particle filters have some shortcomings, such as particle depletion and high computational complexity, which require optimization. This article proposes a sports video object tracking algorithm based on optimized particle filters, aiming to improve the accuracy and stability of object tracking. Particle filter-based human motion video target tracking technology has become a trend. This project intends to apply particle filters to image processing of human activities. Firstly, an improved particle filter model tracks moving video objects. The purpose is to improve the tracking effect further and increase the accuracy. HSV distribution model was used to establish a target observation model. The algorithm is combined with the weight reduction algorithm to realize the human motion trajectory detection in the target observation mode. An examination of sports player videos then confirmed the model. Experiments show that this method can be used to track people in moving images of sports. Compared with other methods, this method has higher computational accuracy and speed. [ABSTRACT FROM AUTHOR]

The intensive pursuit for quantum advantage in terms of computational complexity has further led to a modernized crucial question of when and how will quantum computers outperform classical computers. The next milestone is undoubtedly the realization of quantum acceleration in practical problems. Here we provide a clear evidence and arguments that the primary target is likely to be condensed matter physics. Our primary contributions are summarized as follows: 1) Proposal of systematic error/runtime analysis on state-of-the-art classical algorithm based on tensor networks; 2) Dedicated and high-resolution analysis on quantum resource performed at the level of executable logical instructions; 3) Clarification of quantum-classical crosspoint for ground-state simulation to be within runtime of hours using only a few hundreds of thousand physical qubits for 2d Heisenberg and 2d Fermi-Hubbard models, assuming that logical qubits are encoded via the surface code with the physical error rate of p = 10−3. To our knowledge, we argue that condensed matter problems offer the earliest platform for demonstration of practical quantum advantage that is order-of-magnitude more feasible than ever known candidates, in terms of both qubit counts and total runtime. [ABSTRACT FROM AUTHOR]

Fire and smoke detection is crucial for the safe mining of coal energy, but previous fire-smoke detection models did not strike a perfect balance between complexity and accuracy, which makes it difficult to deploy efficient fire-smoke detection in coal mines with limited computational resources. Therefore, we improve the current advanced object detection model YOLOv8s based on two core ideas: (1) we reduce the model computational complexity and ensure real-time detection by applying faster convolutions to the backbone and neck parts; (2) to strengthen the model's detection accuracy, we integrate attention mechanisms into both the backbone and head components. In addition, we improve the model's generalization capacity by augmenting the data. Our method has 23.0% and 26.4% fewer parameters and FLOPs (Floating-Point Operations) than YOLOv8s, which means that we have effectively reduced the computational complexity. Our model also achieves a mAP (mean Average Precision) of 91.0%, which is 2.5% higher than the baseline model. These results show that our method can improve the detection accuracy while reducing complexity, making it more suitable for real-time fire-smoke detection in resource-constrained environments. [ABSTRACT FROM AUTHOR]

In the article, the extreme problem of finding the optimal placement plan of 5G base stations at certain points within a linear area of finite length is set. A fundamental feature of the author's formulation of the extreme problem is that it takes into account not only the points of potential placement of base stations but also the possibility of selecting instances of stations to be placed at a specific point from a defined excess set, as well as the aspect of inseparable interaction of placed 5G base stations within the framework of SON. The formulation of this extreme problem is brought to the form of a specific combinatorial model. The article proposes an adapted branch-and-bounds method, which allows the process of synthesis of the architecture of a linearly oriented segment of a 5G network to select the best options for the placement of base stations for further evaluation of the received placement plans in the metric of defined performance indicators. As the final stage of the synthesis of the optimal plan of a linearly oriented wireless network segment based on the sequence of the best placements, it is proposed to expand the parametric space of the design task due to the specific technical parameters characteristic of the 5G platform. The article presents a numerical example of solving an instance of the corresponding extremal problem. It is shown that the presented mathematical apparatus allows for the formation of a set of optimal placements taking into account the size of the non-coverage of the target area. To calculate this characteristic parameter, both exact and two approximate approaches are formalized. The results of the experiment showed that for high-dimensional problems, the approximate approach allows for reducing the computational complexity of implementing the adapted branch-and-bounds method by more than six times, with a slight loss of accuracy of the optimal solution. The structure of the article includes Section 1 (introduction and state-of-the-art), Section 2 (statement of the research, proposed models and methods devoted to the research topic), Section 3 (numerical experiment and analysis of results), and Section 4 (conclusions and further research). [ABSTRACT FROM AUTHOR]

To ensure the accuracy of state of charge (SOC) and state of health (SOH) estimation for battery packs while minimizing the amount of pre-experiments required for aging modeling and the scales of computation for online management, a decisive-cell-based estimation method with training-free characteristic parameters and a dynamic-weighted estimation strategy is proposed in this paper. Firstly, to reduce the computational complexity, the state estimation of battery packs is summed up to that of two decisive cells, and a new selection approach for the decisive cells is adopted based on the detection of steep voltage changes. Secondly, two novel ideas are implemented for the state estimation of the selected cells. On the one hand, a set of characteristic parameters that only exhibit local curve shrinkage with aging is chosen, which keeps the corresponding estimation approaches away from training. On the other hand, multiple basic estimation approaches are effectively combined by their respective dynamic weights, which ensures the estimation can maintain a good estimation accuracy under various load profiles. Finally, the experimental results show that the new method can quickly correct the initial setting deviations and have a high estimation accuracy for both the SOC and SOH within 2% for a series battery pack consisting of cells with obvious inconsistency. [ABSTRACT FROM AUTHOR]

The effective segmentation of clouds and cloud shadows is crucial for surface feature extraction, climate monitoring, and atmospheric correction, but it remains a critical challenge in remote sensing image processing. Cloud features are intricate, with varied distributions and unclear boundaries, making accurate extraction difficult, with only a few networks addressing this challenge. To tackle these issues, we introduce a multi-scale receptive field aggregation network (MRFA-Net). The MRFA-Net comprises an MRFA-Encoder and MRFA-Decoder. Within the encoder, the net includes the asymmetric feature extractor module (AFEM) and multi-scale attention, which capture diverse local features and enhance contextual semantic understanding, respectively. The MRFA-Decoder includes the multi-path decoder module (MDM) for blending features and the global feature refinement module (GFRM) for optimizing information via learnable matrix decomposition. Experimental results demonstrate that our model excelled in generalization and segmentation performance when addressing various complex backgrounds and different category detections, exhibiting advantages in terms of parameter efficiency and computational complexity, with the MRFA-Net achieving a mean intersection over union (MIoU) of 94.12% on our custom Cloud and Shadow dataset, and 87.54% on the open-source HRC_WHU dataset, outperforming other models by at least 0.53% and 0.62%. The proposed model demonstrates applicability in practical scenarios where features are difficult to distinguish. [ABSTRACT FROM AUTHOR]

Dehazing individual remote sensing (RS) images is an effective approach to enhance the quality of hazy remote sensing imagery. However, current dehazing methods exhibit substantial systemic and computational complexity. Such complexity not only hampers the straightforward analysis and comparison of these methods but also undermines their practical effectiveness on actual data, attributed to the overtraining and overfitting of model parameters. To mitigate these issues, we introduce a novel dehazing network for non-uniformly hazy RS images: GLUENet, designed for both lightweightness and computational efficiency. Our approach commences with the implementation of the classical U-Net, integrated with both local and global residuals, establishing a robust base for the extraction of multi-scale information. Subsequently, we construct basic convolutional blocks using gated linear units and efficient channel attention, incorporating depth-separable convolutional layers to efficiently aggregate spatial information and transform features. Additionally, we introduce a fusion block based on efficient channel attention, facilitating the fusion of information from different stages in both encoding and decoding to enhance the recovery of texture details. GLUENet's efficacy was evaluated using both synthetic and real remote sensing dehazing datasets, providing a comprehensive assessment of its performance. The experimental results demonstrate that GLUENet's performance is on par with state-of-the-art (SOTA) methods and surpasses the SOTA methods on our proposed real remote sensing dataset. Our method on the real remote sensing dehazing dataset has an improvement of 0.31 dB for the PSNR metric and 0.13 for the SSIM metric, and the number of parameters and computations of the model are much lower than the optimal method. [ABSTRACT FROM AUTHOR]

Long-term coherent integration (CI) can effectively improve the radar detection capability for high-speed targets. However, the range walk (RW) effect caused by high-speed motion significantly degrades the detection performance. To improve detection performance, this study proposes an improved algorithm based on the modified Radon inverse Fourier transform (denoted as IMRIFT). The proposed algorithm uses parameter searching for velocity estimation, designs a compensation function based on the relationship between velocity and distance walk and Doppler ambiguity terms, and performs CI based on the compensated signal. IMRIFT can achieve RW correction, avoid the blind-speed sidelobe (BSSL) effect caused by velocity mismatch, and improve detection performance, while ensuring low computational complexity. In addition, considering the relationship between energy concentration regions and bandwidth in the 2D frequency domain, a fast method based on IMIRFT is proposed, which can balance computational cost and detection capacity. Finally, a series of comparative experiments are conducted to demonstrate the effectiveness of the proposed algorithm and the fast method. [ABSTRACT FROM AUTHOR]

Network dismantling is one of the most challenging problems in complex systems. This problem encompasses a broad array of practical applications. Previous works mainly focus on the metrics such as the number of nodes in the Giant Connected Component (GCC), average pairwise connectivity, etc. This paper introduces a novel metric, the accumulated 2-core size, for assessing network dismantling. Due to the NP-hard computational complexity of this problem, we propose SmartCore, an end-to-end model for minimizing the accumulated 2-core size by leveraging reinforcement learning and graph neural networks. Extensive experiments across synthetic and real-world datasets demonstrate SmartCore's superiority over existing methods in terms of both accuracy and speed, suggesting that SmartCore should be a better choice for the network dismantling problem in practice. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for efficiently solving a trajectory planning problem for swarm robotics in cluttered environments. Recent research has demonstrated high success rates in real-time local trajectory planning for swarm robotics in cluttered environments, but optimizing trajectories for each robot is still computationally expensive, with a computational complexity from O (k (n t , ε) n t 2) to O (k (n t , ε) n t 3) where n t is the number of parameters in the parameterized trajectory, ε is precision, and k (n t , ε) is the number of iterations with respect to n t and ε. Furthermore, the swarm is difficult to move as a group. To address this issue, we define and then construct the optimal virtual tube, which includes infinite optimal trajectories. Under certain conditions, any optimal trajectory in the optimal virtual tube can be expressed as a convex combination of a finite number of optimal trajectories, with a computational complexity of O (n t) . Afterward, a hierarchical approach including a planning method of the optimal virtual tube with minimizing energy and distributed model predictive control is proposed. In simulations and experiments, the proposed approach is validated and its effectiveness over other methods is demonstrated through comparison. [ABSTRACT FROM AUTHOR]

In this study, a control scheme is proposed based on Chaotic Particle Swarm Optimization (CPSO) to enhance the Linear Auto-Disturbance Rejection Controller (LADRC). The focus is on addressing the challenge of high-precision variations in angle-of-attack through dual-motor cooperative control within the lifting wing of a high-speed train. The scheme initiates with the design of a dual-loop structure for LADRC, integrating position and current control. The position loop is further refined. Subsequently, the CPSO algorithm is employed to optimize the parameters of the LADRC controller. Ultimately, the loop is closed by feeding back the position error in the cross-coupled structure to the current loop, thereby achieving high-precision control. The performance of the proposed structure is validated through both Matlab/Simulink simulations and an experimental platform. The experimental results demonstrate that CPSO-LADRC, in comparison to traditional LADRC and Proportion-Integration-Differentiation (PID) control, exhibits an increase in the maximum response time by 3.76 s and 3.3 s, respectively, a reduction in overshoot by 1.12% and 0.8%, as well as a decrease in the maximum synchronization error by 0.45 cm and 1 cm, respectively. These findings validate the effectiveness of the proposed synchronous loop controller method in simplifying computational complexity, enhancing system responsiveness, robustness, and synchronization performance. Additionally, our approach facilitates precise angle-of-attack transformation for the lifting wings of high-speed trains effectively. [ABSTRACT FROM AUTHOR]

In order to address the fragility issues associated with the current prescribed performance control (PPC) strategy and ensure both transient and steady-state performance of the tracking error, a non-fragility prescribed performance control scheme is proposed. A non-fragile prescribed performance control method for robotic systems with model uncertainties and unknown disturbances is developed. This method not only addresses the inherent vulnerability defects of the existing prescribed performance control but also effectively reduces the computational complexity of the controller. Firstly, addressing the fragility issues of existing PPC, a new non-fragile prescribed performance control strategy is proposed. To address the fragile issue with the current PPC, the shift function is employed to handle the tracking error. Based on the non-fragile PPC mentioned above, a new prescribed performance controller is designed without the requirement for approximation or estimation. This effectively reduces the complexity of controller design. At last, the feasibility of achieving non-fragile prescribed performance is verified through stability analysis, and the superiority of the designed controller is confirmed through simulation comparisons. The results show that the designed controller effectively resolves the control singularity issue arising from the inherent limitations of the PPC. [ABSTRACT FROM AUTHOR]

Point cloud segmentation, as the basis for 3D scene understanding and analysis, has made significant progress in recent years. Graph-based modeling and learning methods have played an important role in point cloud segmentation. However, due to the inherent complexity of point cloud data, it is difficult to capture higher-order and complex features of 3D data using graph learning methods. In addition, how to quickly and efficiently extract important features from point clouds also poses a great challenge to the current research. To address these challenges, we propose a new framework, called hypergraph position attention convolution networks (HGPAT), for point cloud segmentation. Firstly, we use hypergraph to model the higher-order relationships among point clouds. Secondly, in order to effectively learn the feature information of point cloud data, a hyperedge position attention convolution module is proposed, which utilizes the hyperedge–hyperedge propagation pattern to extract and aggregate more important features. Finally, we design a ResNet-like module to reduce the computational complexity of the network and improve its efficiency. We have conducted point cloud segmentation experiments on the ShapeNet Part and S3IDS datasets, and the experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art ones. [ABSTRACT FROM AUTHOR]

The development of deep learning greatly promotes the progress of speaker verification (SV). Studies show that both convolutional neural networks (CNNs) and dilated time-delay neural networks (TDNNs) achieve advanced performance in text-independent SV, due to their ability to sufficiently extract the local feature and the temporal contextual information, respectively. Also, the combination of the above two has achieved better results. However, we found a serious gridding effect when we apply the 1D-Res2Net-based dilated TDNN proposed in ECAPA-TDNN for SV, which indicates discontinuity and local information losses of frame-level features. To achieve high-resolution process for speaker embedding, we improve the CNN–TDNN structure with proposed repeated multi-scale feature fusions. Through the proposed structure, we can effectively improve the channel utilization of TDNN and achieve higher performance under the same TDNN channel. And, unlike previous studies that have all converted CNN features to TDNN features directly, we also studied the latent space transformation between CNN and TDNN to achieve efficient conversion. Our best method obtains 0.72 EER and 0.0672 MinDCF on VoxCeleb-O test set, and the proposed method performs better in cross-domain SV without additional parameters and computational complexity. [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]

Hyperspectral Images (HSI) classification is a challenging task due to a large number of spatial-spectral bands of images with high inter-similarity, extra variability classes, and complex region relationships, including overlapping and nested regions. Classification becomes a complex problem in remote sensing images like HSIs. Convolutional Neural Networks (CNNs) have gained popularity in addressing this challenge by focusing on HSI data classification. However, the performance of 2D-CNN methods heavily relies on spatial information, while 3D-CNN methods offer an alternative approach by considering both spectral and spatial information. Nonetheless, the computational complexity of 3D-CNN methods increases significantly due to the large capacity size and spectral dimensions. These methods also face difficulties in manipulating information from local intrinsic detailed patterns of feature maps and low-rank frequency feature tuning. To overcome these challenges and improve HSI classification performance, we propose an innovative approach called the Attention 3D Central Difference Convolutional Dense Network (3D-CDC Attention DenseNet). Our 3D-CDC method leverages the manipulation of local intrinsic detailed patterns in the spatial-spectral features maps, utilizing pixel-wise concatenation and spatial attention mechanism within a dense strategy to incorporate low-rank frequency features and guide the feature tuning. Experimental results on benchmark datasets such as Pavia University, Houston 2018, and Indian Pines demonstrate the superiority of our method compared to other HSI classification methods, including state-of-the-art techniques. The proposed method achieved 97.93% overall accuracy on the Houston-2018, 99.89% on Pavia University, and 99.38% on the Indian Pines dataset with the 25 × 25 window size. [ABSTRACT FROM AUTHOR]

In the field of medical image segmentation, achieving fast and accurate semantic segmentation of tumor cell nuclei and skin lesions is of significant importance. However, the considerable variations in skin lesion forms and cell types pose challenges to attaining high network accuracy and robustness. Additionally, as network depth increases, the growing parameter size and computational complexity make practical implementation difficult. To address these issues, this paper proposes MD-UNet, a fast cell nucleus segmentation network that integrates Tokenized Multi-Layer Perceptron modules, attention mechanisms, and Inception structures. Firstly, tokenized MLP modules are employed to label and project convolutional features, reducing computational complexity. Secondly, the paper introduces Depthwise Attention blocks and Multi-layer Feature Extraction modules. The Depthwise Attention blocks eliminate irrelevant and noisy responses from coarse-scale extracted information, serving as alternatives to skip connections in the UNet architecture. The Multilayer Feature Extraction modules capture a wider range of high-level and lowlevel semantic features during decoding and facilitate feature fusion. The proposed MD-UNet approach is evaluated on two datasets: the International Skin Imaging Collaboration (ISIC2018) dataset and the PanNuke dataset. The experimental results demonstrate that MD-UNet achieves the best performance on both datasets. [ABSTRACT FROM AUTHOR]

Sound zones are used to reproduce individual audio content to multiple people in a room using a set of loudspeakers with controllable input signals. To allow the reproduction of individual audio to dynamically change, e.g., due to moving listeners, changes in the number of listeners, or changing room transfer functions, an adaptive formulation is proposed. This formulation is based on frequency domain block adaptive filters and given room transfer functions. To reduce computational complexity, the system is extended to subband processing without cross-adaptive filters. The computational savings come from recognizing that sound zones consist of part-solutions which are inherently band limited, hence, several subbands can be ignored. To validate the theoretical findings, a 27-channel loudspeaker array was constructed, and measurements were performed in anechoic and reflective environments. The results show that the subband solution performs identically to a full-rate solution but at a reduced computational complexity. [ABSTRACT FROM AUTHOR]

During steel manufacturing, surface defects such as scratches, scale, and oxidation can compromise product quality and safety. Detecting these defects accurately is critical for production efficiency and product integrity. However, current target detection algorithms are often too resource-intensive for deployment on edge devices with limited computing resources. To address this challenge, we propose YOLO-RDP, an enhanced YOLOv7-tiny model. YOLO-RDP integrates RexNet, a lightweight network, for feature extraction, and employs GSConv and VOV-GSCSP modules to enhance the network's neck layer, reducing parameter count and computational complexity. Additionally, we designed a dual-headed object detection head called DdyHead with a symmetric structure, composed of two complementary object detection heads, greatly enhancing the model's ability to recognize minor defects. Further model optimization through pruning achieves additional lightweighting. Experimental results demonstrate the superiority of our model, with improvements in mAP values of 3.7% and 3.5% on the NEU-DET and GC10-DET datasets, respectively, alongside reductions in parameter count and computation by 40% and 30%, and 25% and 24%, respectively. [ABSTRACT FROM AUTHOR]

Symmetry is an important principle and characteristic that is prevalent in nature and artificial environments. In the three-dimensional packing problem, leveraging the inherent symmetry of goods and the symmetry of the packing space can enhance packing efficiency and utilization.The three-dimensional packing problem is an NP-hard combinatorial optimization problem in the field of modern logistics, with high computational complexity. This paper proposes an improved genetic algorithm by incorporating a fusion tabu search strategy to address this problem. The algorithm employs a three-dimensional loading mathematical model and utilizes a wall-building method under residual space constraints for stacking goods. Furthermore, adaptation of fitness variation strategy, chromosome adjustment, and tabu search algorithm are introduced to balance the algorithm's global and local search capabilities, as well as to enhance population diversity and convergence speed. Through testing on benchmark cases such as Bischoff and Ratcliff, the improved algorithm demonstrates an average increase of over 3% in packing space utilization compared to traditional genetic algorithms and other heuristic algorithms, validating its feasibility and effectiveness. The proposed improved genetic algorithm provides new insights for solving three-dimensional packing problems and optimizing logistics loading schedules, offering promising prospects for various applications. [ABSTRACT FROM AUTHOR]

In the field of direction of arrival (DOA) estimation for coherent sources, subspace-based model-driven methods exhibit increased computational complexity due to the requirement for eigenvalue decomposition. In this paper, we propose a new neural network, i.e., the signal space deep convolution (SSDC) network, which employs the signal space covariance matrix as the input and performs independent two-dimensional convolution operations on the symmetric real and imaginary parts of the input signal space covariance matrix. The proposed SSDC network is designed to address the challenging task of DOA estimation for coherent sources. Furthermore, we leverage the spatial sparsity of the output from the proposed SSDC network to conduct a spectral peak search for obtaining the associated DOAs. Simulations demonstrate that, compared to existing state-of-the-art deep learning-based DOA estimation methods for coherent sources, the proposed SSDC network achieves excellent results in both matching and mismatching scenarios between the training and test sets. [ABSTRACT FROM AUTHOR]

Semantic segmentation algorithms leveraging deep convolutional neural networks often encounter challenges due to their extensive parameters, high computational complexity, and slow execution. To address these issues, we introduce a semantic segmentation network model emphasizing the rapid generation of redundant features and multi-level spatial aggregation. This model applies cost-efficient linear transformations instead of standard convolution operations during feature map generation, effectively managing memory usage and reducing computational complexity. To enhance the feature maps' representation ability post-linear transformation, a specifically designed dual-attention mechanism is implemented, enhancing the model's capacity for semantic understanding of both local and global image information. Moreover, the model integrates sparse self-attention with multi-scale contextual strategies, effectively combining features across different scales and spatial extents. This approach optimizes computational efficiency and retains crucial information, enabling precise and quick image segmentation. To assess the model's segmentation performance, we conducted experiments in Changge City, Henan Province, using datasets such as LoveDA, PASCAL VOC, LandCoverNet, and DroneDeploy. These experiments demonstrated the model's outstanding performance on public remote sensing datasets, significantly reducing the parameter count and computational complexity while maintaining high accuracy in segmentation tasks. This advancement offers substantial technical benefits for applications in agriculture and forestry, including land cover classification and crop health monitoring, thereby underscoring the model's potential to support these critical sectors effectively. [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]

Road extraction is a crucial aspect of remote sensing imagery processing that plays a significant role in various remote sensing applications, including automatic driving, urban planning, and path navigation. However, accurate road extraction is a challenging task due to factors such as high road density, building occlusion, and complex traffic environments. In this study, a Spatial Attention Swin Transformer (SASwin Transformer) architecture is proposed to create a robust encoder capable of extracting roads from remote sensing imagery. In this architecture, we have developed a spatial self-attention (SSA) module that captures efficient and rich spatial information through spatial self-attention to reconstruct the feature map. Following this, the module performs residual connections with the input, which helps reduce interference from unrelated regions. Additionally, we designed a Spatial MLP (SMLP) module to aggregate spatial feature information from multiple branches while simultaneously reducing computational complexity. Two public road datasets, the Massachusetts dataset and the DeepGlobe dataset, were used for extensive experiments. The results show that our proposed model has an improved overall performance compared to several state-of-the-art algorithms. In particular, on the two datasets, our model outperforms D-LinkNet with an increase in Intersection over Union (IoU) metrics of 1.88% and 1.84%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, the possibility of improving target detection performance in passive bistatic radar by exploiting a frequency agile (FA) signal is investigated, namely frequency agile signal-based passive bistatic radar (FAPBR) coherent integration. Since the carrier frequency of each pulse signal is agile, FAPBR coherent integration suffers from the problems of random range and Doppler phase fluctuations. To tackle these challenges, a novel FA signal coherent integration target detection scheme for PBR is proposed. In particular, the phase quadratic difference principle is presented for eliminating Doppler phase hopping. Then, frequency rearrangement is adopted to compensate for random range phase fluctuation while obtaining the high-range-resolution profiles (HRRPs) of the detecting target. Further, we innovatively present a sliding-range ambiguity decoupling (S-RAD) method to remove the range ambiguity effect in the case of the high pulse repetition frequency (HPRF). Compared with the existing methods, the proposed method can effectively mitigate Doppler phase hopping without requiring prior target velocity information, offering improved coherent integration performance in frequency agile signals with reduced computational complexity. Moreover, it successfully corrects the range ambiguity issue caused by HPRF. Finally, a series of simulation results are presented to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Visual transformers (ViTs) are widely used in various visual tasks, such as fine-grained visual classification (FGVC). However, the self-attention mechanism, which is the core module of visual transformers, leads to quadratic computational and memory complexity. The sparse-attention and local-attention approaches currently used by most researchers are not suitable for FGVC tasks. These tasks require dense feature extraction and global dependency modeling. To address this challenge, we propose a dual-dependency attention transformer model. It decouples global token interactions into two paths. The first is a position-dependency attention pathway based on the intersection of two types of grouped attention. The second is a semantic dependency attention pathway based on dynamic central aggregation. This approach enhances the high-quality semantic modeling of discriminative cues while reducing the computational cost to linear computational complexity. In addition, we develop discriminative enhancement strategies. These strategies increase the sensitivity of high-confidence discriminative cue tracking with a knowledge-based representation approach. Experiments on three datasets, NABIRDS, CUB, and DOGS, show that the method is suitable for fine-grained image classification. It finds a balance between computational cost and performance. [ABSTRACT FROM AUTHOR]

Skeleton‐based action recognition methods commonly employ graph neural networks to learn different aspects of skeleton topology information However, these methods often struggle to capture contextual information beyond the skeleton topology. To address this issue, a Scene Context‐aware Graph Convolutional Network (SCA‐GCN) that leverages potential contextual information in the scene is proposed. Specifically, SCA‐GCN learns the co‐occurrence probabilities of actions in specific scenarios from a common knowledge base and fuses these probabilities into the original skeleton topology decoder, producing more robust results. To demonstrate the effectiveness of SCA‐GCN, extensive experiments on four widely used datasets, that is, SBU, N‐UCLA, NTU RGB + D, and NTU RGB + D 120 are conducted. The experimental results show that SCA‐GCN surpasses existing methods, and its core idea can be extended to other methods with only some concatenation operations that consume less computational complexity. [ABSTRACT FROM AUTHOR]

A fall k-coloring of a graph G is a proper k-coloring of G such that each vertex has at least one neighbor in each of the other color classes. A graph G which has a fall k-coloring is equivalent to having a partition of the vertex set V (G) in k independent dominating sets. In this paper, we first prove that for any fall k-colorable graph G with order n, the number of edges of G is at least (n (k − 1) + r (k − r)) / 2 , where r ≡ n (mod k) and 0 ≤ r ≤ k − 1 , and the bound is tight. Then, we obtain that if G is k-colorable ( k ≥ 2 ) and the minimum degree of G is at least k − 2 k − 1 n , then G is fall k-colorable and this condition of minimum degree is the best possible. Moreover, we give a simple proof for an NP-hard result of determining whether a graph is fall k-colorable, where k ≥ 3 . Finally, we show that there exist an infinite family of fall k-colorable planar graphs for k ∈ { 5 , 6 } . [ABSTRACT FROM AUTHOR]

An adaptive neuro‐fuzzy inference system is presented which is optimized by a genetic algorithm to classify normal and abnormal brain tumours. The classifier is fast and simple, named genetic algorithm‐adaptive neuro‐fuzzy inference system, and the determined learning rules minimize its error and improve its accuracy. The presented system follows five steps including preprocessing, morphological operation, feature extraction, feature selection, and classification. Morphological operators segment the abnormal regions and calculate the tumour area. The statistical features and the grey‐level co‐occurrence matrix are employed for feature extraction. Magnetic resonance images are considered and 12 statistical features are extracted, then the genetic algorithm‐based selection technique helps to select features and reduce the extracted features and improves the accuracy and decision time. So, the high dimensionality and the computational complexity of the adaptive neuro‐fuzzy inference system are reduced, and the classifier decides more efficiently. The input data are the figshare brain tumour dataset with 670 abnormal and 670 normal magnetic resonance images, and the classifier requires 10.788 s for classification. The efficient performance of the genetic algorithm‐adaptive neuro‐fuzzy inference system is confirmed by the accuracy of 99.85%, sensitivity of 99.7%, specificity of 100%, precision of 100%, and mean square error of 0.0027. [ABSTRACT FROM AUTHOR]

Gaussian boson sampling (GBS) plays a crucially important role in demonstrating quantum advantage. As a major imperfection, the limited connectivity of the linear optical network weakens the quantum advantage result in recent experiments. In this work, we introduce an enhanced classical algorithm for simulating GBS processes with limited connectivity. It computes the loop Hafnian of an n × n symmetric matrix with bandwidth w in O (n w 2 w) time. It is better than the previous fastest algorithm which runs in O (n w 2 2 w) time. This classical algorithm is helpful on clarifying how limited connectivity affects the computational complexity of GBS and tightening the boundary for achieving quantum advantage in the GBS problem. [ABSTRACT FROM AUTHOR]

In public spaces, conducting security checks to detect concealed objects carried on the human body is crucial for enhancing global anti-terrorist measures. Terahertz imaging has recently played a pivotal role in concealed object detection. However, previous studies have faced significant challenges in achieving superior accuracy and performance. To address these issues, we propose a YOLOv5m model for detecting hidden objects beneath human clothing. We employ the CSPDarknet53 block to reduce noise and enhance discriminative power. Object location and size are identified using a PANet and the prediction head. To reduce computational complexity and obtain highly relevant features, we utilize multi-convolutional layers. Duplicate boxes are eliminated and high-quality bounding boxes are accurately detected using the NMS block. Hyper parameter tuning is performed using the Mutation Enabled Salp Swarm Algorithm, resulting in improved detection accuracy and reduced processing time. Our proposed model achieves impressive metrics, including a precision of 98.99%, recall of 97.80%, F1 score of 98.05%, detection rate of 96.50% and execution time of 135 s. Comparatively, our method outperforms existing approaches such as CNN, YOLO3, AC-SDBSCAN, YOLO-v2, RaadNet and SPFAN. We train and test our proposed method using a terahertz video dataset, demonstrating excellent results with high precision. [ABSTRACT FROM AUTHOR]

Numerical methods applicable to the approximation of spectral fractional diffusion operators in multidimensional domains with general geometry are analyzed. Over the past decade, several approaches have been proposed to approximate the inverse operator A - α , α ∈ (0 , 1) . Despite their different origins, they can all be written as a rational approximation. Let the matrix A be obtained after finite difference or finite element discretization of A . The BURA (Best Uniform Rational Approximation) method was introduced to approximate the inverse matrix A - α based on an approximation of the scallar function z α , α ∈ (0 , 1) , z ∈ [ 0 , 1 ] . In this paper we study BURA and BURA-based methods for fractional powers of sparse symmetric and positive definite (SPD) matrices, presentiing the concept, general framework and error analysis. Our contributions concern approximations of A - α and A α for arbitrary α > 0 , thus significantly expanding the range of available currently results. Assymptotically accurate error estimates are obtained. The rate of convergence is exponential with respect to the degree of BURA. Numerical results are presented to illustrate and better interpret the theoretical estimates. [ABSTRACT FROM AUTHOR]

In recent years, the drone swarm has experienced remarkable growth, finding applications across diverse domains such as agricultural surveying, disaster rescue and logistics delivery. However, the rapid expansion of drone swarm usage underscores the necessity for innovative approaches in the field. Traditional algorithms face challenges in adapting to complex tasks, environmental modeling and computational complexity, highlighting the need for more advanced solutions like multi-agent deep reinforcement learning to enhance efficiency and robustness in drone swarm. Our proposed approach tackles this challenge by embracing temporal and spatial. In terms of the temporal, the proposed approach builds upon historical data, it enhances the predictive capabilities regarding future behaviors. In the spatial, the proposed approach leverage graph theory to model the swarm's features, while attention mechanisms strengthen the relationships between individual drones. The proposed approach addresses the unique characteristics of drone swarms by incorporating temporal dependencies, spatial structures and attention mechanisms. Extensive experiments validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This study focuses on a network topology design problem with minimum cost subject to a reliability constraint (NTD-CR). The computational complexity of NTD-CR increases exponentially with an increase in the number of nodes and edges, necessitating the use of efficient algorithms in large-scale networks. Herein, variable neighborhood descent (VND) and weighting local search (WLS) algorithms, along with their hybrid WLS-VND, are used to solve this problem. The VND enables effective exploration within neighborhoods dictated by the characteristics of the problem, whereas WLS employs a dynamic penalty coefficient and weighting function to efficiently navigate the solution space. Numerical experiments confirmed that WLS-VND produced high-quality solutions with reduced dependence on the initial parameter settings. This result aligns with our objectives of developing high-performing practical algorithms for real-world applications and minimizing the need for laborious parameter tuning. Furthermore, WLS-VND holds promise for designing cost-effective and reliable network infrastructures. [ABSTRACT FROM AUTHOR]

Model Predictive Control (MPC) has many advantages in controlling an aero-engine, such as handling actuator constraints, but the computational burden greatly obstructs its application. The current multiplex MPC can reduce computational complexity, but it will significantly decrease the control performance. To guarantee real-time performance and good control performance simultaneously, an intelligent reduced-dimensional scheme of MPC is proposed. The scheme includes a control variable selection algorithm and a control sequence coordination strategy. A constrained optimization problem with low computational complexity is first constructed by using only one control variable to define a reduced-dimensional control sequence. Therein, the control variable selection algorithm provides an intelligent mode to determine the control variable that has the best control effect at the current sampling instant. Furthermore, a coordination strategy is adopted in the reduced-dimensional control sequence to consider the interaction of control variables at different predicting instants. Finally, an intelligent reduced-dimensional MPC controller is designed and implemented on an aero-engine. Simulation results demonstrate the effectiveness of the intelligent reduced-dimensional scheme. Compared with the multiplex MPC, the intelligent reduced-dimensional MPC controller enhances the control quality significantly by 34.06%; compared with the standard MPC, the average time consumption is decreased by 64.72%. [ABSTRACT FROM AUTHOR]

To estimate the state of health, charge, power, and safety (SoX) of lithium-ion batteries (LiBs) in real time, battery management systems (BMSs) need accurate and efficient battery models. The full-order partial two-dimensional (P2D) model is a common physics-based cell-level LiB model that faces challenges for real-time BMS implementation due to the complexity of its numerical solver. In this paper, we propose a method to discretise the P2D model equations using the Finite Volume and Verlet Integration Methods to significantly reduce the computational complexity of the solver. Our proposed iterative solver uses novel convergence criteria and physics-based initial guesses to provide high fidelity for discretised P2D equations. We also include both the kinetic-limited and diffusion-limited models for Solid Electrolyte Interface (SEI) growth into an iterative P2D solver. With these SEI models, we can estimate the capacity fade in real time once the model is tuned to the cell–voltage curve. The results are validated using three different operation scenarios, including the 1C discharge/charge cycle, multiple-C-rate discharges, and the Lawrence Livermore National Laboratory dynamic stress test. The proposed solver shows at least a 4.5 times improvement in performance with less than 1% error when compared to commercial solvers. [ABSTRACT FROM AUTHOR]

Person image generation aims to generate images that maintain the original human appearance in different target poses. Recent works have revealed that the critical element in achieving this task is the alignment of appearance domain and pose domain. Previous alignment methods, such as appearance flow warping, correspondence learning and cross attention, often encounter challenges when it comes to producing fine texture details. These approaches suffer from limitations in accurately estimating appearance flows due to the lack of global receptive field. Alternatively, they can only perform cross‐domain alignment on high‐level feature maps with small spatial dimensions since the computational complexity increases quadratically with larger feature sizes. In this article, the significance of multi‐scale alignment, in both low‐level and high‐level domains, for ensuring reliable cross‐domain alignment of appearance and pose is demonstrated. To this end, a novel and effective method, named Multi‐scale Cross‐domain Alignment (MCA) is proposed. Firstly, MCA adopts global context aggregation transformer to model multi‐scale interaction between pose and appearance inputs, which employs pair‐wise window‐based cross attention. Furthermore, leveraging the integrated global source information for each target position, MCA applies flexible flow prediction head and point correlation to effectively conduct warping and fusing for final transformed person image generation. Our proposed MCA achieves superior performance on two popular datasets than other methods, which verifies the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

It is difficult for lightweight neural networks to produce accurate 6DoF pose estimation effects due to their accuracy being affected by scale changes. To solve this problem, we propose a method with good performance and robustness based on previous research. The enhanced PVNet-based method uses depth-wise convolution to build a lightweight network. In addition, coordinate attention and atrous spatial pyramid pooling are used to ensure accuracy and robustness. This method effectively reduces the network size and computational complexity and is a lightweight 6DoF pose estimation method based on monocular RGB images. Experiments on public datasets and self-built datasets show that the average ADD(-S) estimation accuracy and 2D projection index of the improved method are improved. For datasets with large changes in object scale, the estimation accuracy of the average ADD(-S) is greatly improved. [ABSTRACT FROM AUTHOR]