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Flat bands in 2D twisted materials are key to the realization of correlation-related exotic phenomena. However, a flat band often was achieved in the large system with a very small twist angle, which enormously increases the computational and experimental complexity. In this work, we proposed group-V twisted bilayer materials, including P, As, and Sb in the β phase with large twist angles. The band structure of twisted bilayer materials up to 2524 atoms has been investigated by a deep learning method DeepH, which significantly reduces the computational time. Our results show that the bandgap and the flat bandwidth of twisted bilayer β-P, β-As, and β-Sb reduce gradually with the decreasing of twist angle, and the ultra-flat band with bandwidth approaching 0 eV is achieved. Interestingly, we found that a twist angle of 9.43° is sufficient to achieve the band flatness for β-As comparable to that of twist bilayer graphene at the magic angle of 1.08°. Moreover, we also find that the bandgap reduces with decreasing interlayer distance while the flat band is still preserved, which suggests interlayer distance as an effective routine to tune the bandgap of flat band systems. Our research provides a feasible platform for exploring physical phenomena related to flat bands in twisted layered 2D materials. [ABSTRACT FROM AUTHOR]

The dual-scrambling scheme that combines position transformation and bit-plane transformation is a popular image encryption scheme. However, such schemes need more key information, and the encryption and decryption processes are complicated. In addition, the existing quantum image dual-scrambling schemes mainly deal with square images. In this paper, we propose a hybrid scrambling encryption scheme for multi-mode quantum image representation (MQIR) images based on random permutation, in which the H × W quantum image is represented in MQIR. A random number generator factor s uniquely associates one of the random permutations of integers from 1 to a positive integer, so as to hybrid scramble both the pixel position and the binarized position of each pixel value. Meanwhile, the quantum circuits and some examples of scrambling are given. Furthermore, various analyses of the performance of this scheme were conducted, including effectiveness, key space, and computational complexity. By modifying the random generation factor to construct multiple binary grayscale images, the simulated results on the IBM Quantum Cloud platform demonstrate that the proposed quantum image encryption scheme is effective. In comparison to existing quantum image dual scrambling schemes, it is both simple and effective, offering a large key space, lower computational complexity, and applicability to non-square quantum images. [ABSTRACT FROM AUTHOR]

In this paper, a new mixed integer programming (MIP) formulation is developed for balancing and scheduling of mixed model assembly lines with disjunctive precedence constraints among assembly tasks. To represent alternative precedence relations, AND/OR assembly graph was adopted. In case of alternative precedence relations, for each product multiple assembly plans exist, which can be represented by a set of alternative precedence subgraphs and only one of such subgraphs should be selected for each product. As the number of subgraphs exponentially increases with the number of disjunctive relations among the tasks, the computational complexity of simultaneous balancing and scheduling along with the assembly subgraph selection increases with the number of alternative precedence relations. Unlike the other MIP approaches known from the literature, the new model does not need the alternative assembly subgraphs to be to explicitly enumerated as input data and then used for indexing the variables. Instead, a new disjunctive precedence selection and task assignment variable and new constraints are introduced to optimally choose one relation for each subset of alternative precedence relations. The optimal solutions for computational examples of balancing and scheduling problems illustrate a superior performance of the new modelling approach. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a severe retinal condition primarily affecting diabetic people. This is mostly due to excessive blood sugar levels, which induce retinal blood vessel damage. Diabetic Retinopathy must be detected early to avoid vision loss. Exudates on the retina are the primary indicator of diabetic retinopathy. Therefore, their detection would be crucial for automated illness monitoring and grading. This research provides novel classification and segmentation methods for accurately classifying DR images. The process involves four stages: preprocessing, DR classification, Exudates segmentation, and performance computation. In the preprocessing stage, the source RGB retinal images are separated into three bands, R-Image, G-Image, and B-Image, and each sub-band image is then subjected to data augmentation. The data-augmented images are classified through the proposed Hybrid Centric Convolutional Neural Networks (HCCNN) classifier to produce the classification results (Grade 0 or Grade 1). The proposed exudate lesions segmentation method is utilized to predict and find the exudates in the DR image. The exudates segmented retinal Image is then used to do performance computations for sensitivity (Se), specificity (Sp), and accuracy (Acc), as well as computational complexity analysis. The standard datasets (SYSU and HRF) and the real-time dataset are subjected to the HCCNN approach. The mean Se, Sp, and Acc obtained by this HCCNN method are 98.65%, 98.76%, and 98.96% for the SYSU and 98.58%, 98.65%, and 98.6% for the HRF dataset, respectively. Our method obtains an average detection ratio of 91.66% in the real-time dataset. The suggested HCCNN approach produces findings that outperform the conventional methods and help doctors identify the DR disease early to prevent further vision loss. [ABSTRACT FROM AUTHOR]

This study introduces an innovative hybrid approach for personalized trip recommendations, aiming to enhance existing recommender systems by leveraging multidimensional data. Our proposed method integrates user preferences and diverse contextual factors to address challenges related to data sparsity effectively. To overcome this hurdle, our methodology employs a clustering approach, streamlining the extraction of Points of Interest (PoI) and reducing computational complexity. The framework comprises three key components: I) a unique strategy for context assessment, achieved by combining contextual information in vector form through the Term-Frequency-Inverse-Document-Frequency technique, II) the incorporation of tourist demographic information to alleviate the Cold Start problem, and III) the implementation of an asymmetric schema that elevates the traditional similarity paradigm. Moreover, our approach utilizes personalized PoIs in consecutive travel patterns, enabling the retrieval and ranking of an optimal list of potential routes. The experimental results based on Flickr and Yelp datasets reveal that the proposed method surpasses prior work on all three metrics, achieving a significant 8% increase in precision and an 11% increase in F-Score, thereby enhancing the quality metrics of personalized trip recommendations. [ABSTRACT FROM AUTHOR]

Many everyday tasks require people to solve computationally complex problems. However, little is known about the effects of computational hardness on the neural processes associated with solving such problems. Here, we draw on computational complexity theory to address this issue. We performed an experiment in which participants solved several instances of the 0-1 knapsack problem, a combinatorial optimization problem, while undergoing ultra-high field (7T) functional magnetic resonance imaging (fMRI). Instances varied in computational hardness. We characterize a network of brain regions whose activation was correlated with computational complexity, including the anterior insula, dorsal anterior cingulate cortex and the intra-parietal sulcus/angular gyrus. Activation and connectivity changed dynamically as a function of complexity, in line with theoretical computational requirements. Overall, our results suggest that computational complexity theory provides a suitable framework to study the effects of computational hardness on the neural processes associated with solving complex cognitive tasks. Author summary: Humans are frequently faced with complex decisions, ranging from everyday tasks like grocery shopping to more intricate decisions such as selecting an investment portfolio. These decisions require higher-order problem-solving skills, which remain poorly understood, particularly regarding the neural processes that support deliberation. In this study, we introduce a framework that employs computational complexity theory to investigate the neural activity that occurs during complex problem-solving. We suggest that the inherent characteristics of a problem determine its computational difficulty, and that these intrinsic features could be used to identify consistent neural patterns during complex problem-solving. To test this approach, we applied it to the knapsack problem, a standard computational problem. Participants solved several versions of this problem while their brain activity was monitored using an ultra-high field MRI. By leveraging computational complexity theory, we developed generic metrics of computational difficulty and successfully identified the corresponding neural correlates and their dynamics during problem-solving. The results indicate that the proposed framework, grounded in computational complexity theory, offers a promising method for studying the neural processes involved in complex problem-solving. This approach could provide valuable insights into a topic that has previously resisted formal investigation. [ABSTRACT FROM AUTHOR]

Image processing often requires filtering, which can be effectively performed by using B‐spline surface approximation. This article presents a fast method of approximating data in rectangular (image) form using such surfaces. The method considers dynamic changes of data by representing rows and columns in chordal parameterized form. To improve performance, after parametrization, lines are uniformly resampled using linear interpolation. It allows using the same basis functions with uniform parameterization for approximation of all processed lines. After approximation, reconstruction of original parameterization is required, but its computational complexity is also linear. [ABSTRACT FROM AUTHOR]

In the digitalized world, efficient big data clustering is necessary for massive data generation. The clustering algorithm plays an important role in resolving the computational complexity. The big data arriving from various sources are being processed using the MapReduce framework (MRF) by the knowledge of the clustering algorithms. Moreover, the clustering algorithm is useful for mining the significant information from the dataset. Generally, there are several difficulties in applying the clustering approach to big data as its new challenges are based on computation cost and reasonable time. Hence, this research introduced the Competitive Jaya Leader Harris Hawks Optimization assisted Entropy Weighted Power K-Means Clustering (CJayaLHHO_EWPKMC) for big data clustering. In addition, the overall processing of the devised method for big data clustering is carried out in the MapReduce (MR) framework. In mapper, the feature selection is done using Support vector Machine-Recursive Feature Elimination (SVM-RFE) assisted Jaya Leader Harris Hawks Optimization (JayaLHHO). In the reducer, the big data clustering is established using the EWPKMC method, wherein the weight of EWPKMC is modified with the CJayaLHHO algorithm such that the clustering outcome is attained. The proposed method is scalable, simple, cost-effective, and able to integrate with other technologies. The experimental result portrays that the developed method attained a superior presentation than the conventional methods based on the clustering accuracy is 0.937, the Jaccard coefficient is 0.913, and the rand coefficient is 0.912. [ABSTRACT FROM AUTHOR]

Deep learning (DL)-based approaches are notable for their ability to establish feature associations without relying on physical constraints, unlike traditional strategies that are complex and dependent on expert experience. However, three main challenges hinder the versatility of semantic segmentation models. First, the targets in these images are dense and exist at varying spatial scales, which imposes higher demands on the model for accurate segmentation across scales. Second, the segmentation of small targets in the images is often overlooked, leading to a compromise between fine segmentation and model efficiency. Lastly, the data-intensive nature of remote sensing images and the resource-intensive operations of large-scale networks impose significant communication and computation burdens on edge devices, which may not have sufficient resources to handle them effectively. To address these challenges, this paper proposes a lightweight semantic segmentation method for remote sensing images to achieve high-precision segmentation for multi-scale targets while maintaining low computational complexity. The main components include: (1) embedding the inverted residual block structure to minimize the number of model parameters and computational costs; (2) introducing the parallel irregular space pyramid pooling module to efficiently aggregate multi-scale contextual information for fine-grained recognition of small targets; and (3) embedding transfer learning into the encoder-decoder structure to speed up the convergence rate and improve multi-scale feature fusion capability, thereby reducing semantic information loss. The proposed lightweight method has been extensively tested on real-world high-resolution remote sensing datasets. It achieved PA, MPA, MIoU, and FWIoU scores of 87.90%, 75.76%, 66.29%, and 78.81% on the Vaihingen dataset; 87.03%, 85.31%, 74.85%, and 77.54% on the Potsdam dataset; and 95.37%, 83.33%, 75.70%, and 91.31% on the Aeroscapes dataset. Compared to other popular semantic segmentation models, the proposed method achieved the highest values in all four evaluation indicators, demonstrating its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

The purpose of long-term forecasting is to meet the needs of practical applications, such as the prediction of the development trend of infectious diseases and the planning of electricity consumption. In this paper, we study the long-term forecasting of time series. Studies have shown that previous Transformer-based models have the potential to improve prediction capabilities, but there are also some problems, such as the lack of location information and the slow training speed. To solve these problems, we designed an efficient Transformer-based model called AD-Autoformer, which is specifically designed for long-term series prediction. By introducing position embedding, the model can better understand the patterns and relationships in the sequence, to improve the performance and generalization ability of the model when processing sequence data, and the self-attention distilling mechanism realizes the compression and acceleration of the model by halving the cascading layer input to highlight the dominant attention. This method significantly reduces the computational complexity of the model and improves the training speed of the model while maintaining the performance of the model. Experimental results on five large datasets show that the proposed AD-Autoformer model has different degrees of improvement in MSE and MAE indicators compared with other benchmark methods. [ABSTRACT FROM AUTHOR]

The implementation of turn restrictions represents a critical research challenge in chiplet-based systems, with the objective of achieving deadlock-free communication. Nevertheless, existing methodologies encounter difficulties in terms of computational complexity, which impedes the design process. Moreover, as the scale of the problem increases, the cost of addressing it becomes increasingly untenable. In this paper, we introduce the efficient turn restrictions setting (ETRS) method to reduce the computational cost of implementing turn restrictions for boundary routers in chiplet-based systems. In stage 1, we present a symmetry-based preprocessing algorithm (SBPA). SBPA exploits the symmetry inherent in chiplet topologies by generating multiple sets of identical modes, ensuring that scenarios with the same objective function value are calculated only once per iteration. In stage 2, a heuristic selection algorithm (HSA) for turn restrictions based on NSGA-II is proposed as a means of searching for approximately optimal solutions and of solving the problem quickly. In stage 3, different filtering criteria are introduced to evaluate the Pareto fronts of HSA for making decisions on the placement of boundary routers and their turn restrictions. Evaluation results reveal that the proposed ETRS method surpasses existing solutions in terms of computation efficiency. Moreover, it delivers satisfactory optimal objective values. [ABSTRACT FROM AUTHOR]

Printed circuit boards (PCBs) play a critical role in electronic products. Ensuring these products' long-term reliability and consistent performance requires effective PCB defect detection. Although existing deep learning models for PCB defect detection are not highly accurate, they often neglect capability considerations. This paper introduces a precise, fast, and lightweight defect detection model, CCG-YOLO, based on an enhanced YOLOv5 model to address this issue. The enhancements in CCG-YOLO can be summarized as follows: (1) Improved Backbone network: The feature extraction ability of the Backbone network is enhanced by introducing a C3HB module, which fosters spatial interaction capabilities. (2) Lightweight feature fusion network: A lightweight convolution structure called Ghost-Shuffle Convolution is incorporated in the feature fusion network, remarkably reducing model parameters while maintaining performance. (3) Efficient residual networking: To enhance model performance further, a CNeB module is introduced based on the ConvNeXt network, which replaces the C3 module in the Neck. CNeB improves model detection accuracy and reduces the number of model parameters. The combination of these enhancements results in impressive performance. CCG-YOLO achieves mean average precision (mAP@0.5) of 99.5% and 88.75% in mAP@0.5:0.95 on the TDD-Net public dataset. Compared with the original YOLOv5s algorithm, CCG-YOLO offers a 4.24% improvement in mAP@0.5:0.95, a 1 MB reduction in model size, a 0.472 M decrease in the number of parameters, a 0.6G floating point operation reduction in computational complexity, and a 120 frames per second real-time inference speed. These experimental results underscore that the proposed model excels in accuracy and speed and has a compact size for PCB defect detection. Moreover, CCG-YOLO is easily deployable on low-end devices, making it well-suited for meeting the real-time requirements of industrial defect detection. [ABSTRACT FROM AUTHOR]

In this paper, we study Horn formulas from the perspective of read-once resolution refutations (RORs). A Horn formula is a Boolean formula in conjunctive normal form (CNF), in which each clause contains at most one positive literal. Horn formulas are used in a number of domains, including program verification, logic programming, and econometrics. In particular, deduction in ProLog is based on unification. Unification is based on resolution and instantiation. Resolution is a system used to prove the infeasibility of Boolean formulas. It is important to note that resolution is both sound and complete. However, resolution is inefficient in the following sense: There exist CNF formulas with resolution refutations whose lengths are bounded below by an exponential function of the input size. At the same time, these formulas admit shorter (polynomially bounded) proofs of infeasibility in other proof systems, such as Frege Systems. Despite this inefficiency, resolution is simple and easy to implement and hence used in a wide variety of theorem provers. In this paper, we study two variants of resolution. These are read-once resolution (ROR) and read-once unit resolution (UROR). Both ROR and UROR are sound. However, they are incomplete since there exist infeasible Boolean formulas which do not have either an ROR or a UROR. In this paper, we look at the problems of determining if a Horn formula has an ROR or a UROR. We also examine the problem of finding the optimal length ROR of a Horn formula from both the computational complexity and the approximation perspectives. Finally, we analyze the copy complexity of Horn formulas with respect to URORs. [ABSTRACT FROM AUTHOR]

In the path version of the Travelling Salesman Problem (Path-TSP), a salesman is looking for the shortest Hamiltonian path through a set of n cities. The salesman has to start his journey at a given city s , visit every city exactly once, and finally end his trip at another given city t. In this paper we show that a special case of the Path-TSP with a Demidenko distance matrix is solvable in polynomial time. Demidenko distance matrices fulfill a particular condition abstracted from the convex Euclidian special case by Demidenko (1979) as an extension of an earlier work of Kalmanson (1975). We identify a number of crucial combinatorial properties of the optimal solution and design a dynamic programming approach with time complexity O (n 6). [ABSTRACT FROM AUTHOR]

In this work, we propose a multiple feedback-based successive interference cancellation (SIC) scheme for an ultra-dense Internet of Things (IoT) device network. Non-orthogonal multiple access (NOMA) enables massive connectivity with improved user fairness and spectral efficiency and is envisaged as a multiple access technique for IoT devices. NOMA simultaneously serves multiple users within a single resource block, leading to unbounded yet regulated multi-user interference. SIC is widely adopted in the NOMA system to detect users' symbols. Nevertheless, multi-user interference and error propagation in the SIC layer are inherent challenges in NOMA. Recent studies have aimed to minimize interference and error propagation, imposing stringent conditions on the number of users and power allocation. Thus, this paper proposes novel multiple feedback-based SIC algorithms for the uplink multi-user NOMA scenarios that outperform the conventional SIC. Further, the proposed algorithm's performance is analyzed under the practical case of imperfect channel state information at the receiver node to validate the robustness. The computational complexity of multiple feedback SIC is compared with the conventional SIC. [ABSTRACT FROM AUTHOR]

In the development of web applications, the rapid advancement of Internet technologies has brought unprecedented opportunities and increased the demand for user authentication schemes. Before the emergence of blockchain technology, establishing trust between two unfamiliar entities relied on a trusted third party for identity verification. However, the failure or malicious behavior of such a trusted third party could undermine such authentication schemes (e.g., single points of failure, credential leaks). A secure authorization system is another requirement of user authentication schemes, as users must authorize other entities to act on their behalf in some situations. If the transfer of authentication permissions is not adequately restricted, security risks such as unauthorized transfer of permissions to entities may occur. Some research has proposed blockchain-based decentralized user authentication solutions to address these risks and enhance availability and auditability. However, as we know, most proposed schemes that allow users to transfer authentication permissions to other entities require significant gas consumption when deployed and triggered in smart contracts. To address this issue, we proposed an authentication scheme with transferability solely based on hash functions. By combining one-time passwords with Hashcash, the scheme can limit the number of times permissions can be transferred while ensuring security. Furthermore, due to its reliance solely on hash functions, our proposed authentication scheme has an absolute advantage regarding computational complexity and gas consumption in smart contracts. Additionally, we have deployed smart contracts on the Goerli test network and demonstrated the practicality and efficiency of this authentication scheme. [ABSTRACT FROM AUTHOR]

Summary: Localization algorithms are essential for tracking, fault tolerance, and adding location context to Internet of Things (IoT) networks. Among the various range‐free techniques, DV‐Hop stands out for its lower computational complexity. Despite efforts to minimize manufacturing costs for sensors, they often find themselves deployed in remote and hard‐to‐access locations, making battery replacement a challenging prospect. These sensors can be utilized in a hostile environment, such as a field of battle, fires, floods, and earthquakes. In these types of scenarios, the sensors can become dead and stop communicating with each other. Hence, there is a critical need to improve algorithms that can tolerate and detect faults in network nodes. The faults of the sensor can lower the quality of the algorithm if they are not replaced. In such situations, we need to distribute supplementary target nodes to replace faulty nodes. This paper presented an improved DV‐Hop based method for localization of additional target nodes using grey wolf optimization (GWO). The proposed method will only localize the position of target nodes and minimizes the localization time, which makes the system more energy efficient. Simulation results conclude that the proposed GWO‐based DV‐Hop localization performed well as compared with traditional DV‐hop, genetic DV‐Hop, and Adaptive genetic DV‐hop. [ABSTRACT FROM AUTHOR]

In the emergency rescue and disposal of social public emergencies, supply transportation effectively provides a strong supply foundation and realistic conditions. The trajectory tracking control of emergency supplies transportation robot is the key technology to ensure the timeliness of transportation. In this article, the emergency supplies transportation robot is taken as the research object, based on Koopman operator theory, combined with radial basis function (RBF) neural network disturbance observer and adaptive prediction horizon event‐triggered model predictive control (APET‐MPC) algorithm to investigate the purely data‐driven trajectory tracking control problem of emergency supplies transportation robot when the model parameters and models are unknown. Firstly, the Koopman operator is used to establish a high‐dimensional linear model of the robot. Secondly, the RBF neural network disturbance observer is designed to estimate the disturbance during the robot operation and compensate it to the controller. Thirdly, APET‐MPC is used to optimize the trajectory tracking control of the emergency supplies transportation robot to reduce computational complexity. Finally, the performance of the proposed trajectory tracking controller is verified by Carsim/ Simulink joint simulation. The simulation results show that the model established by Koopman operator theory can achieve the high accuracy approximation of the robot. Compared with the MPC trajectory tracking controller, the APET‐MPC trajectory tracking controller based on RBF neural network disturbance observer (RBF‐APET‐MPC) improves the tracking accuracy of the robot and reduces the total triggering times of the system by more than 50%. [ABSTRACT FROM AUTHOR]

Summary: Power system monitoring relies on the reliability of state estimation (SE) results. SE plays a dominant role in data debugging if sufficient data is available. Criticality analysis (CA) integrates SE as a module in which measurements—taken one‐by‐one or in groups (tuples) of minimal cardinality—are designated crucial. The combinatorial nature of extensive CA (not restricted to identifying low‐cardinality critical tuples) characterizes its computational complexity and imposes challenging limits to go beyond. In simple terms, these limits are established by the number of measurements to be combined, the cardinality of tuples, and the computing time required to check the criticality condition. This paper proposes an innovative computational solution to expand CA limits found to date in the literature. A framework with multi‐threads designed cleverly on a graphics processing unit (GPU) parallel processing environment is built. The conceived architecture favors evaluating massive measurement combinations of diverse cardinality in extensive CA. Numerical results reveal significant speed‐ups with the proposed approach, contrasting with those reported in research efforts published so far. [ABSTRACT FROM AUTHOR]

Emotion plays a vital part in the life of humankind. Emotion Recognition (ER) is the necessary task to find one's inner Emotion through speech, text or face. ER process has inevitable places in applications such as education, healthcare, defence, forensics, automation and scientific-based purposes. However, the recognition models face various issues during the process. Due to the large feature sets, the model's computational time and complexity have been increased; it also produced high errors in recognition, which degrades the efficiency of the system. So, a novel Monkey-based V-Net Framework (MbVF) was developed for this research work to classify the Emotion and sentiment patterns using healthcare data. Initially, the healthcare Artificial Intelligence (AI) conversation data from the Coronavirus Tweets NLP-Text Classification dataset was collected and trained for the system. Next, stemming, stop-words removal, tokenization, and noise feature elimination process were performed in the preprocessing step. Then, the Term Frequency based-Inverse Document Frequency (TF-IDF) features were used to choose the best features depending on the fitness features of the proposed MbVF approach. Finally, the ER process was performed to recognize the emotions, such as positive, neutral and negative, using sentiment analysis with the emotion state transition and emotion pattern. The model attained 99.98% accuracy in predicting emotions, and is higher than the other model. [ABSTRACT FROM AUTHOR]

Developing efficient and secure image encryption techniques for transmitting multiple images has become crucial due to the inadequacy of single-image encryption techniques in handling the increasing volume of big data over unprotected networks. This paper introduces a novel multiple-image encryption (MIE) technique that utilizes mixed image elements in conjunction with the RSA cryptosystem, fractional discrete cosine transform (FrDCT), and Henon chaotic map (HCM). To encrypt k images together, the first step involves making three big images B 1 , B 2 and B 3 from these k images using matrix theory. The three images B 1 , B 2 and B 3 are converted into three indexed images I 1 , I 2 , and I 3 by extracting their color maps. Indexed images I 1 , I 2 and I 3 are then treated as a single RGB image by taking I 1 as a red (R) component, I 2 as a green (G) component and I 3 as a blue (B) component. The RSA cryptosystem is then applied on each component individually, followed by FrDCT and HCM to enhance security and key space. The resulting encrypted image is a single-channel real-valued image that is easy to display, store, and transmit over an unsecured network. The suggested technique offers multi-layer security in frequency, time and coordinate domains. Private keys, their arrangements and parameter positions are critical for decryption. Simulation analysis supports the robustness and effectiveness of the proposed technique. The sensitivity analysis demonstrates its extreme sensitivity towards private keys and their arrangement. Statistical analysis, including measures such as MSE, SSIM, PSNR, NPCR, UACI, entropy analysis, correlation coefficient, histogram analysis, computational complexity and comparison analysis confirm the effectiveness and viability of the introduced algorithm. [ABSTRACT FROM AUTHOR]

Lossless image compression techniques shrink the image size to improve the transmission efficiency and reduce the occupied storage space while ensuring the quality of the image is lossless. Among them, the LOCO‐I/JPEG‐LS algorithm benefits high lossless compression ratio and low computational complexity and thus is widely used for various real‐time applications. However, due to the problems of the context dependency in the LOCO‐I, the parallelism in the algorithm is greatly constrained, which significantly limits the throughput and the real‐time performance of hardware implementations. Existing designs achieve more parallelism by using a lot of hardware costs or straightforward chunking with losing compression ratio. In order to trade off the parallelism and the compression ratio, this paper proposes a chunk‐oriented error modeling scheme for LOCO‐I, which enables parallelism in both compression and decompression and achieves a better compression ratio in chunks. Based on the optimized algorithm, a high‐throughput flexible lossless compression and decompression architecture (HFCD) is proposed, which achieves higher pixel per clock (PPC) with less hardware cost. Additionally, HFCD introduces a parameter sharing mechanism to enable random access of image chunks to improve the flexibility for decompression. Experimental results show that, compared with state‐of‐the‐art works, HFCD achieves 3.02–13.50 times improvement for the PPC of compression. For decompression, benefiting from our optimizations, HFCD achieves 22.4 times speedup compared to the software solution. [ABSTRACT FROM AUTHOR]

The unmanned aerial vehicle (UAV) city patrol is of great significance in ensuring the safety of residents' lives and properties, as well as maintaining the normal operation of the city. However, the detection of UAV images faces challenges such as numerous small-scale objects, complex backgrounds, and high requirements for detection speed. In response to these issues, we introduce a Real-time Small Object Detection network in UAV-vision (RTS-Net), tailored for UAV patrols. Initially, we introduce a multiscale feature fusion module (MFFM) designed to augment the expressiveness of features across scales, thereby enhancing the detection of smaller objects. Subsequently, leveraging attention mechanisms, we present the coordinated attention detection module (CADM), which bolsters the detection model's ability to accurately segregate objects from the background in expansive, complex scenarios. Lastly, a lightweight real-time feature extraction module (RFEM) is crafted to diminish model computational complexity and boost inference speed. On the UAV road patrol image dataset we constructed, our proposed method attains a detection accuracy of 89.9 % mAP, breaking previous records. It surpasses all prevailing detection methods, particularly for small-scale objects. Simultaneously, it achieves an inference speed of 163.9 FPS. The experimental results show that RTS-Net can satisfy the accurate and efficient detection of ground objects by various different UAV platforms in different complex scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a fast L1 formula on tanh meshes is proposed for time fractional Burgers equations with Caputo fractional derivative. The solvability, boundness and convergence of the numerical scheme are rigorously established. Several numerical experiments are provided to support the theoretical results. The results of the experiments showed that the proposed numerical method cannot only effectively deal with the weak singularity of the problem near t = 0, but also significantly reduce the computational complexity of numerical simulation. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Low‐dose computed tomography (LDCT) can mitigate potential health risks to the public. However, the severe noise and artifacts in LDCT images can impede subsequent clinical diagnosis and analysis. Convolutional neural networks (CNNs) and Transformers stand out as the two most popular backbones in LDCT denoising. Nonetheless, CNNs suffer from a lack of long‐range modeling capabilities, while Transformers are hindered by high computational complexity.In this study, our main goal is to develop a simple and efficient model that can both focus on local spatial context and model long‐range dependencies with linear computational complexity for LDCT denoising.In this study, we make the first attempt to apply the State Space Model to LDCT denoising and propose a novel LDCT denoising model named Visual Mamba Encoder‐Decoder Network (ViMEDnet). To efficiently and effectively capture both the local and global features, we propose the Mixed State Space Module (MSSM), where the depth‐wise convolution, max‐pooling, and 2D Selective Scan Module (2DSSM) are coupled together through a partial channel splitting mechanism. 2DSSM is capable of capturing global information with linear computational complexity, while convolution and max‐pooling can effectively learn local signals to facilitate detail restoration. Furthermore, the network uses a weighted gradient‐sensitive hybrid loss function to facilitate the preservation of image details, improving the overall denoising performance.The performance of our proposed ViMEDnet is compared to five state‐of‐the‐art LDCT denoising methods, including an iterative algorithm, two CNN‐based methods, and two Transformer‐based methods. The comparative experimental results demonstrate that the proposed ViMEDnet can achieve better visual quality and quantitative assessment outcomes. In visual evaluation, ViMEDnet effectively removes noise and artifacts, while exhibiting superior performance in restoring fine structures and low‐contrast structural edges, resulting in minimal deviation of denoised images from NDCT. In quantitative assessment, ViMEDnet obtains the lowest RMSE and the highest PSNR, SSIM, and FSIM scores, further substantiating the superiority of ViMEDnet.The proposed ViMEDnet possesses excellent LDCT denoising performance and provides a new alternative to LDCT denoising models beyond the existing CNN and Transformer options. [ABSTRACT FROM AUTHOR]

Suppose that a sequence of n cards, numbered 1 to n , is placed face up in random order. Let k be the number on the first card in the sequence. Then take the first k cards from the sequence, rearrange that subsequence of k cards in reverse order, and return them to the original sequence. Repeat this prefix reversal until the number on the first card in the sequence becomes 1. This is a one-player card game called Topswops. The computational complexity of Topswops has not been thoroughly investigated. For example, letting f (n) denote the maximum number of prefix reversals for Topswops with n cards, values of f (n) for n ≥ 20 remain unknown. In general, there is no known efficient algorithm for finding an initial sequence of n cards that requires exactly ℓ prefix reversals for any integers n and ℓ . In this paper, using a deck of cards, we propose a physical zero-knowledge proof protocol that allows a prover to convince a verifier that the prover knows an initial sequence of n cards that requires ℓ prefix reversals without leaking knowledge of that sequence. We also deal with Botdrops, a variant of Topswops. [ABSTRACT FROM AUTHOR]

Gait recognition is a long-distance biometric technique with significant potential for applications in crime prevention, forensic identification, and criminal investigations. Existing gait recognition methods typically introduce specific feature refinement modules on designated models, leading to increased parameter volume and computational complexity while lacking flexibility. In response to this challenge, we propose a novel framework called GaitAE. GaitAE efficiently learns gait representations from large datasets and reconstructs gait sequences through an autoencoder mechanism, thereby enhancing recognition accuracy and robustness. In addition, we introduce a horizontal occlusion restriction (HOR) strategy, which introduces horizontal blocks to the original input sequences at random positions during training to minimize the impact of confounding factors on recognition performance. The experimental results demonstrate that our method achieves high accuracy and is effective when applied to existing gait recognition techniques. [ABSTRACT FROM AUTHOR]

Aiming to provide solutions for problems proposed by the inaccurate segmentation of long objects and information loss of small objects in real-time semantic segmentation algorithms, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment-and-fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model's awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses an NVIDIA GeForce RTX 3080 Laptop GPU experiment on the road street view Cityscapes and CamVid datasets, with the average simultaneously occurring ratios reaching 77.1% and 77.4%, respectively, and the running speeds reaching 127 frames/s and 112 frames/s, respectively. The experimental results show that the proposed algorithm can achieve a real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance. [ABSTRACT FROM AUTHOR]

Many compartments are prone to pose safety hazards such as loose fasteners or object intrusion due to their confined space, making manual inspection challenging. To address the challenges of complex inspection environments, diverse target categories, and variable scales in confined compartments, this paper proposes a novel GMS-YOLO network, based on the improved YOLOv8 framework. In addition to the lightweight design, this network accurately detects targets by leveraging more precise high-level and low-level feature representations obtained from GhostHGNetv2, which enhances feature-extraction capabilities. To handle the issue of complex environments, the backbone employs GhostHGNetv2 to capture more accurate high-level and low-level feature representations, facilitating better distinction between background and targets. In addition, this network significantly reduces both network parameter size and computational complexity. To address the issue of varying target scales, the first layer of the feature fusion module introduces Multi-Scale Convolutional Attention (MSCA) to capture multi-scale contextual information and guide the feature fusion process. A new lightweight detection head, Shared Convolutional Detection Head (SCDH), is designed to enable the model to achieve higher accuracy while being lighter. To evaluate the performance of this algorithm, a dataset for object detection in this scenario was constructed. The experiment results indicate that compared to the original model, the parameter number of the improved model decreased by 37.8%, the GFLOPs decreased by 27.7%, and the average accuracy increased from 82.7% to 85.0%. This validates the accuracy and applicability of the proposed GMS-YOLO network. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) is an essential component for smart robot operations in unknown confined spaces such as indoors, tunnels and underground. This paper proposes a novel tightly-coupled ranging-LiDAR-inertial simultaneous localization and mapping framework, namely RLI-SLAM, which is designed to be high-accuracy, fast and robust in the long-term fast-motion scenario, and features two key innovations. The first one is tightly fusing the ultra-wideband (UWB) ranging and the inertial sensor to prevent the initial bias and long-term drift of the inertial sensor so that the point cloud distortion of the fast-moving LiDAR can be effectively compensated in real-time. This enables high-accuracy and robust state estimation in the long-term fast-motion scenario, even with a single ranging measurement. The second one is deploying an efficient loop closure detection module by using an incremental smoothing factor graph approach, which seamlessly integrates into the RLI-SLAM system, and enables high-precision mapping in a challenging environment. Extensive benchmark comparisons validate the superior accuracy of the proposed new state estimation and mapping framework over other state-of-the-art systems at a low computational complexity, even with a single ranging measurement and/or in a challenging environment. [ABSTRACT FROM AUTHOR]

Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi localization. Key aspects explored include the significance of signal features, the effects of sampling fluctuations, and overall accuracy measured by mean absolute error. Techniques such as mean-based feature selection, principal component analysis (PCA), and functional discriminant analysis (FDA) were employed to analyze signal features. The proposed algorithm, Ada-LT IP, which incorporates data reduction and transfer learning, shows improved accuracy compared to state-of-the-art methods evaluated in the study. Additionally, the study addresses multicollinearity through PCA and covariance analysis, revealing a reduction in computational complexity and enhanced accuracy for the proposed method, thereby providing valuable insights for improving adaptive long-term Wi-Fi indoor localization systems. [ABSTRACT FROM AUTHOR]

The use of linear array pushbroom images presents a new challenge in photogrammetric applications when it comes to transforming object coordinates to image coordinates. To address this issue, the Best Scanline Search/Determination (BSS/BSD) field focuses on obtaining the Exterior Orientation Parameters (EOPs) of each individual scanline. Current solutions are often impractical for real-time tasks due to their high time requirements and complexities. This is because they are based on the Collinearity Equation (CE) in an iterative procedure for each ground point. This study aims to develop a novel BSD framework that does not need repetitive usage of the CE with a lower computational complexity. The Linear Regression Model (LRM) forms the basis of the proposed BSD approach and uses Simulated Control Points (SCOPs) and Simulated Check Points (SCPs). The proposed method is comprised of two main steps: the training phase and the test phase. The SCOPs are used to calculate the unknown parameters of the LR model during the training phase. Then, the SCPs are used to evaluate the accuracy and execution time of the method through the test phase. The evaluation of the proposed method was conducted using ten various pushbroom images, 5 million SCPs, and a limited number of SCOPs. The Root Mean Square Error (RMSE) was found to be in the order of ten to the power of negative nine (pixel), indicating very high accuracy. Furthermore, the proposed approach is more robust than the previous well-known BSS/BSD methods when handling various pushbroom images, making it suitable for practical and real-time applications due to its high speed, which only requires 2–3 s of time. [ABSTRACT FROM AUTHOR]

Accurate estimation of State-of-Charge (SoC) is essential for ensuring the safe and efficient operation of electric vehicles (EVs). Currently, second-order RC equivalent circuit models do not account for the influence of battery charging and discharging states on battery parameters. Additionally, offline parameter identification becomes inaccurate as the battery ages. Online identification requires real-time parameter updates during the SoC estimation process, which increases the computational complexity and reduces the computational efficiency of real vehicle Battery Management System (BMS) chips. To address these issues, this paper proposes a SoC estimation method that combines online and offline identification based on an optimized second-order RC equivalent circuit model, which distinguishes it from existing methods in the field. On the basis of the traditional second-order RC model, the Ohmic resistance (R0), polarization resistance (R1), polarization capacitance (C1), diffusion resistance (R2), and diffusion capacitance (C2) during the charging and discharging processes are discussed separately. R0, which does not change frequently, is identified offline, while R1, R2, C1, and C2, which dynamically change with time and current, are identified online. To thoroughly verify the feasibility of the proposed method, we construct an SoC estimation test bench, which allows us to adjust the battery's surface temperature in real time using a temperature control chamber. Experimental validation under Federal Urban Driving Schedule (FUDS) (−10 °C to 45 °C, 80% battery capacity) and Dynamic Stress Test (DST) (−10 °C to 45 °C, 8% battery capacity) conditions demonstrate that our method improves SoC estimation accuracy by 16.28% under FUDS and 28.2% under DST compared to the improved GRU-based transfer learning method, while maintaining system SoC estimation efficiency. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) inter-pulse parameter agility technology involves dynamically adjusting parameters such as the pulse width, chirp rate, carrier frequency, and pulse repetition interval within a certain range; this effectively increases the complexity and uncertainty of radar waveforms, thereby countering active deceptive interference signals from multiple dimensions. With the development of active deceptive interference technology, single-parameter agility can no longer meet the requirements, making multi-parameter joint agility one of the main research directions. However, inter-pulse carrier frequency agility can cause azimuth Doppler chirp rate variation, making azimuth compression difficult and compensation computationally intensive, thus hindering imaging. Additionally, pulse repetition interval (PRI) agility leads to non-uniform azimuth sampling, severely deteriorating image quality. To address these issues, this paper proposes a multi-parameter agile SAR imaging scheme based on traditional frequency domain imaging algorithms. This scheme can handle joint agility of pulse width, chirp rate polarity, carrier frequency, and PRI, with relatively low computational complexity, making it feasible for engineering implementation. By inverting SAR images, the echoes with multi-parameter joint agility are obtained, and active deceptive interference signals are added for processing. The interference-suppressed imaging results verify the effectiveness of the proposed method. Furthermore, simulation results of point targets with multiple parameters under the proposed processing algorithm show that the peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) are improved by 12 dB and 10 dB, respectively, compared to the traditional fixed waveform scheme. [ABSTRACT FROM AUTHOR]

The utilization of Synthetic Aperture Radar (SAR) for real-time ship detection proves highly advantageous in the supervision and monitoring of maritime activities. Ship detection in the range-compressed domain of SAR rather than in fully focused SAR imagery can significantly reduce the time and computational resources required for complete SAR imaging, enabling lightweight real-time ship detection methods to be implemented on an airborne or spaceborne SAR platform. However, there is a lack of lightweight ship detection methods specifically designed for the SAR range-compressed domain. In this paper, we propose Fast Range-Compressed Detection (FastRCDet), a novel lightweight network for ship detection in the SAR range-compressed domain. Firstly, to address the distinctive geometric characteristics of the SAR range-compressed domain, we propose a Lightweight Adaptive Network (LANet) as the backbone of the network. We introduce Arbitrary Kernel Convolution (AKConv) as a fundamental component, which enables the flexible adjustment of the receptive field shape and better adaptation to the large scale and aspect ratio characteristics of ships in the range-compressed domain. Secondly, to enhance the efficiency and simplicity of the network model further, we propose an innovative Multi-Scale Fusion Head (MSFH) module directly integrated after the backbone, eliminating the need for a neck module. This module effectively integrates features at various scales to more accurately capture detailed information about the target. Thirdly, to further enhance the network's adaptability to ships in the range-compressed domain, we propose a novel Direction IoU (DIoU) loss function that leverages angle cost to control the convergence direction of predicted bounding boxes, thereby improving detection accuracy. Experimental results on a publicly available dataset demonstrate that FastRCDet achieves significant reductions in parameters and computational complexity compared to mainstream networks without compromising detection performance in SAR range-compressed images. FastRCDet achieves a low parameter of 2.49 M and a high detection speed of 38.02 frames per second (FPS), surpassing existing lightweight detection methods in terms of both model size and processing rate. Simultaneously, it attains an average accuracy (AP) of 77.12% in terms of its detection performance. This method provides a baseline in lightweight network design for SAR ship detection in the range-compressed domain and offers practical implications for resource-constrained embedded platforms. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have significantly advanced in recent years in detecting arbitrary-oriented ships in synthetic aperture radar (SAR) images. However, challenges remain with multi-scale target detection and deployment on satellite-based platforms due to the extensive model parameters and high computational complexity. To address these issues, we propose a lightweight method for arbitrary-oriented ship detection in SAR images, named LSR-Det. Specifically, we introduce a lightweight backbone network based on contour guidance, which reduces the number of parameters while maintaining excellent feature extraction capability. Additionally, a lightweight adaptive feature pyramid network is designed to enhance the fusion capability of the ship features across different layers with a low computational cost by incorporating adaptive ship feature fusion modules between the feature layers. To efficiently utilize the fused features, a lightweight rotating detection head is designed, incorporating the idea of sharing the convolutional parameters, thereby improving the network's ability to detect multi-scale ship targets. The experiments conducted on the SAR ship detection dataset (SSDD) and the rotating ship detection dataset (RSDD-SAR) demonstrate that LSR-Det achieves an average precision (AP50) of 98.5% and 97.2% with 3.21 G floating point operations (FLOPs) and 0.98 M parameters, respectively, outperforming the current popular SAR arbitrary-direction ship target detection methods. [ABSTRACT FROM AUTHOR]

Threshold private set intersection (TPSI) is a special case of secure multi-party computation, which is widely used in many fields such as machine learning, ride-sharing, and fingerprint recognition. However, currently existing solutions are all based on algorithms with high computational complexity and are only implemented under a semi-honest model, resulting in high computational overhead and inability to resist attacks by malicious adversaries. In order to solve the above problems, this paper first proposed a vector oblivious match test(VOMT) protocol and designed an efficient semi-honest TPSI protocol based on VOMT and cuckoo hashing. In addition, it combined VOMT and symmetric key encryption schemes to construct the vector oblivious decryption matching test(VODMT) protocol and designed a TPSI protocol based on VODMT and oblivious pseudorandom functions that could resist malicious adversaries. Subsequently, it proved the security of the protocol under the semihonest model and the malicious model respectively, and analyzed that the computational complexity and communication complexity of the two protocols were linear. When the set size is 4 096, the online running time of the two proposed protocols is 0.81 s and 1.81 s respectively, while the previous work required 5 627 s, so both protocols are efficient. [ABSTRACT FROM AUTHOR]

Testing independence between high-dimensional random vectors is fundamentally different from testing independence between univariate random variables. Taking the projection correlation as an example, it suffers from at least three problems. First, it has a high computational complexity of O { n 3 (p + q) } ⁠ , where n , p and q are the sample size and dimensions of the random vectors; this limits its usefulness substantially when n is extremely large. Second, the asymptotic null distribution of the projection correlation test is rarely tractable; therefore, random permutations are often suggested as a means of approximating the asymptotic null distribution, which further increases the complexity of implementing independence tests. Third, the power performance of the projection correlation test deteriorates in high dimensions. To address these issues, the projection correlation is improved by using a modified weight function, which reduces the complexity to O { n 2 (p + q) } ⁠. We estimate the improved projection correlation with U -statistic theory. Importantly, its asymptotic null distribution is standard normal, thanks to the high dimesnionality of the random vectors. This expedites the implementation of independence tests substantially. To enhance the power performance in high dimensions, we propose incorporating a cross-validation procedure with feature screening into the projection correlation test. The implementation efficacy and power enhancement are confirmed through extensive numerical studies. [ABSTRACT FROM AUTHOR]

The particle swarm optimization-K-Means algorithm is proposed by the related researchers to improve the clustering accuracy of the K-Means algorithm. However, the particle swarm optimization-K-Means algorithm brings more burden to the computation, and the computational efficiency is low when dealing with large data sets. To solve this problem, a parallel particle swarm K-Means algorithm based on MapReduce with multi-threading is proposed. The algorithm performs parallel computation by dividing the particle swarm into several equal-sized sub-populations based on the number of available nodes in the cluster and distributing them to each node. It uses a multi-threaded execution in the evaluation stage, which has the highest computational complexity in the evolutionary process. Experiments show that although splitting the population will affect the optimization effect to some extent, the proposed still can effectively optimize the clustering results of the K-Means algorithm, and the computational efficiency is significantly improved compared with serial particle swarm optimization k-means algorithm and MapReduce-based non-multithreaded particle swarm optimization k-means algorithm, in the experiment with the largest dataset and a configuration of 16 nodes, the proposed algorithm is 58 times faster than the serial algorithm. Furthermore, the computing efficiency can be improved in the clusters with more CPU cores. [ABSTRACT FROM AUTHOR]

Managing the explosion of data from the edge to the cloud requires intelligent supervision, such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively regarding two main factors: connectivity and coverage. The network connectivity is based on fog node deployment, which determines the network's physical topology, while the coverage determines the network accessibility. Both have a significant impact on network performance and guarantee the network quality of service. Determining an optimum fog node deployment method that minimizes cost, reduces computation and communication overhead, and provides a high degree of network connection coverage is extremely hard. Therefore, maximizing coverage and preserving network connectivity is a non-trivial problem. In this paper, we propose a fog deployment algorithm that can effectively connect the fog nodes and cover all edge devices. Firstly, we formulate fog deployment as an instance of multi-objective optimization problems with a large search space. Then, we leverage Marine Predator Algorithm (MPA) to tackle the deployment problem and prove that MPA is well-suited for fog node deployment due to its rapid convergence and low computational complexity, compared to other population-based algorithms. Finally, we evaluate the proposed algorithm on a different benchmark of generated instances with various fog scenario configurations. Our algorithm outperforms state-of-the-art methods, providing promising results for optimal fog node deployment. It demonstrates a 50% performance improvement compared to other algorithms, aligning with the No Free Lunch Theorem (NFL Theorem) Theorem's assertion that no algorithm has a universal advantage across all problem domains. This underscores the significance of selecting tailored algorithms based on specific problem characteristics. [ABSTRACT FROM AUTHOR]

Steganalysis techniques play a crucial role in forensic investigation, aiding professionals in identifying digital images with unsafe content. This work proposes a novel and improved lightweight deep-learning convolution neural network architecture called CIRNet, which provides universal steganalysis capabilities. CIRNet leverages inverted residual blocks integrated with a self-attention mechanism that effectively minimises the detection error rate and the computational complexity for steganalysis. The inverted residual blocks include lightweight depth-wise and point-wise convolutions supplemented by a self-attention module. This integration strengthens the saliency of feature maps corresponding to the embedding regions while reducing the network's parameters and the total floating-point operations. Consequently, CIRNet achieves superior accuracy, making it an excellent choice for deployment on lightweight smart mobile systems. Experimental results validate the superiority of the proposed CIRNet over several state-of-the-art deep-learning networks commonly employed in steganalysis. The results further demonstrate the enhanced generalisation ability, particularly when applied to diverse datasets generated from BOSSbase 1.01 and BOWS2 using HUGO, WOW, and S-UNIWARD steganography methods with two distinct payloads. These findings establish CIRNet as an efficient and elite performer in the domain of lightweight steganalysis. [ABSTRACT FROM AUTHOR]

Hand pose estimation is a fundamental task in computer vision with applications in virtual reality, gesture recognition, autonomous driving, and virtual surgery. Keypoint detection often relies on deep learning methods and high-resolution feature map representations to achieve accurate detection. The HRNet framework serves as the basis, but it presents challenges in terms of extensive parameter count and demanding computational complexity due to high-resolution representations. To mitigate these challenges, we propose a lightweight keypoint detection network called LGCANet (Lightweight Ghost-Coordinate Attention Network). This network primarily consists of a lightweight feature extraction head for initial feature extraction and multiple lightweight foundational network modules called GCAblocks. GCAblocks introduce linear transformations to generate redundant feature maps while concurrently considering inter-channel relationships and long-range positional information using a coordinate attention mechanism. Validation on the RHD dataset and the COCO-WholeBody-Hand dataset shows that LGCANet reduces the number of parameters by 65.9% and GFLOPs by 72.6% while preserving the accuracy and improves the detection speed. [ABSTRACT FROM AUTHOR]

The Bayesian optimization algorithm uses Bayesian networks as the probability model of its solution space. Although the research on this algorithm has steadily developed, there are still some problems in its application process, such as excessive computational complexity. To solve various problems in Bayesian algorithm, reduce its computational complexity, and enable it to better achieve image segmentation. The study chooses to improve the Bayesian algorithm on the basis of immune algorithm, and solves the problem of computational complexity by reducing the number of Bayesian network construction times, thereby improving the individual fitness of the population. Through simulation experiments, it has been shown that the average number of times the improved Bayesian algorithm reaches the optimal value is 30, which is higher than the traditional algorithm's 20 times. Its excellent optimization ability searches for the optimal threshold to complete image segmentation. The improved Bayesian optimization algorithm based on immune algorithm can effectively reduce computational complexity, shorten computational time, and improve convergence. And applying Bayesian algorithm to image segmentation has broadened the application field of the algorithm and found new exploration directions for image segmentation. [ABSTRACT FROM AUTHOR]

Lunar missions are currently experiencing a significant surge in popularity, presenting expansive opportunities for further exploration and development. To thoroughly explore the design margins and potential of lunar landers, and to foster the development of overall designs driven by comprehensive performance objectives, it is crucial to conduct optimization design considering the coupling between key disciplines such as trajectory and propulsion. Considering the significant increase in computational complexity caused by conducting trajectory/propulsion integrated design optimization, the analytical target cascading method is employed to hierarchically decompose and coordinate optimization of the complex systems. This article presents a phased soft-landing strategy on the manned lunar lander propelled by hybrid rocket motors, utilizing powered explicit guidance and Apollo powered descent guidance, and proceeds with the trajectory/propulsion integrated design optimization involving diverse grain shapes and feed systems. This optimization process is separately undertaken utilizing multidisciplinary feasible method and analytical target cascading method. The analysis reveals that integrating trajectory and propulsion considerations into the optimization process facilitates a 5 % reduction in the overall mass relative to optimizations constrained solely by velocity increment and lack comprehensive trajectory design considerations. This highlights the profound impact of trajectory requirements on propulsion system design and the advantages of powered explicit guidance laws in minimizing fuel consumption. Crucially, the use of analytical target cascading achieves the better optimization results, and significantly reduces subsystem evaluation times, enhancing operational efficiency by 48 %, demonstrating the advantage in handling complex, large-scale systems. On another level, with different β values, the Mean Relative Error of the target values for the three schemes obtained by the analytical target cascading method is 0.0016, indicating good stability and strong robustness. The practical exploration in this article provides methods and frameworks for high-performance optimization design of complex aerospace mission profiles in the future. • The trajectory/propulsion integrated design optimization is conducted. • The two-phase guidance strategy for lunar lander descent is proposed. • Based on the strategy, the analytical target cascading-decomposed framework is set. • The influence of trajectory and propulsion coupling on overall design is revealed. • The optimization effectiveness, convergence rate, and robustness of the ATC is shown. [ABSTRACT FROM AUTHOR]

This work establishes a framework for solving inverse boundary problems with the geodesic-based quadratic Wasserstein distance (W_{2}). A general form of the Fréchet gradient is systematically derived from the optimal transportation (OT) theory. In addition, a fast algorithm based on the new formulation of OT on \mathbb {S}^{1} is developed to solve the corresponding optimal transport problem. The computational complexity of the algorithm is reduced to O(N) from O(N^{3}) of the traditional method. Combining with the adjoint-state method, this framework provides a new computational approach for solving the challenging electrical impedance tomography problem. Numerical examples are presented to illustrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Model order reduction technique provides an effective way to reduce computational complexity in large-scale circuit simulations. This paper proposes a new model order reduction method for delay circuit systems based on Hermite expansion technique. The presented method consists of three steps i.e., first the delay elements are approximated using the recursive relation of Hermite polynomials, then in the second step, the reduced order is estimated for the delay circuit system using a delay truncation in the Hermite domain and in the third step, a multi-order Arnoldi process is computed for obtaining the projection matrix. In the following, the reduced order delay circuit model is obtained by the projection matrix. Moment matching and passivity properties of the reduced circuit system are also analyzed. Two circuit examples with delay components are performed to verify the effectiveness of the proposed MOR approach. [ABSTRACT FROM AUTHOR]

Electronic equipment used on a daily basis now frequently includes active noise cancellation. The adaptive filters, which are positioned within, are essential for noise cancellation. An essential component to take into account for the overall performance is the structural and computational complexity of the filter. The filter's structure has an impact on this. The amount of taps determines the structure. Active noise cancellation filters often have set tap lengths and are lengthy, which causes sluggish convergence and delay. As a result, a trade-off between the filter's length and convergence is required. This is conceivable if there is a flexible filter with a tap length that adapts to the environment while still ensuring acceptable convergence. This study proposes a novel Minimum Mean M-estimate method with changeable tap length and uses a sub-band adaptive filtering technique to shorten the filter's length. In order to maximize the filter's efficiency, the advantages of three approaches are specifically merged in this work. They are the proposed algorithm, the proposed method's variable tap length variant, and the sub-band adaptive filtering. The simulation's findings and recommendations are supported. [ABSTRACT FROM AUTHOR]

Speech enhancement performance has improved significantly with the introduction of deep learning models, especially methods based on the Long–Short-Term Memory architecture. However, these methods face challenges such as high computational complexity and redundancy of input features. To address these issues, we propose a U-Net-based approach that utilizes an encoder/decoder to extract more concise features, thereby enhancing single-channel speech performance and reducing computation complexity. The proposed method includes a Cross-Swin-Transformer block and a masked bottleneck module, which down-samples features while preserving the detailed representation through skip connections and carefully designed blocks. The bottleneck module extracts coarse representations of hidden features as masks. We evaluated our method against other U-Net-based approaches on VCTK and DNS corpora using CBAK, eSTOI, PESQ, STOI, and SI-SDR metrics. The results demonstrate that the proposed method achieves promising performance while significantly reducing computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, a sparse smooth twin support vector regression (Sparse‐STSVR) model for power amplifier (PA) behavioral modeling is obtained by pruning the kernel matrix based on Cholesky decomposition. Based on the primal smooth twin support vector regression (STSVR) model, the Nystrom approximate matrix of the kernel matrix is found to replace the original kernel matrix, thus simplifying the Newton iterative parameter extraction process of the primal STSVR model and accelerating the convergence of the algorithm. In addition, the new rank approximation kernel matrix has the characteristic of sparse parameters, which further reduces the computational complexity of the feedforward link of the digital predistorter. The 100 MHz 5G New Radio (NR) signal is used for verify the effect of PA modeling and digital predistortion (DPD) experiment. The results show that the proposed method can improve the normalized mean square error (NMSE) by about 2 ~ 3 dB with fewer coefficients compared with the previously proposed machine learning model, and the predistortion linearization effect improves by nearly 3 dB on the adjacent channel power ratio (ACPR), which achieves a good trade‐off between model performance and computational complexity. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The multi-state flow network (MSFN) serves as a fundamental framework for real-life network-structured systems and various applications. The system reliability of the MSFN, denoted as Rd, is defined as the probability of successfully transmitting at least d units of demand from a source to a terminal. Current analytical algorithms are characterized by their computational complexity, specifically falling into the NP-hard problem to evaluate exact system reliability. Moreover, existing analytical algorithms for calculating Rd are basically designed for predetermined values of d. This limitation hinders the ability of decision-makers to flexibly choose the most appropriate based on the specific characteristics of the given scenarios or applications. This means that these methods are incapable of simultaneously calculating system reliability for various demand levels. Therefore, this paper develops a simulation-based algorithm to estimate system reliability for all possible demand levels simultaneously such that we can eliminate the need to rely on repeat procedures for each specified d. An experimental investigation was carried out on a benchmark network and a practical network to validate the effectiveness and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]