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Summary: With the promotion and application of IPv6 in the world, there is a growing demand for IPv6 alias resolution. How to resolve IPv6 alias efficiently and accurately becomes an urgent problem to be solved. After analyzing the features of IPv6 addresses, this paper proposes a large‐scale adaptive IPv6 alias resolution method based on fingerprint information by combining Too‐Big Trick, UAv6, APD, and other alias resolution algorithms. It sends ICMPv6 probe packets to different types of IPv6 addresses to get the fingerprint information of target hosts. After filtering the classified addresses, our proposed alias resolution method is adaptively selected to resolve alias addresses. In the experiment, we use multi‐thread method to resolve the aliases of large‐scale IPv6 addresses, which greatly improves the efficiency of detection. Meanwhile, we use IPv6 datasets collected from the organizations RIPE and CAIDA. By comparing with Speedtrap and Too‐Big Trick, we confirm the accuracy of this method and obtain more alias pairs. [ABSTRACT FROM AUTHOR]

Integration algorithm is an important mean to solve the discrete time equations of structural dynamics in earthquake engineering. In this paper, a new model‐based explicit integration algorithm, featured by optimization‐based integration parameter updating, is developed for a more accurate and efficient solution of structural dynamic problems. The new integration algorithm is designed to yield minimum error and controllable numerical dispersion under the premise of stability by defining objective functions and constraints. Numerical properties of the new integration algorithm are investigated in details using the amplification matrix method for a linear elastic single degree‐of‐freedom system. Meanwhile, representative numerical examples and complex structure examples are analyzed and compared with other representative algorithms. As a result, the proposed algorithm yields comparable numerical characteristics with other methods, but exhibits pronounced superiority in controllable algorithmic dissipation, higher precision, and better efficiency. Therefore, the new integration algorithm possesses extensive application prospect in solving complicated linear and nonlinear structural dynamic problems. [ABSTRACT FROM AUTHOR]

In this study, to address the issues of sluggish convergence and poor learning efficiency at the initial stages of training, the authors improve and optimise the Deep Deterministic Policy Gradient (DDPG) algorithm. First, inspired by the Artificial Potential Field method, the selection strategy of DDPG has been improved to accelerate the convergence speed during the early stages of training and reduce the time it takes for the mobile robot to reach the target point. Then, optimising the neural network structure of the DDPG algorithm based on the Long Short‐Term Memory accelerates the algorithm's convergence speed in complex dynamic scenes. Static and dynamic scene simulation experiments of mobile robots are carried out in ROS. Test findings demonstrate that the Artificial Potential Field method‐Long Short Term Memory Deep Deterministic Policy Gradient (APF‐LSTM DDPG) algorithm converges significantly faster in complex dynamic scenes. The success rate is improved by 7.3% and 3.6% in contrast to the DDPG and LSTM‐DDPG algorithms. Finally, the usefulness of the method provided in this study is similarly demonstrated in real situations using real mobile robot platforms, laying the foundation for the path planning of mobile robots in complex changing conditions. [ABSTRACT FROM AUTHOR]

Background: This study is the first to address the topic of schematic versus realistic dynamic visualization with particular focus on the human movement effect (HME) when the content to be learned takes the form of a computer algorithm. An AlgoRythmics dance choreography illustration (HM‐realistic) was compared with an abstract computer animation (schematic). Previous research in the field of dynamic visualizations found schematic illustrations to be more effective, but the examined realistic representations did not include HME. Objectives: The objective of this study was to explore the process of comprehension of computer algorithms from these two types of representations (abstract animation vs. dance choreography illustration). Methods: The experiment (pre‐test, study phase, post‐test) involved 84 undergraduate students and included the following conditions (2 × 2 between‐participants‐design): HM‐realistic → HM‐realistic, schematic → schematic, HM‐realistic → schematic, schematic → HM‐realistic. Results and Conclusions: The results reveal that: (i) the group of participants who saw the dance choreography twice outperformed those who saw the computer animation twice; (ii) dance choreography illustration is only beneficial if it is presented as the second visualization. These findings are apparently contrary to the conclusions of some relevant previous research in the field, but they are in line with recent results regarding the HME. Takeaways: Realistic dynamic visualizations can be more effective than schematic ones if they involve human movement. To benefit maximally from realistic visualization, students need to have previously viewed it or to have previously viewed the isomorphic schematic presentation of it. Lay Description: What is already known about this topic: Studies that researched whether animation aids learners' understanding of dynamic phenomena have reported mixed results.When human movement is used to illustrate the learning content, dynamic visualizations have consistently proven to be superior to static visualizations.Various studies have explored dynamic visualizations under different combinations of schematic and/or realistic representations. Participants who were exposed exclusively to realistic animations exhibited significantly lower performance compared to their counterparts in the other conditions. What this paper adds: Realistic dynamic visualizations can be more effective than schematic ones if they involve human movement.This result is apparently contrary to the conclusions of some relevant previous research in the field of schematic versus realistic dynamic visualizations, but it is in line with recent findings regarding the so‐called human movement effect.To benefit maximally from realistic visualization, students need to have previously viewed it or to have previously viewed the isomorphic schematic presentation of it. Implications for practice and/or policy: The study provides a new insight into the research field of dynamic visualizations and valuable guidelines for instructional design. [ABSTRACT FROM AUTHOR]

There is a growing interest to introduce energy‐saving algorithms which balance energy efficiency with residents' comfortability into a building energy management system (BEMS). However, the following issues hinder penetration of the energy‐saving algorithms into markets: difficulties both in embedding the algorithms into the BEMS, and in turning the algorithms suitably to fit varieties of installed equipment in each building. In this paper, A BEMS on micro‐service architecture, and software service modules for the BEMS, were proposed. The micro‐services architecture improves flexibility to introduce newly developed energy‐saving algorithms into the BEMS and software service modules manipulate whole roles (modeling, planning, doing, and action) required for tuning the algorithms on‐line to fit the algorithms to each building. Each software service module was implemented and integrated as elements of BEMS on micro‐service architecture. The BEMS on micro‐service architecture was applied to an actual building for evaluation, and its availability was confirmed through the field test. [ABSTRACT FROM AUTHOR]

Car‐following prevails in daily traffic under various scenarios. However, most connected automated vehicle (CAV) car‐following algorithms in the literature only apply to one‐dimensional CAV control, which limits the application scenarios. This paper presents a two‐dimensional (2D) CAVs car‐following strategy on curved roads in the spatial domain with vehicle‐to‐vehicle and vehicle‐to‐infrastructure communication to expand the car‐following application scenarios. This control strategy constructs a 2D car‐following equilibrium policy by extending the simplified Newell's car‐following model. Based on that, a constrained optimal CAV car‐following control in the spatial domain is constructed to maintain the inter‐vehicle spacing and speed difference while regulating the lane deviation and avoiding work zones. As demonstrated by numerical results, our proposed algorithm maintains a high degree of consistency between equilibrium spacing and vehicle speed while duplicating the trajectory of the leading vehicle, implying empirical string stability. The results also suggest the robustness of handling different scenarios. [ABSTRACT FROM AUTHOR]

The moving target segmentation (MTS) aims to segment out moving targets in the video, however, the classical algorithm faces the huge challenge of real‐time processing in the current video era. Some scholars have successfully demonstrated the quantum advantages in some video processing tasks, but not concerning moving target segmentation. In this paper, a quantum moving target segmentation algorithm for grayscale video is proposed, which can use quantum mechanism to simultaneously calculate the difference of all pixels in all adjacent frames and then quickly segment out the moving target. In addition, a feasible quantum comparator is designed to distinguish the grayscale values with the threshold. Then several quantum circuit units are designed in detail to construct the complete quantum circuits for segmenting the moving target. For a quantum video with 2m frames (every frame is a 2n×2n$2^n \times 2^n$ image with q grayscale levels), the complexity of our algorithm can be reduced to O(n2+q)$(n^2 + q)$. Compared with the classic counterpart, it is an exponential speedup, while its complexity is also superior to the existing quantum algorithms. Finally, the experiment is conducted on IBM Quantum Experience (IBM Q) to show the feasibility of our algorithm in the noisy intermediate‐scale quantum era. [ABSTRACT FROM AUTHOR]

Allpass transform is a key element of the discrete systems which produces signal processing in warped frequency domain. The paper presents a new short‐critical path algorithm for allpass transform, which essentially consists of calculating the outputs of allpass filters chain. The algorithm is based on the proposed dual allpass filter structure, which computes the outputs of a cascade of two allpass filters in one computational pass. The dual allpass filter structure is obtained using the state variable representation of the allpass filter. The efficiency of the proposed algorithm is analyzed in the context of allpass transform implementation using a general purpose CPU. The results of the experiment show that the use of the proposed algorithm accelerates the calculation of the allpass transform by 1.81 times. [ABSTRACT FROM AUTHOR]

In the blind reception of non‐cooperative signals in short‐time burst communication, signal time domain detection is the premise and key of signal modulation parameter estimation, signal demodulation, decoding, interpretation, information acquisition and interference guidance. In this paper, the semi‐blind demodulation method of short‐time burst FM‐MFSK signal is studied under the condition of low signal‐to‐noise ratio. A short‐time burst FM‐MFSK semi‐blind demodulation method based on STFT is proposed. When SNR is higher than 3 dB, the correct demodulation probability for FM‐MFSK signal can reach 90%. [ABSTRACT FROM AUTHOR]

Summary: Fire is a serious threat to human life and property, and effective prediction of fire can reduce the harm of fire to human. In the research of fire alarm automation, the fire alarm system based on wireless sensor network is a common method. When using wireless sensor to monitor the fire alarm information, it is necessary to ensure the safety coverage of the network in the monitoring area. Therefore, it is very necessary to study the network coverage in the fire alarm monitoring, which can ensure the effective work of the monitoring network, and good coverage control can reduce the energy consumption of the network and extend the service life of the network. Based on this, this paper designs a network coverage control algorithm based on multiobjective genetic algorithm optimization considering the coverage and energy consumption of wireless network. In this algorithm, the probability perception model is used to design the network coverage control model for the monitoring area with different coverage requirements, taking the maximum area coverage and the minimum network energy consumption rate as the optimization goal. Then the genetic algorithm is used to solve the model. In addition, because the multiobjective genetic algorithm is easy to fall into the local optimal solution when solving the optimization problem, this paper introduces the cross and variation coefficients of self‐adaptive adjustment to improve the genetic algorithm. Through the simulation analysis, it is shown that when solving the multiobjective optimization problem, the introduction of self‐adaptive adjustment of cross and variation coefficients can well overcome the problem that genetic algorithm falls into the local optimal solution, whereas the network coverage control algorithm designed in this paper based on the multiobjective genetic algorithm optimization can achieve greater coverage and lower energy consumption. Compared with the network coverage control algorithm before optimization, the optimized algorithm improves greatly in coverage, network residual energy, and number of nodes, which can well extend the service life of wireless sensor network, and is conducive to the good application of wireless sensor in fire prediction. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology and social networks, traditional personalized recommendation systems have been unable to provide effective recommendation services for groups composed of multiple users. In order to content the needs of group users, the current group recommendation algorithm mainly uses users' consensus preferences instead of personal preferences for group recommendation, which can only content the preferences of most users and ignore the preferences of a few cold users. In order to improve the fairness of group recommendation, a group recommendation algorithm based on dividing subgroups is proposed. The algorithm introduces the comprehensive trust between users to divide the group into different subgroups, and obtains the initial subgroup recommendation list according to the average strategy. The PageRank idea is used to set the weight of subgroups and assign the number of recommended items to subgroups to generate the group recommendation list. The experimental results show that the algorithm in this paper can content the user's personalized recommendation needs to the greatest extent under the premise of improving the group fair recommendation service. [ABSTRACT FROM AUTHOR]

Traditional YOLOv4 object detection network is difficult to be applied to mobile embedded devices because it has some deficiencies such as complex structure and too many parameters. In this paper, the authors propose a lightweight object detection algorithm. Firstly, Mobienetv3 is used to replace the original feature extraction network, and the Mish function is used to be the activation function, which reduces the number of model parameters. Secondly, the dilated convolution is used to replace the maximum pooling operation in the original spatial pyramid pooling (SPP) structure. Then, a custom Dcn‐Dw structure is used to replace the convolution operation in the original PANet, which improves the accuracy of the model for irregular object detection and reduces the model size. Finally, a CBAM lightweight attention mechanism module is introduced in front of the YOLO Head, which further improves the model accuracy. An experiment on the VOC2007 dataset is carried out, and the results show that the mean average precision (mAP) is 80.3% and frames per second (FPS) is 15. At the expense of 3% accuracy, the detection speed is increased to two to three times, and the model size is reduced to one‐fifth of the original model. The lightweight object detection algorithm can suit for real‐time detection tasks on resource‐constrained embedded devices. [ABSTRACT FROM AUTHOR]

This paper uses a fuzzy set scheme to present an adaptive fractional differential algorithm for image edge enhancement and texture preservation. In the proposed algorithm, an image's membership function and area feature are used to calculate the fuzzy set of images. The function of adaptive fractional differential order (FAFDO) can be constructed by making the linear transformation of the fuzzy set. Then, the fuzzy adaptive fractional differential mask (FAFDM) is obtained by substituting the FAFDO into the fractional differential mask. Finally, the image edge and texture are enhanced and preserved by applying airspace filtering of the FAFDM convolution. The experimental results show that, compared to fractional differential or fuzzy set‐based image enhancement algorithms, the proposed algorithm can adaptively enhance the image edge and preserve the image texture by analysing the fuzziness of the image itself. [ABSTRACT FROM AUTHOR]

There are a lot of flammable materials in the textile workshop, and once a fire occurs, it will cause property damage and casualties. At present, smoke detection in textile workshops mainly relies on temperature‐sensing smoke sensors with low detection rate and poor real‐time performance, which cannot meet the task of smoke detection in complex environments. Therefore, this paper proposes an improved mixed Gaussian and YOLOv5 smoke detection algorithm for textile workshops. In order to reduce the interference of static background in smoke detection, an improved gaussian mixture algorithm is used to extract suspected smoke areas in video by using the dynamic characteristics of smoke. Then, an adaptive attention module is added to the feature pyramid infrastructure of the YOLOv5 target detection network to improve the multi‐scale target recognition ability. In addition, the focal loss function is used to reduce the impact of background and foreground class imbalances on the detection results. The experimental results show that the detection accuracy of the proposed method is 94.7%, and the average detection speed is 66.7 FPS. By comparing with the existing state‐of‐the‐art algorithms, the detection capability of this method has been significantly improved. At the same time, it has high real‐time performance and detection accuracy in smoke detection in textile workshops. [ABSTRACT FROM AUTHOR]

This paper aims to improve the efficiency of parameter identification of the nonlinear state‐space model (SSM). The commonly used particle Markov chain Monte Carlo (PMCMC) method is time‐consuming. The surrogate model is a useful acceleration strategy, but it is expensive to establish a global high‐precision surrogate model. This paper proposes an efficient algorithm that gradually estimates the unknown parameters in two stages. In the first stage, a reduced region is established based on the latest method. We train a local Gaussian Process regression (GPR) of the likelihood function in the reduced region based on the optimum Latin hypercube design (OLHD). In the second stage, we identify the unknown parameters more accurately based on the MCMC method. When the proposal sample is in the reduced region, we use GPR to estimate the likelihood; otherwise, BPF is used to estimate the likelihood. The reduced region is usually the high probability density region, which is why the algorithm is efficient. It is proved that the acceptance rate of any two samples based on the proposed algorithm is theoretically convergent to that of the PMCMC algorithm. Two examples demonstrate that the proposed method performs well in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is a disease of older people, yet factors relating to comorbidity and frailty may threaten treatment tolerability for many of this heterogenous group. There has been increasing interest in defining specific and clinically relevant frailty assessment tools within the MM population, with the goal of using these frailty scores, not just as a prognostic instrument, but also as a predictive tool to allow for a frailty‐adapted treatment approach. This paper reviews the various frailty assessment frameworks used in the evaluation of patients with MM, including the International Myeloma Working Group Frailty Index (IMWG‐FI), the Mayo Frailty Index and the simplified frailty scale. While the IMWG‐FI remains the most widely accepted tool, the simplified frailty scale is the most user‐friendly in busy day‐to‐day clinics based on its ease of use. This paper summarises the recommendations from the Myeloma Scientific Advisory Group (MSAG) of Myeloma Australia, on the use of frailty assessment tools in clinical practice and proposes a frailty‐stratified treatment algorithm to aid clinicians in tailoring therapy for this highly heterogeneous patient population. [ABSTRACT FROM AUTHOR]

We consider the incremental subgradient algorithm employing dynamic step sizes for minimizing the sum of a large number of component convex functions. The dynamic step size rule was firstly introduced by Goffin and Kiwiel [Math. Program., 1999, 85(1): 207‐211] for the subgradient algorithm, soon later, for the incremental subgradient algorithm by Nedic and Bertsekas in [SIAM J. Optim., 2001, 12(1): 109‐138]. It was observed experimentally that the incremental approach has been very successful in solving large separable optimizations and that the dynamic step sizes generally have better computational performance than others in the literature. In the present paper, we propose two modified dynamic step size rules for the incremental subgradient algorithm and analyse the convergence and complexity properties of them. At last, the assignment problem is considered and the incremental subgradient algorithms employing different kinds of dynamic step sizes are applied to solve the problem. The computational experiments show that the two modified ones converges dramatically faster and more stable than the corresponding one in [SIAM J. Optim., 2001, 12(1): 109‐138]. Particularly, for solving large separable convex optimizations, we strongly recommend the second one (see Algorithm 3.3 in the paper) since it has interesting computational performance and is the simplest one. [ABSTRACT FROM AUTHOR]

The ability of satellite instruments to accurately observe long‐term changes in atmospheric temperature depends on many factors including the absolute accuracy of the measurement, the stability of the calibration of the instrument, the stability of the satellite orbit, and the stability of the numerical algorithm that produces the temperature data. We present an example of algorithm instability recently discovered in the temperature data set from the SABER instrument on the NASA TIMED satellite. The instability resulted in derived temperatures that were substantially colder than anticipated from mid‐December 2019 to mid‐2022. This algorithm‐induced change in temperature over 1 to 2 years corresponded to the expected temperature change over several decades from increasing anthropogenic CO2. This paper highlights the importance of algorithm stability in developing Geospace Data Records (GDRs) for Earth's mesosphere and lower thermosphere. A corrected version (Version 2.08) of the temperatures from SABER is described. Plain Language Summary: Instruments on Earth orbiting satellites offer the opportunity to detect long‐term changes in atmospheric temperature. Many factors may affect the ability to identify actual long‐term changes in the temperature and to distinguish these from changes in the instrument or from unintended changes in the algorithm that produces the temperature data from the instrument observations. SABER is an instrument on a NASA satellite that has been observing temperatures from 15 to 110 km (10–68 miles) in altitude for over 20 years. An "instability" in the scientific algorithm used to derive temperature from the instrument observations was recently discovered, beginning in late 2019. An unintended change was made in a parameter central to the derivation of temperature from SABER measurements. The consequence was that the atmospheric temperatures between 85 and 110 km (51–68 miles) from 2020 onward were several degrees colder than they would have been without the unintended change. This has been corrected and an updated version of the SABER temperatures and all other SABER data products, called Version 2.08, is now publicly available. Key Points: The concept of Geospace Data Records (GDRs) and their relevance to accurate detection of long‐term change is introducedAlgorithm instability in a GDR of the 20‐year record of SABER temperatures between 85 and 110 km is described and correctedThe field of Geospace Climate is emerging as a frontier with scientific and economic relevance. Accurate GDRs are essential to both [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is an excellent meta‐heuristic algorithm, which has been widely used in the engineering field. However, there is still room for improvement in terms of convergence rate and solution accuracy. Therefore, this paper proposes an enhanced parallel salp swarm algorithm based on the Taguchi method (PTSSA). The parallel trick is to split the initial population uniformly into several subgroups and then exchange information among the subgroups after a fixed number of iterations, which speeds up the convergence. Communication strategies are an important component of parallelism techniques. The Taguchi method is widely used in the industry for optimizing product and process conditions. In this paper, the Taguchi method is adopted into the parallelization technique as a novel communication strategy, which improves the robustness and accuracy of the solution. The proposed algorithm was also tested under the CEC2013 test suite. Experimental results show that PTSSA is more competitive than some common algorithms. In addition, PTSSA is applied to optimize the operation of a heatless combined cooling‐power system. Simulation results show that the optimized operation provided by PTSSA is more stable and efficient in terms of operating cost reduction. [ABSTRACT FROM AUTHOR]

This paper is dedicated to solving the model reconstruction problem of Boolean networks (BNs) based on incomplete information. By resorting to semi‐tensor product, the issue of finding logical functions is converted into designing corresponding structure matrices. In line with desired attractors, two circumstances, standard BNs and delayed BNs, are taken into consideration, and several new results are presented. For the model reconstruction of standard BNs, an algebraic condition is put forward and an algorithm is developed for reconstructing unknown logical functions. For the model reconstruction of delayed BNs, an augmented system is deduced, based on which, several criteria and the corresponding algorithm are proposed. Finally, as an application to gene regulatory networks, the results obtained in this paper are used to analyze the WNT5A network and the apoptosis network. [ABSTRACT FROM AUTHOR]

The Algorithm Selection Problem (ASP) considers the use of previous knowledge regarding problem features and algorithm performance to recommend the best strategy to solve a previously unseen problem. In the application context, the usual ASP for optimization considers recommending the best heuristics, whenever it faces a new similar problem instance, also known as the Per-Instance ASP. Although ASP for heuristic recommendation is not new, selecting heuristics and also their parameters, or the Per-instance Algorithm Configuration Problem, is still considered a challenging task. This paper investigates the use of meta-learning to recommend six different stochastic local searches and their parameters to solve several instances of permutation flowshop problems. The proposed approach uses several problem features, including fitness landscape metrics, builds the performance database using irace, and trains different multi-label recommendation models on a data set with more than 6000 flowshop problem instances. Experiments show that decision tree-based machine learning models achieve good performance, and the quality of the recommendations is capable of outperforming the state-of-the-art algorithm with tuned configuration. [ABSTRACT FROM AUTHOR]

Rationale The recent development of isotope ratio infrared spectroscopy (IRIS) was quickly followed by the addition of online extraction and analysis systems, making it faster and easier to measure soil and plant water isotopes. However, memory and sample size effects limit the efficiency and accuracy of these new setups. In response, this study presents a scheme dedicated to estimating and eliminating these two effects. Methods Memory effect was determined by injecting two standard waters alternately. Each standard was injected nine times in a row and analyzed using induction module cavity ring-down spectroscopy (IM-CRDS). Memory coefficients were calculated using a new 'multistage jump' algorithm. Sample size effects were evaluated by injecting water volumes ranging from 1 μL to 6 μL. Finally, the influence of cellulose filter paper on the isotopic measurements, the memory, and the sample size effect was evaluated by comparing it with glass filter paper. Results Memory effects were detected for both δ18O and δ2H values, with the latter being stronger. Isotopic differences between replicates of the same plant or soil sample showed a clear decrease after memory correction. A small water volume effect was found only when the injected water volume was larger than 3 μL. However, while the correction method performed well for laboratory-made samples, it did not for field samples, due to the heterogeneity of the isotopic composition of the samples. Stronger memory and water volume effects were found for cellulose filter paper. Conclusions The memory coefficients and the water volume-isotope relationship improved the consistency and accuracy of both laboratory and field data. Our results indicate that cellulose filter paper may not be a suitable medium to measure standard waters and evaluate memory and water volume effects. Finally, a detailed correction and calibration protocol is suggested, along with notes on best practices to obtain good-quality IM-CRDS data. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With the development of the drying rack system, users with limited time tend to use the fully functional drying rack system to realize various intelligent control functions. However, the existing control methods for drying rack system has some defects such as, low intelligence and system delay, which are unsuitable for most users. In this paper, an efficient intelligent control algorithm based on the back‐propagation (BP) neural network is proposed. In this system, STM32F103 is first utilized as the central controller and multiple sensors are used to collect environmental information. Then, the remote control, voice keywords, and buttons can be used to achieve intelligent control. Subsequently, a motor drive intelligence control algorithm based on the BP neural network (MCBP) is proposed to improve the accuracy of the intelligent control. Next, an Application (APP) that can display environmental data such as wind speed, temperature, and humidity is developed. The APP can realize various intelligent control functions such as lifting, panning, rotating, and harvesting. Finally, MCBP is compared with normal control and programmable logic controller control. The accuracy of the MCBP is higher than other two control methods. The final extensive experiments confirm the accuracy and efficiency of the proposed intelligent control algorithm. [ABSTRACT FROM AUTHOR]

In this paper we provide faster algorithms for approximately solving discounted Markov decision processes in multiple parameter regimes. Given a discounted Markov decision process (DMDP) with |S| states, |A| actions, discount factor γ ∈ (0, 1), and rewards in the range [−M, M], we show how to compute an ϵ‐optimal policy, with probability 1 − δ in time (Note: We use O˜$$ \tilde{O} $$ to hide polylogarithmic factors in the input parameters, that is, O˜(f(x))=O(f(x)⋅log(f(x))O(1))$$ \tilde{O}\left(f(x)\right)=O\left(f(x)\cdot \log {\left(f(x)\right)}^{O(1)}\right) $$.) O˜|S|2|A|+|S‖A|(1−γ)3logMϵlog1δ.$$ \tilde{O}\left(\left({\left|S\right|}^2\mid A\mid +\frac{\mid S\Big\Vert A\mid }{{\left(1-\gamma \right)}^3}\right)\log \left(\frac{M}{\epsilon}\right)\log \left(\frac{1}{\delta}\right)\right). $$ This contribution reflects the first nearly linear time, nearly linearly convergent algorithm for solving DMDPs for intermediate values of γ. We also show how to obtain improved sublinear time algorithms provided we can sample from the transition function in O(1) time. Under this assumption we provide an algorithm which computes an ϵ‐optimal policy for ϵ∈0,M1−γ$$ \epsilon \in \left(0,\frac{M}{\sqrt{1-\gamma }}\right] $$ with probability 1 − δ in time O˜|S‖A|M2(1−γ)4ϵ2log1δ.$$ \tilde{O}\left(\frac{\mid S\Big\Vert A\mid {M}^2}{{\left(1-\gamma \right)}^4{\epsilon}^2}\log \left(\frac{1}{\delta}\right)\right). $$ Furthermore, we extend both these algorithms to solve finite horizon MDPs. Our algorithms improve upon the previous best for approximately computing optimal policies for fixed‐horizon MDPs in multiple parameter regimes. Interestingly, we obtain our results by a careful modification of approximate value iteration. We show how to combine classic approximate value iteration analysis with new techniques in variance reduction. Our fastest algorithms leverage further insights to ensure that our algorithms make monotonic progress towards the optimal value. This paper is one of few instances in using sampling to obtain a linearly convergent linear programming algorithm and we hope that the analysis may be useful more broadly. [ABSTRACT FROM AUTHOR]

In geoscience and other fields, researchers use models as a simplified representation of reality. The models include processes that often rely on uncertain parameters that reduce model performance in reflecting real‐world processes. The problem is commonly addressed by adapting parameter values to reach a good match between model simulations and corresponding observations. Different optimization tools have been successfully applied to address this task of model calibration. However, seeking one best value for every single model parameter might not always be optimal. For example, if model equations integrate over multiple real‐world processes which cannot be fully resolved, it might be preferable to consider associated model parameters as random parameters. In this paper, a random parameter is drawn from a wide probability distribution for every singe model simulation. We developed an optimization approach that allows us to declare certain parameters random while optimizing those that are assumed to take fixed values. We designed a corresponding variant of the well known Covariance Matrix Adaption Evolution Strategy (CMA‐ES). The new algorithm was applied to a global biogeochemical circulation model to quantify the impact of zooplankton mortality on the underlying biogeochemistry. Compared to the deterministic CMA‐ES, our new method converges to a solution that better suits the credible range of the corresponding random parameter with less computational effort. Plain Language Summary: In geoscience and other fields, researchers use models as a simplified representation of reality. The models include processes that often rely on uncertain parameters that reduce model performance in reflecting real‐world processes. The problem is commonly addressed by adapting parameter values to reach a good match between model simulations and corresponding observations. However, seeking one best value for every single model parameter might not always be optimal. For example, if model equations integrate over multiple real‐world processes it might be preferable to consider associated model parameters as random parameters that are drawn from a wide probability distribution for every single model simulation. To explore this further, we modified a popular variant of the Covariance Matrix Adaption Evolution Strategy (CMA‐ES) in order to tune the other parameters. The new algorithm was applied to a global biogeochemical circulation model to quantify the impact of zooplankton mortality on the underlying biogeochemistry. Compared to the deterministic CMA‐ES our new method converges to a solution that better suits the credible range of the corresponding random parameter. Key Points: Model calibration is an important task in the view of parametric uncertaintiesWe allow specific uncertain model parameters to be random while optimizing other model parameters, providing an efficient tool for the taskWe apply it to a global biogeochemical circulation model to quantify the impact of zooplankton mortality on the underlying biogeochemistry [ABSTRACT FROM AUTHOR]

Covering array generation (CAG) is the key research problem in combinatorial testing and is an NP‐complete problem. With the increasing complexity of software under test and the need for higher interaction covering strength t, the techniques for constructing high‐strength covering arrays are expected. This paper presents a hybrid heuristic memetic algorithm named QSSMA for high‐strength CAG problem. The sub‐optimal solution acceptance rate is introduced to generate multiple test cases after each iteration to improve the efficiency of constructing high‐covering strength test suites. The QSSMA method could successfully build high‐strength test suites for some instances where t up to 15 within one day cutoff time and report five new best test suite size records. Extensive experiments demonstrate that QSSMA is a competitive method compared to state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Retrograde intrarenal surgery (RIRS) is a widely utilized diagnostic and therapeutic tool for multiple upper urinary tract pathologies. The image‐guided navigation system can assist the surgeon to perform precise surgery by providing the relative position between the lesion and the instrument after the intraoperative image is registered with the preoperative model. However, due to the structural complexity and diversity of multi‐branched organs such as kidneys, bronchi, etc., the consistency of the intensity distribution of virtual and real images will be challenged, which makes the classical pure intensity registration method prone to bias and random results in a wide search domain. In this paper, we propose a structural feature similarity‐based method combined with a semantic style transfer network, which significantly improves the registration accuracy when the initial state deviation is obvious. Furthermore, multi‐view constraints are introduced to compensate for the collapse of spatial depth information and improve the robustness of the algorithm. Experimental studies were conducted on two models generated from patient data to evaluate the performance of the method and competing algorithms. The proposed method obtains mean target error (mTRE) of 0.971 ± 0.585 mm and 1.266 ± 0.416 mm respectively, with better accuracy and robustness overall. Experimental results demonstrate that the proposed method has the potential to be applied to RIRS and extended to other organs with similar structures. [ABSTRACT FROM AUTHOR]

This paper presents a novel adaptive dynamic programming (ADP) method to solve the optimal consensus problem for a class of discrete‐time multi‐agent systems with completely unknown dynamics. Different from the classical RL‐based optimal control algorithms based on one‐step temporal difference method, a multi‐step‐based (also call n‐step) policy gradient ADP (MS‐PGADP) algorithm, which have been proved to be more efficient owing to its faster propagation of the reward, is proposed to obtain the iterative control policies. Moreover, a novel Q‐function is defined, which estimates the performance of performing an action in the current state. Then, through the Lyapunov stability theorem and functional analysis, the proof of optimality of the performance index function is given and the stability of the error system is also proved. Furthermore, the actor‐critic neural networks are used to implement the proposed method. Inspired by deep Q network, the target network is also introduced to guarantee the stability of NNs in the process of training. Finally, two simulations are conducted to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

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

Traditional radar imaging was restricted by the antenna aperture and imaging scene. It was difficult to improve azimuth resolution greatly. The vortex electromagnetic (EM) waves carrying orbital angular momentum have a special helical phase wavefront structure. The vortex EM waves could bring more degrees of freedom when applied to synthetic aperture radar imaging, make it possible for ultra‐high resolution imaging, provide a new possibility for the bottleneck problem of azimuth resolution in traditional radar imaging. In this paper, an OAM‐ωK algorithm was used to compensate the Bessel term and residual azimuth angle term which in the echo signal. Because the azimuth term and azimuth angle term were fused into a new azimuth term, the Doppler bandwidth was broadened and high resolution imaging was achieved. The theory verified the advantages of the algorithm in azimuth resolution. By comparing simulation experiments of single target point and multi‐target points with the traditional ωK algorithm, the proposed OAM‐ωK algorithm had higher azimuth resolution, proving the algorithm's effectiveness. Plain Language Summary: We present and verify the OAM‐ωK Algorithm. In this paper, the proposed algorithm is verified by simulation, and it is proved to be valid in both single target point and multi‐target simulation. In the same imaging scene, we propose that the azimuth resolution of the vortex wave imaging algorithm is about 3 times higher than that of the traditional ωK imaging algorithm when the modes is (−236,236). Key Points: We present and verify the OAM‐ωK Algorithm. The research is based on the side‐looking strip synthetic aperture radar imaging sceneWe propose that the azimuth resolution of the algorithm is about 3 times higher than that of the traditional ωK imaging algorithmThe antenna transmitting vortex waves is a concentric uniform circular arrays [ABSTRACT FROM AUTHOR]

One critical difficulty to high‐level automated driving is the decision‐making process of automated vehicles in complicated traffic environments, especially in situations mixed of pedestrians and vehicles. This paper proposes a differentiated decision‐making algorithm to promote passing capability and efficiency in mixed traffic conditions. First, the behavioural characteristic of pedestrians, denoted as the pedestrian feature index, is estimated by a multi‐layer perception module input with quantitative analysis of pedestrian action. Based on estimation results, the decision algorithm merges pedestrian feature index into intelligent driver model and adjusts corresponding parameters, which used to be unchangeable so that the ego‐vehicle can make differential decisions according to various pedestrian features. Validation on the PIE dataset shows that the accuracy of pedestrian feature estimation is ensured. A simulation scenario is established utilizing cellular automata, and the results indicate that the proposed decision‐making algorithm can greatly improve passing efficiency under safety and manoeuvrability prerequisite. [ABSTRACT FROM AUTHOR]

Orthogonal time frequency space (OTFS) is a new method technique that supports reliable information transmission in a strong Doppler environment. Aiming at the radar target parameter estimation problem of integration of radar and communication (IRC) based on OTFS, this paper proposes a two‐step radar target parameter estimation algorithm combining maximum likelihood (ML) estimation and gradient descent (GD) principle. Firstly, the echo signal of the OTFS integrated signal is constructed and based on this, the ML estimation model is constructed, and the alternating projection (AP) algorithm is employed to make a rough estimation of the target parameters. Then the rough estimation results of the AP‐ML algorithm are employed as the initial value of the GD algorithm for the fine parameter estimation. Simulation results show that the proposed algorithm has better target parameter estimation performance and resolution while reducing the computational complexity. [ABSTRACT FROM AUTHOR]

Due to the problems that International Mobile Subscriber Identity is not encrypted during transmission, key disclosure, and high authentication overhead in Evolved Packet System‐Authentication and Key Agreement, the next generation of high‐speed railway Long‐Term Evolution for Railway train ground authentication protocol, this paper describes the improved Secure Remote Password algorithm and two‐way heterogeneous Long‐Term Evolution for Railway authentication key agreement scheme. First, the improved Secure Remote Password algorithm is used to encrypt the message transmission between User Equipment and Mobility Management Entity, solving the problem of International Mobile Subscriber Identity being not encrypted during transmission. Then, a two‐way heterogeneous digital signature method of Public Key Infrastructure and Identity‐Based Cryptosystem is proposed to authenticate and negotiate with the Mobility Management Entity and Home Subscriber Server, which improves the security of the root key and can effectively prevent man in the middle, replay and other attacks. Finally, in the research results, the random oracle model is used to verify, and the results show that our method is superior to the comparison method in security and authentication cost, and can meet the Long‐Term Evolution for Railway communication requirements. [ABSTRACT FROM AUTHOR]

This paper introduces a Finite Element framework for large deformation adhesive contact between elastic bodies based on volume‐to‐volume interaction. A key component of this framework is a novel, customized arc‐length algorithm designed to follow the solution path through multiple snap‐through and snap‐back instabilities. Another new component is a k‐d tree based algorithm which significantly accelerates the computationally expensive volume‐to‐volume interaction calculations. This optimized Finite Element framework is then used to solve two‐body and three‐body elastic adhesive contact problems with realistic material and interaction parameters, using a 6‐12 Lennard‐Jones interaction potential. The improved efficiency of the k‐d tree based scheme makes it indispensable for interaction calculations. This paper also shows the importance of implementing adhesion‐specific customizations in the arc‐length solver. [ABSTRACT FROM AUTHOR]

The emergence of micro‐sources in the energy market to reduce carbon emissions and exploit more renewable generations increases the frequency oscillations of the system. Hence, this paper attempts to develop a robust nonlinear fractional order proportional integral derivative (NLFOPID) controller for frequency regulation of restructured energy systems. A hybrid atom search‐particle swarm optimization (AS‐PSO) is proposed to optimize the gain values of the NLFOPID controller. The proposed control approach enhances flexibility by providing robustness against the RE intermittency. The optimization technique is coded in MATLAB and applied for frequency regulation of a multi‐area energy system developed in Simulink. Initially, the effectiveness of the hybrid algorithm is validated for standard benchmark functions and then implemented for frequency control. The results demonstrate that the proffered AS‐PSO technique performs significantly for various types of transactions in a deregulated energy market than state‐of‐the‐art. The real‐time applicability of the proposed controller is validated using hardware‐in‐the‐loop simulation of the open energy market in OPAL‐RT. The frequency response obtained from the AS‐PSO optimized NLFOPID controller is remarkable compared to other techniques. Furthermore, the closed‐loop stability of the system is examined using bode analysis through an improved Oustaloup approximation technique. [ABSTRACT FROM AUTHOR]

In this paper, a novel compensation algorithm is proposed for improving multi‐frame drop (MFD) on AMOLED display. The MFD phenomenon occurs as the image switching or sliding when the display with extremely low brightness, resulting in the color shift and distortion at the edge of moving objects. Because the current is too small and the emission time is too short at extremely low brightness, more than one frame is needed to alleviate the impact of the parasitic capacitance between the gate and drain node of driving TFT (DTFT), and the gradual changing of drain voltage (VD) causes the DTFT to move from the linear to saturation region, which also aggravates this phenomenon. This paper analyzes the MFD phenomenon based on the existing 7T1C pixel circuits, records the grayscale changes of multiple frames, and gives the different offsets at dropped frames. By experiment results, this method is not changing the pixel circuit, such as the 8T1C, which could compensate for the dropped frames for reducing MFD issues and is easily implemented in the display driver IC for many different OLED panels. [ABSTRACT FROM AUTHOR]

Carrier aggregation can not only increase communication bandwidth, but also improve spectrum efficiency and communication security. Considering the non‐contiguous multi‐subspectrum aggregation for the spectrum division transmission of arbitrary signal, the paper analyzes the performance impact on the demodulation of aggregated signal in the presence of subspectral amplitude and phase errors in receiver. The theoretical bit‐error rates (BER) of quaternary phase‐shift keying and M‐ary phase‐shift keying are derived to clarify the subspectrum synchronization accuracy requirements in non‐contiguous carrier aggregation. In addition, two adaptive equalizers requiring no pilot overhead are introduced to correct the distortion of aggregated signal caused by the subspectral errors. Both algorithms need no estimation of synchronization error but also have the tracking capability of phase error even with subspectral carrier error. Simulation results show the reliability of the theoretical BER performance and the excellent performance of the equalization algorithms. [ABSTRACT FROM AUTHOR]

The author presents comments on a paper on the use of machine learning to support the system dynamics method. Topics discussed include its interpretation of simulation models and explanation of policy analysis, and the emerging view whereby dynamic problems from endogenous feedback structures can be tackled via wider tools and methodological approaches. Also noted is the resulting potential for greater insights into the modelling process.

Objective: To characterize and validate the landscape of algorithms that use International Classification of Disease (ICD) codes to identify low‐acuity emergency department (ED) visits. Data Sources: Publicly available ED data from the National Hospital Ambulatory Medical Care Survey (NHAMCS). Study Design: We systematically searched for studies that specify algorithms consisting of ICD codes that identify preventable or low‐acuity ED visits. We classified ED visits in NHAMCS according to these algorithms and compared agreements using the Jaccard index. We then evaluated the performance of each algorithm using positive predictive value (PPV) and sensitivity, with the reference group specified using low‐acuity composite (LAC) criteria consisting of both triage and clinical components. In sensitivity analyses, we repeated our primary analysis using only triage or only clinical criteria for reference. Data Collection: We used the 2011–2017 NHAMCS data, totaling 163,576 observations before survey weighting and after dropping observations missing a primary diagnosis. We translated ICD‐9 codes (years 2011–2015) to ICD‐10 using a standard crosswalk. Principal Findings: We identified 15 papers with an original list of ICD codes used to identify preventable or low‐acuity ED presentations. These papers were published between 1992 and 2020, cited an average of 310 (SD 360) times, and included 968 (SD 1175) codes. Pairwise Jaccard similarity indices (0 = no overlap, 1 = perfect congruence) ranged from 0.01 to 0.82, with mean 0.20 (SD 0.13). When validated against the LAC reference group, the algorithms had an average PPV of 0.308 (95% CI [0.253, 0.364]) and sensitivity of 0.183 (95% CI [0.111, 0.256]). Overall, 2.1% of visits identified as low acuity by the algorithms died prehospital or in the ED, or needed surgery, critical care, or cardiac catheterization. Conclusions: Existing algorithms that identify low‐acuity ED visits lack congruence and are imperfect predictors of visit acuity. [ABSTRACT FROM AUTHOR]

Background: Polypharmacy in haemodialysis patients can not only manifest as the continued prescribing of unnecessary medications but also has the potential to increase medication‐related hospital admissions, morbidity, and mortality. A validated deprescribing algorithm has recently been developed, specifically targeted at dialysis patients. This paper audits the experience of implementing this algorithm in Australian dialysis settings. Aim: The aim of this paper was to evaluate the usefulness and applicability of the pharmacist‐led Toronto deprescribing tool in Australian haemodialysis settings. Method: The pharmacist‐led deprescribing algorithm was implemented across two metropolitan sites and one rural site. The audit focused on five medications that could potentially be deprescribed in the target patient group (diuretics, alpha blockers, statins, proton pump inhibitors [PPIs], and quinine). Between 1 and 12 months later, a reaudit was conducted, with patients followed up to confirm if medications remained deprescribed. Results: Two hundred and eleven patients across three sites were reviewed. Application of the algorithm resulted in 168 medications deemed appropriate to deprescribe. Of these 168 medications, 56 (33%) were initially deprescribed, with 50 medications (30%) remaining deprescribed on reaudit. The deprescribing rates varied between the three different services, with initial deprescribing rates ranging from 18% to 61%. After follow‐up, deprescribing changes across target medications were fairly static, with only a small number of patients restarting either their diuretic or PPI. Conclusion: The pharmacist‐led deprescribing algorithm resulted in substantial deprescribing across the three sites. Deprescribing rates varied between the sites due to differences in the team model that the pharmacist worked within and the method of the rollout. Further studies should draw on aspects such as finding enablers to overcome prescriber and patient concerns about deprescribing and the aspects of which team models lead to higher rates of successful deprescribing. [ABSTRACT FROM AUTHOR]

This paper proposes a three‐stage algorithm based on clustering decomposition and task allocation—improved clustering planning algorithm (iK‐iD‐N), aiming at the optimization task allocation problem of drones in actual application to meet the task demand constraints. The algorithm solves the problem of the number of drones demanded and the initial delivery range of each drone by introducing dual‐objective planning into the clustering decomposition. Combining improved Dijkstra algorithm (iK‐D) with neighbourhood insertion algorithm into task allocation, to get high‐quality solutions and solve efficiently. Compared with the existing ant colony algorithm, the iK‐iD‐N algorithm proposed in this paper is more efficient and can obtain the best and stable solutions while evenly distributing tasks. Then it is compared with the improved clustering algorithm combined with the basic iK‐D to get better solutions of the iK‐iD‐N algorithm at any time, and compared with the basic clustering algorithm with the improved task allocation algorithm (K‐iD‐N) that iK‐ iD‐N can get a better solution with high probability. The thesis also simulates and analyzes the impact of uncertainty requirements on the solutions based on drone demand and task allocation models, and discusses the impact of drone load capability and endurance capability constraints on the final solutions. [ABSTRACT FROM AUTHOR]

With the help of the analytical expression of the vertical gravity gradient (VGG) generated by a rectangular prism, an analytical algorithm for predicting seafloor topography using the VGG data has been studied. Nevertheless, ship sounding data are an essential constraint in solving the seafloor topography. This paper combines ship sounding data with VGG anomaly to predict the seafloor topography. The main research contents include the following: Using the ship soundings and VGG data in the study area, the observation equations about sea depth are established, and the stability of the equations are studied; furthermore, considering the influence of seafloor topography outside the study area on the observation equations, these effects are divided into boundary effects and far‐field effects, and different processing methods are proposed. Finally, the method is tested on the East Pacific Rise, only using VGG anomaly and adding the mean value to fix the boundary region, the RMS error of the results is 108.8 m; SIO's model is added to the boundary region and the seven maximum absolute errors are replaced by ship sounding data, the RMS error of the results can reach 94.2 m and the accuracy improvement is 13.42%. Plain Language Summary: The topographic fluctuation can be composed of many different rectangular cylinders, and the vertical gravity gradient generated by a rectangular cylinder on the sea level has an analytical expression. In this paper, the observation equations are established by using this analytical expression to predict the seafloor topography, and different methods are studied to control or weaken the influence of boundary effects and far‐field effects. This study will provide a new idea for the refinement of seafloor topography in the future. Key Points: A new method for predicting seafloor topography from vertical gravity gradient and ship soundings, and the effectiveness of the algorithm is verified by numerical simulationThe sources of interference errors are refined as "boundary effects" and "far‐field effects," and different processing methods are proposed; the algorithm is applied to the test area and the RMS error of prediction results can reach 94.2 m [ABSTRACT FROM AUTHOR]

In order to solve the problems of different visual angles, such as large brightness and colour difference, and obvious image splicing trace, an algorithm of ring image fusion based on colour correction is proposed in this paper. According to the algorithm in this paper, a Matlab toolbox was made for image fusion simulation experiment. Structural similarity (SSIM) was used to evaluate the effectiveness of this method in reducing illumination and contrast difference and the mean value of SSIM was 0.8166. The algorithm is measured in the development board and accelerated by graphic processing unit to achieve the computing efficiency of 18 ms per frame and meet the real‐time requirements of the system. [ABSTRACT FROM AUTHOR]

Summary: Load balancing is a crucial feature of cloud computing that evenly distributes workload between servers, network interfaces, and hard drives. Because of dynamic computing over the internet, cloud computing suffers from request overloading. To address this challenge, this paper proposes a new deep learning‐based deadline constrained (DL‐DC) load balancing technique. The proposed DL‐DC technique will improve resource utilization, reduce cost, latency, and response time, as well as balance load between servers and improve reliability. The proposed DL‐DC technique will direct traffic to a load balancer, which will forward the load with a deadline to a deep Inception ResNet. This network considers some parameters, such as sticky session, content‐based and instance health check and efficiently predicts the schedule for the task. Finally, a predicted task schedule has been derived using the DL‐DC model which is used to distribute the task to the virtual machine. The proposed DL‐DC load balancing algorithm is compared with other existing algorithms such as QMPSO, DQTS, and ACSO in terms of cost, makespan, response time, transmission time and task migration. The proposed method achieves up to 21.08% low response time, 27.3% decrease in make span, 25.5% decrease in task migration, and 38.9% decrease in cost respectively. [ABSTRACT FROM AUTHOR]

Summary: This paper overcomes two key issues within radiation monitoring networks. Secure and accurate transmission of detected gamma radiation in remote areas is the first challenge. The second one is related to the robustness of transmitted signal under different fading scenarios. These issues are handled during signal transmission over worldwide interoperability for microwave access (WiMax) networks. The signals are experimentally captured using a 137Cs point source, a NaI (TI) detector, and an analog‐to‐digital converter with 16 MS/s. For the first issue, several algorithms are implemented for secure and efficient transmission of gamma‐based signals. These algorithms include encoding of captured signal, decoding, interleaving, and frequency transformation of modulated signal. Encoding and decoding of target signals are conducted using the Reed Solomon technique and convolutional encoding with a Viterbi decoder. A superior algorithm for channel interleaver using the combinational interleaving method is introduced. Fast lifting transform (FLT) is applied for transforming modulated signal into frequency domain. The results declare that CC = 1/2 with BPSK‐1/2 coding rate introduces a better BER of 0.00068. A higher SNR of 1.5 dB is accomplished by the combinational interleaving algorithm. For the second issue, several models are suggested for overcoming channel degradations under gamma, exponential, Poisson, and geometric fading. Closed‐form expressions that describe outage probability, bit error rate (BER), and symbol error rate (SER) are suggested. Geometric and gamma fading achieve respectively a lower BER of 0.18 and 0.48. These channels demonstrate similar channel performances at 3.6 dB. The proposed models are validated through comparison with published work. Finally, the proposed algorithms enable higher data rate transmission of detected 137Cs signals under higher robustness using WiMax networks. [ABSTRACT FROM AUTHOR]

Neural architecture search can be used to find convolutional neural architectures that are precise and robust while enjoying enough speed for industrial image processing applications. In this paper, our goal is to achieve optimal convolutional neural networks (CNNs) for multiple‐templates matching for applications such as licence plates detection (LPD). We perform an iterative local neural architecture search for the models with minimum validation error as well as low computational cost from our search space of about 32 billion models. We describe the findings of the experience and discuss the specifications of the final optimal architectures. About 20‐times error reduction and 6‐times computational complexity reduction is achieved over our engineered neural architecture after about 500 neural architecture evaluation (in about 10 h). The typical speed of our final model is comparable to classic template matching algorithms while performing more robust and multiple‐template matching with different scales. [ABSTRACT FROM AUTHOR]

Virtual‐real fusion (VRF) technology plays a crucial role in the meta‐universe by bridging the gap between virtual environments (VEs) and physical environments (PEs). However, the current VRF system has the problem of single function and fixed integration, limiting the popularization and application of VRF technology. Therefore, this paper proposes a framework named synchronous mixed reality (SMR) to personalize the VRF system and maintain the balance between interaction efficiency and environmental immersion. We combine an instance segmentation algorithm with an interaction prediction algorithm to achieve this balance. To evaluate the effectiveness of the SMR framework, we design three scenarios based on the interaction properties of physical objects and measure environmental immersion, systematic interaction efficiency, and user experience. Our results demonstrate that the SMR framework meets diverse needs in various PEs while balancing immersion and interaction efficiency. Additionally, our framework significantly reduces collisions and negative emotions experienced by users in VEs. We anticipate that this framework will serve as a guide for constructing virtual‐real fusion systems in the future. [ABSTRACT FROM AUTHOR]

A software product line (SPL) is a software engineering reusability approach used to create different applications from a shared set of common software features. The product configuration problem (PCP) consists in finding the product, among all the products allowed by the SPL, that best fits the customer's preferences and requirements. PCP is a generalization of the tree knapsack problem (TKP) with additional AND, XOR, and OR constraints. Therefore, this problem is also NP‐hard. Previous work in the literature could solve only instances with up to 500 features. In this paper, we propose a pseudo‐polynomial time dynamic programming algorithm that solves PCP in O(nH)$O(nH)$, where n is the number of features and H is the customer's budget. We also propose an integer linear programming formulation for PCP, which is solved by the CPLEX's branch‐and‐bound algorithm. Computational experiments, performed on realistic instances from the literature, showed that the proposed algorithms were able to find optimal solutions for all the instances with up to 10,000 features. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a power factor correction (PFC) method in a single‐phase AC/DC converter using a predictive control algorithm (PCA) through a boost DC/DC converter. The minimum value of the DC‐link capacitor has been evaluated on the DC distribution side for stable operation of the converter. The proposed technique can be applied to the cascade connection of the converters, that is, the uncontrolled rectifier with boost converter and load. The value of the minimum capacitance is obtained as a function of the reference voltage of the DC‐link capacitor, the frequency of the supply, the input power, and the maximum DC‐link voltage. The proposed design is applied to control the boost converter for PFC applications using PCA at the minimum DC‐link capacitor value. The calculated value of DC‐link capacitance has been reduced to a greater extent as compared to the conventional calculation of DC‐link capacitance. The converter operation with the designed value of DC‐link capacitance has been verified through the MATLAB and Typhoon Simulink environment, and an experimental prototype model is used for the validation by using the FPGA based Typhoon HIL‐402 kit. [ABSTRACT FROM AUTHOR]

White‐box cryptography protects the secret key extraction from software implementations of cryptographic primitives. The principle is to white‐box the key by using obfuscation and hide the key in the whole encryption process to ensure that the key cannot be traced and extracted. The idea of adding internal perturbed terms and additional random equations is given by Bringer et al. to implement white‐box encryption, which has been attacked due to the defect in structure. In this paper, we propose a new secure white‐box encryption method by combining the random perturbation mechanism of local differential privacy protection and adding internal interference term, which can protect the privacy of the intermediate secret key more securely. The further disrupted cipher can also obtain the correct result by voting without increasing the number of parallel systems, and also gives the practical white‐box representation to AEw/oS. Compared with the white‐box implementation proposed by Bringer et al., the memory space of the new white‐box scheme will be 44 bytes more, but in terms of security, it can resist traditional attacks and Mulder's attacks. [ABSTRACT FROM AUTHOR]