Search

An automatic computer vision algorithm that detects individual paper fibres from an image, assesses the possibility of grasping the detected fibres with microgrippers and detects the suitable grasping points is presented. The goal of the algorithm is to enable automatic fibre manipulation for mechanical characterisation, which has traditionally been slow manual work. The algorithm classifies the objects in images based on their morphology, and detects the proper grasp points from the individual fibres by applying given geometrical constraints. The authors test the ability of the algorithm to detect the individual fibres with 35 images containing more than 500 fibres in total, and also compare the graspability analysis and the calculated grasp points with the results of an experienced human operator with 15 images that contain a total of almost 200 fibres. The detection results are outstanding, with fewer than 1% of fibres missed. The graspability analysis gives sensitivity of 0.83 and specificity of 0.92, and the average distance between the grasp points of the human and the algorithm is 220 µm. Also, the choices made by the algorithm are much more consistent than the human choices. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses various reports related to model checking and fault prediction including the SemiBoost for the prediction of software defect, the semi-supervised learning technique for software fault detection and the new algorithm for reachability analysis.

The combination of computer algorithms to diagnose clinical images has attracted more and more attention. This research aims to improve the efficiency of gastric cancer (GC) diagnosis, so deep learning (DL) algorithms are tentatively used to assist doctors in the diagnosis of gastric cancer. In the experiment, the collected 3591 gastroscopic images were divided into network training set and experimental verification test set. The lesion samples in the image are all marked by many endoscopists with many years of clinical experience. In order to improve the experimental effect, 5261 endoscopic images were obtained by expanding the training set. Then the obtained training set is input into the convolutional neural network (CNN) for training, and finally get the algorithm model DLU‐Net. By inputting 598 test set samples into the CNN constructed in this paper, five results such as advanced GC, early GC, precancerous lesions, normal and benign lesions can be identified and output, with a total accuracy of 94.1%. It can be concluded that the DL algorithm model constructed in this paper can effectively identify the staging characteristics of cancer as well as gastroscopic images, greatly improve efficiency, and effectively assist physicians in the diagnosis of GC under gastroscopy. [ABSTRACT FROM AUTHOR]

Analytic methods development, like many other disciplines, relies on experimentation and data analysis. Determining the contribution of a paper or report on a study incorporating data analysis is typically left to the reviewer's experience and good sense, without reliance on structured guidelines. This is amplified by the growing role of machine learning driven analysis, where results are based on computer intensive algorithm applications. The evaluation of a predictive model where cross validation was used to fit its parameters adds challenges to the evaluation of regression models, where the estimates can be easily reproduced. This lack of structure to support reviews increases uncertainty and variability in reviews. In this paper, aspects of statistical assessment are considered. We provide checklists for reviewers of applied statistics work with a focus on analytic method development. The checklist covers six aspects relevant to a review of statistical analysis, namely: (1) study design, (2) algorithmic and inferential methods in frequentism analysis, (3) Bayesian methods in Bayesian analysis (if relevant), (4) selective inference aspects, (5) severe testing properties and (6) presentation of findings. We provide a brief overview of these elements providing references for a more elaborate treatment. The robustness analysis of an analytical method is used to illustrate how an improvement can be achieved in response to questions in the checklist. The paper is aimed at both engineers and seasoned researchers. [ABSTRACT FROM AUTHOR]

Data races are a common problem in concurrent and multi-threaded programming. Experience shows that the classical notion of a data race is not powerful enough to capture certain types of inconsistencies occurring in practice. This paper investigates data races on a higher abstraction layer. This enables detection of inconsistent uses of shared variables, even if no classical race condition occurs. For example, a data structure representing a coordinate pair may have to be treated atomically. By lifting the meaning of a data race to a higher level, such problems can now be covered. The paper defines the concepts ‘view’ and ‘view consistency’ to give a notation for this novel kind of property. It describes what kinds of errors can be detected with this new definition, and where its limitations are. It also gives a formal guideline for using data structures in a multi-threaded environment. © US Government copyright [ABSTRACT FROM AUTHOR]

The satisfaction of linear-time temporal properties within fairness introduces an implicit fairness constraint to the verification process. To be applied to practical verification tasks, weakly continuation-closed abstractions preserve properties satisfied within fairness. Being defined on the complete behaviour of a distributed system, weakly continuation-closed abstractions require, in principle, an exhaustive state space construction prior to abstraction. Constructing the state space of a practically relevant specification exhaustively, however, is usually not feasible. Based on the notion of traces, i.e. certain equivalence classes of behaviours, trace reductions are defined in this paper. Trace reduction is a particular partial-order reduction based on the persistent-set selective search technique. It is shown that a trace reduction can be used on behalf of the complete behaviour of a distributed system in order to compute abstractions as well as to check whether the abstractions are weakly continuation closed. Thus, trace reductions allow one, in the discussed context, to overcome the requirement of an exhaustive state-space construction prior to abstraction. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The authors insist that monotonicity is a necessary property of a good cohesion metric and the violation of the monotonicity property limits the application of CBMC. They also state that the augmented CBMC can also be used as a guideline for quality evaluation and restructuring of poorly designed classes. This paper raises the question about the necessity of monotonicity by analyzing the reason that causes CBMC to violate the monotonicity property. In addition, we give a detailed description of the restructuring procedure based on CBMC. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Applications that utilize a broker-based architecture are often composed of components that need to be tested individually and in combination. Furthermore, adequacy assessment of tests of components is useful in that it assists testers in identifying weaknesses in the tests generated so far and in offering hints on what the new tests must be. Traditional test adequacy criteria have limitations for commercial use, especially when tests for large components are to be assessed for their adequacy. This paper describes a test adequacy criterion based on interface mutation and a method, based on the criterion, to test components. This method requires the mutation of elements only from within a component's interface and not from within the code that implements the interface. The adequacy criterion based on interface mutation was evaluated empirically and compared with coverage criteria based on control flow for its relative effectiveness in revealing errors and in the cost incurred in developing test sets that satisfy the criterion. Copyright © 2001 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A road multi‐object detection algorithm is one of the core algorithms for intelligent road cleaning robots relying on machine vision. Most existing object detection algorithms analyse all image regions and finally calculate the category and location of each object. However, it is not necessary to analyse all areas of the image when detecting objects on the road surface where the background changes little, and the number of objects is small because there will be a lot of invalid calculations. If we can perform targeted local analysis on images instead of analysing all image regions, it will improve the detection efficiency. Therefore, this paper proposes a multi‐object detection method using a binocular camera and a convolutional neural network (CNN) that effectively reduces invalid calculations during the detection and improves detection efficiency. In the developed method, the binocular vision image acquired by the binocular camera is stereo matched and equalized, while linear regression and coordinate transformation eliminate the angle of the camera pair concerning the road surface. Then, the coordinates of the regions of interest (ROI) is calculated in the left vision image and the features within the ROI is extracted from the corresponding CNN's feature map. Next, ROI pooling resizes the extracted feature maps of different sizes to the same size, which are then input to the fully connected layers to output the results. The proposed binocular network and faster R‐CNN (VGG16) are trained and tested on a dataset involving 1000 road waste images. The experimental results demonstrate that the developed binocular network improves the detection accuracy and speed by 28.56% and 78.39%, respectively, compared with faster R‐CNN (VGG16), providing a reliable basis for a machine vision‐based intelligent road cleaning robot. [ABSTRACT FROM AUTHOR]

This paper introduces a real-time rendering method for single-bounce glossy caustics created by GGX microsurfaces. Our method is based on stochastic light culling of virtual point lights (VPLs), which is an unbiased culling method that randomly determines the range of influence of light for each VPL. While the original stochastic light culling method uses a bounding sphere defined by that light range for coarse culling (e.g., tiled culling), we have further extended the method by calculating a tighter bounding ellipsoid for glossy VPLs. Such bounding ellipsoids can be calculated analytically under the classic Phong reflection model which cannot be applied to physically plausible materials used in modern computer graphics productions. In order to use stochastic light culling for such modern materials, this paper derives a simple analytical solution to generate a tighter bounding ellipsoid for VPLs on GGX microsurfaces. This paper also presents an efficient implementation for culling bounding ellipsoids in the context of tiled culling. When stochastic light culling is combined with interleaved sampling for a scene with tens of thousands of VPLs, this tiled culling is faster than conservative rasterization-based clustered shading which is a state-of-the-art culling technique that supports bounding ellipsoids. Using these techniques, VPLs are culled efficiently for completely dynamic single-bounce glossy caustics reflected from GGX microsurfaces. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive performance evaluation algorithm explicitly designed for secondary equipment in substations, specifically targeting the relay protection system. In contrast to the current evaluation systems, this novel method navigates the complex internal interconnections and mechanisms inherent within secondary system equipment. Such complications have previously impeded the accuracy and breadth of evaluations, thereby limiting the degree of precision and innovation attainable within substations. The proposed approach effectively integrates the improved Analytic Hierarchy Process entropy weight (IAHP‐EW) method with the Learning Vector Quantization (LVQ) neural network. Initially, the IAHP‐EW method identified the comprehensive evaluation indicators and their corresponding weights for relay protection devices. Following weight allocation, these evaluation indicators are scrutinized and computed utilizing the multivariate regression analysis algorithm, resulting in performance evaluation outcomes for the relay protection system. These outcomes are subsequently classified and utilized in training the LVQ neural network, promoting the network's capacity to autonomously evaluate the performance status of the relay protection system. To corroborate the viability and effectiveness of this proposed performance evaluation and prediction algorithm, empirical operating data from a local substation is used. The results suggest a significant improvement in the evaluation accuracy of secondary equipment performance, indicating potential for practical application and a valuable contribution to the field through the introduction of a novel approach to performance assessment of substation relay protection systems. [ABSTRACT FROM AUTHOR]

Summary: Massive multi‐input multi‐output has the advantages of high energy efficiency and spectrum use, but it also brings some problems because of cheap hardware use, such as high‐power amplifier (HPA) nonlinearity. When the HPA operates in the nonlinear regime, nonlinear distortions, including amplitude and phase distortions, are introduced into the transmitted symbols, which in turn can cause adjacent channel interference and power losses. In this paper, we first study the deficiency of the conventional precoding algorithm when the HPA is nonlinear. Then, this paper proposes the improved precoding algorithms according to the effect of HPA nonlinearity on the signal, in which the adaptive factor is achieved to largely control the amplitude and phase distortions of the nonlinear HPA. Finally, the simulation results are provided and show that the proposed algorithms in this paper based on the relative block of diagonalization precoding and the forced zero precoding can efficiently decrease the bit error rate and increase the throughput, especially in the case of high signal‐to‐noise ratio. [ABSTRACT FROM AUTHOR]

While computing has been (re)introduced into the basic education curricula in various countries, its actual implementation appears to be inconsistent. There are schools in which computing education is commonplace, while the implementation seems to be lagging behind in others. There is emerging evidence that some teachers do not consider computing education relevant, meaningful and important and, thus, intentionally neglect its provision. This is problematic as understanding the principles of code and computing is crucial for agentic citizenship in the post‐digital era. This paper argues that one main reason for these teachers' reluctance is the economy‐driven discursive framing of computing education, which is in contrast with the socialization‐oriented manner in which teachers approach their work. To contribute to resolving this issue, the present paper introduces a transversal approach to computing education. It conceptualizes code as a sociomaterial text with social and societal histories and consequences. Theoretically and conceptually, the approach draws on the pedagogy of multiliteracies. The leading idea is that digital technologies are examined with students from functional and critical dimensions and through micro and macro perspectives. The use of wearable sports technologies, such as activity wristbands, are used as practical examples to put the theoretical ideas into context. Practitioner notesWhat is already known about this topic Computing has been (re)introduced in the curricula of basic education in various countries.Some teachers are found to be reluctant to teach computing in basic education.What this paper adds This paper introduces a transversal multiliteracies‐based approach for computing education.Implications for practice and policy Computing should be included in curricula and classrooms in a holistic manner that includes both functional and critical approaches to computing. [ABSTRACT FROM AUTHOR]

This paper studies an agent‐based model of consumer demand. Agents are heterogeneous with respect to their preferences and incomes. There are two basic ingredients in the model. The first ingredient is a metric that captures the degree of heterogeneity between agents. The second ingredient is a serial computer algorithm that is used in order to compute a terminal consumption bundle at which income is exhausted and overall utility is maximized. Agents are clustered into heterogeneous groups based on their preferences and incomes. We extract information about the evolution of consumer expenditure under different price regimes and the buildup of optimal demand for varying levels of income and preference parameter values. These features cannot be obtained in the classical framework of static utility maximization. Our agent‐based data‐driven methodology can be applied to any relevant data set and so provide a reliable model for forecasting demand given some agent characteristics. [ABSTRACT FROM AUTHOR]

Crimes, such as robbery and murder, often involve firearms. In order to assist with the investigation into the crime, firearm examiners are asked to determine whether cartridge cases found at a crime scene had been fired from a suspect's firearm. This examination is based on a comparison of the marks left on the surfaces of cartridge cases. Firing pin impressions can be one of the most commonly used of these marks. In this study, a total of nine Ruger model 10/22 semiautomatic rifles were used. Fifty cartridges were fired from each rifle. The cartridge cases were collected, and each firing pin impression was then cast and photographed using a comparison microscope. In this paper, we will describe how one may use a computer vision algorithm, the Histogram of Orientated Gradient (HOG), and a machine learning method, Support Vector Machines (SVMs), to classify images of firing pin impressions. Our method achieved a reasonably high accuracy at 93%. This can be used to associate a firearm with a cartridge case recovered from a scene. We also compared our method with other feature extraction algorithms. The comparison results showed that the HOG‐SVM method had the highest performance in this classification task. [ABSTRACT FROM AUTHOR]

Machine Learning (ML), or the ability of self‐learning computer algorithms to autonomously structure and interpret data, is a methodological approach to solve complicated optimization problems based on abundant data. ML is recently gaining momentum as algorithmic applications, computing potency, and available data sets increased manifold over the past two decades, providing an information‐rich environment in which human reasoning can partially be replaced by computer reasoning. In this paper, we want to assess the implications of ML for Design of Experiments (DoE), a statistical methodology widely used in Quality Management for quantifying effects and interactions of factors with influence on the production quality or the process yield. We specifically want to assess the future role and importance of DoE: Will it remain unaltered by ML, will it be made obsolete, or will it be reinforced? With this, we want to contribute to the discussion of the future use of traditional Quality Management methodologies in production, as our ML assessment can in principle be applied to other statistical methodologies as well. While we are convinced that ML will heavily impact the field of Quality Management and its predominant set of statistical methodologies, we find reason to expect that this impact will be a mutual one. As this is the first paper addressing the joint force potential of the two methodologies ML and DoE, we expect a range of follow‐up papers being written on the subject and a spark in specialized applications addressing DoE's ML‐enhanced vital functionality for process improvements. [ABSTRACT FROM AUTHOR]

Summary: As social networks such as micro‐blogging sites rapidly grow, deciding whom to follow (followee recommendation) becomes a significantly important problem. Most existing works exclusively rely on two traditional factors: the proximity between two users in the network topology or the similarity of the user‐generated contents in the social network, disregarding the effect of users' following behaviors. The challenge of how to effectively combine these two factors remains largely open. Moreover, most research studies simply sort the scores to find top‐k users, which is time‐consuming, especially for large‐scale networks. In this paper, we propose the idea that “predict users' following behaviors by following behaviors themselves.” We consider a user's following to others as a normal process of dynamic and coherent behavior, and we model the potential propagation of the users' following behaviors. Furthermore, based on our previous research on top‐k selection problem, we propose an effective top‐k followee recommendation algorithm, called FRFB. FRFB has low complexity and high scalability and, moreover, good adaptability to real‐life dynamic social networks. We conduct extensive experiments, with two real social network data sets (Wiki and Twitter), which show that FRFB outperforms the well‐known topology‐based followee recommendation algorithms. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the optimal trajectory control problem for a two‐link rigid‐flexible manipulator is considered. Since the two‐link rigid‐flexible system is a distributed system, an ordinary differential equation and partial differential equation (ODE‐PDE) dynamic model of the manipulator is established by Hamilton's principle. Based on the ODE‐PDE model, an optimal trajectory controller is proposed in this paper, which includes 2 stages. In the first stage, the optimal trajectory is created by using the differential evolution algorithm. Energy consumption and deflection of the flexible link are chosen as performance indexes. Cubic spline interpolation is applied to obtain the continuous trajectory. In the second stage, the aim is to regulate 2 joints to follow the optimal trajectory and simultaneously suppress vibration of the flexible link. To achieve it, boundary control laws are designed and the stability analysis is given. In simulations, the effectiveness of the optimal controller is verified by MATLAB. [ABSTRACT FROM AUTHOR]

In this paper, an effective, interactive grasping algorithm is proposed to provide physically realistic interactions in the virtual world. We have provided a solution to lacking dynamic control resulting in penetrations and frictionless contact due to kinematic virtual hand provided by hand-tracking devices, by including a proxy hand in the system. The introduced proxy hand not only provides correct visual feedback but also is used to simulate dynamics between the hand and virtual objects. Our method also provides semiautonomous assistive grasping, coupledwith physics-based grasping, whichmakes the systemhelp the user in achieving a grasp by identifying grasp pose and orienting the object in the hand to be grasped robustly. We have implemented and evaluated our technique with various benchmarks, conducted a user study to compare ourmethod against the state-of-the-art, pinch-based grasping mechanism, and showed that our technique provides physically realistic, stable, and time-efficient real-time interactions. [ABSTRACT FROM AUTHOR]

Workload demands in e‐commerce applications are very dynamic in nature, therefore it is essential for internet service providers to manage server resources effectively to maximize total revenue in server overloading situations. In this paper, a data mining technique is applied to a typical e‐commerce application model for identification of composite association rules that capture user navigation patterns. Two algorithms are then developed based on the derived rules for admission control, service differentiation, and priority scheduling. Our approach takes the following aspects into consideration: (a) only final purchase requests result in company revenue; (b) any other request can potentially lead to final purchase, depending upon the likelihood of the navigation sequence that starts from current request and leads to final purchase; (c) service differentiation and priority assignment are based on aggregated confidence and average support of the composite association rules. As identification of composite association rules and computation of confidence and support of the rules can be pre‐computed offline, the proposed approach incurs minimum performance overheads. The evaluation results suggest that the proposed approach is effective in terms of request management for revenue maximization. This article is categorized under: Application Areas > Science and Technology Algorithmic Development > Association Rules Algorithmic Development > Web Mining [ABSTRACT FROM AUTHOR]

Movement data from athletes are useful to quantify performance or more specifically the workload. Inertial measurement units (IMUs) are useful sensors to quantify body movements. Sensor placement on human body is still an open question that this paper focuses on. A method that develops synthesized inertial data is proposed for determining optimal sensors placement. Comparison between virtual and real inertial data is achieved. Training motion recognition algorithm on synthesized and real inertial data exhibits less than 7% difference. This method highlights the ability of the numerical model to determine relevant sensor placement of IMUs on human body for motion recognition algorithm using virtual sensors. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) is one important branch of artificial intelligence (AI), which intuitively imitates the learning style of human beings. It is commonly derived from solving game playing problems and is extensively used for decision‐making, control and optimization problems. It has been extensively applied for solving complicated problems with the property of Markov decision‐making processes. With data accumulation and comprehensive analysis, researchers are not only satisfied with predicting the results for experimental systems but also hope to design or control them for the sake of obtaining the desired properties or functions. RL is potentially facilitated to solve a large number of complicated biological and chemical problems, because they could be decomposed into multi‐step decision‐making process. In practice, substantial progress has been made in the application of RL to the field of biomedicine. In this paper, we will first give a brief description about RL, including its definition, basic theory and different type of methods. Then we will review some detailed applications in various domains, for example, molecular design, reaction planning, molecular simulation and etc. In the end, we will summarize the essentialities of RL approaches to solve more diverse problems compared with other machine learning methods and also outlook the possible trends to overcome their limitations in the future. This article is categorized under:Data Science > ChemoinformaticsData Science > Computer Algorithms and ProgrammingData Science > Artificial Intelligence/Machine Learning [ABSTRACT FROM AUTHOR]

This volume is largely about nontraditional data; this paper is about a nontraditional visualization: classification trees. Using trees with data will be new to many students, so rather than beginning with a computer algorithm that produces optimal trees, we suggest that students first construct their own trees, one node at a time, to explore how they work, and how well. This build‐it‐yourself process is more transparent than using algorithms such as CART; we believe it will help students not only understand the fundamentals of trees, but also better understand tree‐building algorithms when they do encounter them. And because classification is an important task in machine learning, a good foundation in trees can prepare students to better understand that emerging and important field. We also describe a free online tool—Arbor—that students can use to do this, and note some implications for instruction. [ABSTRACT FROM AUTHOR]

Through network programmability, software defined network can simplify network control and management. Since the current software defined network southbound interface level is low and programming situation is complex, it requires a high-level abstract programming language to simplify programming. First, this paper improves the NetCore programming language to generate NetCore-M language, so that it can support deployment of multipolicies combination including packet drop action. This paper describes in detail the syntax, semanteme, and implementation of NetCore-M language forwarding policy service. Secondly, this paper describes the network policy conflict systematically. Finally, this paper shows that the modified multipolicies combination algorithm can effectively detect policies conflicts based on the implementation of the Pyretic project. [ABSTRACT FROM AUTHOR]

Power cable is mainly responsible for the transmission and distribution of electric energy in the power grid. The performance of a power cable greatly affects the reliability and security of the power supply. In order to solve the problem of cable aging caused by long‐term operation and various internal and external factors, this paper proposes an abnormal line loss identification method for a low‐voltage distribution network station area, aiming at focusing on the monitoring of cable aging. The transmission characteristic model based on the transmission line theory and the dielectric constant model of the aging cable insulation layer is established, and the internal relationship between the transmission characteristic and the aging characteristic is analyzed. The characteristic extraction algorithm is used to extract the characteristics of the transmission characteristic curve. Through simulation analysis, for the abnormal line loss fault, the location accuracy of different fault locations is basically at the level of 0.1–0.3%. The simulation of local defects in the cable shows that the change of material dielectric constant is more clear for the characterization of cable aging. The monitoring method in this paper is helpful to provide the relevant basis for the line maintenance plan and the operation strategy of the power grid and is of great significance to ensure the safe and stable operation of the power grid. [ABSTRACT FROM AUTHOR]

Summary: In this paper proposes a Krill Herd Optimization algorithm with Deep Convolutional neural network fostered Breast Cancer Classification using Mammogram Images (BC‐APPDRC‐DCNN‐KHO). Here, the input images are taken from Real time and MAMMOSET datasets. These images are pre‐processed using Altered Phase Preserving Dynamic Range Compression (APPDRC) technique. This APPDRC is applied for preserving local features, compressing dynamic range of images, and enhancing the speckle noise filtering, these are all necessary for better boundary detection. Then, the Pre‐processed images are classified using Deep Convolutional neural network (DCNN). The DCNN weight parameters are optimized based on Krill Herd Optimization algorithm. The Proposed BCC‐DCNN‐KHO‐MI method classifies the input breast cancer imageries into 3 categories: benign, malignant, and normal. The proposed BCC‐DCNN‐KHO‐MI method in Real time dataset attains 18.505%, 19.45%, 16.19%, 17.56% and 16.19% higher accuracy; 15.38%, 12.06%, 12.71%, 26.62% and 18.902% higher Precision; 3.12%, 10.52%, 13.57%, 22.75% and 14.93% higher F‐score, 59.56%, 41.25%, 56.47%, 42.36% and 37.27% lower computation time; 23.87%, 21.87%, 32.87%, 42.76% and 21.05% higher AUC compared with the existing methods, like BCC‐Google Net‐MI, BCC‐Visual Geometry Group Network‐MI, BCC‐Residual Networks‐MI, BC‐RERNN‐LOA‐MI and BC‐CNN‐MI respectively. [ABSTRACT FROM AUTHOR]

Distributed base station deployment, limited server resources and dynamically changing end users in mobile edge networks make the design of computing offloading schemes extremely challenging. Considering the advantages of deep reinforcement learning (DRL) in dealing with dynamic complex problems, this paper designs an optimal computing offloading and resource allocation strategy. Firstly, the authors consider a multi‐user mobile edge network scenario consisting of Macro‐cell Base Station (MBS), Small‐cell Base Station (SBS) and multiple terminal devices, the communication overhead and calculation overhead generated are formulated and described in detail. Besides, combined with the deterministic delay of tasks, the optimization objective of this paper is clarified to comprehensively consider system energy consumption. Then, a learning algorithm based on Deep Deterministic Policy Gradient (DDPG) is proposed to minimize system energy consumption. Finally, simulation experiments show that the authors' proposed DDPG algorithm can effectively optimize the target value, and the total system energy consumption is only 15.6 J, which is better than other compared algorithms. It is also proved that the proposed algorithm has excellent communication resource allocation ability. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for detecting high‐speed flow channels in the polar cap. The algorithm was applied to Super Dual Auroral Radar Network data, specifically to data from the new Longyearbyen radar. This radar is located at 78.2°N, 16.0°E geographical coordinates looking north‐east, and is therefore at an ideal location to measure flow channels in the high‐latitude polar cap. The algorithm detected >500 events over 1 year of observations, and within this paper two case studies are considered in more detail. A flow channel on "old‐open field lines" located on the dawn flank was directly driven under quiet conditions over 13 min. This flow channel contributed to a significant fraction (60%) of the cross polar cap potential and was located on the edge of a polar cap arc. Another case study follows the development of a flow channel on newly opened field lines within the cusp. This flow channel is a spontaneously driven event forming under strong solar wind driving and is intermittently excited over the course of almost an hour. As they provide a high fraction of the cross polar cap potential, these small‐scale structures are vital for understanding the transport of magnetic flux over the polar cap. Key Points: Polar cap flow channels can account for a substantial amount (40–60%) of the cross polar cap potentialFlow channels can form due to dayside reconnection or appear on the edge of polar cap arcsMagnetic field lines that opened 25 min ago can still cause fast flow channels deep inside the polar cap [ABSTRACT FROM AUTHOR]

Summary: One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime‐ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center. [ABSTRACT FROM AUTHOR]

The paper deals with the synchronisation of subsystems that have individual linear dynamics. To formulate the basic condition under which the subsystems can be synchronised, the notion of system intersection is introduced. Subsystems can be synchronised by an appropriate controller only if their dynamics have a non-empty intersection, which acts as a virtual reference system. The paper describes an algorithm for determining the maximum intersection of linear systems and gives an explicit relation for the initial state in dependence upon the subsystem initial states, for which the virtual reference system generates the synchronous trajectory. [ABSTRACT FROM AUTHOR]

This article describes a method to extend the dynamic range of an LCD display by increasing the room for backlight boosting by hardware and algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Microscopy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Frequency hopping (FH) is a common characteristic of a wide variety of communication systems. On the other hand, software-defined radio (SDR) is an increasingly utilized technology for implementing modern communication systems. The main challenge when trying to realize an SDR FH system is the frequency tuning time, that is, the higher the hopping rate, the lower the required frequency tuning time. In this paper, significant universal hardware driver tuning options (within GNU Radio software) are investigated to discover the tuning option that gives the minimum frequency tuning time. This paper proposes an improved SDR frequency tuning algorithm for the generation of a target signal (with a given target frequency). The proposed algorithm aims to improve the frequency tuning time without any frequency deviation, thus allowing the realization of modern communication systems with higher FH rates. Moreover, it presents the design and implementation of an original GNU Radio Companion block that utilizes the proposed algorithm. The target SDR platform is that of the Universal Software Radio Peripheral USRP-N210 paired with the RFX2400 daughter board. Our results show that the proposed algorithm achieves higher hopping rates of up to 5,000 hops/second. [ABSTRACT FROM AUTHOR]

This paper presents a lightweight true random number generator (TRNG) using beta radiation that is useful for Internet of Things (IoT) security. In general, a random number generator (RNG) is required for all secure communication devices because random numbers are needed to generate encryption keys. Most RNGs are computer algorithms and use physical noise as their seed. However, it is difficult to obtain physical noise in small IoT devices. Since IoT security functions are required in almost all countries, IoT devices must be equipped with security algorithms that can pass the cryptographic module validation programs of each country. In this regard, it is very cumbersome to embed security algorithms, random number generation algorithms, and even physical noise sources in small IoT devices. Therefore, this paper introduces a lightweight TRNG comprising a thin‐film beta‐radiation source and integrated circuits (ICs). Although the ICs are currently being designed, the IC design was functionally verified at the board level. Our random numbers are output from a verification board and tested according to National Institute of Standards and Technology standards. [ABSTRACT FROM AUTHOR]

In this paper, we use 2 iterative learning control schemes (P‐type and PI‐type) with an initial learning rule to achieve the formation control of linear fractional‐order multiagent systems. To realize the finite‐time consensus, we assume repeatable operation environments as well as a fixed but directed communication topology for the fractional‐order multiagent systems. Both P‐type and PI‐type update laws are applied to generate the control commands for each agent. It is strictly proved that all agents are driven to achieve an asymptotical consensus as the iteration number increases. Two examples are simulated to verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Summary: Recently, microblog sites such as Twitter attract a great deal of attention as an information resource for topic detection task. Most of existing feature‐pivot topic detection algorithms in Twitter just take a single feature into account rather than multiple features. Thus, these methods always only detect the topics related to the single feature and miss some important topics, which causes a relatively low performance. In this paper, we build a flexible term representation framework for feature‐pivot topic detection based on four features. A Learning‐based Topic Detection using Multiple Features (LTDMF) method is proposed to improve the performance of topic detection. We define a correlation function based on a specific neural network to integrate various features. A Hierarchical Agglomerative Clustering (HAC) algorithm is applied to cluster terms as topics. Based on multiple features, LTDMF detects all types of topics and improves the accuracy of topic detection to solve the problem of missing topics. Experiments show that LTDMF gets a better performance compared with several baseline methods in terms of precision and recall. [ABSTRACT FROM AUTHOR]

Summary: Device‐to‐device (D2D) communication in a cellular spectrum increases the spectral and energy efficiency of local communication sessions, while also taking advantage of accessing licensed spectrum and higher transmit power levels than when using unlicensed bands. To realize the potential benefits of D2D communications, appropriate mode selection algorithms that select between the cellular and D2D communication modes must be designed. On the other hand, physical‐layer network coding (NWC) at a cellular base station—which can be used without D2D capability—can also improve the spectral efficiency of a cellular network that carries local traffic. In this paper, we ask whether cellular networks should support D2D communications, physical‐layer NWC, or both. To this end, we study the performance of mode selection algorithms that can be used in cellular networks that use physical‐layer NWC and support D2D communications. We find that the joint application of D2D communication and NWC scheme yields additional gains compared with a network that implements only one of these schemes, provided that the network implements proper mode selection and resource allocation algorithms. We propose 2 mode selection schemes that aim to achieve high signal‐to‐interference‐plus‐noise ratio and spectral efficiency, respectively, and take into account the NWC and D2D capabilities of the network. [ABSTRACT FROM AUTHOR]

Optimization problems appear in many different applications in science and engineering. A large number of different algorithms have been proposed for solving them; however, there is no unique general optimization method that performs efficiently across a diverse set of problems. Thus, a multimethod optimization, in which different algorithms cooperate to outperform the results obtained by any of them in isolation, is a very appealing alternative. Besides, as real-life optimization problems are becomingmore andmore challenging, the use ofHPCtechniques to implement these algorithms represents an effective strategy to speed up the time-to-solution. In addition, a parallel multimethod approach can benefit from the effortless access to q large number of distributed resources facilitated by cloud computing. In this paper, we propose a self-adaptive cooperative parallel multimethod for global optimization. This proposal aims to perform a thorough exploration of the solution space by means of multiple concurrent executions of a broad range of search strategies. For its evaluation, we consider an extremely challenging case-study from the field of computational systems biology. We also assess the performance of the proposal on a public cloud, demonstrating both the potential of the multimethod approach and the opportunity that the cloud provides for these problems. [ABSTRACT FROM AUTHOR]

Summary: Object tracking is widely referred as one of the most interesting applications of wireless sensor networks (WSNs). This application is able to detect and track objects and report information about these objects to a central base station. One of the major drawbacks in the current research in WSNs is the quality of the data reporting where the major research focus is dedicated to localization of objects; however, few of these works were concentrated on the data reporting. An efficient data reporting algorithm for object tracking in WSNs is proposed in this paper. The main objective of this paper is to enhance the WSN lifetime by achieving both minimum energy and balancing such consumption in sensor nodes during reporting operation. Furthermore, in our model, the enhancement of network reliability is considered. Finally, it reduces the effects of congestion by sufficiently utilizing the under loaded nodes to improve the network throughput. This paper formulates the object tracking problem in large‐scale WSN into 0/1 integer linear programming problem, and then proposes a reliable energy balance traffic aware approach to solve the optimization problem. From the obtained simulation results, the proposed solution has proved to be able to enhance the network performance in network lifetime, throughput, end‐to‐end delay, energy balance, and complexity for both homogeneous and heterogeneous networks. [ABSTRACT FROM AUTHOR]

Summary: Distributed compressive sensing (DCS) usually improves the signal recovery performance of multi‐signal ensembles by exploiting both intra‐ and inter‐signal correlation and sparsity structure. However, the existing DCS had proposed for a very limited ensemble of signals that has only single common information. This paper proposes a generalized DCS (GDCS) framework which can improve sparse signal detection performance given arbitrary types of common information, which are classified into full common information and partial common information after overcoming against existing limitation. Specifically, the theoretical bound on the required number of measurements under the GDCS is obtained. We also develop a practical algorithm to obtain benefits using the GDCS. At the end of this paper, it simply summarizes the potential security issues when it gets all sensing information in a sensor network. Finally, numerical results verify that the proposed algorithm reduces the required number of measurements for correlated sparse signal detection compared to the DCS algorithm. This research lays down the basis for efficient distributed signal detection so that it can improve the detection performance or it can detect the signal reliably when the number of signal observations is limited. [ABSTRACT FROM AUTHOR]

This paper presents a context-sensitive dynamic slicing technique for the concurrent and aspectized programs. To effectively represent the concurrent aspect-oriented programs, we propose an intermediate graph called the multithreaded aspect-oriented dependence graph (MAODG). The MAODG is a dynamic graph generated from the execution trace of a given program with respect to a particular set of values given as an input. Interference dependencies between the statements are shown by a distinguished edge called the interference dependence edge in the MAODG. Based on this intermediate representation, we propose a precise and accurate dynamic slicing algorithm for the concurrent aspect-oriented programs implemented using AspectJ. The proposed dynamic slicing algorithm is implemented in a slicing tool developed using the ASM framework. Several open source programs are studied and evaluated using the proposed technique along with some existing techniques. The experimentation shows that our proposed slicing algorithm generates slices of the same or smaller size, as compared with the existing algorithms. Furthermore, we found that the slice computation time is significantly less in our proposed algorithm, as compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

The analysis in real time of space data variability is essential for scientists and space mission controllers. Automated tools designed to extract key descriptors of variability are needed and solutions to adapt such algorithms for on‐board computers are rare. This paper describes the design of an automated system for detecting directional discontinuities of a physical quantity and its implementation in Field‐Programmable Gate Array (FPGA). The system is currently adapted for solar wind or terrestrial magnetosheath magnetic field directional discontinuities, that is, sharp changes of the magnetic field directionality. Our detection algorithm uses analysis windows of adjustable width and averaging procedures in order to reduce the effects of random fluctuations. A sliding‐window approach is designed for continuous monitoring and detection of magnetic directional discontinuities. A software implementation of the algorithm was tested using in‐situ magnetic field measurements, and emphasized improvements of performance when using analysis windows of adjustable width. The FPGA implementation of the detection algorithm is built on DILIGENT Nexys 4 DDR featuring a commercial Xilinx Artix‐7 device and is designed to be ported to space qualified infrastructure. The FPGA system was tested with synthetic and laboratory signals, and provides results in very good agreement with the software implementation. The FPGA system provides an efficient real‐time monitoring solution using minimal computational and energy resources, and reducing the main on‐board computer utilization. Key Points: We designed an algorithm to detect directional discontinuities from in‐situ space measurements, suitable for automated applicationA Field‐Programmable Gate Array (FPGA) implementation of the algorithm, designed to be adapted for on‐board operations, is providedTests with laboratory and space measurements of the FPGA and software implementations of the detection algorithm give excellent results [ABSTRACT FROM AUTHOR]

Though artificial intelligence (AI) recommendation is a hot topic in recent marketing research, previous research has shown a convergent tendency for aversion to AI recommendation. It is imperative to find ways to promote AI usage and reduce consumers' AI aversion. This study fills this void by exploring the effect of AI (vs. human) recommenders on consumers' preferences for search versus experience products in the context of e‐commerce. Two studies provide convergent evidence that consumers show less avoidance of algorithms when recommending search products compared to experience products. A behavioral experiment (Study 1, N = 112) validates that consumers are less likely to purchase experience products recommended by AI, while there are no significant differences between AI versus human recommenders when recommending search products. Using event‐related potential (ERP), a further consumer neuroscience study (Study 2, N = 26) shows that consumers have a higher level of cognitive conflict (i.e., a larger magnitude of N2) when AI (vs. human) recommends experience products, while the effect disappears for search products. This paper shows that for search products, marketers can obtain similar evaluations using AI recommenders, which is relatively cheaper and more time‐saving compared with human recommenders. Therefore, our work provides important implications for theory and practice on e‐commerce and marketing communication. [ABSTRACT FROM AUTHOR]

Infrared imaging technology is widely used in the fault diagnosis of power equipment. The power equipment images obtained by infrared thermal cameras generally have the characteristics of low resolution, low contrast, similar geometric features, and complex image backgrounds. The traditional infrared image detection methods have problems of low detection accuracy, low real‐time performance, and missed and fault detection, all of which will lead to great difficulties in the automatic identification process of infrared images of power equipment. This paper adopted an algorithm based on You Only Look Once (YOLO) v4 to realize the detection of electrical equipment under infrared images. First of all, the authors utilized the Fast Super‐Resolution Convolutional Neural Network (FSRCNN) algorithm to perform super‐resolution reconstruction on the infrared images of power equipment to achieve image enhancement. Moreover, the lightweight network Mobilenetv3 was selected as the backbone network of YOLOv4 to improve the running speed by reducing network parameters. Finally, the performance of the YOLOv4 algorithm was compared to verify the effectiveness of the algorithm proposed in this paper. The experimental results showed that the proposed method had strong generalization ability and reliable detection of infrared targets of various types of power equipment, with the mean average precision (mAP) reaching 91.3%. [ABSTRACT FROM AUTHOR]

An arc fault is the most common cause of charging pile fire. The series arc fault current is usually lower than the short‐circuit fault current and is challenging to detect, resulting in the inaction of the circuit breaker. Therefore, a method for detecting series arc faults of charging piles based on generalized Stockwell transform (GST) is proposed in this paper. First, an real time digital simulation system (RTDS)‐based charging pile arc experimental platform is constructed, and the point contact and the carbonization path arc fault current are measured. Then, the fault information is extracted using GST, and the 250‐Hz component is selected as the arc fault identification feature. On this basis, an improved sampling point selection method is proposed. Finally, multiple feature quantities are constructed as feature vectors, and the trained particle swarm optimization _support vector machine (PSO_SVM) is used for arc fault detection. The experimental results show that the accuracy rate reaches 98.75%; thus, the scheme proposed by this paper could detect the series arc fault current accurately and avoid the incorrect action of the circuit breaker. [ABSTRACT FROM AUTHOR]

Three dimensional street art illusions have become more popular in recent years. Many of them are drawn on pavement such as streets, sidewalks, and town squares. It is often known as 3D chalk art, where a 2D artwork is drawn on the street, giving the viewer a 3D optical illusion from a certain perspective. This paper supplies chalk stylized renderings for 3D models and synthesizes 3D objects into a realistic photograph. Users can input one photo for the 3D model. First, a camera position is achieved by using a camera calibration algorithm. Second, a chalk stylized rendering was applied to create an artistic image of the 3D models using the same camera position. Then, the non-photorealistic rendering image is composited into a source photograph using a modified Poisson approach. All of the enhanced pavement texture details are also blended into the object's image. The major contribution of this paper is providing a user to create any interesting and attractive 3D illusionary art without physically drawing pictures on the pavement. Finally, the proposed method is demonstrated using various experimental 3D street art illusion images. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]