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This research studied the strategies that players use in sequential adversarial games. We took the Rock-Paper-Scissors (RPS) game as an example and ran players in two experiments. The first experiment involved two humans, who played the RPS together for 100 times. Importantly, our payoff design in the RPS allowed us to differentiate between participants who used a random strategy from those who used a Nash strategy. We found that participants did not play in agreement with the Nash strategy, but rather, their behavior was closer to random. Moreover, the analyses of the participants' sequential actions indicated heterogeneous cycle-based behaviors: some participants' actions were independent of their past outcomes, some followed a well-known win-stay/lose-change strategy, and others exhibited the win-change/lose-stay behavior. To understand the sequential patterns of outcome-dependent actions, we designed probabilistic computer algorithms involving specific change actions (i.e., to downgrade or upgrade according to the immediate past outcome): the Win-Downgrade/Lose-Stay (WDLS) or Win-Stay/Lose-Upgrade (WSLU) strategies. Experiment 2 used these strategies against a human player. Our findings show that participants followed a win-stay strategy against the WDLS algorithm and a lose-change strategy against the WSLU algorithm, while they had difficulty in using an upgrade/downgrade direction, suggesting humans' limited ability to detect and counter the actions of the algorithm. Taken together, our two experiments showed a large diversity of sequential strategies, where the win-stay/lose-change strategy did not describe the majority of human players' dynamic behaviors in this adversarial situation. [ABSTRACT FROM AUTHOR]

Recommender systems (RS) have been developed to make personalized suggestions and enrich users' preferences in various online applications to address the information explosion problems. However, traditional recommender-based systems act as black boxes, not presenting the user with insights into the system logic or reasons for recommendations. Recently, generating explainable recommendations with deep knowledge graphs (DKG) has attracted significant attention. DKG is a subset of explainable artificial intelligence (XAI) that utilizes the strengths of deep learning (DL) algorithms to learn, provide high-quality predictions, and complement the weaknesses of knowledge graphs (KGs) in the explainability of recommendations. DKG-based models can provide more meaningful, insightful, and trustworthy justifications for recommended items and alleviate the information explosion problems. Although several studies have been carried out on RS, only a few papers have been published on DKG-based methodologies, and a review in this new research direction is still insufficiently explored. To fill this literature gap, this paper uses a systematic literature review framework to survey the recently published papers from 2018 to 2022 in the landscape of DKG and XAI. We analyze how the methods produced in these papers extract essential information from graphbased representations to improve recommendations' accuracy, explainability, and reliability. From the perspective of the leveraged knowledge-graph related information and how the knowledge-graph or path embeddings are learned and integrated with the DL methods, we carefully select and classify these published works into four main categories: the Two-stage explainable learning methods, the Joint-stage explainable learning methods, the Path-embedding explainable learning methods, and the Propagation explainable learning methods. We further summarize these works according to the characteristics of the approaches and the recommendation scenarios to facilitate the ease of checking the literature. We finally conclude by discussing some open challenges left for future research in this vibrant field. [ABSTRACT FROM AUTHOR]

This review explores the historical and current significance of gestures as a universal form of communication with a focus on hand gestures in virtual reality applications. It highlights the evolution of gesture detection systems from the 1990s, which used computer algorithms to find patterns in static images, to the present day where advances in sensor technology, artificial intelligence, and computing power have enabled real-time gesture recognition. The paper emphasizes the role of hand gestures in virtual reality (VR), a field that creates immersive digital experiences through the Ma blending of 3D modeling, sound effects, and sensing technology. This review presents state-of-the-art hardware and software techniques used in hand gesture detection, primarily for VR applications. It discusses the challenges in hand gesture detection, classifies gestures as static and dynamic, and grades their detection difficulty. This paper also reviews the haptic devices used in VR and their advantages and challenges. It provides an overview of the process used in hand gesture acquisition, from inputs and pre-processing to pose detection, for both static and dynamic gestures. [ABSTRACT FROM AUTHOR]

This paper consists of a bibliometric study that covers the topic of 3D object detection from 2022 until the present day. It employs various analysis approaches that shed light on the leading authors, affiliations, and countries within this research domain alongside the main themes of interest related to it. The findings revealed that China is the leading country in this domain given the fact that it is responsible for most of the scientific literature as well as being a host for the most productive universities and authors in terms of the number of publications. China is also responsible for initiating a significant number of collaborations with various nations around the world. The most basic theme related to this field is deep learning, along with autonomous driving, point cloud, robotics, and LiDAR. The work also includes an in-depth review that underlines some of the latest frameworks that took on various challenges regarding this topic, the improvement of object detection from point clouds, and training end-to-end fusion methods using both camera and LiDAR sensors, to name a few. [ABSTRACT FROM AUTHOR]

Pure electric vehicle (PEV) equipped with a dual-motor coupling drive system can make full use of the high efficiency working range of the motor in order to improve vehicle efficiency. In order to further expand the application range of the system and to improve its practical application, this paper designs and proposes a new dynamic coupling drive system of three axis-double working modes, which is based on the Simpson planetary gear train. The new system adopts two planetary gears (P1 and P2), and the two sun gears of double rows, planetary carrier of P1 and gear ring of P2 are bunded. The power output of the P1 gear ring (mode 1) and P2 planetary carrier (mode 2) is realized by a controlling wet clutch. This paper adopts the linear interpolation method, least square method and 5-fold CV cross validation method to establish the full load speed characteristics and efficiency characteristics models of two motors (13 and 30 kW). This paper proposes an optimization design method based on an improved simulated annealing (I-SA) algorithm for new system parameter matching and working mode switching strategy determination. The results show that the modeling accuracy of the two motors is high, and the mean value of MAPE is 4.337%. The proposed optimization design method achieves the demand goal of PEV effectively. The I-SA algorithm has good effectiveness and fast convergence, the mean efficiency of the optimized PEV is 83.91% under all working conditions, the maximum speed is 142.56 km/h and the power utilization rate of the dual-motor is 100%. This study proposes a new hardware system and a design optimization method on software and provides a direct reference for the research of PEV drive systems by combining hardware with software. [ABSTRACT FROM AUTHOR]

After the revival of deep learning in computer vision in 2012, SAR ship detection comes into the deep learning era too. The deep learning-based computer vision algorithms can work in an end-to-end pipeline, without the need of designing features manually, and they have amazing performance. As a result, it is also used to detect ships in SAR images. The beginning of this direction is the paper we published in 2017BIGSARDATA, in which the first dataset SSDD was used and shared with peers. Since then, lots of researchers focus their attention on this field. In this paper, we analyze the past, present, and future of the deep learning-based ship detection algorithms in SAR images. In the past section, we analyze the difference between traditional CFAR (constant false alarm rate) based and deep learning-based detectors through theory and experiment. The traditional method is unsupervised while the deep learning is strongly supervised, and their performance varies several times. In the present part, we analyze the 177 published papers about SAR ship detection. We highlight the dataset, algorithm, performance, deep learning framework, country, timeline, etc. After that, we introduce the use of single-stage, two-stage, anchor-free, train from scratch, oriented bounding box, multi-scale, and real-time detectors in detail in the 177 papers. The advantages and disadvantages of speed and accuracy are also analyzed. In the future part, we list the problem and direction of this field. We can find that, in the past five years, the AP50 has boosted from 78.8% in 2017 to 97.8 % in 2022 on SSDD. Additionally, we think that researchers should design algorithms according to the specific characteristics of SAR images. What we should do next is to bridge the gap between SAR ship detection and computer vision by merging the small datasets into a large one and formulating corresponding standards and benchmarks. We expect that this survey of 177 papers can make people better understand these algorithms and stimulate more research in this field. [ABSTRACT FROM AUTHOR]

Food and fluid intake monitoring are essential for reducing the risk of dehydration, malnutrition, and obesity. The existing research has been preponderantly focused on dietary monitoring, while fluid intake monitoring, on the other hand, is often neglected. Food and fluid intake monitoring can be based on wearable sensors, environmental sensors, smart containers, and the collaborative use of multiple sensors. Vision-based intake monitoring methods have been widely exploited with the development of visual devices and computer vision algorithms. Vision-based methods provide non-intrusive solutions for monitoring. They have shown promising performance in food/beverage recognition and segmentation, human intake action detection and classification, and food volume/fluid amount estimation. However, occlusion, privacy, computational efficiency, and practicality pose significant challenges. This paper reviews the existing work (253 articles) on vision-based intake (food and fluid) monitoring methods to assess the size and scope of the available literature and identify the current challenges and research gaps. This paper uses tables and graphs to depict the patterns of device selection, viewing angle, tasks, algorithms, experimental settings, and performance of the existing monitoring systems. [ABSTRACT FROM AUTHOR]

Ensuring confidentiality and privacy is critical when it comes to sharing images over unsecured networks such as the internet. Since widely used and secure encryption methods, such as AES, Twofish, and RSA, are not suitable for real-time image encryption due to their slow encryption speeds and high computational requirements, researchers have proposed specialized algorithms for image encryption. This paper provides an introduction and overview of the image encryption algorithms and metrics used, aiming to evaluate them and help researchers and practitioners starting in this field obtain adequate information to understand the current state of image encryption algorithms. This paper classifies image encryption into seven different approaches based on the techniques used and analyzes the strengths and weaknesses of each approach. Furthermore, this paper provides a detailed review of a comprehensive set of security, quality, and efficiency evaluation metrics for image encryption algorithms, and provides upper and lower bounds for these evaluation metrics. Finally, this paper discusses the pros and cons of different image encryption approaches as well as the suitability of different image encryption approaches for different applications. [ABSTRACT FROM AUTHOR]

The primary objective of a depalletizing system is to automate the process of detecting and locating specific variable-shaped objects on a pallet, allowing a robotic system to accurately unstack them. Although many solutions exist for the problem in industrial and manufacturing settings, the application to small-scale scenarios such as retail vending machines and small warehouses has not received much attention so far. This paper presents a comparative analysis of four different computer vision algorithms for the depalletizing task, implemented on a Raspberry Pi 4, a very popular single-board computer with low computer power suitable for the IoT and edge computing. The algorithms evaluated include the following: pattern matching, scale-invariant feature transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifier. Each technique is described and their implementations are outlined. Their evaluation is performed on the task of box detection and localization in the test images to assess their suitability in a depalletizing system. The performance of the algorithms is given in terms of accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The results reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of a depalletizing system. [ABSTRACT FROM AUTHOR]

Industrial photogrammetry is a reliable method to achieve submillimeter accuracy when mapping 2D or 3D objects. In the field of fatigue testing of steel welded details, it can be used to find a new method of crack initiation assessment. Fatigue testing is an important method for determining and predicting the durability of structural details in service. The research presented in this paper is based on a computer vision algorithm developed using the open-source code OpenCV library and the Oriented FAST and Rotated BRIEF (ORB) method to provide a solution for the assessment of crack initiation. Within this research, a method for determining the crack initiation period using polynomial functions of a certain degree is developed. The developed algorithm fully automatically determines the test specimen displacement for all imagery and assesses the crack initialization period by polynomial interpolation with a percentage threshold. The algorithm shows us the best results based on a 26th-degree polynomial with a deviation from the critical value of 5%. The validation of the algorithm was carried out using completely independently recorded data from the hydraulic press used for fatigue tests. The results of all test specimens show that the percentage accuracy of determination crack initiation period is between −0.04% for test specimens S355-TA-AW-02 and S355-TA-HFMI-03 and −0.82% for test specimen S355-TA-HFMI-03, with the mean of all results being 0.39%. [ABSTRACT FROM AUTHOR]

Fire detection and monitoring systems based on machine vision have been gradually developed in recent years. Traditional centralized deep learning model training methods transfer large amounts of video image data to the cloud, making image data privacy and confidentiality difficult. In order to protect the data privacy in the fire detection system with heterogeneous data and to enhance its efficiency, this paper proposes an improved federated learning algorithm incorporating computer vision: FedVIS, which uses a federated dropout and gradient selection algorithm to reduce communication overhead, and uses a transformer to replace a traditional neural network to improve the robustness of federated learning in the context of heterogeneous data. FedVIS can reduce the communication overhead in addition to reducing the catastrophic forgetting of previous devices, improving convergence, and producing superior global models. In this paper's experimental results, FedVIS outperforms the common federated learning methods FedSGD, FedAVG, FedAWS, and CMFL, and improves the detection effect by reducing communication costs. As the amount of clients increases, the accuracy of other algorithmic models decreases by 2–5%, and the number of communication rounds required increases significantly; meanwhile, our method maintains a superior detection performance while requiring roughly the same number of communication rounds. [ABSTRACT FROM AUTHOR]

Today, the use of digital medicine is becoming more and more common in medicine. With the use of digital medicine, health data can be shared, processed, and visualized using computer algorithms. One of the problems currently facing digital medicine is the rapid transmission of large amounts of data and their appropriate visualization, even in 3D. Advances in technology offer the possibility to use new image processing, networking, and visualization solutions for the evaluation of medical samples. Because of the resolution of the samples, it is not uncommon that it takes a long time for them to be analyzed, processed, and shared. This is no different for 3D visualization. In order to be able to display digitalized medical samples in 3D at high resolution, a computer with computing power that is not necessarily available to doctors and researchers is needed. COVID-19 has shown that everyday work must continue even when there is a physical distance between the participants. Real-time network streaming can provide a solution to this, by creating a 3D environment that can be shared between doctors/researchers in which the sample being examined can be visualized. In order for this 3D environment to be available to everyone, it must also be usable on devices that do not have high computing capacity. Our goal was to design a general-purpose solution that would allow users to visualize large amounts of medical imaging data in 3D, regardless of the computational capacity of the device they are using. With the solution presented in this paper, our goal was to create a 3D environment for physicians and researchers to collaboratively evaluate 3D medical samples in an interdisciplinary way. [ABSTRACT FROM AUTHOR]

Applications using terrestrial laser scanners (TLS) have been skyrocketing in the past two decades. In a scanning project, the configuration of scans is a critical issue as it has significant effects on the project cost and the quality of the product. In this paper, a practical strategy is proposed to resolve the problem of the optimal placement of the terrestrial laser scanner. The method attempts to reduce the number of viewpoints under the premise that the scenes are fully covered. In addition, the approach is designed in a way that the solutions can be efficiently explored. The method has been tested on 540 polygons simulated with different sizes and complexities. The results have also been compared with a benchmark strategy in terms of the optimality of the solutions and runtime. It is concluded that our proposed algorithm ties or reduces the number of viewpoints in the benchmark paper in 85.6% of the 540 tests. For complex environments, the method can potentially reduce the project cost by 10%. Although with relatively lower efficiency, our method can still reach the solution within a few minutes for a polygon with up to 500 vertices. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid algorithm based on the ant colony and Physarum Polycephalum algorithms. The positive feedback mechanism is used to find the globally optimal path. The crossover and mutation operations of the genetic algorithm are introduced into the path search mechanism for the first time. The Van der Waals force is applied to the pheromone updating mechanism. Simulation results show that the improved algorithm has advantages in quality and speed of solution compared with other mainstream algorithms. This paper provides fast and accurate route methods for solving the Traveling Salesman Problem first and a delivery scheme is also presented for UAVs to realize "contactless delivery" to users in the Changchun Mingzhu District during the COVID-19 epidemic, which confirms the practicability and robustness of the algorithm. [ABSTRACT FROM AUTHOR]

Archived seismograms recorded in the 20th century present a valuable source of information for monitoring earthquake activity. However, old data, which are only available as scanned paper-based images should be digitised and converted from raster to vector format prior to reuse for geophysical modelling. Seismograms have special characteristics and specific featuresrecorded by a seismometer and encrypted in the images: signal trace lines, minute time gaps, timing and wave amplitudes. This information should be recognised and interpreted automatically when processing archives of seismograms containing large collections of data. The objective was to automatically digitise historical seismograms obtained from the archives of the Royal Observatory of Belgium (ROB). The images were originallyrecorded by the Galitzine seismometer in 1954 in Uccle seismic station, Belgium. A dataset included 145 TIFF images which required automatic approach of data processing. Software for digitising seismograms are limited and many have disadvantages. We applied the DigitSeis for machine-based vectorisation and reported here a full workflowof data processing. This included pattern recognition, classification, digitising, corrections and converting TIFFs to the digital vector format. The generated contours of signals were presented as time series and converted into digital format (mat files) which indicated information on ground motion signals contained in analog seismograms. We performed the quality control of the digitised traces in Python to evaluate the discriminating functionality of seismic signals by DigitSeis. We shown a robust approach of DigitSeis as a powerful toolset for processing analog seismic signals. The graphical visualisation of signal traces and analysis of the performed vectorisation results shown that the algorithms of data processing performed accurately and can be recommended in similar applications of seismic signal processing in future related works in geophysical research. [ABSTRACT FROM AUTHOR]

Technological advancements have expanded the range of methods for capturing human body motion, including solutions involving inertial sensors (IMUs) and optical alternatives. However, the rising complexity and costs associated with commercial solutions have prompted the exploration of more cost-effective alternatives. This paper presents a markerless optical motion capture system using a RealSense depth camera and intelligent computer vision algorithms. It facilitates precise posture assessment, the real-time calculation of joint angles, and acquisition of subject-specific anthropometric data for gait analysis. The proposed system stands out for its simplicity and affordability in comparison to complex commercial solutions. The gathered data are stored in comma-separated value (CSV) files, simplifying subsequent analysis and data mining. Preliminary tests, conducted in controlled laboratory environments and employing a commercial MEMS-IMU system as a reference, revealed a maximum relative error of 7.6% in anthropometric measurements, with a maximum absolute error of 4.67 cm at average height. Stride length measurements showed a maximum relative error of 11.2%. Static joint angle tests had a maximum average error of 10.2%, while dynamic joint angle tests showed a maximum average error of 9.06%. The proposed optical system offers sufficient accuracy for potential application in areas such as rehabilitation, sports analysis, and entertainment. [ABSTRACT FROM AUTHOR]

High-pressure pipelines are critical for transporting hazardous materials over long distances, but they face threats from third-party interference activities. Preventive measures are implemented, but interference accidents can still occur, making the need for high-quality detection strategies vital. This paper proposes an end-to-end Artificial Intelligence of Things (AIoT) solution to detect potential interference threats in real time. The solution involves developing a smart visual sensor capable of processing images using state-of-the-art computer vision algorithms and transmitting alerts to pipeline operators in real time. The system's core is based on the object-detection model (e.g., You Only Look Once version 4 (YOLOv4) and DETR with Improved deNoising anchOr boxes (DINO)), trained on a custom Pipeline Visual Threat Assessment (Pipe-VisTA) dataset. Among the trained models, DINO was able to achieve the best Mean Average Precision (mAP) of 71.2% for the unseen test dataset. However, for the deployment on a limited computational-ability edge computer (i.e., the NVIDIA Jetson Nano), the simpler and TensorRT-optimized YOLOv4 model was used, which achieved a mAP of 61.8% for the test dataset. The developed AIoT device captures the image using a camera, processes on the edge using the trained YOLOv4 model to detect the potential threat, transmits the threat alert to a Fleet Portal via LoRaWAN, and hosts the alert on a dashboard via a satellite network. The device has been fully tested in the field to ensure its functionality prior to deployment for the SEA Gas use-case. The AIoT smart solution has been deployed across the 10km stretch of the SEA Gas pipeline across the Murray Bridge section. In total, 48 AIoT devices and three Fleet Portals are installed to ensure the line-of-sight communication between the devices and portals. [ABSTRACT FROM AUTHOR]

New versions of HTTP protocols have been developed to overcome many of the limitations of the original HTTP/1.1 protocol and its underlying transport mechanism over TCP. In this paper, we investigated the performance of modern Internet protocols such as HTTP/2 over TCP and HTTP/3 over QUIC in high-latency satellite links. The goal was to uncover the interaction of the new features of HTTP such as parallel streams and optimized security handshake with modern congestion control algorithms such as CUBIC and BBR over high-latency links. An experimental satellite network emulation testbed was developed for the evaluation. The study analyzed several user-level web performance metrics such as average page load time, First Contentful Paint and Largest Contentful Paint. The results indicate an overhead problem with HTTP/3 that becomes more significant when using a loss-based congestion control algorithm such as CUBIC which is widely used on the Internet. Also, the results highlight the significance of the web page structure and how objects are distributed in it. Among the various Internet protocols evaluated, the results show that HTTP/3 over QUIC will perform better by an average of 35% than HTTP/2 over TCP in satellites links specifically with a more aggressive congestion algorithm such as BBR. This can be attributed to the non-blocking stream multiplexing feature of QUIC and the reduced TLS handshake of HTTP/3. [ABSTRACT FROM AUTHOR]

Derivative-free iterative methods are useful to approximate the numerical solutions when the given function lacks explicit derivative information or when the derivatives are too expensive to compute. Exploring the convergence properties of such methods is crucial in their development. The convergence behavior of such approaches and determining their practical applicability require conducting local as well as semi-local convergence analysis. In this study, we explore the convergence properties of a sixth-order derivative-free method. Previous local convergence studies assumed the existence of derivatives of high order even when the method itself was not utilizing any derivatives. These assumptions imposed limitations on its applicability. In this paper, we extend the local analysis by providing estimates for the error bounds of the method. Consequently, its applicability expands across a broader range of problems. Moreover, the more important and challenging semi-local convergence not investigated in earlier studies is also developed. Additionally, we survey recent advancements in this field. The outcomes presented in this paper can be proved valuable to practitioners and researchers engaged in the development and analysis of derivative-free numerical algorithms. Numerical tests illuminate and validate further the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the Recurrent Singular Spectrum Decomposition (R-SSD) algorithm is proposed as an improvement over the Recurrent Singular Spectrum Analysis (R-SSA) algorithm for forecasting non-linear and non-stationary narrowband time series. R-SSD modifies the embedding step of the basic SSA method to reduce energy residuals. This paper conducts simulations and real-case studies to investigate the properties of the R-SSD method and compare its performance with R-SSA. The results show that R-SSD yields more accurate forecasts in terms of ratio root mean squared errors (RRMSEs) and ratio mean absolute errors (RMAEs) criteria. Additionally, the Kolmogorov–Smirnov Predictive Accuracy (KSPA) test indicates significant accuracy gains with R-SSD over R-SSA, as it measures the maximum distance between the empirical cumulative distribution functions of recurrent prediction errors and determines whether a lower error leads to stochastically less error. Finally, the non-parametric Wilcoxon test confirms that R-SSD outperforms R-SSA in filtering and forecasting new data points. [ABSTRACT FROM AUTHOR]

This paper presents a new method of reducing the inrush current and improving the starting performance of a line-start permanent-magnet synchronous motor (LSPMSM). The novelty of the proposed method relies on the selection of the time instant of the connection of the stator winding to the grid, for which the smallest values of the amplitudes of inrush currents are obtained. To confirm the effectiveness of the developed method of limiting the inrush current, simulations and experimental studies were carried out. The algorithm and dedicated computer code developed by the authors for the analysis of transient coupled phenomena in the LSPMSM were used to study the impact of the time instant of connection of the winding to the grid on the motor start-up process. The algorithm was based on a field model of coupled electromagnetic and thermal phenomena in the studied motor. To verify the developed model of the phenomena and the proposed method, experimental research was carried out on a purpose-built computerised test stand. Good concordance between the results of the experiments and simulations confirmed the high reliability of the proposed model, as well as the effectiveness of the developed approach in limiting the inrush current and improving the starting performance of LSPMSMs. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant increase in the use of structural health monitoring (SHM) technologies as systems for monitoring the integrity of aircraft's structures. The use of compact and embeddable sensor networks, like the ones based on fibre optics (FO), is particularly attractive from the perspective of releasing an integrated structural system with intrinsic sensing capacity. Usually, an SHM system architecture is completed by a dedicated algorithm that processes the data gathered from the sensors to elaborate on the level of damage currently suffered by the structure, with the further possibility of providing information to the relevant specialists involved with its supervision. One of the main SHM applications that is attracting major interest is related to the inspection and detection of anomalies in bonded joints, which is extremely relevant in many composite realizations. Aeronautical regulations allow the use of bonded joints on an aircraft's primary structure but require the implementation of a means to ensure their absolute safety, such as the introduction of further mechanical links aimed at stopping the propagation of a possible flaw or the availability of Non-Destructive Inspection (NDI) systems to prove the absence of relevant damaged areas. Generally, the main typical defects occurring during the manufacturing of bonded joints include adhesive curing, kissing bonds, poor porosity, and poor surface preparation. The current NDI systems more widely used and available to detect defects are still inaccurate due to the lack of standard procedures for the creation of representative defects in a controlled manner, which would allow for the development of reliable methodologies and tools able to ensure the safety of a bonded joint, as required by safety regulations. This paper shows the results relative to the implementation of an SHM system developed by the Italian Aerospace Research Centre (CIRA) aimed at monitoring the bonding lines between spar caps and panels of a typical composite wingbox section and detecting faults in location and length. The work was performed during typical ground static tests by using a fibre optical sensing network embedded within relevant adhesive paste layers during the manufacturing process of the structure. In the reported investigation, the SHM system assumed the function of an NDI system tool. The results show that the developed SHM system has good reliability for the detection of both the position and size of damage areas that were artificially inserted within the test article during the bonding phase, showing its potential as a candidate to be used as a tool to verify the conditions of a bonded joint, as required by aviation authorities' regulations. [ABSTRACT FROM AUTHOR]

The Internet of Things technology provides convenience for data acquisition in environmental monitoring and environmental protection and can also avoid invasive damage caused by traditional data acquisition methods. An adaptive cooperative optimization seagull algorithm for optimal coverage of heterogeneous sensor networks is proposed in order to address the issue of coverage blind zone and coverage redundancy in the initial random deployment of heterogeneous sensor network nodes in the sensing layer of the Internet of Things. Calculate the individual fitness value according to the total number of nodes, coverage radius, and area edge length, select the initial population, and aim at the maximum coverage rate to determine the position of the current optimal solution. After continuous updating, when the number of iterations is maximum, the global output is output. The optimal solution is the node's mobile position. A scaling factor is introduced to dynamically adjust the relative displacement between the current seagull individual and the optimal individual, which improves the exploration and development ability of the algorithm. Finally, the optimal seagull individual position is fine-tuned by random opposite learning, leading the whole seagull to move to the correct position in the given search space, improving the ability to jump out of the local optimum, and further increasing the optimization accuracy. The experimental simulation results demonstrate that, compared with the coverage and network energy consumption of the PSO algorithm, the GWO algorithm, and the basic SOA algorithm, the coverage of the PSO-SOA algorithm proposed in this paper is 6.1%, 4.8%, and 1.2% higher than them, respectively, and the energy consumption of the network is reduced by 86.8%, 68.4%, and 52.6%, respectively. The optimal deployment method based on the adaptive cooperative optimization seagull algorithm can improve the network coverage and reduce the network cost, and effectively avoid the coverage blind zone and coverage redundancy in the network. [ABSTRACT FROM AUTHOR]

With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper, which provides a new idea for solving high-dimensional optimization problems. First, in order to balance the algorithm's search abilities in terms of breadth and depth, the value of parameter G is given by an adaptive dynamic adjustment method. Second, in this paper, a foraging-behavior-improvement strategy is utilized to improve the algorithm's solution accuracy and depth-optimization ability. Third, the artificial fish swarm algorithm (AFSA) is introduced to construct a dual-population collaborative optimization strategy based on chicken swarms and artificial fish swarms, so as to improve the algorithm's ability to jump out of local extrema. The simulation experiments on the 17 benchmark functions preliminarily show that the ADPCCSO algorithm is superior to some swarm-intelligence algorithms such as the artificial fish swarm algorithm (AFSA), the artificial bee colony (ABC) algorithm, and the particle swarm optimization (PSO) algorithm in terms of solution accuracy and convergence performance. In addition, the APDCCSO algorithm is also utilized in the parameter estimation problem of the Richards model to further verify its performance. [ABSTRACT FROM AUTHOR]

The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and active control of the aeroengine. In this paper, an aerodynamic system modeling method combination with the wavelet transform and gray wolf algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion technology based on the feature correlation of chaotic data. Because of the chaotic characteristic represented by the sequence, the correlation-correlation (C-C) algorithm is adopted to reconstruct the phase space of the spatial modal. On the premise of finding out the local law of the dynamic system variety, the machine learning method is applied to model the reconstructed low-frequency components and high-frequency components, respectively. As the key part, the parameters of the SVR model are optimized by the gray wolf optimization algorithm (GWO) from the biological view inspired by the predatory behavior of gray wolves. In the definition of the hunting behaviors of gray wolves by mathematical equations, it is superior to algorithms such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy of the model, the multi-resolution and equivalent frequency distribution of the wavelet transform (WT) are used to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better prediction accuracy and reliability with the wavelet reconstruction coefficients as the inputs. In order to effectively identify the sign of the instability in the modeling system, a wavelet singular information entropy algorithm is proposed to detect the stall inception. By using the three sigma criteria as the identification strategy, the instability early warning can be given about 102r in advance, which is helpful for the active control. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a network of smart objects connected via the Internet, where each object is identified by a unique, accessible, and programmable address, while Blockchain is a technology for storing and transmitting information based on a decentralized system such that the centralized validation of transactions is replaced by a consensus mechanism. The integration of these two technologies requires consensus protocols to overcome the major challenges encountered in IoT systems, including issues related to security, storage capacity limits, computing power, etc. Therefore, different consensus algorithms have been implemented. For this purpose, this paper presents a research study on the criteria that would render a Blockchain consensus algorithm suitable for IoT networks, namely, computing power, latency, storage capacity, and security. [ABSTRACT FROM AUTHOR]

Studies have proven that humans perceive haptic textures through different perceptual dimensions, such as rough/smooth and soft/hard, which provide useful guidance in the design of haptic devices. However, few of these have focused on the perception of compliance, which is another important perceptual property in haptic interfaces. This research was conducted to investigate the potential basic perceptual dimensions of the rendered compliance and quantify the effects of the simulation parameters. Two perceptual experiments were designed based on 27 stimuli samples generated by a 3-DOF haptic feedback device. Subjects were asked to describe these stimuli using adjectives, classify the samples, and rate them according to corresponding adjective labels. Multi-dimensional scaling (MDS) methods were then used to project adjective ratings into 2D and 3D perception spaces. According to the results, hardness and viscosity are considered two basic perceptual dimensions of the rendered compliance, while crispness can be regarded as a subsidiary perceptual dimension. Then, the relations between simulation parameters and perceptual feelings were analyzed by the regression analysis. This paper may provide a better understanding of the compliance perception mechanism and useful guidance for the improvement of rendering algorithms and devices for haptic human–computer interaction. [ABSTRACT FROM AUTHOR]

This paper presents a solution to the problem of providing an autonomous vehicle with a safe control task when moving around many other autonomous vehicles. This is achieved by developing an appropriate computer control algorithm that takes into account the possible risk of a collision resulting from both the impact of environmental disturbances and the imperfection of the rules of maneuvering in situations where many vehicles pass each other, giving the control process a decisive character. For this purpose, three types of algorithms were synthesized: kinematic and dynamic optimization with neural domains, as well as sequential game control of an autonomous vehicle. The control algorithms determine a safe trajectory, which is implemented by the actuators of the autonomous vehicle. Computer simulations of the control algorithms in the Matlab/Simulink software allow for their comparative analysis in terms of meeting the criteria for the optimality and safety of an autonomous vehicle when passing a larger number of other autonomous vehicles. For this purpose, scenarios of multidirectional and one-way traffic of autonomous vehicles were used. [ABSTRACT FROM AUTHOR]

The viscoelastic relaxation spectrum provides deep insights into the complex behavior of polymers. The spectrum is not directly measurable and must be recovered from oscillatory shear or relaxation stress data. The paper deals with the problem of recovery of the relaxation spectrum of linear viscoelastic materials from discrete-time noise-corrupted measurements of relaxation modulus obtained in the stress relaxation test. A class of robust algorithms of approximation of the continuous spectrum of relaxation frequencies by finite series of orthonormal functions is proposed. A quadratic identification index, which refers to the measured relaxation modulus, is adopted. Since the problem of relaxation spectrum identification is an ill-posed inverse problem, Tikhonov regularization combined with generalized cross-validation is used to guarantee the stability of the scheme. It is proved that the accuracy of the spectrum approximation depends both on measurement noises and the regularization parameter and on the proper selection of the basis functions. The series expansions using the Laguerre, Legendre, Hermite and Chebyshev functions were studied in this paper as examples. The numerical realization of the scheme by the singular value decomposition technique is discussed and the resulting computer algorithm is outlined. Numerical calculations on model data and relaxation spectrum of polydisperse polymer are presented. Analytical analysis and numerical studies proved that by choosing an appropriate model through selection of orthonormal basis functions from the proposed class of models and using a developed algorithm of least-square regularized identification, it is possible to determine the relaxation spectrum model for a wide class of viscoelastic materials. The model is smoothed and robust on measurement noises; small model approximation errors are obtained. The identification scheme can be easily implemented in available computing environments. [ABSTRACT FROM AUTHOR]

Improving contour accuracy is one of the significant goals of industrial machining. This paper proposes a contour error estimation and compensation algorithm for five-axis computer numerical control (CNC) machine tools based on modified numerical control (NC) codes. The expected path analyzed by NC data and the actual trajectory collected by sensors are spatially mapped by the hidden Markov model (HMM). Next, an evaluation function that hybrids the tool tip position and tool orientation change trend is proposed as the index of contour error estimation. Finally, spatial iterative learning control (ILC) is used to compensate the contour error, and high-precision machining instructions are obtained after multiple iterations. Experiments with different trajectories are performed on a five-axis platform to verify the proposed algorithm's effectiveness. The results show that the proposed algorithm without using planned trajectories, has the same good control effect as traditional methods, which must know the planning trajectory for simple trajectories. At the same time, the method proposed in this paper has better performance than existing algorithms based on tool tip position nearest principle at sharp corners. In conclusion, on the basis of not depending on the planning trajectories, this method has a better compensation effect for the overall accuracy of trajectories and is easier to implement in industrial applications. [ABSTRACT FROM AUTHOR]

The identification of load and mutual inductance parameters of a wireless power transfer system can make the mathematical model of the system more accurate, which can effectively avoid system errors due to parameter uncertainties in the implementation of control, and provide theoretical support for system interoperability and high efficiency. This paper uses the two-port theorem and fundamental wave analysis to establish the identification model and obtain the relationship between inverter output current and load and between mutual inductance and load based on the equivalent circuit of a LCC-S magnetically coupled wireless power transfer system. To make the identification results more accurate, a particle swarm optimization algorithm with weights is introduced to transform the parameter identification problem of the system into an optimization problem, which can obtain the identification method of the system load and mutual inductance parameters. Both simulation and experimental results verify the feasibility and effectiveness of the method. [ABSTRACT FROM AUTHOR]

At present, the DC busbar design is one of the bottlenecks restricting the improvement of the power density of motor drives. Therefore, this paper proposes a three-dimensional line probe algorithm, which can realize the automatic routing of laminated busbar in motor drives. The specific rules and implementation of this method are introduced in detail in this paper. Finally, an example of busbar design of a vehicle motor drive is given to verify the routing rate and execution speed of the algorithm. [ABSTRACT FROM AUTHOR]

The emergence of Industry 4.0 and 5.0 technologies has enabled the digital transformation of various processes and the integration of sensors with the internet. Despite these strides, many industrial sectors still rely on visual inspection of physical processes, especially those employing analog gauges. This method of monitoring introduces the risk of human errors and inefficiencies. Automating these processes has the potential, not only to boost productivity for companies, but also potentially reduce risks for workers. Therefore, this paper proposes an end-to-end solution to digitize analog gauges and monitor them using computer vision through integrating them into an IoT architecture, to tackle these problems. Our prototype device has been designed to capture images of gauges and transmit them to a remote server, where computer vision algorithms analyze the images and obtain gauge readings. These algorithms achieved adequate robustness and accuracy for industrial environments, with an average relative error of 0.95%. In addition, the gauge data were seamlessly integrated into an IoT platform leveraging computer vision and cloud computing technologies. This integration empowers users to create custom dashboards for real-time gauge monitoring, while also enabling them to set thresholds, alarms, and warnings, as needed. The proposed solution was tested and validated in a real-world industrial scenario, demonstrating the solution's potential to be implemented in a large-scale setting to serve workers, reduce costs, and increase productivity. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of an integrated system that can rapidly and accurately extract the geometrical dimensions of a physical object assisted by a robotic hand and generate a 3D model of an object in a popular commercial Computer-Aided Design (CAD) software using computer vision. Two sets of experiments were performed: one with a simple cubical object and the other with a more complex geometry that needed photogrammetry to redraw it in the CAD system. For the accurate positioning of the object, a robotic hand was used. An Internet of Things (IoT) based camera unit was used for capturing the image and wirelessly transmitting it over the network. Computer vision algorithms such as GrabCut, Canny edge detector, and morphological operations were used for extracting border points of the input. The coordinates of the vertices of the solids were then transferred to the Computer-Aided Design (CAD) software via a macro to clean and generate the border curve. Finally, a 3D solid model is generated by linear extrusion based on the curve generated in CATIA. The results showed excellent regeneration of an object. This research makes two significant contributions. Firstly, it introduces an integrated system designed to achieve precise dimension extraction from solid objects. Secondly, it presents a method for regenerating intricate 3D solids with consistent cross-sections. The proposed system holds promise for a wide range of applications, including automatic 3D object reconstruction and quality assurance of 3D-printed objects, addressing potential defects arising from factors such as shrinkage and calibration, all with minimal user intervention. [ABSTRACT FROM AUTHOR]

With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine and point-of-care ultrasound (POCUS) merge to create this new scope. Teleguided POCUS can minimize staff exposure while preserving patient safety and oversight during bedside procedures. In this paper, we propose the use of teleguided POCUS supported by AI technologies for the remote monitoring of COVID-19 patients by non-experienced personnel including self-monitoring by the patients themselves. Our hypothesis is that AI technologies can facilitate the remote monitoring of COVID-19 patients through the utilization of POCUS devices, even when operated by individuals without formal medical training. In pursuit of this goal, we performed a pilot analysis to evaluate the performance of users with different clinical backgrounds using a computer-based system for COVID-19 detection using lung ultrasound. The purpose of the analysis was to emphasize the potential of the proposed AI technology for improving diagnostic performance, especially for users with less experience. [ABSTRACT FROM AUTHOR]

Autonomous lunar exploration is a complex task that requires the development of sophisticated algorithms to control the movement of lunar rovers in a challenging environment, based on visual feedback. To train and evaluate these algorithms, it is crucial to have access to both a simulation framework and data that accurately represent the conditions on the lunar surface, with the main focus on providing the visual fidelity necessary for computer vision algorithm development. In this paper, we present a lunar-orientated robotic simulation environment, developed using the Unity game engine, built on top of robot operating system 2 (ROS 2), which enables researchers to generate quality synthetic vision data and test their algorithms for autonomous perception and navigation of lunar rovers in a controlled environment. To demonstrate the versatility of the simulator, we present several use cases in which it is deployed on various efficient hardware platforms, including FPGA and Edge AI devices, to evaluate the performance of different vision-based algorithms for lunar exploration. In general, the simulation environment provides a valuable tool for researchers developing lunar rover systems. [ABSTRACT FROM AUTHOR]

The water source, as a significant body of the earth, with a high value, serves as a hot topic to study Underwater Sensor Networks (UWSNs). Various applications can be realized based on UWSNs. Our paper mainly concentrates on the localization algorithms based on the acoustic communication for UWSNs. An in-depth survey of localization algorithms is provided for UWSNs. We first introduce the acoustic communication, network architecture, and routing technique in UWSNs. The localization algorithms are classified into five aspects, namely, computation algorithm, spatial coverage, range measurement, the state of the nodes and communication between nodes that are different from all other survey papers. Moreover, we collect a lot of pioneering papers, and a comprehensive comparison is made. In addition, some challenges and open issues are raised in our paper. [ABSTRACT FROM AUTHOR]

Product assembly is often one of the last steps in the production process. Product assembly is often carried out by workers (assemblers) rather than robots, as it is generally challenging to adapt automation to any product. When assembling complex products, it can take a long time before the assembler masters all the steps and can assemble the product independently. Training time has no added value; therefore, it should be reduced as much as possible. This paper presents a custom-developed system that enables the guided assembly of complex and diverse products using modern technologies. The system is based on pick-to-light (PTL) modules, used primarily in logistics as an additional aid in the order picking process, and Computer Vision technology. The designed system includes a personal computer (PC), several custom-developed PTL modules and a USB camera. The PC with a touchscreen visualizes the assembly process and allows the assembler to interact with the system. The developed PC application guides the operator through the assembly process by showing all the necessary assembly steps and parts. Two-step verification is used to ensure that the correct part is picked out of the bin, first by checking that the correct pushbutton on the PTL module has been pressed and second by using a camera with a Computer Vision algorithm. The paper is supported by a use case demonstrating that the proposed system reduces the assembly time of the used product. The presented solution is scalable and flexible as it can be easily adapted to show the assembly steps of another product. [ABSTRACT FROM AUTHOR]

Breast cancer affects thousands of women across the world, every year. Methods to predict risk of breast cancer, or to stratify women in different risk levels, could help to achieve an early diagnosis, and consequently a reduction of mortality. This paper aims to review articles that extracted texture features from mammograms and used those features along with machine learning algorithms to assess breast cancer risk. Besides that, deep learning methodologies that aimed for the same goal were also reviewed. In this work, first, a brief introduction to breast cancer statistics and screening programs is presented; after that, research done in the field of breast cancer risk assessment are analyzed, in terms of both methodologies used and results obtained. Finally, considerations about the analyzed papers are conducted. The results of this review allow to conclude that both machine and deep learning methodologies provide promising results in the field of risk analysis, either in a stratification in risk groups, or in a prediction of a risk score. Although promising, future endeavors in this field should consider the possibility of the implementation of the methodology in clinical practice. [ABSTRACT FROM AUTHOR]

Connected and autonomous vehicles (CAVs) improve the throughput of intersections by crossing in a lane-free order as compared to a signalised crossing. However, it is challenging to quantify such an improvement because the available frameworks to analyse the capacity of the conventional intersections do not apply to the lane-free ones. This paper proposes a novel framework including a measure and an algorithm to calculate the capacity of the lane-free intersections. The results show that a lane-free crossing of CAVs increases the capacity of intersections by 127% and 36% as compared to a signalised crossing of, respectively, human-driven vehicles and CAVs. The paper also provides a sensitivity analysis indicating that, in contrast to the signalised ones, the capacity of the lane-free intersections improves by an increase in the initial speed, maximum permissible speed and acceleration of vehicles. [ABSTRACT FROM AUTHOR]

Object detection is a fundamental part of computer vision, with a wide range of real-world applications. It involves the detection of various objects in digital images or video. In this paper, we propose a proof of concept usage of computer vision algorithms to improve the maintenance of railway tracks operated by Via Rail Canada. Via Rail operates about 500 trains running on 12,500 km of tracks. These tracks pass through long stretches of sparsely populated lands. Maintaining these tracks is challenging due to the sheer amount of resources required to identify the points of interest (POI), such as growing vegetation, missing or broken ties, and water pooling around the tracks. We aim to use the YOLO algorithm to identify these points of interest with the help of aerial footage. The solution shows promising results in detecting the POI based on unmanned aerial vehicle (UAV) images. Overall, we achieved a precision of 74% across all POI and a mean average precision @ 0.5 (mAP @ 0.5) of 70.7%. The most successful detection was the one related to missing ties, vegetation, and water pooling, with an average accuracy of 85% across all three POI. [ABSTRACT FROM AUTHOR]

Garbage detection and 3D spatial localization play a crucial role in industrial applications, particularly in the context of garbage trucks. However, existing approaches often suffer from limited precision and efficiency. To overcome these challenges, this paper presents an algorithmic architecture that leverages advanced techniques in computer vision and machine learning. The proposed approach integrates cutting-edge computer vision methodologies to improve the precision of waste classification and spatial localization. By utilizing RGB-D data captured by the RealSenseD415 camera, the algorithm incorporates state-of-the-art computer vision algorithms and machine learning models, including the Yolactedge model, for real-time instance segmentation of garbage objects based on RGB images. This enables the accurate prediction of garbage class and the generation of masks for each instance. Furthermore, the predicted masks are utilized to extract the point cloud corresponding to the garbage instances. The oriented bounding boxes of the segmented point cloud is calculated as the spatial location information of the garbage instances using the DBSCAN clustering algorithm to remove the interfering points. The findings indicate that the proposed approach can run at a maximum speed of 150 FPS. The usefulness of the proposed method in achieving accurate garbage recognition and spatial localization in a vision-driving robot grasp system has been tested experimentally on datasets that were custom-collected. The results demonstrate the algorithmic architecture's ability to transform waste management procedures while also enabling intelligent garbage sorting and enabling robotic grasp applications. [ABSTRACT FROM AUTHOR]

The Squirrel Search Algorithm (SSA) is widely used due to its simple structure and efficient search ability. However, SSA exhibits relatively slow convergence speed and imbalanced exploration and exploitation. To address these limitations, this paper proposes a fuzzy squirrel search algorithm based on a wide-area search mechanism named FSSSA. The fuzzy inference system and sine cosine mutation are employed to enhance the convergence speed. The wide-area search mechanism is introduced to achieve a better balance between exploration and exploitation, as well as improve the convergence accuracy. To evaluate the effectiveness of the proposed strategies, FSSSA is compared with SSA on 24 diverse benchmark functions, using four evaluation indexes: convergence speed, convergence accuracy, balance and diversity, and non-parametric test. The experimental results demonstrate that FSSSA outperforms SSA in all four indexes. Furthermore, a comparison with eight metaheuristic algorithms is conducted to illustrate the optimization performance of FSSSA. The results indicate that FSSSA exhibits excellent convergence speed and overall performance. Additionally, FSSSA is applied to four engineering problems, and experimental verification confirms that it maintains superior performance in realistic optimization problems, thus demonstrating its practicality. [ABSTRACT FROM AUTHOR]

Fog, haze, or smoke are standard atmospheric phenomena that dramatically compromise the overall visibility of any scene, critically affecting features such as the illumination, contrast, and contour detection of objects. The decrease in visibility compromises the performance of computer vision algorithms such as pattern recognition and segmentation, some of which are very relevant to decision-making in the field of autonomous vehicles. Several dehazing methods have been proposed that either need to estimate fog parameters through physical models or are statistically based. But physical parameters greatly depend on the scene conditions, and statistically based methods require large datasets of natural foggy images together with the original images without fog, i.e., the ground truth, for evaluation. Obtaining proper fog-less ground truth images for pixel-to-pixel evaluation is costly and time-consuming, and this fact hinders progress in the field. This paper aims to tackle this issue by proposing gradient-based metrics for image defogging evaluation that do not require a ground truth image without fog or a physical model. A comparison of the proposed metrics with metrics already used in the NTIRE 2018 defogging challenge as well as several state-of-the-art defogging evaluation metrics is performed to prove its effectiveness in a general situation, showing comparable results to conventional metrics and an improvement in the no-reference scene. A Matlab implementation of the proposed metrics has been developed and it is open-sourced in a public GitHub repository. [ABSTRACT FROM AUTHOR]

This paper presents a solution for an autonomously driven vehicle (a robotic car) based on artificial intelligence using a supervised learning method. A scaled-down robotic car containing only one camera as a sensor was developed to participate in the RoboCup Portuguese Open Autonomous Driving League competition. This study is based solely on the development of this robotic car, and the results presented are only from this competition. Teams usually solve the competition problem by relying on computer vision algorithms, and no research could be found on neural network model-based assistance for vehicle control. This technique is commonly used in general autonomous driving, and the amount of research is increasing. To train a neural network, a large number of labelled images is necessary; however, these are difficult to obtain. In order to address this problem, a graphical simulator was used with an environment containing the track and the robot/car to extract images for the dataset. A classical computer vision algorithm developed by the authors processes the image data to extract relevant information about the environment and uses it to determine the optimal direction for the vehicle to follow on the track, which is then associated with the respective image-grab. Several trainings were carried out with the created dataset to reach the final neural network model; tests were performed within a simulator, and the effectiveness of the proposed approach was additionally demonstrated through experimental results in two real robotics cars, which performed better than expected. This system proved to be very successful in steering the robotic car on a road-like track, and the agent's performance increased with the use of supervised learning methods. With computer vision algorithms, the system performed an average of 23 complete laps around the track before going off-track, whereas with assistance from the neural network model the system never went off the track. [ABSTRACT FROM AUTHOR]

Image sampling is a fundamental technique for image compression, which greatly improves the efficiency of image storage, transmission, and applications. However, existing sampling algorithms primarily consider human visual perception and discard irrelevant information based on subjective preferences. Unfortunately, these methods may not adequately meet the demands of computer vision tasks and can even lead to redundancy because of the different preferences between human and computer. To tackle this issue, this paper investigates the key features of computer vision. Based on our findings, we propose an image sampling method based on the dominant color component (ISDCC). In this method, we utilize a grayscale image to preserve the essential structural information for computer vision. Then, we construct a concise color feature map based on the dominant channel of pixels. This approach provides relevant color information for computer vision tasks. We conducted experimental evaluations using well-known benchmark datasets. The results demonstrate that ISDCC adapts effectively to computer vision requirements, significantly reducing the amount of data needed. Furthermore, our method has a minimal impact on the performance of mainstream computer vision algorithms across various tasks. Compared to other sampling approaches, our proposed method exhibits clear advantages by achieving superior results with less data usage. [ABSTRACT FROM AUTHOR]

Previously, Rahardja (2020) paper (in the first reference list) developed a (pairwise) multiple comparison procedure (MCP) to determine which (proportions) pairs of Multiple Binomial Proportions (with under-reported data), the significant differences came from. Generally, such an MCP test (developed by Rahardja, 2020) is the second part of a two-stage sequential test. In this paper, we derived two omnibus tests (i.e., the overall equality of multiple proportions test) as the first part of the above two-stage sequential test (with under-reported data), in general. Using two likelihood-based approaches, we acquire two Wald-type (Omnibus) tests to compare Multiple Binomial Proportions (in the presence of under-reported data). Our closed-form algorithm is easy to implement and not computationally burdensome. We applied our algorithm to a vehicle-accident data example. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) is a subdomain of machine learning concerned with achieving optimal behavior by interacting with an unknown and potentially stochastic environment. The exploration strategy for choosing actions is an important component for enabling the decision agent to discover how to obtain high rewards. If constructed well, it may reduce the learning time of the decision agent. Exploration in discrete problems has been well studied, but there are fewer strategies applicable to continuous dynamics. In this paper, we propose a Low-Discrepancy Action Selection (LDAS) process, a novel exploration strategy for environments with continuous states and actions. This algorithm focuses on prioritizing unknown regions of the state-action space with the intention of finding ideal actions faster than pseudo-random action selection. Results of experimentation with three benchmark environments elucidate the situations in which LDAS is superior and introduce a metric for quantifying the quality of exploration. [ABSTRACT FROM AUTHOR]

Phytosanitary treatment is one of the most critical operations in vineyard management. Ideally, the spraying system should treat only the canopy, avoiding drift, leakage and wasting of product where leaves are not present: variable rate distribution can be a successful approach, allowing the minimization of losses and improving economic as well as environmental performances. The target of this paper is to realize a smart control system to spray phytosanitary treatment just on the leaves, optimizing the overall costs/benefits ratio. Four different optical-based systems for leaf recognition are analyzed, and their performances are compared using a synthetic vineyard model. In the paper, we consider the usage of three well-established methods (infrared barriers, LIDAR 2-D and stereoscopic cameras), and we compare them with an innovative low-cost real-time solution based on a suitable computer vision algorithm that uses a simple monocular camera as input. The proposed algorithm, analyzing the sequence of input frames and exploiting the parallax property, estimates the depth map and eventually reconstructs the profile of the vineyard's row to be treated. Finally, the performances obtained by the new method are evaluated and compared with those of the other methods on a well-controlled artificial environment resembling an actual vineyard setup while traveling at standard tractor forward speed. [ABSTRACT FROM AUTHOR]

Advances in acoustic technology and instrumentation now make it possible to explore marine resources. As a significant component of ocean exploration, underwater acoustic target tracking has aroused wide attention both in military and civil fields. Due to the complexity of the marine environment, numerous techniques have been proposed to obtain better tracking performance. In this paper, we survey over 100 papers ranging from innovative papers to the state-of-the-art in this field to present underwater tracking technologies. Not only the related knowledge of acoustic tracking instrument and tracking progress is clarified in detail, but also a novel taxonomy method is proposed. In this paper, algorithms for underwater acoustic target tracking are classified based on the methods used as: (1) instrument-assisted methods; (2) mode-based methods; (3) tracking optimization methods. These algorithms are compared and analyzed in the aspect of dimensions, numbers, and maneuvering of the tracking target, which is different from other survey papers. Meanwhile, challenges, countermeasures, and lessons learned are illustrated in this paper. [ABSTRACT FROM AUTHOR]