Search Area Research Articles

Multi-stage gaze-controlled virtual keyboard using eye tracking.

This study presents a novel multi-stage hierarchical approach to optimize key selection on virtual keyboards using eye gaze. Existing single-stage selection algorithms have difficulty with distant keys on large interfaces. The proposed technique divides the standard QWERTY keyboard into progressively smaller regions guided by eye movements, with boundary fixations first selecting halves and quarters to sequentially narrow the search area. Within each region, keys are highlighted one by one for selection. An experiment compared the multi-stage approach to single-step techniques, having participants copy text using eye gaze alone under both conditions. Metrics on selection speed, words per minute, and usability ratings were significantly improved with the hierarchical technique. Half and quarter selection times decreased over 30% on average while maintaining accuracy, with overall task completion 20% faster. Users also rated the multi-stage approach as more comfortable and easier to use. The multi-level refinement of the selection area optimized interaction efficiency for gaze-based text entry.

Designing fit‐for‐purpose monitoring – A case study of a cryptic songbird

AbstractMonitoring threatened species is essential for understanding their conservation needs and developing effective recovery strategies. However, designing and implementing monitoring programs can be challenging in resource‐constrained environments, where conservation practitioners must balance the need for detailed information with limited resources. We present a case study focusing on the design of a range‐wide monitoring program for the Endangered rufous scrub‐bird (Atrichornis rufescens) in the mountain forests of eastern Australia. We first developed a ‘wish list’ of monitoring questions and then trialed the methods needed for answering them using sequence of pilot studies. We implemented a phased approach comprising (i) a study of whether individuals could be identified by their songs, (ii) identification of a ‘core range’ using species distribution models to refine the search area, and (iii) a trial of the efficacy of a preferred feasible monitoring approach. We offer a practical pathway for designing question‐driven monitoring programs for threatened species. Our study highlights the importance of clarifying explicit monitoring goals and tailoring methods to suit species' ecological and life history characteristics. By following this systematic approach, conservation efforts can obtain valuable information for effective management, even with limited resources and prevailing uncertainty about species ecology.

Passive unmanned maritime search and rescue routing method

Addressing the challenges inherent in passive unmanned search and rescue missions at sea, including difficulties in target identification, broad search areas, and slow route planning, a strategic process was introduced for maritime search and rescue area planning and a route planning model specifically designed for passive unmanned missions. By thoroughly understanding the emergency response operations at sea and the specific needs for route planning, an optimal routing model have been developed considering factors such as the efficiency of search and rescue area coverage and the cost of rescue routes. The objective function is constructed within these constraints and solved using the Whale Optimization Algorithm. The validity of the model is confirmed through designated scenario experiments, indicating that our proposed model for maritime passive unmanned search and rescue route planning is capable of swiftly identifying the search and rescue area and efficiently discovering a route with reduced costs. This innovation offers valuable decision-making support for the planning of future emergency maritime search and rescue operations in both military and civilian sectors.

Online Unmanned Ground Vehicle Path Planning Based on Multi-Attribute Intelligent Reinforcement Learning for Mine Search and Rescue

Aiming to improve the efficiency of the online process in path planning, a novel searching method is proposed based on environmental information analysis. Firstly, a search and rescue (SAR) environmental model and an unmanned ground vehicle (UGV) motion model are established according to the characteristics of a mining environment. Secondly, an online search area path-planning method is proposed based on the gray system theory and the reinforcement learning theory to handle multiple constraints. By adopting the multi-attribute intelligent (MAI) gray decision process, the action selection decision can be dynamically adjusted based on the current environment, ensuring the stable convergence of the model. Finally, experimental verification is conducted in different small-scale mine SAR simulation scenarios. The experimental results show that the proposed search planning method can capture the target in the search area with a smoother convergence effect and a shorter path length than other path-planning algorithms.

A Time and Energy Efficient Multi-UAV Coverage Planning for the Search and Rescue Mission under Wind fields

This paper discusses how to quickly and efficiently search for victims when a disaster occurs. Since the search area is large, the search time of a single UAV will be too long to satisfy the golden window of the search and rescue mission. Therefore, it is necessary to dispatch multiple UAVs to conduct a cooperative search by proposing a multi-UAV area coverage planning under wind fields (MUCPW) algorithm and its variant MUCPW-A to achieve the shortest mission time. It first averagely divides the search area into several regions, formulates equations to calculate the minimum mission time and corresponding energy consumption among all possible area partition methods. As compared to related work, simulation results show that MUCPW and MUCPW-A achieve the shortest mission time and the least energy consumption to search all victims in the search and rescue mission. Further, MUCPW-A achieves the smallest standard deviation in energy consumption.

Solving Optimal Power Flow Using New Efficient Hybrid Jellyfish Search and Moth Flame Optimization Algorithms

This paper presents a new optimization technique based on the hybridization of two meta-heuristic methods, Jellyfish Search (JS) and Moth Flame Optimizer (MFO), to solve the Optimal Power Flow (OPF) problem. The JS algorithm offers good exploration capacity but lacks performance in its exploitation mechanism. To improve its efficiency, we combined it with the Moth Flame Optimizer, which has proven its ability to exploit good solutions in the search area. This hybrid algorithm combines the advantages of both algorithms. The performance and precision of the hybrid optimization approach (JS-MFO) were investigated by minimizing well-known mathematical benchmark functions and by solving the complex OPF problem. The OPF problem was solved by optimizing non-convex objective functions such as total fuel cost, total active transmission losses, total gas emission, total voltage deviation, and the voltage stability index. Two test systems, the IEEE 30-bus network and the Mauritanian RIM 27-bus transmission network, were considered for implementing the JS-MFO approach. Experimental tests of the JS, MFO, and JS-MFO algorithms on eight well-known benchmark functions, the IEEE 30-bus, and the Mauritanian RIM 27-bus system were conducted. For the IEEE 30-bus test system, the proposed hybrid approach provides a percent cost saving of 11.4028%, a percent gas emission reduction of 14.38%, and a percent loss saving of 50.60% with respect to the base case. For the RIM 27-bus system, JS-MFO achieved a loss percent saving of 50.67% and percent voltage reduction of 62.44% with reference to the base case. The simulation results using JS-MFO and obtained with the MATLAB 2009b software were compared with those of JS, MFO, and other well-known meta-heuristics cited in the literature. The comparison report proves the superiority of the JS-MFO method over JS, MFO, and other competing meta-heuristics in solving difficult OPF problems.

UAT:Unsupervised object tracking based on graph attention information embedding

An excellent unsupervised tracker includes a powerful base tracker and an effective unsupervised tracking strategy. However, most base trackers lack internal feature representations for information embedding processes. Most unsupervised trackers are not robust enough in complex environments and lack an effective template update strategy. We propose an unsupervised object tracking based on graph attention information embedding (UAT) to solve the above problems. UAT combines graph attention mechanism with multi-scale features to construct a multi-scale graph attention module (MGA). MGA module dynamically and efficiently completes the information embedding between the template branch and the search area branch. The response map obtained by fusing the feature maps of the two branches is more informative about the location of the target. An attention based information reinforcement update module (RUM) improves the robustness of the tracker. RUM enhances the representation of the feature map in both the spatial dimension and the channel dimension. Template features are also updated indirectly through information transfer between the two branches. RUM suppresses background interference and improves network perception during tracking. Experiments on challenging benchmarks such as VOT2018, VOT2019, TrackingNet, OTB100, LaSOT and UAV123 demonstrate that the proposed UAT achieves state-of-the-art performance in unsupervised trackers.

Site selection for a high-level nuclear waste repository in Germany: a short guide

The government agency BGE started a site selection process for a high-level nuclear waste repository in 2017. The first phase of data screening was completed in 2020 and resulted in the identification of search areas covering 54% of the national territory. The actual second phase will be completed by 2027 or later, and the areas for aboveground investigations will be identified. The obligatory nongovernment input is provided by follow-up regional conferences, which are best supported by external scientific expertise. The large amount of text documents and maps and the interactive internet tools such a 3D viewer provide a suitable basis for external evaluation. The rationale is to apply stricter criteria than those used in the first phase to drastically reduce the size of the search area. With this updated set of criteria, the size of the search area can be reduced to 2,662 km2 or 0.7% of the national territory. This size is convenient for detailed aboveground investigations such as high-density 3D seismic surveys.

An approximate method of estimating the depth of objects using wavelet-based transformation

Modern three-dimensional computer vision technologies are actively developing, offering new solutions based on the analysis of environmental details. One of the key tasks is to determine the depth of location of objects in images, which requires efficient and fast methods of their processing. This study proposes a new depth estimation method for 3D computer vision based on wavelet transform optimization. Solutions are offered to simplify the disparity calculation, which is traditionally used for the construction of depth maps, with the possibility of describing contours with adjustable detail based on the wavelet transform. The main advantage of this method is the increase in performance due to the allocation of the Haar wavelet carrier length in the extremum search area. The simulation confirmed the effectiveness of the proposed approach for constructing depth maps, which allows us to recommend it for use in unmanned aerial vehicles (UAVs) in conditions of limited computing and energy resources.

A Novel Spotted Hyena Optimizer for the Estimation of Equivalent Circuit Model Parameters in Li-Ion Batteries

Li-ion batteries possess significant advantages like large energy density, fast recharge, and high reliability; hence, they are widely adopted in electric vehicles, portable electronics, and military and aerospace applications. Albeit having their merits, accurate battery modeling is subjected to problems like prior information on internal chemical reactions, complexity in problem formulation, a large number of unknown parameters, and the need for extensive experimentation. Hence, this article presents a reliable Spotted Hyena Optimizer (SHO) to determine the equivalent circuit parameters of lithium-ion (Li-ion) batteries. The methodology of the SHO is derived from the living and hunting tactics of spotted hyenas, and it is efficiently applied to solve the battery parameter estimation problem. Nine unknown battery model parameters of a Samsung INR 18650-25R are determined using this method. The model parameters estimated are endorsed for five different datasets with various discharge current values. Further, the effect of parameter range and its selection is also emphasized. Secondly, for validation, various performance metrics such as Integral Squared Error, mean best, mean worst, and Standard Deviation are evaluated to authenticate the superiority of the proposed parameter extraction. From the computed results, the SHO algorithm is able to explore the search area up to 89% in the case of larger search ranges. The chosen model and range of the SHO precisely predict the behavior of the proposed Li-ion battery, and the results are in accordance with the catalog data.

Clinicians’ perspectives of immersive tools in clinical mental health settings: a systematic scoping review

BackgroundVirtual Reality in mental health treatment has potential to address a wide spectrum of psychological and neurocognitive disorders. Despite the proven benefits, integration into clinical practice faces significant challenges. There is a critical need for research into clinicians’ perceptions of virtual reality due to the gap between rapid technological advancements and their adoption in mental health services.MethodA scoping review was conducted to comprehensively understand clinicians’ perspectives on the application of immersive virtual reality technologies within mental health settings. 4 data bases were searched, from inception, with the search areas of clinicians’, technology, perspectives and mental health. The scoping review followed the PRISMA-ScR checklist. All results were thematically analysed to identify and categorise themes with a focus on qualitative analyses of clinicians’ experiences and perceptions of VR applications in therapeutic contexts.Results17 articles were selected, encompassing a range of mental health settings. The findings indicate that the integration of VR in clinical environments is heavily influenced by clinicians’ knowledge and experience, with unfamiliarity often leading to scepticism. Positive attitudes towards VR, bolstered by direct experience and training, were found to drive acceptance, as clinicians’ acknowledged its potential to complement traditional therapies. However, there are still gaps in understanding VR’s therapeutic applications, particularly concerning its impact on human interaction and its suitability for specific patient groups. Balancing VR’s clinical benefits with ethical and safety concerns is crucial, especially when working with vulnerable populations. Furthermore, structural and administrative support is essential to overcoming the financial and logistical challenges of VR implementation, ensuring its safe and effective integration into mental health care.ConclusionWhile VR holds significant potential for enhancing mental health care, its successful integration into clinical practice necessitates addressing existing gaps in knowledge, training, and structural support. By carefully balancing its clinical benefits with ethical, practical, and safety considerations, VR can be effectively utilised as a valuable tool in mental health treatment, providing innovative solutions while ensuring that patient care remains paramount.

Solving power system economic emission dispatch problem under complex constraints via dimension differential learn butterfly optimization algorithm with FDC-based

The economic emission dispatch (EED) aims to minimize the fuel and pollutant emission costs of generator units under various complex constraints. Optimizing the EED problem is of crucial importance for alleviating the current energy and environmental pressures. In this work, nearly all known complex constraints in the EED problem, including the valve-point effect, transmission line power loss, prohibited operating zones, and ramp-rate limits, are taken into account, and an enhanced version of butterfly optimization algorithm (FDCDLBOA) is proposed to solve it. First, a new adaptive fragrance is employed to optimize the instability caused by target differences and improve the convergence performance. Second, the proposed dimension differential learning strategy evolves the position of individuals with the help of superior dimensional information in the population, and this extensive learning exchange can balance global and local search, maintain diversity, and get rid of local optima. Third, the Fitness-Distance-Constraint (FDC) guide selection method is employed for the first time to handle the complex constraints of EED problems, enhancing the ability of individuals to bypass the infeasible search areas. After evaluating the proposed FDCDLBOA on CEC 2022 test suite, it is applied to solve 8 EED cases, encompassing small-, medium- and large-scale systems. Notably, the 280-generator case is the first large-scale test to exceed 200 generators. Compared with 9 representative algorithms, FDCDLBOA performs outstandingly in terms of robustness, improvement index (IF), mean constraint violation (MV), feasibility rate (FR) and Quade multiple comparison, among which IF, MV, FR, and Quade are all employed for evaluating the EED problem for the first time. The presented results confirm that the proposed method effectively enhances the robustness of high-quality solutions and the ability to handle complex constraints, demonstrating strong competitiveness and potential in solving the EED problem.

Temporal relation transformer for robust visual tracking with dual-memory learning

Recently, transformer trackers mostly associate multiple reference images with the search area to adapt to the changing appearance of the target. However, they ignore the learned cross-relations between the target and surrounding, leading to difficulties in building coherent contextual models for specific target instances. This paper presents a Temporal Relation Transformer Tracker (TRTT) for robust visual tracking, providing a concise approach to modeling temporal relations by dual target memory learning. Specifically, a temporal relation transformer network generates paired memories based on static and dynamic templates, which are reinforced interactively. The memory contains implicit relation hints that capture the relations between the tracked object and its immediate surroundings. More importantly, to ensure consistency of target instance identities between frames, the relation hints from previous frames are transferred to the current frame for merging temporal contextual attention. Our method also incorporates mechanisms for reusing favorable cross-relations and instance-specific features, thereby overcoming background interference in complex spatio-temporal interactions through a sequential constraint. Furthermore, we design a memory token sparsification method that leverages the key points of the target to eliminate interferences and optimize attention calculations. Extensive experiments demonstrate that our method surpasses advanced trackers on 8 challenging benchmarks while maintaining real-time running speed.

Full Coverage Path Planning for Torpedo-Type AUVs’ Marine Survey Confined in Convex Polygon Area

In this paper, we present a full coverage path planning (CPP) algorithm for the marine surveys conducted in the convex polygon shaped search area. The survey is supposed to carry out by torpedo-type AUVs (autonomous underwater vehicles). Due to their nonholonomic mechanical characteristics, these vehicles have nonzero minimum turning radius. For any given polygon shaped search area, it can always be partitioned into one or more convex polygons. With this in mind, this paper proposes a novel search algorithm called CbSPSA (Calculation based Shortest Path Search Algorithm) for full coverage of any given convex polygon shaped search area. By aligning the search inter-tracks alongside the edge with the minimum height, we can guarantee the minimum number of the vehicle’s turns. In addition, the proposed method can guarantee the planned path is strictly located inside the polygon area without overlapped or crossed path lines, and also has the total path length as short as possible. Considering the vehicle’s nonzero minimum turning radius, we also propose a sort of smoothing algorithm which can smooth the waypoint path searched by CbSPSA so that the vehicle can exactly follow it. The smoothed path is also guaranteed to be strictly located inside the polygon. Numerical simulation analyses are also carried out to verify the effectiveness of the proposed schemes.

Efficient motion estimation and discrete cosine transform implementation using the graphics processing units.

Motion Estimation (ME) and the two-dimensional (2D) discrete cosine transform (2D-DCT) are both computationally expensive parts of HEVC standard, therefore real-time performance of the HEVC may not be free from glitches. To address this issue, this study deploys the graphics processing units (GPUs) to perform the ME and 2D-DCT tasks. In this concern, authors probed into four levels of parallelism (i.e., frame, macroblock, search area, and sum of the absolute difference (SAD) levels) existing in ME. For comparative analysis, authors involved full search (FS), test zone search (TZS) of HEVC, and hierarchical diamond search (EHDS) ME algorithms. Similarly, two levels of parallelism (i.e., macroblock and sub-macroblock) are also explored in 2D-DCT. Notably, the least computationally complex multithreaded Loeffler DCT algorithm is utilized for computing 2D-DCT. Experimental results show that ME processing task corresponding to 25 frames, with each frame of size (3840×2160) pixels, is accomplished in 0.15 seconds on the NVIDIA GeForce GTX 1080, whereas the 2D-DCT task along with the image reconstruction and differencing corresponding to 25 frames took 0.1 seconds. Collectively, both ME and 2D-DCT tasks are processed in 0.25 seconds, which still leaves enough room for the encoder's remaining parts to be executed within one second. Due to this enhancement, the resultant encoder can safely be used in real-time applications.

Siamese Tracking Network with Multi-attention Mechanism

Object trackers based on Siamese networks view tracking as a similarity-matching process. However, the correlation operation operates as a local linear matching process, limiting the tracker’s ability to capture the intricate nonlinear relationship between the template and search region branches. Moreover, most trackers don’t update the template and often use the first frame of an image as the initial template, which will easily lead to poor tracking performance of the algorithm when facing instances of deformation, scale variation, and occlusion of the tracking target. To this end, we propose a Simases tracking network with a multi-attention mechanism, including a template branch and a search branch. To adapt to changes in target appearance, we integrate dynamic templates and multi-attention mechanisms in the template branch to obtain more effective feature representation by fusing the features of initial templates and dynamic templates. To enhance the robustness of the tracking model, we utilize a multi-attention mechanism in the search branch that shares weights with the template branch to obtain multi-scale feature representation by fusing search region features at different scales. In addition, we design a lightweight and simple feature fusion mechanism, in which the Transformer encoder structure is utilized to fuse the information of the template area and search area, and the dynamic template is updated online based on confidence. Experimental results on publicly tracking datasets show that the proposed method achieves competitive results compared to several state-of-the-art trackers.

Video text tracking with transformer-based local search

Video text tracking is a highly significant branch in the field of multi-object tracking (MOT), aiming to detect all text instances in video frames and construct trajectories for each text. Conventional text tracking methods typically follow the Tracking-By-Detection (TbD) paradigm which involves two separate steps of detection and association. In complex scenarios, abundant detection omission of text instances is caused by exposure, occlusion, motion blur, and the like, leading to text trajectory breakpoints in the TbD methods. To this end, video text tracking with transformer-based local search (LSTrack) is proposed in this paper. We utilize historical trajectory information to estimate the approximate search areas where omitted texts are located. In this case, our local search tracker leverages the text images in historical trajectories as references to directly recall text instances in the search areas. In addition, our tracking framework can combine the explicit semantic information output from an OCR model to obtain the semantic version (SV) of LSTrack, which for the first time uses the text edit distance as the distance measurement in matching stages to achieve better text tracking results. Ultimately, our method achieves an advanced performance on several public benchmarks. In particular, LSTrack (SV) achieves state-of-the-art performance on the Minetto benchmark.

An in-depth survey of the artificial gorilla troops optimizer: outcomes, variations, and applications

A recently developed algorithm inspired by natural processes, known as the Artificial Gorilla Troops Optimizer (GTO), boasts a straightforward structure, unique stabilizing features, and notably high effectiveness. Its primary objective is to efficiently find solutions for a wide array of challenges, whether they involve constraints or not. The GTO takes its inspiration from the behavior of Gorilla Troops in the natural world. To emulate the impact of gorillas at each stage of the search process, the GTO employs a flexible weighting mechanism rooted in its concept. Its exceptional qualities, including its independence from derivatives, lack of parameters, user-friendliness, adaptability, and simplicity, have resulted in its rapid adoption for addressing various optimization challenges. This review is dedicated to the examination and discussion of the foundational research that forms the basis of the GTO. It delves into the evolution of this algorithm, drawing insights from 112 research studies that highlight its effectiveness. Additionally, it explores proposed enhancements to the GTO’s behavior, with a specific focus on aligning the geometry of the search area with real-world optimization problems. The review also introduces the GTO solver, providing details about its identification and organization, and demonstrates its application in various optimization scenarios. Furthermore, it provides a critical assessment of the convergence behavior while addressing the primary limitation of the GTO. In conclusion, this review summarizes the key findings of the study and suggests potential avenues for future advancements and adaptations related to the GTO.

Reinforcement-Learning-Based Multi-UAV Cooperative Search for Moving Targets in 3D Scenarios

Most existing multi-UAV collaborative search methods only consider scenarios of two-dimensional path planning or static target search. To be close to the practical scenario, this paper proposes a path planning method based on an action-mask-based multi-agent proximal policy optimization (AM-MAPPO) algorithm for multiple UAVs searching for moving targets in three-dimensional (3D) environments. In particular, a multi-UAV high–low altitude collaborative search architecture is introduced that not only takes into account the extensive detection range of high-altitude UAVs but also leverages the benefit of the superior detection quality of low-altitude UAVs. The optimization objective of the search task is to minimize the uncertainty of the search area while maximizing the number of captured moving targets. The path planning problem for moving target search in a 3D environment is formulated and addressed using the AM-MAPPO algorithm. The proposed method incorporates a state representation mechanism based on field-of-view encoding to handle dynamic changes in neural network input dimensions and develops a rule-based target capture mechanism and an action-mask-based collision avoidance mechanism to enhance the AM-MAPPO algorithm’s convergence speed. Experimental results demonstrate that the proposed algorithm significantly reduces regional uncertainty and increases the number of captured moving targets compared to other deep reinforcement learning methods. Ablation studies further indicate that the proposed action mask mechanism, target capture mechanism, and collision avoidance mechanism of the AM-MAPPO algorithm can improve the algorithm’s effectiveness, target capture capability, and UAVs’ safety, respectively.

Robust tracking via coarse-to-fine redetection and spatial–temporal reliability evaluation

Correlation-filter-based trackers still suffer from the problem of spatial restriction and have difficulty in handling unreliable tracking results. In this paper, we propose a robust correlation tracking method by integrating coarse-to-fine redetection scheme and spatial–temporal reliability evaluation strategy. Specifically, we first exploit an improved multi-region coarse estimation method to identify multiple possible candidate regions. Then, the dense search is performed in candidate regions and some candidate objects are obtained. Considering that a good sampling center can help obtain more reasonable response values for the target and other objects, the proposed redetection scheme jointly considers the sampling center quality and detection quality to determine the redetection result. To reduce the risk of drifting to similar distractors in the expanded search area, a novel reliability evaluation strategy is developed with spatial–temporal context to measure the sampling center quality and further elaborate the reliability scores of candidate objects. Furthermore, an effective output integration method is proposed to determine the final output by carefully checking the tracking result and redetection result with historical information and distance constraint. Extensive experimental results demonstrate that the proposed method can achieve favorable tracking performance compared with some state-of-the-art trackers.