Search Results (189)

Odometry estimation, which calculates the trajectory of a moving object across timeframes, is a critical and time-consuming function in SLAM (Simultaneous Localization and Mapping) systems. Although LiDAR-based sensing is most popular for outdoor and long-range applications because of its ranging accuracy, the sparsity of laser point cloud poses a significant challenge to feature extraction and matching in odometry estimation. In this paper, we investigate odometry estimation from two aspects, i.e., algorithm optimization, and system design/implementation. In algorithm optimization, we present an image feature-assisted odometry estimation scheme that leverages the richness of image information captured by a companion camera to enhance the accuracy of laser point cloud matching. This also serves as a screening mechanism to reduce the matching size and lower the computing complexity for a higher estimation rate. In addition, various schemes, such as adaptive threshold in image feature point selection, principal component analysis (PCA)-based plane fitting for laser point interpolation, and Gauss–Newton optimization for calculating the transform matrix, are also employed to improve the accuracy of odometry estimation. The performance of improved odometry estimation is verified using an existing FLOAM (Fast Lidar Odometry and Mapping) framework. The KITTI dataset for autonomous vehicles with ground truth was used as the test bench. Simulation results indicate that the translation error and rotation error can be reduced by 16.6% and 1.3%, respectively. Computing complexity, measured as the software execution time, also reduced by 63%. In system implementation, a hardware/software (HW/SW) co-design strategy was adopted, where complexity profiling was first conducted to determine the task partitioning and time-consuming tasks are offloaded to a hardware accelerator. This facilitates real-time execution on a resource-constrained embedded platform consisting of a microprocessor module (Raspberry Pi) and an attached FPGA board (Pynq Z2). Efficient hardware designs for customized DSP functions (adaptive threshold and PCA) were developed in an FPGA capable of completing one data frame in 20ms. The final system implementation met the target throughput of 10 estimations per second, and can be scaled up further. Full article

In the state estimation problem for nonlinear systems, the Unscented Kalman Filter (UKF) has gained attention as an algorithm capable of accurate state estimation based on high-fidelity discretization for strongly nonlinear systems. Furthermore, for applying the UKF to continuous-time state–space models, a method employing the Runge–Kutta method in the time-update equation for sigma points has already been proposed to achieve high-precision state estimation. While this method uses high-order numerical approximations, the associated decrease in computational efficiency due to processing time becomes problematic. It is thus unsuitable for the state estimation of relatively fast-moving objects, such as autonomous vehicles and drones, which require high sampling frequencies. In this study, to reduce computational load while achieving relatively high estimation accuracy, we newly apply the Adams–Bashforth method to the UKF algorithm. The effectiveness of the proposed method is demonstrated by first explaining a low-dimensional model’s state estimation problem, followed by a comparison of estimation accuracy and computation time in state estimation simulations for the UAV model of an Osprey-type drone. Full article

Resident Space Objects (RSOs) are a collection of both man-made and natural objects in near-Earth space. Given their large orbital velocities and rapidly increasing quantity, they pose a collision threat to space assets, necessitating better Space Situational Awareness (SSA). SSA begins with detecting these objects in the first place and can be accomplished by using space-based optical images, such as images from the Fast Auroral Imager (FAI) on the CASSIOPE satellite. However, these short-exposure images are low in resolution and contain various artifacts and noise, posing challenges to traditional source detection methods. Furthermore, the background stars and RSOs both move due to the satellite’s non-constant attitude, posing a challenge for tracking algorithms. Nevertheless, these images are a valuable source of SSA data, which can be used to develop algorithms to ultimately augment the capabilities of current SSA systems. Such augmentations include performing RSO detection as a simultaneous function on existing spacecraft or allowing dedicated SSA payloads to detect RSOs during slew maneuvers, where background stars will similarly move. This paper proposes a rules-based RSO tracking algorithm tailored for low-resolution, short-exposure, space-based imagery with non-constant spacecraft attitude, addressing the challenge of distinguishing RSOs from background stars that are also in motion. This method consists of a custom thresholding algorithm, along with the Iterative Closest Point (ICP) algorithm to correct the motion of the background stars, followed by a tracking algorithm to finally detect the RSOs within the imagery, returning their pixel positions. The algorithm was tested on an 878-image dataset, achieving 79% precision and 71% recall, while detecting 87% of all RSOs at least once. These results prove that the algorithm is a feasible method for detecting RSOs in non-constant-attitude imagery, providing a means to develop current SSA systems. Full article

The growing penetration of Distributed Energy Resources (DERs)—such as photovoltaic generation, battery energy storage, electric vehicles, hydrogen technologies and flexible loads—requires advanced Energy Management Systems (EMS) capable of coordinating their operation and leveraging controllability to optimize microgrid performance and enable flexibility provision to the grid. When the physical, electrical, and economic system model is properly defined, the main sources of performance degradation typically arise from forecast uncertainty and temporal discretization effects, which propagate into sub-optimal schedules and infeasible setpoints. This paper proposes and tests a two-stage deterministic EMS architecture featuring rolling-horizon planning at an upper layer and fast local setpoint adaptation at a lower layer, jointly to reduce the impact of forecast errors and other uncertainties on the objective function. The first stage can be deployed either on the edge or in the cloud, depending on computational requirements, whereas the second stage is executed locally, close to the physical assets, to ensure timely corrective action. In the simulated cloud-executed planning case, moving from hourly to 15 min granularity improves the objective value from −49.39€ to −72.12€, corresponding to an approximate 46% reduction in operating cost. In our case study, the proposed second-stage local adaptation can reduce the mean absolute error (MAE) of the EMS performance loss by approximately 50% compared with applying the first-stage schedule without local correction. Results show that this two-stage hierarchical EMS effectively limits objective-function degradation while preserving operational efficiency and robustness. Full article

Background/Objectives: Type 2 diabetes (T2D) affects more than 38 million Americans and remains a leading public health challenge. Behavioral self-management is central to glycemic control but is often undermined by dysregulated and addictive-like eating behaviors. Continuous glucose monitoring (CGM) offers immediate feedback that may strengthen self-regulation, yet the psychological processes linking CGM use, food addiction (FA), and behavior change are poorly understood. This secondary mixed-methods study examined how CGM-supported group medical visits (GMVs) influence glycemic outcomes and FA symptoms in adults with diabetes. Methods: Adults with T2D participated in a 14-week GMV program integrating CGM review with education on nutrition, physical activity, sleep, stress, and intermittent fasting. Thirteen participants had paired CGM summaries and psychosocial data. Quantitative outcomes included mean glucose, glycemic variability, time-in-range (TIR), and symptoms of food addiction using the modified Yale Food Addiction Scale 2.0 (mYFAS 2.0). Qualitative data came from open-ended surveys analyzed using reflexive thematic analysis. Integration followed a convergent design, merging individual change trajectories with thematic interpretations and case vignettes. Results: Mean glucose decreased by 21 mg/dL and TIR improved by 9 percentage points. Among six participants with baseline FA symptoms, all showed reductions in self-reported mYFAS 2.0 symptom counts. Four moved from mild to no symptoms, one from moderate to no symptoms, and one from severe to no symptoms. Across the full sample, the mean change was a reduction of 1.2 in the mYFAS 2.0 symptom counts per participant. Thematic analysis identified four interrelated psychological mechanisms: enhanced awareness of food–glucose relationships, increased accountability through shared tracking, motivation via gamified self-monitoring, and relief from cognitive burden associated with dietary uncertainty. Conclusions: Integrating CGM feedback into GMVs was associated with improvements in glycemic metrics and reductions in addictive-like eating symptoms in this pilot sample. These findings position CGM as a behavioral intervention tool that complements its traditional monitoring role and highlight the value of combining real-time biofeedback with group-based support in diabetes care. Full article

To enhance a robot’s capacity to perceive and interpret its environment, an advanced vision system tailored specifically for machine perception was developed, moving away from human-oriented visual processing. This system improves robotic functionality by incorporating algorithms optimized for how computerized devices process visual information. Central to this paper’s approach is an improved Fast Discrete Cosine Transform (FDCT) algorithm, customized for robotic systems, which enhances object and obstacle detection in machine vision. By prioritizing higher frequencies and eliminating less critical lower frequencies, the algorithm sharpens focus on essential details. Instead of adapting the data stream for human vision, the FDCT and quantization tables were adjusted to suit machine vision requirements, achieving a file size reduction to about one-third of the original while preserving highly relevant data for robotic processing. This innovative approach significantly improves robots’ ability to navigate complex environments, perform tasks such as object recognition, motion detection, and obstacle avoidance with greater accuracy and efficiency. Full article

Space Surveillance and Tracking (SST) plays a crucial role in ensuring space safety. To this end, accurate and numerous observational resources are needed to build and maintain a catalog of space objects. In particular, it is essential to develop optimal observation strategies to maximize both the number and the quality of detections obtained from a sensor network. This represents a key step in the assessment of the network through simulations. This work presents the integrated development of sensor tasking strategies for optical systems and a track-to-track correlation pipeline within S$\mathrm{\Xi }$NSIT, a software environment designed to simulate sensor network configurations and evaluate cataloging performance. For high-altitude low Earth orbit (HLEO) targets, which are fast-moving and widely distributed, tasking strategies emphasize systematic scans of the Earth’s shadow boundary to exploit favorable phase angles and improve observational accuracy, while medium- and geostationary-Earth orbits (MEO–GEO) rely on equatorial-plane scans. The correlation pipeline employs Two-Body Integrals, uncertainty propagation, and a ${\chi }^2$-test with the Squared Mahalanobis Distance to associate tracks and perform initial orbit determination of newly detected objects. Results indicate that the integrated approach significantly enhances detection coverage, leading to greater catalog build-up efficiency and improved SST performance. Consequently, it facilitates the cataloging of numerous uncataloged objects within a reduced timeframe. Full article

A system-level scheme that couples a multi-dimensional attention-fused vision model and an improved Dijkstra planner is proposed for basketball robots in complex scenes. Fast-moving object detection, cluttered background recognition, and real-time path decision are targeted. For vision, the proposed YOLO11 with Multi-dimensional Attention Fusion (YOLO11-MAF) is equipped with four modules: Coordinate Attention (CoordAttention), Efficient Channel Attention (ECA), Multi-Scale Channel Attention (MSCA), and Large-Separable Kernel Attention (LSKA). Detection accuracy and robustness for high-speed basketballs are raised. For planning, an improved Dijkstra algorithm is proposed. Binary heap optimization and heuristic fusion cut time complexity from $O\left(V^2\right)$ to $O\left(\right(V+E\left)\log V\right)$. Redundant expansions are removed and planning speed is increased. A complete robot platform integrating mechanical, electronic, and software components is constructed. End-to-end experiments show the improved vision model raises mAP@0.5 by 0.7% while keeping real-time frames per second (FPS). The improved path planning algorithm cuts average compute time by 16% and achieves over 95% obstacle avoidance success. The work offers a new approach for real-time perception and autonomous navigation of intelligent sport robots. It lays a basis for future multi-sensor fusion and adaptive path planning research. Full article

Achieving fast and robust object tracking is critical for real-time Unmanned Aerial Vehicle (UAV) applications, where targets often move unpredictably and environmental conditions can rapidly change. In this paper, we propose the Search-Region Adaptive Mamba-ViT Tracker (SAMViTrack), a novel framework that combines the efficiency of Mamba attention with the powerful feature extraction capabilities of Vision Transformer (ViT). Our tracker dynamically adjusts the search region based on the target’s motion and environmental context, ensuring precise tracking even under challenging conditions such as occlusions, fast motion, and scale variations. By integrating an adaptive search mechanism, our SAMViTrack significantly reduces computational overhead without compromising accuracy, making it suitable for real-time deployment on UAVs with limited onboard resources. Extensive experiments on benchmark datasets demonstrate that our method outperforms both traditional and modern trackers, achieving superior accuracy and robustness with improved efficiency. The proposed tracker sets a new baseline, especially by combining Mamba and ViT, for UAV tracking by offering a balance between speed, accuracy, and adaptability in dynamic environments. Full article

Within a power hardware-in-the-loop (PHIL) controller design automation (CDA) framework for voltage feedback grid-forming inverters, a scaled-down inverter system is developed for time-domain response solving. This hardware-based approach effectively addresses the conflicting demands of accuracy, computational efficiency, and modeling cost that are commonly encountered in simulation-based methods. Conventional synchronous sampling in digitally controlled pulse-width modulation (PWM) inverters introduces severe low-frequency distortion and significant ripple components in the step response, leading to non-decaying oscillations that compromise the extraction of settling time and steady-state error. By analyzing the sideband aliasing mechanism in capacitor-voltage sampling and associated harmonic-cancellation conditions, aliasing-free sampling is achieved using 90° phase-shifted anti-aliasing filters combined with synchronous sampling. Although Fast Fourier Transform (FFT) filtering offers the highest fidelity, it suffers from window-boundary distortions and is unsuitable for online use; therefore, four practical filtering schemes are evaluated against the FFT benchmark, among which oversampling with moving-average filtering (MAF) retains dynamics closest to the FFT result while avoiding its distortions. An objective function incorporating step-response metrics is constructed to optimize single-variable active damping and multiple resonant controllers, mitigating severe overshoot encountered in conventional integral-based approaches. Experimental results verify the aliasing mechanism and the effectiveness of the proposed CDA method. Full article

Autonomous navigation is critical for unlocking the full potential of Unmanned Surface Vehicles (USVs) in complex maritime environments. Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for developing self-learning control policies, yet the design of reward functions to balance conflicting objectives, particularly fast arrival at the target position and collision avoidance, remains a major challenge. The precise, quantitative impact of reward parameterization on a USV’s maneuvering behavior and the inherent performance trade-offs have not been thoroughly investigated. Here, we demonstrate that by systematically varying reward function weights within a framework relying on the Proximal Policy Optimization (PPO), it is possible to quantitatively map the trade-off between collision avoidance safety and mission time. Our results, derived from simulations, show that agents trained with balanced reward weights achieve target-reaching success rates exceeding 98% in dynamic multi-obstacle scenarios. Conversely, configurations that disproportionately penalize obstacle proximity lead to overly cautious behavior and mission failure, with success rates dropping to 22% due to workspace boundary violations. This work provides a data-driven methodological framework for reward function design and parameter selection in safety-critical robotic applications, moving beyond ad-hoc tuning towards a more structured parameter influence analysis. Full article

Background/Objectives: Predicting whether a compound is subject to active transport is crucial in drug development. We propose a simple threshold for passive membrane permeability (P_m), derived from the cell’s energy limitation, to identify compounds unlikely to be actively effluxed. Results: By considering fundamental cellular energy constraints, our approach provides a mechanistic rationale for why compounds with very high passive permeability in combination with low applied concentration will not undergo active efflux. This moves beyond the empirical observation (such as in previous systems that associate fast-permeating, poorly soluble compounds with low transporter activity) by grounding the prediction in the cell’s energetic limitations. For MDCK (Madin–Darby canine kidney) cells, this threshold—normalized to the applied compound concentration (C_ext)—was determined to be P_m×C_ext = 10^−1.7 cm/s×µM. Methods: To derive this threshold, we conducted an extensive analysis of literature-reported efflux ratios (ERs) in MDCKII cells overexpressing efflux transporters (MDR1, BCRP, MRP2; 294 datapoints across 136 unique compounds). Concentration-dependent measurements for Amprenavir, Eletriptan, Loperamide, and Quinidine—chosen because these borderline compounds exhibited the highest P_m×C_ext while still being actively effluxed—enabled the most accurate determination of the threshold. Literature ER values were re-evaluated through the experimental determination of reliable P_m values, as well as newly measured ER values with MDCK efflux assays. Conclusions: The results of these assays and the re-evaluation allowed us to reclassify all but three outliers (compounds with ER > 2.5 and log(P_m×C_ext) > −1.7). In contrast, more than 60% of the compounds analyzed without significant ER values (123 compounds) fell above the threshold, in strong agreement with our theory of an energy limitation to active transport. This permeability threshold thus provides a simple and broadly applicable criterion to identify compounds for which active efflux is energetically not feasible and may serve as a practical tool for early drug discovery and optimization, pending further validation in practical applications. Full article

Background/Objectives: Research on restaurants has traditionally emphasized business operations. Considering restaurants’ growing role in shaping dietary patterns and public health outcomes, this study aimed to map the scope, trends, and gaps in scholarly research addressing food-related aspects of restaurants, excluding business-oriented topics. Methods: A bibliometric analysis was conducted using the Web of Science and Scopus databases. Search terms encompassed multiple restaurant categories, including fast food, fast casual, casual dining, and fine dining. After screening, 956 peer-reviewed English-language journal articles were included. Descriptive performance metrics were calculated, and keyword co-occurrence analysis was conducted. Results: Findings revealed that nutrition-related studies dominate the literature, particularly research linking fast food consumption to obesity and the impact of menu labeling policies on consumer behavior. Food science research was comparatively limited and concentrated primarily on food safety and uses for degraded frying oil. The analysis also highlighted a strong research focus on fast food, while fast casual and fine dining restaurants were notably underrepresented. Conclusions: Future studies should move beyond short-term, cross-sectional designs and incorporate longitudinal approaches to better capture how policy interventions, such as menu labeling and reformulation incentives affect consumer food choices and restaurant offerings over time. Understanding how to reduce restaurants’ contribution to the incidence of diet-related noncommunicable disease risk factors such as obesity and hypertension will require research trials that jointly manipulate key factors such as economic (prices and incentives), structural (recipes, assortment, and operations), and behavioral (choice architecture). Research could also investigate strategies to reduce allergen risks by evaluating standardized training programs and integrated menu/POS disclosure systems. In addition, examination of consumer acceptance of sustainable ingredient substitutions and packaging methods is needed. Full article

Background/Objectives: Prediabetes (PreDM) is a heterogeneous condition, impacting hundreds of millions worldwide, associated with a substantially high risk of Type 2 Diabetes Mellitus (T2DM) and cardiovascular complications. Early identification of subgroups within the PreDM population may support tailored prevention strategies. Methods: We conducted a cross-sectional study using data from annual health check-ups of 419 university staff (aged 27–69) in Kazakhstan. Latent Class Analysis (LCA) was applied to identify subgroups of individuals with PreDM based on cardiovascular risk factors. Differences in glucose metabolism markers (fasting glucose, OGTT, HOMA-IR, HOMA-β) were compared across identified classes. Results: PreDM prevalence was 43.4%. LCA revealed four distinct classes: Class 1: healthy, low-risk individuals; Class 2: overweight with moderate metabolic risk; Class 3: older, overweight individuals with high cardio-metabolic risk; and Class 4: obese, middle-aged to older individuals with very high cardio-metabolic risk. Significant differences were found in glucose metabolism profiles across the classes. IFG predominated in Class 1 (95%), while Classes 3 and 4 had higher rates of β-cell dysfunction and combined IFG/IGT patterns. HOMA-β differed significantly between classes (p  <  0.001), while HOMA-IR did not. Conclusions: PreDM is highly prevalent in this working-age Kazakh population and demonstrates marked heterogeneity. Based on easily obtainable cardiovascular risk factors, we have identified four subgroups with distinct glucose profiles that may inform personalized interventions. These distinct subgroups may require differentiated prevention strategies, moving beyond a one-size-fits-all approach. Full article

Category management (CM) is crucial for optimising retailer–supplier partnerships via technological integration and data sharing. However, the role of CM in Saudi Arabia’s unique fast-moving consumer goods sector (FMCG) remains underexplored. This study aimed to answer the following research question: How do cloud-based inventory platforms and real-time data sharing improve forecasting accuracy and inventory turnover for retailer–supplier CM partnerships in Saudi Arabia’s FMCG sector? A systematic review of 87 studies from the Web of Science and Scopus databases was conducted, followed by thematic analysis. The findings indicate that CM improves demand forecasting, inventory optimisation, and collaborative decision-making. Key implementation barriers include cultural resistance to data sharing, high technology costs for small and medium-sized enterprises, and infrastructural limitations. Success relies on phased technology adoption, relational data governance, and trust building that aligns with Saudi cultural norms. The study concludes that CM is essential for leveraging technology and data capabilities, and it offers a contextualised framework to overcome local barriers and support the achievement of Vision 2030 objectives. This study provides practical strategies for sector stakeholders to adopt high-impact, low-cost technology and a basis for future comparative studies in Gulf Cooperation Council markets. Full article