Quantitative calibration experiments were performed on four different GelStereo platforms. The experimental results confirm the proposed calibration pipeline's ability to achieve Euclidean distance errors of less than 0.35 mm. This implies that the proposed refractive calibration method can be effectively utilized in complex GelStereo-type and other similar visuotactile sensing systems. High-precision visuotactile sensors play a crucial role in the advancement of research on the dexterous manipulation capabilities of robots.

A novel omnidirectional observation and imaging system, the arc array synthetic aperture radar (AA-SAR), has emerged. Utilizing linear array 3D imaging data, this paper introduces a keystone algorithm, coupled with arc array SAR 2D imaging, and then presents a modified 3D imaging algorithm using keystone transformations. biological optimisation A crucial first step is the discussion of the target azimuth angle, keeping to the far-field approximation approach of the first-order term. This must be accompanied by an analysis of the forward platform motion's effect on the along-track position, leading to a two-dimensional focus on the target's slant range-azimuth direction. Redefining a new azimuth angle variable within slant-range along-track imaging constitutes the second step. The ensuing keystone-based processing algorithm, operating in the range frequency domain, effectively removes the coupling term stemming from the array angle and slant-range time. To achieve a focused image of the target and perform three-dimensional imaging, the corrected data is employed for along-track pulse compression. Regarding the AA-SAR system's forward-looking spatial resolution, this article provides a comprehensive analysis, substantiated by simulations that verify both resolution changes and algorithm effectiveness.

The capacity for independent living among older adults is frequently undermined by issues such as failing memory and difficulties in making sound judgments. In this work, an integrated conceptual model for assisted living systems is introduced, providing support for elderly individuals with mild memory impairments and their caregivers. A four-part model is proposed: (1) an indoor localization and heading measurement system within the local fog layer, (2) an augmented reality application for user interaction, (3) an IoT-based fuzzy decision-making system for handling user and environmental interactions, and (4) a real-time user interface for caregivers to monitor the situation and issue reminders. A preliminary proof-of-concept implementation is undertaken to demonstrate the suggested mode's efficacy. Functional experiments, founded upon diverse factual situations, provide corroboration for the proposed approach's effectiveness. An exploration of the proposed proof-of-concept system's response time and accuracy is further carried out. The implementation of such a system, as suggested by the results, is likely to be viable and conducive to the advancement of assisted living. The suggested system has the potential to create scalable and customizable assisted living solutions, diminishing the challenges older adults experience with independent living.

For robust localization in the challenging, highly dynamic warehouse logistics environment, this paper proposes a multi-layered 3D NDT (normal distribution transform) scan-matching approach. A layered division of the input 3D point-cloud map and scan measurements was performed, based on variations in the height-related environmental factors. The covariance estimates for each layer were derived using 3D NDT scan-matching. Warehouse localization can be optimized by selecting layers based on the covariance determinant, which represents the estimate's uncertainty. If the layer approaches the warehouse floor, the extent of environmental variations, including the warehouse's disorganized layout and the placement of boxes, would be substantial, despite its numerous favorable characteristics for scan-matching. Should a specific layer's observation prove inadequately explained, alternative layers exhibiting lower uncertainty levels can be selected for localization purposes. As a result, the distinctive feature of this approach is the enhancement of location identification accuracy, even within spaces filled with both obstacles and rapid motion. This study details the proposed method, encompassing simulation-based validation using Nvidia's Omniverse Isaac sim and a comprehensive mathematical framework. The outcomes of this study's assessment provide a sound starting point to explore methods of lessening the impact of occlusions in mobile robot navigation within warehouse settings.

The condition assessment of railway infrastructure is facilitated by monitoring information, which delivers data that is informative concerning its condition. An illustrative piece of this data is Axle Box Accelerations (ABAs), which perfectly illustrates the dynamic interplay between the vehicle and track. To continuously evaluate the condition of railway tracks across Europe, sensors have been integrated into specialized monitoring trains and current On-Board Monitoring (OBM) vehicles. The accuracy of ABA measurements is compromised by data noise, the non-linear complexities of the rail-wheel contact, and variable environmental and operational parameters. Rail weld condition assessment using existing tools is complicated by these uncertainties. Expert opinions are incorporated into this study as an additional data point, enabling a reduction of uncertainties and thereby enhancing the assessment. In Vivo Imaging For the past year, with the Swiss Federal Railways (SBB) providing crucial support, we have developed a database containing expert assessments of the condition of critical rail weld samples, as identified through ABA monitoring. This work uses a fusion of expert feedback and ABA data features for enhanced precision in the identification of defect-prone welds. This task utilizes three models: Binary Classification, a Random Forest (RF) model, and a Bayesian Logistic Regression scheme (BLR). The RF and BLR models demonstrated superior performance compared to the Binary Classification model, the BLR model, in particular, offering predictive probabilities to quantify the confidence of assigned labels. We explain the inherent high uncertainty within the classification task, directly attributable to problematic ground truth labels, and explain the importance of continuous weld condition observation.

The successful implementation of UAV formation technology heavily relies on maintaining strong communication quality in the face of limited power and spectral resources. Simultaneously increasing the transmission rate and the probability of successful data transfer, the convolutional block attention module (CBAM) and value decomposition network (VDN) were implemented within a deep Q-network (DQN) for a UAV formation communication system. This manuscript investigates the combined utilization of UAV-to-base station (U2B) and UAV-to-UAV (U2U) links to fully exploit frequency resources, and identifies the potential for reusing the U2B links in supporting U2U communication links. read more DQN's U2U links, functioning as agents, interact with the system to autonomously learn and select the most efficient power and spectrum allocations. The channel and spatial elements of the CBAM demonstrably affect the training results. In addition, a solution was crafted using the VDN algorithm to overcome the problem of partial observation in a single UAV. This solution leverages distributed execution strategies by decomposing the collective q-function of the team into distinct q-functions for each agent using VDN. The data transfer rate and the probability of successful data transmission exhibited a notable improvement, as shown by the experimental results.

For effective traffic management within the Internet of Vehicles (IoV), License Plate Recognition (LPR) is indispensable, given that license plates serve as a definitive identifier for vehicles. The rising tide of vehicles on the road system has necessitated a more complex approach to traffic management and control systems. The consumption of resources and privacy concerns present substantial challenges, particularly within large urban settings. The development of automatic license plate recognition (LPR) technology within the Internet of Vehicles (IoV) is a crucial area of research to address these concerns. License plate recognition (LPR), by identifying and recognizing license plates found on roadways, can significantly enhance the management and regulation of the transportation system. Implementing LPR in automated transport systems necessitates a cautious approach to privacy and trust concerns, particularly with regard to how sensitive data is collected and used. This study suggests the application of blockchain technology to improve IoV privacy security, specifically using LPR. The blockchain system directly registers a user's license plate, eliminating the need for a gateway. A surge in the number of vehicles navigating the system could result in the database controller experiencing a catastrophic malfunction. License plate recognition, in conjunction with blockchain technology, is utilized in this paper to create a privacy preservation system for the IoV. The LPR system, upon capturing a license plate, transmits the image to the central communication gateway. To obtain a license plate, the user's registration is performed by a blockchain-integrated system, independently of the gateway. Furthermore, the traditional IoV model places the entire responsibility for connecting vehicle identities to public keys in the hands of the central authority. With a growing number of vehicles in the system, there exists a heightened risk of the central server crashing. Vehicle behavior analysis, performed by the blockchain system within the key revocation process, allows for the identification and removal of malicious user public keys.

In ultra-wideband (UWB) systems, this paper proposes IRACKF, an improved robust adaptive cubature Kalman filter, to overcome the problems of non-line-of-sight (NLOS) observation errors and inaccurate kinematic models.