Search Results (2,702)

Simulator-based digital twins are widely used in robotics education and industrial development to accelerate prototyping and enable safe experimentation. However, they often hide implementation details that are essential for understanding, diagnosing, and correcting system failures. This paper introduces a technology-independent model-based design framework that provides students with full visibility of the computational mechanisms underlying robotic controllers while remaining feasible within a 150-h undergraduate course. The approach relies on representing controller behavior using networks of Extended Finite State Machines (EFSMs) and their stacked extension (EFS2M), which unify all abstraction levels of the control architecture—from low-level reactive behaviors to high-level deliberation—under a single formal model. A structured programming template ensures traceable, optimization-free software synthesis, facilitating debugging and enabling self-diagnosis of design flaws. The framework includes real-time synchronized simulation, transparent switching between virtual and physical robots, and a smart data logger that captures meaningful events for model updating and error detection. Integrated into the Intelligent Robots course, the system supports topics such as kinematics, control, perception, and simultaneous localization and mapping (SLAM) while avoiding dependency on specific middleware such as Robot Operating System (ROS) 2. Over three academic years, students reported positive hands-on experiences, strong adaptability to diverse modeling approaches, and consistently high survey ratings reflecting the course’s overall quality. The proposed environment thus offers an effective methodology for teaching end-to-end robot controller design through transparent, simulation-driven digital twins. Full article

This paper details the design of a guidance architecture, in the form of a layered, finite state machine, meant to enable safe and autonomous rendezvous operations. The onboard software uses relative state parametrization based on relative orbital elements which provide significant geometrical insight into the shape of the relative orbit. The development is structured in two main steps: first, novel closed-form impulsive control schemes, derived from the Gauss Variational Equations expressed in a velocity-aligned frame, are formulated. These complement available strategies from the literature and generalize them for arbitrarily eccentric reference orbits. Secondly, the definition of the guidance layer provides the chaser spacecraft with the capability to select, schedule, and execute the proper maneuvers to complete a given rendezvous scenario, ensuring operational safety and predictability. The functionality and performance of the implemented architecture are analyzed through numerical tests in a linear propagator and a high-fidelity non-linear simulator. The results provide validation of the developed maneuvers’ strategies, as well as demonstrating how the proposed guidance architecture can be used in a straightforward fashion across different target orbit scenarios, while guaranteeing the same level of passive safety. Full article

This paper presents initial developments towards a high-frequency condition monitoring framework designed for Autonomous Mobile Robots (AMRs) in Smart Factory environments. The proposed approach focuses on data acquisition and edge-level processing at the ultrasound range specifically (>20 kHz), using Micro-Electro-Mechanical System (MEMS) sensors. The system integrates real-time data acquisition, embedded fixed-point frequency-domain processing via a 1024-point FFT, and the integration of Industrial Internet-of-Things (IIoT) infrastructure based on the TIG (Telegraf, InfluxDB, and Grafana) stack, for data aggregation and remote visualization. To ensure timing precision at a sampling rate of 160 kHz, a software-based calibration routine is implemented to compensate for microcontroller overhead. Furthermore, the architecture’s alignment with IEEE 1451 principles is discussed to support interoperable and scalable sensor integration. Experimental results validate the reliable acquisition and processing of ultrasonic signals up to 80 kHz using controlled acoustic sources. This work provides a foundational infrastructure for condition-based monitoring, enabling future development of automated anomaly detection for mechanical components, such as bearings, which exhibit early-stage fault signatures in the ultrasonic spectrum. Full article

Preserving critical data, preventing unauthorized access, and securing communication are aspects of information security. Implementing them as hardware is more reliable than software. There are various hardware solutions that suggest using a separate computational unit, which is capable of providing various security enhancements. This article describes a heterogeneous security architecture with a tightly coupled security core to the CPU. A security interface that allows direct control and monitoring of the security core over the CPU is proposed. In the article analysis of how the interface interacts with the controlled and monitored CPU is done. This analysis explains the benefits and why for certain aspects, control is implemented seeking performance, while for others, using less logic. Full article

The rapid proliferation of small satellite missions demands flight software that combines reliability, reusability, and rapid development cycles. NASA’s Core Flight System (cFS), with its layered architecture and component-based design, offers a promising solution. However, its resource-intensive design poses significant challenges for deployment on microcontroller (MCU) platforms commonly used in nanosatellites. This paper presents a comprehensive approach to porting cFS to the TMS570 safety microcontroller running FreeRTOS. We address critical challenges including Operating System Abstraction Layer (OSAL) adaptation for lightweight real-time operating systems and file system virtualization using RAM disk. As a core architectural contribution, we propose a hierarchical memory architecture that partitions high-speed internal RAM from external SDRAM, enabling all five cFE core services to operate within 256 KB on-chip RAM by offloading latency-tolerant data structures to SDRAM and releasing 37.5% of internal memory for mission applications. Performance evaluation yields two key quantitative findings: (1) Software Bus latency on SDRAM scales non-linearly from 1.85× to 7.67× relative to internal RAM as message size increases from 64 B to 4 KB, revealing that memory bandwidth—not fixed routing overhead—dominates large-transfer cost; (2) the cFS framework introduces a constant additive overhead of approximately 82.5 $\mathsf{\mu }$s per task cycle, independent of computational load, remaining below 0.1% of the execution budget at typical 1–10 Hz control rates. System stability is validated through 72 h continuous operation encompassing over 2.5 million task cycles with zero unplanned resets. This work establishes quantitative design guidelines—including memory placement criteria and task granularity thresholds—that provide a reusable technical pathway for deploying reliable, extensible flight software on resource-constrained embedded platforms. Full article

Current research in consent management techniques focuses on isolated aspects of data security, privacy, or auditability, but important issues like (i) dynamically integrating regulatory updates into form generation, (ii) support in content generation with verifiable audit trails, and (iii) tools that make compliance reasoning transparent for non-legal users are not yet addressed. This paper introduces CONSENT, an architecture that integrates AI-based consent reasoning using Large Language Models (LLMs) for automated consent-form drafting and compliance evaluation, alongside blockchain technology for secure and auditable storage. The architecture builds on prior work to address the aforementioned issues by introducing three supporting mechanisms: (a) Specialized AI models coordinated through expert routing which coordinate subtasks such as automation in form generation and regulatory compliance, (b) Retrieval-Augmented Generation (RAG) that supports the integration of regulatory updates into forms, and (c) Explainable AI (XAI) for the reasoning behind form content and compliance assessments. CONSENT architecture is evaluated through 250 test cases and a pilot case study for clinical trial consent management involving 20 engineers and attorneys, who evaluated the prototype on form quality (i.e., coherence, conciseness, factuality, fluency, and relevance) as well as time and effort efficiency. Results show that CONSENT substantially reduces the manual effort in consent-form creation while providing transparent, audit-ready compliance assessments, highlighting its potential for dynamic, user-centric consent management. Full article

This article proposes a video streaming solution together with a measurement setup for energy consumption analysis. The proposed solution, including three different types of hardware and their corresponding software configurations, has a direct application in remote mobile surveillance applications. The article presents the hardware and software architectures for each version, and an efficiency and power consumption analysis for different running scenarios and configurations. This analysis measures the energy efficiency in various scenarios, with the purpose of increasing the system’s longevity and reducing operational costs. Full article

Landslides triggered by extreme events, such as heavy rainfall, are often unpredictable and cause significant damage to people and infrastructure. Calculating landslide susceptibility and associated risk in real time is challenging on several fronts, but it would provide valuable assistance in the event of major disasters. In this context, this research project aims to present a cutting-edge system for dynamic landslide susceptibility estimation based on open-source software, open data, and Open Geospatial Consortium (OGC) standards. Using real-time precipitation and geospatial data, the system allows for the calculation of susceptibility following extreme rainfall events, combining Analytic Hierarchy Process (AHP) and Random Forest processing. The proposed framework represents a prototypical, Digital Twin-ready terrain system, where dynamic geospatial data and real-time precipitation data are integrated in a predictive machine learning model and published within a WebGIS-based architecture. The system dynamically updates landslide susceptibility information, supporting local authorities and planners in identifying critical areas and enabling timely intervention in the event of imminent danger. The automated WebGIS processing and visualization environment provides a scalable and extensible foundation for future integration of physically based simulations and bidirectional feedback mechanisms, oriented to Digital Twinning Twinning solutions. Full article

This paper outlines the design of the space radiation detection experiment RADS to demonstrate new shielding materials in space during the Athene-1 mission, as well as the Gena-OT1 CubeSat precursor mission. The experiment compares new materials in the form of functional layers integrated into fibre-reinforced composite structures against traditional aluminium shielding. Trapped-particle motion is considered to maximise the exposure of the experiment in space. The radiation sensing units are based on off-the-shelf electronic components. Dosimeters based on a floating-gate MOSFET architecture are used to represent the damage mechanism in electronic devices exposed to space radiation. To account for particle- and energy-specific dose enhancement effects in the silicon of the dosimeters, the concept of a Cobalt-60 equivalent dose is introduced to serve as a calibration baseline. The structural design and software aspects are considered to increase ease of use for future satellite missions. Full 3D radiation simulations were conducted using FastRAD to validate the measurement concept of the sensor units in conjunction with the housing unit and the new shielding material. The experimental design has been verified, showcasing a unique method for evaluating new shielding materials in space. Full article

Large language models (LLMs) are increasingly used in software development, yet their ability to generate and maintain large, multi-module systems through natural language interaction remains insufficiently characterized. This study presents an empirical analysis of developing a 7420-line Terminal User Interface (TUI) framework for the Ring programming language using a prompt-driven workflow with Claude Code (Opus 4.5), employing an iterative testing and corrective feedback. The system was produced through 107 prompts: 21 feature requests, 72 bug fix prompts, 9 prompts sharing information from Ring documentation, 4 prompts providing architectural guidance, and 1 prompt dedicated to generating documentation. Development progressed across five phases, with the Window Manager phase requiring the most interaction (35 prompts), followed by complex UI systems (25 prompts) and control expansion (20 prompts). Bug-related prompts covered redraw issues, event-handling faults, runtime errors, and layout inconsistencies, while feature requests focused primarily on new widgets, window-manager capabilities, and advanced UI components. Most prompts were brief (mean ≈ 258 characters; median = 207 characters), reflecting a highly iterative workflow in which the human role was limited to specifying requirements, validating behavior, and issuing corrective prompts—without writing any code manually. The resulting framework contains 28 classes, 334 methods and includes a windowing subsystem, event-driven architecture, interactive widgets, hierarchical menus, grid and tree components, tab controls, and a multi-window desktop environment. By combining quantitative prompt analysis with qualitative assessment of model behavior, this study provides empirical evidence that modern LLMs can preserve architectural coherence across iterations and support the construction of new libraries and tools for emerging programming languages, highlighting prompt-driven development as a viable methodology within software-engineering practice. Full article

Background/Objectives: Accurate evaluation of root angulation is essential for assessing root parallelism and orthodontic treatment outcomes. In routine clinical practice, this assessment is often performed by visual inspection of panoramic radiographs, which is subjective and prone to observer variability. The objective of this study was to develop and validate an artificial intelligence (AI)–based algorithm for automated, quantitative assessment of mesiodistal root angulations on panoramic radiographs and to evaluate its accuracy relative to conventional manual measurements. Methods: A total of 214 panoramic radiographs (orthopantomograms), comprising 4280 posterior teeth, were retrospectively selected after applying strict inclusion and exclusion criteria. Individual teeth were automatically segmented using a U²-Net–based deep learning architecture. Tooth long-axis orientation was calculated using principal component analysis, with exclusion of the apical third to minimize the influence of root curvature. Angular deviation was measured relative to fixed horizontal reference lines. Manual measurements performed by experienced examiners using 3D Slicer software served as the reference standard. Intra- and inter-examiner reliability, agreement between AI-based and manual measurements, intraclass correlation coefficients (ICC), and Bland–Altman analyses were calculated. Results: Manual measurements demonstrated excellent reliability, with intra-examiner and inter-examiner ICC values of 0.972 and 0.963, respectively. Agreement between the AI-based algorithm and manual measurements was also excellent (ICC = 0.941). Bland–Altman analysis showed a mean difference of −0.10°, with 95% limits of agreement ranging from −1.60° to 1.41°, indicating minimal bias and no proportional error. Conclusions: The proposed AI-based algorithm provides accurate, objective, and reproducible measurements of posterior tooth root angulations on panoramic radiographs. This approach may support clinical decision-making, reduce observer-related variability, and facilitate efficient assessment of root parallelism in orthodontic practice. Full article

Orthopaedic plates are long-established medical devices conventionally manufactured from metals, most notably titanium alloys. The introduction of Additive Manufacturing (AM) has created new opportunities to design implants with complex internal architectures, enabling precise control over infill patterns and densities that directly influence mechanical properties and fatigue performance. Biodegradable polymers such as polylactic acid (PLA) have attracted growing interest in biomedical engineering, potentially reducing the need for secondary implant-removal surgery if degradation rates are carefully controlled and clinically approved. Additionally, AM offers the ability to customise internal structure for improved mechanical performance and load-bearing, while also providing the possibility of integrating advanced functionalities, such as controlled drug delivery. Building on previous work by our research group at the University of Belgrade, this study investigates the fatigue behaviour of the best-performing AM-optimised orthopaedic plate design. Numerical models incorporating honeycomb infill structures with the full range of achievable densities were developed to assess structural integrity under fatigue loading. Fatigue crack growth was simulated in ANSYS Mechanical (ANSYS Inc., Canonsburg, PA, USA) software, employing a four-point bending configuration in accordance with the ASTM F382 standard. A validated PLA material model was implemented at a reduced load level (10%) relative to previous studies. Direct comparison with titanium plates was avoided due to fundamentally different material properties, focusing instead on infill architecture to identify optimal AM design strategies for orthopaedic plates. Full article

Optical networks constitute the backbone of contemporary communication infrastructures, supporting massive bandwidth, low-latency services, and high levels of scalability across core, metro, and access domains. As these systems evolve toward elastic, software-defined, and multi-domain architectures, their exposure to sophisticated security threats increases significantly. This paper provides a comprehensive survey of vulnerabilities and countermeasures in modern optical networks, spanning the physical, control, and cross-layer dimensions. We analyze major architectures—including WDM, TDM, PON, EON, and IP-over-WDM—and examine how their structural properties shape their security posture. A threat taxonomy is presented covering physical-layer attacks such as fiber tapping, optical jamming, crosstalk exploitation, and signal injection; control-plane risks including spoofing, malicious signaling, and SDN manipulation; and broader cross-layer attack vectors. We review state-of-the-art defense mechanisms, including physical-layer security (PLS), spectrum randomization, chaotic optical coding, device-level authentication, survivability techniques, intelligent monitoring, and quantum-secure solutions such as QKD. By integrating insights from recent experimental and operational studies, the survey highlights emerging challenges and identifies open problems related to secure orchestration, multi-tenant environments, and quantum-era resilience. The objective is to guide researchers, engineers, and network operators toward robust and future-proof security strategies for next-generation optical infrastructures. Full article

In a globalized world where data play a critical role in system operation, process automation, and decision-making, the development of real-time control systems is essential, as it enables operators and supervisors to monitor the current status of a process based on its physical variables. Consequently, a wide range of software and hardware platforms is currently available for implementing real-time control systems, including Arduino, ESP32, and PIC microcontrollers. However, these platforms lack sufficiently robust hardware features for closed-loop control applications, as they were primarily designed for general-purpose use. To address the limitations of conventional embedded systems, this paper presents a novel approach for the implementation of digital controllers using Texas Instruments embedded systems applied to experimental plants designed with different control strategies. The proposed contribution focuses on the development of an experimental framework that integrates multi-platform programming, automatic code generation, and the use of dedicated real-time control modules, such as the Control Law Accelerator available in the LAUNCHXL-F28379D LaunchPad embedded system. The results highlight the capability of Texas Instruments microcontrollers to execute real-time control loops applied to different physical systems and operating under various control parameters. In conclusion, the findings demonstrate that Texas Instruments embedded systems equipped with advanced microcontroller architectures represent a promising alternative not only for scalable control applications but also for industrial-level control system development. Full article

Software-Defined Networking (SDN) has emerged as a fundamental architecture for future Internet systems by enabling centralized control, programmability, and fine-grained traffic management. However, the logical centralization of the SDN control plane also introduces critical vulnerabilities, particularly to Distributed Denial-of-Service (DDoS) attacks that can severely disrupt network availability and performance. To address these challenges, machine-learning (ML) techniques have been increasingly adopted to enable intelligent, adaptive, and data-driven DDoS detection mechanisms within SDN environments. This study presents a PRISMA-guided systematic literature review of recent ML-based approaches for DDoS detection in SDN-based networks. A comprehensive search of IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar identified 38 primary studies published between 2021 and 2025. The selected studies were systematically analyzed to examine learning paradigms, experimental environments, evaluation metrics, datasets, and emerging architectural trends. The synthesis reveals that while single machine-learning classifiers remain dominant in the literature, hybrid and ensemble-based approaches are increasingly adopted to improve detection robustness under dynamic and high-volume traffic conditions. Experimental evaluations are predominantly conducted using SDN emulation platforms such as Mininet integrated with controllers, including Ryu and OpenDaylight, with performance commonly measured using accuracy, precision, recall, and F1 score, alongside emerging system-level metrics such as detection latency and controller resource utilization. Public datasets, including CICIDS2017, CICDDoS2019, and InSDN, are widely used, although a significant portion of studies rely on custom SDN-generated datasets to capture control-plane-specific behaviors. Despite notable advances in detection accuracy, several challenges persist, including limited generalization to low-rate and unknown attacks, dependency on synthetic traffic, and insufficient validation under real-time operational conditions. Based on the synthesized findings, this review highlights key research directions toward intelligent, scalable, and resilient DDoS defense mechanisms for future Internet architectures, emphasizing adaptive learning, lightweight deployment, and integration with programmable networking infrastructures. Full article