Search Results (75)

Marker-based motion capture systems are widely used to estimate joint kinematics, yet their accuracy depends strongly on how anatomical segments and coordinate systems are defined. This proof-of-concept study aimed to describe and technically evaluate a structured and reproducible framework for upper-limb segment definition and joint-angle computation. Reflective markers were placed on anatomical landmarks of the trunk and upper limbs, and joint angles were computed using custom-developed MATLAB R2022b (MathWorks, Natick, MA, USA) routines. Baseline-corrected model-derived joint angles were compared with composite reference measurements obtained using a universal manual goniometer and a twin-axis biosignalsplux goniometer under predefined static conditions in two adult participants. Side-specific mean absolute error values ranged from 0.80° to 5.24°, while root mean square error values ranged from 0.86° to 5.24°. The largest discrepancies were observed during maximum wrist extension and left maximum radial deviation. The evaluated static observations showed close correspondence in several joint positions, although larger discrepancies occurred in selected end-range wrist positions. Given the limited sample, single recordings, and controlled static conditions, these findings should be interpreted as an initial demonstration of technical feasibility rather than evidence of generalisable validity or repeatability. The explicit framework provides a basis for further evaluation using larger samples, repeated marker applications, repeated trials, and dynamic multi-planar upper-limb tasks. Full article

Vehicle vibration and noise are predominantly driven by road-surface excitation, making robust prediction of these phenomena based on pavement roughness a central challenge in automotive development. This paper presents a fully automated pipeline that begins with high-resolution laser-triangulation scans of actual test tracks to produce centerline elevation profiles. These profiles are processed and classified by a MATLAB routine using ISO 8608-based power-spectral-density analysis to extract the Gh₀ roughness coefficient. Concurrently, in-service acoustic and chassis-vibration data, collected at two representative speeds, are transformed into feature vectors comprising statistical PSD descriptors. A regression model then learns the mapping from these features to Gh₀, evaluating the feasibility of mapping vehicle-borne signatures to roughness metrics. Predicted Gh₀ values drive a profile-synthesis algorithm to generate two-dimensional height grids, which are exported as CRG files and imported into a multibody simulation software (MSC ADAMS) as well as driver-in-the-loop platforms. Simulation results closely reproduce the primary excitation characteristics of the physical tracks, demonstrating a preliminary proof-of-concept pipeline for virtual road surface generation. While the cross-validated regression model indicates limited generalization on the current small dataset ($R^2=-0.2783$), the end-to-end workflow establishes the baseline integration required for future data-driven NVH simulation. To extend applicability beyond a single test vehicle, a set of Vehicle Calibration Transforms is proposed to adapt power-spectral-density features from arbitrary vehicles into the calibrated feature domain. The complete workflow promises to streamline virtual NVH validation, reduce prototype testing, and support full NVH simulator engineering in future research. Full article

Non-invasive characterization of painting stratigraphy is challenged by light attenuation in optically heterogeneous opaque materials composing micrometric layers. Optical coherence tomography (OCT) provides suitable, non-invasive, depth-resolved imaging capabilities; however, large-area stratigraphic analysis is limited by the restricted field of view of individual stacks, high data dimensions, and the lack of robust methods for stitching volumes acquired at different focal depths. In addition, conventional layer thickness estimation relies on manual identification of intensity peaks along A-scans, which is time-consuming, operator-dependent, and unsuitable for large-scale analysis. Building upon our prior work, which introduced an AI-enhanced method for OCT volume analysis, we present an automated workflow integrating supervised semantic segmentation, volumetric mosaic stitching, and pixel-wise layer thickness quantification. OCT B-scans are segmented using a Random Forest classifier within the Trainable Weka Segmentation framework to delineate material interfaces. Adjacent OCT volumes are then combined into a continuous mosaic, and interfacial distances are computed using a custom MATLAB routine to obtain pixel-wise thickness measurements over extended fields of view. The model discriminates air/paint and paint/primer interfaces with an accuracy ranging from 96.4% to 97.1% and a weighted average F1-score exceeding 0.95, enabling quantitative reconstruction of paint thickness with micrometric resolution. This method enables efficient and reproducible analysis of large OCT datasets, extends stratigraphic characterization to macroscopic fields of view while maintaining micrometric resolution, and reduces processing time while improving consistency compared to manual approaches. Full article

To enhance drive performance, this paper introduces an advanced speed controller architecture intended for a brushless DC motor (BLDCM) operating under field-oriented control (FOC). This newly developed controller integrates decision tree theory (DTT) with sliding mode theory (SMT). Initially, the regression algorithm from the classification and regression tree (CART) framework is applied to partition the deviation between the actual motor speed and the target command into 10 distinct error zones. These intervals serve as the basis for configuring three critical parameters of a standard exponential reaching law sliding mode controller (ERLSMC): namely, the sliding mode dynamic trajectory control gain, the exponential reaching gain, and the constant speed reaching gain. Following each split, the mean squared error (MSE) of the respective nodes is evaluated to determine the root node. The dataset is recursively bifurcated into dual subsets using the chosen split variables and thresholds, establishing a structured decision pathway through each successive child node. As a result, the sliding mode speed controller receives dynamically optimized modifications for its three key gains in real time during BLDCM operation. In addition, the controller continuously computes an updated sliding mode dynamic trajectory control gain by tracking the derivative of the speed error. Tuning these three operational gains effectively mitigates the transient overshoot typically induced by the conventional exponential reaching law (ERL) across diverse running states. This mechanism ensures that the speed response of the BLDCM drive system dynamically and accurately follows target commands under fluctuating conditions. Advantageously, the introduced control strategy avoids intensive computational routines and eliminates the need for extensive training datasets, ensuring straightforward implementation. To validate this approach, the proposed methodology is applied to the BLDCM drive system using the Matlab/Simulink environment. Its execution is benchmarked against conventional sliding mode controllers (SMCs) configured with three distinct control strategies: the constant speed reaching law (CSRL), the standard ERL, and the extension theory combined with exponential reaching law (ETERL). The resulting simulation data confirms that the proposed adaptive controller delivers superior performance over the alternative three reaching laws regarding both transient command tracking and robustness in load regulation. Full article

This paper presents a practical numerical procedure for the study of structures on foundations subjected to soil consolidation settlements, using the Finite Element Method (FEM) coupled with the Boundary Element Method (BEM). A theoretical application is presented for a structure built on saturated soft soil, employing the Kelvin–Voigt and Boltzmann viscoelastic models. The Kelvin–Voigt model is suitable for situations where uniform or negligible initial settlements are assumed before the onset of soil consolidation, whereas the Boltzmann model allows for the consideration of differential movements, including both immediate and time-dependent displacements. This study shows that, although the same viscoelastic parameters are adopted for both models, the differences in internal forces and resulting displacements can be significant due to the distinct relative stiffnesses. The choice of viscoelastic model directly impacts the prediction of structural behavior. The analyses were conducted considering an iterative coupling between the FEM and BEM systems, using an MATLAB R2024a routine developed by the authors. Despite the differences between the models, the results obtained were consistent with the technical literature, reinforcing the applicability of the proposed procedure. Full article

This article evaluates two model-based systems engineering (MBSE) toolchains that support aircraft certification under EASA CS-23 Amendment 6. Airworthiness requirements and associated acceptable means of compliance are digitalized as Systems Modeling Language (SysML) models that preserve document structure and encode parameters and expressions needed for substantiation. The maneuvering and gust flight envelope required by CS-23 Subpart C is used as a representative case to compare workflow integration, robustness, and artifact generation. One implementation combines Eclipse Papyrus with MATLAB to export and parse the SysML model and to execute automated calculations and reporting. The second uses CATIA Magic Systems of Systems Architect (MSoSA) to export stereotype fields to JSON and to run C++ routines orchestrated by activity diagrams. Both toolchains generate certification-relevant outputs, including design airspeeds, limit load factors, and flight envelope plots, while improving traceability relative to document-centric practice. The comparison indicates that the Papyrus/MATLAB approach supports rapid prototyping but is more sensitive to regulatory text changes, whereas the MSoSA-based approach reduces dependence on text–pattern parsing and provides more integrated execution. These results suggest that MBSE can improve the efficiency of preparing certification evidence, with adoption trade-offs driven by licensing cost, integration effort, and organizational maturity. Full article

We developed a hardware-in-the-loop simulation of a battery management system (BMS) using controller area network (CAN) as the communication backbone for electric-powered response boats in flood rescue. A LiFePO4 pack and discharge motor/charger were modeled in MATLAB/Simulink/Simscape, while an STM32 Nucleo-F446RE executed CAN messaging. The BMS monitored voltage, current, temperature, and state of charge. Results indicate CAN’s reliability under rescue-like disturbances: priority arbitration delivered over-temperature and over-current warnings ahead of routine telemetry; error detection and retransmission preserved data integrity; and bus-load analysis showed low latency for urgent frames without interrupting state-of-charge reporting, improving situational awareness and reducing operator risk. Full article

Timber frame diaphragms play a central role in the lateral stability of modern timber buildings, yet current design codes insufficiently capture their nonlinear behaviour and governing failure mechanisms. This study evaluates two finite element modelling strategies to improve the prediction of diaphragm response. The first strategy, implemented in MATLAB^®, explicitly models the nonlinear behaviour of sheathing-to-framing (STF) connections using an oriented orthogonal multilinear damage law. Validation against experimental tests on partially anchored and fully anchored diaphragms as well as in-plane bending specimens demonstrated accurate predictions of stiffness and force–displacement behaviour in both the linear-elastic and elastoplastic ranges. Deviations in peak load predictions for the detailed model reached up to approximately 25%, while stiffness predictions remained within approximately 10% of the experimental values. The second approach, implemented in commercial structural engineering software, represents STF connections by uncoupled elastoplastic spring elements. Although post-peak softening cannot be captured, peak capacities were predicted within approximately 3–5% for several configurations, with reliable stiffness estimates in most cases. A quantitative comparison using the normalised root mean square error between experimental and numerical force-displacement curves yielded values between approximately 5% and 14%, indicating good agreement between the numerical predictions and the experimental behaviour. Overall, the detailed model enables high-fidelity nonlinear analysis and insight into failure mechanisms, whereas the simplified spring approach offers a practical and computationally efficient modelling strategy suitable for routine engineering design. Full article

Accurate estimation of structural damping is essential for seismic performance assessment and design for earthquake-resistant buildings. From a sustainability perspective, reliable evaluation of dynamic properties is crucial in extending the service life of existing structures and reducing the need for material-intensive interventions. Ambient vibration measurements enable non-invasive identification of damping characteristics, supporting sustainable assessment of the built environment. This paper presents an analysis of the dynamic response of a four-story reinforced concrete structure. Ambient vibration recordings are obtained with Geodas Aquisition Station and one-second velocity sensors made by Butan Service And Tokio Soil Ltd., available from CERS (Seismic Risk Assessment Research Center) research center from TUCEB (Technical University of Civil Engineering of Bucharest). The sensors were installed at the top level of the analyzed structure. The method used for estimating the damping ratio is the Random Decrement Technique (RDT). The influence of the several parameters involved in the method is investigated, such as the triggering value, the dimension of the time window sub-samples, and the number of cycles considered within a window relative to the natural period of the structure. For the analysis of the parameters specific to the RDT method, computational routines were developed using syntax compatible with OCTAVE/MATLAB R2019b. Filters were applied to isolate the natural vibration modes. The variability in the parameters demonstrates that the developed method is robust. Full article

Background: Radiomics has emerged as a promising approach to non-invasively characterize the molecular landscape of gliomas, providing quantitative, high-dimensional data derived from routine MRI. Given the recent shift toward molecularly driven classification, radiomics may support precision oncology by predicting key genomic, epigenetic, and phenotypic alterations without the need for invasive tissue sampling. This systematic review aimed to synthesize current radiomics applications for the non-invasive prediction of molecular biomarkers in gliomas, evaluating methodological trends, performance metrics, and translational readiness. Methods: This review followed the PRISMA 2020 guidelines. A systematic search was conducted in PubMed, Ovid MEDLINE, and Scopus on 10 January 2025, and updated on 1 February 2025, using predefined MeSH terms and keywords related to glioma, radiomics, machine learning, deep learning, and molecular biomarkers. Eligible studies included original research using MRI-based radiomics to predict molecular alterations in human gliomas, with reported performance metrics. Data extraction covered study design, cohort size, MRI sequences, segmentation approaches, feature extraction software, computational methods, biomarkers assessed, and diagnostic performance. Methodological quality was evaluated using the Radiomics Quality Score (RQS), Image Biomarker Standardization Initiative (IBSI) criteria, and Newcastle–Ottawa Scale (NOS). Due to heterogeneity, no meta-analysis was performed. Results: Of 744 screened records, 70 studies met the inclusion criteria. A total of 10,324 patients were included across all studies (mean 140 patients/study, range 23–628). The most frequently employed MRI sequences were T2-weighted (59 studies, 84.3%), contrast-enhanced T1WI (53 studies, 75.7%), T1WI (50 studies, 71.4%), and FLAIR (48 studies, 68.6%); diffusion-weighted imaging was used in only 7 studies (12.8%). Manual segmentation predominated (52 studies, 74.3%), whereas automated approaches were used in 13 studies (18.6%). Common feature extraction platforms included 3D Slicer (20 studies, 28.6%) and MATLAB-based tools (17 studies, 24.3%). Machine learning methods were applied in 47 studies (67.1%), with support vector machines used in 29 studies (41.4%); deep learning models were implemented in 27 studies (38.6%), primarily convolutional neural networks (20 studies, 28.6%). IDH mutation was the most frequently predicted biomarker (49 studies, 70%), followed by ATRX (27 studies, 38.6%), MGMT methylation (8 studies, 11,4%), and 1p/19q codeletion (7 studies, 10%). Reported AUC values ranged from 0.80 to 0.99 for IDH, approximately 0.71–0.953 for 1p/19q, 0.72–0.93 for MGMT, and 0.76–0.97 for ATRX, with deep learning or hybrid pipelines generally achieving the highest performance. RQS values highlighted substantial methodological variability, and IBSI adherence was inconsistent. NOS scores indicated high-quality methodology in a limited subset of studies. Conclusions: Radiomics demonstrates strong potential for the non-invasive prediction of key glioma molecular biomarkers, achieving high diagnostic performance across diverse computational approaches. However, widespread clinical translation remains hindered by heterogeneous imaging protocols, limited standardization, insufficient external validation, and variable methodological rigor. Full article

Microbiological analysis is crucial for identifying species, assessing infections, and diagnosing infectious diseases, thereby supporting both research studies and medical diagnosis. In response to these needs, accurate and efficient identification of bacterial colonies is essential. Conventionally, this process is performed through manual counting and visual inspection of colonies on agar plates. However, this approach is prone to several limitations arising from human error and external factors such as lighting conditions, surface reflections, and image resolution. To overcome these limitations, an automated bacterial colony counting and classification system was developed by integrating a custom-designed imaging device with advanced deep learning models. The imaging device incorporates controlled illumination, matte-coated surfaces, and a high-resolution camera to minimize reflections and external noise, thereby ensuring consistent and reliable image acquisition. Image-processing algorithms implemented in MATLAB were employed to detect bacterial colonies, remove background artifacts, and generate cropped colony images for subsequent classification. A dataset comprising nine bacterial species was compiled and systematically evaluated using five deep learning architectures: ResNet-18, ResNet-50, Inception V3, GoogLeNet, and the state-of-the-art EfficientNet-B0. Experimental results demonstrated high colony-counting accuracy, with a mean accuracy of 90.79% ± 5.25% compared to manual counting. The coefficient of determination (R² = 0.9083) indicated a strong correlation between automated and manual counting results. For colony classification, EfficientNet-B0 achieved the best performance, with an accuracy of 99.78% and a macro-F1 score of 0.99, demonstrating strong capability in distinguishing morphologically distinct colonies such as Serratia marcescens. Compared with previous studies, this research provides a time-efficient and scalable solution that balances high accuracy with computational efficiency. Overall, the findings highlight the potential of combining optimized imaging systems with modern lightweight deep learning models to advance microbiological diagnostics and improve routine laboratory workflows. Full article

Developing the analytical model of a multibody system is often the initial step in control and optimization. The analytical model (equations of motion) describes a system’s time evolution under specified forcing conditions. Although developing these equations is easy for simple systems, this process becomes more complex for systems composed of multiple bodies. Deriving equations of motion for complex multibody systems requires specialized expertise in multibody dynamics, is time-consuming, and is susceptible to error. To address this issue, this paper presents an open-source, easy-to-use, systematic framework to derive symbolic equations of motion in both Python and MATLAB using the joint coordinate formulation. This formulation results in a set of ordinary differential equations that use the minimum set of coordinates needed to model a system. The symbolic representation provides better insight into the influence of design parameters on system performance, facilitates sensitivity analysis and parameter studies, and supports direct implementation of control and optimization routines. The tool enables numerical simulation for specified parameter sets, is modular for straightforward integration with other tools and libraries, and allows incorporation of hydrodynamics, mooring, and other external forces. The result is a reproducible, extensible pipeline for modeling, simulation, and design of complex multibody systems. The proposed tool is versatile and can be applied to domains such as robotics, control, and design. In addition, we integrated external libraries that provide capabilities for modeling offshore systems such as underwater robots and marine energy converters. Full article

In this paper, complementary metal-oxide semiconductor (CMOS) circuit realization of a low-pass elliptic filter of order (1 + α) is realized using the inverse follow-the-leader feedback (IFLF) topology. The transfer functions to approximate the passband and stopband ripple characteristics of the second-order elliptic low-pass filter are synthesized using the nonlinear least squares (NLS) optimization routine. The elliptic filters of orders 1.4, 1.6, and 1.8 are designed using a cross-coupled operational transconductance amplifier (OTA) in the United Microelectronics Corporation (UMC) 180 nm CMOS process. The dynamic range of the filter was found to be 49.7 dB, 52.08 dB, and 54.02 dB for an order of 1.4, 1.6, and 1.8, respectively. The circuit simulation results such as magnitude, phase, transient, and group delay plots, are validated with the MATLAB simulation plots. Monte Carlo and PVT analyses have demonstrated the accuracy and robustness of the design. The proposed approach supports quality education and industry, innovation, and infrastructure. Full article

Reservoir petrophysical characterization is traditionally performed through the interpretation of well logs validated with routine core analysis (RCAL), often excluding the integration of other tools such as computed tomography (CT), which provides interpretation of higher resolution. In this study, artificial neural network (ANN) models were applied to estimate porosity and permeability by integrating conventional logs with CT-derived data (RHOB and PEF), thereby validating the petrophysical model of Ciénaga de Oro Formation. Neural networks were trained in MATLAB^® using a feed-forward regression network based on a multilayer perceptron (MLP) architecture, with RCAL measurements serving as a reference. Model performance was assessed by comparing predictions with laboratory data from two wells, yielding high accuracy (R² = 0.98 for permeability and R² = 0.90 for porosity) with mean absolute errors below 5%. Additional validation was performed using well logs and CT data from complete 3 ft sections, with the trained models successfully reproducing core heterogeneities at millimetric resolution. These results confirm the potential of integrating well logs and CT data with ANN to enhance petrophysical characterization and extend property estimation to wells lacking core or laboratory measurements. Furthermore, an interactive MATLAB^® tool was developed, enabling users to load well logs and CT files as flat inputs, generate high-resolution predictions, validate results, and export the estimated values. Full article

Cracks serve as a critical indicator of tunnel structural degradation. Manual inspections are difficult to meet engineering requirements due to their time-consuming and labor-intensive nature, high subjectivity, and significant error rates, while traditional image processing methods exhibit poor performance under complex backgrounds and irregular crack morphologies. To address these limitations, this study developed a high-quality dataset of tunnel crack images and proposed an improved lightweight semantic segmentation network, LiteSqueezeSeg, to enable precise crack identification and quantification. The model was systematically trained and optimized using a dataset comprising 10,000 high-resolution images. Experimental results demonstrate that the proposed model achieves an overall accuracy of 95.15% in crack detection. Validation on real-world tunnel surface images indicates that the method effectively suppresses background noise interference and enables high-precision quantification of crack length, average width, and maximum width, with all relative errors maintained within 5%. Furthermore, an integrated intelligent detection system was developed based on the MATLAB (R2023b) platform, facilitating automated crack feature extraction and standardized defect grading. This system supports routine tunnel maintenance and safety assessment, substantially enhancing both inspection efficiency and evaluation accuracy. Through synergistic innovations in lightweight network architecture, accurate quantitative analysis, and standardized assessment protocols, this research establishes a comprehensive technical framework for tunnel crack detection and structural health evaluation, offering an efficient and reliable intelligent solution for tunnel condition monitoring. Full article