Ground-point classification (GPC) is essential for light detection and ranging (LiDAR)-derived terrain and vegetation products, yet most of the well-established GPC algorithms often require parameter calibration across varying terrain, vegetation, point densities, and sensor modalities—airborne laser scanning (ALS), unoccupied aerial vehicle laser scanning (ULS), mobile laser scanning (MLS), and terrestrial laser scanning (TLS). This study introduces FAST-GC, a fully adaptive, self-tuning, parameter-free GPC algorithm designed for ALS, ULS, MLS, and TLS LiDAR sensors. FAST-GC was evaluated against cloth simulation filtering (CSF), progressive TIN densification (PTD), and progressive morphological filtering (PMF) across benchmark sites (n = 6), with additional terrain-complexity, slope, and sampling-density assessments. FAST-GC maintained high ground completeness with limited commission errors, particularly under complex topography. At slopes of 70–80°, the FAST-GC-derived digital terrain model (DTM) mean absolute error (MAE) was 1.00 m, compared with 3.12–12.75 m for the reference counterparts—CSF, PMF, and PTD. Across 20 ALS sites, reducing median point density from 9.84 to 3.84 points/m2 maintained F1 ≈ 0.97, intersection over union (IoU) ≈ 0.95, and Matthews correlation coefficient (MCC) ≈ 0.85, while DTM MAE increased slightly from 0.07 to 0.11 m. FAST-GC-derived DTM (0.5 m) from concurrent ALS, ULS, and TLS showed strong cross-platform agreement (R2 and concordance correlation coefficient (CCC) ≥ 0.99; RMSE = 0.10–0.16 m). Processing 382 ALS tiles (~23 km2) further demonstrated scalable execution and automated terrain-product generation. These findings establish FAST-GC as a reproducible, sensor-aware framework that preserves terrain morphology across heterogeneous LiDAR datasets without scene-specific parameter calibration.
A low-temperature post-processing Al-migration module is used to achieve separate absorption, charge, and multiplication (SACM) functionality in vertical Ge-on-Si photodiodes without dedicated charge-layer formation by implantation or epitaxy. During a 400 °C alloying step, Al migrates from the contacts through perimeter openings of selectively grown Ge islands and modifies the Ge/Si interface, increasing the conduction-band potential barrier height. This mechanism is supported by I–V and C–V characterization and spatially resolved photoresponse under selective illumination of Ge and Si. At low reverse bias, the barrier suppresses carrier collection from Ge, resulting in uniformly low near-infrared (NIR) responsivity. Increasing reverse bias lowers the barrier and increases responsivity before avalanche. Room-temperature optical gain is demonstrated at 1310 and 1550 nm, reaching approximately 5 at 1310 nm. The photoresponse remains spatially uniform across the Ge island at all reverse biases. Light-emission mapping and wavelength-selective laser probing indicate that multiplication occurs predominantly in Si. Separate 1D and 2D TCAD simulations investigate the impact of interface charge and Ge-island geometry on electric-field redistribution, showing that moderate negative interface charge shifts the peak field into Si, while corner-induced field crowding, which will locally lower the interface barrier, facilitates the transport of photogenerated carriers from Ge into high-field regions in Si. The proposed module is potentially compatible with back-end-of-line processing and applicable to other vertical Ge-on-Si structures.
This study proposes a non-invasive experimental setup that integrates environmental and physiological sensors with personal parameters (age, gender, and Body Mass Index (BMI)) to measure the perceived thermal comfort of occupants via a Machine Learning (ML) framework. An experimental campaign involving 23 participants was conducted across cold (17–20 °C), neutral (22–24 °C), and warm conditions (26–28 °C). Data collected combined environmental measurements, continuous wearable physiological signals, subjective Thermal Sensation Votes (TSVs), and a multidimensional Global Sensation Vote (GSV). Evaluating six distinct datasets via a Leave-One-Subject-Out (LOSO) cross-validation strategy revealed that the value of personal data depends heavily on the underlying sensor architecture. While integrating personal data introduced slight noise in purely environmental linear models (decreasing accuracy by 4.77%), it yielded significant quantitative increments as sensor complexity shifted toward the individual, boosting accuracy by 2.03% in physiological setups and a remarkable 13.6% in bare-minimum sensor networks. Overall predictive performance reached an accuracy of 73% using a Support Vector Machine (SVM-LIN) for environmental configurations, while an XGBoost model achieved a robust 68% accuracy in a highly reduced, low-cost sensor setup by leveraging personal context. The innovative aspect of this research is the simultaneous use of sensors to monitor both physiological and environmental parameters, which, when combined with subjective feedback and personal data, significantly increases the prediction of TSV in practical cases. This integrated approach suggests prospective applications for vulnerable categories whose thermoregulatory responses deviate from standardized models, though it requires future empirical validation among these specific demographics. Overall, the integration of personal parameters into data-driven models contributes to generalizing individual physiological differences and supports the development of more effective, human-centric IoT monitoring systems.
Device-to-Device (D2D) communication is a key technology for 5G-Advanced and 6G networks, enabling nearby User Equipment (UE) to communicate directly or via a relay when the direct link is blocked or unreliable. At Millimeter-Wave (mmWave) frequencies, frequent blockage and intermittent connectivity make relay selection a sequential decision problem under uncertainty, naturally formulated as a Multi-Armed Bandit (MAB). This paper develops and evaluates two Thompson Sampling (TS) approaches for D2D relay selection using a 3GPP TR 38.901 UMi-Street Canyon channel model. The first TS approach uses a Beta-Bernoulli model to maximize link-success probability, whereas the second TS uses a Gaussian-conjugate model to maximize continuous spectral efficiency. Over T=2000 rounds with N=10 candidate relays and shared channel realizations, both approaches identify the best relay within about 100–150 rounds and direct more than 94% of selections to it. Compared with random selection, they raise the mean spectral efficiency by ≈3×, raise the link-success rate to above 99%, and cut the outage probability to 0.9% (TS-Bernoulli) and 0.4% (TS-Gaussian). These results demonstrate the effectiveness of TS for relay selection under realistic mmWave propagation conditions and highlight the importance of aligning the learning objective with the target performance metric.
Industrial noise can degrade acoustic fault diagnosis differently across input representations. This study evaluated whether classifier-independent waveform restoration could reduce this disparity in BLDC motor diagnosis. A Wave-U-Net denoiser was trained without classifier losses or outputs and applied before generating time-domain waveform and STFT magnitude inputs for six CNN, LSTM, and CNN–LSTM classifiers, which remained unchanged. The dataset comprised 13,790 one-second acoustic segments recorded from a single BLDC motor under normal operating conditions and bearing, eccentricity, and electrical fault conditions. Industrial noise environments were simulated by mixing the recorded motor signals with external noise recordings from the MIMII dataset. At 0 dB, restoration increased mean classification accuracy from 69.27% to 97.37% and reduced the mean performance gap between the two input representations from 48.61 to 1.01 percentage points. Improvements were also observed at evaluated SNRs below the training range and under noise types excluded from denoiser training. Grad-CAM and feature-space analyses indicated that restoration shifted decision patterns and class-wise feature structures toward those observed without added industrial noise. These findings support waveform restoration as a common preprocessing approach for reducing representation-dependent degradation under the evaluated conditions.
