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To study the influence of the geometric structure of the probe coil on the electromagnetic characteristics of the eddy current probe in the process of eddy current testing, based on the principle of eddy current testing, different probe coil models were established using finite element software. These geometric structure parameters include the difference between the inner and outer radius, thickness, and equivalent radius. The magnetic field distribution around the probe is simulated and analyzed under different parameters, and the detection performance of the probe is judged in combination with the change rate of the magnetic field around the probe coil. The simulation results show that at a closer position, increasing the difference between the inner and outer radii, reducing the thickness, and reducing the equivalent radius are beneficial to improve the resolution of the probe coil. At a far position, reducing the difference between the inner and outer radii, increasing the thickness, and reducing the equivalent radius are beneficial to improve the resolution of the probe coil. At the same time, the accuracy of the simulation data is verified by comparing the theoretical values with the simulated values under different conditions. Therefore, the obtained conclusions can provide a reference and basis for the optimal design of the probe structure.

Indoor occupancy prediction is a prerequisite for the management of energy consumption, security, health, and other systems in smart buildings. Previous studies have shown that buildings that automatize their heating, lighting, air conditioning, and ventilation systems through considering the occupancy and activity information might reduce energy consumption by more than 50%. However, it is difficult to use high-resolution sensors and cameras for occupancy prediction due to privacy concerns. In this paper, we propose a novel solution for predicting occupancy using multiple low-cost and low-resolution heat sensors. We suggest two different methods for fusing and processing the data captured from multiple heat sensors and we use a Convolutional Neural Network for predicting occupancy. We conduct experiments to assess both the performance of the proposed solutions and analyze the impact of sensor field view overlaps on the prediction results. In summary, our experimental results show that the implemented solutions show high occupancy prediction accuracy and real-time processing capabilities.

Tactile sensing is increasingly vital in robotics, especially for tasks like object manipulation and texture classification. Among tactile technologies, optical and electrical sensors are widely used, yet no rigorous direct comparison of their performance has been conducted. This paper addresses that gap by presenting a comparative study between a high-resolution optical tactile sensor (a modified TacTip) and a low-resolution electrical sensor combining accelerometers and piezoelectric elements. We evaluate both sensor types on two tasks: texture classification and coefficient of dynamic friction prediction. Various configurations and resolutions were explored, along with multiple machine learning classifiers to determine optimal performance. The optical sensor achieved 99.9% accuracy on a challenging texture dataset, significantly outperforming the electrical sensor, which reached 82%. However, for dynamic friction prediction, both sensors performed comparably, with only a 5~% accuracy difference. We also found that the optical sensor retained high classification accuracy even when image resolution was reduced to 25% of its original size, suggesting that ultra-high resolution is not essential. In conclusion, the optical sensor is the better choice when high accuracy is required. However, for low-cost or computationally efficient systems, the electrical sensor provides a practical alternative with competitive performance in some tasks.

The monitoring of irrigated areas still represents a complex and laborious challenge in land use classification. The extent and location of irrigated areas vary in both methodology and scale. One major reason for discrepancies is the choice of spatial resolution. This study evaluates the influence of spatial resolution on the mapped extent and spatial patterns of irrigation using an NDVI threshold approach with Sentinel-2 and operational PROBA-V data. The influence of resolution on irrigation mapping was analyzed in the USA, China and Sudan to cover a broad range of agricultural systems by comparing results from original 10 m Sentinel-2 data with mapped coarser results at 20 m, 40 m, 60 m, 100 m, 300 m, 600 m and 1000 m and with results from PROBA-V. While the mapped irrigated area in China is constant independent of resolution, it decreases in Sudan (−29%) and the USA (−48%). The differences in the mapping result can largely be explained by the spatial arrangement of the irrigated pixels at a fine resolution. The calculation of landscape metrics in the three regions shows that the Landscape Shape Index (LSI) can explain the loss of irrigated area from 10 m to 300 m (r > 0.9).

High-resolution sensors onboard satellites are generally reputed for rapidly producing land-use/land-cover (LULC) maps with improved spatial detail. However, such maps are subject to uncertainties due to several factors, including the training sample size. We investigated the effects of different training sample sizes (from 1000 to 12,000 pixels) on LULC classification accuracy using the random forest (RF) classifier. Then, we analyzed classification uncertainties by determining the median and the interquartile range (IQR) of the overall accuracy (OA) values through repeated k-fold cross-validation. Results showed that increasing training pixels significantly improved OA while minimizing model uncertainty. Specifically, larger training samples, ranging from 9000 to 12,000 pixels, exhibited narrower IQRs than smaller samples (1000–2000 pixels). Furthermore, there was a significant variation (Chi2 = 85.073; df = 11; p < 0.001) and a significant trend (J-T = 4641, p < 0.001) in OA values across various training sample sizes. Although larger training samples generally yielded high accuracies, this trend was not always consistent, as the lowest accuracy did not necessarily correspond to the smallest training sample. Nevertheless, models using 9000–11,000 pixels were effective (OA > 96%) and provided an accurate visual representation of LULC. Our findings emphasize the importance of selecting an appropriate training sample size to reduce uncertainties in high-resolution LULC classification.

The use of data from multiple sensors is often required to ensure data coverage and continuity, but differences in the spectral characteristics of sensors result in spectral index values being different. This study investigates spectral response function effects on 48 spectral indices for cultivated grasslands using simulated data of 10 very high spatial resolution sensors, convolved from field reflectance spectra of a grass covered dike (with varying vegetation condition). Index values for 48 indices were calculated for original narrow-band spectra and convolved data sets, and then compared. The indices Difference Vegetation Index (DVI), Global Environmental Monitoring Index (GEMI), Enhanced Vegetation Index (EVI), Modified Soil-Adjusted Vegetation Index (MSAVI2) and Soil-Adjusted Vegetation Index (SAVI), which include the difference between the near-infrared and red bands, have values most similar to those of the original spectra across all 10 sensors (1:1 line mean 1:1R2 > 0.960 and linear trend mean ccR2 > 0.997). Additionally, relationships between the indices’ values and two quality indicators for grass covered dikes were compared to those of the original spectra. For the soil moisture indicator, indices that ratio bands performed better across sensors than those that difference bands, while for the dike cover quality indicator, both the choice of bands and their formulation are important.

Excess thermal energy can cause significant damage to the mechanical parts of various systems. Temperature sensors can measure and control the temperature in these systems to prevent such damage. However, the applications of existing temperature sensors are limited by their shape as it limits their ability to attach to different surfaces. Consequently, it is challenging to measure the temperature of curved surfaces. In this study, we propose a smart paint that can measure the temperature of curved surfaces to address the above issues. The smart paint fabrication method must be optimized because the resistance characteristics of these paints differ according to the manufacturing conditions. In this study, a curing process was used to manufacture the smart paint; the optimal curing temperature and curing time were determined experimentally. Furthermore, a coating solution was applied to increase the surface stability of the fabricated paint. The coating was applied using a spin coater at a specific rotation time and speed. We also determined the optimal coating conditions. The coating was confirmed to increase the surface stability of the smart paint.

A high-resolution sensor using a piezoelectric drum transducer is proposed for power frequency current sensing (50 Hz or 60 Hz). The utilization of the magnetic circuit helps to enhance the response to the electric currents in the power cords. The high sensitivity of the sensor originates from the superposition of the Ampere forces and the amplified piezoelectric effect of the drum transducer. The feasibility of the sensor was verified by experiments. The device exhibits a broad 3 dB bandwidth of 67.4 Hz without an additional magnetic field bias. The average sensitivity is 31.34 mV/A with a high linearity of 0.49%, and the resolution of the sensor attains 0.02 A. The resolution is much higher than that of the previous piezoelectric heterostructure for two-wire power-cords. Error analysis shows that the uncertainty reaches 0.01865 mV at the current of 2.5 A. Meanwhile, the device can generate a load power of 447.9 nW with an optimal load resistance of 55 KΩ at 10A (f = 50 Hz) in energy harvesting experiments. The features of high sensitivity, excellent linearity, high resolution, low costs, and convenient installation demonstrate the application prospect of the proposed device for measuring power frequency currents in electric power grids.

The use of Very High Spatial Resolution (VHSR) imagery in remote sensing applications is nowadays a current practice whenever fine-scale monitoring of the earth’s surface is concerned. VHSR Land Cover classification, in particular, is currently a well-established tool to support decisions in several domains, including urban monitoring, agriculture, biodiversity, and environmental assessment. Additionally, land cover classification can be employed to annotate VHSR imagery with the aim of retrieving spatial statistics or areas with similar land cover. Modern VHSR sensors provide data at multiple spatial and spectral resolutions, most commonly as a couple of a higher-resolution single-band panchromatic (PAN) and a coarser multispectral (MS) imagery. In the typical land cover classification workflow, the multi-resolution input is preprocessed to generate a single multispectral image at the highest resolution available by means of a pan-sharpening process. Recently, deep learning approaches have shown the advantages of avoiding data preprocessing by letting machine learning algorithms automatically transform input data to best fit the classification task. Following this rationale, we here propose a new deep learning architecture to jointly use PAN and MS imagery for a direct classification without any prior image sharpening or resampling process. Our method, namely M u l t i R e s o L C C , consists of a two-branch end-to-end network which extracts features from each source at their native resolution and lately combine them to perform land cover classification at the PAN resolution. Experiments are carried out on two real-world scenarios over large areas with contrasted land cover characteristics. The experimental results underline the quality of our method while the characteristics of the proposed scenarios underline the applicability and the generality of our strategy in operational settings.

We have demonstrated metal-on-silicon thermocouples with a noticeably high Seebeck coefficient and an excellent temperature-sensing resolution. Fabrication of the thermocouples involved only simple photolithography and metal-liftoff procedures on a silicon substrate. The experimentally measured Seebeck coefficient of our thermocouple was 9.17 × 10 −4  V/°K, which is 30 times larger than those reported for standard metal thin-film thermocouples and comparable to the values of alloy-based thin-film thermocouples that require sophisticated and costly fabrication processes. The temperature-voltage measurements between 20 to 80 °C were highly linear with a linearity coefficient of 1, and the experimentally demonstrated temperature-sensing resolution was 0.01 °K which could be further improved up to a theoretical limit of 0.00055 °K. Finally, we applied this approach to demonstrate a flexible metal-on-silicon thermocouple with enhanced thermal sensitivity. The outstanding performance of our thermocouple combined with an extremely thin profile, bending flexibility, and simple, highly-compatible fabrication will proliferate its use in diverse applications such as micro-/nanoscale biometrics, energy management, and nanoscale thermography.