Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying Floating Plastic Debris at Sea Using Vessel-Based Optical Data and Artificial Intelligence

Despite recent advances in remote sensing of large accumulations of floating plastic debris, mainly in coastal regions, the quantification of individual macroplastic (> 0.5 m) remains a challenge. Here, we have trained an object-detection algorithm by selecting and labeling footage of floating plastic debris recorded offshore with GPS-enabled action cameras aboard vessels of opportunity. Macroplastic numerical concentrations are estimated by combining the object detection solution with bulk processing of the optical data. The first results are promising, and the data is comparable to previous observations using different sampling methods.
Medium-Sized Highly Coupled Planar Arrays with Maximum Aperture Efficiency

This paper presents a technique to design strongly coupled planar arrays with very high aperture efficiency. The key innovation is that, based on an irregular 2 × 1 array, very compact medium-sized arrays of size 2 × 2, 2 × 4, and 2 × 6 are constructed with very strong and constructive mutual coupling between the elements. In this way, a maximum aperture efficiency is reached for a given footprint of the array. The occupied space of the antenna in comparison with conventional linear patch arrays is studied. A prototype 2 × 4 array operating around 5.8 GHz is designed, fabricated, built, and measured. The results show a large bandwidth of 20% and a very high aperture efficiency of 100%, which is the largest found in the literature for similarly sized arrays. These results are important in view of the future Internet of Things, where small and medium-sized arrays are planned to be mounted on numerous devices where a very limited physical area is available.
Kinematic Zenith Tropospheric Delay Estimation with GNSS PPP in Mountainous Areas

The use of global navigation satellite systems (GNSS) precise point positioning (PPP) to estimate zenith tropospheric delay (ZTD) profiles in kinematic vehicular mode in mountainous areas is investigated. Car-mounted multi-constellation GNSS receivers are employed. The Natural Resources Canada Canadian Spatial Reference System PPP (CSRS-PPP) online service that currently processes dual-frequency global positioning system (GPS) and Global’naya Navigatsionnaya Sputnikovaya Sistema (GLONASS) measurements and is now capable of GPS integer ambiguity resolution is used. An offline version that can process the above and Galileo measurements simultaneously, including Galileo integer ambiguity resolution is also tested to evaluate the advantage of three constellations. A multi-day static data set observed under open sky is first tested to determine performance under ideal conditions. Two long road profile tests conducted in kinematic mode are then analyzed to assess the capability of the approach. The challenges of ZTD kinematic profiling are numerous, namely shorter data sets, signal shading due to topography and forests of conifers along roads, and frequent losses of phase lock requiring numerous but not always successful integer ambiguity re-initialization. ZTD profiles are therefore often only available with float ambiguities, reducing system observability. Occasional total interruption of measurement availability results in profile discontinuities. CSRS-PPP outputs separately the zenith hydrostatic or dry delay (ZHD) and water vapour content or zenith wet delay (ZWD). The two delays are analyzed separately, with emphasis on the more unpredictable and highly variable ZWD, especially in mountainous areas. The estimated delays are compared with the Vienna Mapping Function 1 (VMF1), which proves to be highly effective to model the large-scale profile variations in the Canadian Rockies, the main contribution of GNSS PPP being the estimation of higher frequency ZWD components. Of the many conclusions drawn from the field experiments, it is estimated that kinematic profiles are generally determined with accuracy of 10 to 20 mm, depending on the signal harshness of the environment.
An RSS Transform—Based WKNN for Indoor Positioning

An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.
Improving the Resolution of GRACE Data for Spatio-Temporal Groundwater Storage Assessment

Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1° to a higher resolution (0.25°). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash–Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about −9.54 ± 1.27 km3 at the rate of −0.68 ± 0.09 km3/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales.
The Effects of the Structural and Acoustic Parameters of the Skull Model on Transcranial Focused Ultrasound

Transcranial focused ultrasound (tFUS) has great potential in brain imaging and therapy. However, the structural and acoustic differences of the skull will cause a large number of technical problems in the application of tFUS, such as low focus energy, focal shift, and defocusing. To have a comprehensive understanding of the skull effect on tFUS, this study investigated the effects of the structural parameters (thickness, radius of curvature, and distance from the transducer) and acoustic parameters (density, acoustic speed, and absorption coefficient) of the skull model on tFUS based on acrylic plates and two simulation methods (self-programming and COMSOL). For structural parameters, our research shows that as the three factors increase the unit distance, the attenuation caused from large to small is the thickness (0.357 dB/mm), the distance to transducer (0.048 dB/mm), and the radius of curvature (0.027 dB/mm). For acoustic parameters, the attenuation caused by density (0.024 dB/30 kg/m3) and acoustic speed (0.021 dB/30 m/s) are basically the same. Additionally, as the absorption coefficient increases, the focus acoustic pressure decays exponentially. The thickness of the structural parameters and the absorption coefficient of the acoustic parameters are the most important factors leading to the attenuation of tFUS. The experimental and simulation trends are highly consistent. This work contributes to the comprehensive and quantitative understanding of how the skull influences tFUS, which further enhances the application of tFUS in neuromodulation research and treatment.
Using Unmanned Aerial Vehicle and LiDAR-Derived DEMs to Estimate Channels of Small Tributary Streams

Defining stream channels in a watershed is important for assessing freshwater habitat availability, complexity, and quality. However, mapping channels of small tributary streams becomes challenging due to frequent channel change and dense vegetation coverage. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to obtain a 3D Digital Surface Model (DSM) to estimate the total in-stream channel and channel width within grazed riparian pastures. We used two methods to predict the stream channel boundary: the Slope Gradient (SG) and Vertical Slope Position (VSP). As a comparison, the same methods were also applied using low-resolution DEM, obtained with traditional photogrammetry (coarse resolution) and two more LiDAR-derived DEMs with different resolution. When using the SG method, the higher-resolution, UAV-derived DEM provided the best agreement with the field-validated area followed by the high-resolution LiDAR DEM, with Mean Squared Errors (MSE) of 1.81 m and 1.91 m, respectively. The LiDAR DEM collected at low resolution was able to predict the stream channel with a MSE of 3.33 m. Finally, the coarse DEM did not perform accurately and the MSE obtained was 26.76 m. On the other hand, when the VSP method was used we found that low-resolution LiDAR DEM performed the best followed by high-resolution LiDAR, with MSE values of 9.70 and 11.45 m, respectively. The MSE for the UAV-derived DEM was 15.12 m and for the coarse DEM was 20.78 m. We found that the UAV-derived DEM could be used to identify steep bank which could be used for mapping the hydrogeomorphology of lower order streams. Therefore, UAVs could be applied to efficiently map small stream channels in order to monitor the dynamic changes occurring in these ecosystems at a local scale. However, the VSP method should be used to map stream channels in small watersheds when high resolution DEM data is not available.
EMG Based Analysis of Gait Symmetry in Healthy Children

The purpose of this study was to examine the changes in muscular activity between the left and right lower legs during gait in healthy children throughout temporal parameters of EMG and symmetry index (SI). A total of 17 healthy children (age: 8.06 ± 1.92 years) participated in this study. Five muscles on both legs were examined via the Vicon 8-camera motion analysis system synchronized with a Trigno EMG Wireless system and a Bertec force plate; onset–offset intervals were analyzed. The highest occurrence frequency of the primary activation modality was found in the stance phase. In the swing phase, onset–offset showed only a few meaningful signs of side asymmetry. The knee flexors demonstrated significant differences between the sides (p < 0.05) in terms of onset–offset intervals: biceps femoris in stance, single support, and pre-swing phases, with SI values = −6.45%, −14.29%, and −17.14%, respectively; semitendinosus in single support phase, with SI = −12.90%; lateral gastrocnemius in swing phase, with SI = −13.33%; and medial gastrocnemius in stance and single support phases, with SI = −13.33% and −23.53%, respectively. The study outcomes supply information about intra-subject variability, which is very important in follow-up examinations and comparison with other target groups of children.
Spatiotemporal Monitoring of Soil CO2 Efflux in a Subtropical Forest during the Dry Season Based on Field Observations and Remote Sensing Imagery

The CO2 efflux from forest soil (FCO2) is one of the largest components of the global carbon cycle. Accurate estimation of FCO2 can help us better understand the carbon cycle in forested areas and precisely predict future climate change. However, the scarcity of field-measured FCO2 data in the subtropical forested area greatly limits our understanding of FCO2 dynamics at regional and global scales. This study used an automatic cavity ring-down spectrophotometer (CRDS) analyzer to measure FCO2 in a typical subtropical forest of southern China in the dry season. We found that the measured FCO2 at two experimental areas experienced similar temporal trends in the dry season and reached the minima around December, whereas the mean FCO2 differed apparently across the two areas (9.05 vs. 5.03 g C m−2 day−1) during the dry season. Moreover, we found that both abiotic (soil temperature and moisture) and biotic (vegetation productivity) factors are significantly and positively correlated, respectively, with the FCO2 variation during the study period. Furthermore, a machine-learning random forest model (RF model) that incorporates remote sensing data is developed and used to predict the FCO2 pattern in the subtropical forest, and the topographic effects on spatiotemporal patterns of FCO2 were further investigated. The model evaluation indicated that the proposed model illustrated high prediction accuracy for the training and testing dataset. Based on the proposed model, the spatiotemporal patterns of FCO2 in the forested watershed that encloses the two monitoring sites were mapped. Results showed that the spatial distribution of FCO2 is obviously affected by topography: the high FCO2 values mainly occur in relatively high altitudinal areas, in slopes of 10–25°, and in sunny slopes. The results emphasized that future studies should consider topographical effects when simulating FCO2 in subtropical forests. Overall, our study unraveled the spatiotemporal variations of FCO2 and their driving factors in a subtropical forest of southern China in the dry season, and demonstrated that the proposed RF model in combination with remote sensing data can be a useful tool for predicting FCO2 in forested areas, particularly in subtropical and tropical forest ecosystems.
A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array

In response to one of the most important challenges of the century, i.e., the estimation of the food demands of a growing population, advanced technologies have been employed in agriculture. The potato has the main contribution to people’s diet worldwide. Therefore, its different aspects are worth studying. The large number of potato varieties, lack of awareness about its new cultivars among farmers to cultivate, time-consuming and inaccurate process of identifying different potato cultivars, and the significance of identifying potato cultivars and other agricultural products (in every food industry process) all necessitate new, fast, and accurate methods. The aim of this study was to use an electronic nose, along with chemometrics methods, including PCA, LDA, and ANN as fast, inexpensive, and non-destructive methods for detecting different potato cultivars. In the present study, nine sensors with the best response to VOCs were adopted. VOCs sensors were used at various VOCs concentrations (1 to 10,000 ppm) to detect different gases. The results showed that a PCA with two main components, PC1 and PC2, described 92% of the total samples’ dataset variance. In addition, the accuracy of the LDA and ANN methods were 100 and 96%, respectively.
Improving Biomass and Grain Yield Prediction of Wheat Genotypes on Sodic Soil Using Integrated High-Resolution Multispectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

Sodic soils adversely affect crop production over extensive areas of rain-fed cropping worldwide, with particularly large areas in Australia. Crop phenotyping may assist in identifying cultivars tolerant to soil sodicity. However, studies to identify the most appropriate traits and reliable tools to assist crop phenotyping on sodic soil are limited. Hence, this study evaluated the ability of multispectral, hyperspectral, 3D point cloud, and machine learning techniques to improve estimation of biomass and grain yield of wheat genotypes grown on a moderately sodic (MS) and highly sodic (HS) soil sites in northeastern Australia. While a number of studies have reported using different remote sensing approaches and crop traits to quantify crop growth, stress, and yield variation, studies are limited using the combination of these techniques including machine learning to improve estimation of genotypic biomass and yield, especially in constrained sodic soil environments. At close to flowering, unmanned aerial vehicle (UAV) and ground-based proximal sensing was used to obtain remote and/or proximal sensing data, while biomass yield and crop heights were also manually measured in the field. Grain yield was machine-harvested at maturity. UAV remote and/or proximal sensing-derived spectral vegetation indices (VIs), such as normalized difference vegetation index, optimized soil adjusted vegetation index, and enhanced vegetation index and crop height were closely corresponded to wheat genotypic biomass and grain yields. UAV multispectral VIs more closely associated with biomass and grain yields compared to proximal sensing data. The red-green-blue (RGB) 3D point cloud technique was effective in determining crop height, which was slightly better correlated with genotypic biomass and grain yield than ground-measured crop height data. These remote sensing-derived crop traits (VIs and crop height) and wheat biomass and grain yields were further simulated using machine learning algorithms (multitarget linear regression, support vector machine regression, Gaussian process regression, and artificial neural network) with different kernels to improve estimation of biomass and grain yield. The artificial neural network predicted biomass yield (R2 = 0.89; RMSE = 34.8 g/m2 for the MS and R2 = 0.82; RMSE = 26.4 g/m2 for the HS site) and grain yield (R2 = 0.88; RMSE = 11.8 g/m2 for the MS and R2 = 0.74; RMSE = 16.1 g/m2 for the HS site) with slightly less error than the others. Wheat genotypes Mitch, Corack, Mace, Trojan, Lancer, and Bremer were identified as more tolerant to sodic soil constraints than Emu Rock, Janz, Flanker, and Gladius. The study improves our ability to select appropriate traits and techniques in accurate estimation of wheat genotypic biomass and grain yields on sodic soils. This will also assist farmers in identifying cultivars tolerant to sodic soil constraints.
Data Quality Management in the Internet of Things

Nowadays, IoT is being used in more and more application areas and the importance of IoT data quality is widely recognized by practitioners and researchers. The requirements for data and its quality vary from application to application or organization in different contexts. Many methodologies and frameworks include techniques for defining, assessing, and improving data quality. However, due to the diversity of requirements, it can be a challenge to choose the appropriate technique for the IoT system. This paper surveys data quality frameworks and methodologies for IoT data, and related international standards, comparing them in terms of data types, data quality definitions, dimensions and metrics, and the choice of assessment dimensions. The survey is intended to help narrow down the possible choices of IoT data quality management technique.
Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human–Robot Conversations

Mental health is as crucial as physical health, but it is underappreciated by mainstream biomedical research and the public. Compared to the use of AI or robots in physical healthcare, the use of AI or robots in mental healthcare is much more limited in number and scope. To date, psychological resilience—the ability to cope with a crisis and quickly return to the pre-crisis state—has been identified as an important predictor of psychological well-being but has not been commonly considered by AI systems (e.g., smart wearable devices) or social robots to personalize services such as emotion coaching. To address the dearth of investigations, the present study explores the possibility of estimating personal resilience using physiological and speech signals measured during human–robot conversations. Specifically, the physiological and speech signals of 32 research participants were recorded while the participants answered a humanoid social robot’s questions about their positive and negative memories about three periods of their lives. The results from machine learning models showed that heart rate variability and paralinguistic features were the overall best predictors of personal resilience. Such predictability of personal resilience can be leveraged by AI and social robots to improve user understanding and has great potential for various mental healthcare applications in the future.
Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms

Plant Ecological Unit’s (PEUs) are the abstraction of vegetation communities that occur on a site which similarly respond to management actions and natural disturbances. Identification and monitoring of PEUs in a heterogeneous landscape is the most difficult task in medium resolution satellite images datasets. The main objective of this study is to compare pixel-based classification versus object-based classification for accurately classifying PEUs with four selected different algorithms across heterogeneous rangelands in Central Zagros, Iran. We used images of Landsat-8 OLI that were pan-sharpened to 15 m to classify four PEU classes based on a random dataset collected in the field (40%). In the first stage, we applied the following classification algorithms to distinguish PEUs: Minimum Distance (MD), Maximum Likelihood Classification (MLC), Neural Network-Multi Layer Perceptron (NN-MLP) and Classification Tree Analysis (CTA) for pixel based method and object based method. Then, by using the most accurate classification approach, in the second stage auxiliary data (Principal Component Analysis (PCA)) was incorporated to improve the accuracy of the PEUs classification process. At the end, test data (60%) were used for accuracy assessment of the resulting maps. Object-based maps clearly outperformed pixel-based maps, especially with CTA, NN-MLP and MD algorithms with overall accuracies of 86%, 72% and 59%, respectively. The MLC algorithm did not reveal any significant difference between the object-based and pixel-based analyses. Finally, complementing PCA auxiliary bands to the CTA algorithms offered the most successful PEUs classification strategy, with the highest overall accuracy (89%). The results clearly underpin the importance of object-based classification with the CTA classifier together with PCA auxiliary data to optimize identification of PEU classes.