Feature Weaken: Vicinal Data Augmentation for Classification

Deep learning usually relies on training large-scale data samples to achieve better performance. However, over-fitting based on training data always remains a problem. Scholars have proposed various strategies, such as feature dropping and feature mixing, to improve the generalization continuously. For the same purpose, we subversively propose a novel training method, Feature Weaken, which can be regarded as a data augmentation method. Feature Weaken constructs the vicinal data distribution with the same cosine similarity for model training by weakening features of the original samples. In especially, Feature Weaken changes the spatial distribution of samples, adjusts sample boundaries, and reduces the gradient optimization value of back-propagation. This work can not only improve the classification performance and generalization of the model, but also stabilize the model training and accelerate the model convergence. We conduct extensive experiments on classical deep convolution neural models with five common image classification datasets and the Bert model with four common text classification datasets. Compared with the classical models or the generalization improvement methods, such as Dropout, Mixup, Cutout, and CutMix, Feature Weaken shows good compatibility and performance. We also use adversarial samples to perform the robustness experiments, and the results show that Feature Weaken is effective in improving the robustness of the model.Comment: 9 pages,6 figure

Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images

Lodging has been recognized as one of the major destructive factors for crop quality and yield, resulting in an increasing need to develop cost-efficient and accurate methods for detecting crop lodging in a routine manner. Using structure-from-motion (SfM) and novel geospatial computing algorithms, this study investigated the potential of high resolution imaging with unmanned aircraft system (UAS) technology for detecting and assessing lodging severity over an experimental maize field at the Texas A&M AgriLife Research and Extension Center in Corpus Christi, Texas, during the 2016 growing season. The method was proposed to not only detect the occurrence of lodging at the field scale, but also to quantitatively estimate the number of lodged plants and the lodging rate within individual rows. Nadir-view images of the field trial were taken by multiple UAS platforms equipped with consumer grade red, green, and blue (RGB), and near-infrared (NIR) cameras on a routine basis, enabling a timely observation of the plant growth until harvesting. Models of canopy structure were reconstructed via an SfM photogrammetric workflow. The UAS-estimated maize height was characterized by polygons developed and expanded from individual row centerlines, and produced reliable accuracy when compared against field measures of height obtained from multiple dates. The proposed method then segmented the individual maize rows into multiple grid cells and determined the lodging severity based on the height percentiles against preset thresholds within individual grid cells. From the analysis derived from this method, the UAS-based lodging results were generally comparable in accuracy to those measured by a human data collector on the ground, measuring the number of lodging plants (R2 = 0.48) and the lodging rate (R2 = 0.50) on a per-row basis. The results also displayed a negative relationship of ground-measured yield with UAS-estimated and ground-measured lodging rate

Simplified indoor localization using Bluetooth beacons and received signal strength fingerprinting with smartwatch

Variations in Global Positioning Systems (GPSs) have been used for tracking users’ locations. However, when location tracking is needed for an indoor space, such as a house or building, then an alternative means of precise position tracking may be required because GPS signals can be severely attenuated or completely blocked. In our approach to indoor positioning, we developed an indoor localization system that minimizes the amount of effort and cost needed by the end user to put the system to use. This indoor localization system detects the user’s room-level location within a house or indoor space in which the system has been installed. We combine the use of Bluetooth Low Energy beacons and a smartwatch Bluetooth scanner to determine which room the user is located in. Our system has been developed specifically to create a low-complexity localization system using the Nearest Neighbor algorithm and a moving average filter to improve results. We evaluated our system across a household under two different operating conditions: first, using three rooms in the house, and then using five rooms. The system was able to achieve an overall accuracy of 85.9% when testing in three rooms and 92.106% across five rooms. Accuracy also varied by region, with most of the regions performing above 96% accuracy, and most false-positive incidents occurring within transitory areas between regions. By reducing the amount of processing used by our approach, the end-user is able to use other applications and services on the smartwatch concurrently.The authors thank the Open Access Publication Fund provided by the Mary and Jeff Bell Library at Texas A&M University-Corpus Christi. This research was partially funded by the University Research Enhancement Grant from Texas A&M University-Corpus Christi

Mapping relative sea-level rise with satellite geodesy along subsiding coast near Galveston, Texas

The combined effect of absolute sea-level rise (ASLR) and coastal subsidence has been long monitored via tide gauges (TGs), which measure sea-level rise relative to land-fixed benchmarks, referred to as relative sea- level rise (RSLR). The importance of TG observations lies in dynamically reflecting land-water interaction which also is shaping the coastal living environment. However, TGs are usually sparsely distributed along coastline, providing limited information about the spatial patterns and variability of RSLR. Thanks to emerging satellite geodesy technologies such as satellite radar altimetry (SRA) and interferometric synthetic aperture radar (InSAR), changes of sea surface height can be measured in the ocean and largescale high- accuracy land deformation can be estimated. This study combines ASLR data derivied from SRA with coastal land deformation derived from InSAR to estimate and map RLSR along coastline near Galveston, Texas, one of leading subsidence hotspots in the United States. Specifically, the radar altimetry product “MEaSUREs” from NASA’s Jet Propulsion Laboratory (JPL) was used to extract the time series of sea surface height anomalies for estimating ASLR rate. Meanwhile, the persistent scatter (PS) InSAR technique was utilized to generate land subsidence from Sentinel-1 data between 2017 and 2021. The RSLR map is generated by combining ASLR and InSAR data via designed grid pattern defined by geographic information system (GIS) analysis near the coastline. The performance of the RSLR grid map is validated through comparing against results obtained by TG measurements. This study hopes to provide improved capability for monitoring RSLR along coastline in response to increased demands for coastal resilience and sustainable development

Long-term vertical-land-motion investigation with space and terrestrial geodetic techniques near San Leon, Texas, USA

Monitoring vertical land motion (VLM) along coastlines, which influences the dynamics of sea level changes in relation to the land, is a challenging task due to its inherent high spatiotemporal variability and limited availability of observations. This study aimed to investigate the rates, patterns, and drivers of land subsidence near the coastal town of San Leon, TX, United States, since the 1990s, utilizing a range of space and terrestrial geodetic techniques. These techniques included interferometric synthetic aperture radar (InSAR), global navigation satellite systems (GNSS), tide gauge (TG), and satellite radar altimetry (SRA). The small baseline subset (SBAS) InSAR method was adopted to process 254 images from three synthetic aperture radar (SAR) sensors, i.e., ERS-2 between 1995 and 1999, ALOS-1 PALSAR between 2006 and 2011, and Sentinel-1 between 2016 and 2020. The results from InSAR subsidence maps were verified by comparing with high-accuracy vertical positioning observations at ten continuously operating GNSS (cGNSS) stations. Within the study area, a special attention was given to the Eagle Point TG station where sea level has been significantly rising relative to the sinking land. Long-term time series of land subsidence at Eagle Point obtained from sea-level difference between TG and SRA observations were confirmed and compared against InSAR and the cGNSS station observations recorded in close proximity. Gaussian Process regression (GPR) was then employed to model the VLM processes at Eagle Point using: (1) the combined results of InSAR and sea-level difference (i.e., GPR 1), and (2) the InSAR results alone (i.e., GPR 2). A 0.9 mm/yr divergence was found between GPR 1 and GPR 2 models, indicating the potential to accurately estimate long-term VLM with InSAR standalone measurements even if multi-year observation gaps intermittently occur, especially for inland areas where measurement data from other geodetic techniques, such as GNSS, TG, and SRA, are not available. Further investigations suggest that land subsidence around Eagle Point since 1998 was related to anthropogenic activities such as hydrocarbon pumping from oil and gas wells that were situated in close proximity to the TG station.This research was supported by the U.S. Department of Commerce-National Oceanic and Atmospheric Administration (NOAA) through the University of Southern Mississippi (USM) under the terms of Agreement [No. NA13NOS4000166]. This work was also supported by the National Science Foundation (NSF) under award No. 2131263. The authors also thank the Open Access Publication Fund provided by the Mary and Jeff Bell Library at Texas A&M University-Corpus Christi (TAMU-CC) . Opinions expressed by authors herein do not necessarily reflect views of NOAA, NSF or USM. The authors are grateful to Dr. Laha Ale for his valuable review and insightful comments on the article

Simulation and characterization of wind impacts on sUAS flight performance for crash scene reconstruction

Small unmanned aircraft systems (sUASs) have emerged as promising platforms for the purpose of crash scene reconstruction through structure-from-motion (SfM) photogrammetry. However, auto crashes tend to occur under adverse weather conditions that usually pose increased risks of sUAS operation in the sky. Wind is a typical environmental factor that can cause adverse weather, and sUAS responses to various wind conditions have been understudied in the past. To bridge this gap, commercial and open source sUAS flight simulation software is employed in this study to analyze the impacts of wind speed, direction, and turbulence on the ability of sUAS to track the pre-planned path and endurance of the flight mission. This simulation uses typical flight capabilities of quadcopter sUAS platforms that have been increasingly used for traffic incident management. Incremental increases in wind speed, direction, and turbulence are conducted. Average 3D error, standard deviation, battery use, and flight time are used as statistical metrics to characterize the wind impacts on flight stability and endurance. Both statistical and visual analytics are performed. Simulation results suggest operating the simulated quadcopter type when wind speed is less than 11 m/s under light to moderate turbulence levels for optimal flight performance in crash scene reconstruction missions, measured in terms of positional accuracy, required flight time, and battery use. Major lessons learned for real-world quadcopter sUAS flight design in windy conditions for crash scene mapping are also documented.Small unmanned aircraft systems (sUASs) have emerged as promising platforms for the purpose of crash scene reconstruction through structure-from-motion (SfM) photogrammetry. However, auto crashes tend to occur under adverse weather conditions that usually pose increased risks of sUAS operation in the sky. Wind is a typical environmental factor that can cause adverse weather, and sUAS responses to various wind conditions have been understudied in the past. To bridge this gap, commercial and open source sUAS flight simulation software is employed in this study to analyze the impacts of wind speed, direction, and turbulence on the ability of sUAS to track the pre-planned path and endurance of the flight mission. This simulation uses typical flight capabilities of quadcopter sUAS platforms that have been increasingly used for traffic incident management. Incremental increases in wind speed, direction, and turbulence are conducted. Average 3D error, standard deviation, battery use, and flight time are used as statistical metrics to characterize the wind impacts on flight stability and endurance. Both statistical and visual analytics are performed. Simulation results suggest operating the simulated quadcopter type when wind speed is less than 11 m/s under light to moderate turbulence levels for optimal flight performance in crash scene reconstruction missions, measured in terms of positional accuracy, required flight time, and battery use. Major lessons learned for real-world quadcopter sUAS flight design in windy conditions for crash scene mapping are also documented

Designing UAV swarm experiments: A simulator selection and experiment design process

The rapid advancement and increasing number of applications of Unmanned Aerial Vehicle (UAV) swarm systems have garnered significant attention in recent years. These systems offer a multitude of uses and demonstrate great potential in diverse fields, ranging from surveillance and reconnaissance to search and rescue operations. However, the deployment of UAV swarms in dynamic environments necessitates the development of robust experimental designs to ensure their reliability and effectiveness. This study describes the crucial requirement for comprehensive experimental design of UAV swarm systems before their deployment in real-world scenarios. To achieve this, we begin with a concise review of existing simulation platforms, assessing their suitability for various specific needs. Through this evaluation, we identify the most appropriate tools to facilitate one’s research objectives. Subsequently, we present an experimental design process tailored for validating the resilience and performance of UAV swarm systems for accomplishing the desired objectives. Furthermore, we explore strategies to simulate various scenarios and challenges that the swarm may encounter in dynamic environments, ensuring comprehensive testing and analysis. Complex multimodal experiments may require system designs that may not be completely satisfied by a single simulation platform; thus, interoperability between simulation platforms is also examined. Overall, this paper serves as a comprehensive guide for designing swarm experiments, enabling the advancement and optimization of UAV swarm systems through validation in simulated controlled environments.This research received no external funding

Inferring Human Activity in Mobile Devices by Computing Multiple Contexts

This paper introduces a framework for inferring human activities in mobile devices by computing spatial contexts, temporal contexts, spatiotemporal contexts, and user contexts. A spatial context is a significant location that is defined as a geofence, which can be a node associated with a circle, or a polygon; a temporal context contains time-related information that can be e.g., a local time tag, a time difference between geographical locations, or a timespan; a spatiotemporal context is defined as a dwelling length at a particular spatial context; and a user context includes user-related information that can be the user’s mobility contexts, environmental contexts, psychological contexts or social contexts. Using the measurements of the built-in sensors and radio signals in mobile devices, we can snapshot a contextual tuple for every second including aforementioned contexts. Giving a contextual tuple, the framework evaluates the posteriori probability of each candidate activity in real-time using a Naïve Bayes classifier. A large dataset containing 710,436 contextual tuples has been recorded for one week from an experiment carried out at Texas A&M University Corpus Christi with three participants. The test results demonstrate that the multi-context solution significantly outperforms the spatial-context-only solution. A classification accuracy of 61.7% is achieved for the spatial-context-only solution, while 88.8% is achieved for the multi-context solution