Emerging Sensor Technologies Research Articles

A Review of Emerging Sensor Technologies for Tank Inspection: A Focus on LiDAR and Hyperspectral Imaging and Their Automation and Deployment

This paper reviews various sensor technologies for tank inspection, focusing on Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) as advanced solutions for corrosion detection. These technologies are evaluated alongside traditional methods such as ultrasonic, electromagnetic, and thermographic inspections. This review highlights their potential to enhance inspection accuracy, reduce the limitations of manual inspection, and support integrated data analysis for comprehensive asset management. Additionally, this paper proposes a pathway for automating these techniques to streamline inspection processes and improve implementation in practical applications.

Potential of Emerging Sensor Technologies like LiDar for Enhanced Spatial Computing: Unlocking the Future of Technology

In recent years, the synergistic integration of Light Detection and Ranging (LiDAR) technology with the burgeoning field of spatial computing has ignited a transformative spark across diverse industries. This groundbreaking fusion has revolutionized how we perceive and interact with the digital world, unlocking unprecedented possibilities in augmented reality (AR), virtual reality (VR), autonomous driving, and environmental mapping. These advancements transcend mere technological enhancements; they represent a paradigm shift, reshaping entire ecosystems and fostering a new era of innovation. This research paper delves deeper into the immense potential of LiDAR as an emerging sensor technology, specifically exploring its profound impact on the landscape of spatial computing. We will meticulously examine its diverse applications, dissect the inherent benefits it offers, and analyze its far-reaching consequences on the digital sphere. Additionally, we will utilize Apple Vision Pro as a compelling case study, meticulously dissecting how this innovative AR/MR headset leverages the power of LiDAR technology to achieve superior spatial awareness and deliver unparalleled user experiences. Through this multifaceted examination, we aim to illuminate the transformative potential of LiDAR and its role in shaping the center of spatial computing.

Chemical Sensor Technologies for Sustainable Development: Recent Advances, Classification, and Environmental Monitoring

AbstractOne of the significant issues of concern in recent times is detecting, quantifying, and monitoring various chemical contaminants in the environment. Numerous inorganic and organic substances are discharged into the environment from various anthropogenic activities, thereby polluting the ecosystems' air, water, soil, and other components. These contaminants have been documented to have deleterious effects on humans, animals, and the environment. Therefore, the need for monitoring and continual quantification of the contents of these contaminants in various environmental matrices becomes paramount. Many diverse sensors are fabricated for multiple utilizations, including liquids and gas sensors, wireless sensors, electronic tongues, and noses for detecting volatile airborne substances, pollutants, and effluents. This review focuses on cutting‐edge technology used in chemical sensors for environmental protection and sustainable development. It also discusses the fabrication and development of chemical sensors and their working principles. In addition, highlights about the applications of chemical sensors in environmental monitoring are also elaborated. This review article discusses emerging sensor technologies and their application in ecological monitoring. Further, the recognition of chemical warfare agents and homeland security applications with the help of bioelectronic nose and tongue is discussed. Finally, some challenges associated with chemical sensors and future trends are also attempted.

Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms.

The integration of advanced technologies is revolutionizing classrooms, significantly enhancing their intelligence, interactivity, and personalization. Central to this transformation are sensor technologies, which play pivotal roles. While numerous surveys summarize research progress in classrooms, few studies focus on the integration of sensor and AI technologies in developing smart classrooms. This systematic review classifies sensors used in smart classrooms and explores their current applications from both hardware and software perspectives. It delineates how different sensors enhance educational outcomes and the crucial role AI technologies play. The review highlights how sensor technology improves the physical classroom environment, monitors physiological and behavioral data, and is widely used to boost student engagements, manage attendance, and provide personalized learning experiences. Additionally, it shows that combining sensor software algorithms with AI technology not only enhances the data processing and analysis efficiency but also expands sensor capabilities, enriching their role in smart classrooms. The article also addresses challenges such as data privacy protection, cost, and algorithm optimization associated with emerging sensor technologies, proposing future research directions to advance educational sensor technologies.

Optimizing multi-spectral ore sorting incorporating wavelength selection utilizing neighborhood component analysis for effective arsenic mineral detection

Arsenic contamination not only complicates mineral processing but also poses environmental and health risks. To address these challenges, this research investigates the feasibility of utilizing Hyperspectral imaging combined with machine learning techniques for the identification of arsenic-containing minerals in copper ore samples, with a focus on practical application in sorting and processing operations. Through experimentation with various copper sulfide ores, Neighborhood Component Analysis (NCA) was employed to select essential wavelength bands from Hyperspectral data, subsequently used as inputs for machine learning algorithms to identify arsenic concentrations. Results demonstrate that by selecting a subset of informative bands using NCA, accurate mineral identification can be achieved with a significantly reduced the size of dataset, enabling efficient processing and analysis. Comparison with other wavelength selection methods highlights the superiority of NCA in optimizing classification accuracy. Specifically, the identification accuracy showed 91.9% or more when utilizing 8 or more bands selected by NCA and was comparable to hyperspectral data analysis with 204 bands. The findings suggest potential for cost-effective implementation of multispectral cameras in mineral processing operations. Future research directions include refining machine learning algorithms, exploring broader applications across diverse ore types, and integrating hyperspectral imaging with emerging sensor technologies for enhanced mineral processing capabilities.

Towards a wireless sensing infrastructure for smart mobility

This paper presents a suite of wireless sensing solutions for the continuous monitoring of transportation infrastructure using traditional and emerging sensor technologies. First, various pavement sensing technologies are discussed followed by a suitability assessment survey to select suitable inter-sensor and gateway-to-cloud communication technologies for the sensors requiring lower and higher bandwidths. Selection recommendations were then made based on multi-disciplinary analyses of strengths, weaknesses, opportunities, and threats (SWOT) of various communication technologies and proposed wireless architectures for sensing infrastructure for smart mobility (SISM). Further, recommendations were made concerning the implementation of proposed wireless architectures for wireless and continuous monitoring of transportation infrastructure at the proposed Illinois Autonomous and Connected Track (I-ACT), which will be established at the University of Illinois Urbana-Champaign.

How Emerging Sensor Technologies Contribute to Artificial Intelligence; Machine Learning and Deep Learning Data Acquisition Techniques

Computers Munge on Data and Without data to munge computers can as well be famished; to say literally.Deep Learning as an off-shoot of Machine Learning which itself is a sub-field of Artificial Intelligence relies solely on advancements in emerging computing Technologies which provide the computing capacity for running large scale data processing ; most importantly these include (i.e. but not limited to)advanced high level computing power and myriad data acquisition Sources as the ubiquitous Sensors proliferating the technology space of portable computing devices and IoT.Sensors Technology help in data collection in several be-spoke ways for these emerging computing technologies(since without data computers are famished typically and with deep learning sufficient data is basically needed)to be able to perform deep learning operations required for inferencing useful informative insights from crude-data.Today data is being generated in an unprecedented manner, as a result of the emergence of several sensor technologies; this has become the leverage for all deep learning computations and its successes today.In this Paper review i will be overviewing and discussing how these emerging sensors technology has been able to complement the computing power today to facilitate deep Learning technologies.

Riboflavin-rich agar enhances the rate of extracellular electron transfer from electrogenic bacteria inside a thin-layer system

Numerous bacteria owe extracellular electron transport (EET) ability, and the rate enhancement of EET is critical for the emerging sensor technology to detect metabolically active pathogens. Here, the considerable enhancement of microbial current signal was firstly demonstrated in a thin layer electrolyte sandwiched between an agar substrate (AS) containing high concentration riboflavin (RF) and a screen-printed electrode. Covering cells with this AS showed a sharply current increase from 0.033 µA to 1.59 μA (47.7-folds) in EET-capable bacteria Shewanella oneidensis MR-1. Differential pulse voltammograms using gene-deletion mutant strains of S. oneidensis MR-1 revealed thin electrolyte between RF-loaded AS and electrode enhanced the rate of electron transfer via complexes between riboflavin and outer membrane c-type cytochrome. A similar effect in Streptococcus mutans UA159, a biofilm-forming pathogen, was also explored. Moreover, capturing and quantifying both metabolically active microbes from the dry solid surface are demonstrated with RF-loaded AS successfully. The considerable enhancement of the EET in the thin layer electrolyte provides a new direction for designing whole-cell biosensors and understanding a microbe/electrode interaction in a micro-sized space.

Multidimensional ground reaction forces and moments from wearable sensor accelerations via deep learning

Introduction: Military task performance is dependent upon the coherent and effective collaboration of its component parts. These are comprised of individuals working in coordinated teams performing tasks that are naturally variable and have physical and cognitive elements, the relative contribution being dependent upon the task. Furthermore, the battlefield is non-linear, and dynamic with human, task, environmental and informational constraints. Complexity is added to team performance assessment by the fact that there is considerable redundancy within military teams. This redundancy is critical and affords the ability for team performance to be protected even when individual performance is degraded by fatigue. The purpose of this work is to propose a conceptual framework for the assessment of military team dynamics, based upon analytical models used in sport in addition to emergent sensor technologies and Artificial Intelligence (AI). Methods: A review of the scientific literature and the developments in team performance assessment, AI and emerging sensor technologies was conducted to inform a conceptual framework designed to assess team dynamics and redundancy in the presence of individual fatigue. Results: Current assessment of team dynamics during mission-critical tasks, the contribution of each individual, and the emergent team behaviours whilst subject to fatigue are typically limited to subjective ratings and post-activity reports. Indeed, there seems to be an institutional belief that the military already assess teams effectively and retrospective After Action Review is key to this success. As such, the ability to reliably monitor and objectively assess military team dynamics and redundancy remains a challenge. In addition, despite advancements in technology, the opportunity to conduct real-time or near real-time observations with which to provide rapid insights is yet to be taken up by the military. Discussion/conclusions: Improvements in analytical methods, sensor technologies, and the maturity of AI tools provide the opportunity to progress from simply describing individual and team performance to understanding the team dynamics by the integration of novel sensors and AI-assisted analytical tools. Devices to rapidly acquire and process data continue to evolve along with wearable, and stand-off sensors and AI-assisted tools. A peer-reviewed, conceptual, interdisciplinary framework for objectively assessing team dynamics is proposed. With careful collaboration, we suggest that the potential to enhance the understanding of team dynamics and redundancy is boundless. Notwithstanding, it is critical that defence organisations have an endorsed strategy for the acceptance and implementation of such analytical tools that would provide a clear operational benefit.

Electrochemical Biosensors Employing Hybridization Chain Reaction: From Structural Design to Applications

AbstractElectrochemical biosensors are one of the most emerging sensor technologies for many applications including disease diagnosis, environmental monitoring, and food safety, etc. They often rely on amplification strategies to achieve ultrasensitivity for the specific analytes of interest, as they feature extremely low abundance, such as ctDNA and other protein‐type cancer biomarkers. Among all the amplification strategies, hybridization chain reaction (HCR) is extremely cost‐effective, simple, enzyme‐free, and reacts under isothermal conditions, thus often employed in the electrochemical biosensors towards the goal of sensitivity enhancement. By coupling HCR and electrochemistry, it has proved to benefit a great variety of analytes, including ions, small molecules, nucleic acids, proteins, and cells, etc. Given the recent advance of HCR‐amplified electrochemical biosensors, here one aims to review the significant achievements of this platform over the past 10 years, covering their design in HCR constructs in response to the respective targets, and their challenges and opportunities.

Performance and Deployment of Low-Cost Particle Sensor Units to Monitor Biomass Burning Events and Their Application in an Educational Initiative.

Biomass burning smoke is often a significant source of airborne fine particles in regional areas where air quality monitoring is scarce. Emerging sensor technology provides opportunities to monitor air quality on a much larger geographical scale with much finer spatial resolution. It can also engage communities in the conversation around local pollution sources. The SMoke Observation Gadget (SMOG), a unit with a Plantower dust sensor PMS3003, was designed as part of a school-based Science, Technology, Engineering and Mathematics (STEM) project looking at smoke impacts in regional areas of Victoria, Australia. A smoke-specific calibration curve between the SMOG units and a standard regulatory instrument was developed using an hourly data set collected during a peat fire. The calibration curve was applied to the SMOG units during all field-based validation measurements at several locations and during different seasons. The results showed strong associations between individual SMOG units for PM2.5 concentrations (r2 = 0.93–0.99) and good accuracy (mean absolute error (MAE) < 2 μg m−3). Correlations of the SMOG units to reference instruments also demonstrated strong associations (r2 = 0.87–95) and good accuracy (MAE of 2.5–3.0 μg m−3). The PM2.5 concentrations tracked by the SMOG units had a similar response time as those measured by collocated reference instruments. Overall, the study has shown that the SMOG units provide relevant information about ambient PM2.5 concentrations in an airshed impacted predominantly by biomass burning, provided that an adequate adjustment factor is applied.

Study workplace space occupancy: a review of measures and technologies

PurposeThe purpose of this paper is to review the use of technologies for measuring space occupancy to guide the selection of appropriate tools for workplace post-occupancy evaluation (POE) studies. The authors focus on how actual space occupancy was measured in previous studies and the pros and cons of the different technologies and tools. This paper also addresses research gaps and directions for future research.Design/methodology/approachThe space occupancy measures/tools are categorized based on the three types of technologies: environmental/ambient sensors, wearable sensors/smartphones and computer vision. A total of 50 studies are reviewed to identify the capabilities and limitations of these measurements.FindingsBased on review results, the authors propose that although sensor technology can be a useful addition to the measures/tools list, a comprehensive review of the research goal, the occupants' behavior, and the environmental settings' characteristics should be conducted beforehand. Selecting appropriate technology is critical for collecting the proper behavioral data type, with a lower level of surveillance and increased validity.Originality/valueThis paper urges critical thinking about existing occupancy measures/tools across various fields, to inform the adoption and creation of new building occupancy measures. The knowledge of emerging sensor technology allows researchers to better study the temporal patterns of occupant behavior over extended periods and in a wide range of settings.

Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review.

BackgroundDiabetes mellitus is a metabolic disorder that affects hundreds of millions of people worldwide and causes several million deaths every year. Such a dramatic scenario puts some pressure on administrations, care services, and the scientific community to seek novel solutions that may help control and deal effectively with this condition and its consequences.ObjectiveThis study aims to review the literature on the use of modern mobile and wearable technology for monitoring parameters that condition the development or evolution of diabetes mellitus.MethodsA systematic review of articles published between January 2010 and July 2020 was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Manuscripts were identified through searching the databases Web of Science, Scopus, and PubMed as well as through hand searching. Manuscripts were included if they involved the measurement of diabetes-related parameters such as blood glucose level, performed physical activity, or feet condition via wearable or mobile devices. The quality of the included studies was assessed using the Newcastle-Ottawa Scale.ResultsThe search yielded 1981 articles. A total of 26 publications met the eligibility criteria and were included in the review. Studies predominantly used wearable devices to monitor diabetes-related parameters. The accelerometer was by far the most used sensor, followed by the glucose monitor and heart rate monitor. Most studies applied some type of processing to the collected data, mainly consisting of statistical analysis or machine learning for activity recognition, finding associations among health outcomes, and diagnosing conditions related to diabetes. Few studies have focused on type 2 diabetes, even when this is the most prevalent type and the only preventable one. None of the studies focused on common diabetes complications. Clinical trials were fairly limited or nonexistent in most of the studies, with a common lack of detail about cohorts and case selection, comparability, and outcomes. Explicit endorsement by ethics committees or review boards was missing in most studies. Privacy or security issues were seldom addressed, and even if they were addressed, they were addressed at a rather insufficient level.ConclusionsThe use of mobile and wearable devices for the monitoring of diabetes-related parameters shows early promise. Its development can benefit patients with diabetes, health care professionals, and researchers. However, this field is still in its early stages. Future work must pay special attention to privacy and security issues, the use of new emerging sensor technologies, the combination of mobile and clinical data, and the development of validated clinical trials.

Anodic stripping voltammetry analysis of gold nanoparticles functionalized one-dimensional single polypyrrole nanowire for arsenic sensing

Emerging sensor technology has led to develop high-performance sensors that satisfy most of the sensing parameters. However, still further advances are needed for sensors to achieve low detection, which is crucial for the detection of low concentration analytes present in the solution. Herein, we designed a sensor electrode with gold nanoparticles (AuNPs) functionalized single polypyrrole nanowire (PpyNW) to detect arsenic (III) in water samples. The chronocoulometry and cyclic voltammetry methods were used to synthesize PpyNW and AuNPs, respectively. The morphological and elemental analysis of PpyNW functionalized with AuNPs was done using field emission scanning electron microscopy (FESEM) and energy dispersive spectroscopy (EDS), respectively. After fixing PpyNW on the electrode, the surface was functionalized with AuNPs. The sensing performance of the AuNPs/PpyNW sensor was monitored using anodic stripping voltammetry method. From the obtained data, the sensor showed high sensitivity of 63.13 mA/mM (i.e. 0.8417 mA/ppb) and an ultra-low detection limit of 0.005 mM (i.e. 0.37 ppb) during arsenic (III) detection in solution. Furthermore, we tested the sensing performance of our sensor in the laboratory-grade and adulterated tap water samples to determine arsenic (III) concentrations.

Using Feedback to Improve Accountability in Global Environmental Health and Engineering.

Engineered environmental health interventions and services in low-income and resource-limited settings-such as water supply and treatment, sanitation, and cleaner household energy services-have had a less than expected record of sustainability and have sometimes not delivered on their potential to improve health. These interventions require both effectively functioning technologies as well as supporting financial, political, and human resource systems, and may depend on user behaviors as well as professionalized service delivery to reduce harmful exposures. In this perspective, we propose that the application of smarter, more actionable monitoring and decision support systems and aligned financial incentives can enhance accountability between donors, implementers, service providers, governments, and the people who are the intended beneficiaries of development programming. Made possible in part by new measurement techniques, including emerging sensor technologies, rapid impact evaluation, citizen science, and performance-based contracting, such systems have the potential to propel the development of solutions that can work over the long-term, allowing the benefits of environmental health improvements to be sustained in settings where they are most critical by improving trust and mutual accountability among stakeholders.

Quanta burst photography

Single-photon avalanche diodes (SPADs) are an emerging sensor technology capable of detecting individual incident photons, and capturing their time-of-arrival with high timing precision. While these sensors were limited to singlepixel or low-resolution devices in the past, recently, large (up to 1 MPixel) SPAD arrays have been developed. These single-photon cameras (SPCs) are capable of capturing high-speed sequences of binary single-photon images with no read noise. We present quanta burst photography, a computational photography technique that leverages SPCs as passive imaging devices for photography in challenging conditions, including ultra low-light and fast motion. Inspired by recent success of conventional burst photography, we design algorithms that align and merge binary sequences captured by SPCs into intensity images with minimal motion blur and artifacts, high signal-to-noise ratio (SNR), and high dynamic range. We theoretically analyze the SNR and dynamic range of quanta burst photography, and identify the imaging regimes where it provides significant benefits. We demonstrate, via a recently developed SPAD array, that the proposed method is able to generate high-quality images for scenes with challenging lighting, complex geometries, high dynamic range and moving objects. With the ongoing development of SPAD arrays, we envision quanta burst photography finding applications in both consumer and scientific photography.

The Internet of Things enhancing animal welfare and farm operational efficiency.

The growth in wirelessly enabled sensor network technologies has enabled the low cost deployment of sensor platforms with applications in a range of sectors and communities. In the agricultural domain such sensors have been the foundation for the creation of decision support tools that enhance farm operational efficiency. This Research Reflection illustrates how these advances are assisting dairy farmers to optimise performance and illustrates where emerging sensor technology can offer additional benefits. One of the early applications for sensor technology at an individual animal level was the accurate identification of cattle entering into heat (oestrus) to increase the rate of successful pregnancies and thus optimise milk yield per animal. This was achieved through the use of activity monitoring collars and leg tags. Additional information relating to the behaviour of the cattle, namely the time spent eating and ruminating, was subsequently derived from collars giving further insights of economic value into the wellbeing of the animal, thus an enhanced range of welfare related services have been provisioned. The integration of the information from neck-mounted collars with the compositional analysis data of milk measured at a robotic milking station facilitates the early diagnosis of specific illnesses such as mastitis. The combination of different data streams also serves to eliminate the generation of false alarms, improving the decision making capability. The principle of integrating more data streams from deployed on-farm systems, for example, with feed composition data measured at the point of delivery using instrumented feeding wagons, supports the optimisation of feeding strategies and identification of the most productive animals. Optimised feeding strategies reduce operational costs and minimise waste whilst ensuring high welfare standards. These IoT-inspired solutions, made possible through Internet-enabled cloud data exchange, have the potential to make a major impact within farming practices. This paper gives illustrative examples and considers where new sensor technology from the automotive industry may also have a role.

1205 Activity Trackers As A Tool In Sleep Research: Determining Discrepancies In Trackers Vs. PSG

Abstract Introduction Fitness-based wearables and other emerging sensor technologies have the potential to track sleep across large populations longitudinally in at-home environments. To understand how these devices can inform research studies, limitations of available trackers need to be compared to traditional polysomnography (PSG). Here we assessed discrepancies in sleep staging in activity trackers vs. PSG in subjects with various sleep disorders. Methods Twelve subjects (age 41-78, 7f, 5m) wore a Fitbit Charge 3 while undergoing a scheduled sleep study. Six subjects had been previously diagnosed with a sleep disorder (5 OSA, 1 CSA). 4 subjects used CPAP throughout the night, 2 had a split night (CPAP 2nd half of the night), and 6 had a PSG only. Activity tracker staging was compared to 2 RPSGTs staging. Results Of the 12 subjects, eight subjects’ sleep was detected in the activity tracker, and compared across sleep stages to the PSG (7 female, 1 male, ages 41-78, AHI 0.3-87, RDI 0.5-94.4, sleep efficiency 74%+/-18, 4 PSG, 1 split, 3 CPAP). The activity tracker matched either tech 52% (+/- 13). The average difference in score tech and activity tracker staging for sleep onset (SO) was 16 +/- 15 minutes and wake after sleep onset was 43.5 +/- 44 minutes. Sensitivity, specificity, and balanced accuracy were found for each sleep stage. Respectively, Wake: 0.45+/-0.27, 0.97+/-0.03, 0.71+/-0.12, REM: 0.41+/-0.30, 0.90+/-0.06, 0.60+/-0.28, Light: 0.71+/-0.09, 0.58+/-0.19, 0.65+/-0.10, Deep: 0.63+/-0.52, 0.88+/-0.05, 0.59+/-0.49. Conclusion From this study of 12 subjects seen at a sleep clinic for suspected sleep disorders, activity trackers performed best in wake, REM and deep sleep specificity (&amp;gt;=88%), while they lacked sensitivity to REM and wake (&amp;lt;=45%) stages. The tracker did not detect sleep in 4 subjects who had elevated AHI or low sleep efficiency. Further analysis can identify whether discrepancies between the Fitbit and PSG can be predicted by distinct patterns in sleep staging and/or identify subject exclusion criteria for activity tracking studies. Support This project in on-going with the support of Academy Diagnostics Sleep and EEG Center and staff.

Detection of Conflicts Between ADS-B-Equipped Aircraft and Unmanned Aerial Systems

Versatile unmanned aerial system (UAS) platforms have grown significantly in popularity by virtue of their low cost relative to manned aircraft, high performance, and operational simplicity. While the Federal Aviation Administration (FAA) currently regulates the operating altitudes, speeds, weights, pilot qualifications, and locations of drones, a lack of capacity and technology prohibits sufficient enforcement of these restrictions. To assess the frequency and severity of manned and unmanned aircraft separation incidents, and to examine the emerging sensor technology available to facilitate such assessment, flight operations in controlled airspace around Orlando Melbourne International Airport (KMLB) were monitored. One sensor system deployed at KMLB reported UAS locations, altitudes, and flight durations, while a second system reported manned aircraft positions, altitudes, and timestamps using ADS-B signals. Evaluation of flight operations data in the vicinity of KMLB revealed eight potential drone incursions over a 2-week period. Aircraft flight paths were retroactively tracked to map these unmanned and manned aerial conflicts; aircraft identification information was also researched to contextualize the incidents. The frequency and magnitude of identified events suggest the need for additional research to further explore the problem scope and potential solutions.

Algorithm to overcome atmospheric phase errors in SAL data.

Synthetic aperture ladar is an emerging sensor technology providing high-resolution imagery of targets from long standoff ranges. Atmospheric turbulence corrupts the collected phase history data with spatially variant phase perturbations, impacting resolution and contrast of reconstructed imagery. We explore the efficacy of model-based reconstruction algorithms with model error corrections to mitigate the deleterious effects of atmospheric turbulence and restore image quality. We present results from model error correction techniques utilizing spatially invariant, spatially variant, and a model-based atmospheric phase error correction. We quantify the performance of all algorithms using an atmospheric ray-trace simulation.