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The multi-receptor tyrosine kinase inhibitor XL092 has been developed to inhibit the activity of oncogenic targets, including MET, VEGFR2, and the TAM family of kinases TYRO3, AXL and MER. Presented here is a preclinical evaluation of XL092. XL092 causes a significant decrease in tumor MET and AXL phosphorylation (P < 0.01) in murine Hs 746T xenograft models relative to vehicle, and a 96% inhibition of VEGFR2 phosphorylation in murine lungs. Dose-dependent tumor growth inhibition with XL092 was observed in various murine xenograft models, with dose-dependent tumor regression seen in the NCI-H441 model. Tumor growth inhibition was enhanced with the combination of XL092 with anti-PD-1, anti-programmed death ligand-1 (PD-L1), or anti-CTLA-4 compared with any of these agents alone in the MC38 murine syngeneic model and with anti-PD-1 in the CT26 colorectal cancer survival model. In vivo, XL092 promoted a decrease in the tumor microvasculature and significant increases of peripheral CD4+ T cells and B cells and decreases in myeloid cells versus vehicle. Significant increases in CD8+ T cells were also observed with XL092 plus anti-PD-1 or anti-PD-L1 versus vehicle. In addition, XL092 promoted M2 to M1 repolarization of macrophages in vitro and inhibited primary human macrophage efferocytosis in a dose-dependent manner. In summary, XL092 was shown to have significant antitumor and immunomodulatory activity in animal models both alone and in combination with immune checkpoint inhibitors, supporting its evaluation in clinical trials.

Agent-wise local design methods to synthesize distributed control gains focus on the individual dynamics of each agent to guarantee the overall stability of the system. They are powerful tools due to their scalability. However, the agent-wise local design methods are incapable of maximizing the overall system performance through, for example, decay rate assignment. On the other hand, design methods, which are predicated on a global condition, leads to nonconvex optimization problems. This article considers a global design of an internal model-based distributed dynamic state feedback control law for the linear cooperative output regulation problem. We present a convex formulation of this global design problem based on a structured Lyapunov inequality. Then, the existence of solutions to the structured Lyapunov inequality is investigated. Specifically, we analytically show that the solutions exist for the systems satisfying the agent-wise local sufficient condition. Finally, we compare the proposed method with the agent-wise local design method through numerical examples in terms of conservatism, performance maximization, graph dependency, and scalability.

The authors would like to thank the following people for participating in the 2018 UAS Safety Symposium at Georgia Tech since that effort provided much of the driver for this research: Roy Burke, Julianna Burke, Anthony Mormino, Eric Richey, Chris Proudlove, Naira Hovakimyan, Jeong Jur, Greg Ourada, and Michael Miller. The authors are also thankful to Olivia Jagiella-Lodise and Philippe Monmousseau for their contributions in editing and formatting this paper.

Increased frequency and occurrence of herbicide-resistant biotypes heightens the need for alternative wild oat management strategies. This study aimed to exploit the height differential between wild oat and crops by targeting wild oat between panicle emergence and seed shed timing. Two field studies were conducted either in Lacombe, AB, or Lacombe, AB and Saskatoon, SK, from 2015 to 2017. In the first study, we compared panicle removal methods: hand clipping, use of a hedge trimmer, and a selective herbicide crop topping application to a weedy check and an industry standard in-crop herbicide application in wheat. These treatments were tested early (at panicle emergence), late (at initiation of seed shed), or in combination at one location over 3 yr. In the second study, we investigated optimal timing of panicle removal via a hedge trimmer with weekly removals in comparison to a weedy check in wheat and lentil. This study was conducted at two locations, Lacombe, AB, and Saskatoon, SK, over 3 yr. Among all the tested methods, the early crop topping treatment consistently had the largest impact on wild oat density, dockage, seedbank, and subsequent year crop yield. The early (at panicle emergence) or combination of early and late (at initiation of seed shed) treatments tended to reduce wild oat populations the following season the most compared to the late treatments. Subsequent wild oat populations were not influenced by panicle removal timing, but only by crop and location interactions. Panicle removal timing did significantly affect wild oat dockage in the year of treatment, but no consistent optimal timing could be identified. However, the two studies together highlight additional questions to be investigated, as well as the opportunity to manage wild oat seedbank inputs at the panicle emergence stage of the wild oat lifecycle.

This work provides an overview of avionic systems, which can be used as an entry point to learn about their architecture and components, and as a guide for studying the recent development of unmanned aerial systems avionics. The development trend of avionics for unmanned aerial systems tends toward the one used for a large aircraft both in the structure of the architecture and in design and implementation processes. However, due to the differences in the operational environment, challenges remain in specifying the proper requirements, and proving their safety and security. The paper reviews the functionalities and importance of avionic systems, especially in unmanned aerial systems. It also provides the historical background and the future trend of system development in terms of safety, security, and certification for the purpose of integrating these unmanned aerial systems into an urban airspace.

Malting barley is important in western Canada, yet many malting cultivars do not meet malt quality standards, in part due to lodging. Lodging can decrease barley yield and quality thereby reducing the acceptability for malting. In other countries, plant growth regulator (PGR) applications are used to mitigate lodging. Chlormequat chloride (chlormequat), trinexapac-ethyl (trinexapac), and ethephon were tested at five locations over 3 yr in western Canada for their ability to limit lodging, as well as their effects on yield, agronomic traits, and pre-malt quality characteristics. PGR applications occurred between Zadoks growth stage (GS) 30–33 for chlormequat and trinexapac and GS 37–49 for ethephon. Seeding rates of 200, 300, and 400 seeds m−2 of CDC Copeland barley were used to increase the likelihood of lodging. Increased seeding rate decreased tillers per plant, height, days to maturity, kernel protein, and kernel weight. Ethephon increased the number of tillers per plant and decreased plant height, kernel plumpness, and kernel weight. Trinexapac decreased plant height and kernel weight. Days to maturity was investigated across site-years, with ethephon increasing maturity in 60% of comparisons. Trinexapac and chlormequat had limited effects on maturity. Lodging was investigated across site-years, with trinexapac showing the largest number of lodging reductions and scale of reductions. Ethephon reduced lodging in 36% of comparisons, while chlormequat had inconsistent effects. None of the products affected yield or grain protein. The results suggest PGRs may not be the solution to lodging for CDC Copeland barley on the Canadian Prairies; however, trinexapac shows the most promise of the products tested.

This paper presents a novel state estimation system for unmanned aerial vehicle landing. A novel vision algorithm that detects a portion of the marker is developed, and this algorithm extends the d...

Wild oat (Avena fatua L.) is one of the most problematic weed species in western Canada due to widespread populations, herbicide resistance, and seed dormancy. In wheat (Triticum aestivum L.), and especially in shorter crops such as lentil (Lens culinaris Medik.), A. fatua seed panicles elongate above the crop canopy, which can facilitate physical cutting of the panicles (clipping) to reduce viable seed return to the seedbank. However, the viability of A. fatua seed at the time of panicle elongation is not known. The objective of this study was to determine the viability of A. fatua seed at successive time intervals after elongation above a wheat or lentil crop canopy. A 2-yr panicle clipping and removal study in wheat and lentil was conducted in Lacombe, AB, and Saskatoon, SK, in 2015 and 2016 to determine the onset of viability in A. fatua seeds at successive clipping intervals. Manual panicle clipping of A. fatua panicles above each crop canopy began when the majority of panicles were visible above respective crop canopies and continued weekly until seed shed began. At the initiation of panicle clipping, A. fatua seed viability was between 0% and 10%. By the last clipping treatment (approximately 6 to 7 wk after elongation), 95% of the A. fatua seeds were viable. Seed moisture and awn angle were not good predictors of A. fatua viability, and therefore were unlikely to provide effective tools to estimate appropriate timing for implementation of A. fatua clipping as a management technique. Based on A. fatua seed viability, earlier clipping of A. fatua is likely to be more effective in terms of population management and easier to implement in shorter crops such as lentil. Investigations into long-term effects of clipping on A. fatua populations are needed to evaluate the efficacy of this management strategy on A. fatua.

Glyphosate-resistant (GR) canola is a widely grown crop across western Canada and has quickly become a prolific volunteer weed. Glyphosate-resistant soybean is rapidly gaining acreage in western Canada. Thus, there is a need to evaluate herbicide options to manage volunteer GR canola in GR soybean crops. We conducted an experiment to evaluate the efficacy of various PRE and POST herbicides applied sequentially to volunteer GR canola and to evaluate soybean injury caused by these herbicides. Trials were conducted across Saskatchewan and Manitoba in 2014 and 2015. All treatments provided a range of suppression (>70%) to control (>80%) of volunteer canola. All treatments with the exception of the glyphosate-treated control reduced aboveground canola biomass by an average of 96%. As well, canola seed contamination was reduced from 36% to less than 5% when a PRE and POST herbicide were both used. Moreover, all combinations of herbicides used had excellent crop safety (

In recent years, soybean acreage has increased significantly in western Canada. One of the challenges associated with growing soybean in western Canada is the control of volunteer glyphosate-resistant (GR) canola, because most soybean cultivars are also glyphosate resistant. The objective of this research was to determine the impact of soybean seeding rate and planting date on competition with volunteer canola. We also attempted to determine how high seeding rate could be raised while still being economically feasible for producers. Soybean was seeded at five different seeding rates (targeted 10, 20, 40, 80, and 160 plants m−2) and three planting dates (targeted mid-May, late May, and early June) at four sites across western Canada in 2014 and 2015. Soybean yield consistently increased with higher seeding rates, whereas volunteer canola biomass decreased. Planting date generally produced variable results across site-years. An economic analysis determined that the optimal rate was 40 to 60 plants m−2, depending on market price, and the optimal planting date range was from May 20 to June 1.

Plant-based protein is generally preferred for human consumption, while vegetable oil is becoming increasingly popular for industrial uses. It is of importance to evaluate the oil and protein production potential of non-traditional Brassicaceae oilseeds for use as feedstock. In this study, oil and protein contents and their relationship with seed yield were determined for oriental mustard (Brassica juncea L.), canola (Brassica napus L.), camelina (Camelina sativa L.), Ethiopian mustard (Brassica carinata A. Braun), and yellow mustard (Sinapis alba L.) grown across diverse environments. These oilseeds were studied at Lethbridge, Scott, and Swift Current on the Canadian prairie during 2014–2016. Seed oil and protein contents had an inverse relationship with a wide variation among the oilseeds. Oil and protein contents of canola were 48 and 22%, respectively, while S. alba had the lowest oil (29%) but the highest protein (34%) contents. The oil yield (oil concentration by seed mass) was significantly higher in canola (1358 kg ha−1) compared with other oilseeds. Protein yield was in a narrow range with the highest in canola and S. alba (617 and 597 kg ha−1, respectively), and the lowest in camelina (456 kg ha−1). Oil yield was generally higher in site-years with higher precipitation combined with lower temperature during grain filling, whereas protein yield did not have a consistent trend with growing conditions. Considering oil and protein yields across site-years, B. juncea and carinata could be considered as potential alternatives to conventional rapeseed as industrial feedstock sources on the Canadian prairies.

Protected areas have developed alongside intensive changes in land use and human settlements in the neighboring landscape. Here, we investigated the occurrence of 21 organochlorine pesticides (OCPs), 14 current use pesticides (CUPs), 47 halogenated flame retardants (HFRs), and 19 organophosphate esters (OPEs) in air around Las Cruces (LC) and La Selva (LS) Biological Stations, Costa Rica, and Kibale National Park (KNP), Uganda using passive air samplers (PAS) with polyurethane foam (PUF) discs (PAS-PUF). Significantly higher concentrations of CUPs were observed around LS, while LC had a higher concentration of OCPs. Land use analysis indicated that LS had a higher fraction of agriculture than LC (33% vs 14%), suggesting the higher CUPs concentration at LS was related to pesticide intensive crops, while higher OCPs concentration at LC may be attributed to the area's long agricultural history characterized by small-scale subsistence farming or long-range transport. In Uganda, CUPs and OCPs were generally lower than in Costa Rica, but high concentrations of HFRs were observed inside KNP, possibly due to human activity at research camps near the protected forest. This is the first study that documented the occurrence of anthropogenic chemicals in the air at protected areas with tropical forests.

Diversifying cropping systems is essential to enhance oilseed production resiliency in a changing climate. Here, we determined the productivity and stability of five oilseed crops under different crop stubble management strategies over all nine environments. A 2-year cropping sequence study was conducted at nine environ-sites (3 sites×3 independent 2-yr cropping sequence phases). Crops in year 1 consisted of chemical fallow, lentil (Lens culinaris L.) and spring wheat (Triticum aestivum L.), which resulted in fallow, lentil and wheat stubbles for the following year crops. Crops in year 2 were five oilseed crops: canola (Brassica napus L.), Ethiopian mustard (Brassica carinata L.), oriental mustard (Brassica juncea L.), yellow mustard (Sinapis alba L.), and camelina (Camelina sativa L.). Over all nine environ-sites, B. juncea had high yield and low yield variation, making it a good substitute for B. napus, the primary oilseed crop in acreage on the Canadian Prairies. Lentil and wheat stubbles reduced oilseed crop yields by 4% and 5% compared with chemical fallow, respectively; however, at the 2-yr cropping sequence level, lentil-oilseed sequence increased system production, expressed by annualized canola equivalent yield, by 33% compared with wheat–oilseed sequence and by 112% compared with fallow–oilseed system. The lentil-oilseed sequence had the lowest variation in production and was most suitable for high-yielding environments compared with fallow- and wheat–oilseed sequences, while B. juncea- and B. napus-based cropping sequences were most stable based on the static and dynamic stabilities among all five oilseed-based cropping sequences. Crops explained more variation in system production than weather (40% vs 5%) because of a dramatic difference of cropping sequences with and without fallow. Under changing climatic conditions, diversifying conventional wheat- or fallow-based canola systems using lentil and B. juncea will enhance system productivity and yield stability across different environments.

Enhancing the stability of crop production is vital for agriculture under climate uncertainty. Conventional fallow and shallow-rooting pulse crops such as lentil (Lens culinaris Medic.) have been incorporated in oilseed-cereal cropping systems to cope with dry conditions on the semiarid Canadian prairies. However, the static and dynamic stability of these adaptation practices at a cropping system level is unclear due to the complexity of interactions. This study assessed the effects of diversified rotation systems as drought adaptation practices on the productivity and stability of oilseed-cereal cropping systems. Nine 3-year crop rotations were tested for two cycles at three sites from 2013 to 2016. The 3-year crop sequences included fallow, lentil, and spring wheat (Triticum aestivum L.) in rotation phase 1, followed by canola (Brassica napus L.), oriental mustard (Brassica juncea L.), and camelina (Camelina sativa L. Crantz) in phase 2, and the rotation phase 3 was durum wheat (Triticum turgidum L. subsp. durum (Desf.) Husn.) in all nine rotation systems. On average, the lentil system increased system productivity, expressed by annualized durum wheat equivalent yield, by 24% and 78% compared with the spring wheat and fallow systems, respectively. Stability analysis revealed that the lentil – B. juncea – durum wheat and lentil – B. napus – durum wheat systems had the least variation across the environments and were well adapted to high-yielding sites. The fully-phased rotations across various environments showed that the drought-induced reductions in system productivity ranged from 3 to 47% compared with yields under normal weather, with the lentil – oilseed – durum systems having least reduction. Quantitative assessments revealed that about 36% of the variation in system productivity was associated with rotation systems and additional 30% was due to weather-related factors. In conclusion, the inclusion of lentil in rotation increases systems productivity and reduces yield variation in oilseed-cereal cropping systems in changing environments.

Flax yield can be severely reduced by weeds. The combination of limited herbicide options and the spread of herbicide-resistant weeds across the prairies has resulted in a need for more weed control options for flax producers. The objective of this research was to evaluate the tolerance of flax to topramezone, pyroxasulfone, flumioxazin, and fluthiacet-methyl applied alone as well as in a mix with currently registered herbicides. These herbicides were applied alone and in mixtures at the 1X and 2X rates and compared with three industry standards and one nontreated control. This experiment was conducted at Carman, MB, and Saskatoon, SK, as a randomized complete block with four replications. Data were collected for crop population, crop height, yield, and thousand-seed weight. Ratings for crop damage (phytotoxicity) were also taken at three separate time intervals: 7 to 14, 21 to 28, and 56+ d after treatment. Crop tolerance to these herbicides varied between site-years. This was largely attributed to differences in spring moisture conditions and the differences in soil characteristics between sites. Herbicide injury was transient. Hence, no herbicide or combination of herbicides significantly impacted crop yield consistently. Flumioxazin was the least promising herbicide evaluated, as it caused severe crop damage (>90%) when conditions were conducive. Overall, flax had excellent tolerance to fluthiacet-methyl, pyroxasulfone, and topramezone. Flax had excellent crop safety to the combination of pyroxasulfone + sulfentrazone. However, mixing fluthiacet-methyl and topramezone with MCPA and bromoxynil, respectively, increased crop damage and would not be recommended.

Climate change, agricultural inputs, cropping diversity, and environment have seldom been combined in analyses of soil organic carbon (SOC), and soil C respired through microbial respiration (MR). This modeling study assessed SOC and MR simulated with the Environmental Policy Integrated Climate (EPIC) model for historical weather (1971–2000) and future climate scenarios (2041–2070) for the Alternative Cropping Systems (ACS) study research site in Saskatchewan, Canada. Nineteen years of field and crop management information from the 1994–2013 ACS study were used to validate and provide parameters to the EPIC model for analyses of climate change scenarios. The ACS study consisted of three levels of agricultural inputs [organic (ORG), reduced (RED), and high (HI)] and three levels of cropping diversity [low (LOW), diversified annual grains (DAG), and diversified annuals and perennials (DAP)]. Changes in future SOC and MR under climate change were explored with ANOVA and recursive partitioning in multivariate analyses of inputs, diversity, growing season precipitation (GSP), growing degree days (GDD), annual average maximum, minimum temperatures, cumulative annual precipitation, and terrain attributes (TA). Under climate change, SOC decreased by 1.3% (from 132.3 to 130.6 Mg ha−1) of original stocks in the 0–90 cm. Microbial respiration was affected by climate change and increased by 17% (from 1.92 to 2.25 Mg C ha−1 y−1) due to an increase in annual maximum and minimum temperatures. The increase in annual maximum and minimum temperature was correlated with 32 and 42% of variation in SOC respectively. Monthly growing season GDD was correlated with 14% of variation of SOC in an analysis independent of annual data. Annual precipitation did not affect SOC, though May GSP accounted for 16% of total variation in SOC, while June temperature accounted for 9% of variation in MR. The combination of input and diversity was correlated with 3 and 7% of variation in SOC and MR, respectively. The combination of RED inputs and DAG diversity are the best option to manage SOC and reduce the amount of soil C respired under climate change relative to organic and conventional systems.

Phenotyping crop performance is critical for line selection and variety development in plant breeding. Canola (Brassica napus L.) flowers, the bright yellow flowers, indeterminately increase over a protracted period. Flower production of canola plays an important role in yield determination. Yellowness of canola petals may be a critical reflectance signal and a good predictor of pod number and, therefore, seed yield. However, quantifying flowering based on traditional visual scales is subjective, time-consuming, and labor-consuming. Recent developments in phenotyping technologies using Unmanned Aerial Vehicles (UAVs) make it possible to effectively capture crop information and to predict crop yield via imagery. Our objectives were to investigate the application of vegetation indices in estimating canola flower numbers and to develop a descriptive model of canola seed yield. Fifty-six diverse Brassica genotypes, including 53 B. napus lines, two Brassica carinata lines, and a Brassica juncea variety, were grown near Saskatoon, SK, Canada from 2016 to 2018 and near Melfort and Scott, SK, Canada in 2017. Aerial imagery with geometric and radiometric corrections was collected through the flowering stage using a UAV mounted with a multispectral camera. We found that the normalized difference yellowness index (NDYI) was a useful vegetation index for representing canola yellowness, which is related to canola flowering intensity during the full flowering stage. However, the flowering pixel number estimated by the thresholding method improved the ability of NDYI to detect yellow flowers with coefficient of determination (R2) ranging from 0.54 to 0.95. Moreover, compared with using a single image date, the NDYI-based flowering pixel numbers integrated over time covers more growth information and can be a good predictor of pod number and thus, canola yield with R2 up to 0.42. These results indicate that NDYI-based flowering pixel numbers can perform well in estimating flowering intensity. Integrated flowering intensity extracted from imagery over time can be a potential phenotype associated with canola seed yield.

Phenotyping crop performance is critical for line selection and variety development in plant breeding. Canola (Brassica napus L.) flowers, the bright yellow flowers, indeterminately increase over a protracted period. Flower production of canola plays an important role in yield determination. Yellowness of canola petals may be a critical reflectance signal and a good predictor of pod number and, therefore, seed yield. However, quantifying flowering based on traditional visual scales is subjective, time-consuming, and labor-consuming. Recent developments in phenotyping technologies using Unmanned Aerial Vehicles (UAVs) make it possible to effectively capture crop information and to predict crop yield via imagery. Our objectives were to investigate the application of vegetation indices in estimating canola flower numbers and to develop a descriptive model of canola seed yield. Fifty-six diverse Brassica genotypes, including 53 B. napus lines, two Brassica carinata lines, and a Brassica juncea variety, were grown near Saskatoon, SK, Canada from 2016 to 2018 and near Melfort and Scott, SK, Canada in 2017. Aerial imagery with geometric and radiometric corrections was collected through the flowering stage using a UAV mounted with a multispectral camera. We found that the normalized difference yellowness index (NDYI) was a useful vegetation index for representing canola yellowness, which is related to canola flowering intensity during the full flowering stage. However, the flowering pixel number estimated by the thresholding method improved the ability of NDYI to detect yellow flowers with coefficient of determination (R2) ranging from 0.54 to 0.95. Moreover, compared with using a single image date, the NDYI-based flowering pixel numbers integrated over time covers more growth information and can be a good predictor of pod number and thus, canola yield with R2 up to 0.42. These results indicate that NDYI-based flowering pixel numbers can perform well in estimating flowering intensity. Integrated flowering intensity extracted from imagery over time can be a potential phenotype associated with canola seed yield.

The field pea has both semi-leafless (SL) and leafed (L) types. Mixing these two types together might improve yield by optimizing pea solar radiation interception, reducing lodging, and decreasing disease. However, an optimum mixing ratio has not yet been established, since previous studies mixed two leaf types from two separate varieties. This study used four near-isogenic pairs of pea genotypes differing only in leaf type to determine the optimal mixing ratio for yield and agronomic traits. Two leaf types were mixed at seeding in five mixing ratios: 0:100, 50:50, 67:33, 83:17, and 100:0 SL/L. With precise UAV quantification of canopy height (r2 = 0.88, RMSE = 2.6 cm), the results showed that a ratio of over 67% semi-leafless pea had a 10% greater lodging resistance when compared to the leafed monoculture. For mycosphaerella blight and Uromyce viciae-fabae rust diseases, the 83:17 mixture decreased disease severity by 4% when compared with the leafed monoculture. Regression analysis of yield estimated that the 86:14 ratio provided an 11% increase to the leafed monoculture, but there was no increase compared with the semi-leafless monoculture. Mixing the two types in a high semi-leafless ratio can reduce leafed lodging and prevent yield loss but does not increase the overall yield over the semi-leafless monoculture.

Landing an aircraft on a ship deck in high sea states can be challenging due to the risk for adverse interactions between the moving ship deck and aircraft components. When deck motion is significant, aircraft must sometimes wait for an extended duration until a calm period is detected to land safely. This paper presents a deck motion estimation (DME) algorithm that can decrease landing times. The DME algorithm runs in real-time allowing for fully autonomous ship deck landings without a human operator in the loop. The proposed solution uses generalized predictive control to predict future ship deck states based on prior observations. A landing is commanded when ship deck states are predicted to be within quiescent bounds for a pre-determined prediction horizon in which a safe landing could be completed. Monte-Carlo simulations utilizing ship state data show that the deck motion estimation can accurately predict quiescent landing periods. Further, flight test experiments have demonstrated the feasibility of implementing the system for an unmanned aircraft.

The accurate prediction of the wildfire spread-rate is a challenging task, due to the high number of parameters involved and the underlying complex dynamic multi-physics processes which drive the phenomenon. For these reasons, data-driven prediction tools could be useful to provide a more accurate prediction of the fire front. In this scenario, systematic fire data gathering becomes crucial and using an Unmanned Aircraft Vehicle (UAV) is strategic to reduce considerably the risk associated with flying a manned aircraft into low visibility and extremely turbulent air, sustained by the fire-induced convective motions. Moreover the employment of the UAV is beneficial, as the possibility of flying at very low altitudes maximizes the on-board Electro-Optical (EO) sensor effectiveness. The aim is to develop a real time data-driven fire propagator to support wildfire fighting operations and to facilitate the risk assessment and decision making process. In order to collect data, the fire front position has to be measured using an infra-red (IR) camera so as to overcome the limitations associated to a visible camera in low visibility (smoky)conditions and night operations. To reduce the computational cost associated to the image processing, a Line Camera (LC) configuration has been preferred. Because of the mono-dimensionality of the measure, classical edge detector, like the Canny method, or contour algorithms, developed for 2D images, can not be applied. In this paper, a mono-dimensional noise-resistant algorithm for edge detection is presented. The generality of the proposed method opens the possibility to a variety of heterogeneous problems of different nature. The robustness of this algorithm resides in the use of known physical characteristics of the target of interest, to increase the feature edge discontinuity. Its straightforwardness guarantees fast computation, making it very attractive for real time image processing, remote sensing applications and for UAV surveillance tasks.

Evidential reasoning, developed by Glenn Shafer and Arthur Dempster in the 1960s and 1970s, has been extensively applied to risk analysis, sensor fusion, and system failure analysis. Use in real-time systems for health analysis has been more limited due to computational complexity requiring less either comprehensive analysis or significant computing power. Evidential reasoning networks, also known as valuation networks, reduce computational requirements by eliminating hypothesis combinations which are infeasible. Recent extensions enable these networks to learn the relationships between nodes based on evidence inputs, enabling these networks to adapt without the need of subject matter experts defining each relationship update. This system is applied to unmanned system health analysis, demonstrating the capability to run complex belief analyses in real-time on autopilot systems with low computational power using the GUST autopilot system developed at the Georgia Institute of Technology. Comparisons are made between the evidential combination approach and more traditional contingency management that uses time delays and worst-case scenario assumptions for contingency responses. Simulation training is used as a surrogate for high volumes of flight testing, and operational results are primarily tested through GUST simulation of a representative mission. Results show that evidential reasoning networks are an effective approach to real-time health analysis of unmanned systems, using the novel update rules to understand relationships based on operational outcomes. Flight demonstration is included to show the capability to run this system in real operations. This work has implications on integration of unmanned systems into the national airspace as well as on Urban Air Mobility. Results from the network are explainable, enabling human oversight of operational decisions. Real-time implementation enables integration into avionics systems. Further, the data-driven approach to learning relationships enables this system to adapt as information concerning unmanned systems steadily changes.

In this paper, an Unmanned Aerial System (UAS) navigation solution using the Intel RealSense T265, a commercially available Visual Inertial Odometry device, is developed and presented in conjunction with an Extended Kalman Filter framework. Comparisons of raw and estimated position and yaw angle data from the device are made against ground truth measurements obtained via a motion capture system. Preliminary results from hand-carry tests show promising localization capability as the device continues to gather information about its environment. Further localization improvements may be achievable with varied software configurations. The performance of the Extended Kalman Filter during closed-loop flight is also evaluated, and shows smoothing of noisy measurements from the T265 and generally precise trajectory following capabilities. Future work to extend this characterization shall involve testing the performance of the device across varying flight envelopes, and especially for longer durations.

Sensor fusion is a topic central to aerospace engineering and is particularly applicable to unmanned aerial systems (UAS). Evidential Reasoning, also known as Dempster-Shafer theory, is used heavily in sensor fusion for detection classification. High computing requirements typically limit use on small UAS platforms. Valuation networks, the general name given to evidential reasoning networks by Shenoy, provides a means to reduce computing requirements through knowledge structure. However, these networks use conditional probabilities or transition potential matrices to describe the relationships between nodes, which typically require expert information to define and update. This paper proposes and tests a novel method to learn these transition potential matrices based on evidence injected at nodes. Novel refinements to the method are also introduced, demonstrating improvements in capturing the relationships between the node belief distributions. Finally, novel rules are introduced and tested for evidence weighting at nodes during simultaneous evidence injections, correctly balancing the injected evidenced used to learn the transition potential matrices. Together, these methods enable updating a Dempster-Shafer network with significantly less user input, thereby making these networks more useful for scenarios in which sufficient information concerning relationships between nodes is not known a priori.

In this paper, we study the problem of cooperative control of heterogeneous linear time-invariant multiagent systems over networks with limited bandwidth. Our analysis include two different setups; namely, (i) one-channel network and (ii) multi-channel network. To this end, we utilize a time- delay approach to the networked control systems to derive some upper bounds for the performance parameters; specifically, the decay rate and the L 2 -gain of the closed-loop system. We then present methods predicated on linear matrix inequalities to estimate those parameters. Finally, we illustrate how the network imperfections affect the performance and stability of the closed-loop system through a numerical example.

Dr. Ken Atwater, President of Hillsborough Community College (HCC), saw the email flash across his monitor, “2018-19 Performance Funding: Hillsborough Community College” sent from the Chancellor of the Florida College System (FCS). The email was 12 months in the making. In 2015, the Florida Legislature created the first performance funding-based incentive program in its General Appropriations Act (Laws of Florida Ch. 2015-232. (n.d.)). Proviso language required the State Board of Education to allocate performance funds pursuant to a performance funding model. The performance model had four performance funding metrics: retention, graduation, wages and job placement (see Exhibit 1). This one message would answer a burning question that had been lingering in the college’s top administrators’ minds: Where would the college land in another year of performance funding? Atwater contemplatively read the email, “A Bronze ranking, again.” This Bronze designation meant the college was not eligible for new state distributed performance funds meaning almost $2 million would not be appropriated to HCC. Atwater asked himself “what needs to be done so HCC is eligible for this funding?” The 2015 Florida Legislature inserted language into its General Appropriations Act creating the FCS’ performance funding-based incentive program. The direction of millions of dollars distributed throughout Florida colleges had been determined including a final ranking of Gold, Silver, Bronze or Purple for each college, with Gold being the highest ranking. This ranking determined whether HCC received millions in new dollars; money that in an environment of budget cuts to the entire FCS over the last two years would be extremely important to the students, faculty, and administrators across Hillsborough County. Atwater knew the college needed to improve its score, thus allowing HCC to move into a Silver or Gold category. The improvement in the score to gain the additional dollars boiled down to concentrated efforts in providing the best education for students while equipping faculty with the right resources to improve effectiveness. Atwater thought, "Easier said than done. I am faced with the proverbial chicken before the egg or egg before the chicken. I may need funding to make the necessary changes to improve the scores. However, without the necessary changes to show improvement in scores, we will not receive the funding." Regardless of the dilemma, the question had to be asked, "What strategies should be implemented to increase scores in the four performance metrics that the college would be judged on? Should the college expand tracking of the cohort of students that is examined? Should new student success initiatives be rolled out to help students?" Atwater wanted answers. He had approximately two million reasons why.

In visual-inertial odometry (VIO), inertial measurement unit (IMU) dead reckoning acts as the dynamic model for flight vehicles while camera vision extracts information about the surrounding environment and determines features or points of interest. With these sensors, the most widely used algorithm for estimating vehicle and feature states for VIO is an extended Kalman filter (EKF). The design of the standard EKF does not inherently allow for time offsets between the timestamps of the IMU and vision data. In fact, sensor-related delays that arise in various realistic conditions are at least partially unknown parameters. A lack of compensation for unknown parameters often leads to a serious impact on the accuracy of VIO systems and systems like them. To compensate for the uncertainties of the unknown time delays, this study incorporates parameter estimation into feature initialization and state estimation. Moreover, computing cross-covariance and estimating delays in online temporal calibration correct residual, Jacobian, and covariance. Results from flight dataset testing validate the improved accuracy of VIO employing latency compensated filtering frameworks. The insights and methods proposed here are ultimately useful in any estimation problem (e.g., multi-sensor fusion scenarios) where compensation for partially unknown time delays can enhance performance.

With the advent of unmanned aerial vehicles (UAVs), a major area of interest in the research field of UAVs has been vision-aided inertial navigation systems (V-INS). In the front-end of V-INS, image processing extracts information about the surrounding environment and determines features or points of interest. With the extracted vision data and inertial measurement unit (IMU) dead reckoning, the most widely used algorithm for estimating vehicle and feature states in the back-end of V-INS is an extended Kalman filter (EKF). An important assumption of the EKF is Gaussian white noise. In fact, measurement outliers that arise in various realistic conditions are often non-Gaussian. A lack of compensation for unknown noise parameters often leads to a serious impact on the reliability and robustness of these navigation systems. To compensate for uncertainties of the outliers, we require modified versions of the estimator or the incorporation of other techniques into the filter. The main purpose of this paper is to develop accurate and robust V-INS for UAVs, in particular, those for situations pertaining to such unknown outliers. Feature correspondence in image processing front-end rejects vision outliers, and then a statistic test in filtering back-end detects the remaining outliers of the vision data. For frequent outliers occurrence, variational approximation for Bayesian inference derives a way to compute the optimal noise precision matrices of the measurement outliers. The overall process of outlier removal and adaptation is referred to here as &ldquo, outlier-adaptive filtering&rdquo, Even though almost all approaches of V-INS remove outliers by some method, few researchers have treated outlier adaptation in V-INS in much detail. Here, results from flight datasets validate the improved accuracy of V-INS employing the proposed outlier-adaptive filtering framework.