Isolation of two phenylethanoid glycosides from Eremophila gilesii

The leaves of Eremophila gilesii have been used traditionally to treat colds, headaches, sores, and chest pains. Our previous screening of Australian native plants showed that the methanol extract of the aerial parts of E. gilesii demonstrated notable inhibition of ADP-induced human platelet aggregation and serotonin release. Subsequent fractionation on the methanol extract led to the isolation of two phenylethanoid glycosides, verbascoside (1) and poliumoside (2). This is the first study reporting the presence of phenylethanoid glycosides in E. gilesii.

Ectopic overexpression of a mungbean vacuolar Na+/H+ antiporter gene (VrNHX1) leads to increased salinity stress tolerance in transgenic Vigna unguiculata L. Walp

Salinity is a major threat to sustainable agriculture worldwide. Plant NHX exchangers play an important role in conferring salt tolerance under salinity stress. In this study, a vacuolar Na+/H+ antiporter gene VrNHX1 (Genbank Accession No. JN656211.1) from mungbean (Vigna radiata) was introduced into cowpea (Vigna unguiculata) by the Agrobacterium tumefaciens-mediated transformation method. Polymerase chain reaction and Southern blot hybridization confirmed the stable integration of VrNHX1 into the cowpea genome. Comparative expression analysis by semi-quantitative RT-PCR revealed higher expression of VrNHX1 in transgenic cowpea plants than wild-type. Under salt stress conditions, T2 transgenic 35S:VrNHX1 cowpea lines exhibited higher tolerance to 200 mM NaCl treatment than wild-type. Furthermore, T2 transgenic 35S:VrNHX1 lines maintained a higher K+/Na+ ratio in the aerial parts under salt stress and accumulated higher [Na+] in roots than wild-type. Physiological analysis revealed lower levels of lipid peroxidation, hydrogen peroxide and oxygen radical production but higher levels of relative water content and proline, ascorbate and chlorophyll contents in T2 transgenic 35S:VrNHX1 lines.

The effects of geochemical conditions on the establishment and growth of mangrove seedlings in acid sulfate soil environments

Acid sulfate soils (ASS) is a stress factor that is responsible for the failure of some mangrove restoration projects, including abandoned aquaculture ponds converted from mangrove ecosystems. Through experimental and field studies, this research provides a better understanding of the biogeochemistry of ASS disturbance and the response of mangrove seedlings (Rhizophoraceae) under high metal levels and acidic conditions. This study found that mangrove restorations under ASS disturbance can work but with lower numbers of survived seedlings. To prevent toxicity under high levels of metal, seedlings retained metals in their roots and sparingly distributed them into aerial parts with low mobility. The presence of high levels of potential acidity parameters would allow pyrite to oxidise, thus increasing metal levels and acidity, which in turn affected the survival and growth of the seedlings.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Automatic Detection of Marine Mammal from Aerial Imagery

The following technical report describes the approach and algorithm used to detect marine mammals from aerial imagery taken from manned/unmanned platform. The aim is to automate the process of counting the population of dugongs and other mammals. We have developed and algorithm that automatically presents to a user a number of possible candidates of these mammals. We tested the algorithm in two distinct datasets taken from different altitudes. Analysis and discussion is presented in regards with the complexity of the input datasets, the detection performance.

Is the sum more than the corporate entrepreneurship parts? the different effects of social capital on innovation, venturing, and renewal processes

Principal Topic Although corporate entrepreneurship is of vital importance for long-term firm survival and growth (Zahra and Covin, 1995), researchers still struggle with understanding how to manage corporate entrepreneurship activities. Corporate entrepreneurship consists of three parts: innovation, venturing, and renewal processes (Guth and Ginsberg, 1990). Innovation refers to the development of new products, venturing to the creation of new businesses, and renewal to redefining existing businesses (Sharma, and Chrisman, 1999; Verbeke et al., 2007). Although there are many studies focusing on one of these aspects (cf. Burgelman, 1985; Huff et al., 1992), it is very difficult to compare the outcomes of these studies due to differences in contexts, measures, and methodologies. This is a significant lack in our understanding of CE, as firms engage in all three aspects of CE, making it important to compare managerial and organizational antecedents of innovation, venturing and renewal processes. Because factors that may enhance venturing activities may simultaneously inhibit renewal activities. The limited studies that did empirically compare the individual dimensions (cf. Zahra, 1996; Zahra et al., 2000; Yiu and Lau, 2008; Yiu et al., 2007) generally failed to provide a systematic explanation for potential different effects of organizational antecedents on innovation, venturing, and renewal. With this study we aim to investigate the different effects of structural separation and social capital on corporate entrepreneurship activities. The access to existing and the development of new knowledge has been deemed of critical importance in CE-activities (Floyd and Wooldridge, 1999; Covin and Miles, 2007; Katila and Ahuja, 2002). Developing new knowledge can be facilitated by structurally separating corporate entrepreneurial units from mainstream units (cf. Burgelman, 1983; Hill and Rothaermel, 2003; O'Reilly and Tushman, 2004). Existing knowledge and resources are available through networks of social relationships, defined as social capital (Nahapiet and Ghoshal, 1998; Yiu and Lau, 2008). Although social capital has primarily been studied at the organizational level, it might be equally important at top management level (Belliveau et al., 1996). However, little is known about the joint effects of structural separation and integrative mechanisms to provide access to social capital on corporate entrepreneurship. Could these integrative mechanisms for example connect the separated units to facilitate both knowledge creation and sharing? Do these effects differ for innovation, venturing, and renewal processes? Are the effects different for organizational versus top management team integration mechanisms? Corporate entrepreneurship activities have for example been suggested to take place at different levels. Whereas innovation is suggested to be a more bottom-up process, strategic renewal is a more top-down process (Floyd and Lane, 2000; Volberda et al., 2001). Corporate venturing is also a more bottom-up process, but due to the greater required resource commitments relative to innovation, it ventures need to be approved by top management (Burgelman, 1983). As such we will explore the following key research question in this paper: How do social capital and structural separation on organizational and TMT level differentially influence innovation, venturing, and renewal processes? Methodology/Key Propositions We investigated our hypotheses on a final sample of 240 companies in a variety of industries in the Netherlands. All our measures were validated in previous studies. We targeted a second respondent in each firm to reduce problems with single-rater data (James et al., 1984). We separated the measurement of the independent and the dependent variables in two surveys to create a one-year time lag and reduce potential common method bias (Podsakoff et al., 2003). Results and Implications Consistent with our hypotheses, our results show that configurations of structural separation and integrative mechanisms have different effects on the three aspects of corporate entrepreneurship. Innovation was affected by organizational level mechanisms, renewal by integrative mechanisms on top management team level and venturing by mechanisms on both levels. Surprisingly, our results indicated that integrative mechanisms on top management team level had negative effects on corporate entrepreneurship activities. We believe this paper makes two significant contributions. First, we provide more insight in what the effects of ambidextrous organizational forms (i.e. combinations of differentiation and integration mechanisms) are on venturing, innovation and renewal processes. Our findings show that more valuable insights can be gained by comparing the individual parts of corporate entrepreneurship instead of focusing on the whole. Second, we deliver insights in how management can create a facilitative organizational context for these corporate entrepreneurship activities.

Enhancing digital road map with lane details extracted from large-scale stereo aerial imagery using object-oriented image analysis

Precise, up-to-date and increasingly detailed road maps are crucial for various advanced road applications, such as lane-level vehicle navigation, and advanced driver assistant systems. With the very high resolution (VHR) imagery from digital airborne sources, it will greatly facilitate the data acquisition, data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lane information from aerial images with employment of the object-oriented image analysis method. Our proposed algorithm starts with constructing the DSM and true orthophotos from the stereo images. The road lane details are detected using an object-oriented rule based image classification approach. Due to the affection of other objects with similar spectral and geometrical attributes, the extracted road lanes are filtered with the road surface obtained by a progressive two-class decision classifier. The generated road network is evaluated using the datasets provided by Queensland department of Main Roads. The evaluation shows completeness values that range between 76% and 98% and correctness values that range between 82% and 97%.

Towards an automatic road lane marks extraction based on ISODATA segmentation and shadow detection from large-scale aerial images

The automatic extraction of road features from remote sensed images has been a topic of great interest within the photogrammetric and remote sensing communities for over 3 decades. Although various techniques have been reported in the literature, it is still challenging to efficiently extract the road details with the increasing of image resolution as well as the requirement for accurate and up-to-date road data. In this paper, we will focus on the automatic detection of road lane markings, which are crucial for many applications, including lane level navigation and lane departure warning. The approach consists of four steps: i) data preprocessing, ii) image segmentation and road surface detection, iii) road lane marking extraction based on the generated road surface, and iv) testing and system evaluation. The proposed approach utilized the unsupervised ISODATA image segmentation algorithm, which segments the image into vegetation regions, and road surface based only on the Cb component of YCbCr color space. A shadow detection method based on YCbCr color space is also employed to detect and recover the shadows from the road surface casted by the vehicles and trees. Finally, the lane marking features are detected from the road surface using the histogram clustering. The experiments of applying the proposed method to the aerial imagery dataset of Gympie, Queensland demonstrate the efficiency of the approach.

Automated road pavement marking detection from high resolution aerial images based on Multi-resolution image analysis and anisotropic Gaussian filtering

Road features extraction from remote sensed imagery has been a long-term topic of great interest within the photogrammetry and remote sensing communities for over three decades. The majority of the early work only focused on linear feature detection approaches, with restrictive assumption on image resolution and road appearance. The widely available of high resolution digital aerial images makes it possible to extract sub-road features, e.g. road pavement markings. In this paper, we will focus on the automatic extraction of road lane markings, which are required by various lane-based vehicle applications, such as, autonomous vehicle navigation, and lane departure warning. The proposed approach consists of three phases: i) road centerline extraction from low resolution image, ii) road surface detection in the original image, and iii) pavement marking extraction on the generated road surface. The proposed method was tested on the aerial imagery dataset of the Bruce Highway, Queensland, and the results demonstrate the efficiency of our approach.

Multi-objective mission flight planning in civil unmanned aerial systems

Unmanned Aerial Vehicles (UAVs) are emerging as an ideal platform for a wide range of civil applications such as disaster monitoring, atmospheric observation and outback delivery. However, the operation of UAVs is currently restricted to specially segregated regions of airspace outside of the National Airspace System (NAS). Mission Flight Planning (MFP) is an integral part of UAV operation that addresses some of the requirements (such as safety and the rules of the air) of integrating UAVs in the NAS. Automated MFP is a key enabler for a number of UAV operating scenarios as it aids in increasing the level of onboard autonomy. For example, onboard MFP is required to ensure continued conformance with the NAS integration requirements when there is an outage in the communications link. MFP is a motion planning task concerned with finding a path between a designated start waypoint and goal waypoint. This path is described with a sequence of 4 Dimensional (4D) waypoints (three spatial and one time dimension) or equivalently with a sequence of trajectory segments (or tracks). It is necessary to consider the time dimension as the UAV operates in a dynamic environment. Existing methods for generic motion planning, UAV motion planning and general vehicle motion planning cannot adequately address the requirements of MFP. The flight plan needs to optimise for multiple decision objectives including mission safety objectives, the rules of the air and mission efficiency objectives. Online (in-flight) replanning capability is needed as the UAV operates in a large, dynamic and uncertain outdoor environment. This thesis derives a multi-objective 4D search algorithm entitled Multi- Step A* (MSA*) based on the seminal A* search algorithm. MSA* is proven to find the optimal (least cost) path given a variable successor operator (which enables arbitrary track angle and track velocity resolution). Furthermore, it is shown to be of comparable complexity to multi-objective, vector neighbourhood based A* (Vector A*, an extension of A*). A variable successor operator enables the imposition of a multi-resolution lattice structure on the search space (which results in fewer search nodes). Unlike cell decomposition based methods, soundness is guaranteed with multi-resolution MSA*. MSA* is demonstrated through Monte Carlo simulations to be computationally efficient. It is shown that multi-resolution, lattice based MSA* finds paths of equivalent cost (less than 0.5% difference) to Vector A* (the benchmark) in a third of the computation time (on average). This is the first contribution of the research. The second contribution is the discovery of the additive consistency property for planning with multiple decision objectives. Additive consistency ensures that the planner is not biased (which results in a suboptimal path) by ensuring that the cost of traversing a track using one step equals that of traversing the same track using multiple steps. MSA* mitigates uncertainty through online replanning, Multi-Criteria Decision Making (MCDM) and tolerance. Each trajectory segment is modeled with a cell sequence that completely encloses the trajectory segment. The tolerance, measured as the minimum distance between the track and cell boundaries, is the third major contribution. Even though MSA* is demonstrated for UAV MFP, it is extensible to other 4D vehicle motion planning applications. Finally, the research proposes a self-scheduling replanning architecture for MFP. This architecture replicates the decision strategies of human experts to meet the time constraints of online replanning. Based on a feedback loop, the proposed architecture switches between fast, near-optimal planning and optimal planning to minimise the need for hold manoeuvres. The derived MFP framework is original and shown, through extensive verification and validation, to satisfy the requirements of UAV MFP. As MFP is an enabling factor for operation of UAVs in the NAS, the presented work is both original and significant.

Extraction of road lanes from high-resolution stereo aerial imagery based on maximum likelihood segmentation and texture enhancement

Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.

Unmanned Aerial Vehicles UAVs attitude, height, motion estimation and control using visual systems

This paper presents an implementation of an aircraft pose and motion estimator using visual systems as the principal sensor for controlling an Unmanned Aerial Vehicle (UAV) or as a redundant system for an Inertial Measure Unit (IMU) and gyros sensors. First, we explore the applications of the unified theory for central catadioptric cameras for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV’s attitude. Then we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Additionally, we show the use of a stereo system to calculate the aircraft height and to measure the UAV’s motion. Finally, we present a visual tracking system based on Fuzzy controllers working in both a UAV and a camera pan and tilt platform. Every part is tested using the UAV COLIBRI platform to validate the different approaches, which include comparison of the estimated data with the inertial values measured onboard the helicopter platform and the validation of the tracking schemes on real flights.

Evaluation of aerial remote sensing techniques for vegetation management in power line corridors

The following paper presents an evaluation of airborne sensors for use in vegetation management in powerline corridors. Three integral stages in the management process are addressed including, the detection of trees, relative positioning with respect to the nearest powerline and vegetation height estimation. Image data, including multi-spectral and high resolution, are analyzed along with LiDAR data captured from fixed wing aircraft. Ground truth data is then used to establish the accuracy and reliability of each sensor thus providing a quantitative comparison of sensor options. Tree detection was achieved through crown delineation using a Pulse-Coupled Neural Network (PCNN) and morphologic reconstruction applied to multi-spectral imagery. Through testing it was shown to achieve a detection rate of 96%, while the accuracy in segmenting groups of trees and single trees correctly was shown to be 75%. Relative positioning using LiDAR achieved a RMSE of 1.4m and 2.1m for cross track distance and along track position respectively, while Direct Georeferencing achieved RMSE of 3.1m in both instances. The estimation of pole and tree heights measured with LiDAR had a RMSE of 0.4m and 0.9m respectively, while Stereo Matching achieved 1.5m and 2.9m. Overall a small number of poles were missed with detection rates of 98% and 95% for LiDAR and Stereo Matching.