922 research outputs found
Ecology of the Festuca Grassland in Central Saskatchewan
No full textFor some time it has been realized that the grassland occuring in the 'park belt' or 'aspen grove region' bounding the Canadian prairies on the north, and forming the transition between it and the boreal forest, does not consist merely of extensions of the adjacent grassland communities (true prairie or mixed prairie) among the patches of woodland which occur in that region; but is characterized by a different group of dominant grasses from those occuring elsewhere in North America.
Although other investigators have mentioned this fact and written about the flora in other parts of the region, their data have been based largely on methods of estimation chiefly of a qualitative nature. In view of this, it was thought that an ecological survey based on quantitative methods might provide a better basis for classifying the vegetation of the area.
Accordingly, while being employed as an assistant in the Department of Plant Ecology of the University of Saskatchewan the author was given the opportunity, under the supervision of Dr. R.T. Coupland, to carry out investigations in those parts of the aspen grove region north and northwest of Saskatoon.
The author is considerably indebted to Dr. B.W. Currie of the Physics Department, for furnishing data on the climate of the area of research, and to Mr. H.C. Moss and his assistants, of the Saskachewan Soil Survey, who kindly undertook to examine and report on the soil samples which were collected in several of the sites studied. The author also wishes to express his gratitide to Mr. Jack F. Alex and Mr. W. Budz of the Department of Plant Ecology, who assisted greatly in both the field and laboratory work for this project. The financial assistance of the Saskatchewan Agricultural Research Foundation, which made this study possible, is also appreciated.
The author experienced a great deal of difficulty as a result of unfamiliarity with the topography, in particular with the vegetation of the plants, since he is more accustomed to the mountains and forests of British Columbia. Many problems of identifications were encountered in dealing with the wealth of species of grasses and composites, as well as problems of relationship between the communities found in certain locations and their topographical situation
Coordination in an Adaptive Traffic Signal Control System
No full textCoordination between the intersections is used in traffic signal control for quite a long time. The objective of this thesis is to determine how beneficial network coordination is in an adaptive traffic signal control system that follows the multi-agent approach. As part of the research: Simulation results show that coordination in a multi-agent controller can reduce average delay of the users on the network depending on the average demand. The best performing coordination measure is platooning cars on the main streams at the first intersection of the arterial. This provide time for the downstream intersections to serve side streams and ensured that the main stream is not stopped at the downstream intersections on the arterial. The tested coordination measures reduced delay with 10% compared to the original settings.Transport and PlanningTransport & PlanningCivil Engineering and Geoscience
The implementation of a system description language and its semantic functions
No full textElectrical Engineering, Mathematics and Computer Scienc
Distributed Gaussian Process Hyperparameter Optimization for Multi-Agent Systems
No full textGaussian Process (GP) is a flexible non-parametric method which has a wide variety of applications e.g., field estimation using multi-agent systems. However, the training of the hyperparameters suffers from high computational complexity. Recently, distributed hyperparameter optimization with proximal gradients has been proposed to reduce complexity, however only for a network with a central station. In this work, exploiting edge-based constraints, we propose two fully-distributed algorithms pxADMMfd and pxADMMfd,fast for a network of multi-agent systems, which do not rely on a central station. In addition, asynchronous versions of the algorithms are also proposed to reduce the synchronization overhead in heterogeneous networks. Simulations are conducted for a field estimation problem, using both artificial, and real-world datasets, which show that the proposed fully-distributed algorithms successfully converge, at the cost of an increased number of iterations.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
RadioAstron gravitational redshift experiment: status update
No full textA test of a cornerstone of general relativity, the gravitational redshift effect, is currently being conducted with the RadioAstron spacecraft, which is on a highly eccentric orbit around Earth. Using ground radio telescopes to record the spacecraft signal, synchronized to its ultra-stable on-board H-maser, we can probe the varying flow of time on board with unprecedented accuracy. The observations performed so far, currently being analyzed, have already allowed us to measure the effect with a relative accuracy of 4 × 10−4 . We expect to reach 2.5×10−5 with additional observations in 2016, an improvement of almost a magnitude over the 40-year old result of the GP-A mission
Relative Space-Time Kinematics of an Anchorless Network
No full textThe work described in this thesis was in part financially supported by STW-sponsored OLFAR project (Contract Number: 10556) within the Dutch ASSYS perspectief programSignal Processing System
Multi-Sensor Fusion for Localization of a Lunar Micro Rover Using Non-Vision Sensors
No full textAutonomous navigation is a critical aspect of robotic systems, particularly in hostile and uncertain environments, and robot localization is central to navigation. Robot localization establishes its position within its surroundings. This thesis addresses the challenge of robot localization, focusing on a lunar-like environment with constraints such as limited computational resources and the use of non-visual based sensors. In this thesis, three sensors — wheel encoders (WE), Sun sensor (SS), and inertial measurement unit (IMU) — are employed for localization. Each sensor contributes distinct information regarding position and orientation. However, individual sensor measurements suffer from inherent inaccuracies and errors, especially the IMU’s reliance on integration over time, leading to significant drift. To mitigate these challenges, three fusion methods are explored: sensor selection based on predefined thresholds, Kalman filtering, and weighted fusion. Results indi- cate substantial improvements in localization accuracy compared to individual sensor measurements. The weighted fusion method, in particular, demonstrates superior per- formance by assigning appropriate importance, according to their accuracy, to each sensor’s information, resulting in significantly reduced positioning errors. The maxi- mum localization error using this method is 92m, which is smaller than reported in the literature. Further, the maximum localization percentage error over 65m is around 8%, which is comparable to the literature with visual sensors. The weighted fusion method introduces only a marginal increase in the computational complexity. Thus, this method stands out for its simplicity and delivers results superior to those documented in existing literature for non-visual sensors. Despite promising results, the research is met with certain hurdles, notably the avail- ability and consistency of datasets. The reliance on existing datasets, such as the Devon Island Rover dataset, highlights the need for standardized and comprehensive datasets for thorough testing and validation. Calibration inconsistencies and verification issues further underscore the complexity of real-world implementation. Nevertheless, the findings of this thesis offer insights into the integration of multiple sensors for enhanced localization in lunar-like environments. By leveraging complementary sensor data and employing efficient fusion techniques, the proposed approach enables more accurate and reliable navigation of lunar micro rovers, thus advancing the capabilities of autonomous robotic systems for future lunar exploration missions.Electrical Engineering | Signals and System
Cooperative Localization of Unmanned Aerial Vehicles using ADS-B
No full textAs unmanned aerial systems (UAS) turn into a full-fledged industry, the sky will be much more crowded in the future. Large-scale UAV applications make reliable UAV navigation a pressing need. Traditionally, global navigation satellite system (GNSS) is extensively used as the primary positioning, navigation, and timing (PNT) service. However, GNSS is vulnerable to intentional radio interference such as spoofing, jamming, and repeating. Hence, alternative PNT (APNT) attracted many researchers' attention. In this thesis, instead of GNSS signals, ADS-B signals from piloted aircraft are leveraged for UAV navigation. We propose a cooperative navigation strategy for multiple UAVs in GNSS-denied environments. It consists of: 1) a system-level, leader-follower cooperative strategy; 2) a sensor fusion algorithm for individual UAV navigation based on the extended Kalman filter. Furthermore, the effects of asynchronous clocks are studied and a joint relative positioning and synchronization algorithm is applied to tackle this problem. Finally, Monte Carlo experiments in a multi-UAV scene are performed to verify the proposed algorithms. The results show that the proposed algorithms achieve a performance comparable to civilian GNSS on the selected data set and under the system assumptions we made. Moreover, the proposed cooperative navigation framework only needs one ground station of limited service capacity as external aid. Compared with large-scale, specialized terrestrial APNT service networks, our proposed framework is more flexible and the system can be deployed in areas without infrastructure.Electrical Engineering | Circuits and System
Distributed Gaussian Process for Multi-agent Systems
No full textThis work is focused on the distributed system, i.e. Multi-agent Systems (MAS), with application in environmental monitoring and learning. The specific task is to develop algorithms, i.e. Gaussian Process (GP), that are robust, accurate and fully-distributed to learn the unknown spatial environmental field. The two main problems are (1). how to optimize GP hyperparameters in fully-distributed manner, and (2). how to aggregate predictions from agents.The state-of-the-art solution for distributed GP hyperparameter optimization problem is proximal alternated direction method of multipliers (pxADMM) algorithm, which requires a center station in MAS. Based on pxADMM, two fully-distributed pxADMM algorithms are proposed such that the center station is no longer needed. Asynchronous behavior is also introduced into the proposed algorithms, so that they can deal with heterogeneous processing time of agents. Simulations are carried out on both artificial and real datasets. Results show that the proposed methods all achieve stable convergence.The aggregation methods can be classified based on whether the local datasets are assumed to be independent or not. Under independent assumption, PoE and BCM families of methods can be distributed by applying discrete time consensus filter (DTCF). In this project, primal-dual method of multiplier (PDMM) is proposed to replace DTCF so that the aggregation converges faster. Without independent assumption, the Nested Pointwise Aggregation of Experts (NPAE) considers the cross-correlation among local datasets to achieve consistent aggregation. The current NPAE-JOR algorithm distributes NPAE in complete graph, where flooding variables across network is required before aggregation. In this project, CON-NPAE is proposed to extend NPAE to fully-distributed version in connected graph, where flooding is not required. Simulations on artificial and real datasets are performed. Results show that the proposed PDMM based algorithm reduces the iterations needed for fully-distributed PoE and BCM families of methods. The CON-NPAE is fully-distributed and makes better aggregations than independence assumption based methods in networks with high connectivity. The connection between its performance and MAS structure requires further study.In conclusion, fully-distributed and asynchronous algorithms are proposed for GP hyperparameter optimization based on pxADMM. The fully-distributed PoE and BCM methods are accelerated by applying PDMM. The CON-NPAE is proposed to make NPAE fully-distributed. In future work, the theoretical convergence of fully-distributed pxADMM should be researched. For GP aggregation, the effect of network structure on the performance of CON-NPAE can be studied. Also, inducing points method is a possible solution to alleviate the flooding overhead of distributed NPAE.Electrical Engineerin
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