Dario Schor

Satellite ground station emulator: An architecture and implementation proposal

The need to train satellite ground station operators is of critical importance for the success of a space mission. An operator is responsible for monitoring the health of a spacecraft and recording of payload data. This paper proposes an architecture that uses an existing ground station for training purposes in the form of emulation. Telemetry is simulated and transmitted to a ground station from a remote location, thus allowing the use of all the available physical equipment. In addition, means are provided for the simulator to inject noise and incorrect telemetry that test the operators understanding not only of a satellite mission, but also their ability to react to anomalies that may occur. The paper also suggests incremental modes of training that guide an operator through different scenarios leading to communicating with an amateur satellite.

A study of optimal topologies in swarm intelligence

The Particle Swarm Optimization (PSO) algorithm was proposed by Kennedy and Eberhart to solve unconstrained, nonlinear optimization problems. This paper examines the merits of different neighbourhood topologies using the original PSO algorithm. The global, ring, star, torus, trees, and a newly proposed hierarchical topologies are tested against the Sphere, Rosenbrock, Rastrigin, and Griewank functions. The study looks at the number of iterations until the function converges (when the fitness function does not change by more than a convergence error for 50 iterations) and the mean fitness achieved by each test. The results indicate that the torus and a Gov-7 topologies performs well for all functions tested due to the degrees of separation and multiple paths for information flow that allow information about a good solution to be propagated to the rest of the particles. This work also shows how special nodes can serve as filters that reject local solutions in swarm topologies. This work furthers the understanding of swarms and the information flow through the network.

A Command and Data Handling unit for pico-satellite missions

This paper outlines the design and implementation of Phase Two of the Command and Data Handling (CDH) unit for the WinCube project. WinCube is a multi-year, multi-disciplinary project conducted at the University of Manitoba in which students from different departments participate in the design of a CubeSat. The purpose of the WinCube project is to provide an opportunity for engineering students to design and implement a space-ready satellite. The project also acts as a vessel for a high school science experiment, which will run an experiment while in space, and relay results down to a satellite base station. The CDH unit must manage the operations and internal communications of the various units inside the satellite. This unit includes a power management application, serial communications protocols to relay information, and a trustworthy timing system. The power management application must monitor the state of the battery, the output of the solar panels, as well as control the amount of power provided to each of the primary units. Serial communications are used to interact with other devices on the satellite. Finally, an external real time clock and watchdog timer were used to enhance the reliability of the system. Special measures were taken to protect the CDH unit from the harsh operating environment and to monitor the health of the system beyond temperature and battery readings by performing periodic tests of components.

A study of particle swarm optimization for cognitive machines

This paper presents a study of the properties of optimization algorithms for use in cognitive machines through five key measures: (i) speed of convergence, (ii) degree of exploration of the parameter space, (iii) storage and system size, (iv) adaptability, and (v) multi-scale capabilities. Based on these factors, a novel study of the trajectories of a particle in the particle swarm optimization algorithm is performed both in the time and frequency domain. The analysis shows that the trajectories of particles can be separated into a transient and a steady state periods where the transient is wide-sense stationary with long term dependancies that show the evolutionary properties of the algorithm as it converges on a solution. The steady state shows an increased degree of exploration of the parameter space that allow the algorithm to improve on the solution found over time. The results show the advantages of particle swarm optimization and inherent properties that make this optimization algorithm a suitable choice for use in cognitive machines. The information learned from this analysis can further be used to extract complexity measures to classify the behavior and control of particle swarm optimization, and make proper quick decisions on what to do next. The decision process often requires more alternatives to be considered in a short window of time than it is physically possible for a real-time system [Kins04]. Thus, in order to make good decisions without exploring all possible paths, a cognitive system requires optimization techniques that can survey the possible options, and quickly select the best option possible. The paper reviews the requirements for an ideal optimization technique for use in cognitive systems and proposes the particle swarm optimization algorithm as one technique that is designed to satisfy these requirements. In order to show the properties of PSO, a novel trajectory, time, and frequency domain analyses of single particles along individual dimensions are presented.

Time and Frequency Analysis of Particle Swarm Trajectories for Cognitive Machines

This paper examines the inherited persistent behavior of particle swarm optimization and its implications to cognitive machines. The performance of the algorithm is studied through an average particles trajectory through the parameter space of the Sphere and Rastrigin function. The trajectories are decomposed into position and velocity along each dimension optimized. A threshold is defined to separate the transient period, where the particle is moving towards a solution using information about the position of its best neighbors, from the steady state reached when the particles explore the local area surrounding the solution to the system. Using a combination of time and frequency domain techniques, the inherited long-term dependencies that drive the algorithm are discerned. Experimental results show the particles balance exploration of the parameter space with the correlated goal oriented trajectory driven by their social interactions. The information learned from this analysis can be used to extract complexity measures to classify the behavior and control of particle swarm optimization, and make proper decisions on what to do next. This novel analysis of a particle trajectory in the time and frequency domains presents clear advantages of particle swarm optimization and inherent properties that make this optimization algorithm a suitable choice for use in cognitive machines.

The T-Sat1 Nanosatellite Team of Teams

Designing complex machines and systems that operate in very difficult remote locations, with largely unknown conditions, is very challenging. Specifications for such systems must be extremely detailed and extensive, with input from professionals who have designed such systems before, and who gained considerable experience from their operations. Since much of the operating environment is not not known in advance, cognitive informatics and computing should play a critical role in the design and operation. This paper describes such a system, the T-Sat1 nanosatellite. The satellite is being developed by undergraduate and graduate students at the University of Manitoba, as an extension of the standard classroom experience. Particular attention is given to the formation and maintenance of a team of teams, including the teams focusing on the satellite subsystems, assembly, integration and testing, as well as the teams of advisors from academia, aerospace industry, other industries, business, military, government, and other organizations such as the radio community. The paper describes the satellite characteristics, its mission, subsystems, as well as the development of specifications, protocols for verification, testing, launch and early operating procedures, as ell as concepts for nominal operations.

Implementation of a nanosatellite attitude determination and control system for the T-Sat1 mission

The design of attitude determination and control of nanosatellites requires innovative solutions that are low-cost, small in size, and minimized power consumption. The University of Manitoba T-Sat1 project has created a simple complement of sensors and actuators that can provide stable determination and orientation. The linear region of photodiode readings was combined with magnetometer readings in a sensor-fusion attitude determination algorithm. The actuators were custom-built torque discs on printed circuit boards that proved easy to mount and generate fields 25 nT that are sufficient to orient the satellite while in orbit. The torque disc manufacturing process is described with details on the imperfections and lessons learned from the prototypes. This paper describes the hardware and software design and implementation for the attitude determination and control of the T-Sat1 nanosatellite.

Towards agent Swarm Optimization

This paper examines particles in Particle Swarm Optimization (PSO) in terms of situated agents to improve control of the long term behaviour of particles as required for cognitive machines. In PSO, the particles lack goals and temporal knowledge for personal/global best solutions, thus many of the decisions for advancing the position of the particle diverge away from the solution causing the algorithm to take longer as the particles return to their intended path. This paper proposes novel modifications to the standard PSO algorithm to incorporate self-adjusting temporal knowledge to help guide the particles towards the optimal solution faster. The temporal knowledge is incorporated as a weighted sum of L previous steps taken by the particle, where L is automatically adjusted to maintain a certain multiscale measure that satisfies a balance between exploration and seeking the goal. Additional improvements based on the dynamics of the particles behaviour are described that would allow for real-time predicting of parameters.