software
Fatemeh SalarKaleji; Parvin Shavandi; Alireza Omranian; Oveys Kazemi; Farzad Emami; shahrokh jalilian; Alireza Khani; Abolfazl Dayyani; Mohammad Sayanjali
Abstract
PARS1 Satellite is a Remote Sensing satellite with a 3-years mission. Main mission is imaging from earth by three camera named MS, SWIR, TIR. PARS1 OnBoard Software (OBSW) is developed as a performance platform, satellite components control, data management, algorithm management included normal status ...
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PARS1 Satellite is a Remote Sensing satellite with a 3-years mission. Main mission is imaging from earth by three camera named MS, SWIR, TIR. PARS1 OnBoard Software (OBSW) is developed as a performance platform, satellite components control, data management, algorithm management included normal status control and event handling. Because of OBSW complexity and its different features, it has more priority than other subsystems in satellite. So PARS1 satellite OBSW design, development and test is a useful platform for gaining of worthwhile experiences in the field of satellite onboard software which is very wide and complicated in its field. So we decided in this paper to present here as lesson-learned and results experiences. These results is gained from development and test phase from developer and tester view. Use of these experiences will be very efficient in smoothing of test and development path in future projects and works in Satellite Research Institute.
Computer
Athena Abdi; shahrokh jalilian
Abstract
In this paper, a task scheduling and mapping method based on multi-objective particle swarm optimization (MOPSO) algorithm is presented to improve lifetime reliability of multiprocessors systems on chip. This method considers power consumption temperature and performance along with the lifetime reliability ...
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In this paper, a task scheduling and mapping method based on multi-objective particle swarm optimization (MOPSO) algorithm is presented to improve lifetime reliability of multiprocessors systems on chip. This method considers power consumption temperature and performance along with the lifetime reliability due to the antagonistic relations of these parameters. These antagonistic and dependent relations make the design space exploration and optimization processes complex. The proposed method is based on MOPSO algorithm and starts with an initial population of candidate solutions which are generated randomly and represents valid task scheduling and mapping solutions. By changing the scheduling and mapping parameters during the MOPSO algorithm, new solutions are produced and the design space is explored based on the objective of the target problem of this method. Several experiments on random and real-life benchmarks are performed to verify the effectiveness of our proposed method. The results demonstrate the capability of the proposed method in effective design space exploration and generating the Pareto front. Moreover, comparisons to the related research show 35%, 23%, 19% and 3% improvements in performance, lifetime reliability, temperature, and power consumption on average.
