In this paper, we measure the memory performance throughout the Phoronix test "RAMspeed SMP". We decide to test this specific benchmark because we know how important is the memory for the system performance. This document shows how much the memory performance could change if we modify some variables in the linux kernel.
The Linux kernel controls the way tasks (or processes) are managed in the running system. The task scheduler, sometimes called process scheduler, is the part of the kernel that decides which task to run next. In this project its analyzed the behavior of scheduler by changing a default value from the runtime scheduling. The default value is 950000µs, or 0.95 seconds for the sched\_rt\_runtime\_us or scheduler realtime running variable. Meaning that 5% of the CPU time is reserved for processes that don't run under a real-time or deadline scheduling policy. This value in this file specifies how much of the "period" time can be used by all real-time and deadline scheduled processes on the system. The AIO-Stress which shows the obtained results in the different tests is an a-synchronous I/O benchmark created by SuSE which is is a German Linux distribution provider and business unit of Novell, Inc.
In this paper we will study an algorithm designed by Madgwick which is commonly used to determine the orientation of a quadcopter. The algorithm uses a group of accelerometers, gyroscopes and magnetometers integrated in what is called an IMU as input. Some differences have been found between the results obtained by the original paper and the implementation done by the author. Therefore, a thorough study has been made, finding a miscalculation in the equations. The results show a relative average error in the orientation of 1,44 ppm.
Linear regression is one of the most widely used statistical methods available today. It is used by data analysts and students in almost every discipline. However, for the standard ordinary least squares method, there are several strong assumptions made about data that is often not true in real world data sets. This can cause numerous problems in the least squares model. One of the most common issues is a model overfitting the data. Ridge Regression and LASSO are two methods used to create a better and more accurate model. I will discuss how overfitting arises in least squares models and the reasoning for using Ridge Regression and LASSO include analysis of real world example data and compare these methods with OLS and each other to further infer the benefits and drawbacks of each method.