Abstract. This research paper describes the method for the calibration of accelerometer and magnetometer for tilt compensated electronic compass. The electronic compass is implemented using triaxial MEMS accelerometer and triaxial MEMS magnetometer. The heading of the compass is generally influenced by scale factors, offsets and misalignment errors of these sensors. The proposed calibration method determines twelve calibration parameters in six stationary positions for accelerometer and twelve calibration parameters in 3D rotations for magnetometer.

This papers' aim is to provide the Artificial Intelligence community with a better tool for symbolic regression. In this paper, the method of analytical programming and constant resolving is revisited and extended. Nowadays, analytical programming mainly uses two methods for constant resolving. The first method is meta-evolution, in which the second evolutionary algorithm is used for constant resolving. The second method uses non-linear fitting algorithm. This paper reveals the third method, which use the basic mathematics to generate constants. The findings of this study have a number of important implications for future practice.

This paper evaluates the usage of analytical programming and different fitness functions for software effort estimation. Analytical programming and differential evolution generate regression functions. These functions are evaluated by the fitness function which is part of differential evolution. The differential evolution requires a proper fitness function for effective optimization. The problem is in proper selection of the fitness function. Analytical programming and different fitness functions were tested to assess insight to this problem. Mean magnitude of relative error, prediction 25 %, mean squared error (MSE) and other metrics were as possible candidates for proper fitness function. The experimental results shows that means squared error performs best and therefore is recommended as a fitness function. Moreover, this work shows that analytical programming method is viable method for calibrating use case points method. All results were evaluated by standard approach: visual inspection and statistical significance.

No abstract

Abstract. Heading angle of magnetic compasses is estimated using the triaxial magnetometer readings. The accuracy of these readings is influenced by many factors such as sensor errors (scale factors, non-orthogonality, and offsets), and magnetic deviations (soft-iron and hard-iron interference). To calculate the true heading angle, the magnetic calibration of magnetometer is necessary. This research paper describes a calibration procedure for triaxial low-cost MEMS magnetometer. The proposed calibration method determines twelve calibration parameters in 3D arbitrary rotations of magnetometer.