Browsing by Subject "LMS"

Now showing 1 - 3 of 3

Open Access
Big data signal processing using boosted RLS algorithm
(IEEE, 2016) Civek, Burak Cevat; Kari, Dariush; Delibalta, İ.; Kozat, Süleyman Serdar
We propose an efficient method for the high dimensional data regression. To this end, we use a least mean squares (LMS) filter followed by a recursive least squares (RLS) filter and combine them via boosting notion extensively used in machine learning literature. Moreover, we provide a novel approach where the RLS filter is updated randomly in order to reduce the computational complexity while not giving up more on the performance. In the proposed algorithm, after the LMS filter produces an estimate, depending on the error made on this step, the algorithm decides whether or not updating the RLS filter. Since we avoid updating the RLS filter for all data sequence, the computational complexity is significantly reduced. Error performance and the computation time of our algorithm is demonstrated for a highly realistic scenario.
Open Access
Robust regression and applications
(1996) Kıracı, Arzdar
This study analyzes the effect of outliers in the regression analysis with the help of a written program in the programing language of GAUSS. The analysis relies on the subject of Robust Regression, which is explained and supported by experiments and applications. The applications contain examples to show the superiority of this technique.
Open Access
Video processing algorithms for wildfire surveillance
(2015-05) Günay, Osman
We propose various image and video processing algorithms for wild re surveillance. The proposed methods include; classi er fusion, online learning, real-time feature extraction, image registration and optimization. We develop an entropy functional based online classi er fusion framework. We use Bregman divergences as the distance measure of the projection operator onto the hyperplanes describing the output decisions of classi ers. We test the performance of the proposed system in a wild re detection application with stationary cameras that scan prede ned preset positions. In the second part of this thesis, we investigate di erent formulations and mixture applications for passive-aggressive online learning algorithms. We propose a classi er fusion method that can be used to increase the performance of multiple online learners or the same learners trained with di erent update parameters. We also introduce an aerial wild re detection system to test the real-time performance of the analyzed algorithms. In the third part of the thesis we propose a real-time dynamic texture recognition method using random hyperplanes and deep neural networks. We divide dynamic texture videos into spatio-temporal blocks and extract features using local binary patterns (LBP). We reduce the computational cost of the exhaustive LBP method by using randomly sampled subset of pixels in the block. We use random hyperplanes and deep neural networks to reduce the dimensionality of the nal feature vectors. We test the performance of the proposed method in a dynamic texture database. We also propose an application of the proposed method in real-time detection of ames in infrared videos. Using the same features we also propose a fast wild re detection system using pan-tilt-zoom cameras and panoramic background subtraction. We use a hybrid method consisting of speeded-up robust features and mutual information to register consecutive images and form the panorama. The next step for multi-modal surveillance applications is the registration of images obtained with di erent devices. We propose a multi-modal image registration algorithm for infrared and visible range cameras. A new similarity measure is described using log-polar transform and mutual information to recover rotation and scale parameters. Another similarity measure is introduced using mutual information and redundant wavelet transform to estimate translation parameters. The new cost function for translation parameters is minimized using a novel lifted projections onto convex sets method.