Search results for "algoritmit"

Simultaneous Noise and Impedance Fitting to Transition-Edge Sensor Data Using Differential Evolution

2020

We discuss a robust method to simultaneously fit a complex multi-body model both to the complex impedance and the noise data for transition-edge sensors. It is based on a differential evolution (DE) algorithm, providing accurate and repeatable results with only a small increase in computational cost compared to the Levenberg–Marquardt (LM) algorithm. Test fits are made using both DE and LM methods, and the results compared with previously determined best fits, with varying initial value deviations and limit ranges for the parameters. The robustness of DE is demonstrated with successful fits even when parameter limits up to a factor of 10 from the known values were used. It is shown that the…

Linear Feature Extraction for Ranking

2018

We address the feature extraction problem for document ranking in information retrieval. We then propose LifeRank, a Linear feature extraction algorithm for Ranking. In LifeRank, we regard each document collection for ranking as a matrix, referred to as the original matrix. We try to optimize a transformation matrix, so that a new matrix (dataset) can be generated as the product of the original matrix and a transformation matrix. The transformation matrix projects high-dimensional document vectors into lower dimensions. Theoretically, there could be very large transformation matrices, each leading to a new generated matrix. In LifeRank, we produce a transformation matrix so that the generat…

Algorithms and software for biological multiscale image analysis

2013

Tietokoneohjelma epälineaaristen elektroniikkapiirien simulointiin

2014

Tässä työssä esitetään kuinka lineaarisissa ja epälineaarisissa elektroniikkapiireissä toimiva piirisimulaattoriohjelma voidaan toteuttaa oliokielellä. Työ aloitettiin itse kehitetyn TICER-yksinkertaistusalgoritmin pohjalta ja yhtenä työn motivaationa olikin tutkia kuinka kyseistä algoritmia voi käyttää muiden analysointimenetelmien apuna. Työn edetessä osoittautuikin, että TICER-yksinkertaistusta kannattaa käyttää muiden analysointimenetelmien apuna, sillä se voi nopeuttaa huomattavasti RC-piirien analysointia. TICER ei kuitenkaan nopeuttanut ohjelman toimintaa poikkeuksetta, mutta se ei hidastanutkaan analysointia koskaan niin paljon, etteikö sitä olisi kannattanut käyttää. Ohjelman pääas…

Serendipity in recommender systems

2018

The number of goods and services (such as accommodation or music streaming) offered by e-commerce websites does not allow users to examine all the available options in a reasonable amount of time. Recommender systems are auxiliary systems designed to help users find interesting goods or services (items) on a website when the number of available items is overwhelming. Traditionally, recommender systems have been optimized for accuracy, which indicates how often a user consumed the items recommended by system. To increase accuracy, recommender systems often suggest items that are popular and suitably similar to items these users have consumed in the past. As a result, users often lose interest…

Memory-saving optimization algorithms for systems with limited hardware

2011

On data mining applications in mobile networking and network security

2014

Taming big knowledge evolution

2016

Information and its derived knowledge are not static. Instead, information is changing over time and our understanding of it evolves with our ability and willingness to consume the information. When compared to humans, current computer systems seem very limited in their ability to really understand the meaning of things. On the other hand, they are very powerful when it comes down to performing exact computations. One aspect which sets humans apart from machines when trying to understand the world is that we will often make mistakes, forget information, or choose what to focus on. To put this in another perspective, it seems like humans can behave somehow more randomly and still outperform …

Automatic social distance estimation for photographic studies: Performance evaluation, test benchmark, and algorithm

2022

The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitabl…

Optimization of Linearized Belief Propagation for Distributed Detection

2020

In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP algorithm in a network of distributed agents can be approximated by a linear fusion of all the local log-likelihood ratios. The proposed approach clarifies how the BP algorithm works, simplifies the statistical analysis of its behavior, and enables us to develop a performance optimization framework for the BP-based distributed inference systems. Next, we propose a blind learning-adaptation scheme to optimize the system performance when there is no information available a pr…