Search results for "VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550"
showing 10 items of 469 documents
The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements
2022
Author's accepted manuscript Since its early beginning, the paradigm of Learning Automata (LA), has attracted much interest. Over the last decades, new concepts and various improvements have been introduced to increase the LA’s speed and accuracy, including employing probability updating functions, discretizing the probability space, and implementing the “Pursuit” concept. The concept of incorporating “structure” into the ordering of the LA’s actions is one of the latest advancements to the field, leading to the ϵ-optimal Hierarchical Continuous Pursuit LA (HCPA) that has superior performance to other LA variants when the number of actions is large. Although the previously proposed HCPA is …
Learning automata based energy-efficient AI hardware design for IoT applications: Learning Automata based AI Hardware
2020
On Novel Variants of the Hierarchical Stochastic Searching on the Line
2012
Master's thesis Mechatronics MAS500 - University of Agder, 2012 Konfidensiell til / confidential until 01.07.2017
Detection of Depression Using Weighted Spectral Graph Clustering With EEG Biomarkers
2023
The alarming annual growth in the number of people affected by Major Depressive Disorder (MDD) is a problem on a global scale. In the primary scrutiny of depression, Electroencephalography (EEG) is one of the analytical tools available. Machine Learning (ML) and Deep Neural Networks (DNN) methods are the most common techniques for MDD diagnosis using EEG. However, these ML methods heavily rely on manually annotated EEG signals, which can only be generated by experts, for training. This also necessitates a large amount of memory and time constraints. The requirement of huge amounts of data to foresee emerging tendencies or undiscovered alignments is enforced. This article develops an unsuper…
Towards Thompson Sampling for Complex Bayesian Reasoning
2020
Paper III, IV, and VI are not available as a part of the dissertation due to the copyright. Thompson Sampling (TS) is a state-of-art algorithm for bandit problems set in a Bayesian framework. Both the theoretical foundation and the empirical efficiency of TS is wellexplored for plain bandit problems. However, the Bayesian underpinning of TS means that TS could potentially be applied to other, more complex, problems as well, beyond the bandit problem, if suitable Bayesian structures can be found. The objective of this thesis is the development and analysis of TS-based schemes for more complex optimization problems, founded on Bayesian reasoning. We address several complex optimization proble…
SECURITY ON THE MIS AND THE INTERFACING MODEM
2012
Master's thesis Mechatronics MAS500 - University of Agder, 2012 Konfidensiell til / confidential until 01.07.2017
The Use of Cross-Platform Frameworks for Google Play Store Apps
2022
In this paper, we describe the harnessing and analyses of a large sample (n = 661705) of Android apps and associated metadata available on the Google Play Store. The analyses and scrutiny are in the context of cross-platform mobile development, as we report on the technologies used to develop apps for the Android ecosystem. Specifically, we quantify the use of 13 technical frameworks for cross-platform development, identify their distribution across Google Play Store categories, present an overview of framework usage from 2008 to 2019, app file size (.apk size), and lastly discuss our findings in the context of current industry trends and directions. Our findings indicate that cross-platfor…
Online Machine Learning for Graph Topology Identification from Multiple Time Series
2020
High dimensional time series data are observed in many complex systems. In networked data, some of the time series are influenced by other time series. Identifying these relations encoded in a graph structure or topology among the time series is of paramount interest in certain applications since the identified structure can provide insights about the underlying system and can assist in inference tasks. In practice, the underlying topology is usually sparse, that is, not all the participating time series in influence each other. The goal of this dissertation pertains to study the problem of sparse topology identification under various settings. Topology identification from time series is a …
The Relationship Between Outbound and Inbound Communication in Government-to-Citizen Interaction
2020
Synthetic Micro-Doppler Signatures of Non-Stationary Channels for the Design of Human Activity Recognition Systems
2021
The main aim of this dissertation is to generate synthetic micro-Doppler signatures and TV-MDSs to train the HACs. This is achieved by developing non-stationary fixed-tofixed (F2F) indoor channel models. Such models provide an in-depth understanding of the channel parameters that influence the micro-Doppler signatures and TV-MDSs. Hence, the proposed non-stationary channel models help to generate the micro-Doppler signatures and the TV-MDSs, which fit those of the collected measurement data. First, we start with a simple two-dimensional (2D) non-stationary F2F channel model with fixed and moving scatterers. Such a model assumes that the moving scatterers are moving in 2D geometry with simpl…