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On the pattern recognition and classification of stochastically episodic events
(Lecture Notes in Computer Science; 7190;, Chapter; Peer reviewed, 2012)Researchers in the field of Pattern Recognition (PR) have traditionally presumed the availability of a representative set of data drawn from the classes of interest, say ω 1 and ω 2 in a 2-class problem. These samples are ... -
On the problems of symbol-spaced tapped-delay-line models for WSSUS channels
(Peer reviewed; Journal article, 2009)This paper analyzes the validity and statistical behavior of symbol-spaced tapped-delay-line (SSTDL) models for wide-sense stationary uncorrelated scattering (WSSUS) mobile radio channels. SSTDL models are obtained by ... -
On the spatial configuration of scatterers for given delay-angle distributions
(Journal article; Peer reviewed, 2014)This paper investigates the distribution of scatterers located around the mobile station (MS) for given delay-angle distributions. The delay-angle distribution function represents the joint probability density function ... -
On the spectral moments of non-WSSUS mobile-to-mobile double Rayleigh fading channels
(IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops;2017, Journal article; Peer reviewed, 2018)This paper deals with the mathematical analysis of the spectral moments of non-wide-sensestationary uncorrelated-scattering (non-WSSUS) mobile-to-mobile (M2M) double-Rayleigh fading channels. The point of departure is a ... -
On the Statistical Analysis of the Channel Capacity of Double Rayleigh Channels with Equal Gain Combining in V2V Communication Systems
(Journal article; Peer reviewed, 2010)In this article, we present a detailed study on the statistical properties of the channel capacity of vehicle-to-vehicle (V2V) fading channels with equal gain combining (EGC). Assuming perfect channel state information ... -
On the Statistical Properties of Equal Gain Combining over Mobile-to-Mobile Fading Channels in Cooperative Networks
(Journal article; Peer reviewed, 2010)This article deals with the statistical analysis of equal gain combining (EGC) over mobile-to-mobile (M2M) fading channels in a dual-hop amplify-and-forward relay network. Here, we analyze narrowband M2M fading channels ... -
On the Theory and Applications of Hierarchical Learning Automata and Object Migration Automata
(Doctoral dissertations at University of Agder; no. 444, Doctoral thesis, 2023)The paradigm of Artificial Intelligence (AI) and the sub-group of Machine Learning (ML) have attracted exponential interest in our society in recent years. The domain of ML contains numerous methods, and it is desirable ... -
On using "Stochastic learning on the line" to design novel distance estimation methods
(Journal article; Peer reviewed, 2018) -
On Using Novel "Anti-Bayesian" Techniques for the Classification of Dynamical Data Streams
(Chapter; Peer reviewed, 2017) -
On using prototype reduction schemes to optimize locally linear reconstruction methods
(Journal article; Peer reviewed, 2012)This paper concerns the use of prototype reduction schemes (PRS) to optimize the computations involved in typical k-nearest neighbor (k-NN) rules. These rules have been successfully used for decades in statistical pattern ... -
On using the theory of regular functions to prove the ε-Optimality of the Continuous Pursuit Learning Automaton
(Lecture Notes in Computer Science;7906, Chapter; Peer reviewed, 2013)There are various families of Learning Automata (LA) such as Fixed Structure, Variable Structure, Discretized etc. Informally, if the environment is stationary, their ε-optimality is defined as their ability to converge ... -
On utilizing an enhanced object partitioning scheme to optimize self-organizing lists-on-lists
(Journal article; Peer reviewed, 2020) -
On Utilizing Association and Interaction Concepts for Enhancing Microaggregation in Secure Statistical Databases
(Journal article; Peer reviewed, 2010)This paper presents a possibly pioneering endeavor to tackle the microaggregation techniques (MATs) in secure statistical databases by resorting to the principles of associative neural networks (NNs). The prior art has ... -
On utilizing dependence-based information to enhance micro-aggregation for secure statistical databases
(Journal article; Peer reviewed, 2013)We consider the micro-aggregation problem which involves partitioning a set of individual records in a micro-data file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best ... -
On utilizing weak estimators to achieve the online classification of data streams
(Journal article; Peer reviewed, 2019) -
Online Joint Nonlinear Topology Identification and Missing Data Imputation over Dynamic Graphs
(Journal article; Peer reviewed, 2022)Extracting causal graph structures from multivariate time series, termed topology identification, is a fundamental problem in network science with several important applications. Topology identification is a challenging ... -
Online Machine Learning for Inference from Multivariate Time-series
(Doctoral Dissertations at the University of Agder; no: 429, Doctoral thesis, 2023)Inference and data analysis over networks have become significant areas of research due to the increasing prevalence of interconnected systems and the growing volume of data they produce. Many of these systems generate ... -
An Online Multiple Kernel Parallelizable Learning Scheme
(Journal article; Peer reviewed, 2023)The performance of reproducing kernel Hilbert space-based methods is known to be sensitive to the choice of the reproducing kernel. Choosing an adequate reproducing kernel can be challenging and computationally demanding, ... -
Online or offline – Does it matter?: A study of in-service teachers' perceptions of learning outcomes in Norway
(Peer reviewed; Journal article, 2020) -
Optimal sampling for estimation with constrained resources using a learning automaton-based solution for the nonlinear fractional knapsack problem
(Journal article; Peer reviewed, 2010)While training and estimation for Pattern Recognition (PR) have been extensively studied, the question of achieving these when the resources are both limited and constrained is relatively open. This is the focus of this ...