The estimation of pollution fields, especially in densely populated areas, is an important application in the field of environmental science due to the significant effects of air pollution on public health. In this paper, we investigate the spatial distribution of three air pollutants in Tehran’s atmosphere: carbon monoxide (CO), nitrogen dioxide (NO2), and atmospheric particulate matters less ...

Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of non-linear non-Gaussian dynamical systems. Classical sequential Monte Carlos suffer from weight degeneracy which is where the number of distinct particles collapse. Traditionally this is addressed by resampling, which effectively replaces high weight particles w...

Motivated by recent results in joint source-channel coding and decoding, we consider the problem of decoding of variable-length codes using soft channel values. We present results of decoding of selected codes using the maximum a posteriori (MAP) decoder and the sequential decoder, and show the performance gains over decoding using hard decisions alone. The objective behind this work is to prov...

Maximum likelihood decoding algorithms for Gaussian MIMO linear channels are considered. Linearity over the field of real numbers facilitates the design of maximum likelihood decoders using number theoretic tools for searching the closest lattice point. These decoders are collectively referred to as sphere decoders in the literature. In this paper, a fresh look at this class of decoding algorit...

9.1. Problem Setting .......................................................................................................................1 9.2. The Gauss Algorithm...............................................................................................................2 9.2.1. Sequential Algorithm..............................................................................................

The estimation of pollution fields, especially in densely populated areas, is an important application in the field of environmental science due to the significant effects of air pollution on public health. In this paper, we investigate the spatial distribution of three air pollutants in Tehran’s atmosphere: carbon monoxide (CO), nitrogen dioxide (NO2), and atmospheric particulate matters less ...

MPC of an unknown system that is modelled by GP techniques is studied in this paper. Using GP, the variances computed during the modelling and inference processes allow us to take model uncertainty into account. The main issue in using MPC to control systems modelled by GP is the propagation of such uncertainties within the control horizon. In this paper, two approaches to solve this problem, c...

In this thesis, we study the performance of the maximum-likelihood soft-decision sequential decoding algorithm (MLSDA) for convolutional codes, which is previously proposed in [1]. Instead of using the conventional Fano metric, the proposed algorithm employs a new metric based on a variation of the Wagner rule, which is referred to as the second form of the maximum-likelihood decoding rule. The...

Flexible and low-complexity variable rate coding schemes based on rate-compatible con-volutional codes are presented. The codes have a wide range of code rates and are optimized for good performance on both AWGN and Rayleigh fading channels. Furthermore, the application of these codes for rate matching, combined coding and spreading in a DS-CDMA system and hybrid type-II ARQ schemes are demonst...

In this paper, we analyze a generic algorithm scheme for sequential global optimization using Gaussian processes. The upper bounds we derive on the cumulative regret for this generic algorithm improve by an exponential factor the previously known bounds for algorithms like GPUCB. We also introduce the novel Gaussian Process Mutual Information algorithm (GP-MI), which significantly improves furt...