Universal MMSE Filtering With Logarithmic Adaptive Regret

We consider the problem of online estimation of a real-valued signal corrupted by oblivious zero-mean noise using linear estimators. The estimator is required to iteratively predict the underlying signal based on the current and several last noisy observations, and its performance is measured by the mean-square-error. We describe and analyze an algorithm for this task which: 1. Achieves logarithmic adaptive regret against the best linear filter in hindsight. This bound is assyptotically tight, and resolves the question of Moon and Weissman [1]. 2. Runs in linear time in terms of the number of filter coefficients. Previous constructions required at least quadratic time.Comment: 14 page

Determination of Secchi Disc depths in Lake Eymir using remotely sensed data

AbstractIn this study Secchi disk depths (SDD) are determined in an eutrophic Eymir Lake in Ankara using the multi-spectral image obtained from the Quickbird satellite. For this purpose, empirical models given in literature and artificial neural networks (ANN) are used. SDDs at 17 sampling points in Eymir Lake are measured via field studies. In the satellite image, pixel values at the sampling points are determined using ERDAS Imagine. Results indicate very low correlations between the SDD values calculated using the empirical models and the ones measured in-situ. Correlation of determination values (R2) up to 0.92 are achieved when ANN modeling is applied. In ANN models developed, Levenberg-Marquardt (LM) and gradient decent algorithm (GDA) are the training algorithms that provided the best results. This study indicates that ANN is an important tool in obtaining information from the remotely sensed data

Cooperative Vehicles versus Non-Cooperative Traffic Light: Safe and Efficient Passing

Connected and automated vehicles (CAVs) will be a key component of future cooperative intelligent transportation systems (C-ITS). Since the adoption of C-ITS is not foreseen to happen instantly, not all of its elements are going to be connected at the early deployment stages. We consider a scenario where vehicles approaching a traffic light are connected to each other, but the traffic light itself is not cooperative. Information about indented trajectories such as decisions on how and when to accelerate, decelerate and stop, is communicated among the vehicles involved. We provide an optimization-based procedure for efficient and safe passing of traffic lights (or other temporary road blockage) using vehicle-to-vehicle communication (V2V). We locally optimize objectives that promote efficiency such as less deceleration and larger minimum velocity, while maintaining safety in terms of no collisions. The procedure is computationally efficient as it mainly involves a gradient decent algorithm for one single parameter