Estimator Selection: End-Performance Metric Aspects

Recently, a framework for application-oriented optimal experiment design has been introduced. In this context, the distance of the estimated system from the true one is measured in terms of a particular end-performance metric. This treatment leads to superior unknown system estimates to classical experiment designs based on usual pointwise functional distances of the estimated system from the true one. The separation of the system estimator from the experiment design is done within this new framework by choosing and fixing the estimation method to either a maximum likelihood (ML) approach or a Bayesian estimator such as the minimum mean square error (MMSE). Since the MMSE estimator delivers a system estimate with lower mean square error (MSE) than the ML estimator for finite-length experiments, it is usually considered the best choice in practice in signal processing and control applications. Within the application-oriented framework a related meaningful question is: Are there end-performance metrics for which the ML estimator outperforms the MMSE when the experiment is finite-length? In this paper, we affirmatively answer this question based on a simple linear Gaussian regression example.Comment: arXiv admin note: substantial text overlap with arXiv:1303.428

Preamble-Based Channel Estimation for CP-OFDM and OFDM/OQAM Systems: A Comparative Study

In this paper, preamble-based least squares (LS) channel estimation in OFDM systems of the QAM and offset QAM (OQAM) types is considered, in both the frequency and the time domains. The construction of optimal (in the mean squared error (MSE) sense) preambles is investigated, for both the cases of full (all tones carrying pilot symbols) and sparse (a subset of pilot tones, surrounded by nulls or data) preambles. The two OFDM systems are compared for the same transmit power, which, for cyclic prefix (CP) based OFDM/QAM, also includes the power spent for CP transmission. OFDM/OQAM, with a sparse preamble consisting of equipowered and equispaced pilots embedded in zeros, turns out to perform at least as well as CP-OFDM. Simulations results are presented that verify the analysis

Σχεδιασμός υβριδικού συστήματος φωτοβολταϊκού-αντλίας θερμότητας με ταυτόχρονο ενεργειακό συμψηφισμό

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Συστήματα Αυτοματισμού

Age determination and growth of leaping mullet, (Liza saliens R.1810) from the Messolonghi Etoliko lagoon (western Greece)

This study is the first detailed work on the age and growth of the leaping mullet (Liza saliens, Risso 1810) in the central Mediterranean. During the period 1991-1995 the age and growth of leaping mullet from the Messolonghi -Etoliko lagoon system (western Greek coast) were studied. Age and growth determinations were based upon otolith samples taken from 537 fish. Marginal increment analysis was used to validate age determination. Annulus formation took place around November each year. The back-calculated lengths at age estimated from the otoliths showed no differences between sub-areas of the lagoon system and the recorded limited between-years variability showed no persistent temporal pattern. The maximum age of leaping mullet in the Messolonghi - Etoliko lagoon was 5 years for males and 6 years for females. The von Bertalanffy equation (L‡=32.99±1.25 cm, k=0.258 ±0.017 year-1, t0=-0.47±0.04 year) accurately describes the growth of the total length of leaping grey mullet for all life stages (fry, juveniles and adults). A large spread and length overlap characterized the age groups. The estimated Length-Weight relationships were common for the two sexes (W=0.0079L3.01)

A Provably Improved Algorithm for Crowdsourcing with Hard and Easy Tasks

Crowdsourcing is a popular method used to estimate ground-truth labels by collecting noisy labels from workers. In this work, we are motivated by crowdsourcing applications where each worker can exhibit two levels of accuracy depending on a task's type. Applying algorithms designed for the traditional Dawid-Skene model to such a scenario results in performance which is limited by the hard tasks. Therefore, we first extend the model to allow worker accuracy to vary depending on a task's unknown type. Then we propose a spectral method to partition tasks by type. After separating tasks by type, any Dawid-Skene algorithm (i.e., any algorithm designed for the Dawid-Skene model) can be applied independently to each type to infer the truth values. We theoretically prove that when crowdsourced data contain tasks with varying levels of difficulty, our algorithm infers the true labels with higher accuracy than any Dawid-Skene algorithm. Experiments show that our method is effective in practical applications