Interfacing to Time-Triggered Communication Systems

Time-triggered communication facilitates the construction of multi-component real-time systems whose components are in control of their temporal behavior. However, the interface of a time-triggered communication system has to be accessed with care, to avoid that the temporal independence of components gets lost. This paper shows two interfacing strategies, one for asynchronous interface access (in two variants, one being the new Rate-Bounded Non-Blocking Communication protocol) and one for time-aware, synchronized interface access, that allow components to maintain temporal independence. The paper describes and compares the interfacing strategies.Final Accepted Versio

SASWeave: Literate Programming Using SAS

SASweave is a collection of scripts that allow one to embed SAS code into a LATEX document, and automatically incorporate the results as well. SASweave is patterned after Sweave, which does the same thing for code written in R. In fact, a document may contain both SAS and R code. Besides the convenience of being able to easily incorporate SAS examples in a document, SASweave facilitates the concept of "literate programming": having code, documentation, and results packaged together. Among other things, this helps to ensure that the SAS output in the document is in concordance with the code.

Accounting for Location Uncertainty in Azimuthal Telemetry Data Improves Ecological Inference

Background: Characterizing animal space use is critical for understanding ecological relationships. Animal telemetry technology has revolutionized the fields of ecology and conservation biology by providing high quality spatial data on animal movement. Radio-telemetry with very high frequency (VHF) radio signals continues to be a useful technology because of its low cost, miniaturization, and low battery requirements. Despite a number of statistical developments synthetically integrating animal location estimation and uncertainty with spatial process models using satellite telemetry data, we are unaware of similar developments for azimuthal telemetry data. As such, there are few statistical options to handle these unique data and no synthetic framework for modeling animal location uncertainty and accounting for it in ecological models. We developed a hierarchical modeling framework to provide robust animal location estimates from one or more intersecting or non-intersecting azimuths. We used our azimuthal telemetry model (ATM) to account for azimuthal uncertainty with covariates and propagate location uncertainty into spatial ecological models. We evaluate the ATM with commonly used estimators (Lenth (1981) maximum likelihood and M-Estimators) using simulation. We also provide illustrative empirical examples, demonstrating the impact of ignoring location uncertainty within home range and resource selection analyses. We further use simulation to better understand the relationship among location uncertainty, spatial covariate autocorrelation, and resource selection inference. Results: We found the ATM to have good performance in estimating locations and the only model that has appropriate measures of coverage. Ignoring animal location uncertainty when estimating resource selection or home ranges can have pernicious effects on ecological inference. Home range estimates can be overly confident and conservative when ignoring location uncertainty and resource selection coefficients can lead to incorrect inference and over confidence in the magnitude of selection. Furthermore, our simulation study clarified that incorporating location uncertainty helps reduce bias in resource selection coefficients across all levels of covariate spatial autocorrelation. Conclusion: The ATM can accommodate one or more azimuths when estimating animal locations, regardless of how they intersect; this ensures that all data collected are used for ecological inference. Our findings and model development have important implications for interpreting historical analyses using this type of data and the future design of radio-telemetry studies

How To Make A Pie: Reproducible Research for Empirical Economics & Econometrics

Empirical economics and econometrics (EEE) research now relies primarily on the application of code to datasets. Handling the workflow linking datasets, programs, results and finally manuscript(s) is essential if one wish to reproduce results, which is now increasingly required by journals and institutions. We underline here the importance of “reproducible research” in EEE and suggest three simple principles to follow. We illustrate these principles with good habits and tools, with particular focus on their implementation in most popular software and languages in applied economics

Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals

Dictionary Learning has proven to be a powerful tool for many image processing tasks, where atoms are typically defined on small image patches. As a drawback, the dictionary only encodes basic structures. In addition, this approach treats patches of different locations in one single set, which means a loss of information when features are well-aligned across signals. This is the case, for instance, in multi-trial magneto- or electroencephalography (M/EEG). Learning the dictionary on the entire signals could make use of the alignement and reveal higher-level features. In this case, however, small missalignements or phase variations of features would not be compensated for. In this paper, we propose an extension to the common dictionary learning framework to overcome these limitations by allowing atoms to adapt their position across signals. The method is validated on simulated and real neuroelectric data.Comment: 9 pages, 5 figures, minor correction

How To Make A Pie: Reproducible Research for Empirical Economics & Econometrics

Empirical economics and econometrics (EEE) research now relies primarily on the application of code to datasets. Handling the workflow linking datasets, programs, results and finally manuscript(s) is essential if one wish to reproduce results, which is now increasingly required by journals and institutions. We underline here the importance of “reproducible research” in EEE and suggest three simple principles to follow. We illustrate these principles with good habits and tools, with particular focus on their implementation in most popular software and languages in applied economics

Improving Prediction of Wireline Data Using Artificial Neural Network

This paper is dedicated to investigate the capabilities of artificial neural network (ANN) to improve prediction of petrophysical properties. Furthermore, this project is intended to test capability of network to predict the logging tools readings based on other tools readings. For petrophysical data prediction, it will be limited to predicting values of porosity by comparing predicted values from different models and values obtained from core data. Data obtained for core is considered to be the most accurate representation of petrophysical data. Hence, it is used as a reference data for testing capabilities of the model and training ANN networks

Universal Variable-to-Fixed Length Lossy Compression at Finite Blocklengths

We consider universal variable-to-fixed length compression of memoryless sources with a fidelity criterion. We design a dictionary codebook over the reproduction alphabet which is used to parse the source stream. Once a source subsequence is within a specified distortion of a dictionary codeword, the index of the codeword is emitted as the reproduced string. Our proposed dictionary consists of coverings of type classes in the boundary of transition from low to high empirical lossy rate. We derive the asymptotics of the \epsilon-coding rate (up to the third-order term) of our coding scheme for large enough dictionaries