Spatial inference of traffic transition using micro-macro traffic variables

This paper proposes an online traffic inference algorithm for road segments in which local traffic information cannot be directly observed. Using macro-micro traffic variables as inputs, the algorithm consists of three main operations. First, it uses interarrival time (time headway) statistics from upstream and downstream locations to spatially infer traffic transitions at an unsupervised piece of segment. Second, it estimates lane-level flow and occupancy at the same unsupervised target site. Third, it estimates individual lane-level shockwave propagation times on the segment. Using real-world closed-circuit television data, it is shown that the proposed algorithm outperforms previously proposed methods in the literature

Psychic embedding — vision and delusion

The paper introduces the idea that the human brain may apply complex mathematical modules in order to process and understand the world. We speculate that the substrate of what appears outwardly as intuition, or prophetic power, may be a mathematical apparatus such as time-delay embedding. In this context, predictive accuracy may be the reflection of an appropriate choice of the embedding parameters. We further put this in the perspective of mental illness, and search for the possible differences between good intuition and delusive ideation. We speculate that the task at which delusional schizophrenic patients falter is not necessarily of perception, but rather of model selection. Failure of the psychotic patient to correctly choose the embedding parameters may readily lead to misinterpretation of an accurate perception through an altered reconstructed of the object perceived

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

This paper proposes a method for the identification of different partial discharges (PDs) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm. Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure. The result of the unsupervised classification is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. The methods and graphical tools detailed in this paper have been coded and published as a contributed package of the R environment under a GNU/GPL license

Limitations and opportunities for wire length prediction in gigascale integration

Wires have become a major source of bottleneck in current VLSI designs, and wire length prediction is therefore essential to overcome these bottlenecks. Wire length prediction is broadly classified into two types: macroscopic prediction, which is the prediction of wire length distribution, and microscopic prediction, which is the prediction of individual wire lengths. The objective of this thesis is to develop a clear understanding of limitations to both macroscopic and microscopic a priori, post-placement, pre-routing wire length predictions, and thereby develop better wire length prediction models. Investigations carried out to understand the limitations to macroscopic prediction reveal that, in a given design (i) the variability of the wire length distribution increases with length and (ii) the use of Rent s rule with a constant Rent s exponent p, to calculate the terminal count of a given block size, limits the accuracy of the results from a macroscopic model. Therefore, a new model for the parameter p is developed to more accurately reflect the terminal count of a given block size in placement, and using this, a new more accurate macroscopic model is developed. In addition, a model to predict the variability is also incorporated into the macroscopic model. Studies to understand limitations to microscopic prediction reveal that (i) only a fraction of the wires in a given design are predictable, and these are mostly from shorter nets with smaller degrees and (ii) the current microscopic prediction models are built based on the assumption that a single metric could be used to accurately predict the individual length of all the wires in a design. In this thesis, an alternative microscopic model is developed for the predicting the shorter wires based on a hypothesis that there are multiple metrics that influence the length of the wires. Three different metrics are developed and fitted into a heuristic classification tree framework to provide a unified and more accurate microscopic model.Ph.D.Committee Chair: Dr. Jeff Davis; Committee Member: Dr. James D. Meindl; Committee Member: Dr. Paul Kohl; Committee Member: Dr. Scott Wills; Committee Member: Dr. Sung Kyu Li