In this paper, we present a novel approach to estimate traffic speed using a sequence of images from an uncalibrated camera. We assert that exact calibration is not necessary to estimate speed. Instead, we use 1) geometric relationships inherently available in the image, 2) some common-sense assumptions that reduce the problem to a one-dimensional (1-D) geometry, 3) frame differencing to isolat...

To investigate the applicability of ARIMA models in wholesale vegetable market models are built taking sales data of one perishable vegetable from Ahmedabad wholesales market in India. It is found that these models can be applied to forecast the demand with Mean Absolute Percentage Error (MAPE) in the range of 30%. This error is acceptable in fresh produce market where the demand and prices are...

This article investigates a Cautious Minimum Variance (CMV) control approach for controlling industrial process variability when the model parameters are estimated from data and subject to uncertainty. CMV control has a number of advantages over traditional robust control methods. It incorporates probabilistic, as opposed to deterministic, measures of parameter uncertainty, which are more consi...

An accurate cost-model that accounts for dataset size and structure can help optimize geoscience data analysis. We develop and apply a computational model to estimate data analysis costs for arithmetic operations on gridded datasets typical of satelliteor climate model-origin. For these dataset geometries our model predicts data reduction scalings that agree with measurements of widely-used geo...

We study the autocorrelation structure and the spectral density function of aggregates from a discrete-time process. The underlying discrete-time process is assumed to be a stationary AutoRegressive Fractionally Integrated Moving-Average (ARFIMA) process, after suitable number of differencing if necessary. We derive closed-form expressions for the limiting autocorrelation function and the norma...