response surface methodology (rsm) is a statistical method useful in the modeling and analysis of problems in which the response variable receives the influence of several independent variables, in order to determine which are the conditions under which should operate these variables to optimize a corrugated box production process. the purpose of this research is to create response surface mode...

The error in a designed experiment, σ2, is the natural variation in the response when one of the experimental combinations is replicated. One important challenge in a designed experiment is obtaining an unbiased estimate of σ2. Too often, experimenters do not realize the impact that data collection and analysis assumptions have on the estimate of σ2. If the estimate is biased, tests of the effe...

In parameter estimation, assumptions about the model are typically considered which allow us to build optimal estimation methods under many statistical senses. However, it is usually the case where such models are inaccurately known or not capturing the complexity of the observed phenomenon. A natural question arises to whether we can find fundamental estimation bounds under model mismatches. T...

In this paper, we derive an asymptotically optimal multi-layer coding scheme for entropy-coded scalar quantizers (SQ) that minimizes the weighted mean-squared error (WMSE). The optimal entropy-coded SQ is non-uniform in the case of WMSE. The conventional multi-layer coder quantizes the base-layer reconstruction error at the enhancement-layer, and is sub-optimal for the WMSE criterion. We consid...

Learning to predict future images from a video sequence involves the construction of an internal representation that models the image evolution accurately, and therefore, to some degree, its content and dynamics. This is why pixel-space video prediction is viewed as a promising avenue for unsupervised feature learning. In this work, we train a convolutional network to generate future frames giv...

Theory for jointly optimizing nonuniform analysis and synthesis FIR filter banks with arbitrary filter lengths and an arbitrary delay through the filter bank is developed. The FIR subband coder is optimized with respect to the minimum mean square error between the output and the input signals under a bit constraint. The subband quantizers are modeled as additive noise sources. Theoretical compa...

The aim of this paper is to find a multitaper-based spectrum estimator that is mean square error optimal for cepstrum coefficient estimation. The multitaper spectrum estimator consists of windowed periodograms which are weighted together, where the weights are optimized using the Taylor expansion of the log-spectrum variance and a novel approximation for the log-spectrum bias. A thorough discus...

Most of the real life classification problems have ill defined, imprecise or fuzzy class boundaries. Feedforward neural networks with conventional backpropagation learning algorithm are not tailored to this kind of classification problem. Hence, in this paper, feedforward neural networks, that use backpropagation learning algorithm with fuzzy objective functions, are investigated. A learning al...

This paper regards randomized discrete-time consensus systems that preserve the average on expectation. As a main result, we provide an upper bound on the mean square deviation of the consensus value from the initial average. Then, we particularize our result to systems where the interactions which take place simultaneously are few, or weakly correlated; these assumptions cover several algorith...