A new subspace algorithm consistently identifies stochastic state space models directly from given output data, using only semi-infinite block Hankel matrices. Ala~raet-In this paper, we derive a new subspace algorithm to consistently identify stochastic state space models from given output data without forming the covariance matrix and using only semi-infinite block Hankel matrices. The algori...

Feature Selection is the process of selecting the momentous feature subset from the original ones. This technique is frequently used as a preprocessing technique in data mining. In this study, a new feature selection algorithm is proposed and is called Modified Fisher Score Principal Feature Analysis (MFSPFA). The new algorithm is developed by combining the proposed Modified Fisher Score (MFS) ...

Alec Feinberg is senior principal reliability engineer at M/A-COM. He received his Ph.D. in Physics from Northeastern University. He has 17 years of reliability physics experience. He previously worked at TASC and AT&T Bell Laboratories. Dr. Feinberg has actively published in the area of reliability physics since 1986 and at the IEST on a wide variety of topics from accelerated reliability grow...

in this article, the effect of cells' radius on the behavior of wavelength switching optical filter andthe effect of the radius of the optical filters' key characteristics such as wavelength resonance onan optical filter based on photonic crystals, have been investigated. currently, the most commonapplied mechanism for designing optical filter based on photonic crystals is using twome...

We propose a way of measuring the risk of a sovereign debt portfolio by using a simple two-factor short rate model. The model is calibrated from data and then the changes in the bond prices are simulated by using a Kalman filter. The bond prices being simulated remain arbitrage-free, in contrast with principal component analysis based strategies for simulation and risk measurement of debt portf...

The recurrent least squares (RLS) learning approach is proposed for controlling the learning rate in parallel principal subspace analysis (PSA) and in a wide class of principal component analysis (PCA) associated algorithms with a quasi{parallel extraction ability. The purpose is to provide a useful tool for applications where the learning process has to be repeated in an on{line self{adaptive ...

We developed a new noise-reduction algorithm based on a joint spectro-temporal representation of signals. The algorithm was inspired by the discovery in our laboratory of higher-level avian auditory cortical neurons that showed invariant responses to communication signals. The algorithm consists of an analysis step and a synthesis step. In the analysis step, the sound is first decomposed into n...

A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter...

The selection of filters plays a fundamental role for the setup and the performance of a multispectral image acquisition system. When the number of available filters is large, the full search method becomes computationally expensive. In this paper the filter selection problem is defined as a dimensionality reduction process by selecting the filters that contains most of the essential informatio...