Information Matrix

Multiple attribute group decision making with linguistic variables and complete unknown weight information

Interval type-2 fuzzy sets, each of which is characterized by the footprint of uncertainty, are a very useful means to depict the linguistic information in the process of decision making. In this article, we investigate the group decision making problems in which all the linguistic information provided by the decision makers is expressed as interval type-2 fuzzy decision matrices where each of ...

Sensitivity analysis for criteria values in decision making matrix of SAW method

MRA parseval frame multiwavelets in L^2(R^d)

In this paper, we characterize multiresolution analysis(MRA) Parseval frame multiwavelets in L^2(R^d) with matrix dilations of the form (D f )(x) = sqrt{2}f (Ax), where A is an arbitrary expanding dtimes d matrix with integer coefficients, such that |detA| =2. We study a class of generalized low pass matrix filters that allow us to define (and construct) the subclass of MRA tight frame multiwa...

Quality function deployment: Integrating comprehensive matrix and SWOT analysis for effective decision making

In this ever-changing business scenario, the manufacturing product industries have to be in position to rec-ognize the ever changing pulse and demands of the market. Customer satisfaction and quality management has become a strategic issue for companies in the new millennium. Quality Function Deployment (QFD) lit-erature suggests that building up the House of Quality (HoQ) is not a difficult ta...

Using Exciting and Spectral Envelope Information and Matrix Quantization for Improvement of the Speaker Verification Systems

Speaker verification from talking a few words of sentences has many applications. Many methods as DTW, HMM, VQ and MQ can be used for speaker verification. We applied MQ for its precise, reliable and robust performance with computational simplicity. We also used pitch frequency and log gain contour for further improvement of the system performance.

A Novel Noise Reduction Method Based on Subspace Division

This article presents a new subspace-based technique for reducing the noise of signals in time-series. In the proposed approach, the signal is initially represented as a data matrix. Then using Singular Value Decomposition (SVD), noisy data matrix is divided into signal subspace and noise subspace. In this subspace division, each derivative of the singular values with respect to rank order is u...