We present here a series of sediment mass accumulation rate (MAR) for the Santa Barbara Basin, California. The 1117-1992 series was computed from varve thickness and water content to account for the compaction trend. Singular Spectrum Analysis (SSA) was employed to investigate multi-annual oscillations, as was previously done on varve thickness alone (Biondi et al. 1997). Singular spectrum anal...

There are many models that have been used to simulate the rainfall-runoff relationship. The artificial neural network (ANN) model was selected to investigate an approach of improving daily runoff forecasting accuracy in terms of data preprocessing. Singular spectrum analysis (SSA) as one data preprocessing technique was adopted to deal with the model inputs and the SSA-ANN model was developed. ...

Watermarking is to embed a structure called a watermark into the target data such as images. The watermark can be used, for example, in order to secure the copyright and detect tampering. This paper presents a new robust watermarking method that adds a watermark into a 3D polygonal mesh in the spectral domain. In this algorithm, a shape of a 3D polygonal model is regarded as a sequence of verti...

[1] Observational data sets in space physics often contain instrumental and sampling errors, as well as large gaps. This is both an obstacle and an incentive for research, since continuous data sets are typically needed for model formulation and validation. For example, the latest global empirical models of Earth’s magnetic field are crucial for many space weather applications, and require time...

Nonlinear Laplacian spectral analysis (NLSA) is a recently developed technique for spatiotemporal analysis of high-dimensional data, which represents temporal patterns via natural orthonormal basis functions on the nonlinear data manifold. Through such basis functions, determined efficiently via graph-theoretic algorithms, NLSA captures intermittency, rare events, and other nonlinear dynamical ...

In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. this paper, the performance of SSA tech nique considered by applying it well-known data set, namely, monthly accidental deaths USA. The results are com pared with those obtained using Box-Jenkins SARIMA models, ARAR algorithm Holt-Win...

The Lee-Carter model and its extensions are the most popular methods in field of forecasting mortality rate. But, spite introducing several different rate so far, there is no general method applicable to all situations. Singular Spectrum Analysis (SSA) a relatively new, powerful non parametric time series analysis that capability has been proven various sciences. In this paper, we investigate f...