– We use a new approach to combine magnetic and electrical geophysical data from two different archaeological areas in Greece (Europos & Doriskos). Using the curvelet transform we express feature of images as function spatial coordinates, scale, orientation. A combined image is created domain through objective selection most salient features. The results show several potential structures.

I. TRANSFORM LEARNING While the idea of learning a synthesis [1] or analysis [2], [3] dictionary for sparse signal representation has received recent attention, these formulations are typically non-convex and NP-hard, and the approximate algorithms are still computationally expensive. In this work, we focus instead on the learning of square sparsifying transforms W ∈ Rn×n, and develop efficient...

I. TRANSFORM LEARNING The formulations for learning synthesis [1] and analysis [2], [3] sparsifying dictionaries are typically non-convex and NP-hard, and the approximate algorithms are still computationally expensive. As an alternative, we recently introduced an approach for learning square sparsifying transforms W ∈ Rm×n, m = n [4], which are competitive with overcomplete synthesis or analysi...

An algorithm for efficient and accurate computation of the fractional Fourier transform is given. For signals with time-bandwidth product N , the presented algorithm computes the fractional transform in O( N log N ) time. A definition for the discrete fractional Fourier transform that emerges from our analysis is also discussed.

An algorithm for efficient and accurate computation of the fractional Fourier transform is given. For signals with time-bandwidth product N , the presented algorithm computes the fractional transform in O( N log N ) time. A definition for the discrete fractional Fourier transform that emerges from our analysis is also discussed.

When it comes to compiler design, there has been a dispute as to whether it is useful to transform the source code using the continuation passing style (CPS) transform first. Some have observed that it is easier to optimize CPS transformed code, while others maintain that direct style compilation is better. Sabry and Felleisen resolve the dispute by showing how get the benefits of CPS without t...

Transform learning has been introduced and studied in [1],[2], [3] and [4]. An optimal transform learning for structured and overcomplete matrix was proposed in [5]. However, several issues (optimality, convergence and computational complexity) related to learning an incoherent, well-conditioned, non-structured and overcomplete sparsifing transform still remain open. Let X ∈ <N×L be a data matr...