Hamming star-convexity packing in information storage.
Hermite functions and uncertainty principles for the Fourier and the windowed Fourier transforms.
We extend an uncertainty principle due to Beurling into a characterization of Hermite functions. More precisely, all functions f on Rd which may be written as P(x)exp(-(Ax,x)), with A a real symmetric definite positive matrix, are characterized by integrability conditions on the product f(x)f(y). We then obtain similar results for the windowed Fourier transform (also known, up to elementary changes of functions, as the radar ambiguity function or the Wigner transform). We complete the paper with...
Heterogeneous questionnaires and Rényi's entropy
Heuristic decoding of convolutional codes
Heuristic methods of construction of sequential questionnaire
Hierarchical models, marginal polytopes, and linear codes
In this paper, we explore a connection between binary hierarchical models, their marginal polytopes, and codeword polytopes, the convex hulls of linear codes. The class of linear codes that are realizable by hierarchical models is determined. We classify all full dimensional polytopes with the property that their vertices form a linear code and give an algorithm that determines them.
Hierarchical residue number systems with small moduli and simple converters
In this paper, a new class of Hierarchical Residue Number Systems (HRNSs) is proposed, where the numbers are represented as a set of residues modulo factors of 2k ± 1 and modulo 2k . The converters between the proposed HRNS and the positional binary number system can be built as 2-level structures using efficient circuits designed for the RNS (2k-1, 2k, 2k+1). This approach allows using many small moduli in arithmetic channels without large conversion overhead. The advantages resulting from the...
High-rate wireless data communications: An underwater acoustic communications framework at the physical layer.
High-resolution quantization and entropy coding for fractional Brownian motion.
Hilbert Spaces of Distributions Having an Orthogonal Basis of Exponentials.
How similarity matrices are?
In finite sets with n elements, every similarity relation (or fuzzy equivalence) can be represented by an n x n-matrix S = (sij), sij ∈ [0,1], such that sii = 1 (1 ≤ i ≤ n), sij = sji for any i,j and S o S = S, where o denotes the max-min product of matrices. These matrices represent also dendograms and sets of closed balls of a finite ultrametric space (vid. [1], [2], [3]).
How to handle fuzzy-quantities?