Dale Joachim

Binary image transformation using two-dimensional chaotic maps

We present an algorithm for binary image transformation using chaotic maps. Because of its random-like behavior, chaos is a good candidate for encryption. We show that a two-dimensional discrete time dynamical system with one positive Lyapunov exponent allows the transformation of the image in an unpredictable manner. The suggested algorithm acts on the pixel position, where the diffusion property resulting from the sensitivity to the initial states is used to accomplish the transformation in a random-like way. The suggested algorithm uses three types of keys: initial state, external parameters and the number of iterations. Using the so-called Henon map as an example, we show that the algorithm produces almost uncorrelated images even when the keys are slightly changed, making it an attractive and fast method for image encryption.

Multiweight optimization in optimal bounding ellipsoid algorithms

Optimal Bounding Ellipsoid (OBE) algorithms offer an attractive alternative to traditional least-squares methods for identification and filtering problems involving affine-in-parameters signal and system models. The benefits-including low computational efficiency, superior tracking ability, and selective updating that permits processor multi-tasking-are enhanced by multiweight (MW) optimization in which the data history is considered in determining update times and optimal weights on the observations. MW optimization for OBE algorithms is introduced, and an example MW-OBE algorithm implementation is developed around the recent quasi-OBE algorithm. Optimality of the solution is discussed, and simulation studies are used to illustrate performance benefits.

Multiweight optimization in OBE algorithms for improved tracking and adaptive identification

Optimal bounding ellipsoid (OBE) algorithms offer an attractive alternative to traditional least squares methods for identifying linear-in-parameters signal and system models due to their low computational efficiency, superior tracking ability, and selective updating that permits processor sharing among tasks. These benefits are further enhanced by multiweight optimization (MWO) which yields improved per-point parameter convergence. This paper introduces the MWO process and describes advances in its implementation including the incorporation of a forgetting factor for improved tracking, a new method for efficient weight computation, and extensions to volume-minimizing OBE algorithms. Simulation studies illustrate the results.

Practical considerations in the use of a new OBE algorithm that blindly estimates error bounds

Optimal bounding ellipsoid (OBE) identification algorithms require precise knowledge of bounds on model disturbance sequences, and such bounds are often difficult to ascertain in practice. The OBE algorithm with automatic bound estimation (OBE-ABE) theoretically obviates the need for precise a priori bound estimates, thereby removing the major obstacle to practical application of these powerful and interesting methods. Performance assessment of OBE-ABE, particularly with regard to its favorable convergence behavior, has involved asymptotic analysis over infinite frames of data. This paper discusses application of OBE-ABE to short-time frames of data, suggesting that the favorable asymptotic results apply to finite processing if care is taken in the choice of certain parameters. Example case studies, one using real speech data, illustrate the theoretical discussions.

OBE set estimates in classification problems

This paper explores the use of alternative estimates arising from the feasibility set of an optimal bounded ellipsoid (OBE) algorithm. The central estimator is interpretable as a least squares result, but all others in the bounding set are consistent with the observations and error bounds as well. The purpose of the present paper is to focus attention on the set estimates in an effort to stimulate further research into their utility in applications. As an example, we suggest the use of set estimates in classification problems in which the classes can be represented by distinct linear-in-parameters models, or by related sets.

Mapping speech signals to musical scores through prosodic extraction

Detached from its semantic content, a speech signal can be interpreted as a musical structure, containing rhythm, intonation, timbre and pitch. The musical components of speech can be extracted through algorithmic prosodic analysis to be mapped into musical notation. In this paper, we present a system that extracts pitch and rhythm from an utterance for mapping into a musical score.

Benefits of multi-weight optimization in OBE algorithms

Optimal bounding ellipsoid identification algorithms feature a unique data selection process which recursively checks observations for innovation, then assigns weights in accordance with information content. Multi-weight optimization enhances this process by reoptimizing over a number of past weights. This technique offers faster unbiased convergence of the central estimator, improved tracking, increased selectivity, and can be used to decrease computational complexity. These properties potentially decrease idle time in multi-processor systems.

Robustness optimization of parametric speech watermarking

Parameter-embedded watermarking is effected through slight perturbations of parametric models of some deeply-integrated dynamics of the speech signal. In set-membership filtering (SMF) based parametric watermarking, linear predictor (LP) coefficients of the original speech are modified subject to an objective fidelity constraint. SMF is used to obtain sets of allowable parameter perturbations (i.e., watermarks) subject to a constraint on the error between the watermarked and original material. This paper discusses the robustness of SMF based watermarking to filtering, quantization and combination attacks. An important consideration in watermark robustness is the energy of the watermark signal (difference between watermarked and original signals). The most robust watermark is obtained from perturbed LP coefficients at the boundary of the membership set. A constrained optimization problem is solved to obtain the best watermarks for filtering and quantization attacks

Set-membership filtering strategies for multipulse coding

This paper describes a new class of multi-pulse coders based on bounded-error identification. Multi-pulse coders provide effective representations of speech signals by extracting linear prediction parameters and approximating the effect of excitation signals with strategic sparse impulse sequences. These sparse impulses can be derived according to a set-membership optimization set volume reduction strategy. The proposed method offers the added benefit of improved scalar quantization through the choice of coder parameters from a feasibility set

DF Experimentation through Parametric Simulation

In most direction-finding experiments, beamforming is one of the simplest and most robust means of spatial filtering. Beamforming implementation is typically done using ordinary or some type of commercial style microphones. We plan to fabricate a microphone array on a micro-scale, which will allow for several more applications in direction-finding. However, this approach can be difficult due to the requirements for direction-finding and the constraints that are practical in micro-electro mechanical systems (MEMS). The purpose of this work is to model and simulate the entire direction-finding system. The results from these simulations will provide insight for an optimal design of a sensor that can be applied to direction-finding. This project will also provide the flexibility to alter parameters in order to test the microphone performance. Furthermore, an optimal design of the microphone will lead to a more accurate direction-finding system