A method and apparatus are disclosed for approving a transaction request between an electronic transaction system and a portable electronic authorization device (PEAD) carried by a user using an electronic service authorization token The method includes the steps of receiving at the PEAD first digital data representing the transaction request The PEAD provides information to the user regarding an ability to approve the transaction request When the transaction request is approved by the user, the PEAD receives second digital data representing the electronic service authorization token In one aspect of the invention, the method and apparatus include a remote agent server that provides a bridge between the electronic transaction system and the PEAD In an embodiment providing a further level of security, the private key is stored on the portable device, encrypted The decryption key is stored outside of the device, at a trusted 3rd party location When the user attempts to make a signature the software sends a request for the decryption key, along with the user's password or pass phrase keyed in at the keyboard of the PDA, smart phone, or cell phone, to a server belonging to the trusted 3rd party This password is usually, but not always, different than the password stored in the PEAD The server checks the password or pass phrase and, if it is correct sends the decryption key to the portable device, where it is used once and immediately discarded In yet another aspect of the invention, the user's password is securely encoded in the method and apparatus and are used at a point-of-sale location Advantages of the invention include the ability to securely and conveniently perform transactions in a portable device

Electronic transaction systems and methods therefor

This paper presents a Volterra filtered-X least mean square (LMS) algorithm for feedforward active noise control. The research has demonstrated that linear active noise control (ANC) systems can be successfully applied to reduce the broadband noise and narrowband noise, specifically, such linear ANC systems are very efficient in reduction of low-frequency noise. However, in some situations, the noise that comes from a dynamic system may he a nonlinear and deterministic noise process rather than a stochastic, white, or tonal noise process, and the primary noise at the canceling point may exhibit nonlinear distortion. Furthermore, the secondary path estimate in the ANC system, which denotes the transfer function between the secondary source (secondary speaker) and the error microphone, may have nonminimum phase, and hence, the causality constraint is violated. If such situations exist, the linear ANC system will suffer performance degradation. An implementation of a Volterra filtered-X LMS (VFXLMS) algorithm based on a multichannel structure is described for feedforward active noise control. Numerical simulation results show that the developed algorithm achieves performance improvement over the standard filtered-X LMS algorithm for the following two situations: (1) the reference noise is a nonlinear noise process, and at the same time, the secondary path estimate is of nonminimum phase; (2) the primary path exhibits the nonlinear behavior. In addition, the developed VFXLMS algorithm can also be employed as an alternative in the case where the standard filtered-X LMS algorithm does not perform well.

Adaptive Volterra filters for active control of nonlinear noise processes

The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

/pdf/recent-advances-on-active-noise-control-open-issues-and-1wg2ydx57i.pdf

Recent Advances on Active Noise Control: Open Issues and Innovative Applications

Audio frames are classified as either speech, non-transient background noise, or transient noise events. Probabilities of speech or transient noise event, or other metrics may be calculated to indicate confidence in classification. Frames classified as speech or noise events are not used in updating models (e.g., spectral subtraction noise estimates, silence model, background energy estimates, signal-to-noise ratio) of non-transient background noise. Frame classification affects acceptance/rejection of recognition hypothesis. Classifications and other audio related information may be determined by circuitry in a headset, and sent (e.g., wirelessly) to a separate processor-based recognition device.

Apparatus and method to classify sound to detect speech

Over the past few decades, structural system identification based on vibration measurements has attracted much attention in the structural dynamics field. The well-known extended Kalman filter (EKF) is often used to deal with nonlinear system identification in many civil engineering applications. In spite of that, applying an EKF to highly nonlinear structural systems is not a trivial task, particularly those subject to severe loading. Unlike the EKF, a new technique, the unscented Kalman filter (UKF) is applicable to highly nonlinear systems. In this paper, the EKF and UKF are compared and applied for nonlinear structural system identification. Simulation results show that the UKF produces better state estimation and parameter identification than the EKF and is also more robust to measurement noise levels. Copyright © 2006 John Wiley & Sons, Ltd.

Application of the unscented Kalman filter for real-time nonlinear structural system identification

There has been much research on active noise control (ANC) systems and transaural sound reproduction (TSR) systems. In those fields, multichannel FIR adaptive filters are extensively used. For the learning of FIR adaptive filters, recursive-least-squares (RLS) algorithms are known to produce a faster convergence speed than stochastic gradient descent techniques, such as the basic least-mean-squares (LMS) algorithm or even the fast convergence Newton-LMS, the gradient-adaptive-lattice (GAL) LMS and the discrete-cosine-transform (DCT) LMS algorithms. In this paper, multichannel RLS algorithms and multichannel fast-transversal-filter (FTF) algorithms are introduced, with the structures of some stochastic gradient descent algorithms used in ANC: the filtered-x LMS, the modified filtered-x LMS and the adjoint-LMS. The new algorithms can be used in ANC systems or for the deconvolution of sounds in TSR systems. Simulation results comparing the convergence speed, the numerical stability and the performance using noisy plant models for the different multichannel algorithms are presented, showing the large gain of convergence speed that can be achieved by using some of the introduced algorithms.

Multichannel recursive-least-square algorithms and fast-transversal-filter algorithms for active noise control and sound reproduction systems

Active control of sound and vibration has been the subject of a lot of research, and examples of applications are now numerous. However, few practical implementations of nonlinear active controllers have been realized. Nonlinear active controllers may be required in cases where the actuators used in active control systems exhibit nonlinear characteristics, or in cases when the structure to be controlled exhibits a nonlinear behavior. A multilayer perceptron neural-network based control structure was previously introduced as a nonlinear active controller, with a training algorithm based on an extended backpropagation scheme. This paper introduces new heuristical training algorithms for the same neural-network control structure. The objective is to develop new algorithms with faster convergence speed and/or lower computational loads. Experimental results of active sound control using a nonlinear actuator with linear and nonlinear controllers are presented. The results show that some of the new algorithms can greatly improve the learning rate of the neural-network control structure, and that for the considered experimental setup a neural-network controller can outperform linear controllers.

Improved training of neural networks for the nonlinear active control of sound and vibration

In the field of adaptive signal processing, it is well known that affine projection algorithms or their low-computational implementations fast affine projection algorithms can produce a good tradeoff between convergence speed and computational complexity. Although these algorithms typically do not provide the same convergence speed as recursive-least-squares algorithms, they can provide a much improved convergence speed compared to stochastic gradient descent algorithms, without the high increase of the computational load or the instability often found in recursive-least-squares algorithms. In this paper, multichannel affine and fast affine projection algorithms are introduced for active noise control or acoustic equalization. Multichannel fast affine projection algorithms have been previously published for acoustic echo cancellation, but the problem of active noise control or acoustic equalization is a very different one, leading to different structures, as explained in the paper. The computational complexity of the new algorithms is evaluated, and it is shown through simulations that not only can the new algorithms provide the expected tradeoff between convergence performance and computational complexity, they can also provide the best convergence performance (even over recursive-least-squares algorithms) when nonideal noisy acoustic plant models are used in the adaptive systems.

Multichannel affine and fast affine projection algorithms for active noise control and acoustic equalization systems

A method for a multi microphone noise reduction in a complex noisy environment is proposed. A left and a right noise power spectral density for a left and a right noise input frame is estimated for computing a diffuse noise gain. A target speech power spectral density is extracted from the noise input frame. A directional noise gain is calculated from the target speech power spectral density and the noise power spectral density. The noisy input frame is filtered by Kalman filtering method. A Kalman based gain is generated from the Kalman filtered noisy frame and the noise power spectral density. A spectral enhancement gain is computed by combining the diffuse noise gain, the directional noise gain, and the Kalman based gain. The method reduces different combinations of diverse background noise and increases speech intelligibility, while guaranteeing to preserve the interaural cues of the target speech and directional background noises.

Method and system for a multi-microphone noise reduction

In recent years, a few articles describing the use of neural networks for nonlinear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a nonlinear controller and one as a nonlinear plant model), steepest descent algorithms based on two distinct gradient approaches were introduced for the training of the controller network. The two gradient approaches were sometimes called the filtered-x approach and the adjoint approach. Some recursive-least-squares algorithms were also introduced, using the adjoint approach. In this paper, an heuristic procedure is introduced for the development of recursive-least-squares algorithms based on the filtered-x and the adjoint gradient approaches. This leads to the development of new recursive-least-squares algorithms for the training of the controller neural network in the two networks structure. These new algorithms produce a better convergence performance than previously published algorithms. Differences in the performance of algorithms using the filtered-x and the adjoint gradient approaches are discussed in the paper. The computational load of the algorithms discussed in the paper is evaluated for multichannel systems of nonlinear active control. Simulation results are presented to compare the convergence performance of the algorithms, showing the convergence gain provided by the new algorithms.

Martin Bouchard

Papers

Multichannel recursive-least-square algorithms and fast-transversal-filter algorithms for active noise control and sound reproduction systems

Improved training of neural networks for the nonlinear active control of sound and vibration

Multichannel affine and fast affine projection algorithms for active noise control and acoustic equalization systems

Method and system for a multi-microphone noise reduction

New recursive-least-squares algorithms for nonlinear active control of sound and vibration using neural networks