Frequency Selection Algorithm Research Articles

Experimental Design and Algorithmics for a Multichannel Spectral Data Acquisition System

An optically-encoded mechanical modulation system and an electrical signal extraction algorithm are introduced. The system employs the beam chopping in a highly parallel scheme to impart multiple modulation channels onto the spectrum of a white-light source. The optically-multiplexed beam is directed through an experimental sample, and the absorption of the sample at each of the individual modulation channel wavelengths is resolved. A MATLAB-based parallel frequency-selection algorithm is ultimately used to resolve the signals. The low-noise benefits of optical lock-in detection and the practicality of parallel encoding/extraction are demonstrated in this design.

Autonomous Frequency Selection Algorithm under Existing Cellular System and Its Experimental Results (Rural Model)

A frequency selection algorithm leveraging the capability of a handset to autonomously select idle channels of a public communication system for use in a private communication system was previously proposed, and its effectiveness was verified through experiments conducted in a metropolitan area [1]. This paper describes the results of an experiment verifying the algorithm's effectiveness in rural areas with relatively low public communication system traffic.

Frequency selective adaptive time delay estimation

A frequency-selective algorithm for time delay estimation between two channel data is proposed, and its performance is studied. The adaptive stochastic gradient scheme provides a direct estimate of the time delay. It has a low numerical complexity, and it is an alternative to data prefiltering and time delay estimation when the source and noise components have different spectral characteristics.

Reduction in correlation-filter sensitivity to background clutter by the automatic spatial frequency selection algorithm

We propose an automatic spatial frequency selection correlation filter that reduces the sensitivity to nonoverlapping noise or background clutter. This is achieved by inclusion of distorted versions of the reference images surrounded by nonoverlapping background clutter. Furthermore, we impose that the window functions of the reference images give response zero-correlation amplitudes. Simulation results are provided in the case of a two-class pattern-recognition problem and show that the results are appreciably increased. The results are compared with a normal automatic spatial frequency selection.

Automatic spatial frequency selection algorithm for pattern recognition by correlation

We describe an approach to compute filters that automatically performs a spatial frequency selection to improve interclass discrimination and to reduce intraclass sensitivity. This approach is achieved by using as input to the filter synthesis a set of reference images to compute the filters and a set of distorted images to introduce the distortion or noise model of the reference images. Simulation results of correlation examples are provided for two pattern-recognition problems and are compared with the ones obtained with the standard minimum average correlation energy filters.

A frequency selective adaptive algorithm

A frequency selective adaptive algorithm (FSAA) is proposed. This frequency-domain algorithm is based on a weighted least squares (WLS) criterion, and is developed for short-time pole-zero selective spectral matching. First, we give the optimal solution for the parameters of the selective spectral envelope characterizing an N-point windowed sequence. A gradient algorithm, namely the FSAA, is then given for sample-by-sample and block-by-block updates of the envelope. The FSAA can be used in speech processing applications, such as efficient harmonic voice representation and vocal tract characterization. Results are given for the spectral matching (selective and non-selective) of the short-time speech spectrum. These are compared to those obtained using the classical adaptive predictor.