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Convex Analysisの二,三の進展について

Many signals are naturally described by continuous contours in the time-frequency plane, but standard time-frequency methods disassociate continuous structures into isolated “atoms” of energy Here we propose a method that represents any discrete time-series as a set of time-frequency contours The edges of the contours are defined by fixed points of a generalized reassignment algorithm These edges are linked together by continuity such that each contour represents a single phase-coherent region of the time-frequency plane By analyzing the signal across many time-scales, an over-complete set of contours is generated, and from this redundant set of shapes the simplest, most parsimonious forms may be selected The result is an adaptive time-frequency analysis that can emphasize the continuity of long-range structure The proposed method is demonstrated with a few examples

Sparse Contour Representations of Sound

Presented is a method to blindly estimate the location of a transmitter from the signal observed by a single
moving receiver. This process is based on the observation that the observed Doppler characteristics are essentially
uniquely determined by the transmission frequency, the location of the transmitter, and the time-varying flight
path of the receiver. We accurately estimate the instantaneous frequency of the received signal and blindly
calculate the transmitted frequency from the received signal and the instantaneous position and velocity of the
receiver. The transmitter location is then estimated by minimizing a cost function representing the difference
between the Doppler characteristics calculated from the relative geometry of the transmitter and receiver and
the Doppler characteristics estimated from the received signal. The method has the advantages that only one
receiving antenna is required and the emitter may be located with no a priori knowledge of the emitter location
or frequency. In addition, the process is essentially independent of the flight path of the receiver.

Target location from the estimated instantaneous received frequency

An accurate electromyography (EMG) classification algorithm to control a virtual hand prosthesis with 12 degrees of freedom using two surface EMG electrodes is presented in this paper. We propose the application of independent component analysis (ICA) for blind-source separation of the EMG signals obtained from two electrodes. One of the problems affecting the EMG classification accuracy is the location dependence of the EMG signal due to the superposition of signals from multiple sources. ICA is used to separate the two signals obtained from two surface electrodes into two independent EMG signals prior to the feature extraction and classification processes. We demonstrate that the EMG classification accuracy can be improved using the ICA algorithm. We also propose a novel eigen-based feature that is extracted from the short-time Fourier transform (STFT) magnitude spectrum. Our new feature not only decreases feature dimensions but also performs better than other well-known features. We also implement the EMG classification scheme on the virtual robot arm. The performance shows promising result as indicated by a decrease in the Davies–Bolden (DB) index after applying the ICA © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

An accurate forearm EMG signal classification method using two‐channel electrode

Gaussian Minimum Shift Keying (GMSK) is a modulation method used by GSM phone networks and the
Automatic Identification System (AIS) used by commercial ships. Typically these systems transmit data in
short bursts and accomodate a large number of users by time, frequency and power management. Co-channel
interference is not a problem unless the system is heavily loaded. This system load is a function of the density of
users and the footprint of the receiver. We consider the problem of demodulation of burst GMSK signals in the
presence of severe noise and co-channel interference. We further examine the problem of signal detection and
blind estimation and tracking of all of the parameters required in the demodulation process. These parameters
include carrier frequency, carrier phase, baud rate, baud phase, modulation index and the start and duration of
the signal.

GMSK co-channel demodulation

We describe a new linear time-frequency model in which the instantaneous value of each signal component is mapped to the curve functionally representing its instantaneous frequency. This transform is linear, uniquely defined by the signal decomposition, and satisfies linear marginal-like distribution properties. We further demonstrate the transform generated surface may be estimated from the short time Fourier transform by a concentration process based on the phase of the short-time Fourier transform (STFT), differentiated with respect to time. Interference may be identified on the concentrated STFT surface, and the signal with the interference removed may be estimated by applying the linear-time-marginal to the concentrated STFT surface from which the interference components have been removed

D.C. Smith

Papers

A linear model for TF distribution of signals