Blind channel identification based on second-order statistics: a frequency-domain approach

In this communication, necessary and sufficient conditions are presented for the unique blind identification of possibly nonminimum phase channels driven by cyclostationary processes. Using a frequency domain formulation, it is first shown that a channel can be identified by the second-order statistics of the observation if and only if the channel transfer function does not have special uniformly spaced zeros. This condition leads to several necessary and sufficient conditions on the observation spectra and the channel impulse response. Based on the frequency-domain formulation, a new identification algorithm is proposed

A new class of random processes with application to helicopter noise

The concept of dividing random processes into classes (e.g., stationary, locally stationary, periodically correlated, and harmonizable) has long been employed. A new class of random processes is introduced which includes many of these processes as well as other interesting processes which fall into none of the above classes. Such random processes are denoted as linearly correlated. This class is shown to include the familiar stationary and periodically correlated processes as well as many other, both harmonizable and non-harmonizable, nonstationary processes. When a process is linearly correlated for all t and harmonizable, its two-dimensional power spectral density S(x)(omega 1, omega 2) is shown to take a particularly simple form, being non-zero only on lines such that omega 1 to omega 2 = + or - r(k) where the r(k's) are (not necessarily equally spaced) roots of a characteristic function. The relationship of such processes to the class of stationary processes is examined. In addition, the application of such processes in the analysis of typical helicopter noise signals is described

Computational Morphology: Three-Dimensional Computer Graphics for Electron Microscopy

This paper describes a method for the computer reconstruction of surfaces from a sequence of electron micrographs, and a data structuring approach to the problem of representing and analyzing objects of physiological importance. The reconstruction technique involves the following stages: 1) object outlines are traced from each section, 2) the computer chain encodes these outlines, 3) the chain codes are reduced to the minimum number of boundary points which satisfactorily define the boundary, 4) polygons are mapped onto the boundary points between sections to approximate the surface, and 4) color coded, shaded surface views are computed of any subset of objects viewed and illuminated from arbitrary locations

Adaptation of methods for cyclo-stationary processes for noisy structural health data

The kind supports of Czech Science Foundation project No. 21-32122J and of the RVO 68378297 institutional support are gratefully acknowledged

Wavelet Based Periodic Autoregressive Moving Average Models

This paper proposes a wavelet-based method for analysing periodic autoregressive moving average (PARMA) time series. Even though Fourier analysis provides an effective method for analysing periodic time series, it requires the estimation of a large number of Fourier parameters when the PARMA parameters do not vary smoothly. The wavelet-based analysis helps us to obtain a parsimonious model with a reduced number of parameters. We have illustrated this with simulated and actual data sets

Spectrum sensing methods in cognitive radio network

Cognitive radio is a capable technology, which has provided a different way to increase the efficiency of the electromagnetic spectrum utilization. CR allows unlicensed users or secondary users (SUs) to use the licensed spectrum through dynamic channel assignment strategies or spectrum access when the primary users (PUs) are in a dormant state to improve the spectrum utilization and hence avoid spectrum scarcity. For this we need intelligent spectrum sensing techniques which can detect the presence of spectrum holes and allocate them to the secondary users without interfering with the activities of the primary users. This thesis specifically investigates the Cyclo-stationary detector, the energy detector and their simulation in MATLAB to know the presence of licensed users. Energy detector is a semi blind spectrum sensing technique, which do not need any prior information about the signal to know the presence of primary users. It is simple and easy to implement, but requires high SNR conditions for optimal performance, which is in accordance with our simulation results. The poor performance of ED in low SNR conditions provides option for new spectrum sensing techniques which performs better in LOW SNR conditions. In that Sense the Cyclo-stationary detector overcomes the problem as it gives optimal performance even at low SNR conditions. The wireless microphone signal which is specified in IEEE 802.22 standard (first standard based on cognitive radio) is used as the test signal for the performance evaluation of the energy detector as well as the cyclo-stationary detector