• Course Description: Time and frequency representation of signals. Spectral density and autocorrelation. A/D and D/A conversion. PAM and PCM systems. Detection of binary and M-ary signals in Gaussian noise. Matched filter and correlator receivers. Pulse shaping. Band pass modulation and demodulation techniques. Error performance for binary and M-ary systems. Spectral Analysis of digital signal...

We consider the problem of detecting an unknown signal from an unknown noise type. We restrict the signal type to a class of slowly varying periodic signals with harmonic components, a class which includes real signals such as the electroencephalogram or speech signals. This paper presents two methods designed to detect these signal types: the ambiguity filter and the time-frequency correlator....

After reviewing the standard hypothesis test and the matched filter technique to identify gravitational waves under Gaussian noises, we introduce two methods to deal with non-Gaussian stationary noises. We formulate the likelihood ratio function under weakly non-Gaussian noises through the Edgeworth expansion and strongly non-Gaussian noises in terms of a new method we call Gaussian mapping whe...

In this paper we consider the generic problem of detecting a transmitted signal when one of M known signals is transmitted. Instead of using a classical matched filter (MF) detector, matched to the transmitted signals, we propose using an orthogonal matched filter (OMF) detector, which is matched to a set of orthogonal signals that are closest in a least-squares sense to the transmitted signals...

The recently introduced theory of compressed sensing enables the reconstruction of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist rate samples. Interestingly, it has been shown that random projections are a satisfactory measurement scheme. This has inspired the des...

In Cognitive radio networks, the optimum utilisation of the spectrum can be ensured by detecting the unused spectrum by spectrum sensing techniques and sharing it without harmful interference to other users. One important requirement in a cognitive radio network is sensing spectrum holes reliably and efficiently. This paper outlines the different spectrum sensing techniques like matched filter ...