Ternary Chaotic Pulse Compression Sequences

In this paper method available for generating ternary sequences is discussed. These sequences are useful in many applications but specifically in synchronization of block codes and pulse compression in radar. The ternary sequences are derived from chaotic maps. It is feasible to achieve simultaneously superior performances in detection range and range resolution using the proposed ternary sequences. The properties of these sequences like autocorrelation function, Peak Side Lobe Ratio (PSLR), ambiguity diagram and performance under AWGN noise background has been studied. The generation of these sequences is much simpler, and the available number of sequences is virtually infinite and not limited by the length of the sequence

Separation Of �, �, � & � Activities In EEG To Measure The Depth Of Sleep And Mental Status

The electrical activity of the human brain i.e. the EEG and its classification into various frequency bands has been of interest to researches dealing with neurology. So, here our aim is targeted to classify EEG signals traces into different fundamental frequency rhythms and determine whether a relatively short EEG record taken in a routine laboratory is normal or abnormal. The primary aim of computerized EEG analysis is to support electroencephalographer’s evaluation, by representing the data in numerical and or graphical form. EEG analysis however, can go further, actually extending the electroencephalographer’s capabilities giving them new tools with which they can perform such difficult and time consuming tasks as quantitative duration EEG in epileptic patients and sleep and psychopharmacological studies. This method is having several advantages over the visual screening by which it is very difficult to extract EEG information. The choice of analytic method should be determined mainly by the goal of the application. The frequency domain tool is used for EEG analysis. The system performs continuous analysis in graphical form and tabular form of recorded EEG signal. Algorithm is implemented by using C language. It is helpful to classify the depth of sleep and mental status from the percentage power in each band i.e., delta, alpha, beta and theta