Practical realizations of optimized adaptive digital signal processing solutions implemented in preset time count rate meters

Optimized discrete wavelet transform to real-time digital signal processing

In this paper, we propose optimized method of discrete wavelet transform. There is many use of wavelet transform in digital signal processing (compression, wireless sensor networks, etc.). In those fields, it is necessary to have digital signal processing as fast as it possible. The new segmented discrete wavelet transform (SegWT) has been developed to process in real-time. It is possible to pr...

Digital Signal Processing Final Solutions

(b) There are several ways to construct linear-phase FIR filters, including windowing the IDTFT of an ideal response, but in order to ensure a minimax-optimal filter (i.e., a filter that minimizes the maximum deviation from a piecewise-constant ideal specification), the way we know is via the Parks-McClellan approach. In fact. this filter was designed in Matlab as: >> h = firpm(8, [0 0.55 0.65 ...

Real - Time Digital Signal Processing

Signals can be divided into three categories: continuous-time (analog) signals, discrete-time signals, and digital signals. The signals that we encounter daily are mostly analog signals. These signals are defined continuously in time, have an infinite range of amplitude values, and can be processed using analog electronics containing both active and passive circuit elements. Discrete-time signa...

Digital Signal Processing Midterm 2 Solutions

Adaptive Digital Signal Processing JAVA Teaching Tool

This publication presents a JAVA program for teaching the rudiments of adaptive digital signal processing (DSP) algorithms and techniques. Adaptive DSP is on of the most important areas of signal processsing, and provides the core algorithmic means to implement applications ranging from mobile telephone speech coding, to noise cancellation, to communication channel equalization. Over the last 3...