Students learn the basic principles of filtering as well as how to apply digital filters to extract part of an audio signal by using an interactive online demo website. They apply this knowledge in order to isolate a voice recording from a heavily noise-contaminated sound wave. After completing the associated lesson, expect students to be able to attempt (and many successfully finish) this activity with minimal help from the instructor.

A digital signal processing (DSP) textbook, and other materials, aimed at computer science students. Why one specifically for CS students? DSP has traditionally been taught as part of the electrical engineering curriculum and, as such, is accessible to CS students only to the extent that they have taken its prerequisites. In some engineering schools, that may be the case, but it is often the case that the CS math curriculum lacks some of the expected prerequisites. Moreover, DSP is most often taught as an upper-level EE course, and therefore, practically speaking, inaccessible to most non-EE majors.

This class teaches the fundamentals of signals and information theory with emphasis on modeling audio/visual messages and physiologically derived signals, and the human source or recipient. Topics include linear systems, difference equations, Z-transforms, sampling and sampling rate conversion, convolution, filtering, modulation, Fourier analysis, entropy, noise, and Shannon’s fundamental theorems. Additional topics may include data compression, filter design, and feature detection. The undergraduate subject MAS.160 meets with the two half-semester graduate subjects MAS.510 and MAS.511, but assignments differ.

The examples and supporting code for this book are in Python. You should know core Python and you should be familiar with object-oriented features, at least using objects if not defining your own. If you are not already familiar with Python, you might want to start with my other book, Think Python, which is an introduction to Python for people who have never programmed, or Mark Lutz’s Learning Python, which might be better for people with programming experience.

Abstract— Wireless sensor networks are mainly resource constrained with less memory space, limited power supply, processing speed and availability of bandwidth for communication. One of the most important challenges in wireless sensor networks is to design energy-efficient data gathering a network which increases the lifetime of wireless sensor networks. Due to an enormous deployment of sensors, a tremendous data isgenerated by these sensor networks. Processing and transportation of such a huge data increase the energy consumption of sensor nodes along with an increase in network traffic. It is observed that processed data requires less power as compared to transmitting data in the wireless medium. Hence, it is more significant to apply compressed sensing algorithm at sensing node. Compressive sensing (CS) technique generates a sparse signal of few nonzero samples from the original signal at sub-Nyquist sampling rate where reconstruction of the original signal is possible even with few sparse samples. Thus, all the necessary and more accurateinformation can be obtained from the data gathered by wireless sensor networks with less number of samples. In this paper, we compare three types of data gathering technique.