Signal Processing Software

The cybernetic revolution that began on the eve of twenty-first century effected our life in all dimensions. The collection, organization, transformation, and interpretation of information are cornerstones of science, industry, business, and government. Each aspect of information management uses mathematics as a tool.

Signal processing is all about taking a signal, applying some changes to it and then getting a new signal out. The change might be amplification or filtration or something else, but nearly all electronic circuits can be considered as signal processors. Thus, the signal processor might be composed of discrete components like capacitors and resistors, or it could be complex integrated circuits, or it could be a digital system that accepts a signal on its input and outputs the changed signal. Digital signal processing (DSP) is the processing of signals by digital means. The term “digital” comes from “digit,” meaning a number and so “digital” literally means numerical. A signal carries a stream of information representing anything from stock prices to data from a remote sensing satellite.

Applications of DSP include audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, radar, sonar, seismology and biomedicine. Examples include speech compression and transmission in digital mobile phones, room matching equalization of sound, analysis and control of industrial process, seismic data processing, medical imaging such as CAT scans and MRI, MP3 compression, image manipulation, computer-generated animations in movies, high fidelity loud speaker crossovers and equalization, audio effects, etc. Digital signal processing is often implemented using specialized microprocessors such as the DSP56000, the TMS320 or the SHARC. Multi-core implementations of DSPs have started to emerge from companies including Free scale and stream processors.

The two main problems that digital signal processing are related to matrix decompositions:

Algorithms used and developed in digital signal processing have a broad value and may be used in many other applications.

People make mistakes. But human limitations prevent us from detecting mistakes instantly. We usually do not see flaws in our own exposition, necessitating a review process. There is a remedy to rapidly correct errors---software packages. An example would be the sort of built-in "auto correct" or "auto complete" features in many apps. This makes coding and programming an important tool/skill which improves your text.

There are many computing resources available that will aid in your exploration and understanding of digital signal processing. The main software packages that we recommend are as follows:

All these packages have syntax commands closely related to traditional pen-and-paper mathematical language. According to the National Council of Teachers of Mathematics, the goal of teaching mathematics is to help all students develop mathematical concepts including the abilities of students in converting symbolic information into graphical and technological information. This can be achieved and constructed using programming that is useful at any stage of mathematical thinking especially in conjecturing. In our days, computational thinking should be considered a fundamental analytic skill in education, along with reading, writing, and arithmetic. This is a vision for the twenty-first century supported by both the National Research Council of the Academy of Science and the National Council of Teachers of Mathematics.

The author feels deeply that computing should be an integral part of any course. Graphics capabilities alone warrant its use. Of course. technology is not a substitute for actual knowledge of material. Technology is a tool to gain insights into complex problems and it can be a valuable tool to better understand difficult concepts and learn the scientific methods of inquiry. The author believes that computing brings additional insight and helps to develop curiosity that theory alone cannot achieve. As a result, the qualitative aspects of signal processing could be explored in greater depth.

Encouragement

The following resources are recommended:

  1. Bellanger, M., Digital Signal Processing: Theory and Practice, 10th Edition, Wiley, 2024. ISBN-13 ‏: ‎ 978-1394182664
  2. Kassakian J.G., Perreault, D.J., & 2 more, Principles of Power Electronics, 2nd Edition, Cambridge University Press, 2023. ISBN-13 ‏: ‎ 978-1316519516
  3. Ingle, V.K. and Proakis, J.G., Digital Signal Processing Using MATLAB®, 4th Edition, 2017, Cengage Learning, Boston, MA
  4. Mitra, S.K., Digital Signal Processing : A Computer-Based Approach, McGraw-Hill Higher Education; 2nd edition, 2001.
  5. Oppenheim, A.V. and Schafer. R.W., Discrete-Time Signal Processing, 3rd Edition (2009), Pearson Education, Hoboken, NJ
  6. Palani, S., Principles of Digital Signal Processing, Springer, 2022.
  7. Proakis, J.G. and Manolakis, D.G., Digital Signal Processing, 5th Edition (2022), Pearson Education, Hoboken, NJ.
  8. Vaidyanathan, P.P., Signals, Systems, and Signal Processing, ‎ Cambridge University Press; 1st edition, 2024.