Dear Colleagues,

The engineering applications of Digital Signal Processing (DSP) are vast and may be said to be a fundamental aspect of our “Digital Society”. Today, many aspects of electrical and electronic engineering are essentially applications of DSP. This is because of the focus on processing information in the form of digital signals using specialist DSP hardware designed to execute software which, in turn, is often an algorithmic solution to a specific engineering problem.

The design of any DSP system is inextricably connected with the simulation of the system. This requires accurate mathematical and/or statistical models to be developed that are relatively complete statements of the physical conditions that ‘reflect’ the engineering application in which the system will function. Further, each system is typically based on a library of signal processing algorithms, and hence, software engineering is a key component of DSP.

DSP has traditionally been associated with electrical and electronic engineering but, in recent years, its applications have diversified radically. Any stream of digital data that requires some form of numerical analysis and processing to produce a well defined output can be classified as DSP. This includes, for example, financial time series analysis which is based on “tick” data and refers to any market data defining the price and volume at regular intervals of time. This data feed is more commonly grouped into “candlestick data” which is a compressed form of tick data and therefore more readily available as it requires significantly less bandwidth to distribute to traders world-wide. The point here is that financial time series analysis is a growing example of DSP using a range of real-time programming environments such as Metatrader, for the relatively new and fundamentally important field of “financial engineering”.

Many other examples on the applications of DSP can now be implemented on specialist programming environments designed for real time systems. In biomedical signal analysis and medical image processing, for example, DICOM (Digital Imaging and Communications in Medicine) viewers such as OsiriX provide excellent platforms for implementing X-code based applications albeit limited to Mac and other Linux based operating systems. The development of Apps in general relates to a wide range of multi-media products, many of which are based on DSP, most notably in the area of audio engineering. In this context, music technology is almost exclusively related to real time DSP and involves the continuous development (through the introduction of new Apps) of systems such as ProTools for music composition and audio post-production.

Specialist programming environments now provide excellent facilities for implementing DSP algorithms in real time for the application of process control engineering, avionics and communications engineering, for example. The integration of DSP with intelligent systems and Artificial Neural Networks is now common place as is the use of evolutionary computing for aiding the design of DSP algorithms for stochastic signal analysis. In the area of Cryptology, for example, DSP methods are now an integral component of current and future developments especially with regard to information hiding and Stegacryptology (hiding encrypted data) which are finding value in Digital Rights Management. With respect to the Internet, it is estimated that by 2016, annual global IP traffic will exceed one trillion Gigabytes with 3.4 billion people using the World Wide Web (~45% of the world’s projected population). In this context, there is an urgent need for research and innovation into internet data security (e.g., Cloud Computing) using DSP.

Coupled with the wealth of programming environments for implementing real time DSP, the diversity of DSP applications has grown rapidly in recent years. This special issue of the Journal of Applied Sciences “Digital Signal Processing and Engineering Applications” aims to cover recent advances in the development of DSP algorithms and systems associated with any modern engineering application. The issue is especially interested in promoting research that is related to working prototypes and commercial products with an emphasis on (but not exclusively related to) real time applications.

Prof. Dr. Jonathan Blackledge
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• linear and non-linear modeling of digital signals
• statistical modeling of stochastic digital signals
• audio signal processing
• biomedical signal processing
• coding and encryption of digital signals
• hiding information in digital signals
• adaptive systems
• DSP in control engineering
• intelligent systems engineering
• software engineering methods for DSP
• financial signal processing
• DSP using evolutionary computing
• DSP in communications engineering
• DSP in optics and image processing
• real time DSP
• VLSI, ASIC and FPGAs for DSP