Special Issue "Digital Signal Processing and Engineering Applications"

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A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (15 August 2013)

Special Issue Editor

Guest Editor
Prof. Dr. Jonathan Blackledge

School of Electrical and Electronic Engineering, Dublin Institute of Technology, Kevin Street, Dublin 8, Ireland
Website | E-Mail
Interests: computer aided engineering; financial engineering; communications engineering specializing in information and communications security, energy management

Special Issue Information

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

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 monthly 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 800 CHF (Swiss Francs).


Keywords

  • 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

Published Papers (3 papers)

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Research

Open AccessArticle Compressed Sensing-Based Distributed Image Compression
Appl. Sci. 2014, 4(2), 128-147; doi:10.3390/app4020128
Received: 7 October 2013 / Revised: 21 January 2014 / Accepted: 28 February 2014 / Published: 31 March 2014
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Abstract
In this paper, a new distributed block-based image compression method based on the principles of compressed sensing (CS) is introduced. The coding and decoding processes are performed entirely in the CS measurement domain. Image blocks are classified into key and non-key blocks and
[...] Read more.
In this paper, a new distributed block-based image compression method based on the principles of compressed sensing (CS) is introduced. The coding and decoding processes are performed entirely in the CS measurement domain. Image blocks are classified into key and non-key blocks and encoded at different rates. The encoder makes use of a new adaptive block classification scheme that is based on the mean square error of the CS measurements between blocks. At the decoder, a simple, but effective, side information generation method is used for the decoding of the non-key blocks. Experimental results show that our coding scheme achieves better results than existing CS-based image coding methods. Full article
(This article belongs to the Special Issue Digital Signal Processing and Engineering Applications)
Open AccessArticle Round-Off Noise of Multiplicative FIR Filters Implemented on an FPGA Platform
Appl. Sci. 2014, 4(2), 99-127; doi:10.3390/app4020099
Received: 28 November 2013 / Revised: 7 February 2014 / Accepted: 19 February 2014 / Published: 25 March 2014
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Abstract
The paper analyzes the effects of round-off noise on Multiplicative Finite Impulse Response (MFIR) filters used to approximate the behavior of pole filters. General expressions to calculate the signal to round-off noise ratio of a cascade structure of Finite Impulse Response (FIR) filters
[...] Read more.
The paper analyzes the effects of round-off noise on Multiplicative Finite Impulse Response (MFIR) filters used to approximate the behavior of pole filters. General expressions to calculate the signal to round-off noise ratio of a cascade structure of Finite Impulse Response (FIR) filters are obtained and applied on the special case of MFIR filters. The analysis is based on fixed-point implementations, which are most common in digital signal processing algorithms implemented in Field-Programmable Gate-Array (FPGA) technology. Three well known scaling methods, i.e., L2 bound; infinity bound and absolute bound scaling are considered and compared. The paper shows that the ordering of the MFIR stages, in combination with the scaling methods, have an important impact on the round-off noise. An optimal ordering of the stages for a chosen scaling method can improve the round-off noise performance by 20 dB. Full article
(This article belongs to the Special Issue Digital Signal Processing and Engineering Applications)
Open AccessArticle Undersampling in Orthogonal Frequency Division Multiplexing Telecommunication Systems
Appl. Sci. 2014, 4(1), 79-98; doi:10.3390/app4010079
Received: 12 September 2013 / Revised: 24 February 2014 / Accepted: 28 February 2014 / Published: 17 March 2014
Cited by 3 | PDF Full-text (561 KB) | HTML Full-text | XML Full-text
Abstract
Several techniques have been proposed that attempt to reconstruct a sparse signal from fewer samples than the ones required by the Nyquist theorem. In this paper, an undersampling technique is presented that allows the reconstruction of the sparse information that is transmitted through
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Several techniques have been proposed that attempt to reconstruct a sparse signal from fewer samples than the ones required by the Nyquist theorem. In this paper, an undersampling technique is presented that allows the reconstruction of the sparse information that is transmitted through Orthogonal Frequency Division Multiplexing (OFDM) modulation. The properties of the Discrete Fourier Transform (DFT) that is employed by the OFDM modulation, allow the estimation of several samples from others that have already been obtained on the side of the receiver, provided that special relations are valid between the original data values. The inherent sparseness of the original data, as well as the Forward Error Correction (FEC) techniques employed, can assist the information recovery from fewer samples. It will be shown that up to 1/4 of the samples can be omitted from the sampling process and substituted by others on the side of the receiver for the successful reconstruction of the original data. In this way, the size of the buffer memory used for sample storage, as well as the storage requirements of the Fast Fourier Transform (FFT) implementation at the receiver, may be reduced by up to 25%. The power consumption of the Analog Digital Converter on the side of the receiver is also reduced when a lower sampling rate is used. Full article
(This article belongs to the Special Issue Digital Signal Processing and Engineering Applications)

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