Digital Signal Processing
1.1 Motivation
Digital signal processing (DSP) has been a major player in the current technical advancements such as noise filtering, system identification, and voice prediction [2]. But, standard DSP techniques, are not equipped enough to solve these problems effectively and obtain almost desirable results. Adaptive filtering is an answer to the problem and is being implemented to promote accurate solutions and a timely convergence to that solution. Therefore, because of the high end capabilities of the adaptive techniques these are being widely implemented in the fields like radar, communications, seismology, mechanical design and biomedical electronic equipments.
No matter how sophisticated adaptive…

The Impact of Digital Signal Processing
2184 Words  9 PagesThere are a great number of applications for Digital Signal Processing and in order to better understand why DSP has such a large impact on multiple aspects of society, it helps to better understand the wide variety of applications it can be used for. Here we will briefly look into the following applications of Digital Signal Processing and their uses; speech and audio compression, communications, biomedical signal processing and applications in the automobile manufacturing industry. Li Tan [1] goes…

Delta Sigma Based Digital Signal Processing
9726 Words  39 PagesThe proposed research focuses on Delta Sigma based Digital Signal Processing (DSP) circuits on Very Large Scale Integration (VLSI) systems for lowpower intelligent sensors in particular on building systematic tools to study their design principles and fundamental performance limits of energyefﬁcient lowcomplexity architectures and on the analysis of their practical advantages and limits. Integrated intelligent sensors have emerged in a wide range of applications including healthcare, surveillance…

The Fundamental Concepts Behind Signal Processing
1102 Words  5 PagesA signal is a time dependent, numerical representation of events in the physical world. In typical applications, the signal is in the form of a current or a voltage. For the signal to be useful, it must be modeled. Signal processing takes time dependent data, and manipulates it to create a mathematical model useful to practical problem solvers. Many techniques for signal processing exist, including Fourier Transforms, moving averages, filtering, and spectral analysis. Spectral analysis uses sampled…

Applications Of Digital Signal Processing
2973 Words  12 PagesApplications of Digital Signal Processing in Biomedical field: A Survey 1Ashish Mistry, 2 Ishan Mehta, 3Shantanu Patel, 4Hardik Modi 1,2,3Students, 4Assistant Professor, Charotar University of Science and Technology, Changa388421, Gujarat, India 1ashish31093@gmail.com,2 ishanmehta1805@gmail.com, 3shantanoopatel@gmail.com Abstract: This paper discusses about the applications digital signal processing in the biomedical field, the recent advancements in the field of signal processing with new instruments…

The Effect Of Digital Analog Audio Signal Processing
1434 Words  6 Pagesthe research of digital signal processing is undergoing rapid development. At present, it has been used in many fields such as communications industry, voice and acoustics applications, radar and image. The processing of the speech signal is one of the key areas of DSP application. So far, it has formed a number of research directions, such as speech analysis, speech enhancement, speech recognition, voice communication, etc.. With the development of IT technology and voice processing technology, people…

Digital Time Signal Processing
7459 Words  30 Pages8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.3 0.2 0 0.1π 0.2π 0.4π 0.6π 0.8π w π Phase Response ∅ 0.4π 0.3π 0.2π 0.1π 0 –0.1π –0.2π –0.3π –0.4π w 0 0.2π 0.4π 0.6π 0.8π π Solution : (a) (ii) To find response : The frequency components present in the input signal x(n) are, w1 = π 2 π 4 and w2 = π 2 w2 = At ⎛ π⎞ M ( w) = 2 cos ⎜ 2 ⎟ = 2 cos (π ) = 2 ⎝ 2⎠ ⎛π⎞ φ = −2⎜ ⎟ + π = 0 ⎝2⎠ DSP Help Line : 9987030881 www.guideforengineers.com B E EXTC At w2 = π 4 DTS P DEC 2004 3 π ⎛ π⎞ M ( w) = 2 cos ⎜…

The Digital Signal Processing Applications
2919 Words  12 PagesCHAPTER 1 INTRODUCTION With advent of modern highperformance signal processing applications, high throughput is in great demand. Digital Signal Processing is perhaps the most important enabling technology behind the last few decade’s communication and multimedia revolutions. Most recent research in the digital signal processing (DSP) area has focused on new techniques that explore parallel processing architectures for solutions to the DSP problems .DSP is used in a numerous real time application…

Using Kalman Filter Is Digital Signal Processing Based Filter
853 Words  4 PagesVIDEO DENOISING Nowadays digital cameras which is used to capture images and videos are storing it directly in digital form. But this digital data ie. images or videos are corrupted by various types of noises. It may cause due to some disturbances or may be impulse noise. To suppress noise and improve the image performances we use image processing schemes. In this paper they uses Kalman filter to remove the impulse noise. The Kalman filter is digital signal processing based filter. It estimates…

Digital Signal Processing
936 Words  4 Pagesthe need for methods to process digital signals is more important than ever. Now that I am on the threshold of embarking on a career that will encompass a major part of my adult life, I think it is natural that I veer towards Signal processing. As I look back, I feel that my natural inclination and excellence in mathematics from childhood has led me along this path. Digital Signal processing incorporates the use of mathematics to manipulate an information signal to modify or improve it in some way…

Delta Sigma Based Digital Signal Processing
9737 Words  39 PagesThe proposed research focuses on Delta Sigma based Digital Signal Processing (DSP) circuits on Very Large Scale Integration (VLSI) systems for lowpower intelligent sensors in particular on building systematic tools to study their design principles and fundamental performance limits of energyefﬁcient lowcomplexity architectures and on the analysis of their practical advantages and limits. Integrated intelligent sensors has emerged in a wide range of applications including health care, surveil…
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