Digital Technologies Essay Temp

Digital Technologies Essay Temp
'Will the challenges emerging with digital knowledge contexts, for which digital literacy is being
deployed, open new horizons for the human art of thinking and creating knowledge?' (Belisle 2006,
p.55).
Reference: Harvard Style
Knowledge can be interpreted in different ways where one scholar defines knowledge as a learnt
education another interprets knowledge as intelligence. Knowledge is continuing a process
commencing at birth. Belisle (2006) states
Knowledge is much more than the transmitted or acquired information. It is the awareness and
understanding of facts, truths or beliefs resulting from perception, learning and reasoning.
A thorough knowledge is gained through education as well as real life experiences. Dependency on
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Visual images make the written data and oral presentation easier to understand and assist in the
decision making process. Good decisions are based on research, digital information and Records
management contribute to making decisions based on fact. Timely decisions are facilitated by ready
access to information. Understanding past decisions is assisted by access to the digital data that
originally supported the decision (National Archives Australia 2015). In the transition from literature
to digital and from text to visual, an individual has to be aware that a dependency on digital
technology can create issues.
The internet is a bundle of information that allows the expression of ones' self. This expression on a
personal web page is likely to be a more considered controlled impression than one gained from an
offline or public encounter. A web page can provide an emotional release enabling artistic ability to
be incorporated in its layout. Chatrooms are a form of communication that gives the opportunity to
speak to someone in a different country without the cost of long distance phone charges. It is instant
and not like the olden times where an individual had to wait for mail to be delivered. Technology
makes it easy to communicate when we wish and disengage at will. Educational websites offer
phenomenal information covering a wide range of topics that vary from
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Design Of A Low Power Fixed Point 16 Bit Digital
DESIGN OF A LOW–POWER FIXED–POINT 16–BIT DIGITAL
SIGNAL PROCESSOR USING 65NM SOTB PROCESS
ABSTRACT:
Power consumption is the main problem in designing circuits or devices now a day. Since so many
years the researches are going towards the progression of this issue. One of the progressive
technique is SOTB technology. By using this technology, we will design a 16–bit digital signal
processor by using 65nm SOTB CMOS circuit or device. The chip of DSP takes very less power
consumption 282μW at the initialize voltage 0.55V and operation frequency 200MHz. The main aim
of this project is to design a processor such that it requires very less power to operate and also to
dissipate less heat during and after the process so that the performance of the circuit is increased.
SOTB process is type in SOI (silicon on insulator) technology. The silicon–on–thin–buried–oxide
(SOTB) CMOS is a strong candidate for ultralow–power (ULP) electronics because of its less
instability and back–bias control. These advantages of SOTB CMOS enable power and performance
developed with given Vth control at ULV and can achieve ULP operation with acceptably high
speed and low leakage. These characteristics are suitable for such new applications as energy
harvesting sensor network systems, and long lasting wearable computers.
INTRODUCTION: Highly energy efficient CMOS circuits are required in the internet–of–things
(IoT) era since a great number of small electronic apparatuses process and communicate data.

Ofdm Transmission By Stephen Kiambi
OFDM transmission
Author: Stephen Kiambi
Department of Electrical and Information Engineering, University of Nairobi
OFDM or Orthogonal Frequency Division Multiplexing is a transmission scheme that is widely used
in broadcast and wireless communication technologies. Some of the applications employing OFDM
include Digital Audio Broadcast (DAB), Digital Video Broadcasting Terrestrial (DVB–T2),
Wireless–LAN, Worldwide Interoperability for Microwave Access (WiMAX) and 4G Long Term
Evolution (LTE) radio technologies [1, 2]. Two of the main reasons to use OFDM are to increase
date rates and robustness against frequency–selective fading. In the rest of this document an
overview of OFDM transmission theory is given.
1 Basic principles of OFDM
OFDM is a multicarrier transmission technique that splits the total available bandwidth into many
narrowband sub–channels at equidistant frequencies. The sub–channel spectra overlap each other
but the subcarrier signals are still orthogonal. The single high–rate data stream is subdivided into
many low–rate data streams for the sub–channels. Each sub–channel is modulated individually and
all the sub–channels are transmitted simultaneously in a superimposed and parallel form.
An OFDM transmit signal therefore consists of adjacent orthogonal subcarriers spaced by the
frequency distance on the frequency axis. All subcarrier signals are mutually orthogonal within the
symbol duration, , if the –th unmodulated subcarrier signal is described

Engineering Application Essay
STATEMENT OF PURPOSE
The course of the evolution of my interest in engineering dates back to my childhood. It has begun
in my high school. The kind of environment in which I grew up is, both my parents were graduates
in Mathematics and were proficient in the field of teaching Mathematics in engineering level and
high school level. So, due to their influence on me, I had developed my interest in the subject and
maths was at my fingertips. At that point, I was enthusiastic to learn about the applications of
Mathematics. This lead to the important stage in my life, the field of engineering.
My high school education and my performance in various competitive exams laid the path for me to
pursue my bachelor of technology degree in the field of Electronics and Communication
Engineering which suited me better because of my fondness for mathematics and my interest to
study the applications of the subject. The place of study is Amrita School of Engineering, a college
under Amrita University, Bangalore which is known for its academic excellence which is ranked
among the top 25 educational institutions in India.
During my bachelor's degree, I had been introduced to many versatile courses in ECE like Electric
circuits, Digital systems, Electromagnetics, Signals and Systems etc.,. I had scored good grades in
all my subjects. I had to choose from paths in front of me – Digital Electronics, Microelectronics,
Signal Processing and Radio Frequency Engineering as my area of interest. Due to

Characterization And Classification On Ultrasound Signals...
Microstructural characterization and classification on ultrasound signals Using DCT transformation
in PCA framework Masoud Vejdannik a, Ali Sadr b a,b School of Electrical Engineering, Iran
University of Science & Technology (IUST), Narmak, Tehran 16844, Iran a
m_vejdannik@yahoo.com, b sadr@iust.ac.ir Abstract Purpose: Method: Results: Conclusions:
Introduction Nb–bearing nickel–based superalloys, like the Inconel 625 alloy studied here, exhibit
an outstanding combination of mechanical properties and resistance to pitting, crevice and
intergranular corrosion due to the stiffening effect of chromium, molybdenum and niobium in its
nickel matrix. These properties make precipitation hardening treatments unnecessary [––]. The
extraordinary ... Show more content on Helpwriting.net ...
This segregation and precipitation of the secondary phases can change the mechanical properties of
the alloy and decrease its resistance to corrosion [––]. In addition, the Nb–rich Laves phase has a
low melting point that causes an increase in the temperature solidification range, making the alloy
susceptible to solidification cracking [––]. However, an adequate selection of the welding conditions
can minimize the formation of the Nb–rich Laves phases and consequently, reduce its susceptibility
to solidification cracking. Therefore, it is also important to investigate the phase transformation
process. Nowadays, researchers are evaluating the use artificial intelligence techniques to
characterize microstructures. For example, Albuquerque, Filho, Cavalcante, and Tavares [––]
quantified the porosity of synthetic materials from optical microscopic images successfully, and the
solution proposed, which was based on an artificial neuronal network (ANN), proved to be more
reliable. Albuquerque, de Alexandria, Cortez, and Tavares [––] characterized the microstructures in
images of nodular, grey, and malleable cast irons using a multilayer perceptron

My Statement Of Purpose For Electrical Engineering
Passionate, Curiosity to ask till you are convinced and Clarity such that it sounds simple but not
simpler. The quench for this would be fulfilled by understanding the unknown and what lies
undiscovered. Intrigued by the laws of nature I have always been inquisitive about science and
technology and how it can be most useful to the society. Also over the years, I have developed a
deep interest in a combination of technology and how it can be most useful to the society. I also
have developed an interest in a combination of technologies using electronics and computers. My
special inclination towards Electrical Engineering has been primarily because of subjects namely
Microprocessors, Microcontrollers and Computer Organization which I learned during my
undergraduate program. I am equally fascinated by the processors or microcontrollers used in our
computers and smart phones and the amazing number of possibilities they can be made in. I am
strongly determined to learn the working of these systems and pursue a research career in the same.
Hence, I resolved that pursuing M.S. will be an ideal step towards the achievement of my goal. ...
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These topics have fascinated me ever since. When I studied the fundamentals of analog and digital
signals, I almost decided that Digital Signal Processing will be the career for me. What got me more
enthused in this area was when I learned about microprocessors. I had the opportunity to study some
of the processors during my third and fourth (final) year of undergraduate. That's when I realized
how the two fields could be merged together by having dedicated processors. I was certain that my
interest in this will increase by taking Digital Signal Processing and Computer Architecture as
electives in my final semester of undergraduate

Advantages And Disadvantages Of Analog Signal Processing
Firstly I will explain what is signal ,signal processing ,analogue viruses digital signal types of signal
processing their advantages and disadvantages and their comparison .I–e which one is better
.......why analog signal processing (ASP) is replaced with digital signal processing (DSP). What is
Signal???
Any physical quantity that varies with time or any other independent variable is called signal.
What is signal processing???
Signal processing is concerned with the improvement of quality of a signal that is under observation.
Its particular aim is to reduce noise that will not reduce by the careful design of measurement
system.in signal processing we basically apply some algorithm on analogues and digital signal to
make them higher quality signal as compere to original signal.
Mostly we deal with two following two type of signal processing
1) Analog signal processing
2) Digital signal processing
First I will explain what is analogue signal and digital signal their advantages and disadvantages and
their application .then I will explain ... Show more content on Helpwriting.net ...
Its implementation is very easy but its difficulty in calibration and maintains increases as the order
of filter increases.one can easily design and modify a filter using digital signal processing
(DSP).Filter function in digital signal processing (DSP) is software based, flexible and repeatable. In
digital signal processing (DSP) filter with higher order only require software modification .whereas
in analogue circuit we need additionally hardware implementation also. In short digital signal
processing (DSP) is defined as the converting the continuously changing waveform (analog) into a
series of discrete levels

A New And Accurate Digital Approximation Of The Fractional...
Abstract–In this article a new and accurate digital approximation of the fractional–order
differentiator (FOD) in the form of FIR filter is presented. This approach is based on power series
expansion of fractional order systems. First, the first–order digital differentiators can be simply
derived from a classical continuous–time approximate differentiator by using the Bilinear
transformation. Then, the transfer function of digital FOD is obtained by taking fractional power of
the transfer function of conventional first order digital differentiator. Next, the power series
expansion is used to design digital FIR fractional order differentiator, with closed form formula.
Finally, some examples are presented through the paper to illustrate the performance and the
effectiveness of the proposed design method. Keywords–Fractional order differentiator; FIR filter;
power series expansion; Bilinear transformation. I. INTRODUCTION In the last decades,
considerable interest in fractional order operators has been simulated by the applications of these
concepts in different areas of engineering [1–10]. Analog and digital implementations of this type of
operators are then important research topics. Because they cannot be exactly implemented, only a
limited implementation can however be achieved in a frequency band of interest using
approximation techniques. In reference [4], we can find a good summary of the existing methods of
their analog implementation by rational functions. In

Wearable Medical Devices Using Delta Sigma Processing...
3.3 Research Objective 3: Wearable Medical Devices using Delta–Sigma Processing Circuits
The goal of this research objective is to apply the proposed Delta–Sigma DSP circuits to wearable
medical sensors, more specifically to an EEG signal processor. Examples of questions that we wish
to address are: How much power can be saved by using Delta–Sigma circuits instead of
conventional DSP circuits? What is the trade–off between processing accuracy and power
consumption? How can an efficient interface be designed between Delta–Sigma Processing circuits
and peripheral devices?
3.3.1 Background and Rationale
Wearable EEG signal processors are imminently expected by researchers, clinical doctors and
patients [127–131]. One of the main ... Show more content on Helpwriting.net ...
It would also allow the patients to wear the device outside the laboratory, creating more
opportunities to capture elusive phenomena [134]. This would require a low–power device
implementing a hardware efficient EEG processing algorithm.
The Cross frequency coupling (CFC) algorithm is one of the most promising EEG signal processing
methods [135–137]. CFC, which highlights the correlation between the high–frequency amplitude
and the low–frequency phase of EEG signals, is an attractive means of studying cognitive brain
tasks [138] and disorders such as epilepsy [136] and schizophrenia [137]. Several metrics have been
developed to measure this coupling, including the modulation index [136] and the phase–locking
value (PLV) [135]. However, like other EEG processing algorithms, CFC involves a large number of
MAC operations. If it is implemented in an ASIC using conventional circuits, it consumes too much
circuit area and power, increasing the cost of the device and reducing its battery lifetime. Based on
our preliminary results, we propose to use Delta–Sigma based linear processing circuits for CFC–
based EEG processors. The proposed project could dramatically reduce circuit area and power and
create a wearable CFC EEG processor.
Figure 10: System block diagram for calculating CFC modulation index.
3.3.2 Preliminary Research and Result Insights
An integrated CFC EEG processor was developed in our lab [97,98]. The CFC EEG processor

Case Study Of Internet Of Things
Case study of Internet of Things in area of
Agriculture, ?AGCO?s Fuse Technology 's
'Connected Farm Services '?
Rashi Chaudhary
B.Tech. (Electrical and Electronics Engineering)
Krishna Institute of Engineering and Technology
Ghaziabad, India
Jalaj Ranjan Pandey
B.Tech. (Information Technology)
Noida Institute of Engineering and Technology
Greater Noida, India
Prakhar Pandey
B.Tech. (Electrical and Electronics Engineering)
Krishna Institute of Engineering and Technology
Ghaziabad, India
Pulkit Chaudhary
B.Tech. Final Year (Manufacturing Engineering)
Central Institute of Plastics Engineering and Technology
Lucknow, India
Abstract: Embedded with Internet of Things, Smart
Agriculture vision is analyzed in this case study. Smart

Farming techniques offered by leading Agriculture Company
AGCO?s Fuse Technology 's 'Connected Farm Services ' is
under study. Study presents the future of information
technology used with the IOT (Internet of Things) technology
and rapid development of formulated applications in
agriculture field. SWOT analysis has been used to analyze the
issue under study. Case has been explained in majorly five
portions, introduction, Smart farming operational
Fundamentals, Smart Agriculture by AGCO, SWOT
Analysis, Conclusion.
Keywords: Internet of Things, Smart Agriculture, Smart
Farm, Smart Farm Security
I.INTRODUCTION
Internet of Things is a global network connecting
physical

An Evaluation Of Lms Based Adaptive Filtering
ABSTRACT Current Method of speech enhancement has been developed with adaptive filtering
approach. The removal of unwanted signal i.e. noise from speech signals have applications ranging
from cellular communications to front ends for speech recognition system. This paper describes
proficient algorithm for removal of noise from speech. An optimal evaluation of LMS based
adaptive filtering has been implemented for the observed noisy speech. This Algorithm is basic
adaptive algorithm. This Adaptive algorithm has been used in many practical applications as a result
of its robustness and simplicity. In Future Enhancement Unbiased and Normalized Adaptive noise
reduction will use for speech improvement. Keywords – Adaptive filtering, LMS algorithm, MSE,
Speech Enhancement, UNANR. I. INTRODUCTION In Practical situations speech signals are
corrupted by several different forms of noise such as speaker sound, background noise like door
slam fan running in background, car noise, TV noise and also they are concern to distortion caused
by communication channels; examples are low–quality microphone, room reverberation, etc. In all
such situations extraction of high resolution signals is an important task. Filtering techniques are
mainly classified as adaptive and non adaptive filtering techniques. Speech enhancement improves
quality of signal by suppression of noise and reduction of distortion. The speech enhancement tells
about the growth of communication system. Enhancement means

Texas Instruments Essay
Texas Instruments
The Internet Era is here and the advances in digital technology are completely changing the way we
live. From digital cellular phones to handheld computers not much bigger than a stack of playing
cards, digital technology has created an unprecedented explosion of new products that allow
consumers to communicate with one another as well as integrate the numerous products they use in
their daily lives. This increase in integration along with the increased speed of communication has
created the "global" economy in which most business must operate to remain competitive. "Hardly a
day goes by that we do not encounter products that are enabled by DSP technology. From consumer
electronics including cell phones, ... Show more content on Helpwriting.net ...
These digital signal processors are one of the vital components in digital electronics such as digital
cellular phones, camcorders, VCR's, modems and even automobiles. The company is also the
world's leading maker of analog chips. (Hoovers, 2000) These DSP's and analog chips are
considered to be the semiconductors of the Internet age.
This company has set a corporate vision to "become a premier electronics company providing world
leadership in digital solutions for the networked society". TI plans to accomplish corporate goals by
producing products and technologies that set the company and their customers apart from the
competition, by competing in high–growth markets and by providing consistently good financial
performance. (Texas Instruments Homepage, 2000)
Texas Instruments Inc. is divided into three divisions. The first of these is the semiconductor
business. This division accounts for 84% of 1999 revenues. The next largest division is the Materials
& Controls division, which accounts for 11% of revenues. This business sells electrical and
electronic controls, connectors and sensors into commercial and industrial markets. The third and
smallest division is the Educational & Productivity Solutions division that represents 5% of
revenues. This division is a leading supplier of education and graphing calculators selling primarily
through retailers and to educational institutions. (Standard &

Statement Of Sop For Electrical Engineering
"The only thing permanent in life is change". This perfectly holds true to the world of
Electrical Engineering where novel ideas are not a novelty. It is this novelty that entices me to this
field and gives me a deep sense of professional pride. In this context, I would like to pursue my
masters in Electrical Engineering, which not only supplements the knowledge that
I possess but also provides a strong platform for my career goals.
I Introduce myself as an undergraduate student specialized in Electronics and Communication
Engineering from "Sree Vidyanikethan Engineering College", Andhra Pradesh which has
empowered me to gain an open mindset and intense bits of knowledge. The course curriculum has
been a fine blend of theory and practical knowledge during which I developed a keen interest on
applying theory in practical. ... Show more content on Helpwriting.net ...
I was then interested by the complexities of different structures and their working. The
transformation of computers from the large size to small palmtops enticed me to know about the
factors that are responsible for making computers, also the electronic gadgets so small. I was taken
aback after seeing a small chip for the first time in my school days. Furthermore, when it came to
my knowledge that this single chip contains more than 1000 transistors, it really became impossible
for me to believe and "Integrated circuits" was the answer I received, but wasn't able to penetrate the
fundamentals of it. From then, everyday I came across with small circuits and spent hours to solve
the mystery The interest and fascination has evolved into a determination to study the subject deeply
and to do some valuable research in this

The Effect Of Digital Analog Audio Signal Processing
Abstract
As technology advances, the 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 have more and
more high quality requirements of audio. However, the traditional analog audio signal processing
has been impossible to meet people 's needs, at this time, digital signal processing began to ... Show
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In this case, this paper designs and implements an audio signal acquisition and processing system
which based on DSP technology.
In the processing of the audio signal, it needs to go to the digital filter. Digital filter is a numerical
system which used to filter the time discrete signal. According to the time domain characteristics of
unit impulse response function, it can be divided into two kinds of filter, infinite impulse response
(IIR) filter and finite impulse response (FIR) filter. Compared with the IIR filter, the FIR filter has
just one zero point, and has no pole in the z plane except the origin, so it is always stable and can be
easily realized. Even more important, the strict linear phase property can be obtained by the FIR
filter, which is difficult to achieve by the IIR filter, so it is widely used in the field of high fidelity
signal processing, such as digital audio, image processing, data transmission, biomedical and other
fields.
This project is about using DSP technology and digital filter to build a music instrument which can
do sound signal acquisition and processing. At last, the suggestion and prospect of how to improve
the system will be put forward.
Key word: DSP, Audio signal processing, FIR filter
Acknowledgments
I am deeply glad and appreciating my dissertation supervisor Dr. Itagaki for his expert advice and
throughout the whole project
A warm thanks

The Principle Of Ultrasound Imaging System
DESCRIPTION:
PRINCIPLE OF ULTRASOUND IMAGING SYSTEM:
The main principle of ultra sound imaging system is to transmit ultra sound burst signals into the
particular organ and produced echo is processed for imaging.
The entire system is mainly classified as two PCB boards:
i. FPGA based transmitter board and control. ii. AWG and transceiver.
 The system architecture mainly includes
i. Transducer array ii. 8–Channel Transmitter iii. 8– Channel Transceiver iv. High voltage pulse
v. Digital TX beam former vi. FPGA device vii. User interface (Computer) viii. Analog front
end(AFE) ix. Signal processing modules
a. Transducer array. Medical ultra sound imaging transducer are excited in two modes
i. Linear array mode ii. Phased array mode
Linear array mode: Subset of transducer elements are excited
Phased array mode: All elements are excited to focus at the sharp ultra sound beam at the defined
focal point.
 For the transducer array; the parameter piezo–electric element (electric charge generated with
respect to the applied mechanical stress) pitch size is to smaller than that of half of the wavelength.
Figure () : PIEZO–ELECTRIC EFFECT REPRESENTATION
 In transducer arrays, ultra sound transducer are arranged in the form of an array
DIAGRAM
A large piezo–electric element by crystal is divided into smaller active elements. These elements are
placed in different compartments which can be prevented

Questions On Properties And Properties
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1. Introduction
Filter is a device used that removes unwanted noise in the signal.
Classification of filter
Filters are classified into different categories depending on their transfer function.
Linear and nonlinear
Time variant and time invariant
Casual and non casual Analog and digital Passive a ndactive filter
Infinite impulse response (IIR) or finite impulse response (FIR) filter.
IIR(Infinite impulse response

IIR filters are digital filters with infinite impulse response. They have the feedback (a recursive part
of a filter) and are known as recursive digital filters. They are the time invariant systems. IIR filter
has better frequency response than FIR filters.
FIR(Finite impulse response) The transfer function of FIR filter approaches the ideal as the filter
order increases, thus increasing the complexity and amount of time needed for processing input
samples of a signal being filtered.
IIR filters require more multiplication and addition when compared to FIR, because FIR is of a
higher order in comparison to IIR, which is of lower order
Digital filter
A digital filter uses a digital processor

The Fundamental Concepts Behind Signal Processing
A 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 data to reconstruct a given signal. Though conceptually simple, sampling is typically
impractical for most applications due to the large quantity of data involved in the calculations.
However, the fundamental concepts behind signal processing ... Show more content on
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Fourier Series require an infinite number of frequencies, but the sampling frequency is subdivided
into a finite number of frequency ranges to reduce calculations. One cosine and one sine function is
needed to represent the signal for each subdivision of the sampling frequency. If the sampled data is
represented by a vector, it can be written as a linear combination of two vectors, each composed of
the appropriate sinusoidal entries. Let b m contain the cosine entries at frequency subdivision m,
and c contain the sine entries at m subdivision m. Then, B=[b  b] , C=[c  c] , D=[B C] ,
and the columns 0m0m of D form a basis for the vector space V. It can be shown that the columns of
D are, in fact, an orthogonal basis because the dot product of any two vectors in D is zero.
The signal s∈V , and s=BuCv . This can be rewritten as s=Dw where w=[u] .
Given that the columns of D form an orthogonal basis, the weights can be
v
[s⋅b ] [s⋅c ] calculated using the following relation: u = m , v = m . This discussion forms the
foundation for the calculations in the following example. m  m c⋅c bm⋅bm m m

EXAMPLE: SAMPLING AT 60 HZ
Take the following signal: s={1, 5, 9, 1, 2, 1} where s is sampled at at a rate of 60 Hz. Subdividing
into 6 equal frequency ranges yields the following sinusoidal vectors:
10
1033 b = [ ] c = [ ] b = c = cos sin 33
10
1 0 cos2 sin2
0,0,1 ,1 , 1 0 cos sin
[][] cos5

Dsp Manual
www.jntuworld.com
Department of Electronics and Communication VCET,Hyderabad.
DIGITAL SIGNAL PROCESSING
LAB MANUAL
III YEAR II SEMESTER (ECE)
Prepared by: K. Ashok Kumar Reddy
Department of Electronics & Communications Engineering,
Visvesvaraya College of Engineering & Technology,
Ibrahimpatnam.
Digital Signal Processing Lab Manual
Page 1
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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD III Year B.Tech.
ECE – II Sem L T/P/D C 0 –/3/2
DIGITAL SIGNAL PROCESSING LAB The programs shall be implemented in software (Using
MATLAB / Lab view / C programming/ Equivalent) and hardware (Using TI / Analog devices /
Motorola / Equivalent DSP ... Show more content on Helpwriting.net ...
Furthermore, it allows you to run such lists of commands such that you pass in data
Digital Signal Processing Lab Manual Page 4
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and/or get data back out (i.e. the list of commands is like a function in most programming
languages). Once you save a function, it becomes part of your toolbox (i.e. it now looks to you as if
it were part of the basic toolbox that you started with). For those with computer programming
backgrounds: Note that MATLAB runs as an interpretive language (like the old BASIC). That is, it
does not need to be compiled. It simply reads through each line of the function, executes it, and then
goes on to the next line. (In practice, a form of compilation occurs when you first run a function, so
that it can run faster the next time you run it.) MATLAB Windows : MATLAB works with through
three basic windows Command Window : This is the main window .it is characterized by MATLAB
command prompt >> when you launch the application program MATLAB puts you in this window
all commands including those for user–written programs ,are typed in this window at the MATLAB
prompt Graphics window: the output of all graphics commands typed in the command window are
flushed to the graphics or figure window, a separate gray window with white background color the
user can create as many windows as the system memory will allow Edit window: This is where you

Why Won 't You Fix The Printer?
"Why won't you fix the printer?"
"I can't, mom. I don't know how..." I said exasperatedly.
"You're studying electrical engineering, this thing is electric, what is the problem here?"
"Mom... no... that's not exactly how it goes works..."
One of the things I absolutely love (and hate in the case above) about electrical engineering is just
how broad it is. I can do anything from building robots to designing power distribution grids to
fabricating transistors in a clean room. It's a Choose Your Own Adventure–esque major, with
immense depth into each specialty. I happened to choose a specialty that skimped out on specifics of
printer design and troubleshooting. My passion is focused specifically on signal process, and to me,
it is an art form. There ... Show more content on Helpwriting.net ...
My first exposure to this field was Professor Joseph Havlicek's Signals and Systems class. I enjoyed
learning various types of Fourier Transforms in both continuous and discrete domain and the signal
characteristics they represent. This class sparked in me an interest for signal characteristics.
Although basic filtering techniques were taught in circuits class, Signals and Systems went on to
explain their underlying spectral theories. I am currently taking Digital Signal Processing, with a
strong emphasis on discrete filtering and sampling techniques. Concurrent to DSP, I am also taking a
graduate level Weather Radar Theory class that applies signal processing to stochastic weather radar
signals. This class introduced me to unique applications of signal processing in the study of
meteorology. I thoroughly enjoy the utilization of DSP concepts such as power spectral density and
fast Fourier transforms in relaying vital weather information. For the upcoming semester, I am
scheduled to take Digital Image Processing and Biomedical Signals and Systems to expand my
existing knowledge in signal processing and applications.
One of the most amazing opportunities I experienced was a summer of NSF funded research at the
University of Tennessee. This Research Experience for Undergraduates (REU) program was my first
time doing research. I worked on the topic of distribution network reconfiguration under the
guidance of Professor Fran Li. Over the course of 8

Mechanisms of Data Communication and Signals
Data communication refers to the transmission of electronic information over shared communication
mediums. This transmission can be generated from electronic sources such as computers, phones,
cameras and even audio sources such as speakers. Due to the various types of sources from which
data is derived, each source requires a mechanism that gathers and prepares the data for
transmission. This may include digitizing data, or encoding to ensure the privacy of the data during
transmission. This data is then passed along a communications channel to the destination, or
receiver, in the form of an analog or digital signal and mixed with other multiple pieces of data
through a shared channel. In order to avoid confusion, mechanisms must be in place that can
identify each source and ensure that the data sent from those sources arrive at the intended
destination and not become lost and confused with other data in the same channel. (Comer, 2014).
The propagation of data is accomplished by conducting energy in the form of electromagnetic waves
called signals. Depending on the source and the devices that operate in the communication channels,
analog and digital are the two types of signals transmitted to the receiver. Signals travel along
different types of media including copper wire, fiber optics, and space. (Cao, n.d.). Analog signals
can be found in audio sources including voices, telephones, and cameras. They are continuously
variable and potentially

Implementing A Flexible Open Architecture Based Radio System
Software defined radio (SDR) aims to overcome the limitation of the conventional radio by building
a flexible open architecture based radio system. Functional modules of the radio system are
implemented on reprogrammable/reconfigurable hardware platforms using software. So highly
flexible. Useful for global roaming services as SDR is able to interoperate with different wireless
protocols, incorporate new services, and upgrade to new standards. Hence for implementing a given
radio scheme, we required to identify the BBBs, place them in appropriate position, replace the
BBBs by the FUs and connect them through the switches. Data routing from the output of one FU to
the input(s) of one or more FUs can be achieved by providing appropriate control signals to the
switches which is generated by the software. Thus, by changing control signals of these switches,
different communication schemes can be established. However, the BBUs that are used in most of
the schemes can be made static to reduce switching delay. The signal processing functions (like
adder, delay, multipliers, carrier oscillator generators etc) are computationally intensive and they
exhibit spatial or temporal parallelism or both. In SDR, the signal processing function are
implemented in Software by simply downloading a new program and hence it is possible to
incorporate to new services. Efficient Signal processor for SDR is either a DSP processor or ASIC
or FPGA. ASIC /SOC offer low power Consumption, highest

Statement Of Purpose For Electronic Engineering
With this impression I often tried to find, "What is the difference between a successful person and
an ordinary person. And since then, I have been in a constant efforts to bridge the gap that separates
the extraordinary from the ordinary.
My inclination towards automobiles , machineries and the latest technology exposed me to the
wonders and possibilities of being an Engineer. I excelled academically at school. I was a topper; I
was in the top 10 students throughout in high school. I secured 88.67% in school. I secured good
marks in science subject, as that was my favourite subject and hence took admission for Diploma in
Electronics & Telecommunication for further studies. I secured top grades in my diploma studies
due to which i got admission in College of Engineering Pune (COEP). COEP is an autonomous
institute of Government of Maharashtra which comes under top 50 engineering institutes all over
India. ... Show more content on Helpwriting.net ...
I take this opportunity to describe my educational background and career objectives that motivated
me to gain a research career in Electronic engineering. My undergraduate education has provided
me a thorough exposure to the various opportunities available in Electronics and
telecommunications

Audio Amplifier System With Digital Delay Effects
Introduction
CE 3.1
The project "Audio amplifier system with digital delay effects" was completed as a major aspect of
the Bachelor of engineering degree from Dr. Ambedkar Institute of Technology.
Chronology : 2009
Geographical location : Bangalore
Organization Name : Dr. Ambedkar Institute of Technology
Project Name : Audio amplifier system with digital delay effects
Title of Position : Student
Background
CE 3.2
This career episode briefly outlines my commitment for the effective work in completing my task
and the duties where I dealt with specialized issues according to my learning. I have taken required
care in furnishing all the details of veritable ... Show more content on Helpwriting.net ...
CE 3.3.1
In our project, we implemented these effects using the concept of digital delay. Four types of effects
are produced using a digital signal processing kit TMS320C6713 which is programmed using C
language. I converted an analog signal to a digital signal and processed for the desired effect using a
Digital Signal Processor (DSP). The enhanced output from the DSP is then converted back to an
analog signal and fed to a speaker. Digital circuitry was a perfect fit for the process of storing a
signal and then playing it back at a specified time interval. It also solved the problem of losing the
high frequencies on longer delay times.
CE 3.3.2
I looked at the delay and realized that it is used as a sound enhancement and also can be used on
virtually any instrument and on vocals. I researched why delay is used in this application and
electrical signal move at a much higher speed than sound. I learnt Delay is an audio effect which
records an input signal to an audio storage medium, and then plays it back after a period of time.
Audio storage refers to techniques and formats used to store audio with the goal to reproduce the
audio later using audio signal processing to something that resembles the original. I used the FIR
echo algorithm that calculates the current output buffer value by adding the current input buffer
value to an input buffer value that occurred a certain amount of time in the

The Digital Signal Processing Applications
CHAPTER 1
INTRODUCTION
With advent of modern high–performance 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 multi–media 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
related with the VLSI technology such as wireless communication, transmission system,
multimedia, digital video, digital audio and radar system. The field of DSP has always been driven
by the advances in VLSI technologies. With the advances in ... Show more content on
Helpwriting.net ...
Loop transformation techniques are applied extensively on loop intensive behaviors in design of
area/energy efficient systems in the domain of multimedia and signal processing applications. These
are also commonly used during high–level synthesis for optimization purposes. One of the most
popular loop transformation techniques is retiming, which improves the performance of the system.
Retiming relocates the delays or registers within a circuit without altering the functionality. As
relocating the delays or registers balances the critical path and reduces the states of the circuit.
CHAPTER 2
LITERATURE SURVEY
Graphical representation are efficient for investigating and analyzing the data flow properties of
DSP system and for exploiting the inherent parallelism among the different subtask. More
importantly graphical representation can be used to map DSP algorithm to hardware
implementation. This graphical representation can build the gap between algorithmic description
and structural implementation. It exhibits all parallelism and data driven properties of the system
and provide an insight into space and time tradeoffs.
2.1 DATA FLOW GRAPH
In Data Flow Graph (DFG) representation, the nodes represent computations and the directed edges
represent data path and each edge has a non–negative number of delays associated with it.
The DFG captures the data driven property of DSP algorithms where any node

Designing A New Low Complexity Approximation For Image...
ABSTRACT
We focus on approximate algorithms for the computation of the 8–point DCT. While not computing
the DCT exactly, approximate methods can provide meaningful estimations at low–complexity
requirements. Prominent techniques include the signed discrete cosine transform (SDCT), the
Bouguezel–Ahmad–Swamy (BAS) series of algorithms, and the level–1 approximation by
Lengwehasatit–Orteg. All above mentioned techniques possess extremely low arithmetic
complexities. These techniques have some defaults, So proposed technique is to overcome all the
defaults and performed well compare to above techniques. The aim of this correspondence is to
introduce a new low–complexity DCT approximation for image compression in conjunction with a
... Show more content on Helpwriting.net ...
A digital image is composed of a finite number of elements, each of which has a particular location
and value. These elements are referred to as picture elements, image
Bhargava R Mamidi
(012607138)
California State University
Long beach, California, United States
Bhargavareddy450@gmail.com
elements and pixels. Pixel is most widely used to denote the elements of a digital image.
Images play the single most important role in human perception. Humans are limited to the visual
band of the electromagnetic spectrum, imaging machines cover almost the entire electromagnetic
spectrum, ranging from gamma to radio waves. They can operate on images generated by sources
that humans are not accustomed to associating with images. These include ultrasound, electron
microscopy, and computer–generated images. Thus, digital image processing encompasses a wide
and varied field of applications.
Digital image processing is the use of computer algorithms to perform image processing on digital
images. Digital image processing has the same advantages over analog image processing as digital
signal processing has over analog signal processing it allows a much wider range of algorithms to be
applied to the input data, and can avoid problems such as the build–up of noise and signal distortion
during processing. Image processing is a subclass of signal processing concerned specifically with

Applications Of Low Power Integrated Intelligent Sensors
Applications of low power integrated intelligent sensors [1–5] has been prolific in recent years, for
instance in environmental observation [8–10], security surveillance [11] and infrastructure
communication [12], and biomedical and healthcare monitoring [6, 7]. In particular, as the baby
boomers enter retirement, medical cost becomes significant. For example, in 2014, health care
accounted for 20% of federal spending in the U.S. []. A major problem in current healthcare systems
is that sensing and processing medical data require significant resources. In order to alleviate this
problem, wearable medical devices are expected to provide automatic monitoring and processing of
physiology signals, and more importantly, identifying abnormal signals and contact medical systems
for help if necessary, as shown in Fig. 1. Although such sensors usually have wireless data
communication capability through body area network, due to limited battery power supply, sending
raw sensed data is usually not an option. This is because that in a wireless sensor, usually radio
communication power is much higher than signal processing power []. Thus, it is expected that the
sensors are capable of providing preprocessing of raw data based on DSP algorithms and just
sending the processed results. Such sensors are called "intelligent sensors" []. Besides the capability
of signal processing and wireless communication, low power, high accuracy, high reliability, and
small footprint are also listed as

The Architecture Of Software Defined Radios Essay
2.1 ARCHITECTURE OF SOFTWARE–DEFINED RADIOS
As Reed (Reed, 2002) and others have stressed out, that the benefits of SDR approach go way
beyond the multimode operations. Other benefits stem from the flexibility that comes with
programmability. For instance, it can simplify design by allowing reuse of a common RF front end
with new designs or modes of operation available through software changes. It also enables
improvements to radio performance and functionality through application of digital signal
processing (DSP) to implement adaptive beamforming, interference mitigation, and numerous other
techniques either proven or in development. As the SDR approach has gained traction, a variety of
hardware and software tools for development and exploration have become available. These tools
are broadly accessible with reasonable cost and ease–of–use enabling new opportunities for
engineering education and research. Examples include National Instruments (NI) universal software
radio peripheral (USRP)/ NI LabVIEW software , the Ettus Research USRP/ GNU's Not Unix
(GNU) Radio, WARP , the Lyrtech SDR Platform and the IMEC SDR Platform. For researchers,
these platforms can provide a starting point for development of prototypes operating on live signals.
Such prototypes can effectively demonstrate the practical viability of an implementation and are a
step toward end deployment.
2.2 THE NEED FOR SDR
The SDR offers a wide range of economic and deployment benefits which has attracted many

Border Security Using Wireless Integrated Network Sensors
A Technical Seminar Report On
WIRELESS INTEGRATED NETWORK SENSORS
Submitted to the
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY,
HYDERABAD
In partial fulfillment of the requirement for the award of the degree of
BACHELOR OF TECHNOLOGY
In
ELECTRONICS AND COMMUNICATION ENGINEERING
By
D.VINEETH KUMAR (09B91A0414)
SCHOOL OF ENGINEERING & TECHNOLOGY
GURU NANAK INSTITUTIONS–TECHNICAL CAMPUS
(Affiliated to JNTU, Hyderabad)
Ibrahimpatanam, RangaReddy District –501506
2012–2013
GURU NANAK INSTITUTIONS TECHNICAL CAMPUS SCHOOL OF ENGINEERING &
TECHNOLOGY
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
CERTIFICATE
This is to certify that the technical seminar report on "Wireless ... Show more content on
Helpwriting.net ...
This sensed signal is then converted into power spectral density and the compared with reference

value of our convenience. Accordingly the compared value is processed using a microprocessor,
which sends appropriate signals to the main node. Thus the stranger is identified at the main node. A
series of interface, signal processing, and communication systems have been implemented in micro
power CMOS circuits. A micro power spectrum analyzer has been developed to enable low power
operation of the entire WINS system. Thus WINS require a Microwatt of power. But it is very
cheaper when compared to other security systems such as RADAR under use. It is even used for
short distance communication less than 1 Km. It produces a less amount of delay. Hence it is
reasonably faster. On a global scale, WINS will permit monitoring of land, water, and air resources
for environmental monitoring. On a national scale, transportation systems, and borders will be
monitored for efficiency, safety, and security.
Wireless Integrated Network Sensors (WINS) combine sensing, signal processing, decision
capability, and wireless networking capability in a compact, low power system. Compact geometry
and low cost allows WINS to be embedded and distributed at a small fraction of the cost of
conventional wire line sensor and actuator systems. On a local, wide–area scale, battlefield
situational awareness will provide personnel health monitoring and enhance security and efficiency.
Also,

Delta Sigma Based Digital Signal Processing
The proposed research focuses on Delta Sigma based Digital Signal Processing (DSP) circuits on
Very Large Scale Integration (VLSI) systems for low–power intelligent sensors –in particular on
building systematic tools to study their design principles and fundamental performance limits of
energy–efﬁcient low–complexity 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– lance, environment monitoring, smart buildings, and Internet–of–Things, etc, and has
significantly benefited society due to alleviating certain monitoring and processing tasks. However,
novel applications such as wearable biomedical devices require further miniaturization of existing
state–of–the–art hardware while having more signal processing capability due to the need to
perform real–time processing, i.e. not only monitoring but also detecting abnormal physiological
signals and providing help by calling a hospital or ambulance. One fundamental problem with these
applications is that the high–performance signal processing circuit consumes too much power,
which limits system battery lifetime or processing capability. Thus, these applications require the
design of low–power signal processing hardware, especially multiply–and–accumulate (MAC)
circuits, which are widely used in linear signal processing algorithms. Accordingly, the goal of the
proposed research is to address these

Soundcraft Vi4, the Digital Console
Soundcraft Vi4 is a digital console by Soundcraft, which came out with the first mixing console in a
flight case in 1973 where the music touring was started. The Vi series were being design as digital
desk and are able to do both the touring, broadcast and installed sound scene.
There are three main components in the Vi4 system which is a control surface, local rack and a stage
box. Vi4 system stage box come with 48 mono inputs on and a total of 24 output busses that can be
used for matrices, auxes, group or masters and all the 48 mono inputs are able to be linked and
create stereo channel. Basically it functions as a stage box that located on the stage and able to send
48 mono channels from the back of the stage box to the back of the local rack just by using one
standard fit CAT 5 cable with Amphenol RJF connectors. The maximum cable run using flexible
reel–mounted Cat5 cable is 100m, but possible to get it run 130m with permanent installation Cat7
cable. Another optional connection is using Fiber Optic cable but the Fiber Optical interface card
need to install separately.
The Local rack place at the same place with control surface and inside it is 2 Digital Signal
Processing engine – Studer Score Live technology, with only 2 DSP cards will be capable of 64
inputs with 40–bit floating–point digital processor and one or multiple D21m I/O frames. The D21m
high density I/O system holds up all the inputs and outputs to produce ultra–low noise balance mic
amp and sends it to

Application Of Low Power Integrated Intelligent Sensors
2 Research Motivation and Significance Applications of low–power integrated intelligent sensors
[1–8] have been prolific in recent years, including, for instance, in environmental observation [9–
11], security surveillance [12, 13], infrastructure monitoring and communication [14–17], and
biomedical health care monitoring [18–21]. In particular, as the baby boomers approach retirement
age, medical expenses become significant. For example, in 2014, health care accounted for 28% of
US federal spending [22]. One major problem with current health care systems is that sensing and
processing medical data require significant resources. To alleviate this problem, wearable medical
devices [23–27] are expected to provide automatic monitoring and ... Show more content on
Helpwriting.net ...
The design challenges of the aforementioned intelligent sensors originate from their high–density
circuits, especially DSP circuits performing multiply–and–accumulate (MAC), i.e., adders and
coefficient multipliers (DSP algorithms usually involve many MAC operations), which require a
large circuit area and high power consumption. This is one of the most critical problems in
integrated intelligent sensor design. For example, in [34] a seizure detector consumed more than
50% of the circuit area and power in a System–on–a–Chip (SOC), even though an advanced lookup
table (LUT) method was applied, which saved more than 50% of the total gate count in the digital
filters [35]. The power cost of the chip may require battery replacement every six hours, which is not
convenient or feasible for many patients. Essentially, the large circuit area and high power
consumption of MAC circuits limit system performance and raise chip costs. Medical applications
generally require higher resolution and higher reliability, which only exacerbates the problem. One
promising solution to the above challenges is Delta–Sigma technology. A Delta–Sigma Modulator
(DSM) converts an analog input signal to a 1–bit Delta–Sigma modulated digital bit–stream, as
shown in Fig. 2 (a). Compared with conventional multi–bit binary systems, the Delta–Sigma system
has several advantages: (1) a Delta–Sigma bit–stream can

Digital Signal Processing
CHAPTER I INTRODUCTION
On systems that perform real–time processing of data, performance is often limited by the
processing capability of the system [1]. Therefore, in order to judge the efficiency of any system it is
very important that we evaluate the performance of the architectures based on which the system is
being built. We can also state that we can make a system more efficient and more capable by
working upon the algorithm on which the system is being built. The more efficient the algorithm is
the more efficient will be our system and vice versa. It is assumed that the way in which this chapter
is written will provide the source of motivation for the thesis, it will also give an insight of the work
which is done and it is organized in the thesis.
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

Delta Sigma Linear Processing Circuits
3 Research Objectives
3.1 Research Objective 1: Delta–Sigma Linear Processing Circuits
The goal of this research objective is to analyze the performance of Delta–Sigma linear processing
circuits, including adders and coefficient multipliers. Examples of questions that we wish to address
include the following. What is the noise shaping performance of high–order digital Delta–Sigma
modulators for Delta–Sigma adders? What is the frequency response of Delta–Sigma coefficient
multipliers? What is the circuit power and area of Delta–Sigma MAC circuits compared to
conventional circuits?
3.1.1 Background and Rationale
Multiply–and–accumulate (MAC) are fundamental linear operations in signal processing. However,
they also represent a bottleneck in mobile signal processing systems insofar that the circuit
complexity and power consumption of conventional digital adders and multipliers are relatively high
whereas mobile systems need to be miniaturized and power–efficient. Although alternative MAC
circuit structures have been proposed, their performances do not yet meet the demand of
applications that require intensive computing operations with limited battery power [34]. A Delta–
Sigma based MAC, therefore, constitutes a promising solution for low–power DSP devices.
Delta–Sigma based MAC circuits use Delta–Sigma bit–streams as their input and output. For
instance, the inputs of a Delta–Sigma adder are digital bit–streams generated by Delta–Sigma
Modulators. The adder also produces

What´s a Digital Filter?
Introduction
In signal processing, the purpose of a filter is to remove unwanted portions of the signal, such as
random noise, or to extract useful parts of the signal, such as the constituents lying inside a precise
frequency range.
Figure 1.1 A block diagram of a basic filter
There are two main kinds of filter, analog and digital. An analog filter uses analog electronic routes
made up from constituents such as resistors, capacitors and op amps to produce the needed filtering
effect. Such filter routes are extensively utilized in such requests as sound reduction, video signal
enhancement, graphic equalizers in hi–fi arrangements, and countless supplementary areas.
A digital filter uses a digital processor to present numerical calculations on sampled values of the
signal. Processor may be a general–purpose computer such as a PC, or a enumerated DSP (Digital
Signal Processor) chip.
As contrasted to analog filters, digital filters are possessing higher precision, larger stability and
reliability. Digital filters do not have matching problems. Digital filters embody period separation
multiplexing that can finished a little filtering tasks that a number of analog filters are incapable of
doing. We can accomplish digital filters by hardware circuits. Additionally we have an alternative
way as computer multimedia programming.
The frequency reply of the filter depends on the worth of its coefficients, or taps. Each multimedia
plans can compute the benefits of the coefficients

Stereochemical Dependence for dSp
Interestingly, studies from our laboratory and others have demonstrated a stereochemical
dependence for dSp processing by enzymes in ODNs. The first instance of a dSp diastereomer
difference was reported from our laboratory when Klenow Fragment (exo–) was allowed to insert
dATP opposite the isomers for which dSp1–containing ODNs were found to be the more favorable
substrate for insertion and bypass.32 This study was extended to in vivo experiments by Neeley, et
al. to include pol II and pol IV, also showing more favorable bypass kinetics for dSp1.34 As further
evidence for in vivo stereochemical dependency in dSp processing, Henderson, et al. utilized the
restriction endonuclease and postlabeling assay (REAP) to determine the mutation profiles for dSp1
and dSp2 in Escherichia coli.28 In the sequence context 5'–d(GSpA)–3', they found a strong
stereochemical dependence in the relative amounts of dG→dT and dG→dC transversion mutations
for dSp1 and dSp2. In subsequent experiments, Delaney, et al. showed a somewhat different
dependence of mutation profiles on the dSp diastereomers in the sequence context 5'–(TSpG)–3'
using the REAP and competitive replication of adduct bypass assays (CRAB).29,30 An additional
observation from our laboratory quantified the digestion kinetics of dSp isomers in the dinucleotides
5'–d(Np[Sp])–3' (N = A, T, G or C) by nuclease P1 that showed dSp1 was the kinetically preferred
substrate in all sequence combinations.33 Lastly, in collaboration

Signal Flow:VI4 is a Digital Soundboard Provided by...
Signal Flow: VI4
The VI4 console is a Digital Soundboard provided by Soundcraft which features 3 input banks of 8
channels for up to 48 total mono inputs, 1 output bank of 8 channels for up to 27 outputs, 24 insert
sends/returns and 24 total buses which can be used as groups, aux or up to 16 total matrixes as well
as midi in/out and 2 power supplies, one for main and another as an auxiliary for redundancy. Its
Local rack has 19 analogue inputs, 8 pairs of AES/EBU, 16 GPIO In/out and a 64ch MADI In. The
Stage rack offers 48 analogue inputs, 24 outputs and 8 GPIO in/out.
Signal Starts with the microphone on one end connected into the stage box with standard XLR
cables, which through analogue to digital conversion in the box, is then sent out into the local rack
via Ethernet networking which sends uncompressed, low latency audio signals over standard
Ethernet network. Type of cable used determines the maximum distance connection can travel until
it begins to have a loss, ie; 80m for Standard Cat5e, 130m for Cat7, 1500m with single optical fibre
and 600m with 3x200m reels of multimode 50/125 optical fibre in series. Aside from a main out for
the Ethernet cable there is an auxiliary out to act as redundancy in the event that the primary
Ethernet cable runs into any problems. The Local Rack acts not only as another A/D Converter if
any mic is connected from this point but also as the Digital Signal Processor which enables
mathematical manipulation of analogue signals which

Hardware Efficient Delta Sigma Linear Processing Circuits
Abstract: This paper presents hardware efficient Delta Sigma linear processing circuits for the next
generation low power VLSI device in the Internet–of–things (IoT). We first propose the P–N pair
method to manipulate both the analog value and length of a first–order Delta Sigma bit sequence.
We then present a binary counter method. Based on these methods, we develop Delta Sigma domain
on–the–ﬂy digital–signal–processing circuits: the Delta Sigma sum adder, average adder, and
coefficient multiplier. The counter–based average adder can work with both first–order and higher–
order Delta Sigma modulators and can also be used as a coefficient multiplier. The functionalities of
the proposed circuits are verified by Matlab simulation and FPGA implementation. We also compare
the area and power between the proposed Delta Sigma adders and a conventional multi–bit adder by
synthesizing both circuits in the IBM 0.18 µm technology. Synthesis results show that the proposed
Delta Sigma processing circuits can extensively reduce circuit area and power. With 100 inputs, a
Delta Sigma average adder saves 94% of the silicon area and 96% of the power compared to a
multi–bit binary adder. The proposed circuits have the potential to be widely used in future
miniaturized low power VLSI circuits.
Keywords: VLSI, Delta Sigma Modulation, Digital Signal Processing, Adder, Coefficient Multiplier,
Low–power Low–complexity Circuits.
1. Introduction
Internet–of–Things (IoT) demand ubiquitous

Fpgas Advantages And Disadvantages
I. INTRODUCTION With the advent of modern controls theory and in the semiconductor
technology, the use of high sophisticated technologies, advance digital controllers and embedded
systems which include microprocessors, DSPs (digital signal processors), ASICs (application–
specific integrated circuits), and FPGAs (field–programmable gate arrays).in the area of AC power
control have become global challenges nowadays[1]. Recently FPGAs have become a good
alternative answer and have been generally accepted as a tool for the controller`s platform in high
performance embedded control system[2]. This device completely give inventors the ad–liberty to
use their design customs adapted to their area of applications, by allowing both hardware and
software to be customized at very low cost[3], it has a key impact on hardware or software co–
design and they are used as devices for rapid pro–to typing, and for final products[4]. This find
application in sophisticated motor drive systems, such as fully integrated controllers, sensor less
control, sensor less control with an extended Kalman filter algorithms, and adaptive fuzzy based
controller[5]. Also in some application like Intelligent maximum power point trackers for
photovoltaic applications, Realization of active power filter based on indirect ... Show more content
on Helpwriting.net ...
Indeed, these devices belong to the so called semi–custom Application Specific Integer Circuits
(ASICs). The latter low cost devices consist of predesigned (by the manufacturer) elementary cells
and interconnections that can be programmed and interconnected by the user. This has the credit to
allow rapid–prototyping solutions and make the design process more flexible and cheaper. This is
not the case for full–custom ASICs, which are manufactured for a specific application and cannot be
user

Design Of Continuous Time Second Order Sigma
Design of Continuous Time Second Order Sigma–Delta Modulator Using Feedback Element Mr.
Ravinder Choudhary University Institute of Engineering and Technology Kurukshetra University
Kurukshetra, India E–mail– erravindera@gmail.com
Abstract: This paper discuss the continuous time second order Sigma–Modulator with the feedback
element having negative feedback. The modulator is of second order that decreases the complexity
of the circuit and also decreases the power consumption of the circuit. The circuit works on the
analog input signal in the form of jitter sine wave. The feedback element generates the feedback
pulse. An efficient
Sigma–Delta modulator is designed with the bandwidth of 10 MHz and sampling frequency of 350
MHz. The modulator is designed using a Matlab code and the circuit is then designed on
SIMULINK. The evaluation indicates that the second order modulator achieves 73.5 dB peak SNDR
and 73 dB of peak SNR.
Keywords: Delta–Sigma Modulator, SNR, SNDR, Dynamic Range
1.INTRODUCTION
In electronics and telecommunications, modulation is the process of varying one or more properties
of a periodic waveform, called the career signal, with a modulating signal that typically contains
information to be transmitted. In telecommunications, modulation is the process of conveying a
message

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