Why Is Fig Universal Radio Peripheral

Why Is Fig Universal Radio Peripheral
Fig : Universal Software Radio Peripheral [1] RTLSDR Till date, the USRP claimed to be a low cost device for some communication
experiments. But now, a $20 revolution from Nooelec has appeared. The idea is to use consumer grade DVB–T Dongle based on
Realtek RTL2832U as a fully–fledged cheap SDR device [1] In February 2012 the first FM radio signal was received with an
RTL2832U RTL–SDR dongle using custom SDR drivers [3]. Since then RTL–SDR came onto the market, and various devices and
software kits became available, produced by a number of companies and developers around the world. RTL–SDR definition As shown
in Fig. 1, the RTL–SDR is a small, compact, and easy–to–use USB stick device that is capable of receiving RF radio signals [1] ...
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However, the RTL–SDR is unstable at this rate and may drop samples. The maximum sample rate that does not drop samples is 2.4
MS/s.The input impedance it's about 75 Ohms. The type of tuner chip used in the dongle defines the frequency range of the RTL–SDR.
In the table below the frequency range of each tuner is shown. Tuner Chip Min Freq (MHz) Max Freq (MHz) Rafael Micro R820T2 24
1766 Rafael Micro R820T 24 1766 Elonics E4000 54 2200 Fitipower FC0013 22 1100 Fitipower FC0012 22 948,6 FCI FC2580 146 –
308 438 –924 [3] Fig RTL–SDR frequency range the Rafael Micro R820T and Elonics E4000 dongles have the greatest frequency
range. A Software Defined Radio Design Environment using RTL–SDR With the capability to tune over the range of 25 MHz to 1.7
GHz, the RTL–SDR can be used to investigate, view the spectra of, and receive and decode a wide range of radio signals transmitted for
various applications using different modulation methods. [1] tab shows some of RF signals from the electromagnetic spectrum that can
be received by the device [4] RF signals Frequency range FM radio 87.5 – 108 Aeronautical 108 – 117 Meteorological ~ 137 Fixed
mobile 140 – 150 Special events broadcast 174 – 217 Fixed mobile (space–earth) 267 – 272 Fixed mobile(
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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

Using Kalman Filter Is Digital Signal Processing Based Filter
2.4 VIDEO 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 three states past, present and future of a system.
[10] To remove noise from video sequences they utilize both temporal and spatial information. In the temporal domain, by collecting
neighbouring frames based on similarities of all images, to remove noise from a video tracking sequence they given a low–rank matrix
recovery phenomena. [11]
3. METHODOLOGY ADOPTED
3.1 Wavelength De–noising
3.2 Bilateral De–noising
3.1 WAVELENGTH DENOISING
Basically a wavelet is small wave, which has its energy concentrated in time to give a tool for the analysis time varying phenomena. It
is easier to remove noise from a contaminated 1D or 2D data using these algorithms to eliminate the small coefficient associated to the
noise. In many signals, mostly concentration of energy is in a small number of dimensions and the coefficients of these dimensions are
relatively large compared to other dimensions (noise) that has its energy spread over a large number of coefficients. In wavelet
thresholding

Robust Video Data Hiding Using Forbidden Zone Data Hiding...
1130 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 21, NO. 8, AUGUST 2011
Robust Video Data Hiding Using Forbidden Zone Data Hiding and Selective Embedding Ersin Esen and A. Aydin Alatan, Member,
IEEE
Abstract–Video data hiding is still an important research topic due to the design complexities involved. We propose a new video data
hiding method that makes use of erasure correction capa– bility of repeat accumulate codes and superiority of forbidden zone data
hiding. Selective embedding is utilized in the proposed method to determine host signal samples suitable for data hiding. This method
also contains a temporal synchronization scheme in order to withstand frame drop ... Show more content on Helpwriting.net ...
They applied quantization index modulation (QIM) to low– frequency DCT coefficients and adapted the quantization parameter based
on MPEG–2 parameters. Furthermore, they varied the embedding rate depending on the type of the frame. As a result, insertions and
erasures occur at the decoder, which causes de–synchronization. They utilized repeat accumulate (RA) codes in order to withstand
erasures. Since they adapted the parameters according to type of frame, each frame is processed separately. RA codes are already
applied in image data hiding. In [3], adaptive block selection results in de–synchronization and they utilized RA codes to handle
erasures. Insertions and erasures can be also handled by convolutional codes as in [4]. The authors used convolutional codes at
embedder. However, the burden is placed on the decoder. Multiple parallel Viterbi decoders are used to correct de–synchronization
errors. However, it is observed [4] that such a scheme is successful when the number of selected host signal samples is much less than
the total number of host signal samples. In [5], 3–D DWT domain is used to hide data. They use LL subband coefficients and do not
perform any adaptive selec– tion. Therefore, they do not use error correction codes robust to erasures. Instead, they use BCH code to
increase error correction capability. The authors performed 3–D interleaving in order to get rid of local burst of errors.

Essay On Space Time Coding Techniques For MIMO-Ofdm
Space time coding techniques for MIMO–OFDM
OFDM provides the frequency selective channel which can able to divides the frequency band into multiple sub channels and each sub
channel carries a different stream of symbols. There is flat frequency response throughput each sub channel when there is narrow
bandwidth of each sub channel. Therefore OFDM able to transform a frequency selective channel into set of multiple flat–fading
channels. Simultaneously when M transmit antennas used an OFDM transmitter, and N receive antenna used OFDM front end then L
flat fading MIMO channel is formed from MIMO frequency selective channel with having dimensions equals to MxN. Earlier
traditional space time codes were not optimal for takeout the additional ... Show more content on Helpwriting.net ...
Receiver Processing Only
In this type of MIMO system the processing of signals occurs at the receiver only. Receiver processing system is mostly beneficial in
the uplink scenario as the signal processing is restricted to the receiver and as at the mobile station no MIMO signal would be required.
In case of uplink scenario, there is a single data stream which is demultiplexed into the N number of substream, and each substream is
further modulated and passed it into N number of transmitters.
The power from each individual antenna is proportional to the 1/M. There is algorithm that is V–BLAST algorithm which is used to
perform at the receiver site. Taking into account, in the case of uplink there is number of conventional receiver at the base station and
they are receiving the signals which are generated from N number of transmitters and therefore they must have considerable
interference between the data stream. V–BLAST algorithm at the receiver is used to mitigate the interference between different streams
and try to generate the original data stream at the receiver. V–BLAST used optimal combining and interference cancellation technique.
The signal which is having the best SNR is retrieved at the receiver. In the figure shown below which illustrates the good performance
achieved at the receiver. V–BLAST technique is referred as non–linear technique as it generates

The Importance Of Multicarrier Techniques For A Single...
Chapter 1 Multicarrier techniques
This section deals with the importance of multicarrier techniques.
1.1 Multi carrier techniques
In a single carrier system, single fade causes the whole data stream and undergo distortion i.e frequency selective fading. The Single
carrier systems also suffer with heavy Inter Symbol Interference . In telecommunication Inter Symbol Interference was a form of
distortion of signal in which one symbol interferes with the subsequent symbol. Thus it causes an unwanted phenomena as the previous
symbols have similar effect as noise thus making communication less reliable . ISI occurs when the signal bandwidth is less than the
coherence bandwidth or when the delay spread is greater than symbol duration. To combat the problem multicarrier techniques have
been proposed for high data rate transmission. Multicarrier techniques divide the whole bandwidth into large number of narrow band
orthogonal subcarriers [1, 2]. Thus the signal bandwidth becomes very less compared with coherence bandwidth ensuring no ISI in time
domain and flat fading in frequency domain. Multicarrier systems such as Orthogonal Frequency Division Multiplexing (OFDM) and
Multi Carrier Code Division Multiple Access (MC–CDMA) were considered to be the best technologies for 4G wireless
communication [1, 2]. Fig. explains the spectrum of multicarrier and single carrier systems. In the single carrier system the information
symbols are loaded into one of

The GSM / GPS Module communication and the distance...
The GSM / GPS Module communication and the distance Sensor, checking the car's distance from the surrounding cards so that the
relay will be ON and turn off the engine. The goal to provide minimum 12 feet between the stolen car and the behind car
The Choosing of the GSM/GPS to communicate with the Owner will provide a cheap, effective way to allow the owner to communicate
with the stolen car. Also choosing the ultrasound distance sensor to provide the safe distance for the car to stop.
Different Microcontroller alternative were available :
PIC18F4550: Has large amounts of RAM memory for buffering and Enhanced Flash program memory make it ideal for embedded
control and monitoring applications.
Arduino Uno R3 (Atmega328): ... Show more content on Helpwriting.net ...
In the following it is assumed that the Ardunio is able to capture time within a microsecond, therefore smallest measure of time is
0.000001 or 1micro second. Then form the law :
Distance = 0.5 * Speed of Sound * ( smallest measurable time increment )
Distance= 0.5 * 344 * (0.000001) = 0.0002 meters
Therefore Ideally, the displacement of an object, can be measured with a precision of about 0.2 millimeters.
Considering Doppler effect may affect the Ultrasound sensor accuracy, therefore It is better to measure time difference with Ardunio
instead measuring frequency. Since frequency can be expressed in terms of the time, (Frequency = 1/T).
The relationship above can be expressed in terms of time instead of frequency, which mean:
V = C * ( Ttx / Trx – 1 ) ..For the approaching car ( from back)
V = C * ( 1 – Ttx / Trx ) .. For the retreating car (from front)
Where :
T = Time period between wave peaks
C = Speed of sound
V = Velocity of object Therefore practical consideration must done when using the Ardunio Uno with the Distance sensor to measure
the safe distance 12 ft. and a safe velocity of minimum 15 mile/hour.:
If the frequency of the transmitted wave is 40Khz, then the time period between peaks is 1/40000 equivalent to 25 microseconds.
The Ardunio can only count to 25 in 25 microseconds. Ardunio can only tell that a wave front was received

Light Field Characterization Essay
Light Field Characterization
The field synthesis is only completed by sampling the synthesized transients. For that we have opted "Attosecond streaking"
technique3,4 for sampling the synthesized transients, which gives an access to the field of the synthesized waveform. For this sampling
technique, the generation of isolated EUV attosecond pulse is essential. The sampling takes place at the focus of these transients where
the planned experiment takes place in order to attain high intensities. The isolated EUV attosecond pulse is generated by focusing (f~ –
35 cm) the light transients generated at the exit of the synthesizer apparatus into a quasi–static gas cell, a thin nickel tube (~2mm inner
diameter), filled with neon gas and kept at a ... Show more content on Helpwriting.net ...
This can stand for weeks and allow for having the same synthesized light transient on the sampling and experiment point for a long
period of time. The driving radiation travels through the gas cell is transmitted around the margins of the Zr disc to create an annular
beam. The beam profiles of the four channels pulses are not affected by these wires.
The multilayer coated mirror, together with the high pass Zr filter, comprise a band pass filter of width centered at , leading the isolation
of a single attosecond pulse4,7. A double mirror module comprising a concave multilayer coated inner and metallic Al–coated outer
mirror (12.5 cm) focuses the light transients and the EUV attosecond probe into a second neon gas nozzle placed near the entrance of a
time–of–flight (TOF) spectrometer. The striking spectrogram is recorded and the electric field of the synthesized waveform is retrieved.
The compression of the two octaves supercontinuum led to the generation of subcycle transient generated by the four–channel
synthesizer, carried at central wavelength , has τFWHM pulse duration of ~ 1.7 fs as shown in Figure 6.
Attosecond light field synthesis
The ultrabroadband of the supercontinuum light source spans over

What Is The Intuitive Approach To The Orthogonal Matching...
Chapter 4
4. Orthogonal Matching Pursuit
4.1 Basic Pursuit
4.2 Orthogonal Matching Pursuit
4.3 Compressive Sensing in Image Processing
4.4 Modified OMP
Chapter 4
4. Orthogonal Matching Pursuit
Before discussing OMP we will go through the very basic algorithm used in CS i.e. Basic pursuit.
4.1 Basic Pursuit
The intuitive approach to the compressive sensing problem of recovering a sparse vector x ∈ RN from its measurement vector Φx=y ∈
Rm, where m < N, consists in the l0 –minimization problem min┬(xϵ R^n )⁡
〖‖x‖_(0 ) subject to Φx=y 〗 (4.1)
This is a non convex problem that it is NP–hard in general. However, keeping in mind that ‖z‖_q^(q ) approaches ‖z‖_0as q > 0 tends to
zero, we can approximate (4.1) by the problem ... Show more content on Helpwriting.net ...
For 0 < q < 1, (4.2) is again a nonconvex problem, which is also NP–hard in general. But for the critical value q = 1, it becomes the
following convex problem (interpreted as the convex relaxation of

Stb Case Study
Multitaper SVD cite{haykin2007multitaper,rezaei2014adaptive,alghamdi2009performance,alghamdi2010local,huang2011optimal} has
been studied to provide a reliable estimation of spectral interference temperature .Thus we've got so many benefits from Multi taper as a
perfect candidate for reliable estimation as it was an output of enhanced variance for spectrum without compromising the bias. On the
other hand ,SVD is a perfect tool to discover the environment interference . Haykin cite{haykin2007multitaper} has the first initiative
for using that method as he was collecting the measurements from different sensing nodes with different taper and then he collect them
together in the following measurement matrixlabel{eq2} } ... Show more content on Helpwriting.net ...
However, he wasn't able to provide spatial distribution of interference and MTM coefficient. Meanwhile we obtained them from left
and right singular value respectively. On the opposite side, the author in this paper cite{rezaei2014adaptive} proposed a two modified
scheme where he had provided lower computational complexity algorithm with retaining MTM–SVD functionality. the first scheme
was SWASVD algorithm. The second scheme was 3D higher tensor decomposition where the author benefits from tensor higher order
decomposition to compute the estimation power over all OFDM blocks at once. Therefore, the measurements which were taken from
Multitaper will be arranged in 3 dimensional matrix. These measurements are applied to higher order tensor decomposition in order to
take new singular value computation as the tensor core G(l,m,k). Although MTM–SVD provides a reliable level of Performance
detection, however the system facing many difficulties from performance degradation in the worst environmental conditions and
specific SNR . So Our motivation behind this is to use Multitaper based Cepstrum detection.} % section{Note: Alternative statement }
% paragraph{ Although MTM–SVD provides a reliable performance detection, however it induces a higher sensing time, so our
motivation behind this is to use enhanced MTM detection model based on Cepstrum estimation. } paragraph{The objective

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 more content on Helpwriting.net ...
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

Cognitive Signal Processing : Feature Estimation
Behavioural Signal Processing: Feature Estimation in Speech
Abstract
Human behaviour interrelates closely with the human beings' mental state. The behavioural information reflects communication, social
interaction and even personality. To build a bridge to the human mind over engineering advances, an operational method, Behavioural
Signal Processing (BSP) technology, has been introduced, which aims to analyse speech–based human behaviour. The main task of this
project is the feature estimation based on BSP. In feature extraction, there are numerous analysis modes, of which the Mel–frequency
cepstral (MFC) represents the short–term power spectrum of a speech signal. As MFC is about the power spectrum and requires Fourier
Transform, this ... Show more content on Helpwriting.net ...
[1]
Moreover, behavioural research for psychological estimations deeply depends on cautious evaluation of numerous observational
indications. [5] Both the observation theory and current experimental results state that significant other's behaviour is relevant with
plentiful factors including age, gender, education background, cultural background, life experiences as well as recent mental and
physical health conditions such as stress, illness, or disability. [3] Also, the affection extent between intimate relationships matters much
about how people act or communicate/speak. In this project, the audio data is extracted from a dialogue in drama to simulate a marital
discussion. Behavioural Signal Processing
According to the main reference, "Toward automating a human behavioural coding system for married couples' interactions using
speech acoustic features", in this project, the self–reports of observable behaviours can be extremely untrustworthy to some extent.
Additionally, many of speaking recognition algorithms centres more on demonstrating objective human behaviour, for instance, the
exact words, that is to say, what words or sentences people exactly say to their partners. [1] Consequently, the paper states that one of
effective methods to analyse the audio data is Behavioural Signal Processing

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 Helpwriting.net ...
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

Optical Sensor Essay
2.8. Optical ring resonator based sensor performance metrics
To compare different kinds of optical RR based sensors, it is necessary to define some performance metrics. The suitability of optical
sensors for a particular application will depend on their performance across number of sensor requirements. Some metrics such as
sensitivity, and sensor cost, can be defined numerically (either by means of experimental or theoretical calculations). Other metrics such
as portability is more subjective or simply comparable but can have a significant influence on the commercial success of the methods.
In this section a number of metrics that are typically considered to determine the performance of SOI RR based bio–chemical sensors
are briefly ... Show more content on Helpwriting.net ...
However for the intensity interrogation scheme it becomes .Where, ΔI and Δn are the intensity variation and the surrounding effective
refractive index. Sensitivity can be divided into two types, device sensitivity, and waveguide sensitivity.
In practice, the sensitivity of device is defined by the wavelength shift per unit concentration of an analyte, it depends on its properties
which is related to optical parameters longer resonant wavelength or smaller effective refractive index .The normalized sensitivity of
cavity resonator device operating at different wavelengths is given by Whereas, the sensitivity of optical waveguide is given by the
relation between the variations of effective index with respect to the waveguide parameter affected by the analyte to be detected. It is
related to structure of the waveguide regardless of the devices type. The surrounding refractive index change is yielded by a refractive
index variation of cover medium or by thickness variation of sensing layer []. It differs with the sensing mechanism whether
homogeneous or surface sensing
SH represents homogeneous, SS is surface sensing, Δnc change in cladding index, Δts change in add layer, and Δλ change in resonance
wavelength
2.8.2. Selectivity
Selectivity is a measure of how specific the response of a sensor is to the

Bottom-Up Processing
Curious about how your brain processes incoming signals sent from all the sensory organs? There are two ways in which the brain
processes signals: top–down and bottom–up processing. Top–down processing takes place when the brain only focuses on target signals
and prior knowledge. Bottom–up processing takes place when the brain perceives all incoming signals and combines each part into the
total entity. The assumption has been presented that distractor interference is a byproduct of the attention and a bottom–up process, but
not a top–down process. Distractors are defined as noises in the environment that makes it harder to detect the target signal. This
research examined this assumption through an experimental process using letters and digits ... Show more content on Helpwriting.net ...
This also supports another point from the lecture. It is harder to confirm evidence but easier to eliminate alternative explanations (Psych
240 lecture, 9/07/16). Here, the researchers, based on their current results, could only infer that the top–down processing affects both
target and distractor. There could be other speculations that propose alternate explanations for findings in the future. Currently, this
research study has provided enough evidence to eliminate alternative explanations that the interactions are due to the change in
category. The second experiment changed the font to show the distinct differences between S, 5 and O, 0 and the results demonstrated
the effect of incongruency was significant in both letters and

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

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

Software Is Tightly Integrated With The Matlab Environment
Simulink software is tightly integrated with the MATLAB environment. It requires MATLAB to run, depending on it to define and
evaluate model and block parameters. Simulink can also utilize many MATLAB features. For example, Simulink can use the MATLAB
environment to:
Define model inputs.
Store model outputs for analysis and visualization.
Perform functions within a model, through integrated calls to MATLAB operators and functions
Concept of signal and logic flow:
In Simulink, data/information from various blocks is sent to another block by lines connecting the relevant blocks. Signals can be
generated and fed into blocks dynamic / static).Data can be fed into functions. Data can then be dumped into sinks, which could be
scopes, ... Show more content on Helpwriting.net ...
Unused signals must be terminated, to prevent warnings about unconnected signals. Sources and sinks
Continuous and discrete systems:
All dynamic systems can be analyzed as continuous or discrete time systems. Simulink allows you to represent these systems using
transfer functions, integration blocks, delay blocks etc.
continous and descrete systems
Non–linear operators:
A main advantage of using tools such as Simulink is the ability to simulate non–linear systems and arrive at results without having to
solve analytically. It is very difficult to arrive at an analytical solution for a system having non–linearities such as saturation, signup
function, limited slew rates etc. In Simulation, since systems are analyzed using iterations, non–linearities are not a hindrance. One such
could be a saturation block, to indicate a physical limitation on a parameter, such as a voltage signal to a motor etc. Manual switches are
useful when trying simulations with different cases. Switches are the logical equivalent of if–then statements in programming. simulink
blocks
Mathematical operations:
Mathematical operators such as products, sum, logical operations such as and, or, etc. .can be programmed along with the signal flow.
Matrix multiplication becomes easy with the matrix gain block. Trigonometric functions such as sin or tan inverse (at an) are also
available. Relational operators such as 'equal to', 'greater than' etc. can also be used in logic

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

Cs/3590 Assignment 5
CS 3590 Assignment 2–Fall 2016 Hung Nguyen–wu8247 Question 1: 3.6. Define fundamental frequency. A signal may contain many
frequencies. If frequency B is a multiple of frequency A, then frequency A is the fundamental frequency. B is called the harmonic
frequency. Question 2: 3.7. What is the relationship between a signal's spectrum and its bandwidth? Signal's spectrum is the range of
frequency in which the signal is contained. The bandwidth is measured by the difference between the highest and the lowest
frequencies. Therefore, bandwidth is the width of the signal's spectrum. The higher the bandwidth, the higher amount of data can be
transmitted. Question 3: 3.8. What is attenuation? As the data pass through medium over the distance,

Nt1330 Unit 4.2 Paper
4.2 Implementation Details 4.2.1 Data Source Random Bernoulli generator generates binary data on a per frame basis. In data output,
144 samples per frame are used, and data rate is 36 Mbps. Figure 4.2: Data Source 4.2.2 Forward Error Correction Coding A typical
problem in OFDM based systems is a high Peak–to–Average Power Ratio (PAPR). The available RF power amplifiers usually have a
limited linear dynamic range. This is not a problem for constant amplitude signals, however, in OFDM, due to IFFT operation, some of
the sub–carriers add constructively to large amplitudes while others add destructively. The amplitude of the signals thus varies
randomly and for large amplitude signals, non–linear amplifications cause errors. Therefore, error correction codes are employed. In
HiperLan2, the data is coded with a convolutional encoder of rate 1/2. The convolutional encoder uses the industry–standard generator
polynomials, g0 = 1338 and g1 = 1718, and is shown in Figure 4.3. The bit denoted as "A" is the first output from the encoder while the
bit denoted "B" is the second one. Higher code rates are derived via puncturing. In puncturing, some of the encoded bits in the
transmitter are removed ... Show more content on Helpwriting.net ...
The stream of complex numbers is rearranged so that the pilots can be inserted. In each OFDM symbol, four pilot signals are inserted in
order to make the coherent detection robust against frequency offsets and phase noise. The pilots are BPSK modulated by a pseudo
binary sequence to prevent the generation of spectral lines [18]. A zero padded block adds zeros, in the right places, to adjust the IFFT
bin size to length N. Selector block rearranges the sub–carriers so that real signal output can be generated. The IFFT block then
computes the Inverse Fast Fourier Transform (IFFT) of length N, where, for ease of implementation, N is a power of

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

Wireless Body Area Network ( Wban )
Abstract. A Wireless Body Area Network (WBAN) is a body worn system which provides the user with a set of mobile services. A
BAN service platform for mobile healthcare and several healths BANs targeting different clinical applications have been developed.
Each specialization of the BAN is equipped with a certain set of devices and associated application components, as appropriate to the
clinical application. Different kinds of clinical data may be captured, transmitted and displayed, including text, numeric values, images
and multiple bio–signal streams. Timely processing and transmission of such multimedia clinical data in a distributed mobile
environment requires smart strategies. The efficiency and embedded ability of WBANs ... Show more content on Helpwriting.net ...
Wireless devices still have limited memory and processing power, and are especially restricted by of battery life. State of the art
wireless communications technologies now handle high bandwidth applications, however transmission of some kinds of clinical data
strings that exceeds the normal capacity available today. More importantly applications need to adapt to the dynamically changing
communications environment and situation of the user. For this and other reasons m–health applications need to be context aware
The University of Twente and partners have been developing mobile health systems based on Body Area Networks (WBANs) since
2001. A number of WBANs and a BAN service platform targeted at the healthcare domain were developed during the course of several
European and Dutch projects. We define a health BAN as a network of communicating devices (sensors, actuators, multimedia devices
etc.). A BAN for health monitoring incorporates one or more sensors capturing bio–signals, which are transmitted to a remote
healthcare location for viewing by health professionals.
One of the BAN devices, the Mobile Base Unit (MBU), acts as a communication gateway to other networks and takes care of local
storage and processing. The MBU has been implemented on a number of different PDAs (Personal Digital Assistant) and smart phones.
BAN data has

Digital Time Signal Processing
BE EXTC D T S P DEC– 2004 By Kiran Talele ( talelesir@yahoo.com ) Q 1. (a) FIR filter described by the difference equation : y(n) =
x (n) + x (n – 4) (i) Compute and sketch magnitude and phase response. [4] ⎛π ⎞ ⎛π ⎞ (ii) Find its response to the input x(n) = cos⎜ n
⎟ + cos⎜ n ⎟, − ∞ < n < ∞. ⎝2 ⎠ ⎝4 ⎠ [4] Solution : (i) To find Magnitude and Phase Response Given (i) By ZT, y (n) = x (n) + x (n
– 4) Y (z) = x (z) + z –4 x (z = x (z) (1 + z –4) H (z) = 1 + z –4 z = e jw H (e jw) = 1 + e –j4w Put = e − j2 w e j2 w + e − j2 w H (e jw )
= e − j2 w [2 cos (2w )] (i) (ii) (iii) w 0 0.1 π 0.2 π 0.3 π 0.4 π 0.5 π 0.6 π 0.7 π 0.8 π 0.9 π π [ ] Magnitude Response M (w) = | Hr(w) | =
| 2 cos (2w) | Phase Response : φ ( w ) = e − j2 w Phase ... Show more content on Helpwriting.net ...
y (n) = x (n) * h (n) To find Linear convolution using circular convolution, I. Select N=L+M–1=4+3–1=6 II. Append x[n] by (N – L =
2) zeros and h (n) by (N – m = 3) zeros x (n) = { 1, 2, 3, 4, 0, 0 } ∴ h (n) = { 2, 3, 1, 0, 0, 0 } III. Find y(n) using circular convolution N
−1 y( n ) = x ( n ) ⊗ h ( n ) = y(n ) = 5 m =0 ∑ x ( m ) h ( n − m) where N = 6 m =0 ∑ x ( m ) h ( n − m) 5 i) n = 0, y (0) = m=0 ∑ x (
m) h ( − m) 2 y(0) = (1)( 2) + ( 2)(0) + (3)(0) + (4)(0) + (0)(1) + (0)(3) = ii) n = 1, y(1) = y(1) = (1)(3) + ( 2)( 2) + (3)(0) + ( 4)(0) + (0)
+ (0)(1) = iii) n = 2, y(2) = m =0 ∑ x(m) h (1 − m) 7 5 5 y(2) = (1)(1) + ( 2)(3) + (3)( 2) + 0 + 0 + 0 = 13 DSP Help Line : 9987030881
m =0 ∑ x ( m) h ( 2 − m ) www.guideforengineers.com B E EXTC iv) n = 3, y(3) = 5 DTS P DEC– 2004 6 y(3) = 0 + ( 2)(1) + (3)(3)
+ (4)( 2) + 0 + 0 = 19 v) n = 4, y(4) = m =0 ∑ x(m) h (3 − m) 5 y(4) = 0 + 0 + (3)(1) + ( 4)(3) + (0) + (0) = 15 m =0 ∑ x ( m) h ( 4 − m)
5 vi) n = 5, y(5) = = 0 + 0 + 0 + ( 4)(1) + 0 + 0 = m =0 ∑ x(m) h(5 − m) 4 ANS y[n] = { 2 , 7, 13, 19, 15, 4 } ↑
––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
Q 2. (a) The real sequence of length 8 is given as x[n] = {1, 2, 2, 0, 1, 1, 1 } Find the 8 point DFT X[k], by using 4 point DFTs only.
Prove the property which is used. Solution (a) To find X[k] Given

Advantages And Disadvantages Of Traffic Control
DENSITY–BASED TRAFFIC CONTROL SYSTEM A new density based traffic control system is described here, which is capable of
detecting, counting, and controlling vehicles, thereby reducing the traffic congestion. Before explaining about the proposed system,
some of the existing traffic control methods and their disadvantages are described below.
4.1 EXISTING TRAFFIC CONTROL SYSTEMS
In real world there are many traffic controlling schemes established already. These schemes are described below.
4.1.1 Manual Controlling
This is the simplest form of traffic control. As the name indicates it require man power to control the traffic. Depending on the countries
and states the traffic polices are allotted for a required area or city to control the traffic by analyzing the traffic conditions. A traffic
police stands in the middle of road and monitors flow of traffic. The traffic polices will have things like sign board, sign light and
whistle to control the traffic. In time of congestion he gives signals to the vehicle driver whether to drive or stop. He is also able to
recognize emergency case, so he can choose which lane needs more priority than other.
4.1.2 Automatic Timer Control ... Show more content on Helpwriting.net ...
This system includes simple three color traffic signal. A fixed time is allotted for each lane. The lights are automatically getting ON and
OFF depending on the timer value changes. While using electrical sensors it will capture the availability of the vehicle and signals on
each phase, depending on the signal the lights automatically switch ON and OFF. Before green light, yellow light flashes for few
second, signifying to start your vehicle and be ready to go. For all the time red light is on, ordering each vehicle to stop. This method is
efficient than manual controlling

A Short Note On Earthquake Alarm Structures Of A Wireless...
IV. EARTHQUAKE ALARM STRUCTURES
The earthquake alarm wireless system is divided into two parts,
1) Transmitter part (sends signal) 2) Receiver part (receive signal)
Fig:2 transmitting part of a wireless earthquake system.
Fig:3 receiving part of a wireless earthquake system.
Here, the transmitting part includes the ADXL335 accelerometer which is mainly a analog devices and generates analog signal. It
[6]and compares it with a predetermined threshold value. The signal which accelerometer generates is analog signal and the
microcontroller only functions on digital there is a ADC which is connected to microcontroller fro this coversion.. The signal generated
by a microcontroller is then send to a Xbee S2.
ADXL335: ADXL335 is a type of 3–axis accelerometer which is very thin, low power and small and sends voltage outputs to the
microcontroller. It is a kind of gadget which can be utilized to recognize stuns, vibration and it likewise has some different applications,
for example, for gaming, cell phones, picture pivot and cameras.Instead of using some additional temperature circuits in building this
system ADXL335 can be used as it is very efficient and has very less error. It can measure in 3–xis with a measurement range of ±3g. In
this system is connected to ADC pins for analog to digital conversion.
ADC (analog to digital conversion): it is connected so that an analog signal generated from an accelerometer can be converted to digital

Systems Biology and Computational Modeling
Systems biology integrates computational modelling with experimental techniques in order to better understand the function of living
organisms, the regulation of their cellular processes and how these cells react to environmental perturbations [1]. Among the different
computational approaches, kinetic modelling gives the most detailed representation of the biological system. These models build on the
stoichiometry of the reactions, incorporating the dynamic interactions between different components of the network. The dynamics in
kinetic models are driven through ordinary differential equations (ODEs) that represent the internal reaction mechanism as a function of
species concentration and parameters. These model parameters play a crucial role in describing the correct dynamics of the model.
However, it is only possible to measure a fraction of these kinetic parameters in wet lab experiments due to high cost, difficulty and
limitations in current techniques or methods [2]. Therefore these parameters are indirectly determined through computational methods
from other measurement quantities, in particular the time course data of metabolite concentrations. However, as biological models are
often multi–modal it is not uncommon for traditional parameter estimation methods to become stuck in local optima [3]. In addition,
traditional methods tend to perform badly in the presence of high measurement noise. Furthermore most of these methods do not
consider any form of model

Digital Mammographic Systems
Digital mammographic systems revolve around the working principle of an X–ray detector. While screen film has been the standard
detector used in conventional mammography, developments in technology have opened avenues to more advanced imaging techniques
using digital mammography. The motivation behind advancing from screen film imaging to digital mammography include potentially
lower dose, improved image quality, computer aided diagnosis and soft copy review and digital archiving.
II. Types of digital detectors and their working principles
There are two categories of digital detectors for mammography which are indirect and direct digital detectors. Indirect conversion
requires an intermediate step: X–ray energy is first converted into light by a scintillating medium before conversion into electrical
charges by a photodiode. Direct digital detectors on the other hand utilize a direct conversion method where X–rays are absorbed and
electronic signals are created instantaneously in a single step within a semiconductor device. According to Vedantham (2000) 1, indirect
systems suffer from resolution degradation caused by the scatter of light within the scintillator during the conversion compared to direct
systems.
Phosphor flat panel detector is a type of indirect digital detector. The working principle of this type of detector involves x–rays being
absorbed by a layer of thallium activated cesium iodide phosphor CsI(Tl) that is eventually deposited onto photodiodes. The

Digital Signal Processing
With the advent of digitization in every field from communication to media, the 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, fitting naturally into my area of strength and interest.
I graduated from high school with 97% in Physics, Chemistry and Maths as a result of which I was admitted in SSN, ranked amongst
the top engineering colleges in India. During my undergraduate study in Electronics and Communications Engineering I developed a
liking for subjects like Digital Communication, Digital Image Processing, Digital Signal Processing.etc which provided me with a
fundamental knowledge about digital signals and a thirst to explore more.
My actual venture into DSP, started during fifth semester holidays when I attend a course on "DSP Applications" conducted by IIT
Madras and Analog Devices. This gave me a great opportunity to work with several professors in IIT on projects like Noise
Cancellation, Image Restoration using Kalman filter.etc. Intrigued by this experience, I devoted more time towards studying various
fields in

A Research Study On Health
Abstract– Health is the most important part of the existence, and is in today's age the most critical, costly and time consuming area.
People want to safeguard their health and take all the necessary precautions all by themselves, moving to the healthcare facilities only at
dire need. A leading technology paradigm is the use of biosensors in quantifying the biological and physiological parameters and
transferring the result to a GUI for health monitoring without the assistance of any healthcare personal. These biosensor enabled devices
are becoming very popular in the masses as they can themselves monitor their health. Biosensors coupled with the wireless technology
offer pervasive ubiquitous remote health monitoring and being extremely small enhances the user's mobility with simultaneous
monitoring. The work has discussed the biosensor structure and its importance in healthcare. Various breakthroughs in the biosensor
innovations have been listed. A varied discussion about the integration of the wireless technology with the biosensors has been done,
emphasizing the importance of real–time healthcare monitoring, and lastly a literature survey for the above–mentioned areas has been
given.
Keywords–– BAN, Biosensors, BSN, Implantables, IoT, Wearables, WBAN, WBSN, Wireless Healthcare.
I. INTRODUCTION
One discovery, one innovation leads to another and so forth fashioning a never–ending chain reaction of changes and innovations. The
innovations that today's world is seeing can

Neural Recording And Processing Of The Neural Signals
Neural Recording and Processing
The most critical element of a Brain Machine Interface (BMI) is the recording and processing of the neural signal. We use an invasive
neural signal recording to achieve higher performance of the BMI and to obtain better resolution. We will be recording the neural signal
on central sulcus located in the cortex region of the brain. This recording is referred to as Electrocorticography (ECoG). We will be
using subdural grid electrodes (surface electrodes) with 48 contact points spanning across the upper limb region of the central sulcus.
The signal will be picked up by the electrodes and it will be transferred to the signal processing unit through the encapsulated leads. The
leads will be connecting the ... Show more content on Helpwriting.net ...
The signal gets amplified, filtered and then get digitised. The digitised data is fed into the decoder.
Signal Decoding
We intend to design a signal decoder as an ASIC. It will perform the decoding of neural signals and predict the brain's intention to
perform arm movements. Though we use multiple electrodes to record the neural signal, the number of features that can be extracted
from the recorded signal is very less. The neurons in the brain perform a task by sequential generation of action potentials which are
then transmitted to other neurons through the synapse. These action potentials are referred to as spikes.
The first step in decoding is to extract features from the recorded neural signal. The features we will be interested are the spikes. The
efficient method to detect spikes is threshold crossing. This is a simple approach where we will setup threshold amplitude and count the
number of threshold crossings across a time bin. Whenever a spike or set of spikes cross the threshold value it is detected and assigned
to a single or a group of neuron. This method does not affect the decoding performance and has gained acceptance widely. We will
adopt the threshold crossing technique to extract features from the neural signal.
We will identify the spikes detected based on their frequency, timing and location. The information found is referred to as rate coding,
temporal coding and population coding respectively. We will then classify the spikes based on this

Future Of Sensors : A Study Conducted By Intechno Consulting
Future of sensors
Trends
A study conducted by Intechno Consulting [ ] shows that the non–military, open market for sensors grew from EUR 81.6 billion in 2006
to EUR 119.4 billion in 2011 and can be expected to grow to EUR 184.1 billion until 2016. This equates to an annual average growth of
8.5%. Of the EUR 184.4 billion EUR global market for sensors in 2016, 9.7% will go to the machinery industries, 8.9% to the process
industries, and 22.8% to the vehicle industries including airplanes, ships and rail vehicles.
The figures below show the declining market shares of Western Europe, in spite of Germany's strong overall position in the sensors
market. The main reason for this is the fact that Western European sensor buyers are found mostly in the established industries, while
the USA and Japan lead in information and communication technology–the main growth sector for the future sensors markets.
Figure 5: Forecast of the Non–Military Open World Market for Sensors until 2016: Subdivision by Regions [26]
This is also confirmed in a survey conducted by PwC [ ], where respondents from Asia were more likely to say their companies are
investing in sensors, followed closely by Latin America. On the other hand, North American respondents were least likely to say their
companies are investing in sensors and they have no plans to close the global gap. Asia (26%) and Africa (18%) expect to invest more
in sensors while only 8% of respondents from European companies and 7% of

Digital Signal Processing Quiz
TUTORIAL SHEET –1 (DIGITAL SIGNAL PROCESSING) 1.What are the basic elements of digital signal processing. List the
advantages of digital signal processing over Analog signal processing? 2. Give the classification of signals (a) Continuous time signals
and discrete time signals. (b) Deterministic and Non–Deterministic signals (c) Periodic and Aperiodic signals (d) Even and Odd signals
(e) Energy and Power signal 3.Determine whether the following signals are power or Energy signals or neither (a) x(t)=A sin t –∞ < t <
∞ (b) x(t)= u(t) (c) x(t)=r(t)= tu(t) 4.Determine the even and odd parts of ... Show more content on Helpwriting.net ...
List the advantages of digital signal processing over Analog signal processing? 2. Give the classification of signals (a) Continuous time
signals and discrete time signals. (b) Deterministic and Non–Deterministic signals (c) Periodic and Aperiodic signals (d) Even and Odd
signals (e) Energy and Power signal 3.Determine whether the following signals are power or Energy signals or neither (a) x(t)=A sin t –
∞ < t < ∞ (b) x(t)= u(t) (c) x(t)=r(t)= tu(t) 4.Determine the even and odd parts of the unit step signal u(n). 5. Determine whether the
corresponding system is causal or Non–causal. (a) y(t)=sin x(t) (b) y(n)=nx(n) (c) y(n)=x(–n) TUTORIAL SHEET –1 (DIGITAL
SIGNAL PROCESSING) 1.What are the basic elements of digital signal processing. List the advantages of digital signal processing
over Analog signal processing? 2. Give the classification of signals (a) Continuous time signals and discrete time signals. (b)
Deterministic and Non–Deterministic signals (c) Periodic and Aperiodic signals (d) Even and Odd

Wireless Ad Hoc Networks
Incorporating Data from Multiple Sensors for Localizing Nodes in Mobile Ad Hoc Networks, IEEE TRANSACTION ON MOBILE
COMPUTING
SUMMARY
Wireless Ad hoc Network are important emerging technology that is low cost, most effective. Although a large amount of research has
been done on mobile Ad hoc Network technology, yet truly understanding these networks has been low and the implementation of these
networks is very less. This is because in some areas it is difficult to implement wireless networks as it is an expensive process and
implementing new algorithm and new protocols is difficult replacing all the protocols that are currently used. Although the important
fact is that these networks are not used to its full capacity. Large amount of research is going on the Ad hoc networks and exploit these
networks in far more better way. Ad hoc network technology has many future applications. It can be used widely used for military
application, can be used for UAV's , for detecting of the chemicals and materials underground. " Ad hoc network localization problem
can be defined as the task for finding the estimates the physical location of the nodes know its exact location" ( < Gergely V. Zaruba>
<Rui Huang>, September 2009, 1 ) . This research paper proposes the solution of the Ad hoc network localization problem and
proposes different algorithm that can provide better localization accuracy and higher localization coverage. The paper revolves around
the results obtained by

Phasor Measurement Unit Essay
Large–scale smart grid initiatives require fast and accurate monitoring of power system network. Phasor Measurement Unit (PMU) is
one of the major components which are increasingly deployed in the power system network to provide the necessary infrastructure to
cater smart grid requirements. PMUs provide time synchronized measurement of voltage and current phasors (Phadke and Thorp 2008)
from different nodes of the power system network, which is used for monitoring and control. NASPI (March 2015) and NERC
(September 2016) suggest use of phasor data from the PMUs to perform verification of dynamic models as well as validating
parameters of power plant models. A parameter identification toolbox for power system models developed using the ... Show more
content on Helpwriting.net ...
The mismatch in PMU measurements and dynamic simulators lead to anomalous result for application such as event location,
oscillation detection, islanding detection, and dynamic line rating as indicated in (Zhao et al. 2015). While PMUs can measure and time
synchronize the output phasor with great accuracy, the internal signal processing may utilize several cycles of the input signal
measurements to calculate each phasor values. Thus, even if the PMU report phasor each cycle, the measurements is not representative
of the current cycle but of a number of the preceding and following cycles depending on the sliding window of data used for the phasor
estimation. This typically smooths out/filters faster changes that may be present in the simulation output, rendering PMU output not
comparable to simulated response for the same disturbance when fast changing responses are present (applicable for a very small time
constant parameters in the model of the physical system). Phasor estimation can be performed in electromagnetic simulators in which
point of wave signal is available. Phasor estimation of a point of wave signal is applied in electromagnetic simulator using enhanced
DFT based algorithm in (Romano, Pignati, and Paolone 2015). A mechanism for streaming simulated phasors from TSAT in C37.118
format is proposed in (Zheng, Howell, and Wang 2015). However, these phasors do not reflect the PMU characteristics as

Different Types of Sensors and Their Applications
Capacitive Sensors
Capacitive sensors use an array capacitor plates to image the fingerprint. Skin is conductive enough to provide a capacitive coupling
with an individual capacitive element on the array. Ridges, being closer to the detector, have a higher capacitance and valleys have a
lower capacitance. Some capacitive sensors apply a small voltage to the finger to enhance the signal and create better image contrast.
Capacitive sensors can be sensitive to electrostatic discharge (shock) but they are insensitive to ambient lighting and are more resist
contamination issues than some optical designs.
Optical Sensors
Optical sensors use arrays of photodiode or phototransistor detectors to convert the energy in light incident on the detector into
electrical charge. The sensor package usually includes a light–emitting–diode (LED) to illuminate the finger.
There are two detector types used by optical sensors, charge–coupled–devices (CCD) and CMOS based optical imagers. CCD detectors
are sensitive to low light levels and are capable of making excellent grayscale pictures. However, CCD fabrication is relatively
expensive and neither low–light sensitivity or grayscale imaging are required for fingerprint recognition. CMOS optical imagers are
manufactured in quantity and can be made with some of the image processing steps built into the chip resulting in a lower cost.
Optical sensors for fingerprints may be affected by a number of real world factors such as stray light and

A Short Note On Internal And Internal Assessment
IB Mathematics HL Internal Assessment
Judy Taylor
IB HL Mathematics
Word Count: 1311
Table of Contents Abstract.......................................................................................Page 3
Introduction...................................................................................Page 4
Exploration....................................................................................Page 5
Applications...................................................................................Page 8
Conclusion.....................................................................................Page 9
References....................................................................................Page 11 Abstract The sine integral, written as Si (x), is important factor
in signal processing as it causes overshoot, ringing artifacts, and is considered as the convolution of the cardinal sine function
(represented as sin (x)/ x or sinc (x)) and the Heaviside step function that corresponds to shortening the Fourier series, which causes the
Gibbs ... Show more content on Helpwriting.net ...
After some research, I found out that cardinal sine could not be solved by elementary means and that its integral was, in fact, a crucial
part of the Gibbs phenomenon, which will be expanded upon in the Applications section (Pg. 8) of this investigation. My aim for this
investigation is to prove the integral of cardinal sine to be sine integral and to approximate five (5) values of the sine integral function.
Afterwards, I will show how this is relevant in both mathematics and other, more practical, applications. Exploration In order to
organize the mathematical exploration most effectively, the exploration will begin with the following problem: Prove that ∫▒sin⁡
x/x is
equal to Si (x). Approximate Si (0), Si (π/6), Si (π/4), Si (π/3), and Si (π/2) using a Taylor polynomial of order 3 at 0 for f(x) = Si (x). In
order to verify the results of the approximations, Wolfram Alpha, an online calculator capable of processing the sine integral function,
will be used.
To begin with, the Taylor series representation of the sine integral function is as such:
Si (x)=∑_(n=0 )^∞▒((–1)^n x^(2n+1))/(2n+1)(2n+1)! At this point, it is necessary to find the Taylor series representation of sin⁡
x/x.
First, the Taylor series representation of sin (x) is:
∑_(n=0)^∞▒(〖(–1)〗^n x^(2n+1))/(2n+1)!
Therefore, sin⁡
x/x is equal to:
1/x ∑_(n=0)^∞▒(〖(–1)〗^n x^(2n+1))/(2n+1)!
Which is equal to:
∑_(n=0)^∞▒(〖(–1)〗^n x^(2n+1))/x(2n+1)!

Alzheimer's Disease Cross Recurrence Summary
Cross Recurrence Analysis of EEG Alzheimer's Disease Cross Recurrence Analysis of EEG Alzheimer's Disease Cross Recurrence
Analysis of EEG Alzheimer's Disease Cross Recurrence Analysis of EEG Alzheimer's Disease Cross Recurrence Analysis of EEG
Alzheimer's Disease Cross Recurrence Analysis of EEG Alzheimer's Disease Cross Recurrence Analysis of EEG Alzheimer's Disease
Cross Recurrence Analysis of EEG Alzheimer's Disease Cross Recurrence Analysis of EEG Alzheimer's Disease Cross Recurrence
Analysis of EEG Alzheimer's Disease Cross Recurrence Analysis of EEG Alzheimer's Disease Cross Recurrence Analysis of EEG
Alzheimer's Disease Cross Recurrence Analysis of EEG Alzheimer's Disease Cross Recurrence Analysis of EEG Alzheimer's Disease
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Each type of memory activates a different area in the brain during recall. Working memory resides in the frontal lobe and lasts less than
a minute. Short–term memory resides in the inside of the temporal lobe and lasts a few minutes to a few weeks before being erased.
Long–term memory can last a lifetime though scientists are not yet certain which brain areas are involved in this function. Depending
on the severity of disease related memory loss, a patient may satisfy criteria for either MCI or dementia. In recent studies it is found that
inferior parietal lobe lesions are associated with short term memory loss and non dominant parietal lobe lesions produce impaired
attention. Most of the current studies are in resting EC condition, comparing AD/MCI to controls. Some studies are conducted on EEG
of MCI subjects in working memory task state have suggested the presence of compensatory processes in MCI subjects which enhances
inter and intra hemispheric coherence in these

Photoplethysmogram (Ppg) To Estimate Respiratory Rate
Autoregressive Spectral Analysis on Photoplethysmogram(PPG) to estimate Respiratory Rate.
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Abstract– Abnormal respiratory rate is considered to be a important predictor of conditions like cardiac arrest, tachypnea and
hypervolemia. Estimating respiration rate of an individual using non invasive techniques such as by using Photoplethysmogram (PPG)
and Electrocardiogram(ECG) have been widely discussed and several methods have been proposed to extract respiratory rate
information from these signals. However, maintaining a high degree of accuracy in measuring respiratory rate has been a problem. This
paper uses Autoregressive Modeling (AR) technique and digital filters to estimate respiratory rate from the PPG signal revealing that
this technique has better results in term of accuracy and computational efficiency.
Keywords–Respiratory Rate; Photoplethysmography; Autoregressive (AR) model.
I. INTRODUCTION
Respiration rate is one of the main vital signs used in all clinical settings. An increased respiratory rate is an important predictor for

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