Traffic Control Density System Reduces Congestion

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.
4.1.1Manual 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 ofroad
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.
4.1.2Automatic 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
... Get more on HelpWriting.net ...

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.
1.1Motivation
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

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

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

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 morebetter 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

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 insignal
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 в€«в
–’sinвЃЎx/x is equal to Si (x). Approximate Si (0), Si (ПЂ/6), Si (ПЂ/4), Si (ПЂ
/3), and Si (ПЂ/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)=в€‘_(n=0 )^в€
ћв–’((–1)^n x^(2n+1))/(2n+1)(2n+1)! At this point, it is necessary to find the Taylor series representation of sinвЃЎx/x. First, the
Taylor series representation of sin (x) is:
в€‘_(n=0)^в€
ћв–’(гЂ–(–1)гЂ—^n x^(2n+1))/(2n+1)!
Therefore, sinвЃЎx/x is equal to:
1/x в€‘_(n=0)^в€
ћв–’(гЂ–(–1)гЂ—^n x^(2n+1))/(2n+1)!
Which is equal to:
в€‘_(n=0)^в€
ћв–’(гЂ–(–1)гЂ—^n x^(2n+1))/x(2n+1)!

Photoplethysmogram (Ppg) To Estimate Respiratory Rate
Autoregressive Spectral Analysis on Photoplethysmogram(PPG) to estimate Respiratory Rate.
Subtitle as needed (paper subtitle)
Authors Name/s per 1st Affiliation (Author) line 1 (of Affiliation): dept. name of organization line 2–name of organization, acronyms acceptable line
3–City, Country line 4–e–mail address if desired
Authors Name/s per 2nd Affiliation (Author) line 1 (of Affiliation): dept. name of organization line 2–name of organization, acronyms acceptable line
3–City, Country line 4–e–mail address if desired
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

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

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 П„FWHM pulse duration of ~ 1.7
fs as shown in Figure 6.
Attosecond light field synthesis
The ultrabroadband of the supercontinuum light source spans over

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

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
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 bо‚ m
contain the cosine entries at frequency subdivision m, and cо‚ contain the sine entries at m subdivision m. Then, B=[bо‚ о‚‹ bо‚ ] , C=[cо‚ о‚‹
cо‚ ] , 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 о‚ sв€€V , and о‚ s=Bо‚ uо‚ѓCо‚ v . This can be rewritten as о‚ s=Dwо‚ where wо‚ =[о‚ u] . Given that the columns of D form an
orthogonal basis, the weights can be
о‚ v
[о‚ sв‹…bо‚ ] [о‚ sв‹…cо‚ ] calculated using the following relation: u = m , v = m . This discussion forms the foundation for the calculations in
the following example. m о‚ о‚ m cо‚ в‹…cо‚ bmв‹…bm m m
пїјпїј
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
пїјо‚ 10о‚ 33 b = [ ] cо‚ = [ ] b = cо‚ = cosо‚ћоѓ†о‚џ sinо‚ћоѓ†о‚џ 33
пїјпїј10
1 0 cosо‚ћ2оѓ†о‚џ sinо‚ћ2оѓ†о‚џ
пїјпїј0,0,1 ,1 , 1 0 cosо‚ћоѓ†о‚џ sinо‚ћоѓ†о‚џ
[][] cosо‚ћ5оѓ†о‚џ

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

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 –в€
ћ < t < в€
ћ (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 –в€
ћ < t < в€
ћ (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

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

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

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
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

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

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
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 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

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

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

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 appliessignal 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

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

Robust Video Data Hiding Using Forbidden Zone Data Hiding...
1130IEEE 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 newvideo 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.

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

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, О”I and О”n 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, О”nc change in cladding index, О”ts change in add layer, and О”О» change in resonance
wavelength
2.8.2. Selectivity
Selectivity is a measure of how specific the response of a sensor is to the

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–efп¬Ѓcient 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

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] вЋ›ПЂ вЋћ вЋ›ПЂ вЋћ (ii) Find its response to the input x(n) = cosвЋњ n вЋџ +
cosвЋњ n вЋџ, в€’ в€
ћ < n < в€
ћ. вЋќ2 вЋ вЋќ4 вЋ [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 в€’ j2 w e j2 w + e в€’ j2 w H (e jw ) = e в€’ j2 w
[2 cos (2w )] (i) (ii) (iii) w 0 0.1 ПЂ 0.2 ПЂ 0.3 ПЂ 0.4 ПЂ 0.5 ПЂ 0.6 ПЂ 0.7 ПЂ 0.8 ПЂ 0.9 ПЂ ПЂ [ ] Magnitude Response M (w) = | Hr(w) | =
| 2 cos (2w) | Phase Response : П† ( w ) = e в€’ 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 } в€
ґ h (n) = { 2, 3, 1, 0, 0, 0 } III. Find y(n) using circular convolution N в€’1 y( n ) = x ( n ) в
Љ— h
( n ) = y(n ) = 5 m =0 в€‘ x ( m ) h ( n в€’ m) where N = 6 m =0 в€‘ x ( m ) h ( n в€’ m) 5 i) n = 0, y (0) = m=0 в€‘ x ( m) h ( в€’ 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 в€‘ x(m) h
(1 в€’ m) 7 5 5 y(2) = (1)(1) + ( 2)(3) + (3)( 2) + 0 + 0 + 0 = 13 DSP Help Line : 9987030881 m =0 в€‘ x ( m) h ( 2 в€’ 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
в€‘ x(m) h (3 в€’ m) 5 y(4) = 0 + 0 + (3)(1) + ( 4)(3) + (0) + (0) = 15 m =0 в€‘ x ( m) h ( 4 в€’ m) 5 vi) n = 5, y(5) = = 0 + 0 + 0 + ( 4)(1) + 0 + 0 = m
=0 в€‘ x(m) h(5 в€’ m) 4 ANS y[n] = { 2 , 7, 13, 19, 15, 4 } в†‘
––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––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

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 в€€ RN from its measurement vector О¦x=y в€€ Rm,
where m < N, consists in the l0 –minimization problem minв”¬(xПµ R^n )вЃЎгЂ–вЂ–xвЂ–_(0 ) subject to О¦x=y гЂ— (4.1)
This is a non convex problem that it is NP–hard in general. However, keeping in mind that вЂ–zвЂ–_q^(q ) approaches вЂ–zвЂ–_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

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
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, BehaviouralSignal 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

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]... Show more content on Helpwriting.net ...
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 R820T2241766 Rafael Micro R820T24 1766
Elonics E400054 2200 Fitipower FC001322 1100 Fitipower FC001222 948,6 FCI FC2580146 –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 Aeronautical108 – 117
Meteorological~ 137 Fixed mobile 140 – 150 Special events broadcast 174 – 217 Fixed mobile (space–earth)267 – 272 Fixed mobile(

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 Cross Recurrence... Show more content on Helpwriting.net ...
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

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

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

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

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 В±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

Cs/3590 Assignment 5
CS 3590 Assignment 2–Fall 2016Hung 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,

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