Category: MATLAB Projects

The DES algorithm is a 64- bit block cipher with a 56-bit key. This Project describes DES technology for secure data transmission while maintaining the authenticity and integrity of the message. In this, the message is encrypted before the data transmission process starts. The decryption and encryption of data are done by using the data encryption standard algorithm in MATLAB using GUI.

DES Encryption Results

 

Result for DES Encryption when given registration numbers as input (“A” is and)

DES Decryption Results

Result for DES Decryption when given Encryption output

we got our registration number as DES Decryption output, which we have given as input for DES Encryption (A= “and” in output)

Providing a secure mechanism for data transmission is very important, as we are moving towards a society where automated information resources are highly used. This project shows how we can encrypt and Decrypt plain text using DES in GUI. But DES is currently considered an insecure encryption method in some applications, such as banking systems.

There are some findings that show the theoretical weaknesses in cipher. so, it is very important to augment this algorithm by adding a new level of security to it. In the future, we can change this algorithm by changing the function implementation, and S-box design, and replacing the old XOR with new operations.

Download the Data Encryption Standard using GUI in MATLAB project report and matlab source code.

A system which provides ​prediction of the consumption of electricity for rural region ​ by considering the climatic conditions.

The input parameters of which will be :

Temperature
Precipitation
Relative Humidity
Wind speed
Max Temperature
Min Temperature
Sunshine hours
Air pressure
Rainfall
evaporation

The output will be the electricity consumption in kWh for the region (graphical form and numeric value)
Using ANFIS algorithm ​will be applied on the datasets to obtain estimates of the consumption which will give an estimate of the energy requirements of the area

Platform to be used: MATLAB

The main requirements of prediction of the consumption of electricity for rural regionwill be:
1.​ ​Complete training of the data by applying the neuro-fuzzy model
2.​ ​The gui with user side page showing options for selecting the month ,year ,region which will
then produce a graph showing the estimated values of electricity consumption along with
comparison with actual values to show accuracy
3.​ ​The fuzzy rule set and neural model code. (we have been told by our mentor to avoid to use
any inbuilt functions and code the rule set use in Fuzzy logic and neural network algorithms and
rule sets.)
4.​ ​The user side should be able to login to the system so the gui should also include login page
after which it asks the user to enter the region for which consumption is to be known
5. The application should be able to predict at the electrical load one week and month ahead
6.The coding should be done for feed forward network or bpn for neural part for the given data.
7. Minimum rule sets required for fuzzification of the data for clustering.
8. Output required in charts and numeric value
9.
The basic outline for the neural-fuzzy network.Neural training takes place in the input layer.
10.Minimum tuples required 500-600 for training

Problem Statement

The objective of this work is to develop a system for detection of Alzheimer’s disease from MRI scan of a patient using machine learning approach.

The system will take MRI scan of a patient and will attempt to detect Alzheimer’s based on pre-determined set of features. It will mainly focus on physical characteristics such as texture, area and shape of the hippocampus region which is ill affected in the early stages of the Alzheimer’s.

The output of the system will be a binary classification label suggesting either positive or negative case of Alzheimer’s.

It is basically categorized under neural network matlab based project

Proposed Approach

The proposed approach can be divided into two phases: i) training phase and ii) classification phase

1. Training phase: A set of labelled MRI scans is pre-processed for noise reduction, normalization, segmentation and ROI extraction. The training phase can be summarized as follows:
2. Extract features such as texture, mean, skewness, variance, standard deviation, area, perimeter, etc. from the pre-processed MRI scans.
3. Train an ANN classifier using this feature set.

The output of the training phase is a trained classifier capable of predicting binary classification label based on features of MRI scan.

The performance of the trained classifier can be evaluated using measures like accuracy, sensitivity and specificity.

1. Classification: This phase can be summarized as follows:
2. Take as input, MRI scan of a patient.
3. Pre-process the MRI scan.
4. Extract the required features from the patient’s MRI scan.
5. Use the trained classifier to predict the classification label for the patient’s MRI scan.

The output of this phase is a binary classification label suggesting either positive or negative classification label for the patient’s MRI scan.

The metrics used to determine performance of the trained classifier can be used to determine performance of the proposed approach and its comparison with existing methods.

The flowchart in figure 2 depicts the working of the proposed approach.

 

Abstract

Object detection is the process of finding instances of real world objects such as faces, vehicles and buildings in images or videos. Face Detection and Pedestrian Detection comes under the Object detection.

Vehicle detection is a part of Object detection. Vehicle detection mainly focus on detecting the vehicle. Automated Vehicle detection will be done by first obtaining the images or videos of vehicles in traffic areas under surveillance. This can be done by using Image Sensors. After the Image or video being captured the vehicles in the image or video have to detected. Finally,after detecting the vehicles the vehicles have to be counted. Depending on the count the traffic volume will be detected.

EEE Project List (BTech/M-Tech):Simulation MAT-LAB 

Course of Instruction:

Basics of MATLAB programming/Simulink

Designing of Simple circuits in Simulink

Steady State Space Analysis of RLC Circuits

Electrical Drives/Machines

• Basic Concepts of Motor
• AC/DC motors
• Modeling of Induction Motors
• Electrical Drives
• Various speed controlling techniques of AC/DC motors
• Fuzzy/Neural Networks

Power Electronics:

• Uncontrollable/controllable Converters
• Various PWM Techniques (PWM/SPWM/SVPWM/DPWM/GDPWM)
• Multilevel Inverters
• Harmonics, Active/Passive Filters
• DC to DC converters(Buck/Buck Boost/Cuk/Sepic)
• Fuzzy/Neural Networks

Power Systems

• Transmission/Distribution/Protection
• HVAC/HVDC
• Concepts of Facts(UPFC/SSSC/Statcom/UPQC/TCSC)
• Modeling of Facts Devices in Simulink/Matlab
• Distributed Generation
• Non-Conventional Energy Sources
• Fuzzy/Neural Networks

PROJECT TITTLES:

1. An Islanding Detection Method for Inverter-Based Distributed Generators Based on the Reactive Power Disturbance IEEE 2016 * R.E, P.S
2. A Novel Control Method for Transformer less H-Bridge
   Cascaded STATCOM With Star Configuration IEEE 2015 * P.E
3. An Observer-Based Optimal Voltage Control Scheme for Three-Phase UPS Systems IEEE 2015 D* P.S
4. A new hybrid Active Neutral point Clamped Flying capacitor Multilevel Inverter IEEE 2015 * P.E
5. Five-level multi pole PWM AC-AC Converters with reduced components circuits IEEE 2015 * P.E
6. New three phase symmetrical Multilevel Voltage source inverter IEEE 2015* P.E
7. A Wireless Charging System Applying Phase-Shift and Amplitude Control to Maximize Efficiency and Extractable Power IEEE 2015 D* P.E,P.S
8. Performance of Three Phase II-level Inverter with reduced number of switches using different PWM Techniques IEEE 2015 * P.E
9. Asymmetrical Multilevel Inverter for Higher Output Voltage Levels IEEE 2015 *D P.E
10. Five-Level Inverter Using POD PWM Technique IEEE 2015 * P.E
11. Analysis of Phase – Shifted Carrier Modulation for modular multilevel converters IEEE 2015 * P.E
12. A Bidirectional Modular Multilevel DC–DC Converter of Triangular Structure IEEE 2015 * P.E
13. Analysis and Operation of Modular Multilevel Converters With Phase-Shifted Carrier PWM IEEE 2015 * P.E
14. A New isolated Multilevel inverter based on Cascaded Three – phase converter Blocks IEEE 2015 *P.E

15. Advanced Control Scheme for an IPM Synchronous Generator-Based Gearless Variable Speed Wind Turbine – IEEE 2014 * P.E.D, C.S, R.E, P.S

16. Control scheme for a stand-alone wind energy conversion system – IEEE 2014 * E.D, C.S, R.E, P.S

17. An Effective Control Method for Quasi-Z-Source Cascade Multilevel Inverter-Based Grid-Tie Single-Phase Photovoltaic Power System – IEEE 2014 * P.E, R.E

18. Voltage Source Converter Topology for High-Power Applications Serializing Three-Phase Converters and H-Bridges – IEEE 2014 *E

19. Topology and Modulation for a New Multilevel Diode-Clamped Matrix Converter – IEEE 2014 * E

20. New Three-Phase Multilevel Inverter With Reduced Number of Power Electronic Components – IEEE 2014 *E

21. Solar PV and Battery Storage Integration using a New Configuration of a Three-Level NPC Inverter With Advanced Control Strategy – IEEE 2014 *E,P.S,R.E,C.S

22. An adjustable-speed PFC bridgeless Buck-Boost – IEEE 2014 * P.E

23. A single-phase voltage controlled grid-connected photo voltaic system with power quality conditioner functionality – IEEE 2014 *E,R.E,P.S

24. High frequency AC-Link PV Inverter – IEEE 2014 *E,P.S

25. Pulse width modulation of Z-source inverters with minimum inductor current ripple – IEEE 2014 *E

26. Zero voltage switching Technique for Bidirectional DC/DC converters – IEEE 2014 *E
27. NEW BREED OF NETWORK FAULT TOLERANT VOLTAGE-SOURCE-CONVERTER HVDC TRANSMISSION SYSTEM – IEEE 2013* P.S

28. Improved Active Power Filter Performance for Renewable Power Generation Systems– IEEE 2013*P.S,R.E

29. A Novel DC voltage control for STATCOM by using H-Bridge multilevel converter – IEEE 2013 *P.S

30. Voltage unbalanced compensation using dynamic voltage restorerbased on super capacitor – ELSEVIER 2013 *P.S

31. Dual Buck half bridge Voltage Balancer – IEEE 2014 *P.E

32. Modeling and Simulation of a Distribution STATCOM (D-STATCOM) for Power Quality Problems-Voltage Sag and Swell Based on Sinusoidal Pulse Width Modulation (SPWM) –IEEE *P.S

33. Modeling and analysis of canal type small hydro power plant and performance enhancement using PID controller– ISOR *P.S

34. Flexible Power Electronic Transformer – IEEE *P.E,P.S

35. Generalized UPQC system with an improved Control Method under Distorted and Unbalanced Load Conditions. – IEEE *P.S

36. Control of grid interfacing inverters with integrated voltage unbalance correction*P.E,P.S,C.S

37. POWER-MANAGEMENT STRATEGIES FOR A GRID-CONNECTED PV-FC HYBRID SYSTEM – IEEE* P.E,P.S,R.E

38. A Modified Three-phase Four Wire UPQC Topology with Reduced DC-link Voltage Rating-IEEE *P.S,C.S

39. DESIGN AND ANALYSIS OF DYNAMIC VOLTAGE RESTORER FOR DEEP VOLTAGE SAG AND HARMONIC COMPENSATION* P.E

40. A three-phase interleaved dc–dc converter with active clamp for fuel cells IEEE*P.E,P.S

41. An Improved UPFC Control for Oscillation Damping * P.E

42. Direct Power and Torque Control for Three-level NPC Based PWM ac/dc/ac Converter IEEE * P.E,P.S.C.S

43. Design of a hybrid pid plus fuzzy controller for speed control of induction Motors IEEE*P.E,P.S,C.S

44. Switching losses and harmonic investigations in multilevel inverters IETE*P.E

45. Fuzzy logic based control of variable speed induction machine wind generation system IEEE*P.E,R.E,P.S

46. Voltage flicker compensation using STATCOM – IEEE* P.E

47. Modeling and Real-Time Simulation of Non-Grid- Connected Wind Energy Conversion System IEEE*P.E,P.S,R.E,C.S

48. Enhancement of power quality in distribution system using D-STATCOM IEEE* P.S

49. An Improved UPFC Control for Oscillation Damping IEEE* P.S

50. A Single-Phase Voltage-Controlled Grid-Connected Photovoltaic System With Power Quality Conditioner Functionality IEEE* P.E,P.S,R.E,C.S

51. An Integrated Hybrid Power Supply for Distributed Generation Applications Fed by Nonconventional Energy Sources IEEE* P.E,P.S,R.E

52. Dynamic Modeling and Simulation of Hybrid Power Systems Based on Renewable Energy IEEE* P.E,P.S,R.E,C.S

53. Novel direct torque control based on space vector modulation with adaptive stator flux observer for induction motors IEEE* P.E,P.S,P.E.D,C.S

54. Harmonic elimination in single phase systems by means of a hybrid series active filter (hsaf) IEEE* P.E,P.S

55. An efficient ac–dc step-up converter for low-voltage energy harvesting IEEE* P.E,P.S

56. Design of a hybrid pid plus fuzzy controller for speed control of induction Motors IEEE* P.E,P.S,P.E.D,C.S

57. Power-management strategies for a grid-connected pv-fc hybrid system IEEE*P.E,P.S,R.E

58. A Simplified Space -Vector PWM for Three Level Inverters Applied to Passive and Motor Load IEEE* P.E

59. Analysis and Control of Buck-Boost Chopper Type AC Voltage Regulator IEEE* P.E

60. Double Frequency Buck converter IEEE* P.E

61. Modeling of FACTS Devices Based on SPWM VSCs IEEE* P.S

62. A 28-pulse ac-dc converter based smps for telecom power supply (be press) IEEE* P.E,P.S

63. A high performance induction motor drive system using fuzzy logic Controller IEEE*P.E,P.S,C.S

64. An inrush mitigation technique of load transformers for the series voltage sag compensator IEEE*P.E,P.S

65. Control scheme of cascaded h-bridge STATCOM using zero-sequence voltage and negative-sequence current IEEE* P.E,P.S

66. High-efficiency voltage regulator for rural networks IEEE* P.E,P.S

67. Implementation and control of an hybrid multilevel converter with floating dc-links for current waveform improvement IEEE* P.E

68. Instantaneous power control of D-STATCOM with consideration of power factor correction IEEE* P.E,P.S

Note : Any Simulation Design implementation is done here in recent electrical trends 

Abstract

The systems of future mobile communication will be intended to assist huge range of rates of data with complex quality matrix quality. In order to optimize the resource management of radio and also to increase the capacity of system, while meeting the needed service quality from viewpoint of user, it is turning out to be tougher.

By focusing mostly on existing resources in a cell considerably avoiding the effects of architecture of multi-cell, traditional methods have moved towards this issue. The control of multi-cell interference on the usage of overall radio resource will be addressed, and by locating a new way for future research on strategies of resource scheduling in a multi-cell system, a novel approach has been suggested. A concept known as LM (Load Matrix) has been proposed which assists joint interference management in and among cells for distribution of resources of radio.

Major developments have been shown by the simulation results in both the performance of overall network as well as utilization of resource. By making use of strategy of LM, average throughput of cell can be developed as much as 3- percent when related to the algorithm of benchmark. In addition, the results represent that maintaining interference of cell in the margin rather than the hard target can enhance the utilization of resource importantly.  

Table of Contents:

Abstract

Introduction

•            Background
•            Problem Definition

Aims & Objectives

Literature Review

•  Traditional Schedulers
•  Modeling of Communication System
•  Wireless channel Impairments
•  Mathematical Model for Wireless Mediums
•  Inter-cell Intervention

Models of Intervention

Intra-cell

Inter-cell

Research Analysis

•  Resource Allocation
•  Cell Dimensioning
•  Overlay & underlay Structure
•  Cell boundaries Overlapping

UTMS

•    Concepts and terminology
•    Other IMT2000 Technologies
•    Major Concepts Behind UTMS
•   Concepts of UTMS

Introduction

Background

For mobile communication, the demand has been developed significantly these days, and in order to meet this demand, one of the most significant tasks is distribution of resources. The most significant factor in distributing the resources across WSNs (wireless cellular networks) is the distribution of existing bandwidth.

For transmission, several applications such as multimedia need heavy bandwidth. In order to maintain reasonable QoS (Quality of Service) as well as to enhance the utilization of spectrum, resource allocation and efficient scheduling must be assumed so as to comfort this demand. Few factors such as fading, shadowing and interference results in the delay, and the quality of signal will be corrupted by this delay and as a result the circumstance of wireless channels is influenced extremely.

Particularly, when the interference is regarded, the channel is affected in two significant ways such as inter-cell interference and intra-cell interference. The interference of inter-cell is caused between ‘n’ number of cells, whereas the interference of intra-cell is caused by individual users. For the purpose of better strategy of resource allocation, the only given solution is that the radio spectrum’s efficient utilization is needed to meet better standards of Quality of Service.

The radio spectrum utilization is effected by latest factors such as practical scenarios and conditions of deployment. The whole transmission process is achieved in the form of tiny packets, and the transmission of packet can be achieved in efficiejt manner with the capabilities of enhanced uplink and the standards of Universal Mobile Telecommunications System (UTMS), and this process can be defined as HSDPA (High Speed Downlink Packet Access).

The resource allocation quality can be expressed in terms of transmission, equality and throughput. The summary of total available capacity can be utilized to obtain the available entire transmission process’s throughput. Transmission equality is expressed in terms of capacity of users in order to meet the agreements of basic service level during the process of transmission.

These two factors in any of the general scheduling algorithm are referred where the entire complexity of time and algorithm’s performance complexity are depending completely on the transmission’s throughput. Several strategies of resource allocation are present, and few of them are discussed in the following:

Generally, system with ‘n’ number of traffic classes is regarded, and then the distribution of resource was achieved as per particular circumstances of specific traffic, and as a result it results in power consumption’s reduction as well as increase in capacity of channel, and this might not be the better strategy of resource due to the changing requirements of quality of service. Afterwards, a proposal for resource pool’s fixed partition was obtained in which the existing resources are separated into partitions between various classes of service and all the partitions of resource were maintained by independent scheduler of resource and dynamic spectral efficiency will be implemented by means of this partition strategy.

Entire capacity of network will be increased by utility-based approach and thus enhancing the capabilities of resource allocation. Across the link, adaptive transmission is defined to the latest approach utilized across the WCNs. Diversity gains of multi user can be attained by integrating the methods of fast scheduling with mechanism of modulation coding and adaptive transmission, and this process in relation to fast traversing ones is best of low mobility users.

Depending on the network node’s location, the uplink scheduling process can be classified as decentralized and centralized, for instance if the scheduler in general UTMS exists base station then it can be regarded as decentralized approach, and if it is in radio network controller then it is considered to be centralized approach. The decentralized approach when compared to centralized approach can be regarded to be most excellent in terms of quick changing environment in mobile systems and also for rapid allocation of resources.

Problem Definition 

As per the above discussion it is clear that main issue with the current cellular systems is the Resource allocation. The main aim of this particular resource allocation is to allocate the available radio resources to all the individual users and also to achieve the best Quality of Service with in the available system capacity.

The term resources in this context refer to the time and transmission rate assigned to the individual users of the communication process and for better understanding of the process, a single cell is considered and the same concept is extended to multiple cells. 

Aims & Objectives: 

Aims:  To evaluate Load matrix algorithm that allocate resources across wireless cellular networks efficiently using MATLAB simulation.

Objectives:  Following are the project objectives

• To evaluate the existing resource allocation techniques like Round-robin algorithm and understand their limitations
• To prepare literature review on existing techniques of resource allocation techniques
• To evaluate the Load matrix concept and understand how to apply this technique for better resource allocation for wireless cellular networks
• To prepare the Load matrix algorithm and code it in MATLAB
• To simulate the application and evaluate the results.

Results: 

The figure represents the user data’s base band signal. This is produced arbitrarily by the system. Here, the binary data of 8-bit have been considered that is produced arbitrarily by the system for which up to 800 iterations have been sampled. Thus the above graph is obtained.

This above graph represents an increase over thermal noise which is produced for provided user data in networks of 2G, this ROT acts as threshold for resource allocation and detection from BS (base station). it can be observed that the interval of time is starting form 0-55 seconds for which it is arriving at its largets peak point at 19th milliseconds (ms).

 

This graph represents evaluation of performance among the proposed and conventional methodologies of load matrix by an error probability of about -2% and -1%. Here, it can be observed clearly that a better efficiency of about 98 percent is provided by LM but when related with conventional it represents nearly 28% improvement.

This graph represents evaluation of probability distribution function (PDF) of ROT. The highest PDF of ROT can be shown at 4 db by error probability of 0.35%.

 

This figure mainly used to identify the transmitted power produced by the nth user.

 

This graph represents evaluation of performance among the proposed and conventional methodologies of load matrix by an error probability of about +2% and +1%. Here, it can be observed clearly that a better efficiency of about 98 percent is provided by LM but when related with conventional it represents nearly 28% improvement.

Abstract

Carrier frequency offset abbreviated as CFO in the concept of OFDM systems will then decrease the concept of orthogonal in various kinds of sub carriers. Here loss of Orthogonality will be results in particular performance degradation. This is very much crucial to estimate the performance. A sub optimal approach is introduced in order to expect the CFO by using the sub carriers which are made null.

The CFO estimation will need the integer CFO estimation implementation. The carrier frequency offset will estimate the no of sub carriers. When the past work will be compared with the sub carriers’ expectation, the complexity can be maintained and introduced to the particular OFDM symbols. The introduced approach complexity will be specifically lower due to the lack of simple correlation.

This is the main and important component of the OFDM systems. Only single OFDM symbol and the allocation sub carrier are used for estimating the extent of respective approach. This estimation could be done in the inverse sampling duration. There are some other traditional approaches with similar estimation range. This estimation would also contain higher complexity of the OFDM symbols.

Best efficiency can be easily achieved with the sub carrier allocation. This allocation is specifically relying on the binary sequences. These binary sequences are typically introduces the concepts related to extended m-sequences. These sequences are typically represented by simulations that are employed by the criteria regarding sub carrier sequences. These simulations are deployed for achieving higher likelihood estimators.

Introduction

OFDM is considered as the standard technology for the wireless communications. These communications are considered by the multi path effects. These effects are specifically considered by the adoptions of advanced technology standards. Particularly this OFDM is sensitive in order to synchronize the CFO errors.

In particular, this estimation is stimulated through the series of oscillators among the receiver as well as sender in relevant transfers that are made. This particular OFDM is in the active on the research location. Various training signal are known to be training signals and medium statistics of the CFO estimator.

This is typically operated and performed by the likelihood estimator. This is specifically considered as the expansion of the training signals. Here only specific methods are essential o understand. The least square can be deployed for the estimation.  The specific criteria can be used for achieving the correlation techniques. Training signals are specifically periodic and these estimations would be relying on the ML criteria. This approach can be achieved by the training configuration.

Several training symbols are not that much needed for performing the simple correlation. These symbols will be used for sending the information regarding the configuration. This could be expected by the two different identical components. This approach can be used for presenting the estimation range of identical components. There are two different kinds of sub carrier spacing. Narrow spacing estimation approach can be oscillates with the maximum precision. Here higher precision could be used efficiently.

The carrier frequency offset could be estimated by the OFDM inheritance. This is usually referred to the blind approaches of the OFDM systems. Carrier frequency offset estimation is done with the impulsive response of the carrier signals. Edge of the Sub carriers will result in high performance. The efficiency is generally not used to refer the virtual carriers. The carrier impulsive ranges are typically specified by the sub space methods.

Continuous sub carriers are adjacent to the interference suppression’s. This can be easily achieved by using the medium interference. It considers the sub space functions in case of inverse sampling duration. Frequency selective medium can be used for lacking the identifiably feature. These features are used to propose the distinct space coding for the OFDM systems. Here only single training symbol is used to CFO estimation with the help of null sub carriers. All the carriers are made null to the estimation of carrier frequency offset.

This OFDM symbol contains two different similar components. One will be the fractional part which is then cause the Orthogonality loss. This can be estimated for performing the simple correlation operation on the OFDM symbols.

This will typically shifts the indexes regarding sub carriers. These can be complexly estimated by using the null sub carriers. Assigning of indexes could be done at the even positions of t eh sub carriers. Perfect correlation sequences are clearly performed and proposed by the perfect auto-correlation sequences and m- sequences known as modified sequences can be utilized in order to attain maximum efficiency of OFDM system, and an estimation range can be the inverse sampling duration’s according to the Fourier transformations.

This will also use the CFO compensation components. These to identical components are typically used to represent the low complexity. Fractional and integer parts of CFO can be depended on the perfect modified sequences as well as autocorrelation sequences in order to achieve the results of simulation.

In this case, if  only an individual training symbol is used, then the performance of transmission will be achieved by the estimations of CFO. Final CFO Estimation is also specifically described in this section. Optimal estimation with the null carriers from the OFDM symbols.

But the final CFO estimation could be expected depend on the ML criteria. Several allocation approaches are used for investigation of the CFO estimations. This estimation is very much similar to the allocation null sub carriers. The carrier frequency offset can be estimated by using the following two considerations. Perfect autocorrelation approaches and modified sub carriers. These modified carriers are denoted by m-sequences.

Various simulation outcomes will be estimated and these will typically represent the higher efficiency of the introduced allocation approaches of the sub carriers. These allocations are successfully done with the null sub carriers with distinct space coding.

Matlab Source Code:


%%%%% imlementation of pilot carrier frequnecy offset estimation in OFDm systems
%*****************************************************
% wimax parameters %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% N=256,L=16,4-qam or 16 qam modulation length of cycli prefix is
%%% 8,16,32,64
warning off;
clc;clear all;close all;
N=256; %%% length of the subcarriers
Np=N/4; %% No.of Pilot carriers
L=16; %% length of the cp
Nsym=100; %% no of symbols
iter=10; %% no of iterations
M=16; %% for symbol generation and type of modulation
Ep=2; %% energy per symbol
Ip=[1:N/Np:N]; % location of pilots
%%%%%%%%%%%%%%%%%%%%%%%%
offset=0.2571;
SNR = [0:2:20]; % signal to noise ratio vector in dB
for it=1:iter;
for j=1:length(SNR)
snr=SNR(j);
TrDataBit = randint(Nsym,1,M); %% generate random 400 symols eqn(1)
TrDataMod = qammod(TrDataBit,M); %% do IQ modulation eqn(2)
TrDataMod = Ep * TrDataMod; %% multiply with energy per symbol
TrDataIfft = ifft(TrDataMod,N); %% do ifft operation eqn(3)
s=TrDataIfft(1:(N)); %% eqn(1)
m=TrDataIfft(1:N); %% eqn(2)
alp=(N-Np)/N;
tra=[s;m];
TrDataIfftGi = [tra(N- L + 1 : N);tra]; % insert cyclic prefix eqn(4)
%%%%%%%%%%%%%%%%%%%% End of Transmitter %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
RxDataIfftGi = awgn(TrDataIfftGi,SNR(j)); %% additive white gaussian noise is inserted
%%%%%%%%%%%%%%%%%%%%%%%% End of channel %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
RxDataIfft = RxDataIfftGi(L+1:N+L); % eqn(5)
RxDataMod = fft(RxDataIfft); % eqn(6)
RxDataBit=qamdemod(RxDataMod,M);

%%%%%% Hybrid Maximum Likelihood Estimation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for k=1:Np
frk(k)=pdf(‘Normal’,RxDataBit(k),RxDataBit(N),1);
frx1(k)=pdf(‘Normal’,RxDataBit(k),0,1);
frx2(k)=pdf(‘Normal’,RxDataBit(N),0,1); %% eqn(5) in hybrid max likelihood
del(k)=log(frk(k)/(frx1(k)*frx2(k)));
end
for k=1:Np
frx3(k)=pdf(‘Normal’,RxDataBit(k),0,1);
del1(k)=log(frx3(k));
end
el(j)=max([del del1]); %% cyclic prefix based %eqn(6) in hybrid max likelihood
ro=(alp*SNR(j))/(alp*SNR(j)+1); %eqn(8) in hybrid max likelihood
%%%%% cyclic Prefix based Estimation %%%%%%%%
for k=1:Nsym
a1(k)=(RxDataBit(k)^2);
a2(k)=(RxDataBit(k)).*conj((RxDataBit(k))*exp(2*pi*offset));
decp(k)=real(a2(k)-(ro/2)*a1(k));
end
ep(j)=mean(decp/k)/j;

RxDataBit=RxDataBit(RxDataBit>0)*exp(2*pi*offset); %%eqn(17)
%%%%%%%%%%%%%%%%%%% Proposed Method %%%%%%%%%%%%%%%%
for k=1:Np
for i=1:Nsym
Z = RxDataBit(i).^2;
b1(i)=(RxDataBit(i)/Z);
end %% eqn(18)
br=sum(b1.^2);
b2=(sum(b1)/i)^2;
et(k)=(sum(1-(b2/br)))/Np*j;
end
e(it,j)=min(et);
end
end
cpp=fliplr(sort(abs(ep))); %%% cycli prefix
prp=fliplr(sort(mean(e)));
figure,
semilogy(SNR,cpp,’r’);hold on;
semilogy(SNR,prp,’b ‘);
xlabel(‘———-SNR’);ylabel(‘Mean Estimation error’);
title(‘performance evalaution of CFO’)
legend(‘Cyclic Prefix’,’Proposed’);

 

Problem definition:

OFDM systems are widely used across telecommunication world and frequency offset estimation is the tedious job across the OFDM systems. Frequency offset causes performance degradation and loss of orthogonality across the sub carriers. In this project I will estimate the frequency offset using pilot carriers and implement the compensation based on this estimation.

Aims & objectives

• Aims:

To estimate the frequency offset using pilot carriers across OFDM with MATLAB simulation.

• Objectives:

The following are the research objectives of the project:

• To recognize the concept of OFDM systems and the importance of frequency estimation across them.
• To prepare literature review on the existing Frequency offset estimation techniques and evaluate their limitation
• To design a Frequency offset estimator based on pilot carriers
• To implement compensation based on frequency offset estimation using pilot carriers.
• To simulate the proposed system in MATLAB and document the observations

Introduction and Background: 

When ever the normal working conditions of road are suspended, to provide continuity to the blocked traffic always a temporary traffic planning is required. To provide a temporary traffic control, pedestrian moments should be captured and controlled in a proper way for providing better traffic control and moment. Accommodating pedestrian across a temporary traffic control is really a tedious job, because of the changing conditions and there is no perfect pedestrian control technique in place to handle these requests.

Handling the pedestrian traffic control depends on few factors like work location, work type, traffic volumes and road types. Temporary traffic plan and its quality depend on the time taken to make it work and the plan depends on the changing conditions of pedestrian moments.

Quality of devices and the technical aspects also decide the pedestrian rerouting conditions.  In general pedestrians may fall in to some categories like physically disable and vision defects and all these constraints should be considered while planning a pedestrian traffic control plan and rerouting mechanism.

Aim:

To make research on pedestrian traffic control systems and develop a real time tracking for detecting pedestrian using MATLAB simulation

Objectives:

Following are the research objectives

• To understand the concept of pedestrian control systems and study the existing techniques to achieve this
• To prepare critical analysis on pedestrian tracking and detecting and the corresponding advantages and limitations
• To evaluate a pedestrian tracking and detection techniques
• To develop a real time pedestrian tracking and detection system using MATLAB simulation
• To analyze the results and document the observations

Following are the research questions

• What is a pedestrian traffic management system and how it helps in tracking the real time traffic?
• How to model a real time pedestrian traffic control system in MATLAB?
• What characteristics should be considered when modeling a real time pedestrian tracking and how to handle the physically and mentally disabled pedestrians?

Project type 

This is a research and development project, where I will do some basic research on the pedestrian traffic control methodologies and develop a real time tracking for pedestrians

Research methodology: 

I will make use of qualitative methods of research to develop this project. I will collect the data regarding pedestrian tracking and traffic control systems and evaluate the existing system performance and develop a MATLAB based simulation to track and detect the real time pedestrians.