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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.
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 :
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
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
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
The proposed approach can be divided into two phases: i) training phase and ii) classification phase
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:
Extract features such as texture, mean, skewness, variance, standard deviation, area, perimeter, etc. from the pre-processed MRI scans.
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.
Classification: This phase can be summarized as follows:
Take as input, MRI scan of a patient.
Pre-process the MRI scan.
Extract the required features from the patient’s MRI scan.
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.
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.
In Automatic meter reading system project using GPRS, we implemented a GPRS Automatic Meter Reading System in order to enhance security for the electrical suppliers in controlling the unauthorized electrical power usage. Web services based GPRS automatic meter reading provides better solutions for the managements in meter reading of power consumptions accurately with no scope man made errors.
The architecture of web services based GPRS automatic meter reading system is designed using microprocessor, Remote Reading Units (RRU), telephone network i.e. Communication Front End (CFE). The meter readings were collected by the remote reading units and transferred to the communication front end systems at the control system and billing is done.
Need of Automatic meter reading systems:
In order to prevent the bogus seals and tampering of seals.
To reduce Meter tampering, meter tilting, meter interface.
To control Meter bypassing.
To limit the connection changing’s and Direct tapping from line.
Various Automatic meter reading techniques:
In AMR metering technologies we transmit data from transmitter end to receiver end by means of RF technology.
There are various AMR techniques like handheld computer, touch based AMR, Mobile or Drive-by meter reading, Fixed Network AMR, Radio frequency based AMR, Power Line Communication PLC type AMR system, and Wireless Fidelity (Wi-Fi) based AMR.
Youtube video link cover block diagram , system architecture..etc
When compared to conventional metering systems automatic metering systems are quite advantageous in terms of eliminating meter reading costs in manual operations, presenting interference free data transfer from the meter reading units to the billing units, and these are cost effective reliable and free from excess human involvement.
Download Automatic Meter Reading System Project Using GPRS Full Report
The sample model is implemented in a high performance technical computing MATLAB language. The system design is done by SIMULINK software in MATLAB. The process of selecting blocks and initializing them is clearly explained in the coming sections.
The main aim of this Development of color sensor using wireless camera based on MATLAB image processing project is to develop a color sensor using a camera that can be implemented using MATLAB based Image Processing. Color is the most common feature to distinguish between objects, sorting and recognizing. This technology can be used in Packaging industries where the objects moving through a conveyor belt have to get separated. This system provides such an automatic detecting of specific colored object which presented in front of video camera. The video will be transmitted to the PC. At PC section, this can be seen on PC and further processed through MATLAB. It detects the RGB values of an object that is present in front of Camera.
The video from the camera is transmitted to PC with MATLAB. The detecting system of object has many advantages such as advanced performance, high reliability, etc. The detection unit reaches the maximum threshold level, this project can be further extended in many ways like can take the photos, displaying pictures, reading and projecting of the data on walls with help of projector.
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:
Aims & Objectives
Modeling of Communication System
Wireless channel Impairments
Mathematical Model for Wireless Mediums
Models of Intervention
Overlay & underlay Structure
Cell boundaries Overlapping
Concepts and terminology
Other IMT2000 Technologies
Major Concepts Behind UTMS
Concepts of UTMS
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.
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.
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.
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.
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
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
SNR = [0:2:20]; % signal to noise ratio vector in dB
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)
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)
%%%%%% Hybrid Maximum Likelihood Estimation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
frx2(k)=pdf(‘Normal’,RxDataBit(N),0,1); %% eqn(5) in hybrid max likelihood
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 %%%%%%%%
%%%%%%%%%%%%%%%%%%% Proposed Method %%%%%%%%%%%%%%%%
Z = RxDataBit(i).^2;
end %% eqn(18)
cpp=fliplr(sort(abs(ep))); %%% cycli prefix
xlabel(‘———-SNR’);ylabel(‘Mean Estimation error’);
title(‘performance evalaution of CFO’)
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
To estimate the frequency offset using pilot carriers across OFDM with MATLAB simulation.
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
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.
To make research on pedestrian traffic control systems and develop a real time tracking for detecting pedestrian using MATLAB simulation
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?
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
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.