The Engineering Projects

Send data to Serial Port in MATLAB

Hello friends, hope you all are having fun and enjoying life. In today's post we are gonna see how to send data to serial port in MATLAB. Its a requested tutorial, asked by a follower and after giving him the code, I thought to share it on our blog so that others could also get benefit from it. We have discussed serial port many times and have seen how to communicate with it using different software but we haven't yet discussed how to send data to serial port in MATLAB. So, in today's post I am gonna share the complete code for sending data to serial port in MATLAB.

Serial port is most common way of communication, we can send or receive data using serial port. Normally, in engineering projects there's a need to send or receive data from microcontrollers to computer and in such projects, we used serial communication as its easy and quite quick in communication.

Send data to Serial Port in MATLAB

• Its a quite simple project in which I am gonna send character over the serial port in MATLAB.
• In order to do so first of all, I am gonna create an object and assign it to serial port object in MATLAB.
• After that I am gonna set the properties of that serial port object.
• After setting the properties, what we need to do is simple start our serial port object.
• Once its started, now you can send any character via that serial port object.
• After sending your desired characters, now what you need to do is simply close the serial port.
• Closing the serial port is very essential in MATLAB because if its left open then you can't open it again in MATLAB and you need to restart your computer so be careful.
• Here's the simplest code for sending the data:

• Code 1:

tep=serial('COM1', 'BaudRate', 9600); fopen(tep); fprintf(tep,'a'); fclose(tep);

• Now you can see, its too simple, we just set the com port with which we want to communicate and after that we gave the bud rate.
• In the next line, we open our serial port object.
• Now as our serial port is open, we can send any character to it. In this code, I am sending 'a' to serial port in MATLAB.
• And finally I closed the serial port, which is very necessary as otherwise when you run this code again, it will start giving error.
• Here's a bit explanatory code and much more flexible as you can change any property of Serial Port in MATLAB, you want to change.
• Here's the code:

• Code 2:

clc clear all close all disp(' Welcome to TEP!!!'); disp(' '); disp(' www.TheEngineeringProjects.com'); disp(' '); tep=serial('COM1'); % assign serial port object set(tep, 'BaudRate', 9600); % set BaudRate to 9600 set(tep, 'Parity', 'none'); % set Parity Bit to None set(tep, 'DataBits', 8); % set DataBits to 8 set(tep, 'StopBit', 1); % set StopBit to 1 %display the properties of serial port object in MATLAB Window disp(get(tep,{'Type','Name','Port','BaudRate','Parity','DataBits','StopBits'})); fopen(tep); % Open Serial Port Object fprintf(tep,'a'); %Print character 'a' to the serial port disp('Charater sent to Serial Port is "a".'); fclose(tep); %Close Serial Port Object

• The code is quite self explanatory plus I have also added the comments in the code which will help you in understanding the code but still if you have any problem, then ask in comments.

• Here's a screen shot of the above code:

• Wen you run this code, you will get a below response in your MATLAB window:

• Till now, we have seen how to send a single character defined in the m file of MATLAB, now let's make it a bit complex and send user defined data.
• Now before sending the data, we will first ask the user to enter the data, he wants to send to serial port. Tis data could be a single character or could also be a combination of characters.
• In order to do so, we are gonna use Input command in MATLAB and code is as follows:

• Code 3:

clc clear all close all disp(' Welcome to TEP!!!'); disp(' '); disp(' www.TheEngineeringProjects.com'); disp(' '); tep=serial('COM1'); % assign serial port object set(tep, 'BaudRate', 9600); % set BaudRate to 9600 set(tep, 'Parity', 'none'); % set Parity Bit to None set(tep, 'DataBits', 8); % set DataBits to 8 set(tep, 'StopBit', 1); % set StopBit to 1 %display the properties of serial port object in MATLAB Window disp(get(tep,{'Type','Name','Port','BaudRate','Parity','DataBits','StopBits'})); fopen(tep); % Open Serial Port Object data = input('Enter character: ', 's'); %Ask user to Enter character fprintf(tep,data); %Print character 'a' to the serial port disp('Charater sent to Serial Port is:'); disp(data); fclose(tep); %Close Serial Port Object

• The screenshot of code is as follows: (I am adding the screen shot of code because its colored and thus helps better in understanding the code)

• Now when you run this m file, you will get results as shown in below figure and now you can see I have sent my blog url via serial port in Matlab.

• I think we have played enough with sending data via serial port in MATLAB, now you can send any data via serial port in MATLAB, for example you can also create an infinite loop and keep on sending data to serial port.
• That's all for today, in the coming post I will show how to receive data via serial terminal in MATLAB, so stay tuned and also subscribe us via email to get these exciting tutorials straight in your mailbox. Take care. :)

Logical Gates in Ladder Logic for PLC

In the previous post Logical Gates in Ladder Logic for PLC, we had an overview of what is Ladder Logic programming and we have also implemented three basic Logical gates in Ladder Logic form. Today, we are gonna have a look at some complex Logical Gates in Ladder Logic for PLC. So, I hope till now you guys have basic knowledge of Ladder Logic and can implement complex logical gates in it. If you haven't read the previous post then must read because without that knowledge you won't understand this post.

In today's post we are gonna implement few complex logical gates. Its not gonna be much difficult if you have the basic concepts. I am just pointing out few important points here. While implementing any gate in ladder logic, always consider rung as an electrical line having HIGH voltage at one end and LOW voltage at the other, while the inputs are simple switches. Voltage will be supplied to the output only when switch is closed i.e. input is HIGH, otherwise the output will remain OFF. You should also have a look at Introduction to Logic Gates.

You have seen in previous post, while implementing OR gate we have used a second switch in parallel which ends at the first rung so overall its a single rung having two inputs in parallel so input can come either from first switch or from second one. So, now let's start implementing some complex logical gates in Ladder Logic for PLC. Today, we are gonna implement these logic gates:

• NAND Logical gate in Ladder Logic for PLC
• NOR Logical gate in Ladder Logic for PLC
• XOR Logical gate in Ladder Logic for PLC
• XNOR Logical gate in Ladder Logic for PLC

NAND Logical gate in Ladder Logic for PLC

• NAND gate is another type of logical gate, which is normally used. NAND gate is nothing but a simple NOT of AND gate. In simple words, if we add a NOT gate in front of AND gate, we get NAND gate. The truth tablel of NAND gate is shown in the below figure:

• It is quite obvious from the truth table of NAND gate that the output will be OFF only when both the inputs will be ON otherwise output will remain ON. So, now lets implement this gate in ladder logic programming.
• The below image shows the implementation of NAND logical gate in Ladder Logic form:

• Now, if you understand the above figure, then its quite obvious. We have used both inputs in normally closed form so when both inputs are OFF the output will be ON. If we get X0 ON then in still we will get the HIGH voltage from X1. If we make X1 ON then we get HIGH voltage from X0, but if we get both X0 and X1 ON then our Y0 will get OFF. Again we are using inputs in normally closed form so when our actual input is OFF then our X0 is closed. :)

NOR Logical gate in Ladder Logic for PLC

• In NOR gate, we simple place a NOT gate in front of OR gate. Its truth table is shown in below figure:

• From the truth taable of NOR gate, its quite obvious that its output will be ON when both of its input goes OFF otherwise the output will remain ON. Lets implement this NOR logical gate in Ladder Logic diagram.
• The below figure shows the NOR logical gate in ladder logic diagram:

• If you got the ladder logic form of NAND gate, then its not gonna be much problem. Simple two normally closed inputs are placed in series, so now when any of them gets ON, then output will get OFF.

Note:

• If you have noticed, whenever NOT gate is involved somewhere, we use normally closed inputs.

XOR Logical gate in Ladder Logic for PLC

• The truth table of XOR gate is shown in below figure:

• From the truth table, we can get this thing that, output will ON only when the inputs are in opposite states and output will be OFF when inputs are in same state.
• The ladder logic implementation of XOR gate is shown in below figure:

• Now it has gone a little complex but lets understand how's its working. We have placed X0 and X1 in series and also in parallel, but in first string X0 is normally open and X1 is normally closed while in second string X0 is normally closed and X1 is normally open.
• Now, what we need to do is if both inputs are in same state we need to turn OFF the output. That's what we are doing in above logic diagram. Let's say X0 and X1 both are OFF then the normally open switches will be OFF and they wont let the HIGH voltage pass and hence our Y0 will remain OFF. And if both are ON then the normally closed will be OFF and again Y0 will remain OFF.
• Now if X0 is ON and X1 is OFF, then the first string will connect and our output will ON, and if X0 is OFF while X1 is ON then our second string will connect and will make our output ON. Quite simple and easy.

XNOR Logical gate in Ladder Logic for PLC

• Last but not the least XNOR gate, if we add NOT gate in front of XOR gate we get XNOR gate, let's have a look at its truth table below:

• So, in XNOR gate, we get our output ON when both inputs are in same state otherwise its OFF. Let's implement it in ladder logic form below:

• Now in this ladder logic diagram, we are getting introduced with a new symbol, till now we have used normally open output but here for the first time, we are using normally closed output Y0.
• It's exactly the same logic as we used for XOR gate, but the only difference is ere we are using normally closed output. So, simple when the output is gets ON, its actually OFF and when it gets OFF its actually ON ;)
• I hope now you got the clear concept of How to do programming using Ladder Logic and what's its difference with Microcontrollers like Arduino or PIC Microcontroller etc.

That's all for today, hope I have conveyed some knowledge. If you are new to PLC programming then you won't get it in the first attempt so my suggestion is give it some time, read it again and again and you will feel better :) I am not gonna post more about Ladder Logic designing, instead in the next post we are gonna have n overview of PLC simulation software in which we design this ladder logic diagrams. Till then take care and have fun :)

Introduction to Ladder Logic for PLC

Hello everyone, I hope you all are doing great. In today's tutorial, I am going to share the detailed fIn the previous post, we have seen Introduction to PLC, which was quite simple and has the basic introduction to PLC. To day we are gonna have a look at Getting Started With Ladder Logic For PLC. Ladder Logic, also named as Ladder Logic Programming, is the programming language for PLCs. Its normally considered as the most difficult language among the engineers because of its complex structure, but if you ask me then I will say its the most interesting programming language.

Ladder Logic is different from the usual programming language of Microcontrollers like Arduino, PIC Microcontroller etc. Microcontrollers programming usually compiled from top to bottom i.e. the compiler first capture the first statement and then moves downward till it reaches the end line but that's not the case with Ladder Logic Programming for PLC. In ladder logic, the compiler moves from left to right and it gets all the lines at the same time. It seems bit difficult to understand at first but be with me and you will get it at the end. :)

Introduction to Ladder Logic

Ladder Logic is a programming language used for PLC as C for Microcontrollers. Ladder logic is a combination of rungs. Each rung is executed from left to right. For example, have a look at the below figure, a single rung of ladder logic is shown in it.

• In the above figure, a single rung of ladder logic is shown. Now as I mentioned earlier, each rung is executed from left to right, so the above rung will also do the same behavior.
• There are two symbols mentioned in the above rung, one is X0 and the other is Y0. X0 is placed on the left side while Y0 is placed on the right side.
• We have seen in our previous tutorial Introduction to PLC, that X always indicates input and Y indicate output, so in short the above rung has input on the left side while output on the right side.
• So combining all the above discussion, we come to the conclusion that input will be executed first and then output will be executed, as shown below. (I hope you got the basic theory now :) )

• So now the thing is, if we only consider the above rung, the output Y0 will be ON only if input X0 will be ON. If X0 is OFF then Y0 will also be OFF. Consider this rung as a voltage wire as shown in below figure:

• So, now the output will be ON only when it has HIGH and LOW but input is acting as a switch and in normal condition, its OFF so HIGH is not reaching to output so it will remain OFF, as we turn ON the input X0, it will be like the switch is closed and HIGH will pass through the X0 and will reach Y0 and Y0 will turn ON. I tried my best to explain it as simple as I can but still having confusion, ask in comments.
• Now let's have a look on different logical gates i.e. AND gate, OR gate, NAND gate etc, we normally create in ladder logic.

1. Logical AND in Ladder Logic for PLC

• We all know about the Logical AND gate, in AND gate we get output only if both the inputs are HIGH, otherwise OUTPUT remain OFF.
• The below figure shows the same logical AND gate designed in Ladder Logic diagram:

• Now in the above figure Y0 will be ON when both X0 and X1 are ON, otherwise Y0 will be ON, again consider inputs as switches.
• Its a simple 2 input AND logic, we can add as many inputs as we want in it. For example, below image shows a four input AND gate.

2. Logical OR in Ladder Logic for PLC

• In Logical OR gate, output goes ON when any of the inputs is ON, lets implement it in our ladder logic form.
• The below figure shows the ladder logic form for OR gate:

• If you check the above figure, if X1 goes ON then our connection will connect and Y0 will be ON, similarly, if X0 is OFF and X1 is ON then again Y0 will be ON because now voltage is coming from X1. Again consider inputs as switch.

3. Logical NOT in Ladder Logic for PLC

• In logical NOT gate, output is always opposite to input, if input is HIGH then output will be LOW and vice versa.
• In order to implement NOT gate, we have to consider another type of input, the input which we are using till now is normally open input, means it is open (OFF) in its normal condition and gets closed (ON) when input is supplied, but in ladder logic there's another type of input also present named as normally closed input.
• Normally closed input is closed (ON) in its normal condition and goes open (OFF) when it gets actual input.
• So, using this normally closed input, we can quite easily implement this Logical NOT gate in ladder logic as shown in below figure:

• In the above figure X0 will be ON in normal condition and hence, Y0 will also be ON and when we get actual input then X0 will get OFF and our Y0 will also be OFF.

So, that was all for today. As you have got the basic knowledge of Ladder Logic, so now its time to have a look at How to design Logical Gates in Ladder Logics for PLC. Thanks for reading. :)

Introduction to PLC

Hello friends, I hope you all are fine and enjoying good health. Today's tutorial, as the name shows, is on Introduction to PLC. PLC is an abbreviation of Programmable Logic Controller. Recently I worked on a project in which I have to design a Automated coffee Mixing Machine Using PLC. It worked quite good and I had a great time while working on it. After completing that project, it occurred to me that I haven't posted any tutorial on PLC. So I thought of starting this tutorial. This tutorial is not gonna cover in single post so my plan is to divide it in parts.

Today. I am gonna give an overview about PLC. We will have a look on basics i.e. what is PLC? Why we use PLC instead of microcontroller like Arduino or PIC Microcontroller? What's its advantages and disadvantages? I will try to cover all about the basics. After reading this tutorial, you must have a look at Introduction to Ladder Logic for P L C, Ladder Logic is programming language for PLCs.

There are different types of PLCs available in the market manufactured by different companies, so its impossible to cover all of them. In this tutorial, I am gonna discuss Fatek PLC as I have worked on it during my project. The model I have used is Fatek PLC Fbs-20MA. The reason I used this model because it was cheap and has enough input/output ports sufficient for my project. That's why I preferred it as its engineers' task to optimize the cost as well. Let's get started with PLC.

What is PLC?

Its a basic question, which is normally asked by all the starters so I am gonna reply it first for the newbies.

• PLC is nothing but an advanced form of Microcontroller. It is usually used in industries because of its flexibility and ease of use.
• It can be attached quite easily with computer via serial port as well as usb port.
• PLC is used when we need to automate anything just like microcontroller. We attach our sensors and actuators etc with PLC and then insert some programming code in it and let it do its job.
• You have seen automated lifts, they all are operated with PLC.
• We can use timers, counters, registers in PLC and can get any kind of output from it.
• We can program PLC with different languages and the most commonly used language for PLC is named as Ladder Logic.

Internal Overview of PLC

What's inside PLC, which makes it so cool ? That's a good question and normally engineers wonder about it. PLC can be divided into 3 sections, which are as follows:

• Power Supply - Thissection provides power to the PLC, in my case it is operated on 220V AC, so when I provide 220V AC to my PLC, it got activated and start performing functions.
• Centeral Processing Unit (CPU) - Its the actual brain of PLC, it is further divided into several parts i.e. RAM, ROM, EEPROM, microcontroller etc. The programming code is uploaded in this CPU and according to that program, it performs its functions.
• Input / Output Section - This section is the one from where PLC communicates with the external world. We can attach sensors to the inputs of PLC and can operate our motors, actuators etc from the outputs of PLC.

Types of PLCs

• There are different types of PLCs available in the market manufactured by different companies.
• Few famous PLC companies are Siemens, Mitsubishi, Fatek etc.
• Moreover, they are also available in different sizes and functions. The one I used has 14 inputs and 8 outputs. It doesn't support analog inputs as I don't require them.
• There are PLCs available with analog inputs or you can also buy cards which are interfaced with the PLC and make them capable to work on analog inputs.
• Another function which is not available in my PLC is the Serial communication, but such models are available which supports serial communication.
• So in short, there are several models of PLC available in the market and you have to consider your project demands while buying a PLC.

Why use PLC instead of microcontrollers?

• Microcontroller is normally used in small products, where you need to control some sensors or some motors etc but when we talk about big automated plants in industries then PLC is always preferred over microcontroller.
• The reason for preferring PLC over microcontroller in big projects is because of its flexibilty and ease of use. PLC can be programmed frequently with computer, suppose you have an automated system and you find some bug in it which you wanna remove, then what you need to do is simply attach a computer with the PLC of that plant and make changes in the code, which isn't possible with the microcontroller.
• Moreover, PLC has lot of memory, you can add any size of data in it.
• PLC is also long life as compared to microcontroller.
• Last but not the least, PLC has built in cards to control heavy AC voltages, you can get any kind of voltage from PLC i.e. 220V AC etc but if you wanna get such voltages from microcontrollers then you have to add some extra circuitry.
• In short, in all industrial automated plants, PLC is used.

Getting Started With PLC

I think now you have the idea about PLC, so now I am getting started with PLC. I am gonna explain the functioning of Fatek PLC as I have used that one but if you are using another model of PC then no need to panic as all PLCs have same functionality. So, it doesn't matter which one you are using. If you check the below image then you will see I have marked three sections in it.

• Section 1 is indicating the status of input pins. If inputs are off then this section will remain as shown in above figure, but if any of the inputs get high, then the respective pin indicator will also glow into red, which indicates that this pin is ON. It helps while you are writing programming code for the PLC.
• Section 2 indicates the status of PLC. If PLC is powered up then the POW led will go red, if you have uploaded the code in PLC and start it then the RUN led will go red and if your code has some error then ERR led will go red.
• Section 3 indicates the status of output pins, it will tell you which output is currently ON.

In the below image, I have indicated Section 4 and 5, these are the input/ output section. If you have a look at it closely then you can see there are two rows of screws, where you plug your wires for inputs and outputs and above them, they are also labelled with white color. So, it goes like that, first row of labelling is for first row of screws and second row is for second row of screws.

• Section 4 is the inputs pins section, so if you check above there are inputs from X0 to X13, which makes it overall 14 inputs. Moreover, there are two pins labelled as + 24V and - 24V, which PLC is providing us, so if you wanna give any input to this PLC you have to make sure that its 24V, otherwise PLC not gonna recognize it.
• Section 5 is the output pins section. and you can see there are total 9 outputs starting from Y0 to Y8, now you are getting confused with C0 to C6. C pins are actually the voltage setter, let me explain, in projects there are different outputs are required like your motor is running at 12V DC while your solenoid valve is running at 220VAC. So, there's a need to set these voltages at the output. Here C pins are used. Suppose you need to give 12V output at Y0 and Y1 then give this 12V at C0 and when Y0 or Y1 is ON then they will give 12V at output. In short, when output gets ON it is actually connecting with their respective C pin.
• Lastly, check the Port 0 in the above image, this is the port where you plug your serial wire inPLC and connect it with your computer in order to upload the programming code.

That's all for today. I hope you got the basic idea of Programmable logic controller and now its time to have a look at Introduction to Ladder Logic for P L C, ladder logic is programming language for PLC. Your feedback are warmly welcome. In the next tutorial, I am gonna cover about ladder logic and will show you how to program a PLC. Till then Take care and have fun.

Introduction to Multilevel Inverters

Hello friends, today's tutorial is about Introduction to multilevel inverters, which is quite a wide field so I am not gonna discuss everything here. I will post more about it in my coming tutorials. Today, I am going to through some light on the multilevel inverter, i.e. how they operate and will also discuss their types in detail. So, let's start it.

An inverter, also named a power inverter, is an electrical power device that is used to convert direct current (DC) into alternating current (AC). Using a few control circuits and switches, one can get AC at any required voltage and frequency. Inverter plays exactly the opposite role of rectifiers as rectifiers are used for converting alternating current (AC) into direct current (DC). There are different types of inverters available these days. You should also have a look at Pure Sine wave Inverter Design with code and Modified Sine Wave Inverter Design with code. I think you are gonna like that one. Few most commonly used inverter types are:

• Square wave inverters
• Modified sine wave inverters
• Multilevel inverters
• Pure sine wave inverters
• Resonant inverters
• Grid-tie inverters
• Synchronous inverters
• Stand-alone inverters
• Solar inverters

We have designed a 3 level Diode Clamped Multilevel Inverter in Simulink MATLAB, which you can buy by clicking on the lower button. As it's mostly used by the engineering students that's why we have placed a very small price of $10 so that they can buy it easily and we also get rewarded for our efforts. Buy MATLAB Simulink Simulation

Introduction to Multilevel Inverter

• A multilevel inverter is a power electronic device that is capable of providing desired alternating voltage level at the output using multiple lower-level DC voltages as an input.
• Mostly a two-level inverter is used in order to generate the AC voltage from DC voltage.

Now the question arises what’s the need of using a multilevel inverter when we have a two-level inverter. In order to answer this question, first, we need to look at the concept of the multilevel inverter.

Concept of Multilevel Inverter

First, take the case of a two-level inverter. A two-level Inverter creates two different voltages for the load i.e. suppose we are providing Vdc as an input to a two-level inverter then it will provide + Vdc/2 and – Vdc/2 on output. In order to build an AC voltage, these two newly generated voltages are usually switched. For switching mostly PWM is used as shown in Figure 2.1, reference wave is shown in the dashed blue line. Although this method of creating AC is effective but it has few drawbacks as it creates harmonic distortions in the output voltage and also has a high dv/dt as compared to that of a multilevel inverter. Normally this method works but in few applications, it creates problems particularly those where low distortion in the output voltage is required.

PWM voltage output of a two-level inverter

The concept of multilevel Inverter (MLI) is a kind of modification of a two-level inverter. In multilevel inverters we don’t deal with the two-level voltage instead in order to create a smoother stepped output waveform, more than two voltage levels are combined together and the output waveform obtained in this case has lower dv/dt and also lower harmonic distortions. The smoothness of the waveform is proportional to the voltage levels, as we increase the voltage level the waveform becomes smoother but the complexity of the controller circuit and components also increases along with the increased levels. The waveform for the three, five and seven level inverters are shown in the below figure, where we clearly see that as the levels are increasing, the waveform becoming smoother.

A three-level waveform, a _ve-level waveform and a seven-level multilevel waveform, switched at fundamental frequency

Multilevel Inverter Topologies

There are several topologies of multilevel inverters available. The difference lies in the mechanism of switching and the source of input voltage to the multilevel inverters. Three most commonly used multilevel inverter topologies are:

• Cascaded H-bridge multilevel inverters.
• Diode Clamped multilevel inverters.
• Flying Capacitor multilevel inverters.

1. Cascaded H-bridge Multilevel Inverters

This inverter uses several H-bridge inverters connected in series to provide a sinusoidal output voltage. Each cell contains one H-bridge and the output voltage generated by this multilevel inverter is actually the sum of all the voltages generated by each cell i.e. if there are k cells in an H-bridge multilevel inverter then a number of output voltage levels will be 2k+1. This type of inverter has an advantage over the other two as it requires fewer components as compared to the other two types of inverters and so its overall weight and price are also less. Below Figure shows a k level cascaded H-bridge inverter.

One phase of a cascaded H-bridge multilevel inverter

In a single-phase inverter, each phase is connected to a single dc source. Each level generates three voltages which are positive, negative and zero. This can be obtained by connecting the AC source with the DC output and then using different combinations of the four switches. The inverter will remain ON when two switches with opposite positions will remain ON. It will turn OFF when all the inverters switch ON or OFF. To minimize the total harmonic distortion, switching angles are defined and implemented. The calculations for the measurement of switching angle will remain the same. This inventor can be categorized further into the following types:

• 5 levels cascaded H Bridge Multilevel Inverter
• 9 levels cascaded H Bridge Multilevel Inverter

In 5 level cascaded H Bridge Multilevel Inverters, Two H Bridge Inverters are cascaded. It has 5 levels of output and uses 8 switching devices to control whereas in 9 level cascaded H Bridge Multilevel Inverters, Four H Bridge Invertors are cascaded. It has 9 output levels and use and use 16 switching devices.

Applications of Cascaded H-bridge Multilevel Inverters

Cascaded H Bridge Multilevel Inverters are mostly used for static var applications i.e., in renewable resources’ of energy and battery based applications. Cascaded H Bridge Multilevel Inverters can be applied as a delta or wye form. This can be understood by looking at the work done by Peng where he used an electrical system parallel with a Cascade H Bridge. Here inverter is being controlled by regulating the power factor. Best application is when we used as photovoltaic cell or fuel cell. This is the example of Parallel connectivity of the H Bridge Multilevel Inverter.

Example of 3 phase Wye Connection

 H Bridge can also be used in car batteries to run the electrical components of the car. Also, this can be used in the electrical braking systems of the vehicles.

Scientists and engineers have also proposed the multiplicative factor on Cascade H Bridge Multilevel. It means that rather than using a dc voltage with the difference in levels, it uses a multiplying factor between different levels of the multilevel i.e., every level is a multiplying factor of the previous one.

Advantages of Cascade H Bridge Multilevel Inverters

1. Output voltages levels are doubled the number of sources
2. Manufacturing can be done easily and quickly
3. Packaging and Layout are modularized.
4. Easily controllable with a transformer as shown in the Fig 2.5
5. Cheap

 Cascaded Inverter with transformer

Disadvantages of Cascade H Bridge Multilevel Inverters

• Every H Bridge needs a separate dc source.
• Limited applications due to a large number of sources.

2. Diode Clamped Multilevel Inverters

Diode clamped multilevel inverters use clamping diodes in order to limit the voltage stress of power devices. It was first proposed in 1981 by Nabae, Takashi and Akagi and it is also known as a neutral point converter. A k level diode clamped inverter needs (2k – 2) switching devices, (k – 1) input voltage source and (k – 1) (k – 2) diodes in order to operate. Vdc is the voltage present across each diode and the switch. Single-phase diode clamped multilevel inverter is shown in the figure below:

One phase of a diode clamped inverter

The concept of diode clamped inverter can better be understood by looking into a three-phase six-level diode clamped inverter. Here the common dc bus is shared by all the phases, use five capacitors and six levels. Each capacitor has a voltage of Vdc and same is the voltage limit of switching devices. One important fact should be noted while considering the diode clamped inverter is that five switches will remain ON at any time. Six level, three-phase dc clamped multilevel inverter is shown in the figure below.

Six level three phase inverter

Outputs of each phase can be understood by the following table. Here reference voltage is the negative Vo. Condition 0 means switch is OFF and vice versa. Output waveforms of six level dc clamped inverter is shown below:

Waveform of Six Level Inverter

 Vab is the voltage due to the phase lag b and a voltage.

Applications of Diode Clamped Multilevel Inverters

The most common application of diode clamped multilevel inverter is when a high voltage Dc and Ac transmission lines are interfaced. This can also be used in variable speed control of high power drives. Static variable compensation is also an application of diode clamped multilevel inverters.

Advantages of Diode Clamped Multilevel Inverters

• The capacitance of the capacitors used is low.
• Back-to-back inverters can be used.
• Capacitors are precharged.
• At fundamental frequency, efficiency is high.

Disadvantages of Diode Clamped Multilevel Inverters

• Clamping diodes are increased with the increase of each level.
• The DC level will discharge when control and monitoring are not precise.

3. Flying Capacitor Multilevel Inverters

The configuration of this inverter topology is quite similar to previous one except the difference that here flying capacitors is used in order to limit the voltage instead of diodes. The input DC voltages are divided by the capacitors here. The voltage over each capacitor and each switch is Vdc. A k level flying capacitor inverter with (2k – 2) switches will use (k – 1) number of capacitors in order to operate. The figure below shows a five-level flying capacitor multilevel inverter.

A Flying Capacitor Multilevel Inverter with five voltage levels

If we compare above figures, it shows that the number of switches, main diodes and DC-bus capacitors are same in both the cases. The only difference between the two topologies is that the previous one uses clamping diodes in order to limit the voltage while this topology uses flying capacitors for this purpose, and as capacitors are incapable of blocking the reverse voltage, which diodes do, the number of switches also increases. Voltage on each capacitor is differing from the next as it has a ladder structure. Voltage difference between two back to back capacitors determines the voltage in the output frame.

Advantages of Flying Capacitor Multilevel Inverters

Static var generation is the best application of Capacitor Clamped Multilevel Inverters.

• For balancing capacitors’ voltage levels, phase redundancies are available.
• We can control reactive and real power flow.

Disadvantages of Flying Capacitor Multilevel Inverters

• Voltage control is difficult for all capacitors.
• Complex startup.
• Switching efficiency is poor.
• Capacitors are expensive than diodes.

So, that was all about Multilevel Inverters and their topologies. I hope you guys have understood this concept in detail. Please let me know if you have any questions. Till next tutorial, take care and have fun !!! :)

Black Litterman Approach in MATLAB

Buy This Project

This post is the next part of our previous post Financial Calculations in MATLAB named as Implementation of Black Litterman Approach in MATLAB, so if you haven't read that then you can't understand what's going on here so, its better that you should first have a look at that post. Moreover, as this code is designed after a lot of our team effort so its not free but we have placed a very small cost of $20. So you can buy it easily by clicking on the above button.In the previous post, we have covered five steps in which we first get the financial stock data, then converted it to common currency and after that we calculated the expected returns and covariance matrix and then plot the frontiers with and without risk free rate of 3%. Now in this post, we are gonna calculate the optimal asset allocation, average return after the back test, calculation of alphas and betas of the system and finally the implementation of black-litterman approach. First five steps are explained in the previous tutorial and the next four steps for Implementation of Black Litterman Approach in MATLAB are gonna discuss in this tutorial, which are as follows:

• Step 6: Calculate Optimal Asset Allocation
• Step 7: Average Return after the back test
• Step 8: Calculation of Standard Deviation
• Step 9: Calculate alphas & betas of the system
• Step 10: Implementation of Black Litterman Approach

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• How to Create a GUI in MATLAB?
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Step 6: Calculate Optimal Asset Allocation

In this part of the problem,I calculated the optimal asset allocation in the different years by choosing a constant required return. I chose the constant required return equal to 0.01 and used the highest_slope_portfolio function and plot the graphs. The code used in MATLAB is shown in the below:

ConsRet=0.010 for w=1:10 [xoptCR(:,w), muoptCR(w), sigoptCR(w)] = highest_slope_portfolio( covmat{1,w}, ConsRet , estReturn(w,:)', stdRet(w,:) ); End

The result for this part is shown in the Figure 3. For a constant required return of 0.01, portfolio turnover for each year has increased approximately by 2%.

Figure: Optimal asset allocation for constant return of 0.01

Step 7: Average Return after the back test

In this part of the assignment, it was asked to perform a back test for the optimized portfolios and calculate the average return and the standard deviation for this portfolio. Average return is the average of expected return for the corresponding year and it is calculated by the below code in MATLAB, where w is varying from 1 to 10 to calculate for all the 10 years.

BTreturn(w,:)=estReturn(w,:)*xoptCR(:,w);

Results obtained after the back test for the optimized portfolios for average return are shown in table3 below:

Average Return after the back test
1st year	0.1158
2nd year	0.0889
3rd year	0.0797
4th year	0.0628
5th year	-0.0660
6th year	-0.4323
7th year	-5.1212
8th year	-0.6820
9th year	0.2891
10th year	0.1691

Table : Average return after the back test

Step 8: Calculation of Standard Deviation

The standard deviation of the rate of return is a measure of risk. It is defined as the square root of the variance, which in turn is the expected value of the squared deviations from the expected return. The higher the volatility in outcomes, the higher will be the average value of these squared deviations. Therefore, variance and standard deviation measure the uncertainty of outcomes. Symbolically,

Where,

• S = Standard Deviation
• E(r) = Expected return
• p(s) = Probability of each scenario
• r(s) = Holding-price return in each scenario.
• s = number of scenarios.
• n = Maximum number of scenarios.

Matlab code for calculating the standard deviation after the back test is shown below:

BTstd(w,:)=sqrt(stdRet(w,:).^2*(xoptCR(:,w).^2));

The results of standard deviation for the 10 years are as follows:

Standard Deviation after the back test
1st year	0.2942
2nd year	0.1833
3rd year	0.1799
4th year	0.1733
5th year	0.4026
6th year	1.5834
7th year	16.1279
8th year	2.1722
9th year	1.0430
10th year	0.4949

 

Step 9: Calculate Alphas & Betas of the system

In this part, it was asked to use a broad stock index for testing and check whether our portfolio is in line with the CAPM prediction: Hence, first of all I retrieved the historical data for ticker ^FTLC using the function hist_stock_data as I done in the first part of the assignment. The MATLAB code for retrieving the data is shown below:

FTLC= hist_stock_data('01111993','01112013','^FTLC','frequency','m'); LRFTLC=diff(log(FTLC.Close));

After that I calculated the expected return of one unit of each asset based on the model which gave me the total covariance matrix for the single and multi indexed model. Finally, I calculated the alpha and beta for the model. The MATLAB code used for performing these actions is shown below:

for i = 1:7 mdl_si{i} = LinearModel.fit(LRFTLC, logRet(:,i), 'linear'); mdl_ret_s(i) = mdl_si{i}.Coefficients.Estimate(1:2)' * [1; 12*mean(LRFTLC)]; mdl_cov_s(i) = mdl_si{i}.RMSE^2; alpha_s = [mdl_si{i}.Coefficients.Estimate(1), alpha_s]; beta_s = [mdl_si{i}.Coefficients.Estimate(2), beta_s]; end

As a result, alphas and betas of the system are obtained which are shown in the table below:

Aplha	Beta
0.0068	0.1748
0.0057	0.0335
0.0050	0.0528
0.0063	0.0552
0.0030	0.0249
0.0035	0.0083
0.0047	0.0289

Table: Alpha & beta of the model

Step 10: Implementation of Black Litterman approach

In this part, I have finally implemented the Black Litterman approach on the data available. Black Litterman approach involves the below steps:

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1. The Covariance Matrix from Historical Data
2. Determination of a Baseline Forecast
3. Integrating the Manager’s Private Views
4. Revised (Posterior) Expectations
5. Portfolio Optimization

The code used for implementing the black litterman approach is shown below and the results obtained as a result of Black Litterman approach are shown in the below figure. Risk aversion is take as 1.9 while the precision is 0.3.

gamma = 1.9; tau = .3; for w=1:10 ind=[(w-1)*12+1 (w+10)*12]; logRet2=logRet(ind(1):ind(2),:); for i=1:7 OplogRet(:,i)=logRet2(i,:).*xoptCR(i,w)'*100; end covmatOP{1,w}=12*cov(OplogRet); end for w=1:10 Opweig(:,w)=xoptCR(:,w); Pi{w}= gamma *covmatOP{1,w}* Opweig(:,w);   end figure for w=1:10 title('BL'); hold on; bar(Pi{w}) hold on; end

Figure: Bar graph for Black-Litterman approach

That's all for today, hope it will help you all in some way. If you have any questions then ask in comments and I will try my best to resolve them.

Financial Calculations in MATLAB

Buy This Project Hello friends, today I am gonna share a MATLAB project related to finance which I have named as Financial Calculations in MATLAB. As this project is the outcome of our team efforts so its not free and you can buy it quite easily for just $20 by clicking on the above button. In finance studies, there are lot of calculations are required to be done so MATLAB plays a very important role in finance calculations and showing the pattern in graphical form. In this project, I am first gonna take the Historical Stock Data from web search online so in order to run this program, your computer must have internet access. After getting the historical stock data of 7 different markets or sectors, now there's a need to convert them into same currency so that we can compare it with each other. So, I took US$ as a common currency and convert all others to this currency.

After that I calculated Expected Returns & Covariance Matrix for all these data for the last 10 years and finally plot them. Moreover, I have also plotted the frontiers with risk free rate of 3% so that the ideal and realistic conditions can be compared. So, here are the overall steps we are gonna cover in this project:

• Step 1: Getting Historical Stock Data
• Step 2: Conversion of currencies
• Step 3: Calculate Expected Returns & Covariance Matrix
• Step 4: Plotting 11 different frontiers in one figure
• Step 5: Plotting frontiers with risk free rate of 3%.

These are the first five steps of this project. The next five steps are discussed in Implementation of Black-Litterman Approach in MATLAB.

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• How to Create a GUI in MATLAB?
• Protect Code in M File
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Step 1: Getting Historical Stock Data

• First of all, I chose 7 financial stock indices for different markets or sectors. So, I used a function named hist_stock_data. The syntax of this function is as follows:

Stocks = hist_stock_data('X', 'Y', 'Z', 'frequency', 'm')

• X is the starting date and is a string in the format ddmmyyyy.
• Y is the ending date and is also a string in the format ddmmyyyy.
• Z is the Ticker symbol, whose historical stock data is needed to be retrieved.
• M denotes that the function will return historical stock data with a frequency of months.

Hist_stock_data function retrieves historical stock data for the ticker symbol Z between the dates specified by X and Y. The program returns the stock data in a structure giving the Date, Open, High, Low, Close, Volume, and Adjusted Close price adjusted for dividends and splits. I used 7 sectors and retrieved their data using this function. The commands used in MATLAB are:

snp500 = hist_stock_data('01111993','01112013','^GSPC','frequency','m'); NDX=hist_stock_data('01111993','01112013','^NDX','frequency','m'); GSPTSE=hist_stock_data('01111993','01112013','^GSPTSE','frequency','m'); DAX = hist_stock_data('01111993','01112013','^GDAXI','frequency','m'); CAC40=hist_stock_data('01111993','01112013','^FCHI','frequency','m'); FTAS=hist_stock_data('01111993','01112013','^FTAS','frequency','m'); SSMI=hist_stock_data('01111993','01112013','^SSMI','frequency','m');

Step 2: Conversion of currencies

The first two values were in US dollars, while third one was in CAD dollars, fourth and fifth values were in Euros, sixth were in GBP and the last one was in Swiss France. Hence, there was a need to convert all these currencies into one currency as instructed. I converted all of them into US dollars. In order to convert them, I first defined the exchange rate between these currencies and the US dollars using the below code:

GBP2USD=1.6; EURO2USD=1.34; CAD2USD=0.95; SF2USD=1.08;

After defining the exchange rate, I applied it to all the currencies using the below code:

GSPTSE=CurrencyConvert(GSPTSE, CAD2USD); DAX=CurrencyConvert(DAX, EURO2USD); CAC40=CurrencyConvert(CAC40, EURO2USD); FTAS=CurrencyConvert(FTAS, GBP2USD); SSMI=CurrencyConvert(SSMI, SF2USD);

Now all the currencies for the financial stock indices for 7 sectors are obtained in US dollars.

Step 3: Calculate Expected Returns & Covariance Matrix

In this part, we calculated the expected returns and covariance matrix in annual steps for a period of 10 years. I used the data for 7 markets or sectors were obtained in the first step and calculated their expected returns and covariance matrix. The expected rate of return is a probability-weighted average of the rates of return in each scenario. We may write the expected return as:

Where,

• p(s) = Probability of each scenario
• r(s) = Holding-price return in each scenario.
• s = number of scenarios.
• n = Maximum number of scenarios.

First of all, I initialized a column matrix and filled it with the above data and took log of each data separately by using MATLAB commands as follows:

covmat=cell(1,11); ret=flipud([log(SSMI.Close) log(FTAS.Close) log(CAC40.Close) log(DAX.Close) log(GSPTSE.Close) log(snp500.Close) log(NDX.Close)]);

Further, I calculated the differences between adjacent elements of each data using the diff command in MATLAB.

logRet=[diff(ret(:,1)) diff(ret(:,2)) diff(ret(:,3)) diff(ret(:,4)) diff(ret(:,5)) diff(ret(:,6)) diff(ret(:,7))];

Expected return is nothing other than a guaranteed rate of return. However, it can be used to forecast the future value of a market, and it also provides a guide from which to measure actual returns. Hence to calculate the expected return, I first take two indices with w as a variable where w = 1:10 and calculated these indices for all the values of w and finally took mean value of each data with these two indices as follows:

ind=[(w-1)*12+1 (w+10)*12]; estReturn(w,:)=[mean(logRet(ind(1):ind(2),1)) mean(logRet(ind(1):ind(2),2)) mean(logRet(ind(1):ind(2),3)) mean(logRet(ind(1):ind(2),4)) mean(logRet(ind(1):ind(2),5)) mean(logRet(ind(1):ind(2),6)) mean(logRet(ind(1):ind(2),7))];

The above values calculated for all the 10 times and hence it is placed within a for loop. estReturn gives us the estimated return for a single month and now there’s a need to convert it to annual return, which is accomplished by below command, simply by mutilpying it with 12.

estReturn(w,:)=estReturn(w,:)*12;

After the calculation of expected return annually, I used the command var(X) to calculate the variance of the data. Although it was not asked to calculate in the problem but in order to calculate the covariance, it is required to first obtain the data such that each row of the matrix becomes an observation and each column is a variable. The command used for the calculation of variance is as shown below:

stdRet(w,:)=sqrt(12*var([ logRet(ind(1):ind(2),1) logRet(ind(1):ind(2),2) logRet(ind(1):ind(2),3) logRet(ind(1):ind(2),4) logRet(ind(1):ind(2),5) logRet(ind(1):ind(2),6) logRet(ind(1):ind(2),7)]));

Finally, I used the MATLAB command cov(x) to calculate the covariance of the data. The syntax used is:

Y = cov(X)

Where,

• X = Matrix of data whose covariance is required.
• Y = Covariance of data X.

As x is a matrix of data in our case where each row is an observation, and each column is a variable, cov(X) is the covariance matrix. Results for the expected return and covariance matrix are shown in the below tables.

Expected Return	SSMI	FTAS	CAC40	DAX	GSPTSE	SNP50	NDX
1st year	0.0625	0.0373	0.0524	0.0632	0.0700	0.0848	0.0644
2nd year	0.0954	0.0531	0.0762	0.0846	0.0884	0.0921	0.1290
3rd year	0.0872	0.0506	0.0972	0.0940	0.0915	0.0763	0.1004
4th year	0.0742	0.0457	0.0814	0.0925	0.0747	0.0610	0.0835
5th year	0.0006	-0.0064	0.0122	0.0147	0.0321	-0.0058	0.0110
6th year	-0.0112	0.0007	-0.0039	0.0103	0.0537	-0.0055	0.0115
7th year	-0.0144	-0.0069	-0.0356	0.0115	0.0494	-0.0148	-0.0307
8th year	-0.0314	-0.0034	-0.0573	-0.0041	0.0295	-0.0048	-0.0080
9th year	0.0081	0.0180	-0.0209	0.0359	0.0454	0.0198	0.0470
10th year	0.0431	0.0529	0.0228	0.0907	0.0644	0.0575	0.1007

Table 1: Expected Returns for 10 years

 

Covariance Matrix for 1st year
SSMI	FTAS	CAC40	DAX	GSPTSE	SNP50	NDX
0.0334	0.0191	0.0285	0.0324	0.0168	0.0190	0.0196
0.0191	0.0201	0.0234	0.0271	0.0155	0.0169	0.0257
0.0285	0.0234	0.0434	0.0444	0.0211	0.0226	0.0380
0.0324	0.0271	0.0444	0.0609	0.0252	0.0280	0.0527
0.0168	0.0155	0.0211	0.0252	0.0268	0.0198	0.0342
0.0190	0.0169	0.0226	0.0280	0.0198	0.0234	0.0379
0.0196	0.0257	0.0380	0.0527	0.0342	0.0379	0.1411

Covariance Matrix for 2nd year
SSMI	FTAS	CAC40	DAX	GSPTSE	SNP50	NDX
0.0318	0.0179	0.0273	0.0322	0.0162	0.0185	0.0235
0.0179	0.0180	0.0213	0.0258	0.0151	0.0161	0.0269
0.0273	0.0213	0.0404	0.0427	0.0209	0.0219	0.0390
0.0322	0.0258	0.0427	0.0590	0.0255	0.0279	0.0503
0.0162	0.0151	0.0209	0.0255	0.0265	0.0193	0.0367
0.0185	0.0161	0.0219	0.0279	0.0193	0.0230	0.0394
0.0235	0.0269	0.0390	0.0503	0.0367	0.0394	0.1014

Covariance Matrix for 3rd year
SSMI	FTAS	CAC40	DAX	GSPTSE	SNP50	NDX
0.0314	0.0181	0.0280	0.0320	0.0164	0.0186	0.0237
0.0181	0.0181	0.0214	0.0261	0.0152	0.0160	0.0270
0.0280	0.0214	0.0397	0.0433	0.0212	0.0224	0.0399
0.0320	0.0261	0.0433	0.0579	0.0259	0.0282	0.0509
0.0164	0.0152	0.0212	0.0259	0.0266	0.0194	0.0371
0.0186	0.0160	0.0224	0.0282	0.0194	0.0227	0.0395
0.0237	0.0270	0.0399	0.0509	0.0371	0.0395	0.1014

Step 4: Plotting 11 different frontiers in one figure

In this part, it was asked to plot the 11 different frontiers in one figure. The concept of efficient frontier was introduced by Harry Markowitz and it is defined as if a portfolio or a combination of assets has the best expected rate of return for the level of risk, it is facing, then it will be referred as “efficient”.

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In order plot them, I used the below code in MATLAB, I used different markers for different plots so that they could be distinguished from one another quite easily. Moreover, I used the command legend in order to show the respective years for which the graphs are plotted. RF is a variable which indicate the risk-free rate and as it is asked to operate under efficient frontier that’s why risk-free rate is equal to zero.

RF=0; for w=1:10 [xopt(:,w), muopt(w), sigopt(w)] = highest_slope_portfolio( covmat{1,w}, RF, estReturn(w,:)', stdRet(w,:) ); end mker=['o' '+' '*' '.' 'x' 's' 'd' '^' '>' '<'] figure for w=1:10 plot (sigopt(w), muopt(w) ,'Marker', mker(w)); hold on; plot (0, RF, 'o'); hold on; RF_p1 = [0 sigopt(w) 2* sigopt(w)]; opt1_p = [.02 muopt(w) (2 * muopt(w) - RF) ]; line(RF_p1, opt1_p ); end legend('Year 1994', 'Year 1995', 'Year 1996', 'Year 1997','Year 1998','Year 1999','Year 2000', 'Year 2001', 'Year 2002', 'Year 2003')

In this code, first of all I used highest_slope_portfolio function. This function finds the portfolio with the highest slope. Results are shown in the below figure annually:

Figure 1: Graph for 11 different efficient frontier

Step 5: Plotting frontiers with risk free rate of 3%

The plot in Part (c) was about the efficient frontier with risk rate equal to zero i.e. operating in an ideal condition while in this part, it was asked to plot the same graphs but this time include a risk-free rate of 3%. Hence I used the same code as for the previous part but only this time I used RF = 0.03 as I needed to include a risk free rate of 3%. The code used in MATLAB for this part is shown below:

RF=0.03; %the risk free rate for w=1:10 [xoptRF{w}, muoptRF(w), sigoptRF(w)] = highest_slope_portfolio( covmat{1,w}, RF, estReturn(w,:)', stdRet(w,:) ); end figure for w=1:10 plot (sigoptRF(w), muoptRF(w) ,'Marker', mker(w)); hold on; plot (0, RF, 'o'); hold on; RF_p1 = [0 sigoptRF(w) 2* sigoptRF(w)]; opt1_p = [.02 muoptRF(w) (2 * muoptRF(w) - RF) ]; line(RF_p1, opt1_p ); end legend('Year 1994', 'Year 1995', 'Year 1996', 'Year 1997','Year 1998','Year 1999','Year 2000', 'Year 2001', 'Year 2002', 'Year 2003')

The result for this part i.e. after including a risk-free rate of 3% is shown in the figure below. If the Figure 1 and Figure 2 are closely examined then one can see the clear difference. When the risk-free rate was considered zero, all the expected estimate graphs were in positive direction depicting the profit annually while after adding a risk-free rate of 3%, few of the graphs went in the negative direction depicting the loss in those years. Hence adding the risk factor may cause the business to get deceased and it may even cause it to decline continuously which results in disaster.

Figure 2: Graph for 11 different efficient frontier for risk-free rate of 3%

That's all for today, in the coming post, I will calculate more financial terms like Optimal asset allocation, average return, standard deviation and much more, so stay tuned and have fun.

Getting Data From Webserver using Arduino Wifi

In today’s post, as the name suggests, we will see how to get data from online webserver using Arduino Wifi in simple steps. Getting data from web server using Arduino Wifi Shield has always remained a problem for the engineers. Its not much difficult task as its usually considered to be. In today’s post, I will create a small project in which I will control two simple LEDs via online web server. It’s really a very interesting project and when I completed it, I felt like Hurrah!!!

Arduino Wifi Shield is used to connect Arduino board with Wifi. After connectivity with Wifi, one can perform many tasks using this shield. We can built a complete server on it and can also use it as a client. Server designed on an Arduino Wifi Shield are usually quite simple as it doesn’t have much processing power to support heavy server. Arduino Wifi Shield is mostly used in home automation projects where home appliances are controlled by Wifi or can also be used for security purposes. In short, it has numerous applications and is widely used.

In today’s project, we will use Arduino UNO board for programming purposes, and will interface two leds with it and then we will control these leds via an online web server. Using that online web server, we will ON and OFF these leds on command. For controlling leds from an online server, we have to design two things:

• Online Web Server.
• Arduino Web Client.

Note:

• The complete project has been sent to all the subscribed members. If you want this project code, then Subscribe to our mailing list, and it will be automatically emailed to you as well.

Project Description

First of all, I will explain what we are doing and what we want to achieve. In hardware, we will use:

• Arduino UNO
• Arduino Wifi Shield
• LEDs x 2
• 10k ohm x 2

Their arrangement and pin configuration is shown in the Arduino Web Client section. We will arrange them in such a way that two leds will be mounted on the Arduino UNO shield. In web server, we will design a simple page, which will be having four buttons on it, which will be:

• LED 1 ON
• LED 1 OFF
• LED 2 ON
• LED 2 OFF

When someone will open this web page and will pres any of these buttons, respective task will be performed on the Leds. i.e. if someone pressed the LED 1 ON button then Led 1 present on the Arduino board will get ON and when someone press LED 1 OFF button, that Led will go OFF and same function will be performed for second led. There won’t be any connection between the hardware and that web server, the only connection will be the Wifi. The Arduino Shield must have a Wifi connection available and one sitting from across the world can control them. Now let’s discuss these two parts, one by one.

Online Web Server

I have designed the online web server on my own site The Engineering Projects. This is a php page which I have uploaded on my web server. In order to make this page, simply follow the below steps:

• Create a notepad and rename it to ArduinoWifi.php and save it somewhere, from where you can easily access it.
• The webserver code is for sale and you can buy it just for $50. We have input a lot of effort to accomplish this project that's why we have placed a very small amount on it. You can easily buy it by clicking the below button.

Buy WebServer Code

• Save the file again and our web page is now ready to be uploaded. This web page will act as a web server for the Arduino device and will send commands to it.
• Now we need to upload this web page on some website so that it can start working. In order to do so, you must have some web domain and hosting as I have mine on Godaddy or you can also use some free web hosting service.
• What this code is doing, its actually using a file system. Whenever any button on this web page is pressed, it simply create a txt file with a letter. In this code, when someone pressed LE 1 ON button, character “1" is saved in the txt file. Similarly character “2" is saved in the txt file when LED 1 OFF is pressed and so on.

Note:

• When you upload the webpage on the web server, then hit any button and check your web location where this page is uploaded, you must find a data.txt file in the same location.
• If you can’t find the data.txt then generate it by yourself because in some web servers, generation of such files automatically is not granted.

We are done with the Web Server part, now let’s come to the Arduino Web Client part.  

Arduino Web Client

• First of all, place the Arduino Wifi Shield over the Arduino UNO shield, as shown in the below figure:

• Now, you need to place the two leds on two your Arduino UNO. The pin configuration for these LEDs are shown in the below figure:

   

• Two leds are connected to pin no 3 and 4 of Arduino UNO board and are pulled down.
• Now connect your Arduino UNO board with the computer and burn the below sketch into it.

 #include <SPI.h>

 #include <WiFi.h>

char ssid[] = “EvoWingle-12F3“; // your network SSID (name)

 char pass[] = “093B3453“; // your network password (use for WPA, or use as key for WEP)

 int keyIndex = 0; // your network key Index number (needed only for WEP)

int status = WL_IDLE_STATUS;

 char server[] = “www.theengineeringprojects.com“; // name address for Google (using DNS)

 String location = “/Examples/data.txt HTTP/1.0“;

 char inString[500]; // string for incoming serial data

 int stringPos = 0; // string index counter

 byte statusLed = 0;

 char c;

 int led1 = 3;

 int led2 = 4;

 WiFiClient client;

unsigned long lastConnectionTime = 0; // last time you connected to the server, in milliseconds

 boolean lastConnected = false; // state of the connection last time through the main loop

 const unsigned long postingInterval = 10*1000; // delay between updates, in milliseconds

void setup() {

 //Initialize serial and wait for port to open:

 Serial.begin(9600);

 pinMode(led1,OUTPUT);

 pinMode(led2,OUTPUT);

digitalWrite(led1, LOW);

 digitalWrite(led2, LOW);

// check for the presence of the shield:

 if (WiFi.status() == WL_NO_SHIELD) {

 Serial.println(“WiFi shield not present”);

 // don’t continue:

 while(true);

 }

// attempt to connect to Wifi network:

 while ( status != WL_CONNECTED) {

 Serial.print(“Attempting to connect to SSID: “);

 Serial.println(ssid);

 // Connect to WPA/WPA2 network. Change this line if using open or WEP network:

 status = WiFi.begin(ssid, pass);

// wait 10 seconds for connection:

 delay(10000);

 }

 Serial.println(“Connected to wifi”);

 printWifiStatus();

Serial.println(“nStarting connection to server…”);

 // if you get a connection, report back via serial:

 if (client.connect(server, 80)) {

 Serial.println(“connected to server”);

 // Make a HTTP request:

 client.print(“GET “);

 client.println(location);

 client.println(“Host: theengineeringprojects.com”);

 // client.println(“Connection: close”);

 client.println();

 //readPage();

 }else{

 Serial.println(“connection failed”);

 }

 }

 void loop(){

while (client.available()) {

 c = client.read();

 Serial.write(c);

 CheckingStatus();

 }

if (!client.connected() && lastConnected) {

 Serial.println();

 Serial.println(“disconnecting.”);

 client.stop();

 }

 if(!client.connected() && (millis() – lastConnectionTime > postingInterval)) {

 PingRequest();

 }

 lastConnected = client.connected();

 }

 void PingRequest(){

 if (client.connect(server, 80)) {

 // Serial.println(“connected to server”);

 // Make a HTTP request:

 client.print(“GET “);

 client.println(location);

 client.println(“Host: theengineeringprojects.com”);

 client.println(“Connection: close”);

 client.println();

 //readPage();

 lastConnectionTime = millis();

 }else{

 //Serial.println(“connection failed”);

 client.stop();

 }

 }

 void CheckingStatus(){

 inString[stringPos] = c;

 if(c == ‘*’)

 {

 statusLed = inString[stringPos - 1];

 stringPos = 0;

 // Serial.write(statusLed);

 delay(500);

 UpdatingStatus();

 // delay(500);

 // client.flush();

 // delay(10000);

 //PingServer();

 }

 stringPos ++;

}

void UpdatingStatus(){

 if(statusLed == ’1')

 {

 digitalWrite(led1, HIGH);

 // Serial.write(‘OK’);

 }

 if(statusLed == ’2')

 {

 digitalWrite(led1, LOW);

 }

 if(statusLed == ’3')

 {

 digitalWrite(led2, HIGH);

 }

 if(statusLed == ’4')

 {

 digitalWrite(led2, LOW);

 }

 }

void printWifiStatus() {

 // print the SSID of the network you’re attached to:

 Serial.print(“SSID: “);

 Serial.println(WiFi.SSID());

// print your WiFi shield’s IP address:

 IPAddress ip = WiFi.localIP();

 Serial.print(“IP Address: “);

 Serial.println(ip);

// print the received signal strength:

 long rssi = WiFi.RSSI();

 Serial.print(“signal strength (RSSI):”);

 Serial.print(rssi);

 Serial.println(” dBm”);

 }

• Before burning this sketch into your Arduino Shield, you need to change few things which I have written in bold style in the above code, and mentioned below:Now, everything’s done, so power up your Arduino and make sure that it has got connected with the Wifi and then hit the LED 1 ON button on your web page. If everything’s gone fine then the led on your Arduino board will get ON.
  • ssid is the name of your Wifi connection.
  • pass is the password of your wifi connection.
  • server is the server name on which the web page is uploaded, which in my case is my own site.
  • location is the location of the txt file generated by the webpage, the above web page code generated a txt file in the same location and gives it a name data.txt so I simply used that. (This part is bit confusing but read it twice and you will get it.)
• I have added comments with the Arduino code but if still, you get into some trouble, ask in comments and I will try my best to resolve them.

Note:

• Before starting anything, first run the demo code which comes with the Arduino Library and make sure that your Arduino Shield get connected with your Wifi connection.
• Give the link of txt file generated by the web page, not the the web page itself.

That’s all for today, Stay Blessed, take care. :))

Modelling of DVB-T2 system using Consistent Channel Frequency MATLAB

Buy This Project

Hello friends, today I am going to post a complete project designed on MATLAB named as Modelling of DVB-T2 system using Consistent Channel Frequency in MATLAB. This project is designed by our team and it involved a lot of effort to bring it into existence that's why its not free but as usual I have discussed all the details below related to it, which will help you understanding it and if you want to buy it then you can click on the Buy button shown above.

This project aims to implement a DVB-T2 (Digital Video Broadcasting for terrestrial television) system using consistent channel frequency responses. Tthe code is designed to use the same output from a channel model for different transmitter configurations so that consistency of performance results can be obtained. After that the overall project will be modified to repeat an experiment “n” times collecting data so that “x%” confidence intervals can be calculated. Historically, DVB is a project worked by more than 250 companies around Europe at first and now worldwide. DVB-T2 is the world’s most advanced digital terrestrial television (DTT) system, offering more robustness, flexibility and at least 50% more efficiency than any other DTT system. It supports SD, HD, mobile TV, or any combination thereof. The GUI for DVB-T2 parameters selection in MATLAB is shown on the left.

MATLAB Simulations & Results

DVB-T2 is the second generation standard technology used for digital terrestrial TV broadcasting. As it’s a new technology so it has many fields to explore and research, and the best way of researching on any new technology is via simulations. Simulations provide an easy and efficient way to evaluate the performance of any system. For simulation purposes, MATLAB software was chosen in this thesis because of its wide range of tools and ability to show graphical results in a very appropriate form. . Further, this DVB-T2 simulation model could be extended easily to simulate DVB-H, which shares many features with DVB-T2 (only the physical layer that needs modification). The most important feature, I discussed in my simulations are:

• Comparison of Bit Error Rate (BER) and Signal to noise Ratio (SNR).

DVB-T2 scheme can handle wide range of sub carriers from a range of 1k to 32k; these sub carriers can be fixed or mobile. In this thesis, experiments are performed on mobile transmission of signals to 4000 sub carriers. Below are discussed three different mobile scenarios, for different speeds of mobiles user, which are:

• Pedestrian moving at the speed of 0.1 km/h.
• Bus moving at the speed of 5km/h.
• Car moving at the speed of 10 km/h.

In all the scenarios, the factors mentioned below are kept constant so that a real comparison can be obtained and it could be checked that whether the speed affects the signal or not. These constant factors are:

• Transmission Mode is SISO.
• Transmitting Antenna Cross Correlation is 0.5.
• Receiving Antenna Cross Correlation is 0.5.
• Environment considered for these simulations is rural.
• Radio Channel type is Rayleigh with k factor of 1000.
• Carrier frequency is 91.429MHz.

1.1  Initial MATLAB model

During this thesis, help was taken from a MATLAB model of DVB-T2 transmission system designed by a student at Brunel University. First this initial model was studied and then enhanced it to a higher level. The first model designed by the student at Brunel University, performed the iterations on the DVB-T2 system and gives the results for just one cycle. Explanation of this initial model is discussed in detail below.

1.1.1    Explanation of Initial Model

After the user input all the values in the GUI, this model first calculates the below three values depending on the number of subcarriers attached to the DVB-T2 system.

• Useful OFDM period.
• Maximum number of sub carriers.
• IFFT / FFT length.

After getting this information, the model performs the QAM modulation over the signal so that it could be sent from the transmitter to the receiver. Next, depending on the value of Pilot Pattern given by the user, it calculates the scattered Pilot Amplitudes for the system. After that, it calculates the distortion in transmission depending on area in which the signal is propagating.

In order to calculate the distortion, FFT technique is performed on the signals to get their frequency response. As the signal has already sent from the transmitter after QAM modulation so demodulation on the receiver side is necessary. The model performs the same and demodulates the signal and finally it calculates the value of Signal to noise ratio (SNR) and Bit Error Rate (BER). At the end, it simply plots the graphs of SNR and BER for the visual representation.

1.1.2    Results of Initial Model

Different experiments were performed on the initial model and checked its results. The results are given below for three different experiments, which are:

• Speed of mobile = 0.1 km/s , Total Iterations = 5000
• Speed of mobile = 1.0 km/s , Total Iterations = 5000
• Speed of mobile = 10 km/s , Total Iterations = 5000

Results of these experiments are shown in figure 6.1, 6.2 and 6.3 respectively. Table 6.1, 6.2 and 6.3 gives the values of BER and average BER for all the values of SNR. If these three graphs are closely examined then it can be shown that the band limited impulse response increases as the speed increase and so as the BER and SNR.

The reason for such behavior is that because as the speed of the mobile increase, signal distortion also increases and it becomes difficult for the receiver to catch the signal, that’s the main reason that user travelling in high speed vehicle faces more distortion as compared to a pedestrian.

Results of First Experiment (Speed = 0.1km/s, Iterations = 5000)

 

SNR	BER & Average BER
SNR: 0	BER:0.103833
SNR: 0	NoAvrg_BER:0.160358
SNR: 5	BER:0.014366
SNR: 5	NoAvrg_BER:0.033706
SNR: 10	BER:0.000206
SNR: 10	NoAvrg_BER:0.001528
SNR: 15	BER:0.000002
SNR: 15	NoAvrg_BER:0.000107
SNR: 20	BER:0.001543
SNR: 20	NoAvrg_BER:0.002319
SNR: 25	BER:0.000076
SNR: 25	NoAvrg_BER:0.000184
SNR: 30	BER:0.000000
SNR: 30	NoAvrg_BER:0.000164

BER & Average BER Vs. SNR for experiment 1

Results of Second Experiment (Speed = 1km/s, Iterations = 5000)

SNR	BER & Average BER
SNR: 0	BER:0.140855
SNR: 0	NoAvrg_BER:0.195596
SNR:5	BER:0.046527
SNR:5	NoAvrg_BER:0.071364
SNR:10	BER:0.011363
SNR:10	NoAvrg_BER:0.019860
SNR:15	BER:0.003815
SNR:15	NoAvrg_BER:0.006448
SNR:20	BER:0.000604
SNR:20	NoAvrg_BER:0.001222
SNR:25	BER:0.000214
SNR:25	NoAvrg_BER:0.000404
SNR:30	BER:0.000233
SNR:30	NoAvrg_BER:0.000503

BER & Average BER vs. SNR for experiment 2

Results of Third Experiment (Speed = 10km/s, Iterations = 5000)

SNR	BER & Average BER
SNR: 0	BER:0.128177
SNR: 0	NoAvrg_BER:0.182924
SNR:5	BER:0.056198
SNR:5	NoAvrg_BER:0.084254
SNR:10	BER:0.023229
SNR:10	NoAvrg_BER:0.035131
SNR:15	BER:0.006793
SNR:15	NoAvrg_BER:0.010362
SNR:20	BER:0.001748
SNR:20	NoAvrg_BER:0.002801
SNR:25	BER:0.000425
SNR:25	NoAvrg_BER:0.000691
SNR:30	BER:0.000354
SNR:30	NoAvrg_BER:0.000515

BER & Average BER vs. SNR for experiment 3

Although the results given by these simulations were quite accurate but they were not accurate enough to be trusted, as they were performing the process just for one period and getting the results on the basis of that.

Final MATLAB Model after Modifications

The initial MATLAB model is modified in this thesis, in order to use the same output from the channel model with different transmitter configurations to obtain more consistent results that can be compared with each other. Then theDVB-T2model will be modified so that it can be simulated using Matlab n times collecting data so that an x% confidence interval can be measured.

The results obtained after modifications were very consistent as they were performing the whole scenario for N times (defined by the user), this attribute lacks in the initial model as it was performing the complete task just for one cycle of time and any kind of distortion could fluctuate the results. While in modified model, the same process was performed by N times defined by the user and the results obtained are actually the average of all the cycles and hence providing a very consistent output, which couldn’t be distorted by any external factors.

Moreover, this new model further enhanced the initial model to calculate the Mean BER as it will give the overall performance of BER and average BER. Furthermore, calculates the standard BER on the basis of which global BER is also calculated.

As the simulation of DVB-T2 requires a lot of input parameters from the user, that’s why a GUI is also designed in MATLAB, which makes the working of this project user friendly. User can easily change the parameters of the system using that GUI. On startup, the GUI looks like as shown in figure 4.2:

GUI for DVB-T2 parameters selection

As mentioned above, taking all the other parameters constant, three experiments are performed for the mobile user moving at different speeds with different Iterations and no. of repeats, which are:

• Pedestrian moving at the speed of 0.1 km/h, with no of iterations = 500 and N=2.
• Bus moving at the speed of 5km/h, with no of iterations = 200 and N=2.
• Car moving at the speed of 10 km/h, with no of iterations = 1000 and N=2.

1.2.1    Results of First Experiment of Modified Model:

Results of the first experiment are shown in the figure 6.5, 6.6 and 6.7 respectively. While the theoretical values of BER and average BER for the corresponding SNR are shown in table 6.4 and the Mean BER and std BER are shown in table 6.5.

• Value of Global BER for the experiment comes out to be -2.4021.

Results for Experiment 1 (Speed=0.1km/s, Iterations=500, N=2)

Results for Experiment 1 (Speed=0.1km/s, Iterations=500, N=2)

Results for Experiment 1 (Speed=0.1km/s, Iterations=500, N=2)

For N=1		For N=2
SNR: 0	BER:0.131272	SNR: 0	BER:0.131542
SNR: 0	NoAvrg_BER:0.185916	SNR: 0	NoAvrg_BER:0.186218
SNR:1	BER:0.107086	SNR:1	BER:0.106672
SNR:1	NoAvrg_BER:0.157698	SNR:1	NoAvrg_BER:0.157319
SNR:2	BER:0.086805	SNR:2	BER:0.086459
SNR:2	NoAvrg_BER:0.129841	SNR:2	NoAvrg_BER:0.129562
SNR:3	BER:0.087178	SNR:3	BER:0.086924
SNR:3	NoAvrg_BER:0.128066	SNR:3	NoAvrg_BER:0.127755
SNR:4	BER:0.081465	SNR:4	BER:0.081709
SNR:4	NoAvrg_BER:0.116619	SNR:4	NoAvrg_BER:0.116581
SNR:5	BER:0.028071	SNR:5	BER:0.028074
SNR:5	NoAvrg_BER:0.051596	SNR:5	NoAvrg_BER:0.051751
SNR:6	BER:0.016450	SNR:6	BER:0.016439
SNR:6	NoAvrg_BER:0.030762	SNR:6	NoAvrg_BER:0.030725
SNR:7	BER:0.012705	SNR:7	BER:0.012607
SNR:7	NoAvrg_BER:0.022399	SNR:7	NoAvrg_BER:0.022108
SNR:8	BER:0.036446	SNR:8	BER:0.036612
SNR:8	NoAvrg_BER:0.052421	SNR:8	NoAvrg_BER:0.052642
SNR:9	BER:0.026200	SNR:9	BER:0.026378
SNR:9	NoAvrg_BER:0.039987	SNR:9	NoAvrg_BER:0.040434
SNR:10	BER:0.014162	SNR:10	BER:0.014155
SNR:10	NoAvrg_BER:0.023779	SNR:10	NoAvrg_BER:0.023805
SNR:11	BER:0.007526	SNR:11	BER:0.007539
SNR:11	NoAvrg_BER:0.013874	SNR:11	NoAvrg_BER:0.013838
SNR:12	BER:0.015524	SNR:12	BER:0.015382
SNR:12	NoAvrg_BER:0.023693	SNR:12	NoAvrg_BER:0.023602
SNR:13	BER:0.005303	SNR:13	BER:0.005448
SNR:13	NoAvrg_BER:0.008758	SNR:13	NoAvrg_BER:0.008764
SNR:14	BER:0.008712	SNR:14	BER:0.008823
SNR:14	NoAvrg_BER:0.014517	SNR:14	NoAvrg_BER:0.014421
SNR:15	BER:0.013224	SNR:15	BER:0.013144
SNR:15	NoAvrg_BER:0.019547	SNR:15	NoAvrg_BER:0.019305
SNR:16	BER:0.001919	SNR:16	BER:0.001890
SNR:16	NoAvrg_BER:0.003767	SNR:16	NoAvrg_BER:0.003703
SNR:17	BER:0.002873	SNR:17	BER:0.002907
SNR:17	NoAvrg_BER:0.004932	SNR:17	NoAvrg_BER:0.005001
SNR:18	BER:0.000610	SNR:18	BER:0.000641
SNR:18	NoAvrg_BER:0.001197	SNR:18	NoAvrg_BER:0.001243
SNR:19	BER:0.006294	SNR:19	BER:0.006231
SNR:19	NoAvrg_BER:0.009262	SNR:19	NoAvrg_BER:0.009209
SNR:20	BER:0.001799	SNR:20	BER:0.001749
SNR:20	NoAvrg_BER:0.003268	SNR:20	NoAvrg_BER:0.003248
SNR:21	BER:0.000966	SNR:21	BER:0.000998
SNR:21	NoAvrg_BER:0.001677	SNR:21	NoAvrg_BER:0.001636
SNR:22	BER:0.001733	SNR:22	BER:0.001778
SNR:22	NoAvrg_BER:0.002772	SNR:22	NoAvrg_BER:0.002883
SNR:23	BER:0.004920	SNR:23	BER:0.004914
SNR:23	NoAvrg_BER:0.007638	SNR:23	NoAvrg_BER:0.007743
SNR:24	BER:0.000089	SNR:24	BER:0.000098
SNR:24	NoAvrg_BER:0.000220	SNR:24	NoAvrg_BER:0.000234
SNR:25	BER:0.000001	SNR:25	BER:0.000001
SNR:25	NoAvrg_BER:0.000052	SNR:25	NoAvrg_BER:0.000052
SNR:26	BER:0.000408	SNR:26	BER:0.000393
SNR:26	NoAvrg_BER:0.000695	SNR:26	NoAvrg_BER:0.000646
SNR:27	BER:0.000583	SNR:27	BER:0.000600
SNR:27	NoAvrg_BER:0.001222	SNR:27	NoAvrg_BER:0.001242
SNR:28	BER:0.000352	SNR:28	BER:0.000381
SNR:28	NoAvrg_BER:0.000609	SNR:28	NoAvrg_BER:0.000625
SNR:29	BER:0.000107	SNR:29	BER:0.000124
SNR:29	NoAvrg_BER:0.000365	SNR:29	NoAvrg_BER:0.000384
SNR:30	BER:0.000367	SNR:30	BER:0.000351
SNR:30	NoAvrg_BER:0.000720	SNR:30	NoAvrg_BER:0.000695

SNR Vs. BER values for Experiment 1

Mean BER	std BER
-0.8814	0.0006
-0.9711	0.0012
-1.0623	0.0012
-1.0602	0.0009
-1.0884	0.0009
-1.5517	0.0000
-1.7840	0.0002
-1.8977	0.0024
-1.4374	0.0014
-1.5802	0.0021
-1.8490	0.0001
-2.1231	0.0005
-1.8110	0.0028
-2.2696	0.0083
-2.0571	0.0039
-1.8799	0.0019
-2.7203	0.0046
-2.5391	0.0035
-3.2039	0.0151
-2.2033	0.0031
-2.7511	0.0086
-3.0078	0.0099
-2.7556	0.0079
-2.3083	0.0004
-4.0308	0.0285
-6.1938	0
-3.3978	0.0118
-3.2281	0.0086
-3.4365	0.0247
-3.9387	0.0460
-3.4455	0.0137

Mean BER & std BER values for Experiment 1

1.2.2    Results of Second Experiment of Modified Model:

Figure 6.8, 6.9 and 6.10 gives us the results for the experiment 2, when user is travelling at the speed of 1km/s and the no of iterations taken here are 500 with repeat cycle i.e. N=2. Table 6.6 and 6.7 gives us the values of SNR vs. BER and the Mean BER and standard BER respectively.

• Global BER comes out for experiment 2 was –inf.

Results for Experiment 2 (Speed=1km/s, Iterations=500, N=2)

Results for Experiment 2 (Speed=1km/s, Iterations=500, N=2)

Results for Experiment 2 (Speed=1km/s, Iterations=500, N=2)

For N = 1		For N = 2
SNR: 0	BER:0.144963	SNR: 0	BER:0.144537
SNR: 0	NoAvrg_BER:0.200382	SNR: 0	NoAvrg_BER:0.200617
SNR:1	BER:0.103536	SNR:1	BER:0.103496
SNR:1	NoAvrg_BER:0.153318	SNR:1	NoAvrg_BER:0.153312
SNR:2	BER:0.081079	SNR:2	BER:0.081874
SNR:2	NoAvrg_BER:0.123080	SNR:2	NoAvrg_BER:0.123966
SNR:3	BER:0.056279	SNR:3	BER:0.056618
SNR:3	NoAvrg_BER:0.096223	SNR:3	NoAvrg_BER:0.096636
SNR:4	BER:0.070647	SNR:4	BER:0.070241
SNR:4	NoAvrg_BER:0.103436	SNR:4	NoAvrg_BER:0.103023
SNR:5	BER:0.063094	SNR:5	BER:0.063427
SNR:5	NoAvrg_BER:0.089725	SNR:5	NoAvrg_BER:0.090577
SNR:6	BER:0.020785	SNR:6	BER:0.021318
SNR:6	NoAvrg_BER:0.039970	SNR:6	NoAvrg_BER:0.040469
SNR:7	BER:0.024660	SNR:7	BER:0.024455
SNR:7	NoAvrg_BER:0.040979	SNR:7	NoAvrg_BER:0.041170
SNR:8	BER:0.032986	SNR:8	BER:0.032662
SNR:8	NoAvrg_BER:0.052140	SNR:8	NoAvrg_BER:0.052100
SNR:9	BER:0.023306	SNR:9	BER:0.022988
SNR:9	NoAvrg_BER:0.037168	SNR:9	NoAvrg_BER:0.037283
SNR:10	BER:0.009120	SNR:10	BER:0.008878
SNR:10	NoAvrg_BER:0.017749	SNR:10	NoAvrg_BER:0.017499
SNR:11	BER:0.023258	SNR:11	BER:0.023224
SNR:11	NoAvrg_BER:0.034964	SNR:11	NoAvrg_BER:0.034473
SNR:12	BER:0.023534	SNR:12	BER:0.023745
SNR:12	NoAvrg_BER:0.034579	SNR:12	NoAvrg_BER:0.034325
SNR:13	BER:0.000103	SNR:13	BER:0.000101
SNR:13	NoAvrg_BER:0.000588	SNR:13	NoAvrg_BER:0.000648
SNR:14	BER:0.000016	SNR:14	BER:0.000010
SNR:14	NoAvrg_BER:0.000196	SNR:14	NoAvrg_BER:0.000231
SNR:15	BER:0.000009	SNR:15	BER:0.000014
SNR:15	NoAvrg_BER:0.000209	SNR:15	NoAvrg_BER:0.000240
SNR:16	BER:0.001996	SNR:16	BER:0.002008
SNR:16	NoAvrg_BER:0.003367	SNR:16	NoAvrg_BER:0.003535
SNR:17	BER:0.002367	SNR:17	BER:0.002430
SNR:17	NoAvrg_BER:0.003467	SNR:17	NoAvrg_BER:0.003535
SNR:18	BER:0.000002	SNR:18	BER:0.000004
SNR:18	NoAvrg_BER:0.000010	SNR:18	NoAvrg_BER:0.000018
SNR:19	BER:0.001298	SNR:19	BER:0.001367
SNR:19	NoAvrg_BER:0.002071	SNR:19	NoAvrg_BER:0.002116
SNR:20	BER:0.009918	SNR:20	BER:0.009850
SNR:20	NoAvrg_BER:0.014701	SNR:20	NoAvrg_BER:0.014585
SNR:21	BER:0.000472	SNR:21	BER:0.000521
SNR:21	NoAvrg_BER:0.000769	SNR:21	NoAvrg_BER:0.000854
SNR:22	BER:0.001085	SNR:22	BER:0.001169
SNR:22	NoAvrg_BER:0.001855	SNR:22	NoAvrg_BER:0.001917
SNR:23	BER:0.001360	SNR:23	BER:0.001495
SNR:23	NoAvrg_BER:0.002240	SNR:23	NoAvrg_BER:0.002427
SNR:24	BER:0.000595	SNR:24	BER:0.000621
SNR:24	NoAvrg_BER:0.001258	SNR:24	NoAvrg_BER:0.001321
SNR:25	BER:0.000873	SNR:25	BER:0.000820
SNR:25	NoAvrg_BER:0.001457	SNR:25	NoAvrg_BER:0.001422
SNR:26	BER:0.000003	SNR:26	BER:0.000003
SNR:26	NoAvrg_BER:0.000199	SNR:26	NoAvrg_BER:0.000201
SNR:27	BER:0.000326	SNR:27	BER:0.000342
SNR:27	NoAvrg_BER:0.000637	SNR:27	NoAvrg_BER:0.000651
SNR:28	BER:0.000198	SNR:28	BER:0.000216
SNR:28	NoAvrg_BER:0.000270	SNR:28	NoAvrg_BER:0.000262
SNR:29	BER:0.000000	SNR:29	BER:0.000000
SNR:29	NoAvrg_BER:0.000000	SNR:29	NoAvrg_BER:0.000000
SNR:30	BER:0.000000	SNR:30	BER:0.000000
SNR:30	NoAvrg_BER:0.000071	SNR:30	NoAvrg_BER:0.000078

SNR Vs. BER values for Experiment 2

Mean BER	Std BER
0.8394	0.0009
0.9850	0.0001
1.0890	0.0030
1.2483	0.0018
1.1522	0.0018
1.1989	0.0016
1.6767	0.0078
1.6098	0.0026
1.4838	0.0030
1.6355	0.0042
2.0458	0.0083
1.6337	0.0005
1.6264	0.0027
3.9915	0.0072
4.8895	0.1323
4.9486	0.1512
2.6985	0.0018
2.6201	0.0081
5.5089	0.1569
2.8755	0.0159
2.0051	0.0021
3.3048	0.0302
2.9484	0.0231
2.8460	0.0291
3.2160	0.0133
3.0727	0.0192
5.4949	0
3.4765	0.0154
3.6850	0.0273
Inf	NaN
Inf	NaN

Mean BER and std BER values for Experiment 2

Remarks

This thesis presents the design and Implementation of DVB-T2 system in MATLAB software. The basic purpose of this thesis is to check the bit error ratio (BER) and signal to noise ratio (SNR) for DVB-T2 system so that the system could be improved to a better quality. DVB-T2 system is evaluated for mobile users moving at different speeds. It is clearly shown that the mobility has an impact on the received signal, where the SNR goes to zero in some points. This behavior will generate high BER. If the figures for impulse responses are checked for all the three experiments then it is depicted that the Impulse is high for the third experiment where the mobility speed is higher than the first two experiments. The packet data loss is almost zero for the first experiment while it’s increasing in the second and is higher in the third. The number of packet lost confirms this behavior that high losses occurred in the case of high mobility.

Display Serial Data on Real Time Graph in Labview

Hello friends, hope you all are fine and having fun with your life. Today's post, as the name suggests is about How to Display Serial Data on Real Time Graph in Labview. In our previous post, we have seen that how to Get Continuous Data From COM Port in Labview. Today's post is a sequel of this previous post so I would recommend that if you haven't had a look on that post then first visit it as I am gonna start today's post from where I stopped the previous one.

In the last post, we were able to get the continuous data from COM port using Labview and we have also separated the data based on a character which I have used this one "," i.e. comma. Now, suppose you are sending 3 values from your COM port and these are the sensors' values so tthe best way to display these value is by using a graph. In the last post we separated the values and displayed them in a 1D array, so today we are gonna display those values in a graph. As I have taken three values so today I am gonna plot 3 graphs. Each graph will show one of the values and display them in real time. Let's start it.

Display Serial Data on Real Time Graph in Labview

• In the last post, we have the below vi at the end. This vi get continuous data from the COM Port and then display it in the Response and the 1D array.

• In the above image, the Data 1D displays the values separately and differentiate it on the basis of a characters "," i.e. comma.
• In other words, Data 1D has a array, which consists of three values coming from the serial port.
• So if like we have sent the values 120,221,312, then the array(0) will have 120, array(1) will have 221 and finally the array(2) will have 312.
• In order to plot the values on the graph separately, what we need to do is to simply get array(0) and then plot it vs time and so on, which is shown in below figure:

• As you can see in the above figure, the logic we used is quite simple, what we did is we simply get the array just before Data 1D and then broke it into parts i.e. array(0), array(1) and array(2) and then fed it to the Graph plots.
• In this particular example I am using accelerometer IMUv2 and displaying its values on the graph.
• After adding these blocks you are done. Now press Ctrl+T and open the Front Panel. My front Panel looks like as shown in below figure:

• Now first of all select the COM port from which you are receiving the data. If you can't find yours, then click on the Referesh, you will find in the drop down.
• Now, run the vi and then click on the Start button and you will start receiving the values. The values will be displayed in the Response as it is and then in the Data 1D and will also display on these graphs separately as shown in the below figure:

• Now, you can see the data is displayed. In this vi, we have displayed the data in three formats:

1. Response: It shows the complete data coming from the Serial Port.
2. Data1D: It separates the data and then displays it in digital format.
3. Graphs: They plot the real time graph of the coming data.

I have attached the vi below for download but I would recommend you guys to design it by yourself so that you learn the most out of it.

Download the Labview vi