Internet of Things(IOT), Machine Learning and Artificial Intelligence(AI)

 Internet  of  Things(IOT), Machine Learning and Artificial Intelligence(AI)

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

In this 21^st Century of Networking and Technology, we are surrounded by various technologies that are linked with the internet, and as we nowadays see home automation systems, smart audio responsive gadgets which make our work a lot easier than before.

These gadgets, systems, and technologies work on IoT, ML, and AI.
You all might be guessing what all abbreviations I have written up there. Many might be knowing, many might be not. So AI stands for "Artificial Intelligence", ML stands for "Machine Learning" and IoT as described earlier stands for "Internet of Things".
So, this was the brief description of what IoT is and what all things it can do.


The definition of the Internet of things has evolved due to the convergence of multiple technologies, real-time analytics, machine learning, commodity sensors, and embedded systems. Now, as we are talking about Real-time analytics, machine learning, and embedded systems lets see what all these are(but lets keep it short).
Real-Time Analytics -----> Analytics is the discovery, interpretation, and communication of meaningful patterns in data; and the process of applying those patterns towards effective decision making. In other words, analytics can be understood as the connective tissue between data and effective decision making, within an organization. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.

Machine Learning -----> Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of  artificial intelligence. Machine learning algorithms are used to build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.

Artificial Intelligence(AI) ----> By the term Intelligence we understand one’s ability to acquire knowledge and skills and make it happen
whenever its required, or, it is also understood as an individual’s capability of collecting information and data. If we talk about Artificial
Intelligence then it’s mostly about giving Human Intelligence processes to a machine, more precisely, A Computer, to do tasks where
there are Chances for Human Error when done for longer duration. These processes include learning (the acquisition of information and
rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.
 
Devices or components used for the above Things:Generally ESP8266 is the Low-Cost Wi-Fi Module one can use for IoT project, Arduino for controlling, as well as Raspberry-Pi as a
MicroComputer, further more components can be used according to the Project Needs.
For Further Tutorials and Knowledge about Project Making on IoT and All, follow the below Links:
https://learn.adafruit.com/
https://stackoverflow.com/
https://www.hackster.io/

NEED FOR THIS:
AI - We need Artificial Intelligence (AI) because the work that we need to do is increasing day-to-day. So it’s a good idea to automate the
routine work. This saves the manpower of the organization and also increases the productivity. Additionally, through this Artificial
Intelligence, the company can also get the skilled the persons for the development of the company. If I say about the Marketing and Profit
making scenario, the companies today think that they want to mechanize all the regular and routine work, which will increase their productivity.
ML - Machine learning is needed for tasks that are too complex for humans to code directly. Some tasks are so complex that it is impractical
or if not impossible, for humans to work out and code for them explicitly. So instead, we provide a large amount of data to a machine
learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the
programmers have set it out to achieve.
IOT – Benefits of IoT and why one should use it…..More data means better decisions, it means that the maximum amount of sensors or components attached to a particular device will
help to monitor the Device as well as the Surroundings more accurately and effectively.
Ability to track and monitor things, means it will help the people to track small-small things which we miss out in our Day 2 Day
life, e.g., Switching of Fan/Light/AC/etc.
Lighten the workload with automation, who doesn’t wants to live a merry life free from tedious tasks, IoT does it for you,
automatically informing you to.
Increases efficiency by saving money and resources, automation will help in automatic switching off of many devices which will
save energy and consequently will same money.
Better quality of life- when money is saved then way of living and life quality will also enhance.


©SherilThomas 

Analysis of Algorithm

Introduction:-

Analysis of an Algorithm is done in order to estimate the complexity function for arbitrarily large input. The term "analysis of algorithms" was coined by Donald Knuth.

Algorithm analysis is an important part of computational complexity theory, which provides theoretical estimation for the required resources of an algorithm to solve a specific computational problem. Most algorithms are designed to work with inputs of arbitrary length. Analysis of algorithms is the determination of the amount of time and space resources required to execute it. 

the efficiency or running time of an algorithm is stated as a function relating the input length to the number of steps, known as time complexity, or volume of memory, known as space complexity. 

Algorithm Analysis

Efficiency of an algorithm can be analyzed at two different stages, before implementation and after implementation. They are the following −

• A Priori(Pre) Analysis − This is a theoretical analysis of an algorithm. Efficiency of an algorithm is measured by assuming that all other factors, for example, processor speed, are constant and have no effect on the implementation.
• A Posterior(Post) Analysis − This is an empirical analysis of an algorithm. The selected algorithm is implemented using programming language. This is then executed on target computer machine. In this analysis, actual statistics like running time and space required, are collected.

We shall learn about a priori algorithm analysis. Algorithm analysis deals with the execution or running time of various operations involved. The running time of an operation can be defined as the number of computer instructions executed per operation.

Algorithm Complexity

Suppose X is an algorithm and n is the size of input data, the time and space used by the algorithm X are the two main factors, which decide the efficiency of X.

• Time Factor − Time is measured by counting the number of key operations such as comparisons in the sorting algorithm.
• Space Factor − Space is measured by counting the maximum memory space required by the algorithm.

The complexity of an algorithm f(n) gives the running time and/or the storage space required by the algorithm in terms of n as the size of input data.

Space Complexity

Space complexity of an algorithm represents the amount of memory space required by the algorithm in its life cycle. The space required by an algorithm is equal to the sum of the following two components −

• A fixed part that is a space required to store certain data and variables, that are independent of the size of the problem. For example, simple variables and constants used, program size, etc.
• A variable part is a space required by variables, whose size depends on the size of the problem. For example, dynamic memory allocation, recursion stack space, etc.

Space complexity S(P) of any algorithm P is S(P) = C + SP(I), where C is the fixed part and S(I) is the variable part of the algorithm, which depends on instance characteristic I. Following is a simple example that tries to explain the concept −

Algorithm: SUM(A, B)
Step 1 -  START
Step 2 -  C ← A + B + 10
Step 3 -  Stop

Here we have three variables A, B, and C and one constant. Hence S(P) = 1 + 3. Now, space depends on data types of given variables and constant types and it will be multiplied accordingly.

Time Complexity

Time complexity of an algorithm represents the amount of time required by the algorithm to run to completion. Time requirements can be defined as a numerical function T(n), where T(n) can be measured as the number of steps, provided each step consumes constant time.
For example, addition of two n-bit integers takes n steps. Consequently, the total computational time is T(n) = c ∗ n, where c is the time taken for the addition of two bits. Here, we observe that T(n) grows linearly as the input size increases.


Making up an Algorithm beforehandedly clears many things out while making a particular program or doing anything out.



©SherilThomas

Arduino: About its Structure

Arduino

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1. Arduino is an open source platform.

2. It has a programmable circuit board that can be programmed with the help of its user-friendly Arduino IDE(Integrated development environment ).

3. This circuit board consists of programmable micro-controller which can sense and control object in the real world.

Here is the picture of the Arduino board :

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Types of Arduino:

There are different different types of Arduino micro-controller present in the market.

While selecting the Arduino board you have to consider the type of project you are doing.

Here is a picture demonstrating types of Arduino :

For example :

If you are making some wearable electronics you might be considering Lily pad. which is easy to be seen or integrated with wearable.

Arduino pro mini for small projects etc.

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Board Description:

1. Reset: Used to start code again from an initial state(i.e restart loaded code) in Arduino.

2. AREF: Analog Reference used to set external voltage as an upper limit for analog pins(0v to 5v).

3. Ground: It is used to give ground to your circuit. There are several ground pins in the circuit the in all works the same.

4. Digital pins: Used to take inputs and transmit output.

5. PWM :  (~) marked pin are able to simulate analog signals.

6. power: Arduino can be powered in no. of ways one of them is through the USB slot. you can connect Arduino to PC by using USB cable

7. TX & RX : TX(Transmitter) and RX(receiver). TX flashes according to the serial data transmission speed and RX flashes during the receiving process. Flashing depends upon the baud rate.

8. Micro-Controller: It is also known as the brain of the board. IC(Integrated Circuit a micro-controller) differs from board to board. As before uploading sketch you must know what IC you are using.

9. Power Indicator: This led lights every when the board is powered.

10. Regulator: Controls amount of voltage in Arduino board.

11.DC barrel: Used to power Arduino with DC(Direct Current) jack.

12. 3.3 volts: supplies 3.3v to external objects.

13. 5 volts: supplies 5v to external objects.

14.Ground: It is used to give ground to your circuit . There are several ground pins in circuit in all works same.

15. Analog pins: receives analog signals from sensors and convert it into digital signal.

  ©SherilThomas