KALI-LINUX INSTALLATION IN ORACLE VIRTUAL BOX

                                                              

Hi Friends in this post I’m gonna install kali linux in oracle virtual box..

This tutorial is for educational purpose only..

Requirements:

1.       Kali Linux ISO file (https://www.kali.org/downloads/)

Download kali linux 64 Bit ISO 1.0.9a

2.       Oracle virtual Box(https://www.virtualbox.org/wiki/Downloads)

Download Advanced version

Let’s Start the Procedure:

    a.  Install Oracle Virtual Box and launch it.

   b.   NEW

     c.  A Window will pop-up, Enter as follows

                                                         i.            Name                    : Your wish (kali)

                                                       ii.            Type                      : Linux

                                                      iii.            Version                  :Other linux(64-bit)

    Click Next.

               

                    

    d.  Enter the RAM memory size in the next pop-up windows

    Click Next

      e.Select create a virtual hard drive now button.

   Click Create

    f. Select VDI(VirtualBox Disk Image) button

  Click Next

    g. Select Dynamically Allocated Button

   Click Next

    h. Select the size of Hard disk drive

    Click Create

    i. Right Click on the OS created (KALI) (i)----Select SETTINGS(ii)-----STORAGE(iii)----click EMPTY (iv) in the  Controller:IDE---------------in the attributes (right to storage tree)----choose a virtual CD/DVD file(vi) by clicking the disk image(v).(see the pic below)----------Select the Kali ISO file Downloaded.

 Click OK

    j.Right Click on the OS(Kali)(i)-------Click START(ii)

     k. Kali Linux will start running. That’s it ….

Note:

            If you get the error like this:::

UNABLE TO BOOT - PLEASE USE A KERNEL APPROPRIATE FOR YOUR CPU

Then do as follows

·         Right click on KALI-----select SETTINGS----------SYSTEMS(i)-----------PROCESSOR(ii)

Tick EXTENDED FEATURES:  ENABLE PAE/NX(iii)

·         Also be sure that HARDWARE VIRTUALIZATION: ENABLE VT-x/AMD-V

  ENABLE NESTED PAGING(ii) ,are ticked in the ACCELERATION (i) tab.

To see this tutorial in youtube --http://youtu.be/Tnw56i51FWY

 That's it friends..  If you have any doubts regarding installation, just comment below or mail me..THANK U.../

  

Artificial Neural Network-Introduction (L1)

                                                
Introduction:
   
  Artificial Neural Networks(ANN) are  biologicially inspired computational modals which mimics Animal Central nervous system.Researchers from many fields are developing Artificial Neural Network to solve many Engineering Problems like Pattern Recognition, Optimization, Control,etc.
  Biological Inspired  Artificial Neural Networks are massively parallel systems computing model consists of an extremely large number of processing units called nodes which are wired together in a complex communication network. Each unit/nodes is a simplified model of a real neuron which fires (which means sends electrical impulse from one neuron to other) and also get fired.

                                                               Biological Neuron

                                                                Artificial Neuron

                                                     Dendrites            -------    input
                                                     soma                   -------     Processing unit
                                                     Axon                   -------    Output



Advantage:

• Non-Linearity:  Interconnection of non linear neurons  which means we can design Neural Network for non linear Function. Non-linearity is Distributed throughout the Networks.

• Input-Output Mapping

•  Adaptivity:  It can adapt to the free parameters to changes in the surrounding environment.

• Evidential Response: Decision with a measure of "Confidence"

• VLSI implemetation

• Massively Parallel

• Fault Tolerance and

• Learning Ability

Capabilities Of ANN:

•  Prediction:     For a given set of n input function "f" to a sequence of respective independent varibles of the function . The task is to predict the future at n+1. It can be done easily with high tolerance in Neural Network.
• Optimization:  Many of the Problems in Engineering, Scientific, Mathematics,Economics,etc. can be converted into Optimization Problems.The goal of the optimization problem is to maximize or minimize the Objective function for the constrained or unconstrained independent varaiables. One of the classical Example is Traveling Salesman Problem(TSP).
• Function Approximation: Suppose we have training samples input and output for a function f subjected to noise .We can estimate f' from the training samples which can produce most approximated output with less noise than the function f.
• Pattern classification:
• Categorization/Clustering : 

Applications: 

•      Robotics:  Manipulators and trajectory control, Machine vision system,etc..,
•      Electronics: Voice Synthesis, nonlinear modeling, chip failure analysis.,
•      Defence:  Signal/image identification, object discrimination, facial recognition, target tracking,etc..,
•      Manufacturing: Manufacturing process control, analysis of grinding operation, tool wear prediction,planning and management, dynamic modeling of chemical process systems, real-time particle identification, visual quality inspection
• Medical:  Breast cancer cell analysis, hospital quality improvement, emergency room test advertisement
• Telecommunication: Image and data compression, customer payment processing
• Aerospace: Aircraft components fault detection, autopilot enhancement, aircraft component simulation,flight path simulation
• Automotive:  Automobile guidance system, fault analysis system

Control Systems (L1)

                    

INTRODUCTION

                Control  theory is an interdisciplinary branch of engineering  and mathematics. It deals with the response of the Dynamic system for the given input and the change in its behaviour by feedback. Automatic control is important in many engineering application and science.  Automatic control has many applications in process plants, robotics, space-vehicle and in many industrial operations like monitoring and control of temperature, pressure, humidity, etc.

                Commonly used control theories are Conventional/classical control theory, Modern control theory and Robust control theory. Basic knowledge of Laplace Transform, Differential equations, Partial-Fraction Expansion, Vector-Matrix Algebra are required for complete understanding of Control theory.

                Control theories and techniques are roughly classifies into  :

·         Classical Control:  Proportional -Integral-Derivative(PID) controller used by many industries in 1940s to control pressure, temperature, etc., Examples: Process control in chemical plants, Aeroplanes, etc..

·         Optimal Control: Kalman filter, Linear quadratic regulator control developed in 1960s to achieve optimal performance.

·         Modern Control: It is Centered around robust control and associated topics. It is developed in 1980s to 1990s.

·         Robust Control: H_α control, to handle systems with uncertainties, disturbances and  with high performance.

·         Non-linear Control: It is the hot research topic and it is developed to handle non-linear system with high performance. Examples: Missiles.

·         Intelligent Control: These control techniques adapt various AI approaches like Artificial Neural Network (ANN), Fuzzy logic, Knowledge based control, adaptive control, evolutionary computation, genetic algorithm, etc., to control highly dynamic systems. It is developed in 1990s to handle systems with unknown models. Examples: eco-system, human systems.

There are many other classifications in control theory other than the above.  The above classifications are just an introductory to the types of control theories.

BRIEF HISTORY

1868               The control system field begin with the work of a physicist James Clerk Maxwell  on dynamic analysis of Centrifugal Watt governor for the speed control  of a steam engine.

1877               Edward John Routh abstracted Maxwell’s result for the general class of Linear systems. Adolf Hurwitz analysed system stability using differential equations which is known as Routh-Hurwitz theorem

1922               Minorsky worked on automatic controllers for steering ships. He showed how stability could be determined  from the  differential equations describing the system.

1932               Nyquist developed relatively simple procedure for determining the stability of closed-loop systems on the basis of  open-loop response to steady-state sinusoidal inputs.

1934               Hazen introduced the term SERVOMECHANISM  for position control systems.

1940               Frequency-Response method was developed which made engineers to design linear closed-loop control systems.

1940-1950  PID controllers were used in many industrial control systems to control pressure, temperature, etc., Ziegler-Nichols suggested rules for tuning PID controller called Ziegler-Nichols tuning rules. During this period root-locus method due to Evans was fully developed.

1960               Control theories for modern plants with many inputs and many outputs was developed. Such a modern control systems are complex and requires large number of equations. Modern control theory, based on time-domain analysis and synthesis using state variables , time-domain analysis of complex system is possible by the availability of  digital computers.

1960-1980   Optimal control of both deterministic and stochastic systems  were investigated. Also adaptive and learning control of complex system were analysed.

1980-1990    robust control and its associated topics were developed.

References & Sources:

·         Katsushiks Oguta - Modern Control Engineering, V Edition.

·     Wikipedia