RAKESH JHUNJHUNWALA

Rakesh Jhunjhunwala (born 5 July 1960) is an Indian investor and trader. He is a qualified Chartered accountant. He manages his own portfolio as a partner in his asset management firm, Rare Enterprises.Jhunjhunwala was described by India Today magazine as the "pin-up boy of the current bull run" and by Economic Times as "Pied Piper of Indian bourses".Rakesh Jhunjhunwala grew up in Mumbai, India where his father was posted as an income tax officer. He graduated from Sydenham College and thereafter enrolled at the Institute of Chartered Accountants of India where he qualified as a Chartered Accountant.Rakesh Jhunjhunwala plunged into full-time investing soon after completing his education. He started his career in 1985 when the BSE Sensex was at 150. He made his first big profit of Rs 0.5 million in 1986 when he sold 5,000 shares of Tata Tea at a price of Rs 143 which he had purchased for Rs 43 a share just 3 months prior. Between 1986 and 1989 he earned Rs 20-25 lakhs. His first major successful bet was iron mining company Sesa Goa. He bought 4 lakh shares of Sesa Goa in forward trading, worth Rs 1 crore and sold about 2-2.5 lakh shares at Rs 60-65 and another 1 lakh at Rs 150-175. The price rose to Rs 2200 and he sold some shares.

Let us first look at Mr.Jhunjhunwala’s portfolio. His portfolio is mainly comprised of Core and Non core holdings. Core holdings represent the stocks where he has invested a significant chunk of his portfolio and Non core represents the stocks where there is relatively lesser investment.

The Hard Numbers

Core Holdings

 

Name of Stock	Nos of shares held	Value as of 11.01.2013
Titan Ind	91550220	2470,94,04,378
CRISIL	5400000	548,18,10,000
Lupin	8678405	510,02,98,619
Karur Vysya	5629224	299,50,28,629
Rallis India	17607820	263,50,10,263
Geometric	12251250	137,21,40,000
Total Value Of Rakesh Jhunjhunwala Core Holding As On 11.01.2013		4229,36,91,889

Non-Core Holdings

Name of stock	Nos of shares	Value as on 11.01.2013
Delta Corp	15500000	1,184,200,000
NCC	21000000	1,155,000,000
A2Z Maintenance	16823351	947,154,661
Agro Tech Foods	1728259	879,338,179
VIP Industries	10077500	837,440,250
Hind Oil Explor	7272416	789,784,378
Praj Industries	15001624	737,329,820
Prime Focus	11395000	507,647,250
Geojit BNP	18000000	468,000,000
Bilcare	1735425	374,504,715
Sterling Holiday	2505000	270,665,250
Autoline Ind	1251233	170,230,250
Viceroy Hotels	6207566	154,258,015
Mcnally Bh Engg	1498349	150,434,240
Ion Exchange	875000	111,387,500
Zen Tech	900000	74,970,000
Pantaloon (DVR)	260589	45,329,457
Provogue	2010000	30,351,000
Alphageo	125000	5,675,000
Adinath Exim	166020	2,465,397
Total Value Of Rakesh Jhunjhunwala Non-Core Holdings As On 11.01.2013		889,61,65,360 ALL THIS HAPPENED WITH JUST 5000 RUPEES. HE PROVED EVERYTHING IS POSSIBLE IN LIFE.

Elements of visual perception.

connectivity

Defining Connectivity 

Introduction
In binary valued digital imaging, a pixel can either have a value of  1 -when it's part of the pattern- , or  0 -when it's part of the background- i.e. there is no grayscale level. (We will assume that pixels with value 1 are black while zero valued pixels are white).

In order to identify objects in a digital pattern, we need to locate groups of black pixels that are "connected" to each other. In other words, the objects in a given digital pattern are the connected components of that pattern.

In general, a connected component is a set of black pixels, P, such that for every pair of pixels p_i and p_j in P, there exists a sequence of pixels  p_i, ..., p_j   such that:
a) all pixels in the sequence are in the set P i.e. are black, and
b) every 2 pixels that are adjacent in the sequence are "neighbors"

As a result, an important question arises: When can we say that 2 pixels are "neighbors"?
Since we are using square pixels, the answer to the previous question is not trivial. The reason for that is: in a square tessellation, pixels either share an edge, a vertex, or neither. There are 8 pixels sharing an edge or a vertex with any given pixel; these pixels make up the Moore neighborhood of that pixel. Should we consider pixels having only a common vertex as "neighbors" ? Or should 2 pixels have a common edge in order for them to be considered  "neighbors"?

This gives rise to 2 types of connectedness, namely: 4-connectivity and 8-connectivity.


4-Connectivity
When can we say that a given set of black pixels is 4-connected ?
First, we have to define the concept of a 4-neighbor (also known as a direct-neighbor):
Definition of a 4-neighbor:
A pixel, Q, is a 4-neighbor  of a given pixel, P, if Q and P share an edge.
The 4-neighbors of pixel P (namely pixels P2,P4,P6 and P8) are shown in Figure 2 below.
 

  
  
 
Definition of a 4-connected component :
A set of black pixels, P, is a 4-connected component if for every pair of pixels p_i and p_j in P, there exists a sequence of pixels  p_i, ..., p_j such that:
a) all pixels in the sequence are in the set P i.e. are black, and
b) every 2 pixels that are adjacent in the sequence are 4-neighbors

Examples of 4-connected patterns :
The following diagrams are examples of patterns that are 4-connected:
  
  
  
 

8-Connectivity
When can we say that a given set of black pixels is 8-connected ?
First, we have to define the concept of an 8-neighbor (also known as an  indirect-neighbor):
Definition of an 8-neighbor:
A pixel, Q, is an 8-neighbor (or simply a neighbor) of a given pixel, P, if Q and P either share an edge or a vertex.
The 8-neighbors of a given pixel P make up the Moore neighborhood of that pixel.
Definition of an 8-connected component:
A set of black pixels, P, is an 8-connected component (or simply a connected component) if for every pair of pixels p_i and p_j in P, there exists a sequence of pixels p_i, ..., p_j  such that:
a) all pixels in the sequence are in the set P i.e. are black, and
b) every 2 pixels that are adjacent in the sequence are 8-neighbors
NOTE
All 4-connected patterns are 8-connected i.e. 4-connected patterns are a subset of the set of 8-connected patterns.
On the other hand, an 8-connected pattern may not be 4-connected.
Example of 8-connected pattern :
The diagram below is an example of a pattern that is 8-connected but not 4-connected:

  
  
 
Example of a pattern that's not 8-connected:
The diagram below is an example of a pattern that is not 8-connected i.e. is made up of more than one connected component (there are 3 connected components in the diagram below):

  

A Simple Image Model

A 2D light intensity function f(x, y)

•   f(x, y) = i(x, y) r(x, y)

i(x, y): illumination component, i(x, y) > 0
r(x, y): reflectance component, (0, 1)

where r(x, y) = 0: Total Absorption
r(x, y) = 1: Total Reflectance

In theory: f(x, y) > 0
In practice: f(x, y) falls into [Lmin, Lmax] = [iminrmin,imaxrmax]

The physical image is a continuous voltage waveform whose 
amplitude and spatial behavior are related to the physical 
phenomenon being sensed.

Elements of digital image processing systems

SSD vs HDD: What's the Difference?

What is a HDD, What is a SSD? The traditional spinning hard drive (HDD) is the basic nonvolatile storage on a computer. That is, it doesn't "go away" like the data on the system memory when you turn the system off. Hard drives are essentially metal platters with a magnetic coating. That coating stores your data, whether that data consists weather reports from the last century, a high-definition copy of the Star Wars trilogy, or your digital music collection. A read/write head on an arm accesses the data while the platters are spinning in a hard drive enclosure.

An SSD does much the same job functionally (saving your data while the system is off, booting your system, etc.) as an HDD, but instead of a magnetic coating on top of platters, the data is stored on interconnected flash memory chips that retain the data even when there's no power present. The chips can either be permanently installed on the system's motherboard (like on some small laptops and netbooks), on a PCI/PCIe card (in some high-end workstations), or in a box that's sized, shaped, and wired to slot in for a laptop or desktop's hard drive (common on everything else). These flash memory chips differ from the flash memory in USB thumb drives in the type and speed of the memory. That's the subject of a totally separate technical treatise, but suffice it to say that the flash memory in SSDs is faster and more reliable than the flash memory in USB thumb drives. SSDs are consequently more expensive than USB thumb drives for the same capacities.

Describe the fundamental steps of digital image processing with a neat block diagram.

              Fundamental Steps of Digital Image Processing : There are some fundamental steps but as they are fundamental, all these steps may have sub-steps. The fundamental steps are described below with a neat diagram.

            (i)       Image Acquisition : This is the first step or process of the fundamental steps of digital image processing. Image acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling etc.

                (ii)      Image Enhancement : Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image. Such as, changing brightness & contrast etc.

            (iii)    Image Restoration : Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation.

            (iv)     Color Image Processing : Color image processing is an area that has been gaining its importance because of the significant increase in the use of digital images over the Internet. This may include color modeling and processing in a digital domain etc.

            (v)      Wavelets and Multiresolution Processing : Wavelets are the foundation for representing images in various degrees of resolution. Images subdivision successively into smaller regions for data compression and for pyramidal representation.

            (vi)     Compression : Compression deals with techniques for reducing the storage required to save an image or the bandwidth to transmit it. Particularly in the uses of internet it is very much necessary to compress data.

            (vii)   Morphological Processing : Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape.

        (viii)  Segmentation : Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a long way toward successful solution of imaging problems that require objects to be identified individually.

            (ix)     Representation and Description : Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region or all the points in the region itself. Choosing a representation is only part of the solution for transforming raw data into a form suitable for subsequent computer processing. Description deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.

            (x)      Object recognition : Recognition is the process that assigns a label, such as,  “vehicle” to an object based on its descriptors.

                (xi)     Knowledge Base : Knowledge may be as simple as detailing regions of an image where the information of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite complex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing high-resolution satellite images of a region in connection with change-detection applications.