Requesting & Obtaining an HTTP page:
Saturday, 21 January 2012
C# code for connecting to a Web Page & Obtaining its Source Code/ Web Crawler Algorithm in C#
Requesting & Obtaining an HTTP page:
Thursday, 12 January 2012
Live Search / AutoComplete Using XML & PHP
The code for live search can be broken down in three parts viz:
- HTML file
- PHP file
- XML file
Tuesday, 29 November 2011
Weiner Filter
- The inverse filtering is a restoration technique for deconvolution, i.e., when the image is blurred by a known lowpass filter, it is possible to recover the image by inverse filtering or generalized inverse filtering. However, inverse filtering is very sensitive to additive noise. The approach of reducing one degradation at a time allows us to develop a restoration algorithm for each type of degradation and simply combine them. The Wiener filtering executes an optimal tradeoff between inverse filtering and noise smoothing. It removes the additive noise and inverts the blurring simultaneously.
The Wiener filtering is optimal in terms of the mean square error. In other words, it minimizes the overall mean square error in the process of inverse filtering and noise smoothing. The Wiener filtering is a linear estimation of the original image. The approach is based on a stochastic framework. The orthogonality principle implies that the Wiener filter in Fourier domain can be expressed as follows:
are respectively power spectra of the original image and the additive noise, and 
is the blurring filter. It is easy to see that the Wiener filter has two separate part, an inverse filtering part and a noise smoothing part. It not only performs the deconvolution by inverse filtering (highpass filtering) but also removes the noise with a compression operation (lowpass filtering).Just download it from the "Download box" present on ajinkyaspeaks.wordpress.com.
Thursday, 3 November 2011
Robotics & Artificial Intelligence- Prolog Codes
Prolog is a logical and a declarative programming language. The name itself, Prolog, is short for PROgramming in LOGic. Prolog's heritage includes the research on theorem provers and other automated deduction systems developed in the 1960s and 1970s. The inference mechanism of Prolog is based upon Robinson's resolution principle (1965) together with mechanisms for extracting answers proposed by Green (1968). These ideas came together forcefully with the advent of linear resolution procedures. Explicit goal-directed linear resolution procedures, such as those of Kowalski and Kuehner (1971) and Kowalski (1974), gave impetus to the development of a general purpose logic programming system. The "first" Prolog was "Marseille Prolog" based on work by Colmerauer (1970). The first detailed description of the Prolog language was the manual for the Marseille Prolog interpreter (Roussel, 1975). The other major influence on the nature of this first Prolog was that it was designed to facilitate natural language processing.
Prolog is the major example of a fourth generation programming language supporting the declarative programming paradigm. The Japanese Fifth-Generation Computer Project, announced in 1981, adopted Prolog as a development language, and thereby focused considerable attention on the language and its capabilities. The programs in this tutorial are written in "standard" (University of) Edinburgh Prolog, as specified in the classic Prolog textbook by authors Clocksin and Mellish (1981,1992). The other major kind of Prolog is the PrologII family of Prologs which are the descendants of Marseille Prolog. The reference to Giannesini, et.al. (1986) uses a version of PrologII. There are differences between these two varieties of Prolog; part of the difference is syntax, and part is semantics. However, students who learn either kind of Prolog can easily adapt to the other kind.
Prolog Experiment
Click below to download
PROLOG
Prolog is the major example of a fourth generation programming language supporting the declarative programming paradigm. The Japanese Fifth-Generation Computer Project, announced in 1981, adopted Prolog as a development language, and thereby focused considerable attention on the language and its capabilities. The programs in this tutorial are written in "standard" (University of) Edinburgh Prolog, as specified in the classic Prolog textbook by authors Clocksin and Mellish (1981,1992). The other major kind of Prolog is the PrologII family of Prologs which are the descendants of Marseille Prolog. The reference to Giannesini, et.al. (1986) uses a version of PrologII. There are differences between these two varieties of Prolog; part of the difference is syntax, and part is semantics. However, students who learn either kind of Prolog can easily adapt to the other kind.
Prolog Experiment
Click below to download
PROLOG
Thursday, 29 September 2011
Booth Algorithm for Signed Binary Multiplication & IEEE Floating point representation
Booth's Algorithm:
It is a powerful algorithm for signing up a number of multiplication. It generates a 2n bit product and it treats both +ve & -ve number uniformly.
Booth's multiplication algorithm is a multiplication algorithm that multiplies two signed binary numbers in two's complement notation. The algorithm was invented by Andrew Donald Booth in 1950 while doing research on crystallography at Birkbeck College in Bloomsbury, London. Booth used desk calculators that were faster at shifting than adding and created the algorithm to increase their speed. Booth's algorithm is of interest in the study of computer architecture
IEEE Floating point representation:
The standard defines:
1. arithmetic formats: sets of binary and decimal floating-point data, which consist of finite numbers (including signed zeros and subnormal numbers), infinities, and special "not a number" values (NaNs)
2. interchange formats: encodings (bit strings) that may be used to exchange floating-point data in an efficient and compact form
3. rounding rules: properties to be satisfied when rounding numbers during arithmetic and conversions
operations: arithmetic and other operations on arithmetic formats
4. exception handling: indications of exceptional conditions (such as division by zero, overflow, etc.)
Booth Algorithm & IEEE representation
It is a powerful algorithm for signing up a number of multiplication. It generates a 2n bit product and it treats both +ve & -ve number uniformly.
Booth's multiplication algorithm is a multiplication algorithm that multiplies two signed binary numbers in two's complement notation. The algorithm was invented by Andrew Donald Booth in 1950 while doing research on crystallography at Birkbeck College in Bloomsbury, London. Booth used desk calculators that were faster at shifting than adding and created the algorithm to increase their speed. Booth's algorithm is of interest in the study of computer architecture
IEEE Floating point representation:
There are several ways to represent real numbers on computers. Fixed point places a radix point somewhere in the middle of the digits, and is equivalent to using integers that represent portions of some unit. For example, one might represent 1/100ths of a unit; if you have four decimal digits, you could represent 10.82, or 00.01. Another approach is to use rationals, and represent every number as the ratio of two integers.
Floating-point representation - the most common solution - basically represents reals in scientific notation. Scientific notation represents numbers as a base number and an exponent. For example, 123.456 could be represented as 1.23456 × 102. In hexadecimal, the number 123.abc might be represented as 1.23abc × 162.
Floating-point solves a number of representation problems. Fixed-point has a fixed window of representation, which limits it from representing very large or very small numbers. Also, fixed-point is prone to a loss of precision when two large numbers are divided.
Floating-point, on the other hand, employs a sort of "sliding window" of precision appropriate to the scale of the number. This allows it to represent numbers from 1,000,000,000,000 to 0.0000000000000001 with ease.
The IEEE Standard for Floating-Point Arithmetic (IEEE 754) is a technical standard for floating-point computation established in 1985 by the Institute of Electrical and Electronics Engineers (IEEE). Many hardware floating point units use the IEEE 754 standard. The current version, IEEE 754-2008 published in August 2008, includes nearly all of the original IEEE 754-1985 standard and the IEEE Standard for Radix-Independent Floating-Point Arithmetic (IEEE 854-1987). The international standard ISO/IEC/IEEE 60559:2011 (with identical content to IEEE 754) has been approved for adoption through JTC1/SC 25 under the ISO/IEEE PSDO Agreement and published.The standard defines:
1. arithmetic formats: sets of binary and decimal floating-point data, which consist of finite numbers (including signed zeros and subnormal numbers), infinities, and special "not a number" values (NaNs)
2. interchange formats: encodings (bit strings) that may be used to exchange floating-point data in an efficient and compact form
3. rounding rules: properties to be satisfied when rounding numbers during arithmetic and conversions
operations: arithmetic and other operations on arithmetic formats
4. exception handling: indications of exceptional conditions (such as division by zero, overflow, etc.)
Booth Algorithm & IEEE representation
You can download the C++ programs for Booth's algorithm & IEEE representation from the download box present alongside.
Tuesday, 20 September 2011
DSIP University Papers
DSIP papers
Download the Digital Signal And Image Processing question papers from the download box alongside.
Download the Digital Signal And Image Processing question papers from the download box alongside.
Monday, 15 August 2011
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