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    Kotapalli Detection of Botnets Using Honeypots and P2P Botnets Rajab Challoo Dept. of Electrical Engineering & Computer Science Texas A&M University Kingsville Kingsville‚ 78363-8202‚ USA kfrc000@tamuk.edu Raghavendra Kotapalli Dept. of Electrical Engineering & Computer Science Texas A&M University Kingsville Kingsville‚ 78363-8202‚ USA raghavsan@gmail.com Abstract A “botnet” is a group of compromised computers connected to a network‚ which can be used for both recognition and illicit

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    %FACE RECOGNITION SYSTEM % % Face recognition system based on EigenFaces Method. % The system functions by projecting face images onto a feature space % that spans the significant variations among known face images. The % significant features are known as "eigenfaces" because they are the % eigenvectors (principal components) of the set of faces. % % Face images must be collected into sets: every set (called "class") should % include a number of images for each person‚ with some variations

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    fault diagnostics‚ which gives fast accurate diagnostics. The on-line fast motor fault diagnostics clearly indicate the fault type‚ location‚ and severity in running motors. This approach can also be extended to other applications. Key words: fault detection and isolation; gravity-average method; supervisory learning; fuzzy neural networks Introduction Benbouzid[1] lists 365 books‚ conferences‚ and journal papers related to fault diagnostics of induction motors with stator winding inter-turn short

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    The face recognition model developed by Bruce and Young has eight key parts and it suggests how we process familiar and unfamiliar faces‚ including facial expressions. The diagram below shows how these parts are interconnected. Structural encoding is where facial features and expressions are encoded. This information is translated at the same time‚ down two different pathways‚ to various units. One being expression analysis‚ where the emotional state of the person is shown by facial features. By

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    robotics‚ medical imaging. Gesture recognition for human-machine interfaces is one of today’s hottest applications. Air traffic control is another typical application of video object tracking‚ where aircrafts are more or less continuously visible on radar‚ but in case the transponders are absent‚ the identity is only revealed when the pilot reports by radio. The objective of this project is to develop MATLAB programs and SIMULINK models for tracking single and multiple objects. We present various algorithms

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    evaluate Bruce and Young’s model of face recognition (8+16marks) Bruce young’s model of face recognition starts with structural encoding‚ where the face is seen and the features are analysed. The model then splits up into separate compartments one for familiar faces and the other for unfamiliar faces. [AO1] The first of these compartments is the name generation system‚ which consists of eight separate processes. The first stage being the structural encoding where the face perceived is converted in neural

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    Coin Detection

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    INTRODUCTION In all walks of life‚ machine automation is essential to make sophisticated approach to the mankind. Of course the machines cannot be replaced by human in exact recognition of coins. Nowadays‚ most of the work of the human is replaced by machines. The coin classification of various denominations and finding the sum of the coins is a tedious process. Coin counting machine is user friendly and makes customer operation a breeze. This machine is equipped with an operating system. This

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    2013 Beyond Recognition: The Incredible Story of a Face Transplant Summary: In 2007‚ Carmen Tarleton who is a victim of a brutal attacked by her husband‚ Herbert Rogers. She suffered from a three months comatose; her body was spoiled by a deep chemical burn‚ wrapped in bandages. Her eyelids were gone‚ as well as her left ear‚ she even couldn’t blink‚ smile and breathe through her nose. That would just the seventh American patient to undergo a risky experimental procedure known as face transplant

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    ONFERENCE ON C OMPUTER V ISION AND PATTERN R ECOGNITION 2001 Rapid Object Detection using a Boosted Cascade of Simple Features Paul Viola viola@merl.com Mitsubishi Electric Research Labs 201 Broadway‚ 8th FL Cambridge‚ MA 02139 Michael Jones mjones@crl.dec.com Compaq CRL One Cambridge Center Cambridge‚ MA 02142 tected at 15 frames per second on a conventional 700 MHz Intel Pentium III. In other face detection systems‚ auxiliary information‚ such as image differences in video sequences‚ or

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    SYNOPSIS The project titled “CREDIT CARD FRAUD DETECTION” detects the fraudulent card during transactions and alerts the customer regarding the fraud. This project also aims in minimizing the number of false alerts. The concept of genetic algorithm is a novel one in this application domain. The algorithm begins with multi-population of randomly generated chromosomes. These chromosomes undergo the operations of selection‚ crossover and mutation. Crossover combines the information from two parent

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