Michael Mitzenmacher michaelm@eecs.harvard.edu 617-496-7172 Research Interests Education Design and Analysis of Algorithms; Networks and Data Transmission; Information Theory. UNIVERSITY OF CALIFORNIA AT BERKELEY‚ Berkeley‚ CA Ph.D. in Computer Science awarded December‚ 1996. Dissertation: The Power of Two Choices in Randomized Load Balancing. Advisor: Alistair Sinclair. GPA: 4.0/4.0 CAMBRIDGE UNIVERSITY‚ Cambridge‚ England Attended as one of ten recipients of the Churchill Fellowship. Cambridge
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completion of this tutorial/lab‚ you should be able to: Identify inputs‚ outputs‚ and processing components from a problem description. Work out solution algorithm for the defined problem with sequence‚ selection and repetition control structures. Write the solution algorithm with pseudocode. Task 1: Tutorial Short Questions. Task 2: Algorithms Development Problem 1. A simple problem with only sequence Problem 2. A problem with single selection Problem 3. A problem with multiple selections Problem
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SIMULINK models for tracking single and multiple objects. We present various algorithms for tracking objects and determining the velocity of moving object in sequence of frames. Using the segmentation technique we can locate a target in the current frame. Target localization in current frame was computationally much complex in the conventional algorithms. Searching an object in the current frame using these algorithms starts from its location of the previous frame in the basis of attraction probably
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frontiers of capability of these mechanical devices. There are separate courses that deal with circuits and switching theory (computer logic) and with instruction sets and register arrangements (computer ar-chitecture) and with data structures and algorithms and operating systems and compiler design and artificial intelligence and so forth. All of these courses have a theoretical component‚ but they differ from our study in two basic ways. First‚ they deal only with computers that already exist; our
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Adaptive Shape Contour Tracing Algorithm by Emad Attalla‚ Ph.D. ABSTRACT In this paper we are going to present a new shape contour tracing algorithm called ¡§Adaptive Contour Tracing Algorithm¡¨. The algorithm can trace open and closed discontinuous digital shapes and return an ordered set of boundary points that represent the contour of the shape. Unlike other algorithms that return boundary points that are part of the traced shape‚ our algorithm returns background points that are adjacent
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Programming Fundamentals CSHT-102 Discrete Structures Digital Electronics Calculus and Matrices CSHT-203 Data Structures Semester – II CSHT-204 computer Systems Architecture English Calculus and Geometry using C++ Semeser – III CSHT-305 Algorithms CSHT-306 Systems Programmin g CSHT-307 File Structures and Database Basic Probability and Statistics Semester – IV CSHT-408 Operating Systems CSHT-409 Data Communica tion and Computer CSHT-410 Software Engineering Real Analysis / Differential
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Steps in Program Development Computer programming is an art. Many peoplebelieve that a programmer must be good at mathematics‚ have a memory for figures and technical information‚ and be prepared to spend many hours sitting at a computer‚ typing programs. However‚ given the right tools and steps to follow‚ anyone can write well designed programs. It is a task worth doing‚ as it is both stimulating and fullfilling. Programming can be defined as the development of a solution to and identified
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In pattern recognition‚ the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases‚ the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: • In k-NN classification‚ the output is a class membership. An object is classified by a majority vote of its neighbors‚ with the object being assigned to the class most common among its k
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is satisfied. else stop. If not‚ return in current population and go to Step 2. Each repetition of this process is called generation. The entire generation sets are called a run. [14][16]. Complexity The complexity of the genetic algorithm depends on the number of items (N) and the number of chromosomes in each generation (Size). It is
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In this short article‚ we consider the notion of data depth which generalizes the median to higher dimensions. Our main objective is to present a snapshot of the data depth‚ several closely related notions‚ associated optimization problems and algorithms. In particular‚ we briefly touch on our recent approaches to compute the data depth using linear and integer optimization programming. Although the problem is NP-hard‚ there are ways to compute nontrivial lower and upper bounds of the depth. The
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