'State transition' diaporamas de présentation

Finite-State Machines with No Output Longin Jan Latecki Temple University

Based on Slides by Elsa L Gunter, NJIT, and by Costas Busch. Finite-State Machines with No Output Longin Jan Latecki Temple University. Kleene closure. A and B are subsets of V*, where V is a vocabulary The concatenation of A and B is AB={xy: x string in A and y string in B}

1.09k views • 99 slides

Finite-State Machines with No Output Longin Jan Latecki Temple University

Based on Slides by Elsa L Gunter, NJIT, and by Costas Busch. Finite-State Machines with No Output Longin Jan Latecki Temple University. Kleene closure. A and B are subsets of V*, where V is a vocabulary The concatenation of A and B is AB={xy: x string in A and y string in B}

1.09k views • 99 slides

LING / C SC 439/539 Statistical Natural Language Processing

LING / C SC 439/539 Statistical Natural Language Processing. Lecture 16 3 /6/2013. Recommended reading. Brill (1995): transformation-based learning for POS tagging Jurafsky & Martin 22.1 Named Entity Recognition 5.7 Evaluation and error analysis, in the context of POS tagging

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The Design-Methods Comparison Project

The Design-Methods Comparison Project. Terry Bahill Systems and Industrial Engineering University of Arizona Tucson, AZ 85721-0020 terry@sie.arizona.edu http://www.sie.arizona.edu/sysengr/ Copyright ? 1998-2010 Bahill. Reference.

1.01k views • 82 slides

Building System Models for RE

Building System Models for RE. Chapter 13 Modeling System Behaviours. Chap.11: Agents & responsibilities. Chap.12: Operations. what ?. Chap.13: Behaviors - State machines. Building models for RE. Chap.13: Behaviors - Scenarios. The behavior model.

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LIN3022 Natural Language Processing Lecture 7

LIN3022 Natural Language Processing Lecture 7. Albert Gatt. In this lecture. We consider the task of Part of Speech tagging information sources solutions using markov models transformation-based learning. Part 1. POS Tagging overview. The task (graphically). Running text. Tagger.

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HOME APPLIANCE CONTROL SYSTEM

HOME APPLIANCE CONTROL SYSTEM. Prashanti Karnati Aparna Nandakumar Sharayu Patil. Why HACS ???. When: you are in a traffic jam on the highway What: You set microwave oven in a slow cooking mode. Slow cooking mode. Controlled by Remote Systems Co-ordinates Home Appliances.

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Markov Models (Basics)

Markov Models (Basics). Markov Models. Markov Decision Process (MDP). No observation uncertainty, with decision. With observation uncertainty, no decision. Partially Observable Markov Decision Process (POMDP). Markov Chain (MC). Hidden Markov Model (HMM).

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Toolkit Part 5

Toolkit Part 5. Toolkit Objectives . Explain how object-oriented analysis can be used to describe an information system Define object modeling terms and concepts, including objects, attributes, methods, messages, classes, and instances

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Gene Finding

Gene Finding. Finding Genes. Prokaryotes Genome under 10Mb >85% of sequence codes for proteins Eukaryotes Large Genomes (up to 10Gb) 1-3% coding for vertebrates. Introns. Humans 95% of genes have introns 10% of genes have more than 20 introns Some have more than 60

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Estimation and the Kalman Filter

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Title, Wake Forest SPP06-17-05

From brain to neuron to molecule and back again: circular causality in the organization of embodied cognition Walter J Freeman University of California at Berkeley http://sulcus.berkeley.edu. Title, Wake Forest SPP06-17-05. John von Neumann.

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COM3542 Nature-Inspired Computation Artificial Life and Cellular Automata

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COM3542 Nature-Inspired Computation Artificial Life and Cellular Automata

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COUNTERS WITH INPUTS

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Pattern Matching II

COMP171 Fall 2005. Pattern Matching II. A Finite Automaton Approach. A directed graph that allows self-loop. Each vertex denotes a state and each directed edge is labeled by an input item . The directed edge stands for a transition from one state to another.

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Particle filtering

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04/18/13

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Software Requirements Engineering

Software Requirements Engineering. Ch.4 B. Ramamuthy. Topics. Requirements engineering process State machines Event-driven programming Table-driven methods Mealy Machines (fig.4.4, Table 4.3) Petri nets for representing behavior of multitasks (4.8, 4.9, Table 4.5, fig.4.10)

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Figure 5.4. Worst case execution of the resilient termination protocol

Figure 5.4. Worst case execution of the resilient termination protocol.

219 views • 9 slides