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  • e-book 
    Aerodynamics for Students

    Aerodynamics for Students

    This web textbook contains information, data tables and computer programs for the study of aerodynamics, gas dynamics, propulsion, aircraft performance and flight mechanics. Topics relevant to the undergraduate engineering degree at a university level are covered.

    Subjects:
    Aeronautical and Aviation Engineering
    Keywords:
    Aerodynamics
    Resource Type:
    e-book
     
  • Others 
    A Benchmark Dataset for Manipuri Meetei-Mayek Handwritten Character Recognition

    A Benchmark Dataset for Manipuri Meetei-Mayek Handwritten Character Recognition

    This work introduces a handwritten Manipuri Meetei-Mayek character dataset which consists of more than 5000 data samples which were collected from a diverse population group that belongs to different age groups (from 4 years to 60 years), genders, educational backgrounds, occupations, communities from three different districts of Manipur, India (Imphal East District, Thoubal District and Kangpokpi District) during March and April 2019.

    Subjects:
    Computing
    Keywords:
    Optical pattern recognition
    Resource Type:
    Others
     
  • Others 
    SUFR-W (Subtasks of unconstrained face recognition)

    SUFR-W (Subtasks of unconstrained face recognition)

    This package contains: 1. SUFR-W, a dataset of “in the wild” natural images of faces gathered from the internet. The protocol used to create the dataset is described in Leibo, Liao and Poggio (2014) - https://cbmm.mit.edu/publications/conference-abstracts/subtasks-unconstrained-face-recognition 2. The full set of SUFR synthetic datasets, called the “Subtasks of Unconstrained Face Recognition Challenge” in Leibo, Liao and Poggio (2014) - https://cbmm.mit.edu/publications/conference-abstracts/subtasks-unconstrained-face-recognition

    Subjects:
    Computing
    Keywords:
    Human face recognition (Computer science) Optical pattern recognition
    Resource Type:
    Others
     
  • Others 
    Invariant Action Recognition Dataset

    Invariant Action Recognition Dataset

    A dataset of five actors performing five different actions (drink, eat, jump, run and walk) on a treadmill from five different views (0, 45, 90, 135, and 180 degrees from the front of the actor/treadmill; the treadmill rather than the camera was rotated in place to acquire from different viewpoints). The dataset was filmed on a fixed, constant background.

    Subjects:
    Computing
    Keywords:
    Motion perception (Vision) Computer vision Optical pattern recognition
    Resource Type:
    Others
     
  • Courseware 
    Communicating with Mobile Technology

    Communicating with Mobile Technology

    This course covers the basics of J2ME and explores mobile imaging and media creation, GPS location, user-centered design, usability testing, and prototyping. Java experience is recommended.

    Subjects:
    Computing
    Keywords:
    Mobile apps Mobile computing Cell phone systems Application software -- Development
    Resource Type:
    Courseware
     
  • Courseware 
    Computation Structures

    Computation Structures

    This course introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies. It covers the topics including multilevel implementation strategies, definition of new primitives (e.g., gates, instructions, procedures, processes) and their mechanization using lower-level elements. It also includes analysis of potential concurrency, precedence constraints and performance measures, pipelined and multidimensional systems, instruction set design issues and architectural support for contemporary software structures.

    Subjects:
    Electrical Engineering and Computing
    Keywords:
    Digital electronics
    Resource Type:
    Courseware
     
  • Courseware 
    Signals, Systems and Inference

    Signals, Systems and Inference

    This course covers signals, systems and inference in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; and group delay. State feedback and observers. Probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization. Least-mean square error estimation; Wiener filtering. Hypothesis testing; detection; matched filters.

    Subjects:
    Electronic and Information Engineering|Electrical Engineering
    Keywords:
    Signal processing Signal theory (Telecommunication) System analysis
    Resource Type:
    Courseware
     
  • Courseware 
    Database Systems

    Database Systems

    This course introduces the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed, though students who have taken an undergraduate course in databases are encouraged to attend.

    Subjects:
    Computing
    Keywords:
    Database management
    Resource Type:
    Courseware
     
  • Video 
    Deciding if a distribution is Binomial or Poisson

    Deciding if a distribution is Binomial or Poisson

    In this video we look at how to decide for a given scenario (worded problem) if the distribution described is a Binomial distribution or Poisson distribution and whether its probability distribution function or its cumulative distribution function is required to calculate a specified probability.

    Subjects:
    Mathematics and Statistics
    Keywords:
    Binomial distribution Probabilities Poisson distribution Distribution (Probability theory)
    Resource Type:
    Video
     
  • Video 
    Calculation of probabilities in a Binomial distribution, using tables

    Calculation of probabilities in a Binomial distribution, using tables

    In this video we look at how to use statistical tables to calculate probabilities in a Binomial distribution. This includes an example of using the table for the probability density function to determine the probability the random variable takes a particular value and an example of using the table for the cumulative distribution function to determine the probability the random variable is less than or equal to a certain value and an example determining the probability it is greater than or equal to a certain value.

    Subjects:
    Mathematics and Statistics
    Keywords:
    Probabilities Poisson distribution Distribution (Probability theory)
    Resource Type:
    Video