ELEC ENG 7071 - Detection, Estimation & Classification

North Terrace Campus - Semester 1 - 2014

Probability: random variables, distribution functions; Examples of discrete and continuous distributions; Characteristic functions andmoments; Functions of random variables. Statistical hypotheses: Bayes and Neyman-Pearson criteria; Likelihood ratio test; Asymptotic power of a statistical test; Locally optimal detection; Robust detection. Linear minimum variance estimation: Maximum likelihood estimation; Properties of estimators; Error bounds. Linear classification, Quadratic Classification.

  • General Course Information
    Course Details
    Course Code ELEC ENG 7071
    Course Detection, Estimation & Classification
    Coordinating Unit School of Electrical & Electronic Engineering
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Assumed Knowledge Undergraduate signal processing, random processes & statistics
    Course Description Probability: random variables, distribution functions; Examples of discrete and continuous distributions; Characteristic functions andmoments; Functions of random variables.
    Statistical hypotheses: Bayes and Neyman-Pearson criteria; Likelihood ratio test; Asymptotic power of a statistical test; Locally optimal detection; Robust detection.
    Linear minimum variance estimation: Maximum likelihood estimation; Properties of estimators; Error bounds.
    Linear classification, Quadratic Classification.
    Course Staff

    Course Coordinator: Professor Lang White

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    After successful completion of the course, students should be able to formulate detection and parameter estimation problems using statistical signal models. In special cases, closed form
    solutions may be found, but students will also be able to solve more complicated problems numerically. Students will also understand how to characterise system performance both analytically, and by use of simulations.

    This course has a strong emphasis on the development of fundamental concepts and techniques which should equip students with the ability to successfully deal with a large number of detection,
    estimation and classification problems which arise in practice.

    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. Yes
    The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. Yes
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. Yes
    Skills of a high order in interpersonal understanding, teamwork and communication. N/A
    A proficiency in the appropriate use of contemporary technologies. Yes
    A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. Yes
    A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. Yes
    An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. N/A
  • Learning Resources
    Recommended Resources
    Reference books :

    There are a large number of texts, as well as web resources that students can use for this
    course. Note that different authors use different approaches and notation to
    similar problems. I use my own approaches and notation, however students should
    be able to reconcile any differences encountered. Useful books include the
    following :

    A. Papoulis, Probability,  Random Variables and Stochastic Processes, McGraw-Hill, second edition,
    1984.

    H. V. Poor, An  Introduction to Signal Detection and Estimation, Springer-Verlag, second
    edition, 1984.

    Online Learning
    Course notes and copies of the lecture slides will be provided via MyUni.

     

  • Learning & Teaching Activities
    Learning & Teaching Modes

    No information currently available.

    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    There will be a weekly 3 hour session divided between lectures and tutorial. Student interaction is encouraged. There will be two class tests to allow students to assess their progress. There will be a matlab based assignment to support and develop students' ability to apply techniques from lectures to an important practical problem.
    Learning Activities Summary

    No information currently available.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    A 2 hour open book examination held in June worth 50%.

    An assignment worth 20%. The assignment will be available week 4, and will be due at the end
    of week 10.

    Two class tests, each worth 15%. These will be held in weeks 5 and 9. The tests will be marked and returned before the end of semester.

    Assessment Related Requirements
    The exam is a hurdle requirement. Students must obtain at least 40% for the final exam in order to pass the subject. The usual requirements concerning supplementary assessment opportunities apply.
    Assessment Detail

    No information currently available.

    Submission

    No information currently available.

    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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