Signal Processing for Biomedical Engineering

Professor
Prof. Gaetano Giunta

(e-mail)

Time schedule
The course classes hold in the first semester with the following schedule:

    • on Wednesday hours 10-12 am, in lesson room N22 (via Vasca Navale 109).
    • on Thursday hours 8-10 am, in lesson room N22 (via Vasca Navale 109).
    • To access the MS Teams platform, student must register on the new moodle platform (click here) and select the specific course. By this registration, students are allowed to connect on Microsoft Teams.

Reception hours

  • Wednesday h 3-4 pm in the office, new Vasca Navale building, Via Vito Volterra 62, 2nd floor, room 2.22 (send an e-mail for confirmation)
  • to contact Prof. Luca Pallotta by email (luca.pallotta@uniroma3.it), room 3.01, new Vasca Navale building, top floor.
  • Online Reception via Skype (send an e-mail to book)
  • Online Reception via MS Teams (send an e-mail to book)

Exam method
Oral test with preliminary written questions.
In addition to standard exams’ schedule, ongoing tests should be provided to attending students.

Available Master theses

  • Automatic texture characterization by MR images.
  • Identification and classification of tissue features by MRI.
  • Evaluation of statistical parameters from medical data for clinical decision support.
  • Automatic pathology detection for prognostic purposes.

Next exams’ day&time schedule

Teaching programme (6 cfu).

Discrete signals.

Operations on discrete sequences. Correlation. Convolution. Z-Transform. Continuous Fourier Transform. Discrete Fourier Transform. Fast Fourier Transform (FFT).

Linear systems and digital filters.

Frequency response. Transfer function. Inverse filtering and deconvolution. Design of FIR filters. Multi-rate processing of digital sequences. Scale changes in time and space domains. Expansion. Interpolation. Decimation. Scale change with non integer factors.

Random sequences.

Statistical moments of random sequences. Mean, auto-correlation and cross-correlation. Spectral moments. Statistical estimators of sequence parameters. Performance of estimators. Design of optimum FIR filters. Optimum inverse filtering. Linear prediction. Prediction error.

Spectral analysis and modeling.

Periodogram. Averaged periodogram. Moving Average (MA) models. Auto-Regressive (AR) models. AR estimate of power spectral density.

Application to biomedical engineering

Application to telemedicine. Digital imaging and communications in medicine. DICOM standards (including JPEG and JPEG2000 operating principles). Application to digital health systems.

Teaching material

On line teaching material

Suggested books