Signal Processing Project For this project, you will be applying several signal processing algorithms to several instances of a given type of one-dimensional data set of your choice. Possible choices include: A) ECG B) EEG C) speech D) ultrasound-based 1D dataset You will analyze at least 3 waveforms (1D) from the data set of your choice using 3 (or more) algorithms of your choice for a particular purpose. This makes a minimum of 3x3=9 analyses. That purpose could be to detect a feature or attribute of interest (like heart rate or the relative t wave amplitude for ECG), to analyze frequency content, or to reduce noise and enhance the signal, for instance. Possible algorithms include: a) peak finding b) frequency filtering c) smoothing d) noise reduction f) spectrogram g) short-time Fourier Transform (STFT) h) frequency content analysis You will share your preliminary results with a classmate during class and include a summary in your project report (for part of the grade). The final report (3-5 pages, not including code) will cover your own analysis. Specifically, include in a few paragraphs on each algorithm you used, describe: • what you did to implement the algorithm? . what were the results? (include figures) why/when is this useful? In your final project submission include: • analysis.m files data files (if not built in or provided on eLearning) project report document (.docx, .pdf) If you wish to propose a challenging project of your own talk to me individually. Ground rules: You need to do independent original work. While you may use built-in functions from MATLAB, do not copy the examples or use code directly copied from the web. It is OK to use such code for inspiration. Do not use a project from another course. You can get started with waveforms built into MATLAB (some are not available in latest MATLAB versions, so this also available as a .zip in eLearning): load wecg; % ECG signal sampled at 180 Hz load mit200% The ECG data and annotations are taken from the MIT-BIH Arrhythmia Database. The data are sampled at 360 Hz. load mit203 % The ECG data and annotations are taken from the MIT-BIH Arrhythmia Database (with noise). The data are sampled at 360 Hz. load Espiga3; % 23 channel EEG data sampled at 200 Hz Or, you can use waveforms from a UTD lab you are associated with, or from the web (Kaggle.com is a one source).

4. CGM (continuous glucose monitoring) measures the level of glucose in interstitial fluid. In contrast to the measurement of blood glucose, the method is noninvasive and may be conducted at a high frequency, e.g. every 5 min using a wearable device. Multiple studies indicated that, for nondiabetic individuals, the value should not rise above 140 mg/dL for one or two percent of the time every day. File CGMdata.txt contains the recordings from 57 participants. Columns in the file are DisplayTime (time stamp as recorded by the monitor), GlucoseValue (glucose concentration in mg/dL), subjectld (participant id number), and InternalTime (internal time stamp). The pattern below is from one of the participants with poor glucose regulation, showing a large spike following each main meal. The program CGM1_HW.mlx is designed to help you identify this participant by displaying all the CGM data as tiles. Places that need completion are marked as XXX. Due 9/21, 3 points. الميساني 290 50 References: https://github.com/irinagain/Awesome-CGM/wiki/Hall-(2018)

5. What is the reflected intensity and the transmitted intensities for a 3 MHz transducer for impedances of 200 rayls and 50 rayls, given an initial intensity of 14 mW/cm² ?

7. What are the angles of reflection & transmission for a specular oblique reflector, given an angle of incidence of 40 degrees and propagation speed through medium 1 of 2.1 mm/us and propagation speed through medium 2 of 3.4 mm/us?

3. The dataset in breastTissuelmpedance.xlsx was obtained from University of California Irvine Machine Learning Repository. The study used electrical impedance for the diagnosis of freshly biopsied breast tissues, which measures how much AC signals are attenuated and/or delayed as they pass through the tissue. It is hypothesized that tissue impedance is altered by cancer due to changes in tissue structure or cell composition. A total of 9 measurements were made. However, some of them showed similar responses to cancer therefore provide redundant information. Replace XXX with appropriate code in the program breast Tissuelmpedance_HW.mlx to identify pairs for which the Pearson correlation coefficient is >0.8. 3 points.

ASSIGNMENT Need to do in 1-2 pages double spaced MLA Conceptualize a design for biosensor systems to comparatively evaluate the detection of a large tissue sample from a biopsy and a micro sample from say a bacterial or virus sample like COVID. This could be a diagram, or an essay outlines the possible specifications of the design.

Visual T..S T₂ ·235 T₂.525 .48 .49 TA T5 ть T₂ FFF ·57 BE 312 Lab #2 2. Enter the mean reaction time for this exercise in Table 1. Exercise 4 Data Analysis 1. Use the same technique explained in Exercise 1 to measure and record the reaction times of the subject presented with predictable auditory signals. 2. Enter the mean reaction time for this exercise in Table 1. FINAL STEP: Enter your mean RT data into the spreadsheet provided. Name this spreadsheet TeamLxxx_lab2.xlsx where xxx is your team #. Upload this to the Box folder for Lab2. This must be done before Monday. Table 1: Mean Reaction Times for Different Signals. • T₂ TA Ts T₂ T₂- TB 18 Та То Auditory Signal Visual Auditory Prompted Auditory ·5 .52 •605 Report Questions 49 1. Include a completed Table 1 Questions Exercise 1 and 2 Predictable Auditory Mean Reaction Time of Your Subject (ms) Mean Reaction Time of All Subjects (ms) 0.045 0.125 0.035 Shortest Mean Reaction Time in Class (ms) Longest Mean Reaction Time in Class (ms) 2. How does the subject's mean reaction time to visual signals compare to his or her mean reaction time to auditory signals? 3. What would cause a longer reaction time to one type of signal as compared to another? 4. How do your subject's mean reaction times compare to those of other subjects? 5. Do all subjects respond more quickly to the same signal? 71-0-025 T₁-0-125 0.070 Questions Exercise 3 and 4 6. To which auditory signal did your subject respond most quickly? -0.055 7. To which auditory signal did your subject respond to most slowly? For what reasons? 0.055 8. Did your subject respond more quickly or more slowly to same auditory signal as the other members of the class? -0.195 -0.070 Prompted Auditony 9. Using the entire class data, create scatter plots of the Mean Reaction time for all subjects for each of the four cases. Comment on each of these. 0-125 0.115 -0.105 0.035 0.105 0.055 0-170 0.125 0.145 Predictable Auditony 0045 0.125 0.015 0.185 0.035 0.035 0.185 0.055 -0.035 0.135 -0.025