STEM Showcase 2017 Presentation Guidelines

Hi All,

In coming Thursday's class (06/01) we are going to have our STEM Showcase rehearsal. Each group should be well prepared to deliver your presentation. YOU HAVE TO SEND ME YOUR PRESENTATION BY 6:00 PM TUESDAY FOR INITIAL REVIEW. MODIFY AND RESEND IT TO ME BY WEDNESDAY NIGHT FOR FINAL CONSOLIDATION. A few important guidelines are listed below for your reference.

1. Your audience may cover a wide spectrum. Last year, audience included administration, teachers, college professors, parents, friends, future STEM students, and students with general interests in STEM. Be prepared to address their needs.
2. The show should be focusing on the demonstration of your results/solutions to the problems you want to solve. However, you should also concisely explain the methods you adopted to attack the problems. You should also prepare to answer questions about any aspect of your project. Questions can be general or very technical.
3. You should not be afraid to touch in depth of your technical know-how, but, at the same time, present them in a easy-accessible ways.
4. The presentation should be balanced between descriptions and demonstrations. Remember, it's not a class; it's a show! Physical demos, live illustrations, videos, or even interactions with audience are powerful way to convey the messages. Creative demonstration is very important for communication.
5. Even though it's a show, it's a tech show, you only say what you know. Every thing you present should be technically sound.
6. If you have not achieved your final goals, you can still present/demonstrate your results/solutions of different stages of the project.
7. Every group member's role and timing should be well planned and rehearsed.
8. Dress code in the show should be at least business casual. Prepare for it.

 Good luck!

Mr. Lin

STEM Seminar - A Taste of STEM Showcase

STEM Seminar Preparation PowerPoint is available here. Please prepare for the presentation based on this guideline.

Progress Report

First I started off with importing the Times Square Subway with almost all the trains, and the 59th street station trains images onto MATLAB.






















This plate displays numerous colors, but in order to make it easier for the camera to capture them, I have to pick up the colors individually.

First I started off with changing the color of the whole image. Using the following MATLAB code, I turned the whole image to gray.

>> rgb = imread('NQR345.png');
>> figure
>> imshow(rgb)
>> gray_image = rgb2gray(rbg);
imshow(gray_image);


After, I was flexible with changes the colors around, I tired to make the change it to black and white. Since there are so many other letter and numbers on the plate itself, we need to detect the area we have to focus on. So using MATLAB, I was able to make the image black and white and able to capture NQR456.

The code:
>> center6 = center(1:6,:);
>> radius  = radii(1:6);
>> metric6 = metric(1:6);
>> viscircles(center6, radii6, 'EdgeColor', 'r');

**The 6 in the code above could be named anything else, for instance, train.

So far I been successful at taking the colors away, now it's time to detect colors. Any colors I want.
MATLAB has an app called Color Thresholding App designed to mix the color or separate the colors. I played around with it using the Times Square Subway image.
The polynomial to the right in the image helps you separate or join the colors. So in the image above, I took away all the colors expect for red and Orange. It even took away the white letters and numbers. We can still see the numbers 1, 2, 3, and letters N, Q, R, and W because initially they were white, and the background is all black.

This is another example of Color Thresholding. In this image I made the polynomial in a way so that we can see all the trains in one look. Although, it;s not completely clear because we can still see some of the white letters,

To take this to the next level, using MATLAB, I took all the colors away, and took away all the numbers and letters away except for 1, 2, and 3. The code to get this answer is the following.

%image = imread('/Users/admin/Pictures/subwaysignall.jpg');

image = imread('subwaysign.jpg');

J = imresize(I, 0.5);

figure

imshow(image)

title('Original Image')

figure

imshow(J)

title('Resized Image')

%123

%image = imread('subwaysignall.jpg');

figure(1), imshow(image), title('Original');

%image = im2double(image);

%[r c p] = size(image);

%imageR = squeeze(image(:,:,1));

%imageG = squeeze(image(:,:,2));

%imageB = squeeze(image(:,:,3));

%imageBWR = im2bw(imageR, graythresh(imageR));

%imageBWG = im2bw(imageG, graythresh(imageG));

%imageBWB = im2bw(imageB, graythresh(imageB));

%imageBW = imcomplement(imageBWR&imageBWG&imageBWB);

%figure(2), imshow(imageBWR); title('Color Thresholded');

%figure(3), imshow(imageBWG); title('Color Thresholded');

%figure(4), imshow(imageBWB); title('Color Thresholded');

%figure(5), imshow(imageBW); title('Color Thresholded');

red = @createMask;

imageBWRed = red(image);

figure(6), imshow(imageBWRed); title('Red Train');

imageBWRed = bwmorph(imageBWRed,'clean')

% ocrAnn = @insertOCRAnnotation;

ocrEval = @evaluateOCRTraining;

[ocrimageBWRed, results] = ocrEval(imageBWRed);

figure(7), imshow(ocrimageBWRed); title('OCR Red Train');

results.Text;

text = results.Text;

words = ['say ' '-v' 'Victoria' ' subway train ' text(1:1) ' ' text(2:2) ' ' text(3:3)];

%system('say -v "Victoria" words');

system(words);

results.CharacterConfidences;

function [BW,maskedRGBImage] = createMask(RGB)

%createMask  Threshold RGB image using auto-generated code from colorThresholder app.

%  [BW,MASKEDRGBIMAGE] = createMask(RGB) thresholds image RGB using

%  auto-generated code from the colorThresholder App. The colorspace and

%  minimum/maximum values for each channel of the colorspace were set in the

%  App and result in a binary mask BW and a composite image maskedRGBImage,

%  which shows the original RGB image values under the mask BW.

% Auto-generated by colorThresholder app on 03-Dec-2016

%------------------------------------------------------

% Convert RGB image to chosen color space

I = rgb2hsv(RGB);

% Define thresholds for channel 1 based on histogram settings

channel1Min = 0.917;

channel1Max = 0.045;

% Define thresholds for channel 2 based on histogram settings

channel2Min = 0.000;

channel2Max = 1.000;

% Define thresholds for channel 3 based on histogram settings

channel3Min = 0.000;

channel3Max = 1.000;

% Create mask based on chosen histogram thresholds

sliderBW = ( (I(:,:,1) >= channel1Min) | (I(:,:,1) <= channel1Max) ) & ...

    (I(:,:,2) >= channel2Min ) & (I(:,:,2) <= channel2Max) & ...

    (I(:,:,3) >= channel3Min ) & (I(:,:,3) <= channel3Max);

% Create mask based on selected regions of interest on point cloud projection

I = double(I);

[m,n,~] = size(I);

polyBW = false([m,n]);

I = reshape(I,[m*n 3]);

% Convert HSV color space to canonical coordinates

Xcoord = I(:,2).*I(:,3).*cos(2*pi*I(:,1));

Ycoord = I(:,2).*I(:,3).*sin(2*pi*I(:,1));

I(:,1) = Xcoord;

I(:,2) = Ycoord;

clear Xcoord Ycoord

% Project 3D data into 2D projected view from current camera view point within app

J = rotateColorSpace(I);

% Apply polygons drawn on point cloud in app

polyBW = applyPolygons(J,polyBW);

% Combine both masks

BW = sliderBW & polyBW;

% Initialize output masked image based on input image.

maskedRGBImage = RGB;

% Set background pixels where BW is false to zero.

maskedRGBImage(repmat(~BW,[1 1 3])) = 0;

end

function J = rotateColorSpace(I)

% Translate the data to the mean of the current image within app

shiftVec = [0.035456 0.001129 0.241630];

I = I - shiftVec;

I = [I ones(size(I,1),1)]';

% Apply transformation matrix

tMat = [-0.488370 -0.361528 0.000000 0.683321;

    0.012842 -0.028299 0.658630 -0.491234;

    0.282769 -0.623110 -0.029912 8.146246;

    0.000000 0.000000 0.000000 1.000000];

J = (tMat*I)';

end

function polyBW = applyPolygons(J,polyBW)

% Define each manually generated ROI

hPoints(1).data = [0.191929 0.018162;

    0.548562 -0.007794;

    0.502281 -0.306294;

    0.257266 -0.341984];

% Iteratively apply each ROI

for ii = 1:length(hPoints)

    if size(hPoints(ii).data,1) > 2

        in = inpolygon(J(:,1),J(:,2),hPoints(ii).data(:,1),hPoints(ii).data(:,2));

        in = reshape(in,size(polyBW));

        polyBW = polyBW | in;

    end

end

end

function [ocrI, results] = evaluateOCRTraining(I, roi)

% Location of trained OCR language data

trainedLanguage = '/Users/admin/Documents/MATLAB/myLang/tessdata/myLang.traineddata';

% Run OCR using trained language. You may need to modify OCR parameters or

% pre-process your test images for optimal results. Also, consider

% specifying an ROI input to OCR in case your images have a lot of non-text

% background.

layout = 'Block';

if nargin == 2

    results = ocr(I, roi, ...

        'Language', trainedLanguage, ...

        'TextLayout', layout);

else

    results = ocr(I, ...

        'Language', trainedLanguage, ...

        'TextLayout', layout);

end

ocrI = insertOCRAnnotation(I, results);

end

%--------------------------------------------------------------------------

% Annotate I with OCR results.

%--------------------------------------------------------------------------

function J = insertOCRAnnotation(I, results)

text = results.Text;

I = im2uint8(I);

if isempty(deblank(text))

    % Text not recognized.

    text = 'Unable to recognize any text.';

    [M,N,~] = size(I);

    J = insertText(I, [N/2 M/2], text, ...

        'AnchorPoint', 'Center', 'FontSize', 24, 'Font', 'Arial Unicode');

    

else

    location = results.CharacterBoundingBoxes;

    

    % Remove new lines from results.

    newlines = text == char(10);

    text(newlines) = [];

    location(newlines, :) = [];

    

    % Remove spaces from results

    spaces = isspace(text);

    text(spaces) = [];

    location(spaces, :) = [];

    

    % Convert text array into cell array of strings.

    text = num2cell(text);

    

    % Pad the image to help annotate results close to the image border.

    I = padarray(I, [50 50], uint8(255));

    location(:,1:2) = location(:,1:2) + 50;

    

    % Insert text annotations.

    J  = insertObjectAnnotation(I, 'rectangle', location, text);

end

end

As we can see in the image, all the colors have vanished, and we are only left with what we want, 1, 2, 3, in black and white. By using the color thresholding app, I inputed the function to the code. Using the same method, I used the function for the blue line (A, C, E) from the color thresholding app, and the image gave me A, C, E in black and white.

Now, the next step is to actually make this useful for a visually impaired individual. In order for us to make this useful, this image above will actually have to speak to us. So the following code will actually say the subway train 1, 2, 3, N, R, Q.

STEM Progress Meeting

We are going to have our next STEM Progress Meeting next Monday (12/12/2016). Please prepare it thoroughly and be ready for any questions. Each team will have 10 ~ 15 minutes to present depends on the size of the team. Everyone in the team should present and participate in Q & A. The outlines of the presentation are listed below.

STEM Team Progress Report Outlines
1. Topic
   a. Research topic
   b. Team member(s)
2. Research problem (if your problem stays the same, just briefly recap in one slide. If your project is refocused, elaborate it more.)
3. Method (Methods have chosen to solve the problem.)
4. Progress
   a. Gantt chart : current status & next steps
   b. Show project achievements through demonstrations (Must have!)
5. Problems & Risks
   a. Technical issue(s)
   b. Resource issues(s): tools, materials, etc. need purchase (vendor, price, and time frame)
6. References (updated Project Resource page on ....)

Every team member should have the equal opportunity to present/demo. Group and individual performance will be evaluated by peers and teacher.

Good luck!

Technical Paper Access

You can only access this if you are on a school computer. Just click that you go to Bronx Science because they have paid for a membership, so you click Bronx Science and then you will have access to the whole database.
http://www.sciencedirect.com/

Progress Meeting 1 (10/06/2016)

We are going to have our first STEM Progress Meeting this Thursday (10/06/2015). Please prepare it thoroughly and be ready for any questions. Each team will have 10~15 minutes to present depends on the size of the group. The outlines of the presentation are listed below.

STEM Team Progress Report Outlines
1. Topic
   a. Research topic
   b. group members
2. Research problem
   a. Definition
   b. Scope
   c. Functional specification
   d. Performance specification
3. Prior Arts / Resource Inventory  
   a. Survey of literature
   b. Work from last year
4. Method
   a. Possible methods/strategies to solve the problem
   b. The method (currently experimenting) & rationals of choosing
5. Technology Analysis
   a. Mechanical
   b. Electronic
   c. Algorithms 
   d. Software
   e. Chemical
    f. Biological
6. Network Diagram & Gantt chart (Time & Human Resource)
   a. Major activities/milestones
   b. Dependence
   c. Task/resource assignment
   d. Current status
7. Material Resource (Highlight Items Need Purchase)
   a. Tools
   b. Materials
   c. Software
   d. Equipment
8. Progress
   a. Learning progress
   b. Failed experiments
   c. Project achievements
   d. Demonstrations
9. Problems
   a. Technical issues
   b. Resource issues (time, human, material)
10. Safety/Risks Management
   a. Potential show stopper
   b. Safety issues.
   c. Backup plan
11. References
   a. Books/magazines
   b. Video tutorials
   c. Websites
   d. Papers from professional journals
   e. Thesis/dissertations

Project team members should coordinate with each other to create a seamless presentation. Every group member should have the equal opportunity to present/demo. Group and individual performance will be evaluated by peers and teacher.

Good luck!