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Hi! My name is Mike McLernon, and I’m a product marketing engineer with MathWorks. In my role, I look at the trends ongoing in the wireless industry, across lots of different standards (think 5G, WLAN, SatCom, Bluetooth, etc.), and I seek to shape and guide the software that MathWorks builds to respond to these trends. That’s all about communicating within the Mathworks organization, but every so often it’s worth communicating these trends to our audience in the world at large. Many of the people reading this are engineers (or engineers at heart), and we all want to know what’s happening in the geek world around us. I think that now is one of these times to communicate an important milestone. So, without further ado . . .
Bluetooth 6.0 is here! Announced in September by the Bluetooth Special Interest Group (SIG), it’s making more advances in its quest to create a “world without wires”. A few of the salient features in Bluetooth 6.0 are:
- Channel sounding (for accurate distance measurements)
- Decision-based advertising filtering (for more efficient channel scanning)
- Monitoring advertisers (for improved energy efficiency when devices come into and go out of range)
- Frame space updates (for both higher throughput and better coexistence management)
Bluetooth 6.0 includes other features as well, but the SIG has put special promotional emphasis on channel sounding (CS), which once upon a time was called High Accuracy Distance Measurement (HADM). The SIG has said that CS is a step towards true distance awareness, and 10 cm ranging accuracy. I think their emphasis is in exactly the right place, so let’s learn more about this technology and how it is used.
CS can be used for the following use cases:
- Keyless vehicle entry, performed by communication between a key fob or phone and the car’s anchor points
- Smart locks, to permit access only when an authorized device is within a designated proximity to the locks
- Geofencing, to limit access to designated areas
- Warehouse management, to monitor inventory and manage logistics
- Asset tracking for virtually any object of interest
In the past, Bluetooth devices would use a received signal strength indicator (RSSI) measurement to infer the distance between two of them. They would assume a free space path loss on the link, and use a straightforward equation to estimate distance:
where
received power in dBm,
transmitted power in dBm,
propagation loss in dB,
distance between the two devices, in m,
Bluetooth signal wavelength, in m,
Bluetooth signal frequency, in Hz,
speed of light (3 x 1e8 m/s).So in this method, 
. But if the signal suffers more loss from multipath or shadowing, then the distance would be overestimated. Something better needed to be found.
. But if the signal suffers more loss from multipath or shadowing, then the distance would be overestimated. Something better needed to be found.Bluetooth 6.0 introduces not one, but two ways to accurately measure distance:
- Round-trip time (RTT) measurement
- Phase-based ranging (PBR) measurement
The RTT measurement method uses the fact that the Bluetooth signal time of flight (TOF) between two devices is half the RTT. It can then accurately compute the distance d as
, where c is again the speed of light. This method requires accurate measurements of the time of departure (TOD) of the outbound signal from device 1 (the Initiator), time of arrival (TOA) of the outbound signal to device 2 (the Reflector), TOD of the return signal from device 2, and TOA of the return signal to device 1. The diagram below shows the signal paths and the times.
If you want to see how you can use MATLAB to simulate the RTT method, take a look at Estimate Distance Between Bluetooth LE Devices by Using Channel Sounding and Round-Trip Timing.
The PBR method uses two Bluetooth signals of different frequencies to measure distance. These signals are simply tones – sine waves. Without going through the derivation, PBR calculates distance d as
, where
distance between the two devices, in m,
speed of light (3 x 1e8 m/s),
phase measured at the Initiator after the tone at 
completes a round trip, in radians,
phase measured at the Initiator after the tone at 
completes a round trip, in radians,
frequency of the first tone, in Hz,
frequency of the second tone, in Hz.The mod() operation is needed to eliminate ambiguities in the distance calculation and the final division by two is to convert a round trip distance to a one-way distance. Because a given phase difference value can hold true for an infinite number of distance values, the mod() operation chooses the smallest distance that satisfies the equation. Also, these tones can be as close as 1 MHz apart. In that case, the maximum resolvable distance measurement is about 150 m. The plot below shows that ambiguity and repetition in distance measurement.
If you want to see how you can use MATLAB to simulate the PBR method, take a look at Estimate Distance Between Bluetooth LE Devices by Using Channel Sounding and Phase-Based Ranging.
Bluetooth 6.0 outlines RTT and PBR distance measurement methods, but CS does not mandate a specific algorithm for calculating distance estimates. This flexibility allows device manufacturers to tailor solutions to various use cases, balancing computational complexity with required accuracy and adapting to different radio environments. Examples include simple phase difference calculations and FFT-based methods.
Although Bluetooth 6.0 is now out, it is far from a finished version. The SIG is actively working through the ratification process for two major extensions:
- High Data Throughput, up to 8 Mbps
- 5 and 6 GHz operation
See the last few minutes of this video from the SIG to learn more about these exciting future developments. And if you want to see more Bluetooth blogs, give a review of this one! Finally, if you have specific Bluetooth questions, give me a shout and let’s start a discussion!
I am very excited to share my new book "Data-driven method for dynamic systems" available through SIAM publishing: https://epubs.siam.org/doi/10.1137/1.9781611978162
This book brings together modern computational tools to provide an accurate understanding of dynamic data. The techniques build on pencil-and-paper mathematical techniques that go back decades and sometimes even centuries. The result is an introduction to state-of-the-art methods that complement, rather than replace, traditional analysis of time-dependent systems. One can find methods in this book that are not found in other books, as well as methods developed exclusively for the book itself. I also provide an example-driven exploration that is (hopefully) appealing to graduate students and researchers who are new to the subject.
Each and every example for the book can be reproduced using the code at this repo: https://github.com/jbramburger/DataDrivenDynSyst
Hope you like it!
Christmas season is underway at my house:
(Sorry - the ornament is not available at the MathWorks Merch Shop -- I made it with a 3-D printer.)
So I made this.
clear
close all
clc
% inspired from: https://www.youtube.com/watch?v=3CuUmy7jX6k
%% user parameters
h = 768;
w = 1024;
N_snowflakes = 50;
%% set figure window
figure(NumberTitle="off", ...
name='Mat-snowfalling-lab (right click to stop)', ...
MenuBar="none")
ax = gca;
ax.XAxisLocation = 'origin';
ax.YAxisLocation = 'origin';
axis equal
axis([0, w, 0, h])
ax.Color = 'k';
ax.XAxis.Visible = 'off';
ax.YAxis.Visible = 'off';
ax.Position = [0, 0, 1, 1];
%% first snowflake
ht = gobjects(1, 1);
for i=1:length(ht)
ht(i) = hgtransform();
ht(i).UserData = snowflake_factory(h, w);
ht(i).Matrix(2, 4) = ht(i).UserData.y;
ht(i).Matrix(1, 4) = ht(i).UserData.x;
im = imagesc(ht(i), ht(i).UserData.img);
im.AlphaData = ht(i).UserData.alpha;
colormap gray
end
%% falling snowflake
tic;
while true
% add a snowflake every 0.3 seconds
if toc > 0.3
if length(ht) < N_snowflakes
ht = [ht; hgtransform()];
ht(end).UserData = snowflake_factory(h, w);
ht(end).Matrix(2, 4) = ht(end).UserData.y;
ht(end).Matrix(1, 4) = ht(end).UserData.x;
im = imagesc(ht(end), ht(end).UserData.img);
im.AlphaData = ht(end).UserData.alpha;
colormap gray
end
tic;
end
ax.CLim = [0, 0.0005]; % prevent from auto clim
% move snowflakes
for i = 1:length(ht)
ht(i).Matrix(2, 4) = ht(i).Matrix(2, 4) + ht(i).UserData.velocity;
end
if strcmp(get(gcf, 'SelectionType'), 'alt')
set(gcf, 'SelectionType', 'normal')
break
end
drawnow
% delete the snowflakes outside the window
for i = length(ht):-1:1
if ht(i).Matrix(2, 4) < -length(ht(i).Children.CData)
delete(ht(i))
ht(i) = [];
end
end
end
%% snowflake factory
function snowflake = snowflake_factory(h, w)
radius = round(rand*h/3 + 10);
sigma = radius/6;
snowflake.velocity = -rand*0.5 - 0.1;
snowflake.x = rand*w;
snowflake.y = h - radius/3;
snowflake.img = fspecial('gaussian', [radius, radius], sigma);
snowflake.alpha = snowflake.img/max(max(snowflake.img));
end
At the present time, the following problems are known in MATLAB Answers itself:
- Symbolic output is not displaying. The work-around is to disp(char(EXPRESSION)) or pretty(EXPRESSION)
- Symbolic preferences are sometimes set to non-defaults
Hello, MATLAB fans!
For years, many of you have expressed interest in getting your hands on some cool MathWorks merchandise. I'm thrilled to announce that the wait is over—the MathWorks Merch Shop is officially open!
In our shop, you'll find a variety of exciting items, including baseball caps, mugs, T-shirts, and YETI bottles.
Visit the shop today and explore all the fantastic merchandise we have to offer. Happy shopping!
Just shared an amazing YouTube video that demonstrates a real-time PID position control system using MATLAB and Arduino.
I don't like the change
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I really don't like the change
29%
I'm okay with the change
24%
I love the change
11%
I'm indifferent
11%
I want both the web & help browser
11%
38 voti
You can make a lot of interesting objects with matlab primitive shapes (e.g. "cylinder," "sphere," "ellipsoid") by beginning with some of the built-in Matlab primitives and simply applying deformations. The gif above demonstrates how the Manta animation was created using a cylinder as the primitive and successively applying deformations: (https://www.mathworks.com/matlabcentral/communitycontests/contests/8/entries/16252);
Similarly, last year a sphere was deformed to create a face in two of my submissions, for example, the profile in "waking":
You can piece-wise assemble images, but one of the advantages of creating objects with deformations is that you have a parametric representation of the surface. Creating a higher or lower polygon rendering of the surface is as simple as declaring the number of faces in the orignal primitive. For example here is the scene in "snowfall" using sphere with different numbers of input faces:
sphere(100)
sphere(500)
High poly models aren't always better. Low-polygon shapes can sometimes add a little distance from that low point in the uncanny valley.
Next week is MATLAB EXPO week and it will be the first one that I'm presenting at! I'll be giving two presentations, both of which are related to the intersection of MATLAB and open source software.
- Open Source Software and MATLAB: Principles, Practices, and Python Along with MathWorks' Heather Gorr. We we discuss three different types of open source software with repsect to their relationship to MATLAB
- The CLASSIX Story: Developing the Same Algorithm in MATLAB and Python Simultaneously A collaboration with Prof. Stefan Guettel from University of Manchester. Developing his clustering algorithm, CLASSIX, in both Python and MATLAB simulatenously helped provide insights that made the final code better than if just one language was used.
There are a ton of other great talks too. Come join us! (It's free!) MATLAB EXPO 2024
Hi MATLAB Central community! 👋
I’m currently working on a project where I’m integrating MATLAB analytics into a mobile app, mainly to handle data-heavy tasks like processing sensor data and running predictive models. The app is built for Android, and while it’s not entirely MATLAB-based, I use MATLAB for a lot of data preprocessing and model training.
I wanted to reach out and see if anyone else here has experience with using MATLAB for similar mobile or embedded applications. Here are a few areas I’m focusing on:1. Optimizing MATLAB Code for Mobile Compatibility
I’ve found that some MATLAB functions work perfectly on desktop but may run slower or encounter limitations on mobile. I’ve tried using code generation and reducing function calls where possible, but I’m curious if anyone has other tips for optimizing MATLAB code for mobile environments?
2. Using MATLAB for Sensor Data Processing
I’m working with accelerometer and GPS data, and MATLAB has been great for preprocessing. However, I wonder if anyone has suggestions for handling large sensor datasets efficiently in MATLAB, especially if you've managed data in mobile contexts?
3. Integrating MATLAB Models into Mobile Apps
I’ve heard about using MATLAB Compiler SDK to integrate MATLAB algorithms into other environments. For those who have done this, what’s the best way to maintain performance without excessive computational strain on the device?
4. Data Visualization Tips
Has anyone had experience with mobile-friendly data visualizations using MATLAB? I’ve been using basic plots, but I’d love to know if there are any resources or toolboxes that make it easier to create lightweight, interactive visuals for mobile.
If anyone here has tips, tools, or experiences with MATLAB in mobile development, I’d love to hear them! Thanks in advance for any advice you can share!
It would be nice to have a function to shade between two curves. This is a common question asked on Answers and there are some File Exchange entries on it but it's such a common thing to want to do I think there should be a built in function for it. I'm thinking of something like
plotsWithShading(x1, y1, 'r-', x2, y2, 'b-', 'ShadingColor', [.7, .5, .3], 'Opacity', 0.5);
So we can specify the coordinates of the two curves, and the shading color to be used, and its opacity, and it would shade the region between the two curves where the x ranges overlap. Other options should also be accepted, like line with, line style, markers or not, etc. Perhaps all those options could be put into a structure as fields, like
plotsWithShading(x1, y1, options1, x2, y2, options2, 'ShadingColor', [.7, .5, .3], 'Opacity', 0.5);
the shading options could also (optionally) be a structure. I know it can be done with a series of other functions like patch or fill, but it's kind of tricky and not obvious as we can see from the number of questions about how to do it.
Does anyone else think this would be a convenient function to add?
My favorite image processing book is The Image Processing Handbook by John Russ. It shows a wide variety of examples of algorithms from a wide variety of image sources and techniques. It's light on math so it's easy to read. You can find both hardcover and eBooks on Amazon.com Image Processing Handbook
There is also a Book by Steve Eddins, former leader of the image processing team at Mathworks. Has MATLAB code with it. Digital Image Processing Using MATLAB
You might also want to look at the free online book http://szeliski.org/Book/
Go to this page, scroll down to the middle of the long page where you see "Coding Photo editing STEM Business ...." and select "STEM". Voilà!
In the past two years, large language models have brought us significant changes, leading to the emergence of programming tools such as GitHub Copilot, Tabnine, Kite, CodeGPT, Replit, Cursor, and many others. Most of these tools support code writing by providing auto-completion, prompts, and suggestions, and they can be easily integrated with various IDEs.
As far as I know, aside from the MATLAB-VSCode/MatGPT plugin, MATLAB lacks such AI assistant plugins for its native MATLAB-Desktop, although it can leverage other third-party plugins for intelligent programming assistance. There is hope for a native tool of this kind to be built-in.
Mini Hack is brilliant！Let's use MATLAB to create the future!
Pumpkins have been a popular, recurring, and ever-evolving theme in MATLAB during the past few years, and particularly during this time of year. Much of this is driven by the epic work of @Eric Ludlam and expanded upon by many others. The list of material is too extensive to go through everything individually, but I'm listing some of my favourite resources below and I highly recommend these to everyone as they're a lot of fun to play with:
Pumpkins are also particularly prominent during the yearly Mini Hack Contests. This year, I have jumped onto the bandwagon myself with my Floating Pumpkins entry:
In this post, I would like to introduce the concept of masking 3D surfaces in a festive and fun way, by showcasing how to apply it for carving faces on pumpkins step by step.
Let's start by drawing the pumpkin's body. The following was adapted from Eric's code:
n = 600; % Number of faces
% Shape pumpkin's rind (skin)
[X,Y,Z] = sphere(n);
% Shape pumpkin's ribs (ridges)
R = (1-(1-mod(0:20/n:20,2)).^2/12);
X = X.*R; Y = Y.*R; Z = Z.*R;
Z = (.8+(0-linspace(1,-1,n+1)'.^4)*.3).*Z;
function plotPumpkin(X,Y,Z)
figure
surf(X,Y,Z,'FaceColor',[1 .4 .1],'EdgeColor','none');
hold on
box on
axis([-1 1 -1 1 -1 1],'square')
xlabel('x'); xticks(-1:0.5:1)
ylabel('y'); yticks(-1:0.5:1)
zlabel('z'); zticks(-1:0.5:1)
material([.45,.7,.25])
camlight('headlight')
camlight('headlight')
lighting gouraud
end
plotPumpkin(X,Y,Z)
The next step is drawing the face for the mask. This can be done in 2D and can consist of any number of lines that form polygonal closed shapes and are appropriately scaled relative to the coordinates of the pumpkin. A quick example:
% Mouth
xm = [-.5:.1:.5 flip(-.5:.1:.5)];
ym = [.15 -.3 -.25 -.5 -.4 -.6 flip([.15 -.3 -.25 -.5 -.4]) .15 -.05 0 -.25 -.15 -.3 flip([.15 -.05 0 -.25 -.15])];
% Right eye
xr = [-.35 -.05 -.35];
yr = [.1 0 .5];
% Left eye
xl = abs(xr);
yl = yr;
figure('Color','w')
set(gcf,'Position',get(gcf,'Position')/2)
axes('Visible','off','NextPlot','Add')
axis tight square
fill(xm,ym,'k')
fill(xr,yr,'k')
fill(xl,yl,'k')
We then need to apply the 2D mask to the 3D surface. To do that, we project it onto the intersections of the surface with the XY plane. However, as we need the face to appear on the side of the pumpkin, we first need to rotate the pumpkin so that the front side is facing upwards. Essentially, we need to rotate the pumpkin around the x-axis by -π/2 rad.
Let's do this from first principles to better understand the process:
theta = [-pi/2,0,0];
[X,Y,Z] = xyzRotate(X,Y,Z,theta);
function [X,Y,Z] = xyzRotate(X,Y,Z,theta)
% Rotation matrices
Rx = [1 0 0;0 cos(theta(1)) -sin(theta(1));0 sin(theta(1)) cos(theta(1))];
Ry = [cos(theta(2)) 0 sin(theta(2));0 1 0;-sin(theta(2)) 0 cos(theta(2))];
Rz = [cos(theta(3)) -sin(theta(3)) 0;sin(theta(3)) cos(theta(3)) 0;0 0 1];
for i=1:size(X,1)
for j=1:size(X,2)
r=Rx*Ry*Rz*[X(i,j);Y(i,j);Z(i,j)];
X(i,j)=r(1);
Y(i,j)=r(2);
Z(i,j)=r(3);
end
end
end
More information about these transformations can be found here:
When plotting we get:
plotPumpkin(X,Y,Z)
Note that as we have only rotated this around the x-axis, Ry and Rz are equal to eye(3).
We can now apply the mask as discussed. We do this by using one of my favourite functions inpolygon. This gives us the corresponding indices of all the data points located inside our polygonal regions. At this stage, it's important to keep the following in mind:
- The number of faces (n) controls the discretization of the pumpkin. The larger it is, the smoother the mask will be, but at the same time the computational cost will also increase. If you are using this for the contest which has a timeout limit of 235 seconds, you might need to adjust it accordingly.
- You will also need to restrict the Z-coordinates appropriately (Z>=0) so that the mask is only applied on the front side of the pumpkin.
- If you are animating the face mask (more information about this below), and you need the eyes and mouth to fully close at any point, avoid using the second argument of the inpolygon function that gives you the points located on the edge of the regions.
The masking function is given below:
function [X,Y,Z] = Mask(X,Y,Z,xm,ym,xr,yr,xl,yl)
mask = ones(size(Z));
mask((inpolygon(X,Y,xm,ym)|inpolygon(X,Y,xr,yr)|inpolygon(X,Y,xl,yl))&Z>=0) = NaN;
Z = Z.*mask;
end
Applying the mask gives us:
[X,Y,Z]=Mask(X,Y,Z,xm,ym,xr,yr,xl,yl);
plotPumpkin(X,Y,Z)
arrayfun(@(x)light('style','local','position',[0 0 0],'color','y'),1:2)
We can see that MATLAB was thoughtful enough to automatically remove the pulp from inside the pumpkin, proving its convenience time and time again.
We can then rotate the pumpkin back and add the stem to get the final result:
theta = [pi/2,0,0];
[X,Y,Z] = xyzRotate(X,Y,Z,theta);
% Stem
s = [1.5 1 repelem(.7, 6)] .* [repmat([.1 .06],1,round(n/20)) .1]';
[t,p] = meshgrid(0:pi/15:pi/2,linspace(0,pi,round(n/10)+1));
Xs = repmat(-(.4-cos(p).*s).*cos(t)+.4,2,1);
Ys = [-sin(p).*s;sin(p).*s];
Zs = repmat((.5-cos(p).*s).*sin(t)+.55,2,1);
plotPumpkin(X,Y,Z)
arrayfun(@(x)light('style','local','position',[0 0 0],'color','y'),1:2)
surf(Xs,Ys,Zs,'FaceColor','#008000','EdgeColor','none');
And that's it. You can now add some change to the mask's coordinates between frames and play around with the lighting to get results such as these (more information on how to do this on my Teaser entry):
I hope you have found this tutorial useful, and I'm looking forward to seeing many more creative entries during the final week of the contest.
What incredible short movies can be crafted with no more than 2000 characters of MATLAB code? Discover the creativity in our GALLERY from the MATLAB Shorts Mini Hack contest.
Vote on your favorite short movies by Nov.10th. We are giving out MATLAB T-shirts to 10 lucky voters!
Tips: the more you vote, the higher your chance to win.
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