CVE Alert: CVE-2019-25422 - Cdome - Comodo Dome Firewall - https://www.redpacketsecurity.com/cve-alert-cve-2019-25422-cdome-comodo-dome-firewall/

#OSINT #ThreatIntel #CyberSecurity #cve-2019-25422 #cdome #comodo-dome-firewall

CVE Alert: CVE-2019-25419 - Cdome - Comodo Dome Firewall - https://www.redpacketsecurity.com/cve-alert-cve-2019-25419-cdome-comodo-dome-firewall/

#OSINT #ThreatIntel #CyberSecurity #cve-2019-25419 #cdome #comodo-dome-firewall

CVE Alert: CVE-2019-25405 - Cdome - Comodo Dome Firewall - https://www.redpacketsecurity.com/cve-alert-cve-2019-25405-cdome-comodo-dome-firewall/

#OSINT #ThreatIntel #CyberSecurity #cve-2019-25405 #cdome #comodo-dome-firewall

ADORO IL GENIO - SLACK WYRM

Ricordate il cuscino a pois di Ferragus? Beh, sapete come è venuto fuori?

#adoroilgenio #18febbraio #SlackWyrm #Ferragus #reboot #comicart #comics #fumetto #fumetti #fantasy #dracaris📷 #draghi #drago #vignette #Trono #fantasy #divinità #dei #culto #vignettedivertenti #dio #villici #pigrizia #Comodo #Comodità #sogno #humor #comedy

Graph and levelset based surface construction.

The animation shows a graph curve on the left that is coloured towards a desired local radius. Such a curve could stem from an image segmentation such as the centre line for a blood vessel. The local radius would then represent the local blood vessel radius. On the right a slice through a corresponding "levelset image" is shown. The level set image is a signed distance for the vessel surface but normalised by this radius, so a distance of 1.0 means we are 1 local radius value away from the desired surface. An intensity level of 0.0 means we are on the desired surface, negative levels mean are for the inside, and positive levels are for the outside. Once the levelset image is established the surface can be constructed by drawing the isosurface for the level 0.0. Just to test the algorithm, in this animation, I am creating a sinusoidal variation of the radial data. By increasing the frequency there are more and more bumps in the surface. Critically, when features on the surface become too curved or thin with respect to the voxel size, the surface may become too noisy as the coarse voxelisation starts to fail to capture the detail in the geometry.

This is the new #Julialang implementation but my old MATLAB implementation was used here:

https://doi.org/10.1016/j.jbiomech.2021.110896 (open access version: https://doi.org/10.31224/osf.io/qaujs)

and here:
https://doi.org/10.1098/rsos.242025

#opensource #geometryprocessing #Comodo

Se abre al público un pasadizo secreto del Coliseo relacionado con el emperador romano Cómodo

Recientemente se ha abierto al público un pasadizo hasta ahora oculto en el Coliseo de Roma, vinculado con el emperador romano Cómodo, conocido por su controvertida y cuestionada figura histórica. Este corredor secreto era utilizado por Cómodo para acceder a la arena sin ser visto y para organizar s... [Ver más]

A parameterised hexahedral mesh of a branching vessel. In this video I alter the branch angle, the transition shape (Bezier constants), the transition size, and the wall thickness (but any other parameter can be altered if desired). This type of parameterisation allows one to automate the creation of finite element and fluid structure interaction models and study effect of these parameters. Here the main and side branch are straight, but these can also come from non-straight image data derived patient scans.

Side application I did not think of initially: This model is now being adjusted to be suitable for welded tubular structures where the transition region represents a weld.

#Comodo #opensource #Julialang

This work in progress code lives here, join me to make it better:
https://github.com/COMODO-research/Comodo.jl/blob/main/examples/wip_bifurcation_mesh.jl

Challenge: Complete a tibia segmentation within one "Turkish march" (Mozart)

Testing out the #opensource medical image segmentation code I'm working on. Algorithm:
- Click a voxel
- Get voxel coordinates + intensity
- Get the nearest intensity contour line
Next one can edit and combine these contours with operations like cut/merge/smooth/... , and... if all else fails manually draw things.

For this video I set myself the challenge of segmenting a tibia from a crummy MRI within minutes. The tibia's outer contour is dark (hardly any MRI signal) while its core (marrow/spongy bone I believe) looks white. This used to take me 10 times longer but I've added a basic "prediction" of contours for the next slice. It can be improved still as you can see but when the data is well-behaved one could accept the prediction for most slices. Here you see I sometimes have to remove some "sticky-outy" bits (e.g. connective tissue bits at the bone that also look dark in MRI). Also, as I move up these slices, the data gets increasingly noisy (the leg moves away from the acquisition coil), and the dark cortical bone region gets thinner. So you can see me run into some trouble there and have to resort to more manual editing, and I'm relying (a bit too much at times) on special smoothing splines to iron things out.
I'll be adding this to #Comodo, which is the #JuliaLang project I'm working on, and hopefully in time for the Comodo workhshop at #cmbbe2025 CMBBE Symposium

https://github.com/COMODO-research/Comodo.jl

How am I supposed to get any work done when the maths is so pretty! Look at it flutter by like a butterfly!

This is an animation for "biharmonic spline interpolation" (see also: https://doi.org/10.1007/s11004-011-9346-5).

I'm testing a Julia implementation here which I'd like to use to interpolate the Z-coordinate for meshes that should span a closed curve domain where the x,y, and z are known. Here I created a circle and manipulated the z-coordinates using z=sin(x+a) where a is a phase offset. Next I animated the change of the phase offset to create the wave like motion.

#geometryprocessing #JuliaLang #Comodo