Synthetic Post-Dive Doppler Ultrasound
Research Article “An open-source framework for synthetic post-dive Doppler ultrasound audio generation” By: David Q. Le,Andrew H. Hoang,Arian Azarang,Rachel M. Lance,Michael Natoli,Alan Gatrell,S. Lesley Blogg3,Paul A. Dayton,Frauke Tillmans,Peter Lindholm, Richard E. Moon, Virginie Papadopoulou == Before I Read My understanding is that the relationship between venous gas emboli and decompression sickness is not well understood. Intuitively it makes sense, but my hobbiest understanding is that no actual mechanism has been proposed, let alone proven....
Distributed Multi Hypothesis Testing
Bottom Line Up Front Stefano P. Coraluppi suggests we form tracks first, then try to merge those, and that we don’t trust those initial tracks as much as we currently do in MHT. Summary Multi-target tracking (MTT) Sequence of sets of measurements, unknown time-varying number of targets. Overlay of false alarms. Must determine a _set_of trajectories. An intractable posterior probability distribution, both computationally and conceptually. MAP estimation, even if you nailed it, would be useless....
Use Jax on Compute Canada/Digital Research Alliance of Canada
I’ve had luck getting GPUs quickly on graham.computecanada.ca, so I suggest you use something else. On the login node: Previously I was explicitly specifying a new StdEnv, but for now, the latest one is the default. This is not necessary right now but for future reference. 1 module load StdEnv/2023 Figure grab the least stale python and cuda 1 2 module spider python module spider cuda Grab specific versions 1 2 module load python/3....
Compositional Learning Based Planning for Vision Pomdps
Sampada Deglurkar, Michael Lim, Johnathan Tucker, Zachary Sunberg, Aleksandra Faust, Claire Tomlin. Bottom Line Up Front I rate this 8.1 More, smaller, models trained to their own goals end up being more efficient and robust. This is… close to what I was attempting, but I think my “produce an image” thing was insane. Next step: go find this thing and see how it is proposing particles. I think I may be able to adapt this so that I’m keeping a bunch of unrelated particles alive and just deciding which ones to kill....
IEEE Toronto Government Incentives
Talk by Andrew Skeldon == Incentives Landscape in Canada Overview, there are a few buckets: Tax credits, typically no defined budget, if you meet criteria you apply and get it. Defined grants and discretionary, government picks-and-chooses based on many rules. Negotiated, very large investment, company may go directly to government and present business case. There are roughly 3,000 programs in Canada. They have general themes though: R&D Projects, for product and process from basic research to shop floor Capital Investments Employment, creation, conversion, and training....
Micro-Doppler Based Detection and Tracking of UAVs With Multistatic Radar
Folker Hoffmann, Matthew Ritchie, Francesco Fioranelli, Alexander Charlish, Hugh Griffiths Bottom Line Up Front I rate this 7.9. Super cool experiment, I’m frankly shocked that it works as well as it did. I wish I had radars to play with, and results from slightly less cooperative UAVs. Clearly lots of opportunities for ’next steps’ with this exact hardware setup. I want one. Figure Anarchy I know LaTeX and maybe even IEEE insist on letting figures roam the countryside however they please, but good lord, this paper is just a description of figures and none of them are anywhere near the text describing them....
Cognitive Radar Management
Alexander Charlish, Folker Hoffmann Bottom Line Up Front This is a chapter from a larger book that I should read before discussing it with a student working under Alexander Charlish. I rate this 8.8 Easily the coolest thing I’ve seen, I’m 100% in love with the formalization of a POMDP, but I disagree with certain design/implementation choices. Took too long to get there, this chapter should be two chapters, the first of which I’d just ignore and the second I’d re-read 12 times....
Googling for Existing Counter Uas Tech
I took a look on 2023-07-14. Drone Groups So the U.S. military has 5 UAS groups: <20lbs (<9kg), <1,200ft AGL, <100kts 21-55lbs (9-20kg), <3,500ft AGL, <250kts 55-1,320lbs (20-600kg), <18,000ft AGL, <250kts 1,320+lbs, <18,000ft AGL 1,320+lbs >18,000ft AGL The DND challenge is, to a rounding error, only for group 1 and group 2 drones. NorthropGrumman Sensors and radars Many, HAMMR looks like fun Medium Calibre M-ACE “Mobile - Acquisition, Cueing, and Effector” system with video can “cue” a nearby 25mm (or maybe 30mm?...
Canadas Academic Research and Development in Unmanned Aerial Systems 2020 Survey
Hugh H.-T. Liu (Posted on his website)[https://www.flight.utias.utoronto.ca/fsc/wp-content/uploads/2020/06/uasrmap_ca_2020.pdf] Bottom Line Up Front I rate this 1. Summary Introduction The unmanned Aerial System sector is currently military and security. The bulk of predicted growth will come from civilian and commercial applications. I’d bet that commercial will dwarf both military and civilian within a decade. Lets see what this report thinks. Scope and Method of Survey Covers academic centers, intentionally ignores (but acknowledges) industry....
5g Network Based Passive Radar
Piotr Samczyński, Karol Abratkiewicz, Marek Płotka, Tomasz P. Zieliński, Jacek Wszołek, Sławomir Hausman, Piotr Korbel, Adam Ksiȩżyk Bottom Line Up Front I rate this 9.4. I’d never read much about either Passive Radars or 5G, but this paper gave me enough context to feel like I knew what was going on while at the same time doing something cool. Incredibly written, I never felt either overwhelmed or condescended to. Super cool experiment....