Thank You Cassini Outreach Team
https://science.nasa.gov/mission/cassini/
https://science.nasa.gov/saturn/moons/titan/
Cassini Raw Image gif
Image Credit: NASA/JPL-Caltech/Space Science Institute
Assembled by Barley Culiner with GIMP
nasa.gov
BlogBarley
20241210
CIDAR, academic challenge, rough draft
CIDAR, academic challenge, rough draft
algorithm schema https://barleysarthistory.blogspot.com/2024/12/cidar-academic-challenge-rough-draft.html
Digital television based passive bistatic radar system for drone detection https://ieeexplore.ieee.org/abstract/document/7944443
Experimental Research of Multistatic Passive Radar With a Single Antenna for Drone Detection https://ieeexplore.ieee.org/abstract/document/8374028
Experimental Research for CMMB-Based Passive Radar Under a Multipath Environment https://ieeexplore.ieee.org/abstract/document/6809901
https://blogbarley.blogspot.com/2024/11/using-passive-radars-and-satellite.html
NATO Science & Technology Organization research examples:
Passive Coherent Locators on Mobile Platforms SET-242 https://www.sto.nato.int/Lists/test1/activitydetails.aspx?ID=16017
DMPAR Deployment and Assessment in Military Scenario STO-TR-SET-258 https://www.sto.nato.int/publications/STO%20Technical%20Reports/Forms/Technical%20Report%20Document%20Set/docsethomepage.aspx?ID=5065
Advanced Situation-Specific Modeling, Sensing and Vulnerability Mitigation Using Passive Radar Technology STO-TR-SET-207 https://www.sto.nato.int/publications/STO%20Technical%20Reports/Forms/Technical%20Report%20Document%20Set/docsethomepage.aspx?ID=4303&FolderCTID=0x0120D5200078F9E87043356C409A0D30823AFA16F6010066D541ED10A62C40B2AB0FEBE9841A61&List=92d5819c-e6ec-4241-aa4e-57bf918681b1&RootFolder=https://www.sto.nato.int/publications/STO%20Technical%20Reports/STO-TR-SET-207
https://www.youtube.com/watch?v=DtgNtz22nwY
...
#darpa #nato #NATOSTO
20241209
Enceladus gif
Thank You Cassini Outreach Team
Enceladus gif
https://science.nasa.gov/mission/cassini/
https://science.nasa.gov/saturn/moons/enceladus/
Cassini Raw Image gif
Image Credit: NASA/JPL-Caltech/Space Science Institute
Assembled by Barley Culiner with GIMP
nasa.gov
Worldwide data and power network
Shortwave radio frequency
3 to 30 MHz (100 (exactly 99.930819333) to 10 (exactly 9.9930819333) meters)
https://en.wikipedia.org/wiki/Shortwave_radio
Worldwide data and power network
Digital modulation techniques:
Modern shortwave data transmission often uses digital modulation methods like MFSK (Minimum Shift Keying) or Olivia to improve signal reliability in challenging conditions. -Google Search Labs AI
data over wireless power
Schema
Solar network
20241207
dashboard/desktop/HUD Project G/Alpha 008
dashboard/desktop/HUD Project G/Alpha 008
similar to proven technology from Microsoft
https://www.microsoft.com/en-us/hololens
https://youtube.com/shorts/Zio3gd_GItU?si=Hblp4-FR4Lgc7dTx
Augmented Reality at Space Lockheed Martin
power system for Project G/Alpha 008
Saline power system
powered by human tears
a flexible battery as thin as a human cornea: https://www.optometrytimes.com/view/researchers-develop-ultra-thin-battery-powered-by-saline-for-smart-contact-lenses
https://www.ntu.edu.sg/news/detail/an-ultra-thin-battery-powered-by-saline-for-smart-contact-lenses
NTU scientists develop tear-based batteries for smart contact lenses NTUsg
wireless data/power system
Simultaneous Wireless Power Transfer and Data Communication Using Synchronous Pulse-Controlled Load Modulation https://pmc.ncbi.nlm.nih.gov/articles/PMC5710816/
A Wireless Power and Bi-Directional Data Transfer System Using A Single Inductive Link for Biomedical Implants https://ieeexplore.ieee.org/document/10388617
Project G/Alpha 008
20241206
Sol 4384: Mast Camera (Mastcam) stitch
Sol 4384: Mast Camera (Mastcam) stitch
Images taken by MAST_LEFT onboard NASA's Mars rover Curiosity on Sol 4384 (2024-12-05T18:53:28.000Z) to (2024-12-05T18:55:10.000Z)
Credits: NASA/JPL-Caltech/MSSS
Assembled by Barley Culiner with Microsoft ICE
Additional editing with GIMP
Sol 4384: Mast Camera (Mastcam) stitch
Images taken by MAST_LEFT onboard NASA's Mars rover Curiosity on Sol 4384 (2024-12-05T18:53:28.000Z) to (2024-12-05T18:55:10.000Z)
Credits: NASA/JPL-Caltech/MSSS
Assembled by Barley Culiner with Microsoft ICE
Additional editing with GIMP
micro camera for Project G/Alpha 008
micro camera for project Project G/Alpha 008
 |
| https://smart.mit.edu/post/smart-researchers-create-world-s-smallest-led-and-holographic-microscope-that-enable-conversion |
Advantages of Fresnel Lenses https://www.edmundoptics.com/knowledge-center/application-notes/optics/advantages-of-fresnel-lenses/
Dragonfly Aerospace https://dragonflyaerospace.com/satellite-cameras/
https://connectivity.esa.int/projects/mcamv3
 |
| a) Fully fabricated 300mm wafer (b) Close-up of chip die (c) Infrared micrograph with LED turned on (d) Holographic microscope (e) Close-up of reconstructed holographic image (f) ground truth. |
 |
| https://engineering.princeton.edu/news/2021/11/29/researchers-shrink-camera-size-salt-grain |
20241205
Poker beta test
 |
| GIANTMONSTER gif |
https://www.ibm.com/topics/monte-carlo-simulation
Shiny object AI, lightweight Python
Smartphone to contact lens
Eye reflection/MIT RICE shiny object link https://openaccess.thecvf.com/content/CVPR2023/papers/Tiwary_ORCa_Glossy_Objects_As_Radiance-Field_Cameras_CVPR_2023_paper.pdf
Poker match
Eyeglasses, sunglasses, eyeball reflection utilized zoom
 |
| https://www.contactlensjournal.com/article/S1367-0484(21)00021-7/abstract |
Video m, n
Casino Royale (2006) - Final Poker Scene
DR. NO | "Bond, James Bond" – Sean Connery, Eunice Gayson | James Bond
Las Vegas casino test
SLAM for multiple extended targets using 6G, WIFI, & satellite signal
saline power system
example for ORCa:
slim Python embedded on lens microprocessor example:
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computing Virtual Cones for case of sphere "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Objective\n",
"Given spherical surface , set of camera rays and camera points, find virtual cones for each camera ray in parallel.\n",
"\n",
"Starting with including required packaged and adding some helper functions"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib widget\n",
"import torch\n",
"from torch import autograd\n",
"from functools import partial\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import sys\n",
"sys.path.append('.')\n",
"from utils.viz_util import (plot3d_arrow, plot3d_sphere, \n",
" plot3d_point, plot3d_cone)\n",
"\n",
"dot = lambda x,y: (x*y).sum(-1, keepdim=True)\n",
"norm = lambda x: torch.sqrt((x**2).sum(-1, keepdim=True))\n",
"transp = lambda x: x.transpose(-2,-1)\n",
"normalize = lambda x: x/(1e-7+norm(x))\n",
"to_np = lambda x: x[0,0].detach().cpu().numpy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Given quantities\n",
"\n",
"$N$: Number of primary rays\n",
"\n",
"$P$: Number of intersection points per primary ray\n",
"\n",
"$\\mathbf{o}$: Primary ray origin ($N \\times P \\times 3$)\n",
"\n",
"$\\mathbf{d}$: Primary ray direction ($N\\times P \\times 3$)\n",
"\n",
"$t$: Distance along ray for intersection points($N\\times P \\times 1$) \n",
"\n",
"$r$: Radius of primary cone ($N \\times P \\times 3$)\n",
"\n",
"$\\mathbf{n}$: Surface normals at intersection normals ($N \\times P \\times 3$) \n",
"\n",
"$R$: Radius of curvature at the surface ($N \\times P \\times 3$)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Defining one primary ray,then repeating it to $N\\times P$ values to test out the performance of this module in ORCa. Overall code should work with $N$ different primary rays"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"N = 128\n",
"P = 192\n",
"\n",
"expand_NP = lambda x: x[None,None].expand(N,P,-1)\n",
"\n",
"# Example visualization\n",
"o = expand_NP(torch.cuda.FloatTensor([3.,0.,0.])) # (N,P,3)\n",
"d = expand_NP(normalize(torch.cuda.FloatTensor([-1,0.2,0]))) # (N,P,3)\n",
"r = expand_NP(torch.cuda.FloatTensor([0.05])) # (N,P,1)\n",
"up = expand_NP(torch.cuda.FloatTensor([0.,1.,0]))# Up vector of camera Useful for cone rays"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we obtain $t$ value from ray sphere intersection. But in ORCa pipeline, SDFNet would give us t values. Here consider center $c$ and radius of sphere $R$ are known. In ORCa, center won't be needed and radius can be computed from SDF."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"def intersect_ray_sphere(o, d, c, R):\n",
" # Sphere given by <x-c,x-c>=R**2\n",
" # Ray given by x = o+td\n",
" # Point of intersection t follows:\n",
" # a2t^2 + a1t + a0 with following values\n",
" a2 = 1.\n",
" a1 = 2*dot(d, o-c)\n",
" a0 = norm(o-c)**2 - R**2\n",
" # Using quadratic formula finding the smaller of the two roots\n",
" t_int = (-a1 - torch.sqrt(a1**2 - 4*a2*a0))/(2*a2)\n",
"\n",
" return o+t_int*d\n",
"\n",
"\n",
"R = expand_NP(torch.cuda.FloatTensor([1.])) # (N,P,1)\n",
"c = expand_NP(torch.cuda.FloatTensor([0., 0., 0.])) # (N,P,3)\n",
"\n",
"p = intersect_ray_sphere(o,d,c,R)\n",
"# t is distance along ray\n",
"t = norm(p-o) # (N,P,1)\n",
"# surface normal points outwards from center\n",
"n = normalize(p-c) # (N, P, 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we visualize the known quantities. As all the $N \\times P$ values are the same we just visualize the first one. "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f3cec8a9cf19491182a91b364c9cf959",
"version_major": 2,
"version_minor": 0
},
"image/png":
MIT License Copyright (c) 2023 Kushagra Tiwary
...
-access to raw unedited image and video data required for ML training (cloud data)
Project G
Alpha 008
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