Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Visualizing correlated data from the Allen Telescope Array with CyberEther in real-time. That's 28 antennas, 406 baselines, and 96 MHz/86 Gbps per instance. 🤗

10,714 görüntüleme • 3 ay önce •via X (Twitter)

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

SpaceX has introduced a new website for its next-generation Starlink V3 satellites, along with new information about the satellite. • 1 Tbps downlink capacity (~10× higher than Starlink V2). • 160 Gbps uplink capacity (~22× higher than Starlink V2). • 2,048 downlink beams + 2,048 uplink beams (vs. 192 downlink/144 uplink beams on V2). • Upgraded phased array antennas enable the increased user capacity and beam count. • New SpaceX-designed beamformer chips power the phased arrays. • Modem chips handle ~64× more throughput per chip, allowing more efficient simultaneous service and real-time beam allocation based on demand. • Each satellite includes 6 high-capacity 400 Gbps laser links, enabling a redundant petabit-scale laser mesh network for global routing. • Each satellite also has 4 quad-band RF backhaul antennas operating across Ka, E, V, and W bands. • RF backhaul capacity increases to 1.2 Tbps (>8× Starlink V2). • Backhaul uplink supports 60 GHz of spectrum across frequencies and polarizations (4.3× more than V2). • New solar arrays generate ~2× the power of the V2 satellite arrays. • Solar arrays are manufactured using a continuous roll of solar blanket, cut into 19-meter sections, with 4 sections stitched together per array. • Solar arrays are optimized to reduce atmospheric drag in low Earth orbit. • A Starship launch carrying V3 satellites will deploy ~20× more network capacity than a Falcon 9 launch carrying V2 satellites. • V3 technology will support future Starlink Mobile Gen 2 satellites, delivering terrestrial-like LTE speeds directly to unmodified smartphones. Website:

Sawyer Merritt

242,161 görüntüleme • 14 gün önce

Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

12,950 görüntüleme • 10 gün önce