'hardware' 검색 결과

Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration) and ImageMagick (image manipulation, format conversion, batch processing, effects, composition). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.

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Modelo de espaço de estados com complexidade O(n) versus O(n²) dos Transformers. Inferência 5× mais rápida, sequências de milhão de tokens, sem cache KV. SSM seletivo com design aware de hardware. Mamba-1 (d_state=16) e Mamba-2 (d_state=128, multi-head). Modelos 130M-2.8B no HuggingFace.

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Hardware Security Modules (HSMs) are tamper-resistant physical devices that safeguard cryptographic keys and perform cryptographic operations in a hardened environment. Keys stored in an HSM never lea

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Reconstructed reference notes on EASA acceptable means of compliance for airborne software and airborne electronic hardware, built from AMC 20-115D (ED Decision 2017/020/R, AMC-20 Amendment 14, consolidated in Amendment 23) and AMC 20-152A (ED Decision 2020/010/R, AMC-20 Amendment 19, consolidated in Amendment 23), read from the Easy Access Rules AMC-20 compilation (Amendment 23, June 2023) and pinned 2026-09-26. Use for EASA software means of compliance, EASA AEH means of compliance, AltMoC, ETSO software, and the joint software and hardware certification workflow. SCOPE LIMITS: the two AMC sections only; no FAA content (use faa-8110-49); no RTCA DO-178C or DO-254, EUROCAE ED-12C or ED-80, or supplement clause text (use avionics-signpost to name them); no Part-21 body; no bulk AMC-20; AMC 20-193 and AMC 20-189 are name-only; reflects Amendment 23 as consolidated in the June 2023 EAR, not a later AMC-20 amendment; synthesized reference notes, not legal advice and not the official AMC. LICENCE: per-document (EASA site reproduction-with-acknowledgement grant, qualified by ED Decision all-rights-reserved footers); pack-level commercial use is not granted.

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Knowledge base from FAA-HDBK-006D (2020), the FAA's System Reliability, Maintainability, and Availability (RMA) Handbook for National Airspace System (NAS) hardware and software. Use for RMA figures of merit (MTBF, MTBO, MTTR, the availability variants), the bathtub curve and probability distributions, the four-stage RMA lifecycle mapped to the FAA Acquisition Management System (AMS), the six-task RMA technical-management/acquisition process, service-thread criticality and the NAS-RD severity-to-target table, the RMA analysis toolbox (RBD, FMEA/FMECA, FTA, Ishikawa, Monte Carlo, Bayesian, FRACAS, ALT, reliability-growth/recovery tests), software reliability (early prediction + reliability growth), and tailorable example RMA requirements for a Program Requirements Document. Scope limits: it is FAA/NAS-specific guidance (not a requirement, by its own statement), centred on hardware-plus-software availability — it brackets out human/service/facility/communications availability, names but does not reproduce the math derivations or external standards (MIL-STDs, IEEE 1633, RTCA DO-178C/DO-278A, SAE JA1011), is thin on detailed worked numerical examples and on safety-case method, and predates any current revision beyond 006D.

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Use when designing electrical schematics, PCB layouts, KiCad projects, or OpenSCAD enclosures — trace geometry, impedance, stackup, DFM/DRC/ERC sign-off, and hardware-software co-design boundaries.

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Hardware Security Modules (HSMs) are tamper-resistant physical devices that safeguard cryptographic keys and perform cryptographic operations in a hardened environment. Keys stored in an HSM never lea

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Hardware Security Modules (HSMs) are tamper-resistant physical devices that safeguard cryptographic keys and perform cryptographic operations in a hardened environment. Keys stored in an HSM never lea

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Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.

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Lists the hardware, software, and container runtime requirements for running NemoClaw. Use when verifying prerequisites before installation. Trigger keywords - nemoclaw prerequisites, nemoclaw supported platforms, nemoclaw hardware software, nemoclaw quickstart, install nemoclaw openclaw sandbox, nemoclaw windows wsl2 setup, nemoclaw install windows docker desktop.

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Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration), ImageMagick (image manipulation, format conversion, batch processing, effects, composition), and RMBG (AI-powered background removal). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, removing backgrounds from images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.

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Hardware Security Modules (HSMs) are tamper-resistant physical devices that safeguard cryptographic keys and perform cryptographic operations in a hardened environment. Keys stored in an HSM never lea

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TorchTitan과 PyTorch로 대규모 언어 모델 사전 학습을 설정합니다. 여러 GPU에 작업을 나누고 단일·다중 노드 실행과 학습 저장·재개를 지원합니다.

• 단일 노드에서 Llama 3.1 8B 사전 학습
• SLURM으로 다중 노드 학습 실행
• H100용 Float8 학습 설정

Hermes에서 Actual Computer로 자신의 기기에 AI 모델을 실행하도록 연결을 설정합니다. 중계 서비스와 로컬 연결, 모델 선택, 응답 확인 및 오류 점검을 다룹니다.

• Actual 키로 Hermes를 연결합니다
• 자신의 기기에서 모델을 실행합니다
• 로컬 모델을 찾아 내려받고 불러옵니다

실제 iPhone에서 iOS 앱의 각 화면을 촬영하고 Apple의 설계 지침과 프로젝트 디자인 규칙에 따라 외관을 평가합니다. 항목별 점수와 개선 사항을 제공합니다.

• 실제 iPhone에서 앱 외관 확인
• 화면과 프로젝트 디자인 방침 대조
• 평가 점수를 바탕으로 개선 사항 정리

Google Stitch의 화면 생성에 쓸 디자인 방침을 DESIGN.md로 정리합니다. 배색, 서체, 배치, 움직임과 피할 표현을 구체화해 화면 간 일관성을 유지합니다.

• Google Stitch 화면의 공통 규칙을 만듭니다
• 배색, 서체와 구성 요소 동작을 문서화합니다
• 모바일 배치와 움직임 규칙을 정의합니다

Control a running iOS Simulator on macOS from Orca’s live device pane. Perform taps, gestures, typing, button presses, and rotation, and inspect the interface element hierarchy to check app changes in the simulator.

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• Check iOS app changes in the simulator
• Reproduce gestures and text entry
• Check the display after rotating the device

Control an Android phone or emulator through Orca using adb, the Android device connection tool. View the device live, perform touch and keyboard actions, install and launch apps, inspect interface structure, and read logs.

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• Check app behavior on an Android phone or emulator
• Repeat taps, swipes, and text entry
• Install apps and check runtime permissions

Start using RuView by choosing a Docker demo, a source build, or a live ESP32 setup. The skill guides the next concrete step, distinguishing sample data playback from sensing an actual room with WiFi signals.

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• Trying the dashboard without sensing hardware
• Building and testing RuView from source
• Starting a live ESP32 room-sensing setup

Set up radar sensing for contactless heart rate, breathing rate, presence, and distance measurements. It covers supported hardware, firmware, and combining radar with WiFi CSI, sensing data derived from wireless signals, for shared readings and 3D views.

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• Setting up contactless heart rate and breathing measurements
• Detecting presence and approximate distance in a defined zone
• Combining radar and WiFi CSI readings

Set up an ESP32-S3/C6 sensing node for RuView by choosing firmware, checking and flashing binaries, and configuring WiFi and capture settings. Use boot logs to confirm CSI, measurements of the WiFi channel, is being captured.

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• Choosing firmware for an ESP32 board
• Flashing firmware after checksum verification
• Configuring WiFi, channels, and MAC filters

Optimize scientific Python workloads for NVIDIA GPUs by choosing suitable libraries and reducing bottlenecks. Compare outputs with the CPU version and measure complete processing time, including data transfers, to determine whether the GPU implementation provides a useful improvement.

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• Assess whether slow Python processing would benefit from a GPU
• Move NumPy or pandas workloads to suitable GPU libraries
• Validate GPU results against CPU outputs

Inspect CPU, memory, disk, and accelerator information alongside container and scheduler limits. Produce a JSON snapshot with identifying details removed and conservative worker and memory estimates for planning local workloads, without running stress tests.

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• Choosing worker counts before a large local job
• Checking CPU and memory limits inside a container
• Comparing resource snapshots before and after changes

Analyze firmware from routers, cameras, and other IoT devices, from extracting internal files to running services in an emulator. Follow a staged workflow to investigate vulnerabilities and reproduce findings with firmware analysis and testing tools.

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• Auditing router or camera firmware
• Inspecting firmware update packages
• Running firmware without physical hardware