Skill UI

This comprehensive guide outlines the strict criteria for submitting a paper to the SenSys conference after the 2026 merger. It clarifies that the core contribution must be a built, measured sensing or embedded system, demonstrating value based on physical resources like energy, memory, and latency on real hardware, rather than being a pure algorithm or networking mechanism.

A comprehensive guide for authors in data systems and related fields on achieving rigorous, verifiable reproducibility for academic papers. It mandates detailing the provenance of every claimed result by pinning five critical layers: code version, configuration parameters, dataset source, workload characteristics, and hardware specifications. Best practices include reporting distributions (e.g., percentiles, variance) and maintaining a detailed 'repro debt ledger' to withstand intense peer review.

This guide details the structured process for preparing and evaluating research artifacts for major academic conferences like SOSP. It covers badge targeting (Functional, Reusable), cooperative evaluation expectations, and best practices for packaging system-level artifacts, including defining tiers (runnable, provided-hardware, documented-only) and conducting 'kick-the-tires' smoke tests.

A comprehensive methodology for rigorous scientific research in operating systems and complex systems. This guide details how to pin environments (OS, firmware, hardware), automate data capture, and script all figures to ensure that experimental results are auditable and fully reproducible, protecting findings against review challenges.

A comprehensive guide for authors submitting to high-impact scientific journals. It outlines the essential principles for structuring experiments and presenting evidence, emphasizing claims-evidence matching, fair benchmarking, differentiating result sources (simulation, hardware, theory), and ensuring statistical rigor. Covers best practices across computer science, control, communication, and microelectronics.

A comprehensive guide detailing how to package and present research artifacts—including code, hardware designs, and datasets—for major academic conferences. It outlines best practices for both initial review-time evidence and long-term public reuse, ensuring reproducibility and maximizing the chances of adoption by future builders.

This guide outlines the methodology for conducting hardware security research on embedded systems. It covers physical interface analysis (UART, JTAG, SWD), debug pad triage, secure boot evaluation, and offline firmware extraction. It emphasizes systematic, non-destructive testing protocols and responsible handling of physical devices.

Mentor for embedded and IoT hardware projects: helps select MCUs, dev boards, toolchains, and data destinations (phone, PC, dashboard, alert). Provides time/cost estimates and phased build plans from breadboard MVP to production PCB. Use for questions about ESP32, STM32, Arduino, Raspberry Pi Pico, firmware, PCB, KiCad, MQTT, Home Assistant, or hardware tool recommendations.

Design custom laboratory hardware components using parametric Python models with build123d. Export fabrication-ready files like STEP, STL, and DXF for microfluidic chips, mounts, and adapters. Ensure precise interface dimensions against standards for compatibility with lab equipment. Use for physical parts mating with standardized labware, optical tables, or manufacturing processes.