Skill UI

A comprehensive guide for designing and managing document control systems for ISO 13485-compliant Quality Management Systems (QMS), specifically tailored for the medical device industry. This resource covers document numbering, version control, change management workflows, approval processes, and ensures compliance with rigorous standards like 21 CFR Part 11, including electronic signatures and audit trails.

This skill automates the Design Space Exploration (DSE) process, crucial for computer architecture and Electronic Design Automation (EDA). It runs complex simulations, analyzes performance metrics, and systematically tunes parameters (e.g., cache size, pipeline width) across a defined parameter space. It iterates until the objective metric is optimized or a defined timeout is reached, making it ideal for complex hardware tuning and system design.

KubeEye is a comprehensive cluster inspection tool designed for Kubernetes environments, particularly within KubeSphere. It systematically detects issues across workloads, nodes, and configurations by integrating multiple policy engines, including OPA/Rego, PromQL, file integrity checks, kernel parameter validation, and systemd health checks. Users can define custom inspection rules (InspectRule), schedule checks (InspectPlan), and retrieve detailed reports (InspectResult) to ensure cluster compliance and stability.

This tool guides authors on whether their manuscript fits the scope of Advanced Functional Materials. It emphasizes the critical structure-function-application chain, covering highly advanced fields such as optoelectronics, biomedical devices, and soft robotics. It provides criteria for methodology, required evidence, and journal-specific house style.

A comprehensive guide for researchers in electronics and signal processing preparing submissions to Acta Electronica Sinica (AES). It outlines best practices for proactively structuring, documenting, and disclosing research artifacts—including code, datasets, and hardware designs—to maximize the paper's credibility and chances of acceptance, even without mandatory journal badges. Covers anonymization, reproducibility standards, and proper disclosure in the manuscript.

A comprehensive guide for authors submitting to top-tier electronics journals. It provides detailed methodologies on structuring experimental chapters, ensuring comparison fairness, achieving statistical rigor, and presenting reproducible results to withstand demanding peer review scrutiny.

This guide provides comprehensive best practices for ensuring scientific reproducibility in technical papers, particularly for electronics and information processing fields. It covers the critical pillars of documentation: environment setup, data sourcing and preprocessing, full algorithmic disclosure (including pseudo-code and hyperparameters), and robust statistical evaluation. By adopting these standards, authors can transform their work from potentially unverifiable results into credible, verifiable scientific contributions suitable for top-tier journals.

Provides guidelines for creating robust, verifiable, and reusable Electronic Design Automation (EDA) artifacts for submission to academic conferences like DAC. The focus shifts from chasing conference badges to ensuring community trust and scientific reproducibility. Key elements include pinning the toolchain, using standard benchmarks, providing clear setup instructions, and archiving the release in a DOI-issuing repository.

This guide provides a comprehensive checklist for evaluating Electronic Design Automation (EDA) research manuscripts for top-tier conferences like DAC. It emphasizes the necessity of using standard, recognized benchmark suites (e.g., ISPD, EPFL, TAU), comparing results against the true State-of-the-Art (SOTA), reporting full PPA metrics and runtime, and implementing ablation studies to ensure fairness, reproducibility, and generalizability, especially for ML-based methods.

A comprehensive guide for establishing research reproducibility in Electronic Design Automation (EDA). It details best practices for pinning benchmark suites, disclosing specific tool versions (open/commercial), documenting technology nodes (PDKs), and handling stochastic data flows (seeds, variance). Essential for ensuring that semiconductor design claims and ML-for-EDA results are credible and verifiable by the academic community.

This comprehensive guide assists researchers and practitioners in selecting the optimal venue and track (e.g., DAC, ICCAD, ISCA) for their electronic design automation (EDA) or chip-design project. It clarifies the critical distinction between the archival Research Manuscript track and the industry-facing Engineering Track at DAC, emphasizing the need to frame contributions using measurable Quality of Results (QoR) improvements, novel algorithms, or real-world industrial lessons.

This tool guides authors on structuring manuscripts for IEEE Transactions on Antennas and Propagation (TAP). It emphasizes that the core contribution must be in applied electromagnetics, antenna design, or wave propagation theory, requiring a standard evidence chain: sound theory, full-wave simulation, and measured prototype validation. Use it for venue selection and content re-framing.