Combine requirements, interfaces, architecture, and PPA constraints to train models to answer digital circuit design questions with clear reasoning.

Evaluate how accurately models extract key temperature sensor specifications from product pages and technical catalogs.

Evaluate how well models extract and structure key flow sensor specifications from product webpages and technical materials, with field-level annotations and linked source text.

Train models to extract attributes from similar home media device models and variants without misassigning series-level information to a specific product.

The living room smart home abnormal behavior detection video dataset is used to enhance the anomaly detection capability of smart devices.

This dataset focuses on image classification and recognition for smart refrigerators.

Gesture recognition image datasets boost the performance of smart devices.

This dataset provides images of smartwatches along with their classification labels aimed at improving image recognition accuracy.

This dataset focuses on image classification of robotic vacuum cleaners, suitable for visual recognition tasks in the smart home field.

This dataset aids in intelligent lighting design and energy-saving optimization.

Evaluate how well models extract gas sensor operating and fault states, including their triggers and manifestations, from specialized documents.

This dataset provides video trajectories of cleaning robots in various environments to support intelligent navigation and planning research.

This dataset provides high-quality clothes rack image classification data, supporting applications in the smart home field.

This dataset provides image classification data for smart washing machines, aiding research and applications in the field of smart devices.

Extract operating conditions, contact states, and related sensor-state information from temperature sensor web text for focused domain evaluation.

Distinguish environment-induced pseudo-anomalies from genuine sensor faults to improve gas sensor anomaly detection.

Train models to reason across mixed-signal modules and produce evidence-based, verifiable fault diagnosis answers from symptoms, measurements, and design conditions.

Combine timing reports with design context to train models to interpret critical paths and diagnose timing violations.

Train models to derive nonlinear relationships, rearrange formulas, and solve for unknowns using empirical varistor voltage–current models.

This dataset focuses on image classification for smart air conditioners, aiming to improve device recognition and user interaction efficiency.