Designing analog circuits from performance specifications is a complex, multi-stage process encompassing topology selection, parameter inference, and layout feasibility. We introduce FALCON, a unified machine learning framework that enables fully automated, specification-driven analog circuit synthesis through topology selection and layout-constrained optimization. Given a target performance, FALCON first selects an appropriate circuit topology using a performance-driven classifier guided by human design heuristics. Next, it employs a custom, edge-centric graph neural network trained to map circuit topology and parameters to performance, enabling gradient-based parameter inference through the learned forward model. This inference is guided by a differentiable layout cost, derived from analytical equations capturing parasitic and frequency-dependent effects, and constrained by design rules. We train and evaluate FALCON on a large-scale custom dataset of 1M analog mm-wave circuits, generated and simulated using Cadence Spectre across 20 expert-designed topologies. Through this evaluation, FALCON demonstrates >99\% accuracy in topology inference, <10\% relative error in performance prediction, and efficient layout-aware design that completes in under 1 second per instance. Together, these results position FALCON as a practical and extensible foundation model for end-to-end analog circuit design automation.

As an important tool for detecting and diagnosing epilepsy, multi-channel EEG records the neuronal activities of different brain regions. Visual identification of abnormal EEG signals poses challenges, making the use of artificial intelligence techniques for automated seizure detection an inevitable trend. However, existing seizure detection methods often overlook the spatial relationship between EEG channels, which can't take full advantage of brain network structure. In this paper, we design an end-to-end spatiotemporal architecture for seizure detection based on Graph Convolutional Networks (GCN) and Bidirectional Gated Recurrent Units (BiGRU) to efficiently model the spatial dependence and temporal dynamics of EEG. Firstly, the original EEG signals are preprocessed by applying wavelet transform for temporal-frequency analysis. The Pearson correlation matrix is computed for specific frequency bands and GCN is utilized to extract spatial features between EEG channels. Then, these features are sent into the BiGRU network to capture temporal relationships. Finally, the detection decisions are achieved using fully connected layers and the multi-level decision rules are implemented to provide the final results. The proposed method is validated on CHB-MIT EEG dataset, achieving 98.85% sensitivity, 95.83% specificity, 97.35% accuracy, 97.4% F1-score, and 97.33% AUC. This network fusions multiple EEG characteristics in the spatial-temporal-frequency domains to improve the detection performance and the promising result demonstrates that the performance of this model is superior to or on par with existing methods.

Epilepsy is a chronically occurring neurological disorder which is characterized by uninterrupted repetitive seizures that can occur spontaneously, making it one of the most prevalent brain disorders. A novel method for detecting seizures is proposed in this paper, which utilizes uncorrelated multilinear principal component analysis (UMPCA) and metric learning based on doublet support vector machine (doublet-SVM). The system first segmented the EEG signal and performed modified Stockwell transform (MST) to obtain a 2-dimensional time-frequency spectrum, and the third-order tensor for multichannel EEG signals was constructed based on time, frequency and spatial domains. Then, the uncorrelated features were extracted using the UMPCA, which could distinguish seizure and non-seizure characteristics in the massive third-order EEG tensor. After that, the distance metric learning is approached by employing the doublet-SVM algorithm, which transforms it into a kernel classifier problem for efficient EEG classification. The performance of this epilepsy detection model was tested and evaluated on the Freiburg EEG database of 21 patients, and the average sensitivity, specificity and accuracy obtained were 98.74%, 98.11% and 98.12%, respectively. The results demonstrate the significant ability of this algorithm in detecting seizures.