Memristor-based neural networks promise low-power, high-density neuromorphic hardware, yet most studies remain proof-of-concept with small inputs and qualitative reporting. We present a comprehensive investigation of on-chip learning in a scalable memristor network, modeling realistic non-idealities including device-to-device and cycle-to-cycle variability, conductance programming errors, and input noise. Using a 30-memristor architecture, we systematically scale inputs from 3×3 to 11x11 and evaluate classification robustness under noisy conditions. Results show that moderate training noise improves tolerance, enabling up to 99% accuracy with 20% noisy tests, whereas aggressive weight quantization at higher resolutions can degrade accuracy. We quantify average training time and energy across device types; chromium devices are most efficient, achieving 2.4 s average training time and 18.9mJ training energy for 3×3 tasks. We also report energy per classification versus input size, establishing concrete benchmarks often absent from prior work. A Big-O analysis indicates sub-linear growth of training time (n0.26), training energy (n0.28), and inference cost with input dimension, supporting suitability for edge deployment. Unlike earlier demonstrations that emphasized qualitative recognition, this study provides quantitative trade-offs among accuracy, noise robustness, energy, and training time, bridging device-level feasibility and practical memristor neural network accelerators. These results guide design choices for deployable edge systems.

This article presents a comparative study of sampling methods within the FedHome framework, designed for personalized in-home health monitoring. FedHome leverages federated learning (FL) and generative convolutional autoencoders (GCAE) to train models on decentralized edge devices while prioritizing data privacy. A notable challenge in this domain is the class imbalance in health data, where critical events such as falls are underrepresented, adversely affecting model performance. To address this, the research evaluates six oversampling techniques using Stratified K-fold cross-validation: SMOTE, Borderline-SMOTE, Random OverSampler, SMOTE-Tomek, SVM-SMOTE, and SMOTE-ENN. These methods are tested on FedHome's public implementation over 200 training rounds with and without stratified K-fold cross-validation. The findings indicate that SMOTE-ENN achieves the most consistent test accuracy, with a standard deviation range of 0.0167–0.0176, demonstrating stable performance compared to other samplers. In contrast, SMOTE and SVM-SMOTE exhibit higher variability in performance, as reflected by their wider standard deviation ranges of 0.0157–0.0180 and 0.0155–0.0180, respectively. Similarly, the Random OverSampler method shows a significant deviation range of 0.0155–0.0176. SMOTE-Tomek, with a deviation range of 0.0160–0.0175, also shows greater stability but not as much as SMOTE-ENN. This finding highlights the potential of SMOTE-ENN to enhance the reliability and accuracy of personalized health monitoring systems within the FedHome framework.

An automated determination of Laguerre-Gaussian (LG) modes benefits cavity tuning and optical communication. In this paper, we employ machine learning techniques to automatically detect the lowest sixteen LG modes of a laser beam. Convolutional neural networks (CNN) are trained by collecting the experimental and simulated datasets of LG modes that relies only on the intensity images of their unique patterns. We demonstrate that the trained CNN model can detect LG modes with the maximum accuracy greater than 96% after 60 epochs. The study evaluates the CNN's ability to generalize to new data and adapt to experimental conditions.