TinyML is an incredibly powerful piece of software, and you can easily train your own model and deploy it on an ESP32.

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...

Abstract: Equivariant quantum graph neural networks (EQGNNs) offer a potentially powerful method to process graph data. However, existing EQGNN models only consider the permutation symmetry of graphs, ...

Understanding the brain's functional architecture is a fundamental challenge in neuroscience. The connections between neurons ultimately dictate how information is processed, transmitted, stored, and ...

This project contains implementations of simple neural network models, including training scripts for PyTorch and Lightning frameworks. The goal is to provide a modular, easy-to-understand codebase ...

The study investigates using 3D neural organoids made from human pluripotent stem cells (hPSCs) to assess neurotoxicity and neural function. Organoids were imaged to measure spontaneous calcium ...

Abstract: This paper introduces an innovative approach utilizing a deep neural network (DNN) to optimize the modulation scheme for time-modulated antenna array (TMAA) to verify specific side lobe and ...

ABSTRACT: With the advent of the 5G and future 6G, base stations will be used as station controllers. The antenna systems are networked and equipped with a processor to optimize the detection of ...

Installing Python and related applications on a system without a network connection isn’t easy, but you can do it. Here’s how. The vast majority of modern software development revolves around one big ...

The flagship product of the collaboration is Site3D, a 3D point cloud map of Earth’s surface. Credit: Array Labs Array Labs, Umbra Space and Raytheon have signed a partnership to speed up the ...