This project presents a complete workflow for cone detection in Formula Student Driverless scenarios using deep learning. It demonstrates how to use MATLAB® and Simulink® for data preparation and ...

This paper presents miniML, an AI-based framework for the detection of synaptic events. Benchmark results presented in the paper are compelling, demonstrating the superiority of miniML over current ...

Human inventions, namely engineered systems, have relied on fundamental discoveries in physics and mathematics, e.g., Maxwell’s equations, Quantum mechanics, Information theory, etc., thereby applying ...

Abstract: In this paper, we propose a new deep-learningbased framework for the automatic analysis of microorganism images in MATLAB. In fact, this is an effort to improve the accuracy and efficiency ...

In recent years, notable advancements in the design and training of deep learning models have led to significant improvements in image recognition performance, particularly on large-scale datasets.

The recently published book Understanding Deep Learning by [Simon J. D. Prince] is notable not only for focusing primarily on the concepts behind Deep Learning — which should make it highly accessible ...

Abstract: This paper proposes a Design Space Exploration for Edge machine learning through the utilization of the novel MathWorks FPGA Deep Learning Processor IP, featured in the HDL Deep Learning ...

This does not work in R2023b. Here's the output: Pretrained yolox_s network (mat file) already exists. Warning: While loading an object of class 'dlnetwork': Invalid ...

Scientists have developed a deep learning model, RECAST, to forecast earthquake aftershocks. This model demonstrates superior adaptability and scalability compared to the existing ETAS model, ...