Edge computing is an emerging IT architecture that enables the processing of data locally by smartphones, autonomous vehicles, local servers, and other IoT devices instead of sending it to be ...

Abstract: Deep neural networks (DNNs) and especially convolutional neural networks (CNNs) have revolutionized the way we approach the analysis of large quantities of data. However, the largely ad hoc ...

Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of ...

3D rendering—the process of converting three-dimensional models into two-dimensional images—is a foundational technology in computer graphics, widely used across gaming, film, virtual reality, and ...

"For the EstimatorQNN, the expected output shape for the forward pass is (1, num_qubits * num_observables)” In practice, the forward pass returns an array of shape (batch_size, num_observables)—one ...

The integration of neural network models in autonomous robotics represents a monumental leap in artificial intelligence and robotics. These models, mirroring the human brain's complexity and ...

ABSTRACT: Vehicular Ad Hoc Networks (VANETs) are critical for the advancement of Intelligent Transportation Systems (ITS), enabling real-time vehicle-to-vehicle (V2V) and vehicle-to-infrastructure ...

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...

Abstract: This advanced tutorial explores some recent applications of artificial neural networks (ANNs) to stochastic discrete-event simulation (DES). We first review some basic concepts and then give ...