Abstract: Convolutional layers (CLs) are ubiquitous in contemporary deep neural network (DNN) models, commonly used for automatic feature extraction. A CL performs cross-correlation between the input ...

Applying convolutional neural networks to a large number of EEG signal samples is computationally expensive because the computational complexity is linearly proportional to the number of dimensions of ...

RF signals historically are measured using spectrum analyzers, at least that was before oscilloscopes offered sufficient bandwidth for those measurements. With oscilloscope bandwidths over 100 GHz, RF ...

This paper covers the concept of Fourier series and its application for a periodic signal. A periodic signal is a signal that repeats its pattern over time at regular intervals. The idea inspiring is ...

Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech ...

Abstract: Electromagnetic time reversal (EMTR) is drawing increasing interest in short-circuit fault location. In this article, we investigate the classic EMTR fault location methods and find that it ...

Audio equalization is the process of adjusting the balance between frequency components within an audio signal. The purpose of equalization is to make an audio signal sound better, more clear, and ...

In terms of seizure prediction, how to fully mine relational data information among multiple channels of epileptic EEG? This is a scientific research subject worthy of further exploration. Recently, ...

Signals that carry information play a central role in technology and engineering — signals ranging from sound and images to sensors, radar, communication, MRI, ultrasound, touch-screens, GPS, and ...