In the previous episodes of this AI on Edge journey, we have done something exciting. We built real applications directly on our smartphones. We created image classification apps. We implemented ...

A few weeks ago, I asked what your go-to signal processing language is. Python & MATLAB clearly won the race, with Python taking the slight lead. You’ve seen the ...

\item Digital Signal Processing: System Analysis and Design by Paulo S. R. Diniz, Eduardo A. B. da Silva, and Sergio L. Netto ...

The term “differentiable digital signal processing” describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilitating their ...

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the ...

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 ...

Convolutional Neural Networks have been a dominant model architecture for computer vision since the breakthrough of AlexNet. Since the success of self-attention models like Transformers in natural ...

5.3 Filter application in the time domain 77 5.4 Filter application in the frequency domain 78 5.8 Calculating a power spectral density using MATLAB/Octave and Python/SciPy functions 85 5.9 Code for ...

The vast amount of design freedom in disordered systems expands the parameter space for signal processing. However, this large degree of freedom has hindered the deterministic design of disordered ...

We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be ...