Julius contains different Digital Signal Processing algorithms implemented with PyTorch, so that they are differentiable and available on CUDA. Note that all the modules implemented here can be used ...

Signals and Systems 6.003 covers the fundamentals of signal and system analysis, focusing on representations of discrete-time and continuous-time signals (singularity functions, complex exponentials ...

Deep learning has added a new dimension to engineering applications, from 5G signal processing to predictive maintenance in power grids. It automatically detects equipment failures and optimizes ...

At present, in the field of cherry recognition, problems such as dense fruit growth and frequent occlusions of branches and leaves affect the accuracy of the recognition process. To address these ...

Abstract: This paper proposes a reconfigurable convolutional neural network (CNN) acceleration system based on FPGA to address the computational power and energy consumption limitations when deploying ...

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 ...

Using one of the best reverb plugins is a great way to transform dull and lifeless tracks into more atmospheric and 'human' mixes. Reverbs recreate the sound of a room or space, emulating the ...

Automated thyroid nodule classification in ultrasound images is an important way to detect thyroid nodules and to make a more accurate diagnosis. In this paper, we propose a novel deep convolutional ...

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