AIMC Topic: Neural Networks, Computer

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LightSleepNet: A Lightweight Deep Model for Rapid Sleep Stage Classification with Spectrograms.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Deep learning has achieved unprecedented success in sleep stage classification tasks, which starts to pave the way for potential real-world applications. However, due to its enormous size, deployment of deep neural networks is hindered by high cost a...

Time-domain Mixup Source Data Augmentation of sEMGs for Motion Recognition towards Efficient Style Transfer Mapping.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Motion recognition based on surface electromyogram (sEMG) recorded from the forearm is attracting attention for its applicability because it easily integrates with wearable devices and has a high signal-to-noise ratio. Inter-subject variability and i...

Controlled generation of self-sustained oscillations in complex artificial neural networks.

Chaos (Woodbury, N.Y.)
Spatially distinct, self-sustained oscillations in artificial neural networks are fundamental to information encoding, storage, and processing in these systems. Here, we develop a method to induce a large variety of self-sustained oscillatory pattern...

Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events.

Chaos (Woodbury, N.Y.)
The remarkable flexibility and adaptability of both deep learning models and ensemble methods have led to the proliferation for their application in understanding many physical phenomena. Traditionally, these two techniques have largely been treated ...

Global Mittag-Leffler stability and existence of the solution for fractional-order complex-valued NNs with asynchronous time delays.

Chaos (Woodbury, N.Y.)
This paper is dedicated to exploring the global Mittag-Leffler stability of fractional-order complex-valued (CV) neural networks (NNs) with asynchronous time delays, which generates exponential stability of integer-order (IO) CVNNs. Here, asynchronou...

Simplified description of dynamics in neuromorphic resonant tunneling diodes.

Chaos (Woodbury, N.Y.)
In this article, the standard theoretical model accounting for a double barrier quantum well resonant tunneling diode (RTD) connected to a direct current source of voltage is simplified by representing its current-voltage characteristic with an analy...

Model reduction in acoustic inversion by artificial neural network.

The Journal of the Acoustical Society of America
In ultrasound tomography, the speed of sound inside an object is estimated based on acoustic measurements carried out by sensors surrounding the object. An accurate forward model is a prominent factor for high-quality image reconstruction, but it can...

An optimized pulse coupled neural network image de-noising method for a field-programmable gate array based polarization camera.

The Review of scientific instruments
The quality of polarization images is easy to be affected by the noise in the image acquired by a polarization camera. Consequently, a de-noising method optimized with a Pulse Coupled Neural Network (PCNN) for polarization images is proposed for a Fi...

Gloss perception: Searching for a deep neural network that behaves like humans.

Journal of vision
The visual computations underlying human gloss perception remain poorly understood, and to date there is no image-computable model that reproduces human gloss judgments independent of shape and viewing conditions. Such a model could provide a powerfu...

PhacoTrainer: A Multicenter Study of Deep Learning for Activity Recognition in Cataract Surgical Videos.

Translational vision science & technology
PURPOSE: To build and evaluate deep learning models for recognizing cataract surgical steps from whole-length surgical videos with minimal preprocessing, including identification of routine and complex steps.