AIMC Topic: Algorithms

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Discrimination of bursts and tonic activity in multifunctional sensorimotor neural network using the extended hill-valley method.

Journal of neurophysiology
Individual neurons can exhibit a wide range of activity, including spontaneous spiking, tonic spiking, bursting, or spike-frequency adaptation, and can also transition between these activity types. Manual identification of these activity patterns can...

Diagnosis of Alzheimer's disease with Sobolev gradient-based optimization and 3D convolutional neural network.

International journal for numerical methods in biomedical engineering
Alzheimer's disease is a neuropsychiatric, progressive, also an irreversible disease. There is not an effective cure for the disease. However, early diagnosis has an important role for treatment planning to delay its progression since the treatments ...

Where Computation and Dynamics Meet: Heteroclinic Network-Based Controllers in Evolutionary Robotics.

IEEE transactions on neural networks and learning systems
In the fields of artificial neural networks and robotics, complicated, often high-dimensional systems can be designed using evolutionary/other algorithms to successfully solve very complex tasks. However, dynamical analysis of the underlying controll...

Mango Fruit Load Estimation Using a Video Based MangoYOLO-Kalman Filter-Hungarian Algorithm Method.

Sensors (Basel, Switzerland)
Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree ("dual-view") underestimates the fruit yield as fruit can be hidden from view. ...

Data-Driven Automated Cardiac Health Management with Robust Edge Analytics and De-Risking.

Sensors (Basel, Switzerland)
Remote and automated healthcare management has shown the prospective to significantly impact the future of human prognosis rate. Internet of Things (IoT) enables the development and implementation ecosystem to cater the need of large number of releva...

Network stiffness: A new topological property in complex networks.

PloS one
Aiming at serving the interdisciplinary demand in network science, this paper introduces a new concept for complex networks, named network stiffness, which is extracted from structural engineering by assuming that a complex network behaves similarly ...

Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders.

IEEE journal of biomedical and health informatics
Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the a...

Denoising of MR images with Rician noise using a wider neural network and noise range division.

Magnetic resonance imaging
Magnetic resonance (MR) images denoising is important in medical image analysis. Denoising methods based on deep learning have shown great promise and outperform all of the other conventional methods. However, deep-learning methods are limited by the...

Automatic segmentation of hyperreflective foci in OCT images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The leading cause of vision loss in the Western World is Age-related Macular Degeneration (AMD), but together with modern medicines, tracking the number of Hyperreflective Foci (HF) on Optical Coherence Tomography (OCT) imag...

Weighted Transfer Learning for Improving Motor Imagery-Based Brain-Computer Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
One of the major limitations of motor imagery (MI)-based brain-computer interface (BCI) is its long calibration time. Due to between sessions/subjects variations in the properties of brain signals, typically, a large amount of training data needs to ...