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Found 10 papers, 2 papers with code
Pulse-Width Modulation Neuron Implemented by Single Positive-Feedback Device
Positive-feedback (PF) device and its operation scheme to implement pulse width modulation (PWM) function was proposed and demonstrated, and the device operation mechanism for implementing PWM function was analyzed.
Machine-Learning Approach to Analyze the Status of Forklift Vehicles with Irregular Movement in a Shipyard
We use the DBSCAN and k-means algorithms to identify the area in which a particular forklift is operating and the type of work it is performing.
Hardware Implementation of Spiking Neural Networks Using Time-To-First-Spike Encoding
Furthermore, the effect of the device variation on the accuracy in temporally encoded SNN is investigated and compared with that of the rate-encoded network.
Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse using Root-Flipping: DeepRF_SLR
A novel approach of applying deep reinforcement learning to an RF pulse design is introduced.
Geometric Sequence Decomposition with $k$-simplexes Transform
This paper presents a computationally efficient technique for decomposing non-orthogonally superposed $k$ geometric sequences.
Nonlinear Dipole Inversion (NDI) enables Quantitative Susceptibility Mapping (QSM) without parameter tuning
We propose Nonlinear Dipole Inversion (NDI) for high-quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state-of-the-art reconstruction techniques.
Exploring linearity of deep neural network trained QSM: QSMnet+
To overcome this limitation, a data augmentation method is proposed to generalize the network for a wider range of susceptibility.
Image and Video Processing
Unsupervised Online Learning With Multiple Postsynaptic Neurons Based on Spike-Timing-Dependent Plasticity Using a TFT-Type NOR Flash Memory Array
We present a two-layer fully connected neuromorphic system based on a thin-film transistor (TFT)-type NOR flash memory array with multiple postsynaptic (POST) neurons.
Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet
The QSMnet maps of the test dataset were compared with those from TKD and MEDI for image quality and consistency in multiple head orientations.
Image and Video Processing
Adaptive Learning Rule for Hardware-based Deep Neural Networks Using Electronic Synapse Devices
In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics.
