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Found 13 papers, 2 papers with code
Deep Learning Framework for the Design of Orbital Angular Momentum Generators Enabled by Leaky-wave Holograms
We can determine the optimal values for each parameter, resulting in the desired radiation pattern, using a total of 77, 000 generated datasets.
Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification
We are motivated by these facts to pre-train the in-domain representations of remote sensing imagery using contrastive self-supervised learning and transfer the learned features to other related remote sensing datasets.
Simultaneous estimation of wall and object parameters in TWR using deep neural network
In both cases, we consider the permittivity and thickness for the wall, as well as the two-dimensional coordinates of the target's center and permittivity.
A deep learning approach for inverse design of the metasurface for dual-polarized waves
Here, we have used the Deep Neural Network (DNN) for the generation of desired output unit cell structures in an ultra-wide working frequency band for both TE and TM polarized waves.
Through-the-Wall Radar under Electromagnetic Complex Wall: A Deep Learning Approach
There are two target modes available: single target and double targets.
Deep neural network-based automatic metasurface design with a wide frequency range
Beyond the scope of conventional metasurface which necessitates plenty of computational resources and time, an inverse design approach using machine learning algorithms promises an effective way for metasurfaces design.
EEGsig: an open-source machine learning-based toolbox for EEG signal processing
In the quest to realize a comprehensive EEG signal processing framework, in this paper, we demonstrate a toolbox and graphic user interface, EEGsig, for the full process of EEG signals.
On segmentation of pectoralis muscle in digital mammograms by means of deep learning
Subsequently, the predictions are used by the second stage of the algorithm, in which the desired boundary is recovered as a solution to the shortest path problem on a specially designed graph.
Palmprint image registration using convolutional neural networks and Hough transform
One way to address this issue is aligning all palmprint images to a reference image and bringing them to a same coordinate system.
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction
Alternatively, state-of-the-art joint modeling techniques can be used for jointly modeling the longitudinal and event data and compute event probabilities conditioned on the longitudinal observations.
Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions
Treatment effects can be estimated from observational data as the difference in potential outcomes.
ATD: Anomalous Topic Discovery in High Dimensional Discrete Data
Individual AD techniques and techniques that detect anomalies using all the features typically fail to detect such anomalies, but our method can detect such instances collectively, discover the shared anomalous patterns exhibited by them, and identify the subsets of salient features.
Parsimonious Topic Models with Salient Word Discovery
We minimize BIC to jointly determine our entire model -- the topic-specific words, document-specific topics, all model parameter values, {\it and} the total number of topics -- in a wholly unsupervised fashion.
