Search Results for author:
Found 21 papers, 7 papers with code
Hypertension Detection From High-Dimensional Representation of Photoplethysmogram Signals
Although some studies suggest a relationship between blood pressure and certain vital signals, such as Photoplethysmogram (PPG), reliable generalization of the proposed blood pressure estimation methods is not yet guaranteed.
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Zero Access Points 3D Cooperative Positioning via RIS and Sidelink Communications
Reconfigurable intelligent surfaces (RISs) are expected to be a main component of future 6G networks, due to their capability to create a controllable wireless environment, and achieve extended coverage and improved localization accuracy.
RISs and Sidelink Communications in Smart Cities: The Key to Seamless Localization and Sensing
A smart city involves, among other elements, intelligent transportation, crowd monitoring, and digital twins, each of which requires information exchange via wireless communication links and localization of connected devices and passive objects (including people).
S-Rocket: Selective Random Convolution Kernels for Time Series Classification
A population-based optimization algorithm evolves the population in order to find a best state vector which minimizes the number of active kernels while maximizing the accuracy of the classifier.
CAIM: Cooperative Angle of Arrival Estimation using the Ising Method
This paper proposes a cooperative angle-of-arrival(AoA) estimation, taking advantage of co-processing channel state information (CSI) from a group of access points that receive signals of the same source.
A Framework For Pruning Deep Neural Networks Using Energy-Based Models
A typical deep neural network (DNN) has a large number of trainable parameters.
Pruning of Convolutional Neural Networks Using Ising Energy Model
Pruning is one of the major methods to compress deep neural networks.
A Framework for Neural Network Pruning Using Gibbs Distributions
It can be used for structured or unstructured pruning and we propose a number of specific methods for each.
EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks
The energy-based model stochastically evolves the population to find states with lower energy loss.
Regularizing Neural Networks by Stochastically Training Layer Ensembles
Dropout and similar stochastic neural network regularization methods are often interpreted as implicitly averaging over a large ensemble of models.
Survey of Dropout Methods for Deep Neural Networks
Dropout methods are a family of stochastic techniques used in neural network training or inference that have generated significant research interest and are widely used in practice.
Ising-Dropout: A Regularization Method for Training and Compression of Deep Neural Networks
In this paper, we propose an adaptive technique to wisely drop the visible and hidden units in a deep neural network using Ising energy of the network.
Cylindrical Transform: 3D Semantic Segmentation of Kidneys With Limited Annotated Images
The images generated from a cylindrical transform augment a limited annotated set of images in three dimensions.
Recent Advances in Recurrent Neural Networks
Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data.
Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks
Medical datasets are often highly imbalanced with over-representation of common medical problems and a paucity of data from rare conditions.
Interpretation of Mammogram and Chest X-Ray Reports Using Deep Neural Networks - Preliminary Results
Radiology reports are an important means of communication between radiologists and other physicians.
A Survey of Human Activity Recognition Using WiFi CSI
This is done by extracting features from CSI data streams and using machine learning techniques to build models and classifiers.
Image Augmentation using Radial Transform for Training Deep Neural Networks
We propose a sampling method based on the radial transform in a polar coordinate system for image augmentation to facilitate the training of deep learning models from limited source data.
A Convolutional Neural Network for Search Term Detection
Pathfinding in hospitals is challenging for patients, visitors, and even employees.
OCRAPOSE II: An OCR-based indoor positioning system using mobile phone images
Then, it finds depth and angle-of-view of the query using the information provided by the OCR engine in order to refine the location estimate.
Joint Indoor Localization and Radio Map Construction with Limited Deployment Load
In this paper, we aim to design an indoor localization scheme that can be directly employed without building a full fingerprinted radio map of the indoor environment.
