20 accepted papers
This paper proposes a dynamic orthogonal matching pursuit (OMP)-based localization for automotive radar systems. At each time instant, three dictionaries are designed based on prior information on targets’ approximate locations. We use mutual coherence as the criterion for designing each dictionary.…
This paper proposes to integrate reconfigurable intelligent surfaces (RISs) with unmanned aerial vehicles (UAVs) as a resilience mechanism to mitigate outages in UAV networks due to UAV and link failures. The inherent addition of RIS-aided links (UE-RIS-UAV links), combined with their reconfigurabil…
WiFi sensing is an important part of the new WiFi 802.11bf standard, which can detect motion and measure distances. In recent years, some machine learning methods have been proposed for human activity recognition from WiFi signals. However, to the best of our knowledge, none of these methods have ex…
Photoplethysmogram (PPG) is a major indicator of a patient’s physiological status. PPG is generally studied using manually designed algorithms to detect its critical morphological points. However, existing algorithms for analyzing these signals do not serve the purpose effectively and accurately, pa…
In the last decade, there has been a surge of research interest in feature extraction using random sampling. These techniques are fast and scalable and, at the same time, have practical favorability in low-sample size and high-dimensional training data. Convolutional Kitchen Sinks-based methods are…
Machine learning and deep learning models for time series classification generally require a large volume of data to achieve superior performance. However, due to the lack of a sufficient amount of time series in many real-world applications, particularly health care, training these models is more c…
Accurate indoor localization is a challenging problem in a multipath environment. In order to tackle this problem, several methods have been proposed. Direct localization is one of these methods that makes use of a two-dimensional search in a planar geometry. In this paper, we use a compressed sensi…
Anatomical movements of the human body can change the channel state information (CSI) of wireless signals in an indoor environment. These changes in the CSI signals can be used for human activity recognition (HAR), which is a pre-dominant and unique approach due to preserving privacy and flexibility…
Localization in wireless networks is possible by measuring some characteristics of the propagating signal related to the position of the user, which is always corrupted by noise components. In this paper, a correntropy based algorithm is proposed for localization and tracking of a mobile station in…
A typical deep neural network (DNN) has a large number of trainable parameters. Choosing a network with proper capacity is challenging and generally a larger network with excessive capacity is trained. Pruning is an established approach to reducing the number of parameters in a DNN. In this paper, w…
Networks are frequently changing due to new technologies. The growing demand for bandwidth is forcing many carriers to migrate their existing network to a network with a new technology in order to increase network performance. Telecommunication companies are looking for optimization algorithms to ef…
Resampling increasing the variance of the tracking algorithm in Particle Filtering (PF). Instead of utilizing resampling procedures that rely on asymptotic convergence properties, we show that intelligently selecting and replicating a set of samples can better represent the posterior approximation a…
Pruning is one of the major methods to compress deep neural networks. In this paper, we propose an Ising energy model within an optimization framework for pruning convolutional kernels and hidden units. This model is designed to reduce redundancy between weight kernels and detect inactive kernels/hi…
Data representation can facilitate training of deep neural network when limited data is available. We have previously proposed the radial transform sampling method as a data representation technique for training neural networks. In this paper, a probabilistic framework to analyze radial transform is…
Cyber attacks on the power systems can mislead the control center to produce incorrect state and topology estimate. State and topology attacks can have harmful impacts on the operation of a power system. The problem of placing secure phasor measurement units (PMUs) to detect these attacks has been s…
Multipath interference causes the antenna array of an anchor to estimate several angles of arrival (AoA) for a single user. The resulting ambiguity regarding the line of sight (LoS) component can lead to severe errors in location estimation. This work formulates the problem within an outlier rejecti…
Overfitting is a major problem in training machine learning models, specifically deep neural networks. This problem may be caused by imbalanced datasets and initialization of the model parameters, which conforms the model too closely to the training data and negatively affects the generalization per…
Medical datasets are often highly imbalanced with over-representation of common medical problems and a paucity of data from rare conditions. We propose simulation of pathology in images to overcome the above limitations. Using chest X-rays as a model medical image, we implement a generative adversar…
Deep learning models have a large number of free parameters that must be estimated by efficient training of the models on a large number of training data samples to increase their generalization performance. In real-world applications, the data available to train these networks is often limited or i…
In this paper, we propose an image-based localization system, applicable for a number of indoor scenarios including office buildings, airports, chain stores, etc. In such applications, text/numbers are suitable distinctive landmarks for localization. The proposed system takes advantage of OCR to rea…