arXiv research

Bayesian approach for adaptive radio tomography using SLFs.

Classifies compact radio sources in the Galactic plane using machine learning.

Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy. Radio data is readily available and easy to obtain from an antenna, but labeled and curated data is often scarce making supervised …

Deep RL for uncoordinated cognitive radios finds near-optimal policies.

In order to cope with the increased data volumes generated by modern radio interferometers such as LOFAR (Low Frequency Array) or SKA (Square Kilometre Array), fast and efficient calibration algorithms are essential. Traditional radio interferometric calibration is performed using nonlinear optimization techniques such…

The performance of a modulation classifier is highly sensitive to channel signal-to-noise ratio (SNR). In this paper, we focus on amplitude-phase modulations and propose a modulation classification framework based on centralized data fusion using multiple radios and the hybrid maximum likelihood (ML) approach. In order…

Quantum machine learning improves pulsar classification in radio astronomy.

Deep reinforcement learning boosts throughput in RF-powered cognitive radio networks.

Robust state-space radio interferometric imaging using Stochastic Approximation Expectation Maximization

Deep learning models, especially CNNs, can predict radio frequency power faster than traditional methods.

Study improves radio show segmentation using audio embeddings.

Study travel time tomography for transversely isotropic media using modified pseudodifferential calculus.

Research uses CPS to estimate uncertainty in ML radio metric models.

Summary of tensor tomography proofs on manifolds with boundaries.

Paper addresses travel time tomography stability and statistical inversion.

Opportunistic spectrum access is one of the emerging techniques for maximizing throughput in congested bands and is enabled by predicting idle slots in spectrum. We propose a kernel-based reinforcement learning approach coupled with a novel budget-constrained sparsification technique that efficiently captures the envir…

We introduce a new graphical model for tracking radio-tagged animals and learning their movement patterns. The model provides a principled way to combine radio telemetry data with an arbitrary set of userdefined, spatial features. We describe an efficient stochastic gradient algorithm for fitting model parameters to da…

Survey on multiplayer bandits, highlighting theoretical gaps and future directions.

Study uses machine learning to solve photoacoustic tomography's inverse problem.

Automatic modulation classification (AMC) is an important task for modern communication systems; however, it is a challenging problem when signal features and precise models for generating each modulation may be unknown. We present a new biologically-inspired AMC method without the need for models or manually specified…

These are lecture notes for the course "Analysis and X-ray tomography". The course is a broad overview of various tools in analysis that can be used to study X-ray tomography. The focus is on tools and ideas, not so much on technical details and minimal assumptions. Only very basic functional analysis is assumed as bac…

ASKAP observes a region of the Galactic plane, identifying 3963 radio sources.

Sketch Tomography improves quantum state estimation accuracy.

Deep learning classifies mobile traffic from LTE PDCCH.

Paper predicts GNSS phase scintillations with machine learning.

Radio on Free Space Optics (RoFSO), as a universal platform for heterogeneous wireless services, is able to transmit multiple radio frequency signals at high rates in free space optical networks. This paper investigates the optimal design of power allocation for Wavelength Division Multiplexing (WDM) transmission in Ro…

Deep network improves electrical tomography across multiple frequencies.

We propose a scheme to employ backpropagation neural networks (BPNNs) for both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal strengths (FWIPS): radio map construction during the offline stage, and localization during the online stage. Given a training radio map (TRM), i.e., a set of coordinat…

Develops deep learning for optimizing 5G radio resource allocation.

Graph neural networks optimize radio resource management policies for wireless networks.

We propose a method for downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks. The primary contribution of our paper is an algorithm to enhance the trigger of DL CoMP using online machine learning. We use support vector machine (SVM) classifiers to enhance the user downlink…

Study uses deep learning to quickly estimate tissue properties for personalized radio-frequency dosimetry.

A new method speeds up quantum state estimation.

Deep learning (DL), despite its enormous success in many computer vision and language processing applications, is exceedingly vulnerable to adversarial attacks. We consider the use of DL for radio signal (modulation) classification tasks, and present practical methods for the crafting of white-box and universal black-b…

Improved computed tomography reconstruction with deep learning and deep image prior.

New deep learning model estimates scattering timescale of FRBs efficiently.

We develop a 2D travel time tomography method which regularizes the inversion by modeling groups of slowness pixels from discrete slowness maps, called patches, as sparse linear combinations of atoms from a dictionary. We propose to use dictionary learning during the inversion to adapt dictionaries to specific slowness…

New method uses MRI data to improve PET tomography uncertainty quantification.

A novel approach of training data augmentation and domain adaptation is presented to support machine learning applications for cognitive radio. Machine learning provides effective tools to automate cognitive radio functionalities by reliably extracting and learning intrinsic spectrum dynamics. However, there are two im…

Interference among concurrent transmissions in a wireless network is a key factor limiting the system performance. One way to alleviate this problem is to manage the radio resources in order to maximize either the average or the worst-case performance. However, joint consideration of both metrics is often neglected as …

Sharp stability estimate for tensor tomography in non-positive curvature.

Study inverse problems for twisted geodesic flows on manifolds.

We propose a globally convergent alternating minimization (AM) algorithm for image reconstruction in transmission tomography, which extends automatic relevance determination (ARD) to Poisson noise models with Beer's law. The algorithm promotes solutions that are sparse in the pixel/voxel-differences domain by introduci…

TomOpt optimizes muon detector designs using differentiable programming.

Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manual time consuming delineation of radio-sensitive organs at risk (OARs). This planning process can delay treatment, while also introducing int…

Deep kernel learning for complex function modeling.

Dissertation tackles geodesic ray transform on Riemannian manifolds.

New method uses SBI to infer magnetorotational properties of isolated pulsars.