Dr. Alessandro Perelli
School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, Glasgow City, UK
Lecturer in Artificial Intelligence
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Academic Career Length
17Years (2010-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications24 January 2012 in Smart Materials and Structures
Journal Article
Alessandro PerelliLMLuca De Marchi
Luca De Marchi
Alessandro MarzaniNSNicolò Speciale
Nicolò Speciale
1 August 2013 in Mechanical Systems and Signal Processing
Journal Article
LMLuca De Marchi
Luca De MarchiAlessandro PerelliAlessandro Marzani9 July 2015 in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Journal Article
TITommaso Di Ianni
Tommaso Di IanniLMLuca De Marchi
Luca De Marchi
Alessandro PerelliAlessandro MarzaniNumerous nondestructive evaluations and structural health monitoring approaches based on guide waves rely on analysis of wave fields recorded through scanning laser Doppler vibrometers (SLDVs) or ultrasonic scanners. The informative content which can be extracted from these inspections is relevant; however, the acquisition process is generally time-consuming, posing a limit in the applicability of such approaches. To reduce the acquisition time, we use a random sampling scheme based on compressive sensing (CS) to minimize the number of points at which the field is measured. The CS reconstruction performance is mostly influenced by the choice of a proper decomposition basis to exploit the sparsity of the acquired signal. Here, different bases have been tested to recover the guided waves wave field acquired on both an aluminum and a composite plate. Experimental results show that the proposed approach allows a reduction of the measurement locations required for accurate signal recovery to less than 34% of the original sampling grid.
12 September 2023 in IEEE Transactions on Radiation and Plasma Medical Sciences
Journal Article
ABAlexandre Bousse
Alexandre BousseVKVenkata Sai Sundar Kandarpa
Venkata Sai Sundar KandarpaSRSimon Rit
Simon RitAlessandro PerelliMLMengzhou Li
Mengzhou Li
Ge Wang +2
Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: 1) dual-energy CT (DECT) and 2) photon-counting CT (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.
20 September 2013 in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Journal Article
Alessandro PerelliTITommaso Di Ianni
Tommaso Di IanniAlessandro MarzaniLMLuca De Marchi
Luca De MarchiGMGuido Masetti
Guido Masetti
Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression and analysis of high-resolution signals that have a sparse representation in a fixed basis. In this work, we have developed a CS approach for ultrasonic signal decomposition suitable to achieve high performance in Lamb-wave-based defect detection procedures. In the proposed approach, a CS algorithm based on an alternating minimization (AM) procedure is adopted to extract the information about both the system impulse response and the reflectivity function. The implemented tool exploits the dispersion compensation properties of the warped frequency transform as a means to generate the sparsifying basis for the signal representation. The effectiveness of the decomposition task is demonstrated on synthetic signals and successfully tested on experimental Lamb waves propagating in an aluminum plate. Compared with available strategies, the proposed approach provides an improvement in the accuracy of wave propagation path length estimation, a fundamental step in defect localization procedures.
26 June 2018 in IEEE Journal of Selected Topics in Signal Processing
Journal Article
GHGabor Hannak
Gabor HannakAlessandro PerelliNGNorbert Goertz
Norbert GoertzGMGerald Matz
Gerald MatzMDMike E. Davies
Mike E. DaviesBayesian approximate message passing (BAMP) is an efficient method in compressed sensing that is nearly optimal in the minimum mean squared error (MMSE) sense. Multiple Measurement Vector (MMV)-BAMP performs joint recovery of multiple vectors with identical support and accounts for correlations in the signal of interest and in the noise. In this paper, we show how to reduce the complexity of vector BAMP via a simple joint decorrelation (diagonalization) transform of the signal and noise vectors, which also facilitates the subsequent performance analysis. We prove that the corresponding state evolution (SE) is equivariant with respect to the joint decorrelation transform and preserves diagonality of the residual noise covariance for the Bernoulli-Gauss (BG) prior. We use these results to analyze the dynamics and the mean squared error (MSE) performance of BAMP via the replica method, and thereby understand the impact of signal correlation and number of jointly sparse signals. Finally, we evaluate an application of MMV-BAMP for single-pixel imaging with correlated color channels and thereby explore the performance gain of joint recovery compared to conventional BAMP reconstruction as well as Group-LASSO.
About Me
Alessandro Perelli is a Lecturer in Artificial Intelligence at the University of Glasgow, working on computational imaging for medical applications such as spectral X-ray Computed Tomography, Magnetic Resonance Imaging and prediction models for clinical diagnostic.
He received a Royal Academy of Engineering/Leverhulme Trust Research Fellowship in 2024 on the research project "Physics-guided Deep Learning for Automatic Material Decomposition in Photon Counting Computed Tomography".
From 2021 to 2025 Alessandro has been a Lecturer in Biomedical Engineering at the University of Dundee.
From 2018 to 2021, he was an H. C. Ørsted COFUND Postdoctoral Fellow at the Technical University of Denmark - DTU and my research is based on the funded fellowship called DRIFT - Deep and Randomized Imaging For Tomography.
From 2014 to 2018, he was a Research Associate at the University of Edinburgh (UK) at the Institute for Digital Communications working on signal/image processing techniques for exploring the use of low dimensional structure to reduce the computational cost of solving computational imaging problems.
In May 2014 he pursued the Ph.D. degree in Electronic Engineering at the Department of Electrical, Electronics and Information Engineering - DEI, University of Bologna.
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