pwc-archive/papers-with-abstracts huggingface dataset:
Designing and simulating realistic spatial frequency domain imaging systems using open-source 3D rendering software
Authors: Jane Crowley, George S. D. Gordon
Date: 2023-02-24 00:00:00

Spatial frequency domain imaging (SFDI) is a low-cost imaging technique that can deliver real-time maps of absorption and reduced scattering coefficients. However, there are a wide range of imaging geometries that practical SFDI systems must cope with including imaging flat samples ex vivo, imaging inside tubular lumen in vivo such as in an endoscopy, and measuring tumours or polyps of varying shapes, sizes and optical properties. There is a need for a design and simulation tool to accelerate design and fabrication of new SFDI systems. We present such a system implemented using open-source 3D design and ray-tracing software Blender that is capable of simulating media with realistic optical properties (mimicking healthy and cancerous tissue), a wide variety of shapes and size, and in both planar and tubular imaging geometries. We first demonstrate quantitative agreement between Monte-Carlo simulated scattering and absorption coefficients and those measured from our Blender system. Next, we show the ability of the system to simulate absorption, scattering and shape for flat samples with small simulated tumours and show that the improved contrast associated with SFDI is reproduced. Finally, to demonstrate the versatility of the system as a design tool we show that it can be used to generate a custom look-up-table for mapping from modulation amplitude values to absorption and scattering values in a tubular geometry, simulating a lumen. As a demonstrative example we show that longitudinal sectioning of the tube, with separate look-up tables for each section, significantly improves accuracy of SFDI, representing an important design insight for future systems. We therefore anticipate our simulation system will significantly aid in the design and development of novel SFDI systems, especially as such systems are miniaturised for deployment in endoscopic and laparoscopic systems.
TRINIDI: Time-of-Flight Resonance Imaging with Neutrons for Isotopic Density Inference
Authors: Thilo Balke, Alexander M. Long, Sven C. Vogel, Brendt Wohlberg, Charles A. Bouman
Date: 2023-02-24 00:00:00

Accurate reconstruction of 2D and 3D isotope densities is a desired capability with great potential impact in applications such as evaluation and development of next-generation nuclear fuels. Neutron time-of-flight (TOF) resonance imaging offers a potential approach by exploiting the characteristic neutron absorption spectra of each isotope. However, it is a major challenge to compute quantitatively accurate images due to a variety of confounding effects such as severe Poisson noise, background scatter, beam non-uniformity, absorption non-linearity, and extended source pulse duration. We present the TRINIDI algorithm which is based on a two-step process in which we first estimate the neutron flux and background counts, and then reconstruct the areal densities of each isotope and pixel. Both components are based on the inversion of a forward model that accounts for the highly non-linear absorption, energy-dependent emission profile, and Poisson noise, while also modeling the substantial spatio-temporal variation of the background and flux. To do this, we formulate the non-linear inverse problem as two optimization problems that are solved in sequence. We demonstrate on both synthetic and measured data that TRINIDI can reconstruct quantitatively accurate 2D views of isotopic areal density that can then be reconstructed into quantitatively accurate 3D volumes of isotopic volumetric density.
Automated Lesion Segmentation in Whole-Body FDG-PET/CT with Multi-modality Deep Neural Networks
Authors: Satoshi Kondo, Satoshi Kasai
Date: 2023-02-16 00:00:00
Tasks: Lesion Detection, Lesion Segmentation, Segmentation

Recent progress in automated PET/CT lesion segmentation using deep learning methods has demonstrated the feasibility of this task. However, tumor lesion detection and segmentation in whole-body PET/CT is still a chal-lenging task. To promote research on machine learning-based automated tumor lesion segmentation on whole-body FDG-PET/CT data, Automated Lesion Segmentation in Whole-Body FDG-PET/CT (autoPET) challenge is held, and a large, publicly available training dataset is provided. In this report, we present our solution to the autoPET challenge. We employ multi-modal residual U-Net with deep super vision. The experimental results for five preliminary test cases show that Dice score is 0.79 +/- 0.21.
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
Authors: Axi Niu, Kang Zhang, Trung X. Pham, Jinqiu Sun, Yu Zhu, In So Kweon, Yanning Zhang
Date: 2023-02-14 00:00:00
Tasks: Conditional Image Generation, Denoising, Image Generation, Image Super-Resolution, Image-to-Image Translation, Super-Resolution

Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images. Prior attempts at applying the DPM to image super-resolution (SR) have shown that iteratively refining a pure Gaussian noise with a conditional image using a U-Net trained on denoising at various-level noises can help obtain a satisfied high-resolution image for the low-resolution one. To further improve the performance and simplify current DPM-based super-resolution methods, we propose a simple but non-trivial DPM-based super-resolution post-process framework,i.e., cDPMSR. After applying a pre-trained SR model on the to-be-test LR image to provide the conditional input, we adapt the standard DPM to conduct conditional image generation and perform super-resolution through a deterministic iterative denoising process. Our method surpasses prior attempts on both qualitative and quantitative results and can generate more photo-realistic counterparts for the low-resolution images with various benchmark datasets including Set5, Set14, Urban100, BSD100, and Manga109. Code will be published after accepted.
An Efficient Instance Segmentation Approach for Extracting Fission Gas Bubbles on U-10Zr Annular Fuel
Authors: Shoukun Sun, Fei Xu, Lu Cai, Daniele Salvato, Fidelma Dilemma, Luca Capriotti, Min Xian, Tiankai Yao
Date: 2023-02-08 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

U-10Zr-based nuclear fuel is pursued as a primary candidate for next-generation sodium-cooled fast reactors. However, more advanced characterization and analysis are needed to form a fundamental understating of the fuel performance, and make U-10Zr fuel qualify for commercial use. The movement of lanthanides across the fuel section from the hot fuel center to the cool cladding surface is one of the key factors to affect fuel performance. In the advanced annular U-10Zr fuel, the lanthanides present as fission gas bubbles. Due to a lack of annotated data, existing literature utilized a multiple-threshold method to separate the bubbles and calculate bubble statistics on an annular fuel. However, the multiple-threshold method cannot achieve robust performance on images with different qualities and contrasts, and cannot distinguish different bubbles. This paper proposes a hybrid framework for efficient bubble segmentation. We develop a bubble annotation tool and generate the first fission gas bubble dataset with more than 3000 bubbles from 24 images. A multi-task deep learning network integrating U-Net and ResNet is designed to accomplish instance-level bubble segmentation. Combining the segmentation results and image processing step achieves the best recall ratio of more than 90% with very limited annotated data. Our model shows outstanding improvement by comparing the previously proposed thresholding method. The proposed method has promising to generate a more accurate quantitative analysis of fission gas bubbles on U-10Zr annular fuels. The results will contribute to identifying the bubbles with lanthanides and finally build the relationship between the thermal gradation and lanthanides movements of U-10Zr annular fuels. Mover, the deep learning model is applicable to other similar material micro-structure segmentation tasks.
Modeling of Interface Loads for EOD Suit Wearers
Authors: Yuan Gao, Stephanie Epstein, Murat Inalpolat, Yi-Ning Wu, Yan Gu
Date: 2023-02-27 00:00:00
Tasks: State Estimation

Explosive Ordnance Disposal (EOD) suits are widely used to protect human operators to execute emergency tasks such as bomb disposal and neutralization. Current suit designs still need to be improved in terms of wearer comfort, which can be assessed based on the interaction forces at the human-suit contact regions. This paper introduces a simulation-based modeling framework that computes the interaction loads at the human-suit interface based on a wearer's kinematic movement data. The proposed modeling framework consists of three primary components: a) inertial and geometric modeling of the EOD suit, b) state estimation of the wearer's in-suit movement, and c) inverse dynamics analysis to calculate the human-suit interface forces based on the simulated human-suit model and the estimated human movement data. This simulation-based modeling method could be used to complement experimental testing for improving the time and cost efficiency of EOD suit evaluation. The accuracy of the simulated interface load was experimentally benchmarked during three different human tasks (each with three trials), by comparing the predicted interface forces with that measured by commercial pressure sensors.
Iterated Filters for Nonlinear Transition Models
Authors: Anton Kullberg, Isaac Skog, Gustaf Hendeby
Date: 2023-02-27 00:00:00

A new class of iterated linearization-based nonlinear filters, dubbed dynamically iterated filters, is presented. Contrary to regular iterated filters such as the iterated extended Kalman filter (IEKF), iterated unscented Kalman filter (IUKF) and iterated posterior linearization filter (IPLF), dynamically iterated filters also take nonlinearities in the transition model into account. The general filtering algorithm is shown to essentially be a (locally over one time step) iterated Rauch-Tung-Striebel smoother. Three distinct versions of the dynamically iterated filters are especially investigated: analogues to the IEKF, IUKF and IPLF. The developed algorithms are evaluated on 25 different noise configurations of a tracking problem with a nonlinear transition model and linear measurement model, a scenario where conventional iterated filters are not useful. Even in this "simple" scenario, the dynamically iterated filters are shown to have superior root mean-squared error performance as compared with their respective baselines, the EKF and UKF. Particularly, even though the EKF diverges in 22 out of 25 configurations, the dynamically iterated EKF remains stable in 20 out of 25 scenarios, only diverging under high noise.
Symbol-Level Precoding for Integrated Sensing and Communications: A Faster-Than-Nyquist Approach
Authors: Zihan Liao, Fan Liu
Date: 2023-02-27 00:00:00
Tasks: ISAC, parameter estimation

In this paper, we propose a novel symbol-level precoding (SLP) method for a multi-user multi-input multi-output (MU-MIMO) downlink Integrated Sensing and Communications (ISAC) system based on faster-than-Nyquist (FTN) signaling. Our method minimizes the minimum mean squared error (MMSE) for target parameter estimation while guaranteeing per-user quality-of-service by exploiting constructive interference (CI) techniques. We tackle the non-convex problem using an efficient successive convex approximation (SCA) method. Numerical results demonstrate that our FTN-ISAC-SLP design significantly outperforms conventional benchmarks in both communication and sensing performance.
On the Use of Power Amplifier Nonlinearity Quotient to Improve Radio Frequency Fingerprint Identification in Time-Varying Channels
Authors: Lu Yang, Seyit Camtepe, Yansong Gao, Vicky Liu, Dhammika Jayalath
Date: 2023-02-27 00:00:00
Tasks: Classification, Transfer Learning

Radio frequency fingerprint identification (RFFI) is a lightweight device authentication technique particularly desirable for power-constrained devices, e.g., the Internet of things (IoT) devices. Similar to biometric fingerprinting, RFFI exploits the intrinsic and unique hardware impairments resulting from manufacturing, such as power amplifier (PA) nonlinearity, to develop methods for device detection and classification. Due to the nature of wireless transmission, received signals are volatile when communication environments change. The resulting radio frequency fingerprints (RFFs) are distorted, leading to low device detection and classification accuracy. We propose a PA nonlinearity quotient and transfer learning classifier to design the environment-robust RFFI method. Firstly, we formalized and demonstrated that the PA nonlinearity quotient is independent of environmental changes. Secondly, we implemented transfer learning on a base classifier generated by data collected in an anechoic chamber, further improving device authentication and reducing disk and memory storage requirements. Extensive experiments, including indoor and outdoor settings, were carried out using LoRa devices. It is corroborated that the proposed PA nonlinearity quotient and transfer learning classifier significantly improved device detection and device classification accuracy. For example, the classification accuracy was improved by 33.3% and 34.5% under indoor and outdoor settings, respectively, compared to conventional deep learning and spectrogram-based classifiers.
Task-oriented Explainable Semantic Communications
Authors: Shuai Ma, Weining Qiao, Youlong Wu, Hang Li, Guangming Shi, Dahua Gao, Yuanming Shi, Shiyin Li, Naofal Al-Dhahir
Date: 2023-02-27 00:00:00
Tasks: Semantic Communication

Semantic communications utilize the transceiver computing resources to alleviate scarce transmission resources, such as bandwidth and energy. Although the conventional deep learning (DL) based designs may achieve certain transmission efficiency, the uninterpretability issue of extracted features is the major challenge in the development of semantic communications. In this paper, we propose an explainable and robust semantic communication framework by incorporating the well-established bit-level communication system, which not only extracts and disentangles features into independent and semantically interpretable features, but also only selects task-relevant features for transmission, instead of all extracted features. Based on this framework, we derive the optimal input for rate-distortion-perception theory, and derive both lower and upper bounds on the semantic channel capacity. Furthermore, based on the $\beta $-variational autoencoder ($\beta $-VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise. We further design a real-time wireless mobile semantic communication proof-of-concept prototype. Our simulations and experiments demonstrate that our proposed explainable semantic communications system can significantly improve transmission efficiency, and also verify the effectiveness of our proposed robust semantic transmission scheme.
Adaptive CSI Feedback for Deep Learning-Enabled Image Transmission
Authors: Guangyi Zhang, Qiyu Hu, Yunlong Cai, Guanding Yu
Date: 2023-02-27 00:00:00
Tasks: Deep Learning

Recently, deep learning-enabled joint-source channel coding (JSCC) has received increasing attention due to its great success in image transmission. However, most existing JSCC studies only focus on single-input single-output (SISO) channels. In this paper, we first propose a JSCC system for wireless image transmission over multiple-input multiple-output (MIMO) channels. As the complexity of an image determines its reconstruction difficulty, the JSCC achieves quite different reconstruction performances on different images. Moreover, we observe that the images with higher reconstruction qualities are generally more robust to the noise, and can be allocated with less communication resources than the images with lower reconstruction qualities. Based on this observation, we propose an adaptive channel state information (CSI) feedback scheme for precoding, which improves the effectiveness by adjusting the feedback overhead. In particular, we develop a performance evaluator to predict the reconstruction quality of each image, so that the proposed scheme can adaptively decrease the CSI feedback overhead for the transmitted images with high predicted reconstruction qualities in the JSCC system. We perform experiments to demonstrate that the proposed scheme can significantly improve the image transmission performance with much-reduced feedback overhead.
Adaptive Sampling for Linear Sensing Systems via Langevin Dynamics
Authors: Guanhua Wang, Douglas C. Noll, Jeffrey A. Fessler
Date: 2023-02-27 00:00:00

Adaptive or dynamic signal sampling in sensing systems can adapt subsequent sampling strategies based on acquired signals, thereby potentially improving image quality and speed. This paper proposes a Bayesian method for adaptive sampling based on greedy variance reduction and stochastic gradient Langevin dynamics (SGLD). The image priors involved can be either analytical or neural network-based. Notably, the learned image priors generalize well to out-of-distribution test cases that have different statistics than the training dataset. As a real-world validation, the method is applied to accelerate the acquisition of magnetic resonance imaging (MRI). Compared to non-adaptive sampling, the proposed method effectively improved the image quality by 2-3 dB in PSNR, and improved the restoration of subtle details.
Robust Network Topology Inference and Processing of Graph Signals
Authors: Samuel Rey
Date: 2023-02-26 00:00:00

The abundance of large and heterogeneous systems is rendering contemporary data more pervasive, intricate, and with a non-regular structure. With classical techniques facing troubles to deal with the irregular (non-Euclidean) domain where the signals are defined, a popular approach at the heart of graph signal processing (GSP) is to: (i) represent the underlying support via a graph and (ii) exploit the topology of this graph to process the signals at hand. In addition to the irregular structure of the signals, another critical limitation is that the observed data is prone to the presence of perturbations, which, in the context of GSP, may affect not only the observed signals but also the topology of the supporting graph. Ignoring the presence of perturbations, along with the couplings between the errors in the signal and the errors in their support, can drastically hinder estimation performance. While many GSP works have looked at the presence of perturbations in the signals, much fewer have looked at the presence of perturbations in the graph, and almost none at their joint effect. While this is not surprising (GSP is a relatively new field), we expect this to change in the upcoming years. Motivated by the previous discussion, the goal of this thesis is to advance toward a robust GSP paradigm where the algorithms are carefully designed to incorporate the influence of perturbations in the graph signals, the graph support, and both. To do so, we consider different types of perturbations, evaluate their disruptive impact on fundamental GSP tasks, and design robust algorithms to address them.
Sparse Bayesian Learning-Based 3D Spectrum Environment Map Construction-Sampling Optimization, Scenario-Dependent Dictionary Construction and Sparse Recovery
Authors: Jie Wang, Qiuming Zhu, Zhipeng Lin, Qihui Wu, Yang Huang, Xuezhao Cai, Weizhi Zhong, Yi Zhao
Date: 2023-02-25 00:00:00
Tasks: Management

The spectrum environment map (SEM), which can visualize the information of invisible electromagnetic spectrum, is vital for monitoring, management, and security of spectrum resources in cognitive radio (CR) networks. In view of a limited number of spectrum sensors and constrained sampling time, this paper presents a new three-dimensional (3D) SEM construction scheme based on sparse Bayesian learning (SBL). Firstly, we construct a scenario-dependent channel dictionary matrix by considering the propagation characteristic of the interested scenario. To improve sampling efficiency, a maximum mutual information (MMI)-based optimization algorithm is developed for the layout of sampling sensors. Then, a maximum and minimum distance (MMD) clustering-based SBL algorithm is proposed to recover the spectrum data at the unsampled positions and construct the whole 3D SEM. We finally use the simulation data of the campus scenario to construct the 3D SEMs and compare the proposed method with the state-of-the-art. The recovery performance and the impact of different sparsity on the constructed SEMs are also analyzed. Numerical results show that the proposed scheme can reduce the required spectrum sensor number and has higher accuracy under the low sampling rate.
A deep learning approach for patchless estimation of ultrasound quantitative parametric image with uncertainty measurement
Authors: Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hayley Whitson, Hassan Rivaz
Date: 2023-02-24 00:00:00

Quantitative ultrasound (QUS) aims to find properties of scatterers which are related to the tissue microstructure. Among different QUS parameters, scatterer number density has been found to be a reliable biomarker for detecting different abnormalities. The homodyned K-distribution (HK-distribution) is a model for the probability density function of the ultrasound echo amplitude that can model different scattering scenarios but requires a large number of samples to be estimated reliably. Parametric images of HK-distribution parameters can be formed by dividing the envelope data into small overlapping patches and estimating parameters within the patches independently. This approach imposes two limiting constraints, the HK-distribution parameters are assumed to be constant within each patch, and each patch requires enough independent samples. In order to mitigate those problems, we employ a deep learning approach to estimate parametric images of scatterer number density (related to HK-distribution shape parameter) without patching. Furthermore, an uncertainty map of the network's prediction is quantified to provide insight about the confidence of the network about the estimated HK parameter values.
Moving Target Detection via Multi-IRS-Aided OFDM Radar
Authors: Zahra Esmaeilbeig, Arian Eamaz, Kumar Vijay Mishra, Mojtaba Soltanalian
Date: 2023-02-24 00:00:00

An intelligent reflecting surface (IRS) consists of passive reflective elements capable of altering impinging waveforms. The IRS-aided radar systems have recently been shown to improve detection and estimation performance by exploiting the target information collected via non-line-of-sight paths. However, the waveform design problem for an IRS-aided radar has remained relatively unexplored. In this paper, we consider a multi-IRS-aided orthogonal frequency-division multiplexing (OFDM) radar and study the theoretically achievable accuracy of target detection. In addition, we jointly design the OFDM signal and IRS phase-shifts to optimize the target detection performance via an alternating optimization approach. To this end, we formulate the IRS phase-shift design problem as a unimodular bi-quadratic program which is tackled by a computationally cost-effective approach based on power-method-like iterations. Numerical experiments illustrate that our proposed joint design of IRS phase-shifts and the OFDM code improves the detection performance in comparison with conventional OFDM radar.
Adaptive Turbo Equalization of Probabilistically Shaped Constellations
Authors: Edson Porto da Silva, Metodi Plamenov Yankov
Date: 2023-02-24 00:00:00

Fiber nonlinearity compensation of probabilistically shaped constellations with adaptive turbo equalization is investigated for the first time. Potential for more than 100% transmission reach extension is demonstrated by combining probabilistic shaping, single-channel digital backpropagation, and adaptive turbo equalization.