pwc-archive/papers-with-abstracts huggingface dataset:
Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning
Authors: Shanshan Wang, Ruoyou Wu, Sen Jia, Alou Diakite, Cheng Li, Qiegen Liu, Leslie Ying
Date: 2024-02-05 00:00:00
Tasks: Image Reconstruction, Image Restoration

Deep learning (DL) has emerged as a leading approach in accelerating MR imaging. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MR imaging involves physics-based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data-driven approaches. Our review will introduce the significant challenges faced by such knowledge-driven DL approaches in the context of fast MR imaging along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.
DCE-FORMER: A Transformer-based Model With Mutual Information And Frequency-based Loss Functions For Early And Late Response Prediction In Prostate DCE-MRI
Authors: Sadhana S, Sriprabha Ramanarayanan, Arunima Sarkar, Matcha Naga Gayathri, Keerthi Ram, Mohanasankar Sivaprakasam
Date: 2024-02-03 00:00:00
Tasks: SSIM

Dynamic Contrast Enhanced Magnetic Resonance Imaging aids in the detection and assessment of tumor aggressiveness by using a Gadolinium-based contrast agent (GBCA). However, GBCA is known to have potential toxic effects. This risk can be avoided if we obtain DCE-MRI images without using GBCA. We propose, DCE-former, a transformer-based neural network to generate early and late response prostate DCE-MRI images from non-contrast multimodal inputs (T2 weighted, Apparent Diffusion Coefficient, and T1 pre-contrast MRI). Additionally, we introduce (i) a mutual information loss function to capture the complementary information about contrast uptake, and (ii) a frequency-based loss function in the pixel and Fourier space to learn local and global hyper-intensity patterns in DCE-MRI. Extensive experiments show that DCE-former outperforms other methods with improvement margins of +1.39 dB and +1.19 db in PSNR, +0.068 and +0.055 in SSIM, and -0.012 and -0.013 in Mean Absolute Error for early and late response DCE-MRI, respectively.
Angular Integral Autocorrelation for Speed Estimation in Shear Wave Elastography
Authors: Hamidreza Asemani, Irteza Enan Kabir, Juvenal Ormachea, Marvin M Doyley, Jannick P Rolland, Kevin J Parker
Date: 2024-02-02 00:00:00

The utilization of a reverberant shear wave field in shear wave elastography has emerged as a promising technique for achieving robust shear wave speed (SWS) estimation. However, accurately measuring SWS within such a complex wave field remains challenging. This study introduces an advanced autocorrelation estimator based on angular integration known as the angular integral autocorrelation (AIA) approach to address this issue. The AIA approach incorporates all the au-tocorrelation data from various angles during measurements, resulting in enhanced robustness to both noise and imperfect distributions in SWS estimation. The effectiveness of the AIA estimator for SWS estimation is first validated using a k-Wave simulation of a stiff branching tube in a uniform background. Furthermore, the AIA estimator is applied to ultrasound elastography experiments, MRI experiments, and OCT studies across a range of different excitation frequencies on tissues and phantoms, including in vivo scans. The results verify the capacity of the AIA approach to enhance the accuracy of SWS estimation and SNR, even within an imperfect reverberant shear wave field. Compared to simple autocorrelation approaches, the AIA approach can also successfully visualize and define lesions while significantly improving the estimated SWS and SNR in homogeneous background materials, thus providing improved elastic contrast between structures within the scans. These findings demonstrate the robustness and effectiveness of the AIA approach across a wide range of applications, including ultrasound elastography, MRE, and OCE, for accurately identifying the elastic properties of biological tissues in diverse excitation scenarios.
Direct Antenna Frequency-Hopped M-FSK Modulation With Time-Modulated Arrays
Authors: Roberto Maneiro-Catoira, Julio Brégains, José A. García-Naya, Luis Castedo
Date: 2024-02-05 00:00:00

We present an innovative approach that simultaneously enables direct antenna frequency-hopped M-ary frequency shift keying (DAFH-MFSK) modulation and beamsteering through the use of time-modulated arrays (TMAs). The distinctive feature of our approach lies in the modulation of the TMA excitations with binary periodic sequences, which can be easily frequency-adjusted and time-delayed to simultaneously allow for DAFH-MFSK direct antenna modulation and beamsteering. Notably, our TMA proposal offers a distinct advantage over conventional architectures in terms of performance metrics, including reduced insertion losses and enhanced phase resolution for beamsteering, while also simplifying hardware complexity.
New modalities of cortical electrophysiology, perspectives in medical research and human physiology
Authors: Pierre Bourdillon, Linnea Evanson
Date: 2024-02-05 00:00:00

Recent advances in material technology and in micro- and nano-electronics have profoundly changed the design of intracranial electrophysiology electrodes. It is now possible to manufacture electrodes that record cortical activity at a spatial resolution that was previously unthinkable. This high spatial resolution enables recording of the functional structures of the brain, and differentiation of the activity of the different types of neurons composing them. In this paper, we present a review of the different types of electrodes now available, and then suggest one of the first applications for such high resolution electrodes, namely a means to better characterise the mechanisms that generate focal seizures in epileptics. Finally, we reflect more broadly on prospects for their future use.
Semi-Passive Intelligent Reflecting Surface Enabled Sensing Systems
Authors: Qiaoyan Peng, Qingqing Wu, Wen Chen, Shaodan Ma, Ming-Min Zhao, Octavia A. Dobre
Date: 2024-02-05 00:00:00
Tasks: parameter estimation

Intelligent reflecting surface (IRS) has garnered growing interest and attention due to its potential for facilitating and supporting wireless communications and sensing. This paper studies a semi-passive IRS-enabled sensing system, where an IRS consists of both passive reflecting elements and active sensors. Our goal is to minimize the Cram\'{e}r-Rao bound (CRB) for parameter estimation under both point and extended target cases. Towards this goal, we begin by deriving the CRB for the direction-of-arrival (DoA) estimation in closed-form and then theoretically analyze the IRS reflecting elements and sensors allocation design based on the CRB under the point target case with a single-antenna base station (BS). To efficiently solve the corresponding optimization problem for the case with a multi-antenna BS, we propose an efficient algorithm by jointly optimizing the IRS phase shifts and the BS beamformers. Under the extended target case, the CRB for the target response matrix (TRM) estimation is minimized via the optimization of the BS transmit beamformers. Moreover, we explore the influence of various system parameters on the CRB and compare these effects to those observed under the point target case. Simulation results show the effectiveness of the semi-passive IRS and our proposed beamforming design for improving the performance of the sensing system.
Lossy Compression of Adjacency Matrices by Graph Filter Banks
Authors: Kenta Yanagiya, Junya Hara, Hiroshi Higashi, Yuichi Tanaka, Antonio Ortega
Date: 2024-02-05 00:00:00

This paper proposes a compression framework for adjacency matrices of weighted graphs based on graph filter banks. Adjacency matrices are widely used mathematical representations of graphs and are used in various applications in signal processing, machine learning, and data mining. In many problems of interest, these adjacency matrices can be large, so efficient compression methods are crucial. In this paper, we propose a lossy compression of weighted adjacency matrices, where the binary adjacency information is encoded losslessly (so the topological information of the graph is preserved) while the edge weights are compressed lossily. For the edge weight compression, the target graph is converted into a line graph, whose nodes correspond to the edges of the original graph, and where the original edge weights are regarded as a graph signal on the line graph. We then transform the edge weights on the line graph with a graph filter bank for sparse representation. Experiments on synthetic data validate the effectiveness of the proposed method by comparing it with existing lossy matrix compression methods.
Advanced monitoring of rail breakage in double-track railway lines by means of PCA techniques
Authors: F. Espinosa, J. J. García, A. Hernández, M. Mazo, J. Ureña, J. A. Jiménez, I. Fernández, C. Pérez, J. C. García
Date: 2024-02-04 00:00:00

This work describes a classifier designed to identify rail breakages in double-track railway lines, completing the electronic equipment carried out by authors. The main objective of this proposal is to guarantee the integrity of tracks before the railway traffic starts working. In addition, it facilitates maintenance tasks providing information about possible breakages. The detection of breakages is based on the analysis of eight currents provided by the electronic equipment, one per rail, at the ends of the section (emitting and receiving nodes). The imbalance that occurs among the value of these currents implies that there is at least a breakage in the track section under analysis. This analysis is conducted according to three phases. The first one identifies whether there is a breakage, and, in that case, the damaged track is identified. The second phase provides information about which rail is broken (internal, external or both of them) in the previously identified track. Finally, if there is only one breakage, the third phase estimates its most likely zone along the track section. This situation is considered as a classification problem, and solved by means of the Principal Component Analysis technique. This means that a significant number of measurements is required for every breakage pattern (types of breakages) to be considered. Due to the difficulty of having real data, the proposal has been validated using an 8km-long double-track hardware simulator specially designed by the authors, with specific localizations for breakages.
Joint User Detection and Localization in Near-Field Using Reconfigurable Intelligent Surfaces
Authors: Georgios Mylonopoulos, Behrooz Makki, Stefano Buzzi, Gábor Fodor
Date: 2024-02-04 00:00:00
Tasks: Action Detection, Activity Detection

This letter studies the problem of jointly detecting active user equipments (UEs) and estimating their location in the near field, wherein the base station (BS) is unaware of the number of active (or inactive) UEs and their positions. The system is equipped with multiple reconfigurable intelligent surfaces (RISs) that aid the process of inspecting the coverage area of the BS with adequate localization resolution providing a low-complexity solution for detection and location estimation. To address this problem, we propose to utilize the additional degrees of freedom due to the additional inspection points provided by the RISs. Specifically, we propose an iterative detection procedure, where multiple inspections are jointly considered, allowing the BS to assign known pilots to previously detected UEs and thereby to provide a structured channel access. Also, the problem of multiple access interference is explored and identified as a limiting performance factor for the activity detection. The results show that, with a proper implementation of the RISs, our proposed scheme can detect/localize the UEs with high accuracy, augmenting benchmark UE detection schemes to a spatially aware detection.
Block-Sparse Tensor Recovery
Authors: Liyang Lu, Zhaocheng Wang, Zhen Gao, Sheng Chen, H. Vincent Poor
Date: 2024-02-04 00:00:00

This work explores the fundamental problem of the recoverability of a sparse tensor being reconstructed from its compressed embodiment. We present a generalized model of block-sparse tensor recovery as a theoretical foundation, where concepts measuring holistic mutual incoherence property (MIP) of the measurement matrix set are defined. A representative algorithm based on the orthogonal matching pursuit (OMP) framework, called tensor generalized block OMP (T-GBOMP), is applied to the theoretical framework elaborated for analyzing both noiseless and noisy recovery conditions. Specifically, we present the exact recovery condition (ERC) and sufficient conditions for establishing it with consideration of different degrees of restriction. Reliable reconstruction conditions, in terms of the residual convergence, the estimated error and the signal-to-noise ratio bound, are established to reveal the computable theoretical interpretability based on the newly defined MIP, which we introduce. The flexibility of tensor recovery is highlighted, i.e., the reliable recovery can be guaranteed by optimizing MIP of the measurement matrix set. Analytical comparisons demonstrate that the theoretical results developed are tighter and less restrictive than the existing ones (if any). Further discussions provide tensor extensions for several classic greedy algorithms, indicating that the sophisticated results derived are universal and applicable to all these tensorized variants.
Evaluation of Zadoff-Chu, Kasami and Chirp based encoding schemes for Acoustic Local Positioning Systems
Authors: Santiago Murano, Carmen Perez-Rubio, David Gualda, Fernando J. Alvarez, Teodoro Aguilera, Carlos de Marziani
Date: 2024-02-04 00:00:00
Tasks: Robot Navigation

The task of determining the physical coordinates of a target in indoor environments is still a key factor for many applications including people and robot navigation, user tracking, location-based advertising, augmented reality, gaming, emergency response or ambient assisted living environments. Among the different possibilities for indoor positioning, Acoustic Local Positioning Systems (ALPS) have the potential for centimeter level positioning accuracy with coverage distances up to tens of meters. In addition, acoustic transducers are small, low cost and reliable thanks to the room constrained propagation of these mechanical waves. Waveform design (coding and modulation) is usually incorporated into these systems to facilitate the detection of the transmitted signals at the receiver. The aperiodic correlation properties of the emitted signals have a large impact on how the ALPS cope with common impairment factors such as multipath propagation, multiple access interference, Doppler shifting, near-far effect or ambient noise. This work analyzes three of the most promising families of codes found in the literature for ALPS: Kasami codes, Zadoff-Chu and Orthogonal Chirp signals. The performance of these codes is evaluated in terms of time of arrival accuracy and characterized by means of model simulation under realistic conditions and by means of experimental tests in controlled environments. The results derived from this study can be of interest for other applications based on spreading sequences, such as underwater acoustic systems, ultrasonic imaging or even Code Division Multiple Access (CDMA) communications systems.
Multipath Compensation Algorithm for TDMA-Based Ultrasonic Local Positioning Systems
Authors: Teodoro Aguilera, Fernando J. Alvarez, David Gualda, Jose M. Villadangos, Alvaro Hernandez, Jesus Urena
Date: 2024-02-04 00:00:00

This paper proposes a multipath compensation algorithm (MCA) to enhance the performance of an ultrasonic local positioning system under adverse multipath conditions. The proposed algorithm is based on the accurate estimation of the environment impulse response from which the corresponding line of sight for each channel is obtained. Experimental results in two different environments and with different conditions have been conducted in order to evaluate the performance of this proposal. In both environments, results confirm the expected improvements, even under severe multipath conditions where positioning errors have been reduced from 44 to 9 cm for the 95% of the measurements.
Simultaneous Calibration and Navigation (SCAN) of Multiple Ultrasonic Local Positioning Systems
Authors: David Gualda, Jesus Urena, Juan C. Garcia, Enrique Garcia, Jose Alcala
Date: 2024-02-03 00:00:00
Tasks: Position, Simultaneous Localization and Mapping

This paper proposes a Simultaneous Calibration and Navigation (SCAN) algorithm of a multiple Ultrasonic Local Positioning Systems (ULPSs) that cover an extensive indoor area. The idea is the development of the same concept than SLAM (Simultaneous Localization and Mapping), in which a Mobile Robot (MR) estimates the map while it is navigating. The MR calibrates the beacons of several ULPSs while it is moving inside the localization area. The concept of calibration is the estimation of the position of the beacons referenced to a known map. The scenario is composed of some calibrated ULPSs that we denote as Globally Referenced Ultrasonic Local Positioning Systems (GRULPSs) that are located in strategic points like entrances covering the start and the end of a possible trajectory in the environment. Additionally, there are several non-calibrated ULPSs named Locally Referenced Ultrasonic Local Positioning Systems (LRULPSs) that are placed around the localization area. The proposal uses a MR with odometer for calibrating the beacons of the LRULPSs while it is navigating on their coverage area and go from one GRULPS to another. The algorithm is based on multiple filters running in parallel (one filter for each LRULPS and another one for the GRULPSs) that estimate the global and local trajectories of the MR (one trajectory for each local reference system of the LRULPSs) fusing the information related to the Ultrasound Signals (US) and the odometer of the MR. The position of the beacons of the LRULPSs are obtained by a transformation vector for each LRULPS that converts the local coordinates to the global reference system. Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and H-Inf Filter have been tested, in simulations and real experiments, in order to compare their performance in this case.
FAS-assisted Wireless Powered Communication Systems
Authors: Xiazhi Lai, Kangda Zhi, Wanyi Li, Tuo Wu, Cunhua Pan, Maged Elkashlan
Date: 2024-02-03 00:00:00
Tasks: Diversity

Fluid Antenna System (FAS) is recognized as a promising technology for enhancing communication performance. In this context, we explored the potential of FAS-assisted wireless powered communication systems. Specifically, the transmitter, equipped with FAS, harvests the radio frequency (RF) signal from a power beacon and utilizes the harvested energy for data transmission to the receiver. To evaluate the performance of the considered systems, we derive both the analytical and asymptotic expressions of the outage probability. Simulation results indicate that the diversity of the considered network closely aligns with the number of ports. Besides, it is also revealed that the port selection criteria based solely on single-hop configurations yield a diversity order of only one.
Secure Wireless Communication in Active RIS-Assisted DFRC System
Authors: Yang Zhang, Hong Ren, Cunhua Pan, Boshi Wang, Zhiyuan Yu, Ruisong Weng, Tuo Wu, Yongchao He
Date: 2024-02-03 00:00:00

This work considers a dual-functional radar and communication (DFRC) system with an active reconfigurable intelligent surface (RIS) and a potential eavesdropper. Our purpose is to maximize the secrecy rate (SR) of the system by jointly designing the beamforming matrix at the DFRC base station (BS) and the reflecting coefficients at the active RIS, subject to the signal-to-interference-plus-noise-ratio (SINR) constraint of the radar echo and the power consumption constraints at the DFRC-BS and active RIS. An alternating optimization (AO) algorithm based on semi-definite relaxation (SDR) and majorizationminimization (MM) is applied to solve the SR-maximization problem by alternately optimizing the beamforming matrix and the reflecting coefficients. Specifically, we first apply the SDR and successive convex approximation (SCA) methods to transform the two subproblems into more tractable forms, then the MM method is applied to derive a concave surrogate function and iteratively solve the subproblems. Finally, simulation results indicate that the active RIS can better confront the impact of "multiplicative fading" and outperforms traditional passive RIS in terms of both secure data rate and radar sensing performance.
Simulation-Enhanced Data Augmentation for Machine Learning Pathloss Prediction
Authors: Ahmed P. Mohamed, Byunghyun Lee, Yaguang Zhang, Max Hollingsworth, C. Robert Anderson, James V. Krogmeier, David J. Love
Date: 2024-02-03 00:00:00
Tasks: Data Augmentation, Prediction

Machine learning (ML) offers a promising solution to pathloss prediction. However, its effectiveness can be degraded by the limited availability of data. To alleviate these challenges, this paper introduces a novel simulation-enhanced data augmentation method for ML pathloss prediction. Our method integrates synthetic data generated from a cellular coverage simulator and independently collected real-world datasets. These datasets were collected through an extensive measurement campaign in different environments, including farms, hilly terrains, and residential areas. This comprehensive data collection provides vital ground truth for model training. A set of channel features was engineered, including geographical attributes derived from LiDAR datasets. These features were then used to train our prediction model, incorporating the highly efficient and robust gradient boosting ML algorithm, CatBoost. The integration of synthetic data, as demonstrated in our study, significantly improves the generalizability of the model in different environments, achieving a remarkable improvement of approximately 12dB in terms of mean absolute error for the best-case scenario. Moreover, our analysis reveals that even a small fraction of measurements added to the simulation training set, with proper data balance, can significantly enhance the model's performance.