pwc-archive/papers-with-abstracts huggingface dataset:
Generating 3D Pseudo-Healthy Knee MR Images to Support Trochleoplasty Planning
Authors: Michael Wehrli, Alicia Durrer, Paul Friedrich, Volodimir Buchakchiyskiy, Marcus Mumme, Edwin Li, Gyozo Lehoczky, Carol C. Hasler, Philippe C. Cattin
Date: 2024-12-13 00:00:00

Purpose: Trochlear Dysplasia (TD) is a common malformation in adolescents, leading to anterior knee pain and instability. Surgical interventions such as trochleoplasty require precise planning to correct the trochlear groove. However, no standardized preoperative plan exists to guide surgeons in reshaping the femur. This study aims to generate patient-specific, pseudo-healthy MR images of the trochlear region that should theoretically align with the respective patient's patella, potentially supporting the pre-operative planning of trochleoplasty. Methods: We employ a Wavelet Diffusion Model (WDM) to generate personalized pseudo-healthy, anatomically plausible MR scans of the trochlear region. We train our model using knee MR scans of healthy subjects. During inference, we mask out pathological regions around the patella in scans of patients affected by TD, and replace them with their pseudo-healthy counterpart. An orthopedic surgeon measured the sulcus angle (SA), trochlear groove depth (TGD) and D\'ejour classification in MR scans before and after inpainting. The code is available at https://github.com/wehrlimi/Generate-Pseudo-Healthy-Knee-MRI . Results: The inpainting by our model significantly improves the SA, TGD and D\'ejour classification in a study with 49 knee MR scans. Conclusion: This study demonstrates the potential of WDMs in providing surgeons with patient-specific guidance. By offering anatomically plausible MR scans, the method could potentially enhance the precision and preoperative planning of trochleoplasty, and pave the way to more minimally invasive surgeries.
Structurally Consistent MRI Colorization using Cross-modal Fusion Learning
Authors: Mayuri Mathur, Anav Chaudhary, Saurabh Kumar Gupta, Ojaswa Sharma
Date: 2024-12-12 00:00:00
Tasks: Anatomy, Colorization, Feature Compression, Image Colorization

Medical image colorization can greatly enhance the interpretability of the underlying imaging modality and provide insights into human anatomy. The objective of medical image colorization is to transfer a diverse spectrum of colors distributed across human anatomy from Cryosection data to source MRI data while retaining the structures of the MRI. To achieve this, we propose a novel architecture for structurally consistent color transfer to the source MRI data. Our architecture fuses segmentation semantics of Cryosection images for stable contextual colorization of various organs in MRI images. For colorization, we neither require precise registration between MRI and Cryosection images, nor segmentation of MRI images. Additionally, our architecture incorporates a feature compression-and-activation mechanism to capture organ-level global information and suppress noise, enabling the distinction of organ-specific data in MRI scans for more accurate and realistic organ-specific colorization. Our experiments demonstrate that our architecture surpasses the existing methods and yields better quantitative and qualitative results.
Rate-Splitting Multiple Access for Integrated Sensing and Communications: A First Experimental Study
Authors: Xinze Lyu, Sundar Aditya, Bruno Clerckx
Date: 2024-12-16 00:00:00
Tasks: ISAC

A canonical use case of Integrated Sensing and Communications (ISAC) in multiple-input multiple-output (MIMO) systems involves a multi-antenna transmitter communicating with $K$ users and sensing targets in its vicinity. For this setup, precoder and multiple access designs are of utmost importance, as the limited transmit power budget must be efficiently directed towards the desired directions (users and targets) to maximize both communications and sensing performance. This problem has been widely investigated analytically under various design choices, in particular (a) whether or not a dedicated sensing signal is needed, and (b) for different MIMO multiple access techniques, such as Space Division Multiple Access (SDMA) and Rate-Splitting Multiple Access (RSMA). However, a conclusive answer on which design choice achieves the best ISAC performance, backed by experimental results, remains elusive. We address this vacuum by experimentally evaluating and comparing RSMA and SDMA for communicating with two users $(K = 2)$ and sensing (ranging) one target. Over three scenarios that are representative of \emph{vehicular} ISAC, covering different levels of inter-user interference and separation/integration between sensing and communications, we show that RSMA without a dedicated sensing signal achieves better ISAC performance -- i.e., higher sum throughput (up to $50\%$ peak throughput gain) for similar radar SNR (between $20$ to $24{\rm dB}$) -- than SDMA with a dedicated sensing signal. This first-ever experimental study of RSMA ISAC demonstrates the feasibility and the superiority of RSMA for future multi-functional wireless systems.
Soil moisture estimation of bare and vegetation-covered areas using a P/L/C-band SAR
Authors: Gian Oré, Jhonnatan Yepes, Juliana A. Góes, Luciano P. Oliveira, Bárbara Teruel, Hugo E. Hernandez-Figueroa
Date: 2024-12-16 00:00:00
Tasks: Soil moisture estimation

The paper introduces a novel approach for estimating soil moisture in vegetated surfaces, specifically focusing on sugarcane crops throughout various growth stages in agriculture applications. While existing models typically address bare soil scenarios, this model utilizes data from P-, L-, and C-band Synthetic Aperture Radar (SAR) to estimate soil moisture. The semi-empirical Dubois model forms the basis of the proposed model, which has been adapted to accommodate multiband operation and crop height variations. Synthetic datasets are generated using the adjusted model to train two neural networks incorporated into the overall model. Additionally, a linear expression for estimating crop height is integrated into the model. The model is validated in an Experimental Site at the School of Agricultural Engineering, UNICAMP, and an independent area at the Sugarcane Technology Center in Piracicaba, Brazil. The model utilizes a multiband drone-borne SAR system with a 3-meter image resolution and radiometric accuracy of 0.5 dB. The results indicate that the model can estimate soil moisture with root-mean-square errors of 0.05 cm3.cm-3 (5 vol. %) across crop heights ranging from zero to 2.5 meters.
Scalable Data Transmission Framework for Earth Observation Satellites with Channel Adaptation
Authors: Van-Phuc Bui, Shashi Raj Pandey, Israel Leyva-Mayorga, Petar Popovski
Date: 2024-12-16 00:00:00
Tasks: Earth Observation, Semantic Communication

The immense volume of data generated by Earth observation (EO) satellites presents significant challenges in transmitting it to Earth over rate-limited satellite-to-ground communication links. This paper presents an efficient downlink framework for multi-spectral satellite images, leveraging adaptive transmission techniques based on pixel importance and link capacity. By integrating semantic communication principles, the framework prioritizes critical information, such as changed multi-spectral pixels, to optimize data transmission. The process involves preprocessing, assessing pixel importance to encode only significant changes, and dynamically adjusting transmissions to match channel conditions. Experimental results on the real dataset and simulated link demonstrate that the proposed approach ensures high-quality data delivery while significantly reducing number of transmitted data, making it highly suitable for satellite-based EO applications.
Acceleration and Parallelization Methods for ISRS EGN Model
Authors: Ruiyang Xia, Guanjun Gao, Zanshan Zhao, Haoyu Wang, Kun Wen, Daobin Wang
Date: 2024-12-16 00:00:00
Tasks: Computational Efficiency

The enhanced Gaussian noise (EGN) model, which accounts for inter-channel stimulated Raman scattering (ISRS), has been extensively utilized for evaluating nonlinear interference (NLI) within the C+L band. Compared to closed-form expressions and machine learning-based NLI evaluation models, it demonstrates broader applicability and its accuracy is not dependent on the support of large-scale datasets. However, its high computational complexity often results in lengthy computation times. Through analysis, the high-frequency oscillations of the four-wave mixing (FWM) efficiency factor integrand were identified as a primary factor limiting the computational speed of the ISRS EGN model. To address this issue, we propose an accurate approximation method that enables the derivation of a closed-form expression for the FWM efficiency factor without imposing restrictive conditions. Thereby, the scheme proposed in this paper could significantly accelerate the computational speed. Numerical results demonstrate that method in this work could achieve low error levels under high ISRS influence levels, with an MAE of less than 0.001 dB, and no cumulative error over increasing transmission distances, while reducing computation time by over 97%. Furthermore, a parallel computation strategy targeting independent regions within the integration domain is proposed, which could further improve computational efficiency by nearly 11 times.
On-the-Fly Interrogation of Mobile Passive Sensors from the Fusion of Optical and Radar Data
Authors: Ali Hadj Djilani, Dominique Henry, Patrick Pons, Herve Aubert
Date: 2024-12-16 00:00:00

In this paper a new method based on the fusion of optical and radar data is proposed to detect and remotely interrogate mobile and passive sensors. The sensors are detected in real time by using an optical camera, while their remote reading is carried out on the fly using a Frequency-Modulated Continuous-Wave radar. The proof-of-concept is established from the interrogation of a microfluidic temperature sensor placed on a conveyer belt.
Capacity Analysis on OAM-Based Wireless Communications: An Electromagnetic Information Theory Perspective
Authors: Runyu Lyu, Wenchi Cheng, Qinghe Du, Tony Q. S. Quek
Date: 2024-12-16 00:00:00

Orbital angular momentum (OAM) technology enhances the spectrum and energy efficiency of wireless communications by enabling multiplexing over different OAM modes. However, classical information theory, which relies on scalar models and far-field approximations, cannot fully capture the unique characteristics of OAM-based systems, such as their complex electromagnetic field distributions and near-field behaviors. To address these limitations, this paper analyzes OAM-based wireless communications from an electromagnetic information theory (EIT) perspective, integrating electromagnetic theory with classical information theory. EIT accounts for the physical properties of electromagnetic waves, offering advantages such as improved signal manipulation and better performance in real-world conditions. Given these benefits, EIT is more suitable for analyzing OAM-based wireless communication systems. Presenting a typical OAM model utilizing uniform circular arrays (UCAs), this paper derives the channel capacity based on the induced electric fields by using Green's function. Numerical and simulation results validate the channel capacity enhancement via exploration under EIT framework. Additionally, this paper evaluates the impact of various parameters on the channel capacity. These findings provide new insights for understanding and optimizing OAM-based wireless communications systems.
Probabilistic GOSPA: A Metric for Performance Evaluation of Multi-Object Filters with Uncertainties
Authors: Yuxuan Xia, Ángel F. García-Fernández, Johan Karlsson, Kuo-Chu Chang, Ting Yuan, Lennart Svensson
Date: 2024-12-16 00:00:00
Tasks: Object

This paper presents a probabilistic generalization of the Generalized Optimal Sub-Pattern Assignment (GOSPA) metric, termed P-GOSPA. The GOSPA metric has been widely used to evaluate the distance between finite sets, particularly in multi-object estimation applications. The P-GOSPA extends GOSPA into the space of multi-Bernoulli densities, incorporating inherent uncertainty in probabilistic multi-object representations. Additionally, P-GOSPA retains the interpretability of GOSPA, such as its decomposition into localization, missed detection, and false detection errors in a sound and meaningful manner. Examples and simulations are provided to demonstrate the efficacy of the proposed P-GOSPA metric.
Latency Minimization for Movable Antennas-Enabled Relay-aided D2D Mobile Edge Computing Communication Systems
Authors: Yue Xiu, Yang Zhao, Ran Yang, Huimin Tang, Long Qu, Maurice Khabbaz, Chadi Assi, Ning Wei
Date: 2024-12-16 00:00:00
Tasks: Cloud Computing, Edge-computing

Device-to-device (D2D)-assisted mobile edge computing (MEC) is one of the critical technologies of future sixth generation (6G) networks. The core of D2D-assisted MEC is to reduce system latency for network edge UEs by supporting cloud computing services, thereby achieving high-speed transmission. Due to the sensitivity of communication signals to obstacles, relaying is adopted to enhance the D2D-assisted MEC system's performance and its coverage area. However, relay nodes and the base station (BS) are typically equipped with large-scale antenna arrays. This increases the cost of relay-assisted D2D MEC systems and limits their deployment. Movable antenna (MA) technology is used to work around this limitation without compromising performance. Specifically, the core of MA technology lies in optimizing the antenna positions to increase system capacity. Therefore, this paper proposes a novel resource allocation scheme for MA-enhanced relay-assisted D2D MEC systems. Specifically, the MA positions and beamforming of user equipments (UEs), relay, and BS as well as the allocation of resources and the computation task offloading rate at the MEC server, all are optimized herein with the objective of minimizing the maximum latency while satisfying computation and communication rate constraints. Since this is a multivariable non-convex problem, a parallel and distributed penalty dual decomposition (PDD) based algorithm is developed and combined with successive convex approximation (SCA) to solve this non-convex problem. The results of extensive numerical analyses show that the proposed algorithm significantly improves the performance of the MA-enhanced relay-assisted D2D communication system compared to a counterpart where relays and the BS are equiped with traditional fixed-position antenna (FPA).
UAV-Enabled Passive 6D Movable Antennas: Joint Deployment and Beamforming Optimization
Authors: Changhao Liu, Weidong Mei, Peilan Wang, Yinuo Meng, Boyu Ning, Zhi Chen
Date: 2024-12-15 00:00:00

Intelligent reflecting surface (IRS) is composed of numerous passive reflecting elements and can be mounted on unmanned aerial vehicles (UAVs) to achieve six-dimensional (6D) movement by adjusting the UAV's three-dimensional (3D) location and 3D orientation simultaneously. Hence, in this paper, we investigate a new UAV-enabled passive 6D movable antenna (6DMA) architecture by mounting an IRS on a UAV and address the associated joint deployment and beamforming optimization problem. In particular, we consider a passive 6DMA-aided multicast system with a multi-antenna base station (BS) and multiple remote users, aiming to jointly optimize the IRS's location and 3D orientation, as well as its passive beamforming to maximize the minimum received signal-to-noise ratio (SNR) among all users under the practical angle-dependent signal reflection model. However, this optimization problem is challenging to be optimally solved due to the intricate relationship between the users' SNRs and the IRS's location and orientation. To tackle this challenge, we first focus on a simplified case with a single user, showing that one-dimensional (1D) orientation suffices to achieve the optimal performance. Next, we show that for any given IRS's location, the optimal 1D orientation can be derived in closed form, based on which several useful insights are drawn. To solve the max-min SNR problem in the general multi-user case, we propose an alternating optimization (AO) algorithm by alternately optimizing the IRS's beamforming and location/orientation via successive convex approximation (SCA) and hybrid coarse- and fine-grained search, respectively. To avoid undesirable local sub-optimal solutions, a Gibbs sampling (GS) method is proposed to generate new IRS locations and orientations for exploration in each AO iteration. Numerical results validate our theoretical analyses.
Experimental Study on the Effect of Synchronization Accuracy for Near-Field RF Wireless Power Transfer in Multi-Antenna Systems
Authors: Gilles Callebaut, Jarne Van Mulders, Bert Cox, Benjamin J. B. Deutschmann, Geoffrey Ottoy, Lieven De Strycker, Liesbet Van der Perre
Date: 2024-12-15 00:00:00
Tasks: Diversity

Wireless power transfer (WPT) technologies hold promise for enhancing device autonomy, particularly for energy-limited IoT systems. This paper presents experimental results on coherent and non-coherent transmit diversity approaches for WPT, tested in the near field using the Techtile testbed. We demonstrate that a fully synchronized beamfocusing system achieves a 14 dB gain over non-coherent transmission, consistent with the theoretical 14.9 dB gain for a 31-element array. Additionally, phase alignment errors below 20{\deg} result in less than 1 dB of gain loss, while errors exceeding 40{\deg} lead to losses over 3 dB. These findings suggest that phase coherency requirements for WPT can be relaxed, and that scaling the number of antennas is a promising strategy for improving power transfer efficiency.
Reconfigurable Intelligent Surface-Aided Secure Integrated Radar and Communication Systems
Authors: Tong-Xing Zheng, Xin Chen, Lan Lan, Ying Ju, Xiaoyan Hu, Rongke Liu, Derrick Wing Kwan Ng, Tiejun Cui
Date: 2024-12-15 00:00:00

Despite the enhanced spectral efficiency brought by the integrated radar and communication technique, it poses significant risks to communication security when confronted with malicious radar targets. To address this issue, a reconfigurable intelligent surface (RIS)-aided transmission scheme is proposed to improve secure communication in two systems, i.e., the radar and communication co-existing (RCCE) system, where a single transmitter is utilized for both radar sensing and communication, and the dual-functional radar and communication (DFRC) system. At the design stage, optimization problems are formulated to maximize the secrecy rate while satisfying the radar detection constraint via joint active beamforming at the base station and passive beamforming of RIS in both systems. Particularly, a zero-forcing-based block coordinate descent (BCD) algorithm is developed for the RCCE system. Besides, the Dinkelbach method combined with semidefinite relaxation is employed for the DFRC system, and to further reduce the computational complexity, a Riemannian conjugate gradient-based alternating optimization algorithm is proposed. Moreover, the RIS-aided robust secure communication in the DFRC system is investigated by considering the eavesdropper's imperfect channel state information (CSI), where a bounded uncertainty model is adopted to capture the angle error and fading channel error of the eavesdropper, and a tractable bound for their joint uncertainty is derived. Simulation results confirm the effectiveness of the developed RIS-aided transmission scheme to improve the secrecy rate even with the eavesdropper's imperfect CSI, and comparisons between both systems reveal that the RCCE system can provide a higher secrecy rate than the DFRC system.
Martian Dust Storm Detection with THz Opportunistic Integrated Sensing and Communication in the Internet of Space (IoS)
Authors: Haofan Dong, Ozgur B. Akan
Date: 2024-12-14 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper presents the Mars Dust Storm Detector (MDSD), a system that leverages the THz Opportunistic Integrated Sensing and Communications (OISAC) signals between Mars surface assets (rovers and landers) to extract environmental information, particularly dust storm properties. The MDSD system utilizes the multi-parameter sensitivity of THz signal attenuation between Martian communication devices to provide rich, real-time data on storm intensity, particle characteristics, and potentially even electrification state. This approach, incorporating HITRAN spectroscopic data and Martian-specific atmospheric parameters, allows for accurate modeling and analysis. The system's ability to repurpose THz ISAC signals for environmental sensing demonstrates an efficient use of resources in the challenging Martian environment, utilizing communication infrastructure to enhance our understanding of Mars' atmospheric dynamics. The system's performance is evaluated through extensive simulations under various Node Density Factors (NDFs), comparing different interpolation algorithms for dust storm intensity mapping. Results demonstrate that linear interpolation achieves superior accuracy (correlation >0.90) at high NDFs, while nearest-neighbor and IDW algorithms maintain complete spatial coverage in sparse networks. Error analysis identifies dust particle size uncertainty as the primary contributor to estimation errors, though the system shows resilience to Martian atmospheric variations. This work extends the opportunistic use of ISAC technology to planetary exploration, contributing to both Mars atmospheric monitoring capabilities and ISAC applications in the Internet of Space (IoS).
6D Movable Antenna Enhanced Multi-Access Point Coordination via Position and Orientation Optimization
Authors: Xiangyu Pi, Lipeng Zhu, Haobin Mao, Zhenyu Xiao, Xiang-Gen Xia, Rui Zhang
Date: 2024-12-14 00:00:00
Tasks: Position

The effective utilization of unlicensed spectrum is regarded as an important direction to enable the massive access and broad coverage for next-generation wireless local area network (WLAN). Due to the crowded spectrum occupancy and dense user terminals (UTs), the conventional fixed antenna (FA)-based access points (APs) face huge challenges in realizing massive access and interference cancellation. To address this issue, in this paper we develop a six-dimensional movable antenna (6DMA) enhanced multi-AP coordination system for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP's antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and both of the online and offline 6DMA schemes can attain considerable performance improvement compared to the conventional FA-based schemes.