pwc-archive/papers-with-abstracts huggingface dataset:
A Neural Enhancement Post-Processor with a Dynamic AV1 Encoder Configuration Strategy for CLIC 2024
Authors: Darren Ramsook, Anil Kokaram
Date: 2024-01-31 00:00:00
Tasks: Video Compression

At practical streaming bitrates, traditional video compression pipelines frequently lead to visible artifacts that degrade perceptual quality. This submission couples the effectiveness of a neural post-processor with a different dynamic optimsation strategy for achieving an improved bitrate/quality compromise. The neural post-processor is refined via adversarial training and employs perceptual loss functions. By optimising the post-processor and encoder directly our method demonstrates significant improvement in video fidelity. The neural post-processor achieves substantial VMAF score increases of +6.72 and +1.81 at bitrates of 50 kb/s and 500 kb/s respectively.
A Spectral Library and Method for Sparse Unmixing of Hyperspectral Images in Fluorescence Guided Resection of Brain Tumors
Authors: David Black, Benoit Liquet, Sadahiro Kaneko, Antonio Di leva, Walter Stummer, Eric Suero Molina
Date: 2024-01-30 00:00:00
Tasks: regression

Through spectral unmixing, hyperspectral imaging (HSI) in fluorescence-guided brain tumor surgery has enabled detection and classification of tumor regions invisible to the human eye. Prior unmixing work has focused on determining a minimal set of viable fluorophore spectra known to be present in the brain and effectively reconstructing human data without overfitting. With these endmembers, non-negative least squares regression (NNLS) was used to compute the abundances. However, HSI images are heterogeneous, so one small set of endmember spectra may not fit all pixels well. Additionally, NNLS is the maximum likelihood estimator only if the measurement is normally distributed, and it does not enforce sparsity, which leads to overfitting and unphysical results. Here, we analyzed 555666 HSI fluorescence spectra from 891 ex vivo measurements of patients with brain tumors to show that a Poisson distribution models the measured data 82% better than a Gaussian in terms of the Kullback-Leibler divergence and that the endmember abundance vectors are sparse. With this knowledge, we introduce (1) a library of 9 endmember spectra, (2) a sparse, non-negative Poisson regression algorithm to perform physics-informed unmixing with this library without overfitting, and (3) a highly realistic spectral measurement simulation with known endmember abundances. The new unmixing method was then tested on the human and simulated data and compared to four other candidate methods. It outperforms previous methods with 25% lower error in the computed abundances on the simulated data than NNLS, lower reconstruction error on human data, beUer sparsity, and 31 times faster runtime than state-of-the-art Poisson regression. This method and library of endmember spectra can enable more accurate spectral unmixing to beUer aid the surgeon during brain tumor resection.
Optimising O-to-U Band Transmission Using Fast ISRS Gaussian Noise Numerical Integral Model
Authors: Mindaugas Jarmolovičius, Daniel Semrau, Henrique Buglia, Mykyta Shevchenko, Filipe M. Ferreira, Eric Sillekens, Polina Bayvel, Robert I. Killey
Date: 2024-01-31 00:00:00
Tasks: valid

We model the transmission of ultrawideband signals, including wavelength-dependent fibre parameters: dispersion, nonlinear coefficient and effective fibre core area. To that end, the inter-channel stimulated Raman scattering Gaussian noise integral model is extended to include these parameters. The integrals involved in this frequency-domain model are numerically solved in hyperbolic coordinates using a Riemann sum. The model implementation is designed to work on parallel GPUs and is optimised for fast computational time. The model is valid for Gaussian-distributed signals and is compared with the split-step Fourier method, for transmission over standard single-mode fibre (SSMF) in the O-band (wavelengths around the zero-dispersion wavelength), showing reasonable agreement. Further, we demonstrated SNR evaluation over an 80~km SSFM single-span transmission using 589x96 GBaud channels, corresponding to almost 59 THz optical bandwidth, fully populating the O, E, S, C, L and U bands (1260-1675 nm). The SNR evaluation is completed in just 3.6 seconds using four Nvidia V100 16GB PCIe GPUs. Finally, we used this model to find the optimum launch power profile for this system achieving 747 Tbps of potential throughput over 80 km fibre and demonstrating its suitability for UWB optimisation routines.
Fundamental Trade-Offs in Monostatic ISAC: A Holistic Investigation Towards 6G
Authors: Musa Furkan Keskin, Mohammad Mahdi Mojahedian, Jesus O. Lacruz, Carina Marcus, Olof Eriksson, Andrea Giorgetti, Joerg Widmer, Henk Wymeersch
Date: 2024-01-31 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper undertakes a holistic investigation of two fundamental trade-offs in monostatic OFDM integrated sensing and communication (ISAC) systems-namely, the time-frequency trade-off and the spatial trade-off, originating from the choice of modulation order for random data and the design of beamforming strategies, respectively. To counteract the elevated side-lobe levels induced by varying-amplitude data in high-order QAM signaling, we propose a novel linear minimum mean-squared-error (LMMSE) estimator, capable of maintaining robust sensing performance across a wide range of SNRs. Moreover, we explore spatial domain trade-offs through two ISAC transmission strategies: concurrent, employing joint beams, and time-sharing, using separate, time-non-overlapping beams for sensing and communications. Simulations demonstrate superior performance of the LMMSE estimator, especially in detecting weak targets in the presence of strong ones with high-order QAM, consistently yielding more favorable ISAC trade-offs than existing baselines under various modulation schemes, SNR conditions, RCS levels and transmission strategies. We also provide experimental results to validate the effectiveness of the LMMSE estimator in reducing side-lobe levels, based on real-world measurements.
Advancing EEG/MEG Source Imaging with Geometric-Informed Basis Functions
Authors: Song Wang, Chen Wei, Kexin Lou, Dongfeng Gu, Quanying Liu
Date: 2024-01-31 00:00:00
Tasks: EEG

Electroencephalography (EEG) and Magnetoencephalography (MEG) are pivotal in understanding brain activity but are limited by their poor spatial resolution. EEG/MEG source imaging (ESI) infers the high-resolution electric field distribution in the brain based on the low-resolution scalp EEG/MEG observations. However, the ESI problem is ill-posed, and how to bring neuroscience priors into ESI method is the key. Here, we present a novel method which utilizes the Brain Geometric-informed Basis Functions (GBFs) as priors to enhance EEG/MEG source imaging. Through comprehensive experiments on both synthetic data and real task EEG data, we demonstrate the superiority of GBFs over traditional spatial basis functions (e.g., Harmonic and MSP), as well as existing ESI methods (e.g., dSPM, MNE, sLORETA, eLORETA). GBFs provide robust ESI results under different noise levels, and result in biologically interpretable EEG sources. We believe the high-resolution EEG source imaging from GBFs will greatly advance neuroscience research.
Coexistence of Mobile Broadband IMT Systems and UWB Keyless Entry Systems above 6.5 GHz
Authors: Carsten Monka-Ewe, Lukas Berkelmann, Richard Wolf, Jan Oliver Oelerich, Thorsten Schönfelder, Marco Kühnel, Zhichao Chen, Alin Stanescu, Oliver Kushova, Daniel Siekmann, Bert Jannsen
Date: 2024-01-31 00:00:00

The ever-increasing need for spectrum for mobile broadband systems has led to the recent allocation of spectral resources for International Mobile Telecommunication (IMT) services in the upper mid band (6.425 - 7.125 GHz) at the World Radio Conference (WRC-23) as well as to the creation of an agenda item on identifying future IMT bands in the frequency region 7.125 - 10.5 GHz at WRC-27. The severity of the impact of these frequency allocations on existing UWB systems, which have been using this part of the spectrum as a sub-secondary user for many years, is still subject to controversial discussions. This paper contributes a study on the impact of IMT on a real-world vehicular UWB keyless entry system to this discussion. It is shown that both the car's wireless on-board unit and a nearby basestation may drastically affect the system's performance.
Device-free Indoor WLAN Localization with Distributed Antenna Placement Optimization and Spatially Localized Regression
Authors: Osamu Muta, Kazuki Noguchi, Junsuke Izumi, Shunsuke Shimizu, Tomoki Murakami, Shinya Otsuki
Date: 2024-01-31 00:00:00
Tasks: regression

Wireless sensing is a promising technology for future wireless communication networks to realize various application services. Wireless local area network (WLAN)-based localization approaches using channel state information (CSI) have been investigated intensively. Further improvements in detection performance will depend on selecting appropriate feature information and determining the placements of distributed antenna elements. This paper presents a proposal of an enhanced device-free WLAN-based localization scheme with beam-tracing based antenna placement optimization and spatially localized regression, where beam-forming weights (BFWs) are used as feature information for training machine-learning (ML)-based models localized to partitioned areas. By this scheme, the antenna placement at the access point (AP) is determined by solving a combinational optimization problem with beam-tracing between AP and station (STA) without knowing the CSI. Additionally, we propose the use of localized regression to improve localization accuracy with low complexity, where classification and regression based ML models are used for coarse and precise estimations of the target position. We evaluate the proposed scheme effects on localization performance in an indoor environment. Experiment results demonstrate that the proposed antenna placement and localized regression scheme improve the localization accuracy while reducing the required complexity for both off-line training and on-line localization relative to other reference schemes.
Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural Stimuli
Authors: Simon Geirnaert, Yuanyuan Yao, Tom Francart, Alexander Bertrand
Date: 2024-01-31 00:00:00
Tasks: Brain Computer Interface, EEG

Various new brain-computer interface technologies or neuroscience applications require decoding stimulus-following neural responses to natural stimuli such as speech and video from, e.g., electroencephalography (EEG) signals. In this context, generalized canonical correlation analysis (GCCA) is often used as a group analysis technique, which allows the extraction of correlated signal components from the neural activity of multiple subjects attending to the same stimulus. GCCA can be used to improve the signal-to-noise ratio of the stimulus-following neural responses relative to all other irrelevant (non-)neural activity, or to quantify the correlated neural activity across multiple subjects in a group-wise coherence metric. However, the traditional GCCA technique is stimulus-unaware: no information about the stimulus is used to estimate the correlated components from the neural data of several subjects. Therefore, the GCCA technique might fail to extract relevant correlated signal components in practical situations where the amount of information is limited, for example, because of a limited amount of training data or group size. This motivates a new stimulus-informed GCCA (SI-GCCA) framework that allows taking the stimulus into account to extract the correlated components. We show that SI-GCCA outperforms GCCA in various practical settings, for both auditory and visual stimuli. Moreover, we showcase how SI-GCCA can be used to steer the estimation of the components towards the stimulus. As such, SI-GCCA substantially improves upon GCCA for various purposes, ranging from preprocessing to quantifying attention.
Parametric Near-Field Channel Estimation for Extremely Large Aperture Arrays
Authors: Alva Kosasih, Özlem Tuğfe Demir, Emil Björnson
Date: 2024-01-31 00:00:00

Accurate channel estimation is critical to fully exploit the beamforming gains when communicating with extremely large aperture arrays. The propagation distances between the user and receiver, which potentially has thousands of antennas/elements, are such that they are located in the radiative near-field region of each other when considering the Fraunhofer distance of the entire array. Therefore, it is imperative to consider near-field effects to achieve proper channel estimation. This paper proposes a parametric multi-user near-field channel estimation algorithm based on MUltiple SIgnal Classification (MUSIC) method to obtain the essential parameters describing the users' locations. We derive the estimated channel by incorporating the estimated parameters into the near-field channel model. Additionally, we implement a least-squares-based estimation corrector, resulting in a precise near-field channel estimation. Simulation results demonstrate that our proposed scheme outperforms classical least-squares and minimum mean-square error channel estimation methods in terms of normalized beamforming gain and normalized mean-square error.
Compression before Fusion: Broadcast Semantic Communication System for Heterogeneous Tasks
Authors: Mingze Gong, Shuoyao Wang, Fangwei Ye, Suzhi Bi
Date: 2024-01-31 00:00:00
Tasks: Semantic Communication

Semantic communication has emerged as new paradigm shifts in 6G from the conventional syntax-oriented communications. Recently, the wireless broadcast technology has been introduced to support semantic communication system toward higher communication efficiency. Nevertheless, existing broadcast semantic communication systems target on general representation within one stage and fail to balance the inference accuracy among users. In this paper, the broadcast encoding process is decomposed into compression and fusion to improves communication efficiency with adaptation to tasks and channels.Particularly, we propose multiple task-channel-aware sub-encoders (TCE) and a channel-aware feature fusion sub-encoder (CFE) towards compression and fusion, respectively. In TCEs, multiple local-channel-aware attention blocks are employed to extract and compress task-relevant information for each user. In GFE, we introduce a global-channel-aware fine-tuning block to merge these compressed task-relevant signals into a compact broadcast signal. Notably, we retrieve the bottleneck in DeepBroadcast and leverage information bottleneck theory to further optimize the parameter tuning of TCEs and CFE.We substantiate our approach through experiments on a range of heterogeneous tasks across various channels with additive white Gaussian noise (AWGN) channel, Rayleigh fading channel, and Rician fading channel. Simulation results evidence that the proposed DeepBroadcast outperforms the state-of-the-art methods.
Can We Improve Channel Reciprocity via Loop-back Compensation for RIS-assisted Physical Layer Key Generation
Authors: Ningya Xu, Guoshun Nan, Xiaofeng Tao, Na Li, Pengxuan Mao, Tianyuan Yang
Date: 2024-01-31 00:00:00

Reconfigurable intelligent surface (RIS) facilitates the extraction of unpredictable channel features for physical layer key generation (PKG), securing communications among legitimate users with symmetric keys. Previous works have demonstrated that channel reciprocity plays a crucial role in generating symmetric keys in PKG systems, whereas, in reality, reciprocity is greatly affected by hardware interference and RIS-based jamming attacks. This motivates us to propose LoCKey, a novel approach that aims to improve channel reciprocity by mitigating interferences and attacks with a loop-back compensation scheme, thus maximizing the secrecy performance of the PKG system. Specifically, our proposed LoCKey is capable of effectively compensating for the CSI non-reciprocity by the combination of transmit-back signal value and error minimization module. Firstly, we introduce the entire flowchart of our method and provide an in-depth discussion of each step. Following that, we delve into a theoretical analysis of the performance optimizations when our LoCKey is applied for CSI reciprocity enhancement. Finally, we conduct experiments to verify the effectiveness of the proposed LoCKey in improving channel reciprocity under various interferences for RIS-assisted wireless communications. The results demonstrate a significant improvement in both the rate of key generation assisted by the RIS and the consistency of the generated keys, showing great potential for the practical deployment of our LoCKey in future wireless systems.
An IoT system for smart building combining multiple mmWave FMCW radars applied to people counting
Authors: Valentín Barral, Tomás Domínguez-Bolaño, Carlos J. Escudero, José A. García-Naya
Date: 2024-01-28 00:00:00

In contemporary society, the pressing challenge of preserving user privacy clashes with the imperative for smart buildings to efficiently manage their resources, particularly in the context of occupancy monitoring for optimized energy utilization. This paper delves into the application of millimiter wave (mmWave) frequency modulated continuous wave (FMCW) radar technology for occupancy monitoring. mmWave FMCW radar, unlike conventional methods that often require the use of identifiable tags or involve image analysis, operates without the need for such identifiers, mitigating privacy concerns. However, challenges arise when attempting to cover extensive indoor spaces due to the limited range of individual mmWave FMCW radar devices. The present work proposes the use of a flexible software architecture to integrate the measurements of several mmWave FMCW radar devices, so that the whole behaves as a single sensor. To validate the proposal, an example of use in a real environment in an indoor space monitored with three mmWave FMCW radar devices is also presented. The example details the whole process, from the physical installation of the devices to the use of the different software modules that allow the integration of the data into a common internet of things (IoT) management platform such as Home Assistant. All the elements, from the measurements captured during the test to the different software implementations, are shared publicly with the scientific community.
Time-modulated arrays with Haar wavelets
Authors: R. Maneiro-Catoira, J. Brégains, José A. García-Naya, L. Castedo
Date: 2024-01-28 00:00:00

Time-modulated arrays (TMAs) can effectively perform beamsteering over the first positive harmonic pattern by applying progressively delayed versions of stair-step approximations of a sine waveform to the antenna excitations. In this letter, we consider synthesizing such stair-step sine approximations by means of Haar wavelets. Haar functions constitute a complete orthonormal set of rectangular waveforms, which have the ability to represent a given function with a high degree of accuracy using few constituent terms. Hence, when they are applied to the TMA synthesis, employing single-pole double-throw switches, such a feature leads to an excellent rejection level of the undesired harmonics as well as a bandwidth greater than that supported by conventional TMAs with on-off switches.
Time-modulated multibeam phased arrays with periodic Nyquist pulses
Authors: R. Maneiro-Catoira, J. Brégains, José A. García-Naya, L. Castedo
Date: 2024-01-28 00:00:00

We present a single sideband time-modulated multibeam phased array governed by periodic Nyquist pulsed signals. A Nyquist pulse is a physically realizable approach to the ideal sinc function. Hence, its low-pass spectrum suits particularly well for time-modulated arrays (TMAs) to perform harmonic beam steering. Contrarily to switched TMAs and standard solutions based on variable phase shifters, the performance and complexity of the proposed time modulation scheme is rather robust when increasing the number of multibeams.
Recent methods from statistical inference and machine learning to improve integrative modeling of macromolecular assemblies
Authors: Shreyas Arvindekar, Kartik Majila, Shruthi Viswanath
Date: 2024-01-31 00:00:00
Tasks: Bayesian Inference

Integrative modeling of macromolecular assemblies allows for structural characterization of large assemblies that are recalcitrant to direct experimental observation. A Bayesian inference approach facilitates combining data from complementary experiments along with physical principles, statistics of known structures, and prior models, for structure determination. Here, we review recent methods for integrative modeling based on statistical inference and machine learning. These methods improve over the current state-of-the-art by enhancing the data collection, optimizing coarse-grained model representations, making scoring functions more accurate, sampling more efficient, and model analysis more rigorous. We also discuss three new frontiers in integrative modeling: incorporating recent deep learning-based methods, integrative modeling with in situ data, and metamodeling.
Enhancing Cardiovascular Disease Risk Prediction with Machine Learning Models
Authors: Farnoush Shishehbori, Zainab Awan
Date: 2024-01-29 00:00:00
Tasks: Diagnostic

Cardiovascular disease remains a leading global cause of mortality, necessitating accurate risk prediction tools. Traditional methods, such as QRISK and the Framingham heart score, exhibit limitations in their ability to incorporate comprehensive patient data, potentially resulting in incomplete risk factor consideration. To address these shortcomings, this study conducts a meticulous review focusing on the application of machine learning models to enhance predictive accuracy. Machine learning models, such as support vector machines, and Random Forest, as well as deep learning techniques like convolutional neural networks and recurrent neural networks, have emerged as promising alternatives. These models offer superior performance, accommodating a broader spectrum of variables and providing precise subgroup-specific predictions. While machine learning integration holds promise for enhancing risk assessment, it presents challenges such as data requirements and computational constraints. Additionally, large language models have revolutionised healthcare applications, augmenting diagnostic precision and patient care. This study examines the core aspects of cardiovascular disease event risk and presents a thorough review of traditional and machine learning models, alongside deep learning techniques, for improved accuracy. It offers a comprehensive survey of relevant datasets, critically compares ML models with conventional approaches, and synthesizes key findings, highlighting their implications for clinical practice. Furthermore, the potential of machine learning and large language models in cardiovascular medicine is undeniable. However, rigorous validation and optimisation are imperative before widespread application in healthcare. This integration promises more accurate and personalised cardiovascular care.