pwc-archive/papers-with-abstracts huggingface dataset:
SI-FID: Only One Objective Indicator for Evaluating Stitched Images
Authors: Xinrui Zhang, Shengwei Guo, Guobing Sun
Date: 2024-04-22 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Image Stitching

Image quality evaluation accurately is vital in developing image stitching algorithms as it directly reflects the algorithms progress. However, commonly used objective indicators always produce inconsistent and even conflicting results with subjective indicators. To enhance the consistency between objective and subjective evaluations, this paper introduces a novel indicator the Frechet Distance for Stitched Images (SI-FID). To be specific, our training network employs the contrastive learning architecture overall. We employ data augmentation approaches that serve as noise to distort images in the training set. Both the initial and distorted training sets are then input into the pre-training model for fine-tuning. We then evaluate the altered FID after introducing interference to the test set and examine if the noise can improve the consistency between objective and subjective evaluation results. The rank correlation coefficient is utilized to measure the consistency. SI-FID is an altered FID that generates the highest rank correlation coefficient under the effect of a certain noise. The experimental results demonstrate that the rank correlation coefficient obtained by SI-FID is at least 25% higher than other objective indicators, which means achieving evaluation results closer to human subjective evaluation.
A New Multi-Picture Architecture for Learned Video Deinterlacing and Demosaicing with Parallel Deformable Convolution and Self-Attention Blocks
Authors: Ronglei Ji, A. Murat Tekalp
Date: 2024-04-19 00:00:00
Tasks: Demosaicking, Denoising, Super-Resolution, Video Deinterlacing, Video Restoration

Despite the fact real-world video deinterlacing and demosaicing are well-suited to supervised learning from synthetically degraded data because the degradation models are known and fixed, learned video deinterlacing and demosaicing have received much less attention compared to denoising and super-resolution tasks. We propose a new multi-picture architecture for video deinterlacing or demosaicing by aligning multiple supporting pictures with missing data to a reference picture to be reconstructed, benefiting from both local and global spatio-temporal correlations in the feature space using modified deformable convolution blocks and a novel residual efficient top-$k$ self-attention (kSA) block, respectively. Separate reconstruction blocks are used to estimate different types of missing data. Our extensive experimental results, on synthetic or real-world datasets, demonstrate that the proposed novel architecture provides superior results that significantly exceed the state-of-the-art for both tasks in terms of PSNR, SSIM, and perceptual quality. Ablation studies are provided to justify and show the benefit of each novel modification made to the deformable convolution and residual efficient kSA blocks. Code is available: https://github.com/KUIS-AI-Tekalp-Research-Group/Video-Deinterlacing.
Plane-wave compounding with adaptive joint coherence factor weighting
Authors: Nikunj Khetan, Jerome Mertz
Date: 2024-04-18 00:00:00

Coherent Plane Wave Compounding (CPWC) is widely used for ultrasound imaging. This technique involves sending plane waves into a sample at different transmit angles and recording the resultant backscattered echo at different receive positions. The time-delayed signals from the different combinations of transmit angles and receive positions are then coherently summed to produce a beamformed image. Various techniques have been developed to characterize the quality of CPWC beamforming based on the measured coherence across the transmit or receive apertures. Here, we propose a more fine-grained approach where the signals from every transmit/receive combination are separately evaluated using a quality metric based on their joint spatio-angular coherence. The signals are then individually weighted according to their measured Joint Coherence Factor (JCF) prior to being coherently summed. To facilitate the comparison of JCF beamforming compared to alternative techniques, we further propose a method of image display standardization based on contrast matching. We show results from tissue-mimicking phantoms and human soft-tissue imaging. Fine-grained JCF weighting is found to improve CPWC image quality compared to alternative approaches.
Hybrid Fusion for 802.11ax Wi-Fi-based Passive Radars Exploiting Beamforming Feedbacks
Authors: Martin Willame, Hasan Can Yildirim, Laurent Storrer, Francois Horlin, Jerome Louveaux
Date: 2024-04-22 00:00:00

Passive Wi-Fi-based radars (PWRs) are devices that enable the localization of targets using Wi-Fi signals of opportunity transmitted by an access point. Unlike active radars that optimize their transmitted waveform for localization, PWRs align with the 802.11 amendments. Specifically, during the channel sounding session preceding a multi-user multiple-input multiple-output downlink transmission, an access point isotropically transmits a null data packet (NDP) with a known preamble. From these known symbols, client user equipments derive their channel state information and transmit an unencrypted beamforming feedback (BFF) back to the access point. The BFF comprises the right singular matrix of the channel and the corresponding stream gain for each subcarrier, which allows the computation of a beamforming matrix at the access point. In a classical PWR processing, only the preamble symbols from the NDP are exploited during the channel sounding session. In this study, we investigate multiple target localization by a PWR exploiting hybrid information sources. On one hand, the joint angle-of-departure and angle-of-arrival evaluated from the NDP. On another hand, the line-of-sight angle-of-departures inferred from the BFFs. The processing steps at the PWR are defined and an optimal hybrid fusion rule is derived in the maximum likelihood framework. Monte-Carlo simulations assess the enhanced accuracy of the proposed combination method compared to classical PWR processing based solely on the NDP, and compare the localisation performance between client and non-client targets.
Fidelitous Augmentation of Human Accelerometric Data for Deep Learning
Authors: Tracey K. M. Lee, H. W. Chan, K. H. Leo, Effie Chew, L. Zhao, Saeid Sanei
Date: 2024-04-22 00:00:00
Tasks: Deep Learning, Diversity, Time Series

Time series (TS) data have consistently been in short supply, yet their demand remains high for training systems in prediction, modeling, classification, and various other applications. Synthesis can serve to expand the sample population, yet it is crucial to maintain the statistical characteristics between the synthesized and the original TS : this ensures consistent sampling of data for both training and testing purposes. However the time domain features of the data may not be maintained. This motivates for our work, the objective which is to preserve the following features in a synthesized TS: its fundamental statistical characteristics and important time domain features like its general shape and prominent transients. In a novel way, we first isolate important TS features into various components using a spectrogram and singular spectrum analysis. The residual signal is then randomized in a way that preserves its statistical properties. These components are then recombined for the synthetic time series. Using accelerometer data in a clinical setting, we use statistical and shape measures to compare our method to others. We show it has higher fidelity to the original signal features, has good diversity and performs better data classification in a deep learning application.
Localization Based on MIMO Backscattering from Retro-Directive Antenna Arrays
Authors: Marina Lotti, Nicolò Decarli, Gianni Pasolini, Davide Dardari
Date: 2024-04-22 00:00:00
Tasks: Autonomous Driving

In next-generation vehicular environments, precise localization is crucial for facilitating advanced applications such as autonomous driving. As automation levels escalate, the demand rises for enhanced accuracy, reliability, energy efficiency, update rate, and reduced latency in position information delivery. In this paper, we propose the exploitation of backscattering from retro-directive antenna arrays (RAAs) to address these imperatives. We introduce and discuss two RAA-based architectures designed for various applications, including network localization and navigation. These architectures enable swift and simple angle-of-arrival estimation by using signals backscattered from RAAs. They also leverage multiple antennas to capitalize on multiple-input-multiple-output (MIMO) gains, thereby addressing the challenges posed by the inherent path loss in backscatter communication, especially when operating at high frequencies. Consequently, angle-based localization becomes achievable with remarkably low latency, ideal for mobile and vehicular applications. This paper introduces ad-hoc signalling and processing schemes for this purpose, and their performance is analytically investigated. Numerical results underscore the potential of these schemes, offering precise and ultra-low-latency localization with low complexity and ultra-low energy consumption devices.
Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments
Authors: Jiacheng Wang, Hongyang Du, Dusit Niyato, Zehui Xiong, Jiawen Kang, Bo Ai, Zhu Han, Dong In Kim
Date: 2024-04-22 00:00:00

Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also in signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses channel state information (CSI) to estimate the velocity and acceleration of propagation path length change of the human-induced reflection (HIR). Then, given the strong inference ability of the diffusion model, we propose a unified weighted conditional diffusion model (UW-CDM) to denoise the estimation results, enabling the detection of the number of targets. Next, we use the CSI obtained by a uniform linear array with wavelength spacing to estimate the HIR's time of flight and direction of arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i.e., the subflow size. The evaluation based on practical downlink communication signals shows G-HFD's accuracy of subflow size detection can reach 91%. This validates its effectiveness and underscores the significant potential of GAI in the context of wireless sensing.
GNSS Measurement-Based Context Recognition for Vehicle Navigation using Gated Recurrent Unit
Authors: Sheng Liu, Zhiqiang Yao, Xuemeng Cao, Xiaowen Cai
Date: 2024-04-22 00:00:00

Recent years, people have put forward higher and higher requirements for context-adaptive navigation (CAN). CAN system realizes seamless navigation in complex environments by recognizing the ambient surroundings of vehicles, and it is crucial to develop a fast, reliable, and robust navigational context recognition (NCR) method to enable CAN systems to operate effectively. Environmental context recognition based on Global Navigation Satellite System (GNSS) measurements has attracted widespread attention due to its low cost because it does not require additional infrastructure. The performance and application value of NCR methods depend on three main factors: context categorization, feature extraction, and classification models. In this paper, a fine-grained context categorization framework comprising seven environment categories (open sky, tree-lined avenue, semi-outdoor, urban canyon, viaduct-down, shallow indoor, and deep indoor) is proposed, which currently represents the most elaborate context categorization framework known in this research domain. To improve discrimination between categories, a new feature called the C/N0-weighted azimuth distribution factor, is designed. Then, to ensure real-time performance, a lightweight gated recurrent unit (GRU) network is adopted for its excellent sequence data processing capabilities. A dataset containing 59,996 samples is created and made publicly available to researchers in the NCR community on Github. Extensive experiments have been conducted on the dataset, and the results show that the proposed method achieves an overall recognition accuracy of 99.41\% for isolated scenarios and 94.95\% for transition scenarios, with an average transition delay of 2.14 seconds.
Open Datasets for Satellite Radio Resource Control
Authors: Husnain Shahid, Miguel Á. Vázquez, Musbah Shaat, Pol Henarejos, Nuria T Quijada, Enrique Marinu
Date: 2024-04-22 00:00:00
Tasks: Benchmarking, Decision Making, Management

In Non-Terrestrial Networks (NTN), achieving effective radio resource allocation across multi-satellite system, encompassing efficient channel and bandwidth allocation, effective beam management, power control and interference mitigation, poses significant challenges due to the varying satellite links and highly dynamic nature of user traffic. This calls for the development of an intelligent decision-making controller using Artificial Intelligence (AI) to efficiently manage resources in this complex environment. In this context, open datasets can play a crucial role in driving new advancement and facilitating research. Recognizing the significance, this paper aims to contribute the satellite communication research community by providing various open datasets that incorporate realistic traffic flow enabling a variety of uses cases. The primary objective of sharing these datasets is to facilitate the development and benchmarking of advanced resource management solutions, thereby improving the overall satellite communication systems. Furthermore, an application example focused on beam placement optimization via terminal clustering is provided. This assists in optimizing beam allocation task, enabling adaptive beamforming to effectively meet spatiotemporally varying user traffic demands and optimize resource utilization.
Emerging Advancements in 6G NTN Radio Access Technologies: An Overview
Authors: Husnain Shahid, Carla Amatetti, Riccardo Campana, Sorya Tong, Dorin Panaitopol, Alessandro Vanelli Coralli, Abdelhamed Mohamed, Chao Zhang, Ebraam Khalifa, Eduardo Medeiros, Estefania Recayte, Fatemeh Ghasemifard, Ji Lianghai, Juan Bucheli, Karthik Anantha Swamy, Marius Caus, Mehmet Gurelli, Miguel A. Vazquez, Musbah Shaat, Nathan Borios, Per-Erik Eriksson, Sebastian Euler, Zheng Li, Xiaotian Fu
Date: 2024-04-22 00:00:00
Tasks: Management

The efforts on the development, standardization and improvements to communication systems towards 5G Advanced and 6G are on track to provide benefits such as an unprecedented level of connectivity and performance, enabling a diverse range of vertical services. The full integration of non-terrestrial components into 6G plays a pivotal role in realizing this paradigm shift towards ubiquitous communication and global coverage. However, this integration into 6G brings forth a set of its own challenges, particularly in Radio Access Technologies (RATs). To this end, this paper comprehensively discusses those challenges at different levels of RATs and proposes the corresponding potential emerging advancements in the realm of 6G NTN. In particular, the focus is on advancing the prospective aspects of Radio Resource Management (RRM), spectral coexistence in terrestrial and non-terrestrial components and flexible waveform design solutions to combat the impediments. This discussion with a specific focus on emerging advancements in 6G NTN RATs is critical for shaping the next generation networks and potentially relevant in contributing the part in standardization in forthcoming releases
A Data-Driven Condition Monitoring Method for Capacitor in Modular Multilevel Converter (MMC)
Authors: Shuyu Ou, Mahyar Hassanifar, Martin Votava, Marius Langwasser, Marco Liserre, Ariya Sangwongwanich, Subham Sahoo, Frede Blaabjerg
Date: 2024-04-20 00:00:00

The modular multilevel converter (MMC) is a topology that consists of a high number of capacitors, and degradation of capacitors can lead to converter malfunction, limiting the overall system lifetime. Condition monitoring methods can be applied to assess the health status of capacitors and realize predictive maintenance to improve reliability. Current research works for condition monitoring of capacitors in an MMC mainly monitor either capacitance or equivalent series resistance (ESR), while these two health indicators can shift at different speeds and lead to different end-of-life times. Hence, monitoring only one of these parameters may lead to unreliable health status evaluation. This paper proposes a data-driven method to estimate capacitance and ESR at the same time, in which particle swarm optimization (PSO) is leveraged to update the obtained estimations. Then, the results of the estimations are used to predict the sub-module voltage, which is based on a capacitor voltage equation. Furthermore, minimizing the mean square error between the predicted and actual measured voltage makes the estimations closer to the actual values. The effectiveness and feasibility of the proposed method are validated through simulations and experiments.
BERT: Accelerating Vital Signs Measurement for Bioradar with An Efficient Recursive Technique
Authors: Chengyao Tang, Yongpeng Dai, Zhi Li, Yongping Song, Fulai Liang, Tian Jin
Date: 2024-04-20 00:00:00
Tasks: Heart Rate Variability

Recent years have witnessed the great advance of bioradar system in smart sensing of vital signs (VS) for human healthcare monitoring. As an important part of VS sensing process, VS measurement aims to capture the chest wall micromotion induced by the human respiratory and cardiac activities. Unfortunately, the existing VS measurement methods using bioradar have encountered bottlenecks in making a trade-off between time cost and measurement accuracy. To break this bottleneck, this letter proposes an efficient recursive technique (BERT) heuristically, based on the observation that the features of bioradar VS meet the conditions of Markov model. Extensive experimental results validate that BERT measurement yields lower time costs, competitive estimates of heart rate, breathing rate, and heart rate variability. Our BERT method is promising us a new and superior option to measure VS for bioradar. This work seeks not only to solve the current issue of how to accelerate VS measurement with an acceptable accuracy, but also to inspire creative new ideas that spur further advances in this promising field in the future.
A Massive MIMO Sampling Detection Strategy Based on Denoising Diffusion Model
Authors: Lanxin He, Zheng Wang, Yongming Huang
Date: 2024-04-20 00:00:00
Tasks: Denoising

The Langevin sampling method relies on an accurate score matching while the existing massive multiple-input multiple output (MIMO) Langevin detection involves an inevitable singular value decomposition (SVD) to calculate the posterior score. In this work, a massive MIMO sampling detection strategy that leverages the denoising diffusion model is proposed to narrow the gap between the given iterative detector and the maximum likelihood (ML) detection in an SVD-free manner. Specifically, the proposed score-based sampling detection strategy, denoted as approximate diffusion detection (ADD), is applicable to a wide range of iterative detection methods, and therefore entails a considerable potential in their performance improvement by multiple sampling attempts. On the other hand, the ADD scheme manages to bypass the channel SVD by introducing a reliable iterative detector to produce a sample from the approximate posterior, so that further Langevin sampling is tractable. Customized by the conjugated gradient descent algorithm as an instance, the proposed sampling scheme outperforms the existing score-based detector in terms of a better complexity-performance trade-off.
Optimal Training Design for Over-the-Air Polynomial Power Amplifier Model Estimation
Authors: François Rottenberg, Thomas Feys, Nuutti Tervo
Date: 2024-04-19 00:00:00

The current evolution towards a massive number of antennas and a large variety of transceiver architectures forces to revisit the conventional techniques used to improve the fundamental power amplifier (PA) linearity-efficiency trade-off. Most of the digital linearization techniques rely on PA measurements using a dedicated feedback receiver. However, in modern systems with large amount of RF chains and high carrier frequency, dedicated receiver per RF chain is costly and complex to implement. This issue can be addressed by measuring PAs over the air, but in that case, this extra signalling is sharing resources with the actual data transmission. In this paper, we look at the problem from an estimation theory point of view so as to minimize pilot overhead while optimizing estimation performance. We show that conventional results in the mathematical statistics community can be used. We find the least squares (LS) optimal training design, minimizing the maximal mean squared error (MSE) of the reconstructed PA response over its whole input range. As compared to uniform training, simulations demonstrate a factor 10 reduction of the maximal MSE for a L = 7 PA polynomial order. Using prior information, the LMMSE estimator can achieve an additional gain of a factor up to 300 at low signal-to-noise ratio (SNR).
User-Centric Cell-Free (UCCF) Wireless Systems: Principles and Optimization
Authors: Lie-Liang Yang
Date: 2024-04-19 00:00:00

User-centric cell-free (UCCF) wireless networks have a range of distinguished characteristics, which can be exploited for meeting some challenges that the conventional cellular systems are hard to. This chapter is devoted to delivering the fundamentals of wireless communications in UCCF systems, including channel modeling and estimation, uplink (UL) detection, downlink (DL) transmission, and resource optimization. Specifically, the advantages of cell-free networking are examined in contrast to the conventional celluar systems. The global and location-aware distributed UL detection are explored in the principles of minimum mean-square error (MMSE) and brief propagation. Correspondingly, the global and distributed DL transmission schemes are designed based on the MMSE precoding. The optimization of both UL and DL is analyzed with respect to system design and resource-allocation. Furthermore, some challenges for the implementation of UCCF systems in practice are identified and analyzed.
Integrated Sensing and Communication enabled Multiple Base Stations Cooperative UAV Detection
Authors: Xi Lu, Zhiqing Wei, Ruizhong Xu, Lin Wang, Bohao Lu, Jinghui Piao
Date: 2024-04-19 00:00:00
Tasks: Benchmarking, Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) exhibits notable potential for sensing the unmanned aerial vehicles (UAVs), facilitating real-time monitoring of UAVs for security insurance. Due to the low sensing accuracy of single base stations (BSs), a cooperative UAV sensing method by multi-BS is proposed in this paper to achieve high-accuracy sensing. Specifically, a multiple signal classification (MUSIC)-based symbol-level fusion method is proposed for UAV localization and velocity estimation, consisting of a single-BS preprocessing step and a lattice points searching step. The preprocessing procedure enhances the single-BS accuracy by superposing multiple spectral functions, thereby establishing a reference value for subsequent lattice points searching. Furthermore, the lattice point with minimal error compared to the preprocessing results is determined as the fusion result. Extensive simulation results reveal that the proposed symbol-level fusion method outperforms the benchmarking methods in localization and velocity estimation.