pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Training of a Dynamic Context-Aware Deep Denoising Framework for Low-Dose Fluoroscopic Imaging
Authors: Sun-Young Jeon, Sen Wang, Adam S. Wang, Garry E. Gold, Jang-Hwan Choi
Date: 2024-10-29 00:00:00
Tasks: Denoising, Diagnostic, Knowledge Distillation, Motion Compensation

Fluoroscopy is critical for real-time X-ray visualization in medical imaging. However, low-dose images are compromised by noise, potentially affecting diagnostic accuracy. Noise reduction is crucial for maintaining image quality, especially given such challenges as motion artifacts and the limited availability of clean data in medical imaging. To address these issues, we propose an unsupervised training framework for dynamic context-aware denoising of fluoroscopy image sequences. First, we train the multi-scale recurrent attention U-Net (MSR2AU-Net) without requiring clean data to address the initial noise. Second, we incorporate a knowledge distillation-based uncorrelated noise suppression module and a recursive filtering-based correlated noise suppression module enhanced with motion compensation to further improve motion compensation and achieve superior denoising performance. Finally, we introduce a novel approach by combining these modules with a pixel-wise dynamic object motion cross-fusion matrix, designed to adapt to motion, and an edge-preserving loss for precise detail retention. To validate the proposed method, we conducted extensive numerical experiments on medical image datasets, including 3500 fluoroscopy images from dynamic phantoms (2,400 images for training, 1,100 for testing) and 350 clinical images from a spinal surgery patient. Moreover, we demonstrated the robustness of our approach across different imaging modalities by testing it on the publicly available 2016 Low Dose CT Grand Challenge dataset, using 4,800 images for training and 1,136 for testing. The results demonstrate that the proposed approach outperforms state-of-the-art unsupervised algorithms in both visual quality and quantitative evaluation while achieving comparable performance to well-established supervised learning methods across low-dose fluoroscopy and CT imaging.
Diagnostic Performance of Deep Learning for Predicting Gliomas' IDH and 1p/19q Status in MRI: A Systematic Review and Meta-Analysis
Authors: Somayeh Farahani, Marjaneh Hejazi, Mehnaz Tabassum, Antonio Di Ieva, Neda Mahdavifar, Sidong Liu
Date: 2024-10-28 00:00:00
Tasks: Articles, Diagnostic, Prognosis, Sensitivity, Specificity

Gliomas, the most common primary brain tumors, show high heterogeneity in histological and molecular characteristics. Accurate molecular profiling, like isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion, is critical for diagnosis, treatment, and prognosis. This review evaluates MRI-based deep learning (DL) models' efficacy in predicting these biomarkers. Following PRISMA guidelines, we systematically searched major databases (PubMed, Scopus, Ovid, and Web of Science) up to February 2024, screening studies that utilized DL to predict IDH and 1p/19q codeletion status from MRI data of glioma patients. We assessed the quality and risk of bias using the radiomics quality score and QUADAS-2 tool. Our meta-analysis used a bivariate model to compute pooled sensitivity, specificity, and meta-regression to assess inter-study heterogeneity. Of the 565 articles, 57 were selected for qualitative synthesis, and 52 underwent meta-analysis. The pooled estimates showed high diagnostic performance, with validation sensitivity, specificity, and area under the curve (AUC) of 0.84 [prediction interval (PI): 0.67-0.93, I2=51.10%, p < 0.05], 0.87 [PI: 0.49-0.98, I2=82.30%, p < 0.05], and 0.89 for IDH prediction, and 0.76 [PI: 0.28-0.96, I2=77.60%, p < 0.05], 0.85 [PI: 0.49-0.97, I2=80.30%, p < 0.05], and 0.90 for 1p/19q prediction, respectively. Meta-regression analyses revealed significant heterogeneity influenced by glioma grade, data source, inclusion of non-radiomics data, MRI sequences, segmentation and feature extraction methods, and validation techniques. DL models demonstrate strong potential in predicting molecular biomarkers from MRI scans, with significant variability influenced by technical and clinical factors. Thorough external validation is necessary to increase clinical utility.
Integrating RIS into HAP Networks for Improved Connectivity
Authors: Islam M. Tanash, Ayush Kumar Dwivedi, Taneli Riihonen
Date: 2024-11-06 00:00:00
Tasks: Point Processes

This paper investigates a high-altitude platform (HAP) network enhanced with reconfigurable intelligent surfaces (RISs). The arbitrary placement of HAPs and RISs is modeled using stochastic geometry, specifically as homogeneous Poisson point processes. The HAP--RIS links are assumed to follow Rician fading, while the RIS--user links experience shadowed-Rician fading. The system's coverage probability and ergodic capacity are derived analytically and validated through Monte Carlo simulations. The results highlight significant performance gains and demonstrate the influence of various system parameters and fading conditions. The proposed system has potential for enhancing connectivity and data offloading in practical scenarios.
Statistical Radar Cross Section Characterization for Indoor Factory Targets
Authors: Ali Waqar Azim, Ahmad Bazzi, Roberto Bomfin, Hitesh Poddar, Marwa Chafii
Date: 2024-11-05 00:00:00

In this work, we statistically analyze the radar cross section (RCS) of different test targets present in an indoor factory (InF) scenario specified by 3rd Generation Partnership Project considering bistatic configuration. The test targets that we consider are drones, humans, quadruped robot and a robotic arm. We consider two drones of different sizes and five human subjects for RCS characterization. For the drones, we measure the RCS when they are are flying over a given point and while they are rotating over the same point. For human subjects, we measure the RCS while standing still, sitting still and walking. For quadruped robot and robotic arm, we consider a continuous random motion emulating different tasks which they are supposed to perfom in typical InF scenario. We employ different distributions, such as Normal, Lognormal, Gamma, Rician, Weibull, Rayleigh and Exponential to fit the measurement data. From the statistical analysis, we gather that Lognormal distribution can fit all the considered targets in the InF scenario.
Multi-Modal Intelligent Channel Modeling: A New Modeling Paradigm via Synesthesia of Machines
Authors: Lu Bai, Ziwei Huang, Mingran Sun, Xiang Cheng, Lizhen Cui
Date: 2024-11-06 00:00:00

In the future sixth-generation (6G) era, to support accurate localization sensing and efficient communication link establishment for intelligent agents, a comprehensive understanding of the surrounding environment and proper channel modeling are indispensable. The existing method, which solely exploits radio frequency (RF) communication information, is difficult to accomplish accurate channel modeling. Fortunately, multi-modal devices are deployed on intelligent agents to obtain environmental features, which could further assist in channel modeling. Currently, some research efforts have been devoted to utilizing multi-modal information to facilitate channel modeling, while still lack a comprehensive review. To fill this gap, we embark on an initial endeavor with the goal of reviewing multi-modal intelligent channel modeling (MMICM) via Synesthesia of Machines (SoM). Compared to channel modeling approaches that solely utilize RF communication information, the utilization of multi-modal information can provide a more in-depth understanding of the propagation environment around the transceiver, thus facilitating more accurate channel modeling. First, this paper introduces existing channel modeling approaches from the perspective of the channel modeling evolution. Then, we have elaborated and investigated recent advances in the topic of capturing typical channel characteristics and features, i.e., channel non-stationarity and consistency, by characterizing the mathematical, spatial, coupling, and mapping relationships. In addition, applications that can be supported by MMICM are summarized and analyzed. To corroborate the superiority of MMICM via SoM, we give the simulation result and analysis. Finally, some open issues and potential directions for the MMICM are outlined from the perspectives of measurements, modeling, and applications.
Analog Beamforming Codebooks for Wideband Full-Duplex Millimeter-Wave Systems
Authors: Sungho Cho, Ian P. Roberts
Date: 2024-11-06 00:00:00

In full-duplex millimeter-wave (mmWave) systems, the effects of beam squint and the frequency-selectivity of self-interference exacerbate over wide bandwidths. This complicates the use of beamforming to cancel self-interference when communicating over bandwidths on the order of gigahertz. In this work, we present the first analog beamforming codebooks tailored to wideband full-duplex mmWave systems, designed to both combat beam squint and cancel frequency-selective self-interference. Our proposed design constructs such codebooks by minimizing self-interference across the entire band of interest while constraining the coverage provided by these codebooks across that same band. Simulation results using computational electromagnetics to model self-interference suggest that a full-duplex 60 GHz system with our design enjoys lower self-interference and delivers better coverage across bandwidths as wide as 6 GHz, when compared to similar codebook designs that ignore beam squint and/or frequency-selectivity. This allows our design to sustain higher SINRs and spectral efficiencies across wide bandwidths, unlocking the potentials of wideband full-duplex mmWave systems.
Domain Generalization for Cross-Receiver Radio Frequency Fingerprint Identification
Authors: Ying Zhang, Qiang Li, Hongli Liu, Liu Yang, Jian Yang
Date: 2024-11-06 00:00:00
Tasks: Domain Generalization, Federated Learning

Radio Frequency Fingerprint Identification (RFFI) technology uniquely identifies emitters by analyzing unique distortions in the transmitted signal caused by non-ideal hardware. Recently, RFFI based on deep learning methods has gained popularity and is seen as a promising way to address the device authentication problem for Internet of Things (IoT) systems. However, in cross-receiver scenarios, where the RFFI model is trained over RF signals from some receivers but deployed at a new receiver, the alteration of receivers' characteristics would lead to data distribution shift and cause significant performance degradation at the new receiver. To address this problem, we first perform a theoretical analysis of the cross-receiver generalization error bound and propose a sufficient condition, named Separable Condition (SC), to minimize the classification error probability on the new receiver. Guided by the SC, a Receiver-Independent Emitter Identification (RIEI)model is devised to decouple the received signals into emitter-related features and receiver-related features and only the emitter-related features are used for identification. Furthermore, by leveraging federated learning, we also develop a FedRIEI model to eliminate the need for centralized collection of raw data from multiple receivers. Experiments on two real-world datasets demonstrate the superiority of our proposed methods over some baseline methods.
Multi-bit Distributed Detection of Sparse Stochastic Signals over Error-Prone Reporting Channels
Authors: Linlin Mao, Shefeng Yan, Zeping Sui, Hongbin Li
Date: 2024-11-06 00:00:00
Tasks: Quantization

We consider a distributed detection problem within a wireless sensor network (WSN), where a substantial number of sensors cooperate to detect the existence of sparse stochastic signals. To achieve a trade-off between detection performance and system constraints, multi-bit quantizers are employed at local sensors. Then, two quantization strategies, namely raw quantization (RQ) and likelihood ratio quantization (LQ), are examined. The multi-bit quantized signals undergo encoding into binary codewords and are subsequently transmitted to the fusion center via error-prone reporting channels. Upon exploiting the locally most powerful test (LMPT) strategy, we devise two multi-bit LMPT detectors in which quantized raw observations and local likelihood ratios are fused respectively. Moreover, the asymptotic detection performance of the proposed quantized detectors is analyzed, and closed-form expressions for the detection and false alarm probabilities are derived. Furthermore, the multi-bit quantizer design criterion, considering both RQ and LQ, is then proposed to achieve near-optimal asymptotic performance for our proposed detectors. The normalized Fisher information and asymptotic relative efficiency are derived, serving as tools to analyze and compensate for the loss of information introduced by the quantization. Simulation results validate the effectiveness of the proposed detectors, especially in scenarios with low signal-to-noise ratios and poor channel conditions.
Beam Tracking for Full-Duplex User Terminals in Low Earth Orbit Satellite Communication Systems
Authors: Chaeyeon Kim, Joohyun Son, Daesik Hong, Ian P. Roberts
Date: 2024-11-06 00:00:00

This paper introduces a novel beam tracking scheme for full-duplex ground user terminals aiming to transmit uplink and receive downlink from two low Earth orbit (LEO) satellites at the same time and same frequency. Our proposed technique leverages observed phenomena from a recent measurement campaign to strategically select transmit and receive beams which couple low self-interference across the satellites' trajectories, thereby enabling in-band full-duplex operation. Our scheme takes a measurement-driven approach, meaning it does not rely on explicit knowledge of the self-interference channel and can inherently account for hardware impairments or other nonidealities. We show that our proposed scheme reliably selects beams which spatially cancel self-interference to below the noise floor, circumventing the need for digital/analog cancellation. Simulation results using satellite and orbital parameters published in 3GPP and FCC filings show that this substantial reduction in self-interference does not prohibitively compromise beamforming gain, allowing the user terminal to attain near-maximal SINRs, thus unlocking full-duplex operation.
Upper Mid-Band Channel Measurements and Characterization at 6.75 GHz FR1(C) and 16.95 GHz FR3 in an Indoor Factory Scenario
Authors: Mingjun Ying, Dipankar Shakya, Theodore S. Rappaport, Peijie Ma, Yanbo Wang, Idris Al-Wazani, Yanze Wu, Hitesh Poddar
Date: 2024-11-06 00:00:00

This paper presents detailed radio propagation measurements for an indoor factory (InF) environment at 6.75 GHz and 16.95 GHz using a 1 GHz bandwidth channel sounder. Conducted at the NYU MakerSpace in the NYU Tandon School of Engineering campus in Brooklyn, NY, USA, our measurement campaign characterizes the radio propagation in a representative small factory with diverse machinery and open workspaces across 12 locations, comprising 5 line-of-sight (LOS) and 7 non-line-of-sight (NLOS) scenarios. Analysis using the close-in (CI) free space path loss (FSPL) model with a 1 m reference distance reveals path loss exponents (PLE) below 2 in LOS at 6.75 GHz and 16.95 GHz, while in NLOS, PLE is similar to free-space propagation (e.g., PLE = 2). The RMS delay spread (DS) decreases at higher frequencies with a clear frequency dependence. Also, measurements show a wider RMS angular spread (AS) in NLOS compared to LOS at both frequency bands, with a decreasing trend as frequency increases. These observations in a dense-scatterer factory environment demonstrate frequency-dependent behavior that differs from existing industry-standard 3GPP models. Our findings provide crucial insights into complex propagation mechanisms in factory environments, essential for designing robust air interface and industrial wireless networks at the upper mid-band FR3 spectrum.
Sum Rate Maximization in the Constant Envelope MIMO Downlink with the RZF Precoder
Authors: Ferhad Askerbeyli, Wen Xu, Josef A. Nossek
Date: 2024-11-05 00:00:00
Tasks: Quantization

Feeding power amplifiers (PAs) with constant envelope (CE) signals is an effective way to reduce the power consumption in massive multiple-input-multiple-output (MIMO) systems. The nonlinear distortion caused by CE signaling must be mitigated by means of signal processing to improve the achievable sum rates. To this purpose, many linear and nonlinear precoding techniques have been developed for the CE MIMO downlink. The vast majority of these CE precoding techniques do not include a power allocation scheme, which is indispensable to achieve adequate performances in the downlink with channel gain imbalances between users. In this paper, we present two algorithms to produce a power allocation scheme for regularized zero-forcing (RZF) precoding in CE MIMO downlink. Both techniques are based on transforming the CE quantized MIMO downlink to an approximately equivalent system of parallel single-input-single-output (SISO) channels. The first technique is proven to solve the sum rate maximization problem in the approximate system optimally, whereas the second technique obtains the local maximum with lower complexity. We also extend another state-of-the-art quantization aware sum rate maximization algorithm with linear precoding to the CE downlink. Numerical results illustrate significant gains for the performance of the RZF precoder when the CE quantization is taken into account in a power allocation. Another key numerical result is that the proposed RZF techniques achieve almost the identical performance so that the one with lower computational complexity is chosen as the main method. Results also show that the proposed RZF precoding schemes perform at least as good as the state-of-the-art method with an advantage that the main RZF method has significantly lower computational complexity than the state-of-the-art.
Multi-Scale Temporal Analysis for Failure Prediction in Energy Systems
Authors: Anh Le, Phat K. Huynh, Om P. Yadav, Chau Le, Harun Pirim, Trung Q. Le
Date: 2024-11-05 00:00:00
Tasks: Time Series

Many existing models struggle to predict nonlinear behavior during extreme weather conditions. This study proposes a multi-scale temporal analysis for failure prediction in energy systems using PMU data. The model integrates multi-scale analysis with machine learning to capture both short-term and long-term behavior. PMU data lacks labeled states despite logged failure records, making it difficult to distinguish between normal and disturbance conditions. We address this through: (1) Extracting domain features from PMU time series data; (2) Applying multi-scale windows (30s, 60s, 180s) for pattern detection; (3) Using Recursive Feature Elimination to identify key features; (4) Training multiple machine learning models. Key contributions: Identifying significant features across multi-scale windows; Demonstrating LightGBM's superior performance (0.896 precision); Showing multi-scale analysis outperforms single-window models (0.841). Our work focuses on weather-related failures, with plans to extend to equipment failure and lightning events.
LLM-based Framework for Bearing Fault Diagnosis
Authors: Laifa Tao, Haifei Liu, Guoao Ning, Wenyan Cao, Bohao Huang, Chen Lu
Date: 2024-11-05 00:00:00
Tasks: Fault Diagnosis, feature selection

Accurately diagnosing bearing faults is crucial for maintaining the efficient operation of rotating machinery. However, traditional diagnosis methods face challenges due to the diversification of application environments, including cross-condition adaptability, small-sample learning difficulties, and cross-dataset generalization. These challenges have hindered the effectiveness and limited the application of existing approaches. Large language models (LLMs) offer new possibilities for improving the generalization of diagnosis models. However, the integration of LLMs with traditional diagnosis techniques for optimal generalization remains underexplored. This paper proposed an LLM-based bearing fault diagnosis framework to tackle these challenges. First, a signal feature quantification method was put forward to address the issue of extracting semantic information from vibration data, which integrated time and frequency domain feature extraction based on a statistical analysis framework. This method textualized time-series data, aiming to efficiently learn cross-condition and small-sample common features through concise feature selection. Fine-tuning methods based on LoRA and QLoRA were employed to enhance the generalization capability of LLMs in analyzing vibration data features. In addition, the two innovations (textualizing vibration features and fine-tuning pre-trained models) were validated by single-dataset cross-condition and cross-dataset transfer experiment with complete and limited data. The results demonstrated the ability of the proposed framework to perform three types of generalization tasks simultaneously. Trained cross-dataset models got approximately a 10% improvement in accuracy, proving the adaptability of LLMs to input patterns. Ultimately, the results effectively enhance the generalization capability and fill the research gap in using LLMs for bearing fault diagnosis.
Robust Precoding for FDD MISO Systems via Minorization Maximization
Authors: Donia Ben Amor, Michael Joham, Wolfgang Utschick
Date: 2024-11-04 00:00:00

In this work, we propose an approach to robust precoder design based on a minorization maximization technique that optimizes a surrogate function of the achievable spectral efficiency. The presented method accounts for channel estimation errors during the optimization process and is, hence, robust in the case of imperfect channel state information (CSI). Additionally, the design method is adapted such that the need for a line search to satisfy the power constraint is eliminated, that significantly accelerates the precoder computation. Simulation results demonstrate that the proposed robust precoding method is competitive with weighted minimum mean square error (WMMSE) precoding, in particular, under imperfect CSI scenarios.
Bilinear Precoder Based Efficient Rate Splitting Method in FDD Systems
Authors: Sadaf Syed, Donia Ben Amor, Michael Joham, Wolfgang Utschick
Date: 2024-11-04 00:00:00
Tasks: Form

In this work, we propose a low-cost rate splitting (RS) technique for a multi-user multiple-input single-output (MISO) system operating in frequency division duplex (FDD) mode. The proposed iterative optimisation algorithm only depends on the second-order statistical channel knowledge and the pilot training matrix. Additionally, it offers a closed-form solution in each update step. This reduces the design complexity of the system drastically as we only need to optimise the precoding filters in every coherence interval of the covariance matrices, instead of doing that in every channel state information (CSI) coherence interval. Moreover, since the algorithm is based on closed-form solutions, there is no need for interior point solvers like CVX, which are typically required in most state-of-the-art techniques.
Multitarget Bistatic MIMO RADAR
Authors: Nadeem Dar, Athanassios Manikas
Date: 2024-11-04 00:00:00

This paper is concerned with the investigation of the bistatic MIMO radar for estimating various multitarget parameters of interest in the presence of clutter and noise. The parameters of interest include Direction of Departure (DOD), Direction of Arrival (DOA), range and velocity and a novel algorithm is proposed for estimating these target parameters based on the concepts of the "array manifold" and "manifold extenders". The performance of the proposed algorithm is evaluated using Monte Carlo simulation studies.