pwc-archive/papers-with-abstracts huggingface dataset:
Deterministic fast and stable phase retrieval in multiple dimensions
Authors: Cole Brabec, Sivan Trajtenberg-Mills, Luca Daniel, Dirk Englund
Date: 2024-07-01 00:00:00
Tasks: Retrieval

We present the first phase retrieval algorithm guaranteed to solve the multidimensional phase retrieval problem in polynomial arithmetic complexity without prior information. The method successfully terminates in O(N log(N)) operations for Fourier measurements with cardinality N. The algorithm is guaranteed to succeed for a large class of objects, which we term "Schwarz objects". We further present an easy-to-calculate and well-conditioned diagonal operator that transforms any feasible phase-retrieval instance into one that is solved by our method. We derive our method by combining techniques from classical complex analysis, algebraic topology, and modern numerical analysis. Concretely, we pose the phase retrieval problem as a multiplicative Cousin problem, construct an approximate solution using a modified integral used for the Schwarz problem, and refine the approximate solution to an exact solution via standard optimization methods. We present numerical experimentation demonstrating our algorithm's performance and its superiority to existing method. Finally, we demonstrate that our method is robust against Gaussian noise.
Pre-capture Privacy via Adaptive Single-Pixel Imaging
Authors: Yoko Sogabe, Shiori Sugimoto, Ayumi Matsumoto, Masaki Kitahara
Date: 2024-07-01 00:00:00

As cameras become ubiquitous in our living environment, invasion of privacy is becoming a growing concern. A common approach to privacy preservation is to remove personally identifiable information from a captured image, but there is a risk of the original image being leaked. In this paper, we propose a pre-capture privacy-aware imaging method that captures images from which the details of a pre-specified anonymized target have been eliminated. The proposed method applies a single-pixel imaging framework in which we introduce a feedback mechanism called an aperture pattern generator. The introduced aperture pattern generator adaptively outputs the next aperture pattern to avoid sampling the anonymized target by exploiting the data already acquired as a clue. Furthermore, the anonymized target can be set to any object without changing hardware. Except for detailed features which have been removed from the anonymized target, the captured images are of comparable quality to those captured by a general camera and can be used for various computer vision applications. In our work, we target faces and license plates and experimentally show that the proposed method can capture clear images in which detailed features of the anonymized target are eliminated to achieve both privacy and utility.
Infrared Computer Vision for Utility-Scale Photovoltaic Array Inspection
Authors: David F. Ramirez, Deep Pujara, Cihan Tepedelenlioglu, Devarajan Srinivasan, Andreas Spanias
Date: 2024-06-29 00:00:00
Tasks: Anomaly Detection, Fault Detection, Position

Utility-scale solar arrays require specialized inspection methods for detecting faulty panels. Photovoltaic (PV) panel faults caused by weather, ground leakage, circuit issues, temperature, environment, age, and other damage can take many forms but often symptomatically exhibit temperature differences. Included is a mini survey to review these common faults and PV array fault detection approaches. Among these, infrared thermography cameras are a powerful tool for improving solar panel inspection in the field. These can be combined with other technologies, including image processing and machine learning. This position paper examines several computer vision algorithms that automate thermal anomaly detection in infrared imagery. We demonstrate our infrared thermography data collection approach, the PV thermal imagery benchmark dataset, and the measured performance of image processing transformations, including the Hough Transform for PV segmentation. The results of this implementation are presented with a discussion of future work.
Forensic Camera Identification: Effects of Off-Nominal Exposures
Authors: Abby Martin, Roy Maxion, Jennifer Newman
Date: 2024-06-29 00:00:00

Photo response non-uniformity (PRNU) is a technology that can match a digital photograph to the camera that took it. Due to its use in forensic investigations and use by forensic experts in court, it is important that error rates for this technology are reliable for a wide range of evidence image types. In particular, images with off-nominal exposures are not uncommon. This paper presents a preliminary investigation of the impact that images with different exposure types - too dark or too light - have on error rates for PRNU source camera identification. We construct a new dataset comprised of 8400 carefully collected images ranging from under-exposed (too dark) to nominally exposed to over-exposed (too bright). We first establish baseline error rates using only nominally exposed images, resulting in a true-positive rate of 100% and a true-negative rate of 99.92%. When off-nominal images are tested, we find striking results: the true-negative rate for under-exposed images is 99.46% (a false-positive rate of roughly one in two hundred, typically unacceptable in a forensic context), and for over-exposed images the true-positive rate falls to 82.90%. Our results highlight the importance of continued study of error rates for the PRNU source camera identification to assure adherence to the high standards set for admissibility of forensic evidence in court.
Scalable, Trustworthy Generative Model for Virtual Multi-Staining from H&E Whole Slide Images
Authors: Mehdi Ounissi, Ilias Sarbout, Jean-Pierre Hugot, Christine Martinez-Vinson, Dominique Berrebi, Daniel Racoceanu
Date: 2024-06-26 00:00:00
Tasks: Diagnostic, Virtual Staining, whole slide images

Chemical staining methods are dependable but require extensive time, expensive chemicals, and raise environmental concerns. These challenges highlight the need for alternative solutions like virtual staining, which accelerates the diagnostic process and enhances stain application flexibility. Generative AI technologies are pivotal in addressing these issues. However, the high-stakes nature of healthcare decisions, especially in computational pathology, complicates the adoption of these tools due to their opaque processes. Our work introduces the use of generative AI for virtual staining, aiming to enhance performance, trustworthiness, scalability, and adaptability in computational pathology. The methodology centers on a singular H&E encoder supporting multiple stain decoders. This design focuses on critical regions in the latent space of H&E, enabling precise synthetic stain generation. Our method, tested to generate 8 different stains from a single H&E slide, offers scalability by loading only necessary model components during production. We integrate label-free knowledge in training, using loss functions and regularization to minimize artifacts, thus improving paired/unpaired virtual staining accuracy. To build trust, we use real-time self-inspection with discriminators for each stain type, providing pathologists with confidence heat-maps. Automatic quality checks on new H&E slides ensure conformity to the trained distribution, ensuring accurate synthetic stains. Recognizing pathologists' challenges with new technologies, we have developed an open-source, cloud-based system, that allows easy virtual staining of H&E slides through a browser, addressing hardware/software issues and facilitating real-time user feedback. We also curated a novel dataset of 8 paired H&E/stains related to pediatric Crohn's disease, comprising 480 WSIs to further stimulate computational pathology research.
Using Photoplethysmography to Detect Real-time Blood Pressure Changes with a Calibration-free Deep Learning Model
Authors: Jingyuan Hong, Manasi Nandi, Weiwei Jin, Jordi Alastruey
Date: 2024-07-03 00:00:00
Tasks: Photoplethysmography (PPG), Time Series Classification

Blood pressure (BP) changes are linked to individual health status in both clinical and non-clinical settings. This study developed a deep learning model to classify systolic (SBP), diastolic (DBP), and mean (MBP) BP changes using photoplethysmography (PPG) waveforms. Data from the Vital Signs Database (VitalDB) comprising 1,005 ICU patients with synchronized PPG and BP recordings was used. BP changes were categorized into three labels: Spike (increase above a threshold), Stable (change within a plus or minus threshold), and Dip (decrease below a threshold). Four time-series classification models were studied: multi-layer perceptron, convolutional neural network, residual network, and Encoder. A subset of 500 patients was randomly selected for training and validation, ensuring a uniform distribution across BP change labels. Two test datasets were compiled: Test-I (n=500) with a uniform distribution selection process, and Test-II (n=5) without. The study also explored the impact of including second-deviation PPG (sdPPG) waveforms as additional input information. The Encoder model with a Softmax weighting process using both PPG and sdPPG waveforms achieved the highest detection accuracy--exceeding 71.3% and 85.4% in Test-I and Test-II, respectively, with thresholds of 30 mmHg for SBP, 15 mmHg for DBP, and 20 mmHg for MBP. Corresponding F1-scores were over 71.8% and 88.5%. These findings confirm that PPG waveforms are effective for real-time monitoring of BP changes in ICU settings and suggest potential for broader applications.
Movable Antenna-enabled RIS-aided Integrated Sensing and Communication
Authors: Haisu Wu, Hong Ren, Cunhua Pan, Yang Zhang
Date: 2024-07-03 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we investigate a movable antenna (MA)-aided integrated sensing and communication (ISAC) system, where a reconfigurable intelligent surface (RIS) is employed to enhance wireless communication and sensing performance in dead zones. Specifically, this paper aims to maximize the minimum beampattern gain at the RIS by jointly optimizing beamforming matrix at the base station (BS), the reflecting coefficients at the RIS and the positions of the MAs, subject to signal-to-interference-plus-noise ratio (SINR) constraint for the users and maximum transmit power at the BS. To tackle this non-convex optimization problem, we propose an alternating optimization (AO) algorithm and employ semidefinite relaxation (SDR), sequential rank-one constraint relaxation (SRCR) and successive convex approximation (SCA) techniques. Numerical results indicate that the MA and RIS-aided ISAC system outperforms conventional fixed position antenna (FPA) and RIS-aided systems. In addition, the application of MAs can reduce the similarity of user channels and enhance channel gain in the ISAC system.
Electromagnetic Property Sensing Based on Diffusion Model in ISAC System
Authors: Yuhua Jiang, Feifei Gao, Shi Jin, Tie Jun Cui
Date: 2024-07-03 00:00:00
Tasks: ISAC

Integrated sensing and communications (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel ISAC scheme that utilizes the diffusion model to sense the electromagnetic (EM) property of the target in a predetermined sensing area. Specifically, we first estimate the sensing channel by using both the communications and the sensing signals echoed back from the target. Then we employ the diffusion model to generate the point cloud that represents the target and thus enables 3D visualization of the target's EM property distribution. In order to minimize the mean Chamfer distance (MCD) between the ground truth and the estimated point clouds, we further design the communications and sensing beamforming matrices under the constraint of a maximum transmit power and a minimum communications achievable rate for each user equipment (UE). Simulation results demonstrate the efficacy of the proposed method in achieving high-quality reconstruction of the target's shape, relative permittivity, and conductivity. Besides, the proposed method can sense the EM property of the target effectively in any position of the sensing area.
Semantic-Aware Power Allocation for Generative Semantic Communications with Foundation Models
Authors: Chunmei Xu, Mahdi Boloursaz Mashhadi, Yi Ma, Rahim Tafazolli
Date: 2024-07-03 00:00:00
Tasks: Semantic Communication

Recent advancements in diffusion models have made a significant breakthrough in generative modeling. The combination of the generative model and semantic communication (SemCom) enables high-fidelity semantic information exchange at ultra-low rates. A novel generative SemCom framework for image tasks is proposed, wherein pre-trained foundation models serve as semantic encoders and decoders for semantic feature extractions and image regenerations, respectively. The mathematical relationship between the transmission reliability and the perceptual quality of the regenerated image and the semantic values of semantic features are modeled, which are obtained by conducting numerical simulations on the Kodak dataset. We also investigate the semantic-aware power allocation problem, with the objective of minimizing the total power consumption while guaranteeing semantic performance. To solve this problem, two semanticaware power allocation methods are proposed by constraint decoupling and bisection search, respectively. Numerical results show that the proposed semantic-aware methods demonstrate superior performance compared to the conventional one in terms of total power consumption.
Achieving High Throughput with a Trainable Neural-Network-Based Equalizer for Communications on FPGA
Authors: Jonas Ney, Norbert Wehn
Date: 2024-07-03 00:00:00
Tasks: GPU

The ever-increasing data rates of modern communication systems lead to severe distortions of the communication signal, imposing great challenges to state-of-the-art signal processing algorithms. In this context, neural network (NN)-based equalizers are a promising concept since they can compensate for impairments introduced by the channel. However, due to the large computational complexity, efficient hardware implementation of NNs is challenging. Especially the backpropagation algorithm, required to adapt the NN's parameters to varying channel conditions, is highly complex, limiting the throughput on resource-constrained devices like field programmable gate arrays (FPGAs). In this work, we present an FPGA architecture of an NN-based equalizer that exploits batch-level parallelism of the convolutional layer to enable a custom mapping scheme of two multiplication to a single digital signal processor (DSP). Our implementation achieves a throughput of up to 20 GBd, which enables the equalization of high-data-rate nonlinear optical fiber channels while providing adaptation capabilities by retraining the NN using backpropagation. As a result, our FPGA implementation outperforms an embedded graphics processing unit (GPU) in terms of throughput by two orders of magnitude. Further, we achieve a higher energy efficiency and throughput as state-of-the-art NN training FPGA implementations. Thus, this work fills the gap of high-throughput NN-based equalization while enabling adaptability by NN training on the edge FPGA.
Timely Requesting for Time-Critical Content Users in Decentralized F-RANs
Authors: Xingran Chen, Kai Li, Kun Yang
Date: 2024-07-03 00:00:00

With the rising demand for high-rate and timely communications, fog radio access networks (F-RANs) offer a promising solution. This work investigates age of information (AoI) performance in F-RANs, consisting of multiple content users (CUs), enhanced remote radio heads (eRRHs), and content providers (CPs). Time-critical CUs need rapid content updates from CPs but cannot communicate directly with them; instead, eRRHs act as intermediaries. CUs decide whether to request content from a CP and which eRRH to send the request to, while eRRHs decide whether to command CPs to update content or use cached content. We study two general classes of policies: (i) oblivious policies, where decision-making is independent of historical information, and (ii) non-oblivious policies, where decisions are influenced by historical information. First, we obtain closed-form expressions for the average AoI of eRRHs under both policy types. Due to the complexity of calculating closed-form expressions for CUs, we then derive general upper bounds for their average AoI. Next, we identify optimal policies for both types. Under both optimal policies, each CU requests content from each CP at an equal rate, consolidating all requests to a single eRRH when demand is low or resources are limited, and distributing requests evenly among eRRHs when demand is high and resources are ample. eRRHs command content from each CP at an equal rate under an optimal oblivious policy, while prioritize the CP with the highest age under an optimal non-oblivious policy. Our numerical results validate these theoretical findings.
MmWave for Extended Reality: Open User Mobility Dataset, Characterisation, and Impact on Link Quality
Authors: Alexander Marinsek, Sam De Kunst, Gilles Callebaut, Lieven De Strycker, Liesbet Van der Perre
Date: 2024-07-02 00:00:00

User mobility in extended reality (XR) can have a major impact on millimeter-wave (mmWave) links and may require dedicated mitigation strategies to ensure reliable connections and avoid outage. The available prior art has predominantly focused on XR applications with constrained user mobility and limited impact on mmWave channels. We have performed dedicated experiments to extend the characterisation of relevant future XR use cases featuring a high degree of user mobility. To this end, we have carried out a tailor-made measurement campaign and conducted a characterisation of the collected tracking data, including the approximation of the data using statistical distributions. Moreover, we have provided an interpretation of the possible impact of the recorded mobility on mmWave technology. The dataset is made publicly accessible to provide a testing ground for wireless system design and to enable further XR mobility modelling.
Off-Grid Ultrasound Imaging by Stochastic Optimization
Authors: Vincent van de Schaft, Oisín Nolan, Ruud J. G. van Sloun
Date: 2024-07-02 00:00:00
Tasks: Stochastic Optimization

Ultrasound images formed by delay-and-sum beamforming are plagued by artifacts that only clear up after compounding many transmissions. Some prior works pose imaging as an inverse problem. This approach can yield high image quality with few transmits, but requires a very fine image grid and is not robust to changes in measurement model parameters. We present INverse grid-Free Estimation of Reflectivities (INFER), an off-grid and stochastic algorithm that solves the inverse scattering problem in ultrasound imaging. Our method jointly optimizes for the locations of the gridpoints, their reflectivities, and the measurement model parameters such as the speed of sound. This approach allows us to use significantly fewer gridpoints, while obtaining better contrast and resolution and being more robust to changes in the imaging target and the hardware. The use of stochastic optimization enables solving for multiple transmissions simultaneously without increasing the required memory or computational load per iteration. We show that our method works across different imaging targets and across different transmit schemes and compares favorably against other beamforming and inverse solvers. The source code and the dataset to reproduce the results in this paper are available at www.github.com/vincentvdschaft/off-grid-ultrasound.
White-Box 3D-OMP-Transformer for ISAC
Authors: BoWen Zhang, Geoffrey Ye Li
Date: 2024-07-02 00:00:00
Tasks: ISAC

Transformers have found broad applications for their great ability to capture long-range dependency among the inputs using attention mechanisms. The recent success of transformers increases the need for mathematical interpretation of their underlying working mechanisms, leading to the development of a family of white-box transformer-like deep network architectures. However, designing white-box transformers with efficient three-dimensional (3D) attention is still an open challenge. In this work, we revisit the 3D-orthogonal matching pursuit (OMP) algorithm and demonstrate that the operation of 3D-OMP is analogous to a specific kind of transformer with 3D attention. Therefore, we build a white-box 3D-OMP-transformer by introducing additional learnable parameters to 3D-OMP. As a transformer, its 3D-attention can be mathematically interpreted from 3D-OMP; while as a variant of OMP, it can learn to improve the matching pursuit process from data. Besides, a transformer's performance can be improved by stacking more transformer blocks. To simulate this process, we design a cascaded 3D-OMP-Transformer with dynamic small-scale dictionaries, which can improve the performance of the 3D-OMP-Transformer with low costs. We evaluate the designed 3D-OMP-transformer in the multi-target detection task of integrated sensing and communications (ISAC). Experimental results show that the designed 3D-OMP-Transformer can outperform current baselines.
Intelligent Reflecting Surface-Assisted NLOS Sensing With OFDM Signals
Authors: Jilin Wang, Jun Fang, Hongbin Li, Lei Huang
Date: 2024-07-02 00:00:00

This work addresses the problem of intelligent reflecting surface (IRS) assisted target sensing in a non-line-of-sight (NLOS) scenario, where an IRS is employed to facilitate the radar/access point (AP) to sense the targets when the line-of-sight (LOS) path between the AP and the target is blocked by obstacles. To sense the targets, the AP transmits a train of uniformly-spaced orthogonal frequency division multiplexing (OFDM) pulses, and then perceives the targets based on the echoes from the AP-IRS-targets-IRS-AP channel. To resolve an inherent scaling ambiguity associated with IRS-assisted NLOS sensing, we propose a two-phase sensing scheme by exploiting the diversity in the illumination pattern of the IRS across two different phases. Specifically, the received echo signals from the two phases are formulated as third-order tensors. Then a canonical polyadic (CP) decomposition-based method is developed to estimate each target's parameters including the direction of arrival (DOA), Doppler shift and time delay. Our analysis reveals that the proposed method achieves reliable NLOS sensing using a modest quantity of pulse/subcarrier resources. Simulation results are provided to show the effectiveness of the proposed method under the challenging scenario where the degrees-of-freedom provided by the AP-IRS channel are not enough for resolving the scaling ambiguity.
A Survey on Advancements in THz Technology for 6G: Systems, Circuits, Antennas, and Experiments
Authors: Sidharth Thomas, Jaskirat Singh Virdi, Aydin Babakhani, Ian P. Roberts
Date: 2024-07-02 00:00:00

Terahertz (THz) carrier frequencies (100 GHz to 10 THz) have been touted as a source for unprecedented wireless connectivity and high-precision sensing, courtesy of their wide bandwidth availability and small wavelengths, but noteworthy implementation challenges remain to make this a reality. In this paper, we survey recent advancements in THz technology and its role in future 6G wireless networks, with a particular emphasis on the 200-400 GHz frequency range and the IEEE 802.15.3d standard. We provide a comprehensive overview of THz systems, circuits, device technology, and antennas, while also highlighting recent experimental demonstrations of THz technology. Throughout the paper, we review the state-of-the-art and call attention to open problems, future prospects, and areas of further improvement to fully realize the potential of THz communication in next-generation wireless connectivity.