pwc-archive/papers-with-abstracts huggingface dataset:
Ptychography using Blind Multi-Mode PMACE
Authors: Qiuchen Zhai, Gregery T. Buzzard, Kevin Mertes, Brendt Wohlberg, Charles A. Bouman
Date: 2025-01-11 00:00:00

Ptychography is an imaging technique that enables nanometer-scale reconstruction of complex transmittance images by scanning objects with overlapping illumination patterns. However, the illumination function is typically unknown, which presents challenges for reconstruction, especially when using partially coherent light sources. In this paper, we introduce Blind Multi-Mode Projected Multi-Agent Consensus Equilibrium (BM-PMACE) for blind ptychographic reconstruction. We extend the PMACE framework for distributed inverse problems to jointly estimate the complex transmittance image and multiple, unknown, partially coherent probe functions. Importantly, our method maintains local probe estimates to exploit complementary information at multiple probe locations. Our method also incorporates a dynamic strategy for integrating additional probe modes. Through experimental simulations and validations using both synthetic and measured data, we demonstrate that BM-PMACE outperforms existing approaches in reconstruction quality and convergence rate.
ELFATT: Efficient Linear Fast Attention for Vision Transformers
Authors: Chong Wu, Maolin Che, Renjie Xu, Zhuoheng Ran, Hong Yan
Date: 2025-01-10 00:00:00

The attention mechanism is the key to the success of transformers in different machine learning tasks. However, the quadratic complexity with respect to the sequence length of the vanilla softmax-based attention mechanism becomes the major bottleneck for the application of long sequence tasks, such as vision tasks. Although various efficient linear attention mechanisms have been proposed, they need to sacrifice performance to achieve high efficiency. What's more, memory-efficient methods, such as FlashAttention-1-3, still have quadratic computation complexity which can be further improved. In this paper, we propose a novel efficient linear fast attention (ELFATT) mechanism to achieve low memory input/output operations, linear computational complexity, and high performance at the same time. ELFATT offers 4-7x speedups over the vanilla softmax-based attention mechanism in high-resolution vision tasks without losing performance. ELFATT is FlashAttention friendly. Using FlashAttention-2 acceleration, ELFATT still offers 2-3x speedups over the vanilla softmax-based attention mechanism on high-resolution vision tasks without losing performance. Even on edge GPUs, ELFATT still offers 1.6x to 2.0x speedups compared to state-of-the-art attention mechanisms in various power modes from 5W to 60W. Furthermore, ELFATT can be used to enhance and accelerate diffusion tasks directly without training.
Enhanced Acoustic Beamforming with Sub-Aperture Angular Multiply and Sum -- in vivo and in Human Demonstration
Authors: Matthieu Toulemonde, Cameron A. B. Smith, Kai Riemer, Priya Palanisamy, Jaideep Singh Rait, Laura Taylor, Peter D. Weinberg, Karina Cox, Meng-Xing Tang
Date: 2025-01-10 00:00:00

Power Doppler ultrasound is in widespread clinical use for non-invasive vascular imaging but the most common current method - Delay and Sum (DAS) beamforming - suffers from limited resolution and high side-lobes. Here we propose the Sub-Aperture Angular Multiply and Sum (SAMAS) algorithm; it combines the advantages of two recent non-linear beamformers, Frame Multiply and Sum (FMAS) which uses signal temporal coherence and the acoustic sub-aperture (ASAP) algorithm, which uses signal spatial coherence, respectively. Following in vitro experiments to optimise the algorithm, particularly the use of phase information and sub-aperture pairing, it was evaluated in vivo, first in a rabbit kidney and then in human lymph node, using ultrafast ultrasound images obtained with intravenous contrast agents. The SAMAS algorithm improved the CNR and SNR across all tests, on average raising the CNR by 11 dB and the SNR by 18 dB over DAS in vivo. This work demonstrates a promising vascular imaging method that could have widespread clinical utility.
BEN: Using Confidence-Guided Matting for Dichotomous Image Segmentation
Authors: Maxwell Meyer, Jack Spruyt
Date: 2025-01-08 00:00:00
Tasks: Dichotomous Image Segmentation, Image Matting, Image Segmentation, Segmentation, Semantic Segmentation

Current approaches to dichotomous image segmentation (DIS) treat image matting and object segmentation as fundamentally different tasks. As improvements in image segmentation become increasingly challenging to achieve, combining image matting and grayscale segmentation techniques offers promising new directions for architectural innovation. Inspired by the possibility of aligning these two model tasks, we propose a new architectural approach for DIS called Confidence-Guided Matting (CGM). We created the first CGM model called Background Erase Network (BEN). BEN is comprised of two components: BEN Base for initial segmentation and BEN Refiner for confidence refinement. Our approach achieves substantial improvements over current state-of-the-art methods on the DIS5K validation dataset, demonstrating that matting-based refinement can significantly enhance segmentation quality. This work opens new possibilities for cross-pollination between matting and segmentation techniques in computer vision.
Interpretable Auto Window Setting for Deep-Learning-Based CT Analysis
Authors: Yiqin Zhang, Meiling Chen, Zhengjie Zhang
Date: 2025-01-07 00:00:00
Tasks: Computed Tomography (CT), Deep Learning

Whether during the early days of popularization or in the present, the window setting in Computed Tomography (CT) has always been an indispensable part of the CT analysis process. Although research has investigated the capabilities of CT multi-window fusion in enhancing neural networks, there remains a paucity of domain-invariant, intuitively interpretable methodologies for Auto Window Setting. In this work, we propose an plug-and-play module originate from Tanh activation function, which is compatible with mainstream deep learning architectures. Starting from the physical principles of CT, we adhere to the principle of interpretability to ensure the module's reliability for medical implementations. The domain-invariant design facilitates observation of the preference decisions rendered by the adaptive mechanism from a clinically intuitive perspective. This enables the proposed method to be understood not only by experts in neural networks but also garners higher trust from clinicians. We confirm the effectiveness of the proposed method in multiple open-source datasets, yielding 10%~200% Dice improvements on hard segment targets.
NL-COMM: Enhanced Video Streaming via Advanced Non-Linear Processing
Authors: Marcin Filo, George N. Katsaros, Chathura Jayawardena, Konstantinos Nikitopoulos
Date: 2025-01-16 00:00:00

With video streaming now accounting for the majority of internet traffic, wireless networks face increasing demands, especially in densely populated areas where limited spectral resources are shared among many devices. While multi-user (MU)-MIMO technology aims to improve spectral efficiency by enabling concurrent transmissions over the same frequency and time resources, traditional linear processing methods fall short of fully utilizing available channel capacity. These methods require a substantial number of antennas and RF chains, to support a much smaller number of MIMO streams, leading to increased power consumption and operational costs, even when the supported streams are of low rate. In this demo, we present NL-COMM, an advanced non-linear MIMO processing framework, demonstrated for the first time with commercial off-the-shelf (COTS) user equipment (UEs) in a fully 3GPP-compliant environment. In addition, also for the first time, the audience will compare and assess the quality of live, over-the-air video transmission from four concurrently transmitting UE devices, alternating between current state-of-the-art MIMO detection algorithms and NL-COMM. Key gains of NL-COMM include improved stream quality, halving the number of required base station antennas without compromising stream quality compared to linear approaches, as well as achieving antenna overloading factors of 400\%.
Physical Layer Design for Ambient IoT
Authors: Rohit Singh, Anil Kumar Yerrapragada, Radha Krishna Ganti
Date: 2025-01-16 00:00:00

There is a growing demand for ultra low power and ultra low complexity devices for applications which require maintenance-free and battery-less operation. One way to serve such applications is through backscatter devices, which communicate using energy harvested from ambient sources such as radio waves transmitted by a reader. Traditional backscatter devices, such as RFID, are limited by range, interference, low connection density, and security issues. To address these problems, the Third Generation Partnership Project (3GPP) has started working on Ambient IoT (A-IoT). For the realization of A-IoT devices, various aspects ranging from physical layer design, to the protocol stack, to the device architecture should be standardized. In this paper, we provide an overview of the standardization efforts on the physical layer design for A-IoT devices. The various physical channels and signals are discussed, followed by link level simulations to compare the performance of various configurations of reader to device and device to reader channels.
Exploring the Advantages of Sparse Arrays in XL-MIMO Systems: Do Half-Wavelength Arrays Still Offer an Edge in the Near Field?
Authors: Xianzhe Chen, Hong Ren, Cunhua Pan, Cheng-Xiang Wang, Jiangzhou Wang
Date: 2025-01-16 00:00:00
Tasks: Form

Extremely large-scale multiple-input multiple-output (XL-MIMO) has attracted extensive research attention due to its potential to meet the increasingly demanding requirements of future communication systems. Meanwhile, recent researches have indicated that sparse arrays may offer promising advantages for XL-MIMO systems in the near-field. Motivated by these, this paper investigates a downlink near-field XL-MIMO communication system with sparse uniform planar arrays (UPAs). Based on the Green's function-based channel model, the paper focuses on the power distribution of the arrived signal near the focused point of the transmit sparse UPA. In the vicinity of the focused point, along the x-axis and z-axis directions, closed-form expressions for the power distributions are derived. Based on that, expressions for the width and length of the main lobe are obtained in closed form, both of which decrease as the antenna spacing increases. Furthermore, the paper introduces a crucial constraint on system parameters, under which effective degrees-of-freedom (EDoF) of XL-MIMO systems with sparse UPAs can be precisely estimated. Then, the paper proposes an algorithm to obtain a closed-form expression, which can estimate EDoF with high accuracy and low computational complexity. The numerical results verifies the correctness of the main results of this paper. Furthermore, the numerical results reveals the improvement in the performance of XL-MIMO systems with the use of sparse UPAs.
Hierarchical Deep Reinforcement Learning for Adaptive Resource Management in Integrated Terrestrial and Non-Terrestrial Networks
Authors: Muhammad Ahmed Mohsin, Hassan Rizwan, Muhammad Umer, Sagnik Bhattacharya, Ahsan Bilal, John M. Cioffi
Date: 2025-01-16 00:00:00
Tasks: Deep Reinforcement Learning, Management

Efficient spectrum allocation has become crucial as the surge in wireless-connected devices demands seamless support for more users and applications, a trend expected to grow with 6G. Innovations in satellite technologies such as SpaceX's Starlink have enabled non-terrestrial networks (NTNs) to work alongside terrestrial networks (TNs) and allocate spectrum based on regional demands. Existing spectrum sharing approaches in TNs use machine learning for interference minimization through power allocation and spectrum sensing, but the unique characteristics of NTNs like varying orbital dynamics and coverage patterns require more sophisticated coordination mechanisms. The proposed work uses a hierarchical deep reinforcement learning (HDRL) approach for efficient spectrum allocation across TN-NTN networks. DRL agents are present at each TN-NTN hierarchy that dynamically learn and allocate spectrum based on regional trends. This framework is 50x faster than the exhaustive search algorithm while achieving 95\% of optimum spectral efficiency. Moreover, it is 3.75x faster than multi-agent DRL, which is commonly used for spectrum sharing, and has a 12\% higher overall average throughput.
Spectral Eigenfunction Decomposition for Kernel Adaptive Filtering
Authors: Kan Li, Jose C. Principe
Date: 2025-01-15 00:00:00
Tasks: Time Series Prediction

Kernel adaptive filtering (KAF) integrates traditional linear algorithms with kernel methods to generate nonlinear solutions in the input space. The standard approach relies on the representer theorem and the kernel trick to perform pairwise evaluations of a kernel function in place of the inner product, which leads to scalability issues for large datasets due to its linear and superlinear growth with respect to the size of the training data. Explicit features have been proposed to tackle this problem, exploiting the properties of the Gaussian-type kernel functions. These approximation methods address the implicitness and infinite dimensional representation of conventional kernel methods. However, achieving an accurate finite approximation for the kernel evaluation requires a sufficiently large vector representation for the dot products. An increase in the input-space dimension leads to a combinatorial explosion in the dimensionality of the explicit space, i.e., it trades one dimensionality problem (implicit, infinite dimensional RKHS) for another (curse of dimensionality). This paper introduces a construction that simultaneously solves these two problems in a principled way, by providing an explicit Euclidean representation of the RKHS while reducing its dimensionality. We present SPEctral Eigenfunction Decomposition (SPEED) along with an efficient incremental approach for fast calculation of the dominant kernel eigenbasis, which enables us to track the kernel eigenspace dynamically for adaptive filtering. Simulation results on chaotic time series prediction demonstrate this novel construction outperforms existing explicit kernel features with greater efficiency.
A Multi-modal Intelligent Channel Model for 6G Multi-UAV-to-Multi-Vehicle Communications
Authors: Lu Bai, Mengyuan Lu, Ziwei Huang, Xiang Cheng
Date: 2025-01-15 00:00:00

In this paper, a novel multi-modal intelligent channel model for sixth-generation (6G) multiple-unmanned aerial vehicle (multi-UAV)-to-multi-vehicle communications is proposed. To thoroughly explore the mapping relationship between the physical environment and the electromagnetic space in the complex multi-UAV-to-multi-vehicle scenario, two new parameters, i.e., terrestrial traffic density (TTD) and aerial traffic density (ATD), are developed and a new sensing-communication intelligent integrated dataset is constructed in suburban scenario under different TTD and ATD conditions. With the aid of sensing data, i.e., light detection and ranging (LiDAR) point clouds, the parameters of static scatterers, terrestrial dynamic scatterers, and aerial dynamic scatterers in the electromagnetic space, e.g., number, distance, angle, and power, are quantified under different TTD and ATD conditions in the physical environment. In the proposed model, the channel non-stationarity and consistency on the time and space domains and the channel non-stationarity on the frequency domain are simultaneously mimicked. The channel statistical properties, such as time-space-frequency correlation function (TSF-CF), time stationary interval (TSI), and Doppler power spectral density (DPSD), are derived and simulated. Simulation results match ray-tracing (RT) results well, which verifies the accuracy of the proposed multi-UAV-to-multi-vehicle channel model.
The first calibration model for bluetooth angle of arrival: Enhancing positioning accuracy in indoor environments
Authors: Ma'mon Saeed Alghananim, Yuxiang Feng, Washington Yotto Ochieng
Date: 2025-01-15 00:00:00
Tasks: Position

Internet of Things (IoT) applications are increasingly reliant on indoor positioning systems to deliver precise and reliable navigation in GNSS-denied environments, including urban areas, smart warehouses, hospitals, and underground or multi-level parking systems. Bluetooth Angle of Arrival (AoA) positioning offers cost-effective solutions with the potential to provide users with sub-meter position accuracy, which is crucial for applications such as underground navigation, firefighters, and robotic navigation. Bluetooth AoA positioning uses angles to determine the position of Bluetooth tags; these angles, measured in the anchor coordinate system, need to be transferred to the user's coordinate system. This requires models or techniques to compute 3D rotation matrices between the anchor and user coordinate system. Until now, no model or technique has been developed to compute these rotation matrices. Therefore, the development of the AoA positioning model focuses on simulated scenarios. This paper introduces the first model, named the AoA calibration model, capable of estimating these rotation matrices, thereby facilitating the practical application of this technology. In addition, this paper tests the Bluetooth AoA calibration and positioning model on a real dataset and presents end-toend functional architectures for AoA positioning. The results demonstrate that the proposed calibration model can estimate the 3D transformation rotation angles with a standard deviation better than 2.5 degrees. The findings also reveal that AoA positioning can achieve sub-meter accuracy in both static and kinematic modes, with accuracy significantly influenced by the distance to the anchors and the geometry factor.
Near-Field ISAC: Synergy of Dual-Purpose Codebooks and Space-Time Adaptive Processing
Authors: Ahmed Hussain, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil
Date: 2025-01-15 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has emerged as a transformative paradigm, enabling situationally aware and perceptive next-generation wireless networks through the co-design of shared network resources. With the adoption of millimeter-wave (mmWave) and terahertz (THz) frequency bands, ultra-massive MIMO (UM-MIMO) systems and holographic surfaces unlock the potential of near-field (NF) propagation, characterized by spherical wavefronts that facilitate beam manipulation in both angular and range domains. This paper presents a unified approach to near-field beam-training and sensing, introducing a dual-purpose codebook design that employs discrete Fourier transform (DFT)-based codebooks for coarse estimation of sensing parameters and polar codebooks for parameter refinement. Leveraging these range and angle estimates, a customized low-complexity space-time adaptive processing (STAP) technique is proposed for NF-ISAC to detect slow-moving targets and efficiently mitigate clutter. The interplay between codebooks and NF-STAP framework offers three key advantages: reduced communication beam training overhead, improved estimation accuracy, and minimal STAP computational complexity. Simulation results show that the proposed framework can reduce STAP complexity by three orders of magnitude, validating efficacy, and highlighting the potential of the proposed approach to seamlessly integrate NF communication and sensing functionalities in future wireless networks.
A Systematic Method for Optimum Biomedical Wireless Power Transfer using Inductive Links in Area-Constrained Implants
Authors: Asif Iftekhar Omi, Anyu Jiang, Baibhab Chatterjee
Date: 2025-01-15 00:00:00

In the context of implantable bioelectronics, this work provides new insights into maximizing biomedical wireless power transfer (BWPT) via the systematic development of inductive links. This approach addresses the specific challenges of power transfer efficiency (PTE) optimization within the area constraints of bio-implants embedded in tissue. Key contributions include the derivation of an optimal self-inductance with S-parameter-based analyses leading to the co-design of planar spiral coils and L-section impedance matching networks. To validate the proposed design methodology, two coil prototypes -- one symmetric (type-1) and one asymmetric (type-2) -- were fabricated and tested for PTE in pork tissue. Targeting a 20 MHz design frequency, the type-1 coil demonstrated a state-of-the-art PTE of $\sim$ 4\% (channel length = 15 mm) with a return loss (RL) $>$ 20 dB on both the input and output sides, within an area constraint of $<$ 18 $ \times $ 18 mm$^{2}$. In contrast, the type-2 coil achieved a PTE of $\sim$ 2\% with an RL $>$ 15 dB, for a smaller receiving coil area of $<$ 5x5 mm$^{2}$ for the same tissue environment. To complement the coils, we demonstrate a 65 nm test chip with an integrated energy harvester, which includes \asif{a} 30-stage rectifier and low-dropout regulator (LDO), producing a stable $\sim$ 1V DC output within tissue medium, matching theoretical predictions and simulations. Furthermore, we provide a robust and comprehensive guideline for advancing efficient inductive links for various BWPT applications, with shared resources in GitHub available for utilization by the broader community.
Intelligent Reflecting Surfaces for Wireless Networks: Deployment Architectures, Key Solutions, and Field Trials
Authors: Qingqing Wu, Guangji Chen, Qiaoyan Peng, Wen Chen, Yifei Yuan, Zhenqiao Cheng, Jianwu Dou, Zhiyong Zhao, Ping Li
Date: 2025-01-15 00:00:00

Intelligent reflecting surfaces (IRSs) have emerged as a transformative technology for wireless networks by improving coverage, capacity, and energy efficiency through intelligent manipulation of wireless propagation environments. This paper provides a comprehensive study on the deployment and coordination of IRSs for wireless networks. By addressing both single- and multi-reflection IRS architectures, we examine their deployment strategies across diverse scenarios, including point-to-point, point-to-multipoint, and point-to-area setups. For the single-reflection case, we highlight the trade-offs between passive and active IRS architectures in terms of beamforming gain, coverage extension, and spatial multiplexing. For the multi-reflection case, we discuss practical strategies to optimize IRS deployment and element allocation, balancing cooperative beamforming gains and path loss. The paper further discusses practical challenges in IRS implementation, including environmental conditions, system compatibility, and hardware limitations. Numerical results and field tests validate the effectiveness of IRS-aided wireless networks and demonstrate their capacity and coverage improvements. Lastly, promising research directions, including movable IRSs, near-field deployments, and network-level optimization, are outlined to guide future investigations.