pwc-archive/papers-with-abstracts huggingface dataset:
Depth-Aided Color Image Inpainting in Quaternion Domain
Authors: Shunki Tatsumi, Ryo Hayakawa, Youji Iiguni
Date: 2025-03-21 00:00:00
Tasks: Image Inpainting, Matrix Completion

In this paper, we propose a depth-aided color image inpainting method in the quaternion domain, called depth-aided low-rank quaternion matrix completion (D-LRQMC). In conventional quaternion-based inpainting techniques, the color image is expressed as a quaternion matrix by using the three imaginary parts as the color channels, whereas the real part is set to zero and has no information. Our approach incorporates depth information as the real part of the quaternion representations, leveraging the correlation between color and depth to improve the result of inpainting. In the proposed method, we first restore the observed image with the conventional LRQMC and estimate the depth of the restored result. We then incorporate the estimated depth into the real part of the observed image and perform LRQMC again. Simulation results demonstrate that the proposed D-LRQMC can improve restoration accuracy and visual quality for various images compared to the conventional LRQMC. These results suggest the effectiveness of the depth information for color image processing in quaternion domain.
A Pathway to Near Tissue Computing through Processing-in-CTIA Pixels for Biomedical Applications
Authors: Zihan Yin, Subhradip Chakraborty, Ankur Singh, Chengwei Zhou, Gourav Datta, Akhilesh Jaiswal
Date: 2025-03-21 00:00:00
Tasks: Computational Efficiency

Near-tissue computing requires sensor-level processing of high-resolution images, essential for real-time biomedical diagnostics and surgical guidance. To address this need, we introduce a novel Capacitive Transimpedance Amplifier-based In-Pixel Computing (CTIA-IPC) architecture. Our design leverages CTIA pixels that are widely used for biomedical imaging owing to the inherent advantages of excellent linearity, low noise, and robust operation under low-light conditions. We augment CTIA pixels with IPC to enable precise deep learning computations including multi-channel, multi-bit convolution operations along with integrated batch normalization (BN) and Rectified Linear Unit (ReLU) functionalities in the peripheral ADC (Analog to Digital Converters). This design improves the linearity of Multiply and Accumulate (MAC) operations while enhancing computational efficiency. Leveraging 3D integration to embed pixel circuitry and weight storage, CTIA-IPC maintains pixel density comparable to standard CTIA designs. Moreover, our algorithm-circuit co-design approach enables efficient real-time diagnostics and AI-driven medical analysis. Evaluated on the EndoVis tissu dataset (1280x1024), CTIA-IPC achieves approximately 12x reduction in data bandwidth, yielding segmentation IoUs of 75.91% (parts), and 28.58% (instrument)-a minimal accuracy reduction (1.3%-2.5%) compared to baseline methods. Achieving 1.98 GOPS throughput and 3.39 GOPS/W efficiency, our CTIA-IPC architecture offers a promising computational framework tailored specifically for biomedical near-tissue computing.
Region Masking to Accelerate Video Processing on Neuromorphic Hardware
Authors: Sreetama Sarkar, Sumit Bam Shrestha, Yue Che, Leobardo Campos-Macias, Gourav Datta, Peter A. Beerel
Date: 2025-03-21 00:00:00
Tasks: object-detection, Object Detection, Video Object Detection

The rapidly growing demand for on-chip edge intelligence on resource-constrained devices has motivated approaches to reduce energy and latency of deep learning models. Spiking neural networks (SNNs) have gained particular interest due to their promise to reduce energy consumption using event-based processing. We assert that while sigma-delta encoding in SNNs can take advantage of the temporal redundancy across video frames, they still involve a significant amount of redundant computations due to processing insignificant events. In this paper, we propose a region masking strategy that identifies regions of interest at the input of the SNN, thereby eliminating computation and data movement for events arising from unimportant regions. Our approach demonstrates that masking regions at the input not only significantly reduces the overall spiking activity of the network, but also provides significant improvement in throughput and latency. We apply region masking during video object detection on Loihi 2, demonstrating that masking approximately 60% of input regions can reduce energy-delay product by 1.65x over a baseline sigma-delta network, with a degradation in mAP@0.5 by 1.09%.
Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising
Authors: Yinchi Zhou, Huidong Xie, Menghua Xia, Qiong Liu, Bo Zhou, Tianqi Chen, Jun Hou, Liang Guo, Xinyuan Zheng, Hanzhong Wang, Biao Li, Axel Rominger, Kuangyu Shi, Nicha C. Dvorneka, Chi Liu
Date: 2025-03-20 00:00:00
Tasks: Denoising, Federated Learning, SSIM

Low-count positron emission tomography (LCPET) imaging can reduce patients' exposure to radiation but often suffers from increased image noise and reduced lesion detectability, necessitating effective denoising techniques. Diffusion models have shown promise in LCPET denoising for recovering degraded image quality. However, training such models requires large and diverse datasets, which are challenging to obtain in the medical domain. To address data scarcity and privacy concerns, we combine diffusion models with federated learning -- a decentralized training approach where models are trained individually at different sites, and their parameters are aggregated on a central server over multiple iterations. The variation in scanner types and image noise levels within and across institutions poses additional challenges for federated learning in LCPET denoising. In this study, we propose a novel noise-embedded federated learning diffusion model (Fed-NDIF) to address these challenges, leveraging a multicenter dataset and varying count levels. Our approach incorporates liver normalized standard deviation (NSTD) noise embedding into a 2.5D diffusion model and utilizes the Federated Averaging (FedAvg) algorithm to aggregate locally trained models into a global model, which is subsequently fine-tuned on local datasets to optimize performance and obtain personalized models. Extensive validation on datasets from the University of Bern, Ruijin Hospital in Shanghai, and Yale-New Haven Hospital demonstrates the superior performance of our method in enhancing image quality and improving lesion quantification. The Fed-NDIF model shows significant improvements in PSNR, SSIM, and NMSE of the entire 3D volume, as well as enhanced lesion detectability and quantification, compared to local diffusion models and federated UNet-based models.
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions
Authors: Saddam Hussain Khan, Rashid Iqbal
Date: 2025-03-19 00:00:00
Tasks: Action Recognition, Computational Efficiency, Face Recognition, Federated Learning, Language Modeling, Language Modelling, Medical Diagnosis, object-detection, Object Detection, Pose Estimation, speech-recognition, Speech Recognition, text-classification, Text Classification, Transfer Learning

Deep Convolutional Neural Networks (CNNs) have significantly advanced deep learning, driving breakthroughs in computer vision, natural language processing, medical diagnosis, object detection, and speech recognition. Architectural innovations including 1D, 2D, and 3D convolutional models, dilated and grouped convolutions, depthwise separable convolutions, and attention mechanisms address domain-specific challenges and enhance feature representation and computational efficiency. Structural refinements such as spatial-channel exploitation, multi-path design, and feature-map enhancement contribute to robust hierarchical feature extraction and improved generalization, particularly through transfer learning. Efficient preprocessing strategies, including Fourier transforms, structured transforms, low-precision computation, and weight compression, optimize inference speed and facilitate deployment in resource-constrained environments. This survey presents a unified taxonomy that classifies CNN architectures based on spatial exploitation, multi-path structures, depth, width, dimensionality expansion, channel boosting, and attention mechanisms. It systematically reviews CNN applications in face recognition, pose estimation, action recognition, text classification, statistical language modeling, disease diagnosis, radiological analysis, cryptocurrency sentiment prediction, 1D data processing, video analysis, and speech recognition. In addition to consolidating architectural advancements, the review highlights emerging learning paradigms such as few-shot, zero-shot, weakly supervised, federated learning frameworks and future research directions include hybrid CNN-transformer models, vision-language integration, generative learning, etc. This review provides a comprehensive perspective on CNN's evolution from 2015 to 2025, outlining key innovations, challenges, and opportunities.
Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging
Authors: Hanae Elmekki, Saidul Islam, Ahmed Alagha, Hani Sami, Amanda Spilkin, Ehsan Zakeri, Antonela Mariel Zanuttini, Jamal Bentahar, Lyes Kadem, Wen-Fang Xie, Philippe Pibarot, Rabeb Mizouni, Hadi Otrok, Shakti Singh, Azzam Mourad
Date: 2025-03-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability, portability, and real-time capabilities. However, it faces several challenges that limit its applicability, such as operator dependency, variability in interpretation, and limited resolution, which are amplified by the low availability of trained experts. This calls for the need of autonomous systems that are capable of reducing the dependency on humans for increased efficiency and throughput. Reinforcement Learning (RL) comes as a rapidly advancing field under Artificial Intelligence (AI) that allows the development of autonomous and intelligent agents that are capable of executing complex tasks through rewarded interactions with their environments. Existing surveys on advancements in the US scanning domain predominantly focus on partially autonomous solutions leveraging AI for scanning guidance, organ identification, plane recognition, and diagnosis. However, none of these surveys explore the intersection between the stages of the US process and the recent advancements in RL solutions. To bridge this gap, this review proposes a comprehensive taxonomy that integrates the stages of the US process with the RL development pipeline. This taxonomy not only highlights recent RL advancements in the US domain but also identifies unresolved challenges crucial for achieving fully autonomous US systems. This work aims to offer a thorough review of current research efforts, highlighting the potential of RL in building autonomous US solutions while identifying limitations and opportunities for further advancements in this field.
Long-term channel analysis at 60 and 80 GHz for autonomous ground vehicles
Authors: Radek Zavorka, Tomas Mikulasek, Josef Vychodil, Jiri Blumenstein, Hussein Hammoud, Jaroslaw Wojtun, Aniruddha Chandra, Jan M. Kelner, Cezary Ziolkowski, Ales Prokes
Date: 2025-03-24 00:00:00

This paper presents a comprehensive measurement campaign aimed at evaluating indoor-to-indoor radio channels in dynamic scenarios, with a particular focus on applications such as autonomous ground vehicles (AGV). These scenarios are characterized by the height of the antennas, addressing the unique challenges of near-ground communication. Our study involves long-term measurements (20 minutes of continuous recording per measurement) of the channel impulse response (CIR) in the 60 GHz and 80 GHz frequency bands, each with a bandwidth of 2.048 GHz. We investigate the variations in channel characteristics, focusing on parameters such as root mean square (RMS) delay spread and the Rician factor.
Vehicle to vehicle path loss modeling at millimeter wave band for crossing cars
Authors: Anirban Ghosh, Aniruddha Chandra, Tomas Mikulasek, Ales Prokes, Jaroslaw Wojtun, Jan M. Kelner, Cezary Ziolkowski
Date: 2025-03-24 00:00:00

Fifth generation (5G) new radio is now offering sidelink capability, which allows direct vehicle-to-vehicle (V2V) communication. Millimeter wave (mmWave) enables low-latency mission-critical V2V communications, such as forward crash warning, between two vehicles crossing on a road without dividers. In this article, we present a measurement-based path loss (PL) model for V2V links operating at 59.6 GHz mmWave when two vehicles approach from opposite sides and cross each other. Our model outperforms other existing PL models and can reliably model both approaching and departing vehicle scenarios.
Channel modeling for 60 GHz fixed mmWave O2I and O2O uplink with angular misalignment
Authors: Nitisha Singh, Sahaj K. Jha, Aniruddha Chandra, Radek Zavorka, Petr Horky, Tomas Mikulasek, Jiri Blumenstein, Ales Prokes, Jaroslaw Wojtun, Jan M. Kelner, Cezary Ziolkowski
Date: 2025-03-24 00:00:00

In this letter, we examine the effect of misalignment angle on cluster-based power delay profile (PDP) modeling for a 60 GHz millimeter-wave uplink. The analysis uses real-world data, where fixed uplink scenarios are realized by placing the transmitter at ground level and the receiver at the building level. Both outdoor-to-indoor (O2I) and outdoor-to-outdoor (O2O) scenarios are studied. Using the misalignment angle and the scenario as inputs, we propose a statistical PDP simulation algorithm based on the Saleh-Valenzuela model. Different goodness-of-fit metrics reveal that our proposed algorithm is robust to both O2I and O2O scenarios and can approximate the PDPs fairly well, even in case of misalignment.
On the Optimality of Single-label and Multi-label Neural Network Decoders
Authors: Yunus Can Gültekin, Péter Scheepers, Yuncheng Yuan, Federico Corradi, Alex Alvarado
Date: 2025-03-24 00:00:00

We investigate the design of two neural network (NN) architectures recently proposed as decoders for forward error correction: the so-called single-label NN (SLNN) and multi-label NN (MLNN) decoders. These decoders have been reported to achieve near-optimal codeword- and bit-wise performance, respectively. Results in the literature show near-optimality for a variety of short codes. In this paper, we analytically prove that certain SLNN and MLNN architectures can, in fact, always realize optimal decoding, regardless of the code. These optimal architectures and their binary weights are shown to be defined by the codebook, i.e., no training or network optimization is required. Our proposed architectures are in fact not NNs, but a different way of implementing the maximum likelihood decoding rule. Optimal performance is numerically demonstrated for Hamming $(7,4)$, Polar $(16,8)$, and BCH $(31,21)$ codes. The results show that our optimal architectures are less complex than the SLNN and MLNN architectures proposed in the literature, which in fact only achieve near-optimal performance. Extension to longer codes is still hindered by the curse of dimensionality. Therefore, even though SLNN and MLNN can perform maximum likelihood decoding, such architectures cannot be used for medium and long codes.
Clutter Tracking using Variational Message Passing
Authors: Anders Malthe Westerkam, Troels Pedersen
Date: 2025-03-24 00:00:00

We propose a message passing algorithm for tracking of clutter signals in MIMO radar. The method exploits basis expansion to linearise the signal model, to enable mean field approach for tracking the posterior distribution of the clutter as it evolves across time, as well as the mean and precision of the clutter map. The method shows good estimation accuracy in simulations for a scenario that adhere to the statistical model used for derivation as well as one that does not. The complexity of the method is linear in both the amount of parameters chosen and the amount of data under consideration.
Maximum Likelihood Estimation Based Complex-Valued Robust Chinese Remainder Theorem and Its Fast Algorithm
Authors: Xiaoping Li, Shiyang Sun, Qunying Liao, Xiang-Gen Xia
Date: 2025-03-24 00:00:00

Recently, a multi-channel self-reset analog-to-digital converter (ADC) system with complex-valued moduli has been proposed. This system enables the recovery of high dynamic range complex-valued bandlimited signals at low sampling rates via the Chinese remainder theorem (CRT). In this paper, we investigate complex-valued CRT (C-CRT) with erroneous remainders, where the errors follow wrapped complex Gaussian distributions. Based on the existing real-valued CRT utilizing maximum likelihood estimation (MLE), we propose a fast MLE-based C-CRT (MLE C-CRT). The proposed algorithm requires only $2L$ searches to obtain the optimal estimate of the common remainder, where $L$ is the number of moduli. Once the common remainder is estimated, the complex number can be determined using the C-CRT. Furthermore, we obtain a necessary and sufficient condition for the fast MLE C-CRT to achieve robust estimation. Finally, we apply the proposed algorithm to ADCs. The results demonstrate that the proposed algorithm outperforms the existing methods.
Movable Antenna Enabled ISAC: Tackling Slow Antenna Movement, Dynamic RCS, and Imperfect CSI via Two-timescale Optimizati
Authors: Ata Khalili, Robert Schober
Date: 2025-03-24 00:00:00
Tasks: Integrated sensing and communication, ISAC

We investigate resource allocation for a movable antenna (MA) enabled integrated sensing and communication (ISAC) system scanning a sector for sensing and simultaneously serving multiple communication users using multiple variable-length snapshots. To tackle the critical challenges of slow antenna movement speed, dynamic radar cross section (RCS) variation, imperfect channel state information (CSI), and finite precision antenna positioning encountered in practice, we propose a novel two-timescale (TTS) optimization framework. In particular, we jointly optimize the discrete MA positions, the communication and sensing beamforming vectors, and the snapshot durations for minimization of the average transmit power at the base station (BS) while guaranteeing a minimum sensing and communication quality of service (QoS) and accounting for imperfect CSI. To overcome the slow antenna movement speed, the MA positions are adjusted only once per scanning period whereas the beamforming vectors and snapshot durations are adapted in every snapshot. Furthermore, to manage the impact of varying RCSs, a novel chance constraint for the sensing QoS is introduced. To solve the resulting challenging highly non-convex mixed integer non-linear program (MINLP), an efficient iterative algorithm exploiting alternative optimization (AO) is developed and shown to yield a high-quality suboptimal solution. Our simulation results reveal that the proposed MA enabled ISAC system cannot only significantly reduce the BS transmit power compared to systems relying on fixed-position antennas and antenna selection but also exhibits a remarkable robustness to RCS fluctuations and imperfect CSI. Furthermore, the proposed TTS framework achieves a similar performance as a system adjusting the MA positions in every snapshot, while the TTS approach significantly reduces the time used for MA adjustment.
Novel Constructions of 2-D Golay Complementary Array Sets of Flexible Sizes
Authors: Zhaoyu Zhang, Xiaoyu Chen, Yuqiong Du, Luyi Zheng
Date: 2025-03-24 00:00:00

One-dimensional (1-D) Golay complementary sets(GCSs) possess numerous well-known properties and have achieved extensive use in communication engineering. The concept of 1-D GCSs can be extended to two-dimensional (2-D) Golay complementary array sets(GCASs). The letter proposes two constructions of 2-D GCASs based on two-dimensional extended generalized Boolean functions, where the set sizes and matrix parameters are flexible. The set size of GCASs with an array size of b^m b^n in the letter can reach N^m+n+1, which has not been cited in the existing literature. Both structures are assembled directly and are not restricted by the parameters of other existing sequences, thus expanding the theoretical and applicative boundaries of Golay complementary array sets.
ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions
Authors: Yunhao Quan, Chuang Gao, Nan Cheng, Zhijie Zhang, Zhisheng Yin, Wenchao Xu, Danyang Wang
Date: 2025-03-24 00:00:00
Tasks: Few-Shot Learning

In Automatic Modulation Classification (AMC), deep learning methods have shown remarkable performance, offering significant advantages over traditional approaches and demonstrating their vast potential. Nevertheless, notable drawbacks, particularly in their high demands for storage, computational resources, and large-scale labeled data, which limit their practical application in real-world scenarios. To tackle this issue, this paper innovatively proposes an automatic modulation classification model based on the Adaptive Lightweight Wavelet Neural Network (ALWNN) and the few-shot framework (MALWNN). The ALWNN model, by integrating the adaptive wavelet neural network and depth separable convolution, reduces the number of model parameters and computational complexity. The MALWNN framework, using ALWNN as an encoder and incorporating prototype network technology, decreases the model's dependence on the quantity of samples. Simulation results indicate that this model performs remarkably well on mainstream datasets. Moreover, in terms of Floating Point Operations Per Second (FLOPS) and Normalized Multiply - Accumulate Complexity (NMACC), ALWNN significantly reduces computational complexity compared to existing methods. This is further validated by real-world system tests on USRP and Raspberry Pi platforms. Experiments with MALWNN show its superior performance in few-shot learning scenarios compared to other algorithms.
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems
Authors: Zhidi Lin, Ying Li, Feng Yin, Juan Maroñas, Alexandre H. Thiéry
Date: 2025-03-24 00:00:00
Tasks: Computational Efficiency, Gaussian Processes, State Estimation, State Space Models, Time Series Forecasting, Uncertainty Quantification, Variational Inference

Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. However, existing GPSSMs are limited by the use of multiple independent stationary Gaussian processes (GPs), leading to prohibitive computational and parametric complexity in high-dimensional settings and restricted modeling capacity for non-stationary dynamics. To address these challenges, we propose an efficient transformed Gaussian process state-space model (ETGPSSM) for scalable and flexible modeling of high-dimensional, non-stationary dynamical systems. Specifically, our ETGPSSM integrates a single shared GP with input-dependent normalizing flows, yielding an expressive implicit process prior that captures complex, non-stationary transition dynamics while significantly reducing model complexity. For the inference of the implicit process, we develop a variational inference algorithm that jointly approximates the posterior over the underlying GP and the neural network parameters defining the normalizing flows. To avoid explicit variational parameterization of the latent states, we further incorporate the ensemble Kalman filter (EnKF) into the variational framework, enabling accurate and efficient state estimation. Extensive empirical evaluations on synthetic and real-world datasets demonstrate the superior performance of our ETGPSSM in system dynamics learning, high-dimensional state estimation, and time-series forecasting, outperforming existing GPSSMs and neural network-based SSMs in terms of computational efficiency and accuracy.