pwc-archive/papers-with-abstracts huggingface dataset:
Thermoxels: a voxel-based method to generate simulation-ready 3D thermal models
Authors: Etienne Chassaing, Florent Forest, Olga Fink, Malcolm Mielle
Date: 2025-04-06 00:00:00
Tasks: 3D Reconstruction, Image Generation, NeRF

In the European Union, buildings account for 42% of energy use and 35% of greenhouse gas emissions. Since most existing buildings will still be in use by 2050, retrofitting is crucial for emissions reduction. However, current building assessment methods rely mainly on qualitative thermal imaging, which limits data-driven decisions for energy savings. On the other hand, quantitative assessments using finite element analysis (FEA) offer precise insights but require manual CAD design, which is tedious and error-prone. Recent advances in 3D reconstruction, such as Neural Radiance Fields (NeRF) and Gaussian Splatting, enable precise 3D modeling from sparse images but lack clearly defined volumes and the interfaces between them needed for FEA. We propose Thermoxels, a novel voxel-based method able to generate FEA-compatible models, including both geometry and temperature, from a sparse set of RGB and thermal images. Using pairs of RGB and thermal images as input, Thermoxels represents a scene's geometry as a set of voxels comprising color and temperature information. After optimization, a simple process is used to transform Thermoxels' models into tetrahedral meshes compatible with FEA. We demonstrate Thermoxels' capability to generate RGB+Thermal meshes of 3D scenes, surpassing other state-of-the-art methods. To showcase the practical applications of Thermoxels' models, we conduct a simple heat conduction simulation using FEA, achieving convergence from an initial state defined by Thermoxels' thermal reconstruction. Additionally, we compare Thermoxels' image synthesis abilities with current state-of-the-art methods, showing competitive results, and discuss the limitations of existing metrics in assessing mesh quality.
ADA-Net: Attention-Guided Domain Adaptation Network with Contrastive Learning for Standing Dead Tree Segmentation Using Aerial Imagery
Authors: Mete Ahishali, Anis Ur Rahman, Einari Heinaro, Samuli Junttila
Date: 2025-04-05 00:00:00
Tasks: Contrastive Learning, Domain Adaptation, Image-to-Image Translation, Transfer Learning

Information on standing dead trees is important for understanding forest ecosystem functioning and resilience but has been lacking over large geographic regions. Climate change has caused large-scale tree mortality events that can remain undetected due to limited data. In this study, we propose a novel method for segmenting standing dead trees using aerial multispectral orthoimages. Because access to annotated datasets has been a significant problem in forest remote sensing due to the need for forest expertise, we introduce a method for domain transfer by leveraging domain adaptation to learn a transformation from a source domain X to target domain Y. In this Image-to-Image translation task, we aim to utilize available annotations in the target domain by pre-training a segmentation network. When images from a new study site without annotations are introduced (source domain X), these images are transformed into the target domain. Then, transfer learning is applied by inferring the pre-trained network on domain-adapted images. In addition to investigating the feasibility of current domain adaptation approaches for this objective, we propose a novel approach called the Attention-guided Domain Adaptation Network (ADA-Net) with enhanced contrastive learning. Accordingly, the ADA-Net approach provides new state-of-the-art domain adaptation performance levels outperforming existing approaches. We have evaluated the proposed approach using two datasets from Finland and the US. The USA images are converted to the Finland domain, and we show that the synthetic USA2Finland dataset exhibits similar characteristics to the Finland domain images. The software implementation is shared at https://github.com/meteahishali/ADA-Net. The data is publicly available at https://www.kaggle.com/datasets/meteahishali/aerial-imagery-for-standing-dead-tree-segmentation.
Autoregressive High-Order Finite Difference Modulo Imaging: High-Dynamic Range for Computer Vision Applications
Authors: Brayan Monroy, Kebin Contreras, Jorge Bacca
Date: 2025-04-05 00:00:00
Tasks: Autonomous Driving, Image Reconstruction, object-detection, Object Detection, Quantization

High dynamic range (HDR) imaging is vital for capturing the full range of light tones in scenes, essential for computer vision tasks such as autonomous driving. Standard commercial imaging systems face limitations in capacity for well depth, and quantization precision, hindering their HDR capabilities. Modulo imaging, based on unlimited sampling (US) theory, addresses these limitations by using a modulo analog-to-digital approach that resets signals upon saturation, enabling estimation of pixel resets through neighboring pixel intensities. Despite the effectiveness of (US) algorithms in one-dimensional signals, their optimization problem for two-dimensional signals remains unclear. This work formulates the US framework as an autoregressive $\ell_2$ phase unwrapping problem, providing computationally efficient solutions in the discrete cosine domain jointly with a stride removal algorithm also based on spatial differences. By leveraging higher-order finite differences for two-dimensional images, our approach enhances HDR image reconstruction from modulo images, demonstrating its efficacy in improving object detection in autonomous driving scenes without retraining.
Overcoming the Identity Mapping Problem in Self-Supervised Hyperspectral Anomaly Detection
Authors: Yongchuan Cui, Jinhe Zhang, Peng Liu, Weijing Song, Yi Zeng
Date: 2025-04-05 00:00:00
Tasks: Anomaly Detection, Descriptive

The surge of deep learning has catalyzed considerable progress in self-supervised Hyperspectral Anomaly Detection (HAD). The core premise for self-supervised HAD is that anomalous pixels are inherently more challenging to reconstruct, resulting in larger errors compared to the background. However, owing to the powerful nonlinear fitting capabilities of neural networks, self-supervised models often suffer from the Identity Mapping Problem (IMP). The IMP manifests as a tendency for the model to overfit to the entire image, particularly with increasing network complexity or prolonged training iterations. Consequently, the whole image can be precisely reconstructed, and even the anomalous pixels exhibit imperceptible errors, making them difficult to detect. Despite the proposal of several models aimed at addressing the IMP-related issues, a unified descriptive framework and validation of solutions for IMP remain lacking. In this paper, we conduct an in-depth exploration to IMP, and summarize a unified framework that describes IMP from the perspective of network optimization, which encompasses three aspects: perturbation, reconstruction, and regularization. Correspondingly, we introduce three solutions: superpixel pooling and uppooling for perturbation, error-adaptive convolution for reconstruction, and online background pixel mining for regularization. With extensive experiments being conducted to validate the effectiveness, it is hoped that our work will provide valuable insights and inspire further research for self-supervised HAD. Code: \url{https://github.com/yc-cui/Super-AD}.
Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan
Authors: Mengyuan Liu, Yixiao Chen, Anning Tian, Xinmeng Wu, Mozhi Shen, Tianchou Gong, Jeongkyu Lee
Date: 2025-04-05 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Convolutional neural networks like U-Net excel in medical image segmentation, while attention mechanisms and KAN enhance feature extraction. Meta's SAM 2 uses Vision Transformers for prompt-based segmentation without fine-tuning. However, biases in these models impact generalization with limited data. In this study, we systematically evaluate and compare the performance of three CNN-based models, i.e., U-Net, Attention U-Net, and U-KAN, and one transformer-based model, i.e., SAM 2 for segmenting femur bone structures in MRI scan. The dataset comprises 11,164 MRI scans with detailed annotations of femoral regions. Performance is assessed using the Dice Similarity Coefficient, which ranges from 0.932 to 0.954. Attention U-Net achieves the highest overall scores, while U-KAN demonstrated superior performance in anatomical regions with a smaller region of interest, leveraging its enhanced learning capacity to improve segmentation accuracy.
Simultaneous Motion And Noise Estimation with Event Cameras
Authors: Shintaro Shiba, Yoshimitsu Aoki, Guillermo Gallego
Date: 2025-04-05 00:00:00
Tasks: Denoising, Motion Estimation, Noise Estimation, Optical Flow Estimation

Event cameras are emerging vision sensors, whose noise is challenging to characterize. Existing denoising methods for event cameras consider other tasks such as motion estimation separately (i.e., sequentially after denoising). However, motion is an intrinsic part of event data, since scene edges cannot be sensed without motion. This work proposes, to the best of our knowledge, the first method that simultaneously estimates motion in its various forms (e.g., ego-motion, optical flow) and noise. The method is flexible, as it allows replacing the 1-step motion estimation of the widely-used Contrast Maximization framework with any other motion estimator, such as deep neural networks. The experiments show that the proposed method achieves state-of-the-art results on the E-MLB denoising benchmark and competitive results on the DND21 benchmark, while showing its efficacy on motion estimation and intensity reconstruction tasks. We believe that the proposed approach contributes to strengthening the theory of event-data denoising, as well as impacting practical denoising use-cases, as we release the code upon acceptance. Project page: https://github.com/tub-rip/ESMD
A Hybrid Wavelet-Fourier Method for Next-Generation Conditional Diffusion Models
Authors: Andrew Kiruluta, Andreas Lemos
Date: 2025-04-04 00:00:00
Tasks: Conditional Image Generation, Image Generation, Texture Synthesis

We present a novel generative modeling framework,Wavelet-Fourier-Diffusion, which adapts the diffusion paradigm to hybrid frequency representations in order to synthesize high-quality, high-fidelity images with improved spatial localization. In contrast to conventional diffusion models that rely exclusively on additive noise in pixel space, our approach leverages a multi-transform that combines wavelet sub-band decomposition with partial Fourier steps. This strategy progressively degrades and then reconstructs images in a hybrid spectral domain during the forward and reverse diffusion processes. By supplementing traditional Fourier-based analysis with the spatial localization capabilities of wavelets, our model can capture both global structures and fine-grained features more effectively. We further extend the approach to conditional image generation by integrating embeddings or conditional features via cross-attention. Experimental evaluations on CIFAR-10, CelebA-HQ, and a conditional ImageNet subset illustrate that our method achieves competitive or superior performance relative to baseline diffusion models and state-of-the-art GANs, as measured by Fr\'echet Inception Distance (FID) and Inception Score (IS). We also show how the hybrid frequency-based representation improves control over global coherence and fine texture synthesis, paving the way for new directions in multi-scale generative modeling.
Adaptive Robust Unscented Kalman Filter for Dynamic State Estimation of Power System
Authors: Duc Viet Nguyen, Haiquan Zhao, Jinhui Hu, Le Ngoc Giang
Date: 2025-04-10 00:00:00
Tasks: State Estimation

Non-Gaussian noise and the uncertainty of noise distribution are the common factors that reduce accuracy in dynamic state estimation of power systems (PS). In addition, the optimal value of the free coefficients in the unscented Kalman filter (UKF) based on information theoretic criteria is also an urgent problem. In this paper, a robust adaptive UKF (AUKF) under generalized minimum mixture error entropy with fiducial points (GMMEEF) over improve Snow Geese algorithm (ISGA) (ISGA-GMMEEF-AUKF) is proposed to overcome the above difficulties. The estimation process of the proposed algorithm is based on several key steps including augmented regression error model (AREM) construction, adaptive state estimation, and free coefficients optimization. Specifically, an AREM consisting of state prediction and measurement errors is established at the first step. Then, GMMEEF-AUKF is developed by solving the optimization problem based on GMMEEF, which uses a generalized Gaussian kernel combined with mixture correntropy to enhance the flexibility further and resolve the data problem with complex attributes and update the noise covariance matrix according to the AREM framework. Finally, the ISGA is designed to automatically calculate the optimal value of coefficients such as the shape coefficients of the kernel in the GMMEEF criterion, the coefficients selection sigma points in unscented transform, and the update coefficient of the noise covariance matrices fit with the PS model. Simulation results on the IEEE 14, 30, and 57-bus test systems in complex scenarios have confirmed that the proposed algorithm outperforms the MEEF-UKF and UKF by an average efficiency of 26% and 65%, respectively.
Filtering through a topological lens: homology for point processes on the time-frequency plane
Authors: Juan Manuel Miramont, Kin Aun Tan, Soumendu Sundar Mukherjee, Rémi Bardenet, Subhroshekhar Ghosh
Date: 2025-04-10 00:00:00
Tasks: Point Processes, Topological Data Analysis

We introduce a very general approach to the analysis of signals from their noisy measurements from the perspective of Topological Data Analysis (TDA). While TDA has emerged as a powerful analytical tool for data with pronounced topological structures, here we demonstrate its applicability for general problems of signal processing, without any a-priori geometric feature. Our methods are well-suited to a wide array of time-dependent signals in different scientific domains, with acoustic signals being a particularly important application. We invoke time-frequency representations of such signals, focusing on their zeros which are gaining salience as a signal processing tool in view of their stability properties. Leveraging state-of-the-art topological concepts, such as stable and minimal volumes, we develop a complete suite of TDA-based methods to explore the delicate stochastic geometry of these zeros, capturing signals based on the disruption they cause to this rigid, hyperuniform spatial structure. Unlike classical spatial data tools, TDA is able to capture the full spectrum of the stochastic geometry of the zeros, thereby leading to powerful inferential outcomes that are underpinned by a principled statistical foundation. This is reflected in the power and versatility of our applications, which include competitive performance in processing. a wide variety of audio signals (esp. in low SNR regimes), effective detection and reconstruction of gravitational wave signals (a reputed signal processing challenge with non-Gaussian noise), and medical time series data from EEGs, indicating a wide horizon for the approach and methods introduced in this paper.
Learning Higher-Order Interactions in Brain Networks via Topological Signal Processing
Authors: Breno C. Bispo, Stefania Sardellitti, Fernando A. N. Santos, Juliano B. Lima
Date: 2025-04-10 00:00:00
Tasks: Functional Connectivity

Our goal in this paper is to leverage the potential of the topological signal processing (TSP) framework for analyzing brain networks. Representing brain data as signals over simplicial complexes allows us to capture higher-order relationships within brain regions of interest (ROIs). Here, we focus on learning the underlying brain topology from observed neural signals using two distinct inference strategies. The first method relies on higher-order statistical metrics to infer multiway relationships among ROIs. The second method jointly learns the brain topology and sparse signal representations, of both the solenoidal and harmonic components of the signals, by minimizing the total variation along triangles and the data-fitting errors. Leveraging the properties of solenoidal and irrotational signals, and their physical interpretations, we extract functional connectivity features from brain topologies and uncover new insights into functional organization patterns. This allows us to associate brain functional connectivity (FC) patterns of conservative signals with well-known functional segregation and integration properties. Our findings align with recent neuroscience research, suggesting that our approach may offer a promising pathway for characterizing the higher-order brain functional connectivities.
Low-Complexity Optimization of Antenna Switching Schemes for Dynamic Channel Sounding
Authors: Juan Sanchez, Xuesong Cai, Ali Al-Ameri, Fredrik Tufvesson
Date: 2025-04-10 00:00:00

Understanding wireless channels is crucial for the design of wireless systems. For mobile communication, sounders and antenna arrays with short measurement times are required to simultaneously capture the dynamic and spatial channel characteristics. Switched antenna arrays are an attractive option that can overcome the high cost of real arrays and the long measurement times of virtual arrays. Optimization of the switching sequences is then essential to avoid aliasing and increase the accuracy of channel parameter estimates. This paper provides a novel and comprehensive analysis of the design of switching sequences. We first review the conventional spatio-temporal ambiguity function, extend it to dual-polarized antenna arrays, and analyze its prohibitive complexity when designing for ultra-massive antenna arrays. We thus propose a new method that uses the Fisher information matrix to tackle the estimation accuracy. We also propose to minimize the ambiguity by choosing a switching sequence that minimizes side lobes in its Fourier spectrum. In this sense, we divide the sequence design problem into Fourier-based ambiguity reduction and Fisher-based accuracy improvement, and coin the resulting design approach as Fourier-Fisher. Simulations and measurements show that the Fourier-Fisher approach achieves identical performance and significantly lower computational complexity than that of the conventional ambiguity-based approach.
Cross-Laplacians Based Topological Signal Processing over Cell MultiComplexes
Authors: Stefania Sardellitti, Breno C. Bispo, Fernando A. N. Santos, Juliano B. Lima
Date: 2025-04-10 00:00:00

The study of the interactions among different types of interconnected systems in complex networks has attracted significant interest across many research fields. However, effective signal processing over layered networks requires topological descriptors of the intra- and cross-layers relationships that are able to disentangle the homologies of different domains, at different scales, according to the specific learning task. In this paper, we present Cell MultiComplex (CMC) spaces, which are novel topological domains for representing multiple higher-order relationships among interconnected complexes. We introduce cross-Laplacians matrices, which are algebraic descriptors of CMCs enabling the extraction of topological invariants at different scales, whether global or local, inter-layer or intra-layer. Using the eigenvectors of these cross-Laplacians as signal bases, we develop topological signal processing tools for CMC spaces. In this first study, we focus on the representation and filtering of noisy flows observed over cross-edges between different layers of CMCs to identify cross-layer hubs, i.e., key nodes on one layer controlling the others.
System Concept and Demonstration of Bistatic MIMO-OFDM-based ISAC
Authors: Lucas Giroto de Oliveira, Xueyun Long, Christian Karle, Umut Utku Erdem, Taewon Jeong, Elizabeth Bekker, Yueheng Li, Thomas Zwick, Benjamin Nuss
Date: 2025-04-10 00:00:00
Tasks: Direction of Arrival Estimation, Integrated sensing and communication, ISAC

In future sixth-generation (6G) mobile networks, radar sensing is expected to be offered as an additional service to its original purpose of communication. Merging these two functions results in integrated sensing and communication (ISAC) systems. In this context, bistatic ISAC appears as a possibility to exploit the distributed nature of cellular networks while avoiding highly demanding hardware requirements such as full-duplex operation. Recent studies have introduced strategies to perform required synchronization and data exchange between nodes for bistatic ISAC operation, based on orthogonal frequency-division multiplexing (OFDM), however, only for single-input single-output architectures. In this article, a system concept for a bistatic multiple-input multiple-output (MIMO)-OFDM-based ISAC system with beamforming at both transmitter and receiver is proposed, and a distribution synchronization concept to ensure coherence among the different receive channels for direction-of-arrival estimation is presented. After a discussion on the ISAC processing chain, including relevant aspects for practical deployments such as transmitter digital pre-distortion and receiver calibration, a 4x8 MIMO measurement setup at 27.5 GHz and results are presented to validate the proposed system and distribution synchronization concepts.
Learning Joint Source-Channel Encoding in IRS-assisted Multi-User Semantic Communications
Authors: Haidong Wang, Songhan Zhao, Lanhua Li, Bo Gu, Jing Xu, Shimin Gong, Jiawen Kang
Date: 2025-04-10 00:00:00
Tasks: Deep Reinforcement Learning, Scheduling, Semantic Communication

In this paper, we investigate a joint source-channel encoding (JSCE) scheme in an intelligent reflecting surface (IRS)-assisted multi-user semantic communication system. Semantic encoding not only compresses redundant information, but also enhances information orthogonality in a semantic feature space. Meanwhile, the IRS can adjust the spatial orthogonality, enabling concurrent multi-user semantic communication in densely deployed wireless networks to improve spectrum efficiency. We aim to maximize the users' semantic throughput by jointly optimizing the users' scheduling, the IRS's passive beamforming, and the semantic encoding strategies. To tackle this non-convex problem, we propose an explainable deep neural network-driven deep reinforcement learning (XD-DRL) framework. Specifically, we employ a deep neural network (DNN) to serve as a joint source-channel semantic encoder, enabling transmitters to extract semantic features from raw images. By leveraging structural similarity, we assign some DNN weight coefficients as the IRS's phase shifts, allowing simultaneous optimization of IRS's passive beamforming and DNN training. Given the IRS's passive beamforming and semantic encoding strategies, user scheduling is optimized using the DRL method. Numerical results validate that our JSCE scheme achieves superior semantic throughput compared to the conventional schemes and efficiently reduces the semantic encoder's mode size in multi-user scenarios.