pwc-archive/papers-with-abstracts huggingface dataset:
Joint Transmit and Receive Beamforming for Tri-directional Coil-Based Magnetic Induction Communications
Authors: Jinyang Li, Jianyu Wang, Wenchi Cheng, Yudong Fang, Wei Guo
Date: 2025-05-30 00:00:00

In this paper, we enhance the omnidirectional coverage performance of tri-directional coil-based magnetic induction communication (TC-MIC) and reduce the pathloss with a joint transmit and receive magnetic beamforming method. An iterative optimization algorithm incorporating the transmit current vector and receive weight matrix is developed to minimize the pathloss under constant transmit power constraints. We formulate the mathematical models for the mutual inductance of tri-directional coils, receive power, and pathloss. The optimization problem is decomposed into Rayleigh quotient extremum optimization for transmit currents and Cauchy-Schwarz inequality-constrained optimization for receive weights, with an alternating iterative algorithm to approach the global optimum. Numerical results demonstrate that the proposed algorithm converges within an average of 13.6 iterations, achieving up to 54% pathloss reduction compared with equal power allocation schemes. The joint optimization approach exhibits superior angular robustness, maintaining pathloss fluctuation smaller than 2 dB, and reducing fluctuation of pathloss by approximately 45% compared with single-parameter optimization methods.
Wideband channel sensing with holographic interference surfaces
Authors: Jindiao Huang, Haifan Yin
Date: 2025-05-30 00:00:00

The Holographic Interference Surface (HIS) opens up a new prospect for building a more cost-effective wireless communication architecture by performing Radio Frequency (RF) domain signal processing. In this paper, we establish a wideband channel sensing architecture for electromagnetic wave reception and channel estimation based on the principle of holographic interference theory. Dute to the nonlinear structure of holograms, interferential fringes composed of wideband RF signals exhibit severe self-interference effects in the time-frequency domain, which are inherently resistant to the classical signal processing tools. To overcome the self-interference, we propose a holographic channel recovery method, which analyzes the time-domain variation of holograms from a geometrical perspective and constructs an inverse mapping from wideband holograms to object waves. Based on the Wirtinger partial derivative and Armijo condition, we then develop a wideband hologram-based maximum likelihood (WH-ML) estimation method for estimating the channel state information (CSI) from holograms. We also propose a geometric rotation-based object wave sensing (GROWS) algorithm to address the complicated computation of ML estimation. Furthermore, we derive the Cram\'er-Rao lower bound (CRLB) for investigating the achievable performance of wideband holographic channel estimation. Simulation results show that under the wideband channel sensing architecture, our proposed algorithm can accurately estimate the CSI in wideband scenarios.
Channel Knowledge Maps for 6G Wireless Networks: Construction, Applications, and Future Challenges
Authors: Xingchen Liu, Shu Sun, Meixia Tao, Aryan Kaushik, Hangsong Yan
Date: 2025-05-30 00:00:00
Tasks: Integrated sensing and communication

The advent of 6G wireless networks promises unprecedented connectivity, supporting ultra-high data rates, low latency, and massive device connectivity. However, these ambitious goals introduce significant challenges, particularly in channel estimation due to complex and dynamic propagation environments. This paper explores the concept of channel knowledge maps (CKMs) as a solution to these challenges. CKMs enable environment-aware communications by providing location-specific channel information, reducing reliance on real-time pilot measurements. We categorize CKM construction techniques into measurement-based, model-based, and hybrid methods, and examine their key applications in integrated sensing and communication systems, beamforming, trajectory optimization of unmanned aerial vehicles, base station placement, and resource allocation. Furthermore, we discuss open challenges and propose future research directions to enhance the robustness, accuracy, and scalability of CKM-based systems in the evolving 6G landscape.
Fault-Tolerant IoT System Using Software-Based "Digital Twin"
Authors: Tanish Baranwal, Srihari Varada, Santanu Das, Mohammad R. Haider
Date: 2025-05-29 00:00:00

In this article, we present a novel redundancy scheme to realize a fault-tolerant IoT structure for application in high-reliability systems. The proposed fault-tolerant structure uses a centralized data fusion block and triplicated IoT devices, along with software-based "digital twins", that duplicate the function of each of the sensors. In case of a fault in one of the IoT devices, the pertinent digital twin takes over the function of the actual IoT device for some time in the triplicated structure till the faulty device is either replaced or repaired when possible. The use of software-based digital twins as a duplicate for each physical sensor improves the reliability of the operation with minimal increase in the overall system cost.
Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach
Authors: Tanish Baranwal, Arnab Das, Srihari Varada, Santanu Das, Mohammad R. Haider
Date: 2025-05-29 00:00:00
Tasks: Anomaly Detection, Hybrid Machine Learning

The growing adoption of IoT systems in industries like transportation, banking, healthcare, and smart energy has increased reliance on sensor networks. However, anomalies in sensor readings can undermine system reliability, making real-time anomaly detection essential. While a large body of research addresses anomaly detection in IoT networks, few studies focus on correlated sensor data streams, such as temperature and pressure within a shared space, especially in resource-constrained environments. To address this, we propose a novel hybrid machine learning approach combining Principal Component Analysis (PCA) and Autoencoders. In this method, PCA continuously monitors sensor data and triggers the Autoencoder when significant variations are detected. This hybrid approach, validated with real-world and simulated data, shows faster response times and fewer false positives. The F1 score of the hybrid method is comparable to Autoencoder, with much faster response time which is driven by PCA.
Unobservable Systems: No Problem for Noise Identification
Authors: Oliver Kost, Jindrich Dunik, Ivo Puncochar, Ondrej Straka
Date: 2025-05-29 00:00:00

This paper deals with the noise identification of a linear time-varying stochastic dynamic system described by the state-space model. In particular, the stress is laid on the design of the correlation measurement difference method for estimation of the state and measurement noise covariance matrices for both observable and \textit{unobservable} systems with possibly unknown input sequence. The method provides unbiased and consistent estimates and is implemented in a publicly available MATLAB toolbox and numerically evaluated.
MoE-Gyro: Self-Supervised Over-Range Reconstruction and Denoising for MEMS Gyroscopes
Authors: Feiyang Pan, Shenghe Zheng, Chunyan Yin, Guangbin Dou
Date: 2025-05-27 00:00:00
Tasks: Benchmarking, Denoising, Mixture-of-Experts

MEMS gyroscopes play a critical role in inertial navigation and motion control applications but typically suffer from a fundamental trade-off between measurement range and noise performance. Existing hardware-based solutions aimed at mitigating this issue introduce additional complexity, cost, and scalability challenges. Deep-learning methods primarily focus on noise reduction and typically require precisely aligned ground-truth signals, making them difficult to deploy in practical scenarios and leaving the fundamental trade-off unresolved. To address these challenges, we introduce Mixture of Experts for MEMS Gyroscopes (MoE-Gyro), a novel self-supervised framework specifically designed for simultaneous over-range signal reconstruction and noise suppression. MoE-Gyro employs two experts: an Over-Range Reconstruction Expert (ORE), featuring a Gaussian-Decay Attention mechanism for reconstructing saturated segments; and a Denoise Expert (DE), utilizing dual-branch complementary masking combined with FFT-guided augmentation for robust noise reduction. A lightweight gating module dynamically routes input segments to the appropriate expert. Furthermore, existing evaluation lack a comprehensive standard for assessing multi-dimensional signal enhancement. To bridge this gap, we introduce IMU Signal Enhancement Benchmark (ISEBench), an open-source benchmarking platform comprising the GyroPeak-100 dataset and a unified evaluation of IMU signal enhancement methods. We evaluate MoE-Gyro using our proposed ISEBench, demonstrating that our framework significantly extends the measurable range from 450 deg/s to 1500 deg/s, reduces Bias Instability by 98.4%, and achieves state-of-the-art performance, effectively addressing the long-standing trade-off in inertial sensing.
An Open-Source Python Framework and Synthetic ECG Image Datasets for Digitization, Lead and Lead Name Detection, and Overlapping Signal Segmentation
Authors: Masoud Rahimi, Reza Karbasi, Abdol-Hossein Vahabie
Date: 2025-05-26 00:00:00
Tasks: ECG Digitization, Segmentation

We introduce an open-source Python framework for generating synthetic ECG image datasets to advance critical deep learning-based tasks in ECG analysis, including ECG digitization, lead region and lead name detection, and pixel-level waveform segmentation. Using the PTB-XL signal dataset, our proposed framework produces four open-access datasets: (1) ECG images in various lead configurations paired with time-series signals for ECG digitization, (2) ECG images annotated with YOLO-format bounding boxes for detection of lead region and lead name, (3)-(4) cropped single-lead images with segmentation masks compatible with U-Net-based models in normal and overlapping versions. In the overlapping case, waveforms from neighboring leads are superimposed onto the target lead image, while the segmentation masks remain clean. The open-source Python framework and datasets are publicly available at https://github.com/rezakarbasi/ecg-image-and-signal-dataset and https://doi.org/10.5281/zenodo.15484519, respectively.
Speed Radar using HC-SR04 and Arduino: Calibration and Data Analysis
Authors: Allan Zapata
Date: 2025-05-26 00:00:00

Ultrasonic sensors are devices that use sound waves to measure distances. They are useful in various applications, such as robotics or factory automation. In this report, the use of these devices for the study of kinematics is presented, specifically to measure the speed of a remote control toy car. The calibration of the sensor achieving a mean absolute error (MAE) of 0,40 cm is presented and then the use of this sensor to measure the speed of the car using an Arduino. The methods of data collection and data analysis are also presented. It is concluded that the ultrasonic sensor is a useful tool for measuring distances and speeds, although its accuracy can be affected by factors such as temperature and humidity.
A Novel Shape-Aware Topological Representation for GPR Data with DNN Integration
Authors: Meiyan Kang, Shizuo Kaji, Sang-Yun Lee, Taegon Kim, Hee-Hwan Ryu, Suyoung Choi
Date: 2025-05-26 00:00:00
Tasks: GPR, object-detection, Object Detection, Topological Data Analysis

Ground Penetrating Radar (GPR) is a widely used Non-Destructive Testing (NDT) technique for subsurface exploration, particularly in infrastructure inspection and maintenance. However, conventional interpretation methods are often limited by noise sensitivity and a lack of structural awareness. This study presents a novel framework that enhances the detection of underground utilities, especially pipelines, by integrating shape-aware topological features derived from B-scan GPR images using Topological Data Analysis (TDA), with the spatial detection capabilities of the YOLOv5 deep neural network (DNN). We propose a novel shape-aware topological representation that amplifies structural features in the input data, thereby improving the model's responsiveness to the geometrical features of buried objects. To address the scarcity of annotated real-world data, we employ a Sim2Real strategy that generates diverse and realistic synthetic datasets, effectively bridging the gap between simulated and real-world domains. Experimental results demonstrate significant improvements in mean Average Precision (mAP), validating the robustness and efficacy of our approach. This approach underscores the potential of TDA-enhanced learning in achieving reliable, real-time subsurface object detection, with broad applications in urban planning, safety inspection, and infrastructure management.
Active Contour Models Driven by Hyperbolic Mean Curvature Flow for Image Segmentation
Authors: Saiyu Hu, Chunlei He, Jianfeng Zhang, Dexing Kong, Shoujun Huang
Date: 2025-06-07 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Parabolic mean curvature flow-driven active contour models (PMCF-ACMs) are widely used in image segmentation, which however depend heavily on the selection of initial curve configurations. In this paper, we firstly propose several hyperbolic mean curvature flow-driven ACMs (HMCF-ACMs), which introduce tunable initial velocity fields, enabling adaptive optimization for diverse segmentation scenarios. We shall prove that HMCF-ACMs are indeed normal flows and establish the numerical equivalence between dissipative HMCF formulations and certain wave equations using the level set method with signed distance function. Building on this framework, we furthermore develop hyperbolic dual-mode regularized flow-driven ACMs (HDRF-ACMs), which utilize smooth Heaviside functions for edge-aware force modulation to suppress over-diffusion near weak boundaries. Then, we optimize a weighted fourth-order Runge-Kutta algorithm with nine-point stencil spatial discretization when solving the above-mentioned wave equations. Experiments show that both HMCF-ACMs and HDRF-ACMs could achieve more precise segmentations with superior noise resistance and numerical stability due to task-adaptive configurations of initial velocities and initial contours.
Goal-based portfolio selection with mental accounting
Authors: Erhan Bayraktar, Bingyan Han
Date: 2025-06-07 00:00:00

We present a continuous-time portfolio selection framework that reflects goal-based investment principles and mental accounting behavior. In this framework, an investor with multiple investment goals constructs separate portfolios, each corresponding to a specific goal, with penalties imposed on fund transfers between these goals, referred to as mental costs. By applying the stochastic Perron's method, we demonstrate that the value function is the unique constrained viscosity solution of a Hamilton-Jacobi-Bellman equation system. Numerical analysis reveals several key features: the free boundaries exhibit complex shapes with bulges and notches; the optimal strategy for one portfolio depends on the wealth level of another; investors must diversify both among stocks and across portfolios; and they may postpone reallocating surplus from an important goal to a less important one until the former's deadline approaches.
From Initial Data to Boundary Layers: Neural Networks for Nonlinear Hyperbolic Conservation Laws
Authors: Igor Ciril, Khalil Haddaoui, Yohann Tendero
Date: 2025-06-02 00:00:00

We address the approximation of entropy solutions to initial-boundary value problems for nonlinear strictly hyperbolic conservation laws using neural networks. A general and systematic framework is introduced for the design of efficient and reliable learning algorithms, combining fast convergence during training with accurate predictions. The methodology is assessed through a series of one-dimensional scalar test cases, highlighting its potential applicability to more complex industrial scenarios.
A phylogeny of biological patterns formed by nonlocal advection
Authors: Valeria Giunta, Thomas Hillen, Mark A. Lewis, Jonathan R. Potts
Date: 2025-05-31 00:00:00

From tumour invasion to cell sorting and animal territoriality, many biological systems rely on nonlocal interactions that drive complex spatial organisation. Partial differential equations (PDEs) with nonlocal advection are increasingly recognised as powerful tools for capturing such phenomena. However, most research has focused on one-dimensional domains, leaving their two-dimensional behaviour largely unexplored. Here, we present a detailed numerical study of the patterns formed by these systems on 2D domains. Depending on the underlying mechanisms, a wide variety of spatial patterns can emerge - including segregated clusters, stripes, volcanos, and polygonal mosaics - many of which have been observed in natural systems. By systematically varying model parameters, we classify the links between emergent patterns and their underlying movement mechanisms. In comparing these patterns with empirical observations, we show how this modelling framework can help reveal possible mechanisms of self-organisation in various situations within the life sciences, from ecology and developmental biology to cancer research.
Minimax Rates for the Estimation of Eigenpairs of Weighted Laplace-Beltrami Operators on Manifolds
Authors: Nicolás García Trillos, Chenghui Li, Raghavendra Venkatraman
Date: 2025-05-30 00:00:00
Tasks: Density Estimation

We study the problem of estimating eigenpairs of elliptic differential operators from samples of a distribution $\rho$ supported on a manifold $M$. The operators discussed in the paper are relevant in unsupervised learning and in particular are obtained by taking suitable scaling limits of widely used graph Laplacians over data clouds. We study the minimax risk for this eigenpair estimation problem and explore the rates of approximation that can be achieved by commonly used graph Laplacians built from random data. More concretely, assuming that $\rho$ belongs to a certain family of distributions with controlled second derivatives, and assuming that the $d$-dimensional manifold $M$ where $\rho$ is supported has bounded geometry, we prove that the statistical minimax rate for approximating eigenvalues and eigenvectors in the $H^1(M)$-sense is $n^{-2/(d+4)}$, a rate that matches the minimax rate for a closely related density estimation problem. We then revisit the literature studying Laplacians over proximity graphs in the large data limit and prove that, under slightly stronger regularity assumptions on the data generating model, eigenpairs of graph Laplacians induce manifold agnostic estimators with an error of approximation that, up to logarithmic corrections, matches our lower bounds. Our analysis allows us to expand the existing literature on graph-based learning in at least two significant ways: 1) we consider stronger norms to measure the error of approximation than the ones that had been analyzed in the past; 2) our rates of convergence are uniform over a family of smooth distributions and do not just apply to densities with special symmetries, and, as a consequence of our lower bounds, are essentially sharp when the connectivity of the graph is sufficiently high.
Torsion in Persistent Homology and Neural Networks
Authors: Maria Walch
Date: 2025-06-03 00:00:00
Tasks: Sensitivity, Topological Data Analysis

We explore the role of torsion in hybrid deep learning models that incorporate topological data analysis, focusing on autoencoders. While most TDA tools use field coefficients, this conceals torsional features present in integer homology. We show that torsion can be lost during encoding, altered in the latent space, and in many cases, not reconstructed by standard decoders. Using both synthetic and high-dimensional data, we evaluate torsion sensitivity to perturbations and assess its recoverability across several autoencoder architectures. Our findings reveal key limitations of field-based approaches and underline the need for architectures or loss terms that preserve torsional information for robust data representation.
A strengthened bound on the number of states required to characterize maximum parsimony distance
Authors: Mareike Fischer, Steven Kelk, Sofia Vazquez Alferez
Date: 2025-06-11 00:00:00
Tasks: 2k

In this article we prove that the distance $d_{\mathrm{MP}}(T_1,T_2) = k$ between two unrooted binary phylogenetic trees $T_1, T_2$ on the same set of taxa can be defined by a character that is convex on one of $T_1, T_2$ and which has at most $2k$ states. This significantly improves upon the previous bound of $7k-5$ states. We also show that for every $k \geq 1$ there exist two trees $T_1, T_2$ with $d_{\mathrm{MP}}(T_1,T_2) = k$ such that at least $k+1$ states are necessary in any character that achieves this distance and which is convex on one of $T_1, T_2$.
Metaconcepts of rooted tree balance
Authors: Mareike Fischer, Tom Niklas Hamann, Kristina Wicke
Date: 2025-06-10 00:00:00

Measures of tree balance play an important role in many different research areas such as mathematical phylogenetics or theoretical computer science. Typically, tree balance is quantified by a single number which is assigned to the tree by a balance or imbalance index, of which several exist in the literature. Most of these indices are based on structural aspects of tree shape, such as clade sizes or leaf depths. For instance, indices like the Sackin index, total cophenetic index, and $\widehat{s}$-shape statistic all quantify tree balance through clade sizes, albeit with different definitions and properties. In this paper, we formalize the idea that many tree (im)balance indices are functions of similar underlying tree shape characteristics by introducing metaconcepts of tree balance. A metaconcept is a function $\Phi_f$ that depends on a function $f$ capturing some aspect of tree shape, such as balance values, clade sizes, or leaf depths. These metaconcepts encompass existing indices but also provide new means of measuring tree balance. The versatility and generality of metaconcepts allow for the systematic study of entire families of (im)balance indices, providing deeper insights that extend beyond index-by-index analysis.
SVarM: Linear Support Varifold Machines for Classification and Regression on Geometric Data
Authors: Emmanuel Hartman, Nicolas Charon
Date: 2025-06-01 00:00:00

Despite progress in the rapidly developing field of geometric deep learning, performing statistical analysis on geometric data--where each observation is a shape such as a curve, graph, or surface--remains challenging due to the non-Euclidean nature of shape spaces, which are defined as equivalence classes under invariance groups. Building machine learning frameworks that incorporate such invariances, notably to shape parametrization, is often crucial to ensure generalizability of the trained models to new observations. This work proposes SVarM to exploit varifold representations of shapes as measures and their duality with test functions $h:\mathbb{R}^n \times S^{n-1} \to \mathbb{R}$. This method provides a general framework akin to linear support vector machines but operating instead over the infinite-dimensional space of varifolds. We develop classification and regression models on shape datasets by introducing a neural network-based representation of the trainable test function $h$. This approach demonstrates strong performance and robustness across various shape graph and surface datasets, achieving results comparable to state-of-the-art methods while significantly reducing the number of trainable parameters.