pwc-archive/papers-with-abstracts huggingface dataset:
Aspects of density approximation by tensor trains
Authors: Jiří Ajgl, Ondřej Straka
Date: 2025-05-28 00:00:00
Tasks: State Estimation

Point-mass filters solve Bayesian recursive relations by approximating probability density functions of a system state over grids of discrete points. The approach suffers from the curse of dimensionality. The exponential increase of the number of the grid points can be mitigated by application of low-rank approximations of multidimensional arrays. Tensor train decompositions represent individual values by the product of matrices. This paper focuses on selected issues that are substantial in state estimation. Namely, the contamination of the density approximations by negative values is discussed first. Functional decompositions of quadratic functions are compared with decompositions of discretised Gaussian densities next. In particular, the connection of correlation with tensor train ranks is explored. Last, the consequences of interpolating the density values from one grid to a new grid are analysed.
Algorithm Unrolling-based Denoising of Multimodal Graph Signals
Authors: Hayate Kojima, Keigo Takanami, Junya Hara, Yukihiro Bandoh, Seishi Takamura, Hiroshi Higashi, Yuichi Tanaka
Date: 2025-05-28 00:00:00
Tasks: Denoising, Graph Learning

We propose a denoising method of multimodal graph signals by iteratively solving signal restoration and graph learning problems. Many complex-structured data, i.e., those on sensor networks, can capture multiple modalities at each measurement point, referred to as modalities. They are also assumed to have an underlying structure or correlations in modality as well as space. Such multimodal data are regarded as graph signals on a twofold graph and they are often corrupted by noise. Furthermore, their spatial/modality relationships are not always given a priori: We need to estimate twofold graphs during a denoising algorithm. In this paper, we consider a signal denoising method on twofold graphs, where graphs are learned simultaneously. We formulate an optimization problem for that and parameters in an iterative algorithm are learned from training data by unrolling the iteration with deep algorithm unrolling. Experimental results on synthetic and real-world data demonstrate that the proposed method outperforms existing model- and deep learning-based graph signal denoising methods.
Polarforming Design with Phase Shifter Based Polarization Reconfigurable Antennas
Authors: Zijian Zhou, Jingze Ding, Rui Zhang
Date: 2025-05-28 00:00:00

In this paper, we propose a new form of polarization reconfigurable antennas (PRAs) that can form linear, circular, and general elliptical polarizations assisted by phase shifters (PSs). With PRAs, polarforming is achieved, which enables the antenna to shape its polarization into a desired state for aligning with that of the received electromagnetic (EM) wave or reconfiguring that of the transmit EM wave. To demonstrate the benefits of polarforming, we investigate a PRA-aided single-input single-output (SISO) communication system equipped with tunable PSs for polarization adaptation. We characterize the achievable signal-to-noise ratio (SNR) at the receiver as a function of the phase shifts of PS-based PRAs. Moreover, we develop an alternating optimization approach to maximize the SNR by optimizing the phase shifts at both the transmitter and receiver. Finally, comprehensive simulation results are presented, which not only validate the effectiveness of polarforming in mitigating the channel depolarization effects, but also demonstrate its substantial performance improvement over conventional systems.
Target Localization with Coprime Multistatic MIMO Radar via Coupled Canonical Polyadic Decomposition Based on Joint Eigenvalue Decomposition
Authors: Guo-Zhao Liao, Xiao-Feng Gong, Wei Liu, Hing Cheung So
Date: 2025-05-28 00:00:00
Tasks: Computational Efficiency

This paper investigates target localization using a multistatic multiple-input multiple-output (MIMO) radar system with two distinct coprime array configurations: coprime L-shaped arrays and coprime planar arrays. The observed signals are modeled as tensors that admit a coupled canonical polyadic decomposition (C-CPD) model. For each configuration, a C-CPD method is presented based on joint eigenvalue decomposition (J-EVD). This computational framework includes (semi-)algebraic and optimization-based C-CPD algorithms and target localization that fuses direction-of-arrivals (DOAs) information to calculate the optimal position of each target. Specifically, the proposed (semi-)algebraic methods exploit the rotational invariance of the Vandermonde structure in coprime arrays, similar to the multiple invariance property of \added{estimation of signal parameters via rotational invariance techniques} (ESPRIT), which transforms the model into a J-EVD problem and reduces computational complexity. The study also investigates the working conditions of the algorithm to understand model identifiability. Additionally, the proposed method does not rely on prior knowledge of non-orthogonal probing waveforms and is effective in challenging underdetermined scenarios. Experimental results demonstrate that our method outperforms existing tensor-based approaches in both accuracy and computational efficiency.
Theoretical Bounds for Optimized Doppler-Based Motion Detection in UHF-RFID Readers
Authors: Clemens Korn, Joerg Robert
Date: 2025-05-27 00:00:00
Tasks: Motion Detection, TAG

Radio Frequency Identification (RFID) is a widely used technology for identifying and locating objects equipped with low-cost RFID transponders (tags). UHF (Ultra High Frequency) RFID operates in frequency bands around 900 MHz and supports communication distances of up to 15 m between the reader and the tag. Reliable motion detection is therefore a highly relevant feature in modern logistics - for example, to determine whether a tag is actually placed on a conveyor belt or merely in its vicinity. A promising approach for accurate motion detection is the use of the Doppler effect. Some state-of-the-art UHF-RFID readers already support Doppler shift measurements. However, their measurement accuracy is insufficient for many applications. In this paper, we propose an optimized method for the precise Doppler shift estimation using existing RFID systems - an essential step toward enabling RFID-based motion detection in future logistics. Further, we also derive the theoretical bounds for Doppler-based motion detection in UHF-RFID systems based on the Cramer-Rao Lower Bound. These bounds analyze the influence of tag signal strength, signal duration, and the intervals between multiple tag replies on the performance of motion detection and speed estimation algorithms. In addition, we establish theoretical limits that account for hardware constraints in current UHF-RFID readers. The results of this work provide valuable insights into the limitations of Doppler-based motion detection and support system-level performance optimization. They enable prediction of achievable performance based on reader noise figure, aiding in the design and tuning of RFID systems.
Gauss-Ramanujan Functions: Constructions, Properties, and Applications in Communications and Signal Processing
Authors: Sainath Bitragunta
Date: 2025-05-27 00:00:00
Tasks: Benchmarking

In this article, I construct a new set of functions based on Ramanujan sequences (RSEs), Gaussian pulse (GP), and its delayed Gaussian pulse (DGP). The motivation for this construction is based on the special properties of RSEs, GP, and DGP. First, I present a procedure for constructing Gauss-Ramanujan (GauRam) functions using selected RSEs. I develop an insightful analysis for deterministic and stochastic overlap between GP and DGP. Specifically, I present exact and closed form approximation expressions for delay-averaged GP and DGP overlap and then evaluate them numerically. Later, I derive and analyze the mathematical (spectral) properties of selected GauRam functions. I extend the analysis by analyzing the Hilbert transform of the first-order GauRam function and validating orthogonality and its usefulness in analytic signal representations. Furthermore, I present insightful applications of these functions in communications and signal processing. Specifically, I present the continuous-wave Gauss-Ramanujan modulation (GRM) scheme, Gauss-Ramanujan Shift Keying (GRSK) scheme, and Gauss-Ramanujan wavelets and their analysis and comparisons with benchmarking. The desirable properties of these novel modulation schemes and wavelets enable their use in next-generation hybrid and energy-efficient communication systems and signal processing.
Expectation-maximization for multi-reference alignment: Two pitfalls and one remedy
Authors: Amnon Balanov, Wasim Huleihel, Tamir Bendory
Date: 2025-05-27 00:00:00

We study the multi-reference alignment model, which involves recovering a signal from noisy observations that have been randomly transformed by an unknown group action, a fundamental challenge in statistical signal processing, computational imaging, and structural biology. While much of the theoretical literature has focused on the asymptotic sample complexity of this model, the practical performance of reconstruction algorithms, particularly of the omnipresent expectation maximization (EM) algorithm, remains poorly understood. In this work, we present a detailed investigation of EM in the challenging low signal-to-noise ratio (SNR) regime. We identify and characterize two failure modes that emerge in this setting. The first, called Einstein from Noise, reveals a strong sensitivity to initialization, with reconstructions resembling the input template regardless of the true underlying signal. The second phenomenon, referred to as the Ghost of Newton, involves EM initially converging towards the correct solution but later diverging, leading to a loss of reconstruction fidelity. We provide theoretical insights and support our findings through numerical experiments. Finally, we introduce a simple, yet effective modification to EM based on mini-batching, which mitigates the above artifacts. Supported by both theory and experiments, this mini-batching approach processes small data subsets per iteration, reducing initialization bias and computational cost, while maintaining accuracy comparable to full-batch EM.
Label-free Super-Resolution Microvessel Color Flow Imaging with Ultrasound
Authors: Zhengchang Kou, Junhang Zhang, Chen Gong, Jie Ji, Nathiya Vaithiyalingam Chandra Sekaran, Zikai Wang, Rita J. Miller, Yaoheng Yang, Daniel Adolfo Llano, Qifa Zhou, Michael L. Oelze
Date: 2025-05-27 00:00:00
Tasks: Super-Resolution

We present phase subtraction imaging (PSI), a new spatial-temporal beamforming method that enables micrometer level resolution imaging of microvessels in live animals without labels, which are microbubbles in ultrasound super-resolution imaging. Subtraction of relative phase differences between consecutive frames beamformed with mismatched apodizations is used in PSI to overcome the diffraction limit. We validated this method by imaging both the mouse brain and rabbit kidney using different ultrasound probes and scanning machines.
Graph Neural Network Aided Detection for the Multi-User Multi-Dimensional Index Modulated Uplink
Authors: Xinyu Feng, Mohammed El-Hajjar, Chao Xu, Lajos Hanzo
Date: 2025-05-27 00:00:00
Tasks: compressed sensing, Graph Neural Network

The concept of Compressed Sensing-aided Space-Frequency Index Modulation (CS-SFIM) is conceived for the Large-Scale Multi-User Multiple-Input Multiple-Output Uplink (LS-MU-MIMO-UL) of Next-Generation (NG) networks. Explicitly, in CS-SFIM, the information bits are mapped to both spatial- and frequency-domain indices, where we treat the activation patterns of the transmit antennas and of the subcarriers separately. Serving a large number of users in an MU-MIMO-UL system leads to substantial Multi-User Interference (MUI). Hence, we design the Space-Frequency (SF) domain matrix as a joint factor graph, where the Approximate Message Passing (AMP) and Expectation Propagation (EP) based MU detectors can be utilized. In the LS-MU-MIMO-UL scenario considered, the proposed system uses optimal Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) detectors as benchmarks for comparison with the proposed MP-based detectors. These MP-based detectors significantly reduce the detection complexity compared to ML detection, making the design eminently suitable for LS-MU scenarios. To further reduce the detection complexity and improve the detection performance, we propose a pair of Graph Neural Network (GNN) based detectors, which rely on the orthogonal AMP (OAMP) and on the EP algorithm, which we refer to as the GNN-AMP and GEPNet detectors, respectively. The GEPNet detector maximizes the detection performance, while the GNN-AMP detector strikes a performance versus complexity trade-off. The GNN is trained for a single system configuration and yet it can be used for any number of users in the system. The simulation results show that the GNN-based detector approaches the ML performance in various configurations.
CiUAV: A Multi-Task 3D Indoor Localization System for UAVs based on Channel State Information
Authors: Cunyi Yin, Chenwei Wang, Jing Chen, Hao Jiang, Xiren Miao, Shaocong Zheng Zhenghua Chen Senior, Hong Yan
Date: 2025-05-27 00:00:00
Tasks: Indoor Localization

Accurate indoor positioning for unmanned aerial vehicles (UAVs) is critical for logistics, surveillance, and emergency response applications, particularly in GPS-denied environments. Existing indoor localization methods, including optical tracking, ultra-wideband, and Bluetooth-based systems, face cost, accuracy, and robustness trade-offs, limiting their practicality for UAV navigation. This paper proposes CiUAV, a novel 3D indoor localization system designed for UAVs, leveraging channel state information (CSI) obtained from low-cost ESP32 IoT-based sensors. The system incorporates a dynamic automatic gain control (AGC) compensation algorithm to mitigate noise and stabilize CSI signals, significantly enhancing the robustness of the measurement. Additionally, a multi-task 3D localization model, Sensor-in-Sample (SiS), is introduced to enhance system robustness by addressing challenges related to incomplete sensor data and limited training samples. SiS achieves this by joint training with varying sensor configurations and sample sizes, ensuring reliable performance even in resource-constrained scenarios. Experiment results demonstrate that CiUAV achieves a LMSE localization error of 0.2629 m in a 3D space, achieving good accuracy and robustness. The proposed system provides a cost-effective and scalable solution, demonstrating its usefulness for UAV applications in resource-constrained indoor environments.
Channel-Aware Holographic Decision Fusion
Authors: Domenico Ciuonzo, Alessio Zappone, Marco Di Renzo
Date: 2025-05-27 00:00:00

This work investigates Distributed Detection (DD) in Wireless Sensor Networks (WSNs) utilizing channel-aware binary-decision fusion over a shared flat-fading channel. A reconfigurable metasurface, positioned in the near-field of a limited number of receive antennas, is integrated to enable a holographic Decision Fusion (DF) system. This approach minimizes the need for multiple RF chains while leveraging the benefits of a large array. The optimal fusion rule for a fixed metasurface configuration is derived, alongside two suboptimal joint fusion rule and metasurface design strategies. These suboptimal approaches strike a balance between reduced complexity and lower system knowledge requirements, making them practical alternatives. The design objective focuses on effectively conveying the information regarding the phenomenon of interest to the FC while promoting energy-efficient data analytics aligned with the Internet of Things (IoT) paradigm. Simulation results underscore the viability of holographic DF, demonstrating its advantages even with suboptimal designs and highlighting the significant energy-efficiency gains achieved by the proposed system.
CNN-Based Channel Map Estimation for Movable Antenna Systems
Authors: Yitai Huang, Weidong Mei, Xin Wei, Zhi Chen, Boyu Ning
Date: 2025-05-27 00:00:00

Movable antenna (MA) has attracted increasing attention in wireless communications due to its capability of wireless channel reconfiguration through local antenna movement within a confined region at the transmitter/receiver. However, to determine the optimal antenna positions, channel state information (CSI) within the entire region, termed small-scale channel map, is required, which poses a significant challenge due to the unaffordable overhead for exhaustive channel estimation at all positions. To tackle this challenge, in this paper, we propose a new convolutional neural network (CNN)-based estimation scheme to reconstruct the small-scale channel map within a three-dimensional (3D) movement region. Specifically, we first collect a set of CSI measurements corresponding to a subset of MA positions and different receiver locations offline to comprehensively capture the environmental features. Subsequently, we train a CNN using the collected data, which is then used to reconstruct the full channel map during real-time transmission only based on a finite number of channel measurements taken at several selected MA positions within the 3D movement region. Numerical results demonstrate that our proposed scheme can accurately reconstruct the small-scale channel map and outperforms other benchmark schemes.
Ergonomic Assessment of Work Activities for an Industrial-oriented Wrist Exoskeleton
Authors: Roberto F. Pitzalis, Nicholas Cartocci, Christian Di Natali, Luigi Monica, Darwin G. Caldwell, Giovanni Berselli, Jesús Ortiz
Date: 2025-05-27 00:00:00

Musculoskeletal disorders (MSD) are the most common cause of work-related injuries and lost production involving approximately 1.7 billion people worldwide and mainly affect low back (more than 50%) and upper limbs (more than 40%). It has a profound effect on both the workers affected and the company. This paper provides an ergonomic assessment of different work activities in a horse saddle-making company, involving 5 workers. This aim guides the design of a wrist exoskeleton to reduce the risk of musculoskeletal diseases wherever it is impossible to automate the production process. This evaluation is done either through subjective and objective measurement, respectively using questionnaires and by measurement of muscle activation with sEMG sensors.
Recognition of Physiological Patterns during Activities of Daily Living Using Wearable Biosignal Sensors
Authors: Nicholas Cartocci, Antonios E. Gkikakis, Natalia Kurvina, Natnael Takele, Fabio Pera, Maria Teresa Settino, Darwin G. Caldwell, Jesús Ortiz
Date: 2025-05-27 00:00:00
Tasks: Activity Recognition, Prediction

A key aspect of developing fall prevention systems is the early prediction of a fall before it occurs. This paper presents a statistical overview of results obtained by analyzing 22 activities of daily living to recognize physiological patterns and estimate the risk of an imminent fall. The results demonstrate distinctive patterns between high-intensity and low-intensity activity using EMG, ECG, and respiration sensors, also indicating the presence of a proportional trend between movement velocity and muscle activity. These outcomes highlight the potential benefits of using these sensors in the future to direct the development of an activity recognition and risk prediction framework for physiological phenomena that can cause fall injuries.
Dynamic Resource Allocation in Distributed MIMO-LEO Satellite Networks
Authors: Qihao Peng, Qu Luo, Yi Ma, Chuan Heng Foh, Pei Xiao, Maged Elkashlan, Rahim Tafazolli, George K. Karagiannidis
Date: 2025-05-27 00:00:00
Tasks: Scheduling

This paper characterizes the impacts of channel estimation errors and Rician factors on achievable data rate and investigates the user scheduling strategy, combining scheme, power control, and dynamic bandwidth allocation to maximize the sum data rate in the distributed multiple-input-multiple-output (MIMO)-enabled low earth orbit (LEO) satellite networks. However, due to the resource-assignment problem, it is challenging to find the optimal solution for maximizing the sum data rate. To transform this problem into a more tractable form, we first quantify the channel estimation errors based on the minimum mean square error (MMSE) estimator and rigorously derive a closed-form lower bound of the achievable data rate, offering an explicit formulation for resource allocation. Then, to solve the NP-hard problem, we decompose it into three sub-problems, namely, user scheduling strategy, joint combination and power control, and dynamic bandwidth allocation, by using alternative optimization (AO). Specifically, the user scheduling is formulated as a graph coloring problem by iteratively updating an undirected graph based on user requirements, which is then solved using the DSatur algorithm. For the combining weights and power control, the successive convex approximation (SCA) and geometrical programming (GP) are adopted to obtain the sub-optimal solution with lower complexity. Finally, the optimal bandwidth allocation can be achieved by solving the concave problem. Numerical results validate the analytical tightness of the derived bound, especially for large Rician factors, and demonstrate significant performance gains over other benchmarks.
Continuous SpO2 Monitoring Using Reflectance Pulse Oximetry at the Wrist and Upper Arm During Overnight Sleep Apnea Recordings
Authors: Karen Adam, Clémentine Aguet, Patrick Theurillat, Florent Baty, Maximilian Boesch, Damien Ferrario, Mathieu Lemay, Martin Brutsche, Fabian Braun
Date: 2025-05-27 00:00:00

Sleep apnea (SA) is a chronic sleep-related disorder consisting of repetitive pauses or restrictions in airflow during sleep and is known to be a risk factor for cerebro- and cardiovascular disease. It is generally diagnosed using polysomnography (PSG) recorded overnight in an in-lab setting at the hospital. This includes the measurement of blood oxygen saturation (SpO2), which exhibits fluctuations caused by SA events. In this paper, we investigate the accuracy and utility of reflectance pulse oximetry from a wearable device as a means to continuously monitor SpO2 during sleep. To this end, we analyzed data from a cohort of 134 patients with suspected SA undergoing overnight PSG and wearing the watch-like device at two measurement locations (upper arm and wrist). Our data show that standard requirements for pulse oximetry measurements are met at both measurement locations, with an accuracy (root mean squared error) of 1.9% at the upper arm and 3.2% at the wrist. With a rejection rate of 3.1%, the upper arm yielded better results in terms of data quality when compared to the wrist location which had 30.4% of data rejected.
Estimators and Performance Bounds for Short Periodic Pulses
Authors: Sebastian Schertler, Oliver Lang, Jonas Lindenberger, Stefan Schuster, Stefan Scheiblhofer, Alexander Haberl, Clemens Staudinger, Mario Huemer
Date: 2025-05-27 00:00:00

In many industrial applications, signals with short periodic pulses, caused by repeated steps in the manufacturing process, are present, and their fundamental frequency or period may be of interest. Fundamental frequency estimation is in many cases performed by describing the periodic signal as a multiharmonic signal and employing the corresponding maximum likelihood estimator. However, since signals with short periodic pulses contain a large number of noise-only samples, the multiharmonic signal model is not optimal to describe them. In this work, two models of short periodic pulses with known and unknown pulse shape are considered. For both models, the corresponding maximum likelihood estimators, Fisher information matrices, and approximate Cram\'er-Rao lower bounds are presented. Numerical results demonstrate that the proposed estimators outperform the maximum likelihood estimator based on the multiharmonic signal model for low signal-to-noise ratios.