pwc-archive/papers-with-abstracts huggingface dataset:
Non-invasive maturity assessment of iPSC-CMs based on optical maturity characteristics using interpretable AI
Authors: Fabian Scheurer, Alexander Hammer, Mario Schubert, Robert-Patrick Steiner, Oliver Gamm, Kaomei Guan, Frank Sonntag, Hagen Malberg, Martin Schmidt
Date: 2025-05-27 00:00:00

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are an important resource for the identification of new therapeutic targets and cardioprotective drugs. After differentiation iPSC-CMs show an immature, fetal-like phenotype. Cultivation of iPSC-CMs in lipid-supplemented maturation medium (MM) strongly enhances their structural, metabolic and functional phenotype. Nevertheless, assessing iPSC-CM maturation state remains challenging as most methods are time consuming and go in line with cell damage or loss of the sample. To address this issue, we developed a non-invasive approach for automated classification of iPSC-CM maturity through interpretable artificial intelligence (AI)-based analysis of beat characteristics derived from video-based motion analysis. In a prospective study, we evaluated 230 video recordings of early-state, immature iPSC-CMs on day 21 after differentiation (d21) and more mature iPSC-CMs cultured in MM (d42, MM). For each recording, 10 features were extracted using Maia motion analysis software and entered into a support vector machine (SVM). The hyperparameters of the SVM were optimized in a grid search on 80 % of the data using 5-fold cross-validation. The optimized model achieved an accuracy of 99.5 $\pm$ 1.1 % on a hold-out test set. Shapley Additive Explanations (SHAP) identified displacement, relaxation-rise time and beating duration as the most relevant features for assessing maturity level. Our results suggest the use of non-invasive, optical motion analysis combined with AI-based methods as a tool to assess iPSC-CMs maturity and could be applied before performing functional readouts or drug testing. This may potentially reduce the variability and improve the reproducibility of experimental studies.
A Unified RCS Modeling of Typical Targets for 3GPP ISAC Channel Standardization and Experimental Analysis
Authors: Yuxiang Zhang, Jianhua Zhang, Xidong Hu, Jiwei Zhang, Hongbo Xing, Huiwen Gong, Shilin Luo, Yifeng Xiong, Li Yu, Zhiqing Yuan, Guangyi Liu, Tao Jiang
Date: 2025-05-27 00:00:00
Tasks: Integrated sensing and communication, ISAC

Accurate radar cross section (RCS) modeling is crucial for characterizing target scattering and improving the precision of Integrated Sensing and Communication (ISAC) channel modeling. Existing RCS models are typically designed for specific target types, leading to increased complexity and lack of generalization. This makes it difficult to standardize RCS models for 3GPP ISAC channels, which need to account for multiple typical target types simultaneously. Furthermore, 3GPP models must support both system-level and link-level simulations, requiring the integration of large-scale and small-scale scattering characteristics. To address these challenges, this paper proposes a unified RCS modeling framework that consolidates these two aspects. The model decomposes RCS into three components: (1) a large-scale power factor representing overall scattering strength, (2) a small-scale angular-dependent component describing directional scattering, and (3) a random component accounting for variations across target instances. We validate the model through mono-static RCS measurements for UAV, human, and vehicle targets across five frequency bands. The results demonstrate that the proposed model can effectively capture RCS variations for different target types. Finally, the model is incorporated into an ISAC channel simulation platform to assess the impact of target RCS characteristics on path loss, delay spread, and angular spread, providing valuable insights for future ISAC system design.
OpenNIRScap: An Open-Source, Low-Cost Wearable Near-Infrared Spectroscopy-based Brain Interfacing Cap
Authors: Tony Kim, Haotian Liu, Chiung-Ting Huang, Ingrid Wu, Xilin Liu
Date: 2025-05-26 00:00:00

Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive, real-time method for monitoring brain activity by measuring hemodynamic responses in the cerebral cortex. However, existing systems are expensive, bulky, and limited to clinical or research environments. This paper introduces OpenNIRScap, an open-source, low-cost, and wearable fNIRS system designed to make real-time brain monitoring more accessible in everyday environments. The device features 24 custom-designed sensor boards with dual-wavelength light emitters and photodiode detectors, a central electrical control unit (ECU) with analog multiplexing, and a real-time data processing pipeline. Bench validation and pilot tests on volunteers have confirmed the ability of the system to capture cognitively evoked hemodynamic responses, supporting its potential as an affordable tool for cognitive monitoring and portable neurotechnology applications. The hardware, software, and graphical user interface have all been open-sourced and made publicly available at the following link: https://github.com/tonykim07/fNIRS.
CardioPatternFormer: Pattern-Guided Attention for Interpretable ECG Classification with Transformer Architecture
Authors: Berat Kutay Uğraş, Ömer Nezih Gerek, İbrahim Talha Saygı
Date: 2025-05-26 00:00:00
Tasks: Diagnostic, ECG Classification

Accurate ECG interpretation is vital, yet complex cardiac data and "black-box" AI models limit clinical utility. Inspired by Transformer architectures' success in NLP for understanding sequential data, we frame ECG as the heart's unique "language" of temporal patterns. We present CardioPatternFormer, a novel Transformer-based model for interpretable ECG classification. It employs a sophisticated attention mechanism to precisely identify and classify diverse cardiac patterns, excelling at discerning subtle anomalies and distinguishing multiple co-occurring conditions. This pattern-guided attention provides clear insights by highlighting influential signal regions, effectively allowing the "heart to talk" through transparent interpretations. CardioPatternFormer demonstrates robust performance on challenging ECGs, including complex multi-pathology cases. Its interpretability via attention maps enables clinicians to understand the model's rationale, fostering trust and aiding informed diagnostic decisions. This work offers a powerful, transparent solution for advanced ECG analysis, paving the way for more reliable and clinically actionable AI in cardiology.
BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics
Authors: Hui Zheng, Hai-Teng Wang, Yi-Tao Jing, Pei-Yang Lin, Han-Qing Zhao, Wei Chen, Peng-Hu Wei, Yong-Zhi Shan, Guo-Guang Zhao, Yun-Zhe Liu
Date: 2025-05-26 00:00:00
Tasks: Brain Computer Interface, Disentanglement, Quantization

Decoding speech directly from neural activity is a central goal in brain-computer interface (BCI) research. In recent years, exciting advances have been made through the growing use of intracranial field potential recordings, such as stereo-ElectroEncephaloGraphy (sEEG) and ElectroCorticoGraphy (ECoG). These neural signals capture rich population-level activity but present key challenges: (i) task-relevant neural signals are sparsely distributed across sEEG electrodes, and (ii) they are often entangled with task-irrelevant neural signals in both sEEG and ECoG. To address these challenges, we introduce a unified Coarse-to-Fine neural disentanglement framework, BrainStratify, which includes (i) identifying functional groups through spatial-context-guided temporal-spatial modeling, and (ii) disentangling distinct neural dynamics within the target functional group using Decoupled Product Quantization (DPQ). We evaluate BrainStratify on two open-source sEEG datasets and one (epidural) ECoG dataset, spanning tasks like vocal production and speech perception. Extensive experiments show that BrainStratify, as a unified framework for decoding speech from intracranial neural signals, significantly outperforms previous decoding methods. Overall, by combining data-driven stratification with neuroscience-inspired modularity, BrainStratify offers a robust and interpretable solution for speech decoding from intracranial recordings.
Federated Learning-Distillation Alternation for Resource-Constrained IoT
Authors: Rafael Valente da Silva, Onel L. Alcaraz López, Richard Demo Souza
Date: 2025-05-26 00:00:00
Tasks: Federated Learning

Federated learning (FL) faces significant challenges in Internet of Things (IoT) networks due to device limitations in energy and communication resources, especially when considering the large size of FL models. From an energy perspective, the challenge is aggravated if devices rely on energy harvesting (EH), as energy availability can vary significantly over time, influencing the average number of participating users in each iteration. Additionally, the transmission of large model updates is more susceptible to interference from uncorrelated background traffic in shared wireless environments. As an alternative, federated distillation (FD) reduces communication overhead and energy consumption by transmitting local model outputs, which are typically much smaller than the entire model used in FL. However, this comes at the cost of reduced model accuracy. Therefore, in this paper, we propose FL-distillation alternation (FLDA). In FLDA, devices alternate between FD and FL phases, balancing model information with lower communication overhead and energy consumption per iteration. We consider a multichannel slotted-ALOHA EH-IoT network subject to background traffic/interference. In such a scenario, FLDA demonstrates higher model accuracy than both FL and FD, and achieves faster convergence than FL. Moreover, FLDA achieves target accuracies saving up to 98% in energy consumption, while also being less sensitive to interference, both relative to FL.
Identification of Power System Dynamic Model Parameters using the Fisher Information Matrix
Authors: Dawn Virginillo, Asja Derviškadić, Mario Paolone
Date: 2025-05-26 00:00:00

The expected decrease in system inertia and frequency stability motivates the development and maintenance of dynamic system models by Transmission System Operators. However, some dynamic model parameters can be unavailable due to market unbundling, or inaccurate due to aging infrastructure, non-documented tuning of controllers, or other factors. In this paper, we propose the use of a numerical approximation of the Fisher Information Matrix (nFIM) for efficient inference of dynamic model parameters. Thanks to the proposed numerical implementation, the method is scalable to Electromagnetic Transient (EMT) models, which can quickly become computationally complex even for small study systems. Case studies show that the nFIM is coherent with parameter variances of single- and multi-parameter least-squares estimators when applied to an IEEE 9-bus dynamic model with artificial measurements.
OFDMA for Pinching Antenna Systems
Authors: Thrassos K. Oikonomou, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, Yuanwei Liu, George K. Karagiannidis
Date: 2025-05-26 00:00:00
Tasks: Fairness

Pinching-antenna (PA) systems route millimeter wave (mmWave) signals through a leaky waveguide and radiate them at "pinch" apertures, offering low-cost line-of-sight (LoS) coverage. However, when multiple PAs serve multiple users simultaneously, the downlink channel becomes strongly frequency-selective, creating inter-symbol interference (ISI) that existing single-carrier designs overlook. This paper models the overall channel as a finite impulse response (FIR) filter, characterizes its frequency selectivity, and explicitly accounts for the resulting ISI. To overcome ISI, we introduce an orthogonal frequency-division multiple access (OFDMA)-based framework and formulate a max-min resource-allocation problem to achieve user fairness. A lightweight two-stage heuristic-greedy subcarrier assignment, followed by per-user water-filling, achieves near-optimal fairness with polynomial complexity. Simulation results for an indoor layout demonstrate that the proposed scheme notably increases the minimum user rate compared to time-division single-carrier baselines and remains robust under moderate LoS blockage.
Bit Error Rate and Performance Analysis of Multi-User OTFS under Nakagami-m Fading for 6G and Beyond Networks
Authors: Emir Aslandogan, Haci Ilhan
Date: 2025-05-26 00:00:00

Orthogonal Time-Frequency Space modulation stands out as a promising waveform for 6G and beyond wireless communication systems, offering superior performance over conventional methods, particularly in high-mobility scenarios and dispersive channel conditions. Error performance analysis remains crucial for accurately characterizing the reliability of wireless communication systems under practical constraints. In this paper, we systematically investigate the bit error rate performance of OTFS modulation over Nakagami-m fading channels in both single-user and multi-user scenarios. In analytical approaches, mathematical frameworks are employed for distinct receiver configurations: the Single-input Single-output scenario leverages Erlang probability density function of squared-Nakagami variables to derive closed-form BER expressions, while the Single-input Multiple-output case applies moment matching techniques with Gamma approximation to model multiple user interference, subsequently yielding Signal-to-interference-plus-noise Ratio characterizations through Meijer-G functions. This study examines single-path and multi-path channel conditions, evaluating the relationship between path multiplicity and error performance metrics while considering various fading intensities through Nakagami-m fading parameters. The derived closed-form BER expressions are validated through maximum likelihood detection based Monte Carlo simulations, demonstrating strong correlation between analytical and numerical results across various SNR regions. Furthermore, comparative benchmark evaluations against conventional orthogonal frequency division multiplexing with MLD reveal that OTFS consistently achieves superior error performance in high-mobility scenarios. In multipath fading environments, OTFS achieves superior diversity gain compared to conventional OFDM, which refers to enhanced error performance.
Accurate Radar-Based Detection of Sleep Apnea Using Overlapping Time-Interval Averaging
Authors: Kodai Hasegawa, Shigeaki Okumura, Hirofumi Taki, Hironobu Sunadome, Satoshi Hamada, Susumu Sato, Kazuo Chin, Takuya Sakamoto
Date: 2025-05-26 00:00:00

Radar-based respiratory measurement is a promising tool for the noncontact detection of sleep apnea. Our team has reported that apnea events can be accurately detected using the statistical characteristics of the amplitude of respiratory displacement. However, apnea and hypopnea events are often followed by irregular breathing, reducing the detection accuracy. This study proposes a new method to overcome this performance degradation by repeatedly applying the detection method to radar data sets corresponding to multiple overlapping time intervals. Averaging the detected classes over multiple time intervals gives an analog value between 0 and 1, which can be interpreted as the probability that there is an apnea event. We show that the proposed method can mitigate the effect of irregular breathing that occurs after apnea / hypopnea events, and its performance is confirmed by experimental data taken from seven patients.
Water Level Sensing via Communication Signals in a Bi-Static System
Authors: Zhongqin Wang, J. Andrew Zhang, Kai Wu, Y. Jay Guo
Date: 2025-05-26 00:00:00

Accurate water level sensing is essential for flood monitoring, agricultural irrigation, and water resource optimization. Traditional methods require dedicated sensor deployments, leading to high installation costs, vulnerability to interference, and limited resolution. This work proposes PMNs-WaterSense, a novel scheme leveraging Channel State Information (CSI) from existing mobile networks for water level sensing. Our scheme begins with a CSI-power method to eliminate phase offsets caused by clock asynchrony in bi-static systems. We then apply multi-domain filtering across the time (Doppler), frequency (delay), and spatial (Angle-of-Arrival, AoA) domains to extract phase features that finely capture variations in path length over water. To resolve the $2\pi$ phase ambiguity, we introduce a Kalman filter-based unwrapping technique. Additionally, we exploit transceiver geometry to convert path length variations into water level height changes, even with limited antenna configurations. We validate our framework through controlled experiments with 28 GHz mmWave and 3.1 GHz LTE signals in real time, achieving average height estimation errors of 0.025 cm and 0.198 cm, respectively. Moreover, real-world river monitoring with 2.6 GHz LTE signals achieves an average error of 4.8 cm for a 1-meter water level change, demonstrating its effectiveness in practical deployments.
Empirical 3D Channel Modeling for Cellular-Connected UAVs: A Triple-Layer Machine Learning Approach
Authors: Haider A. H. Alobaidy, Mehran Behjati, Rosdiadee Nordin, Muhammad Aidiel Zulkifley, Nor Fadzilah Abdullah, Nur Fahimah Mat Salleh
Date: 2025-05-26 00:00:00
Tasks: feature selection, GPR

This work proposes an empirical air to ground (A2G) propagation model specifically designed for cellular connected unmanned aerial vehicles (UAVs). An in depth aerial drive test was carried out within an operating Long Term Evolution (LTE) network, gathering thorough measurements of key network parameters. Rigid preprocessing and statistical analysis of these data produced a strong foundation for training a new triple layer machine learning (ML) model. The proposed ML framework employs a systematic hierarchical approach. Accordingly, the first two layers, Stepwise Linear Regression (STW) and Ensemble of Bagged Trees (EBT) generate predictions independently, meanwhile, the third layer, Gaussian Process Regression (GPR), explicitly acts as an aggregation layer, refining these predictions to accurately estimate Key Performance Indicators (KPIs) such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength (RSSI), and Path Loss (PL). Compared to traditional single layer ML or computationally intensive ray tracing approaches, the proposed triple layer ML framework significantly improves predictive accuracy and robustness, achieving around 99 percent accuracy in training and above 90 percent in testing while utilizing a minimal but effective feature set log transformed 3D and 2D propagation distances, azimuth, and elevation angles. This streamlined feature selection substantially reduces computing complexity, thus enhancing scalability across various operating environments. The proposed frameworks practicality and efficacy for real world deployment in UAV integrated cellular networks are further demonstrated by comparative analyses, which underscore its substantial improvement.
Foundation Model for Wireless Technology Recognition Using IQ Timeseries
Authors: Mohammad Cheraghinia, Eli de Poorter, Jaron Fontaine, Merouane Debbah, Adnan Shahid
Date: 2025-05-26 00:00:00
Tasks: Computational Efficiency, Unsupervised Pre-training

Wireless Technology Recognition (WTR) is essential in modern communication systems, enabling efficient spectrum management and the seamless coexistence of diverse technologies. In real-world conditions, WTR solutions should be able to handle signals from various resources with different sampling rates, capturing devices, and frequency bands. However, traditional WTR methods, which rely on energy detection, Convolutional Neural Network (CNN) models, or Deep Learning (DL), lack the robustness and adaptability required to generalize across unseen environments, different sampling devices, and previously unencountered signal classes. In this work, we introduce a Transformer-based foundation model for WTR, trained in an unsupervised manner on large-scale, unlabeled wireless signal datasets. Foundation models are designed to learn general-purpose representations that transfer effectively across tasks and domains, allowing generalization towards new technologies and WTR sampling devices. Our approach leverages input patching for computational efficiency and incorporates a two-stage training pipeline: unsupervised pre-training followed by lightweight fine-tuning. This enables the model to generalize to new wireless technologies and environments using only a small number of labeled samples. Experimental results demonstrate that our model achieves superior accuracy across varying sampling rates and frequency bands while maintaining low computational complexity, supporting the vision of a reusable wireless foundation model adaptable to new technologies with minimal retraining.
Movable-Element STARS-Assisted Near-Field Wideband Communications
Authors: Guangyu Zhu, Xidong Mu, Li Guo, Ao Huang, Shibiao Xu
Date: 2025-05-25 00:00:00
Tasks: Heuristic Search

A novel movable-element simultaneously transmitting and reflecting surface (ME-STARS)-assisted near-field wideband communication framework is proposed. In particular, the position of each STARS element can be adjusted to combat the significant wideband beam squint issue in the near field instead of using costly true-time delay components. Four practical ME-STARS element movement modes are proposed, namely region-based (RB), horizontal-based (HB), vertical-based (VB), and diagonal-based (DB) modes. Based on this, a near-field wideband multi-user downlink communication scenario is considered, where a sum rate maximization problem is formulated by jointly optimizing the base station (BS) precoding, ME-STARS beamforming, and element positions. To solve this intractable problem, a two-layer algorithm is developed. For the inner layer, the block coordinate descent optimization framework is utilized to solve the BS precoding and ME-STARS beamforming in an iterative manner. For the outer layer, the particle swarm optimization-based heuristic search method is employed to determine the desired element positions. Numerical results show that:1) the ME-STARSs can effectively address the beam squint for near-field wideband communications compared to conventional STARSs with fixed element positions; 2) the RB mode achieves the most efficient beam squint effect mitigation, while the DB mode achieves the best trade-off between performance gain and hardware overhead; and 3) an increase in the number of ME-STARS elements or BS subcarriers substantially improves the system performance.