pwc-archive/papers-with-abstracts huggingface dataset:
Classifier-guided registration of coronary CT angiography and intravascular ultrasound
Authors: R. L. M. van Herten, José P. Henriques, R. Nils Planken, Joost Daemen, Eline M. J. Hartman, Jolanda J. Wentzel, Ivana Išgum
Date: 2024-12-22 00:00:00
Tasks: Image Registration

Coronary CT angiography (CCTA) and intravascular ultrasound (IVUS) provide complementary information for coronary artery disease assessment, making their registration valuable for comprehensive analysis. However, existing registration methods require manual interaction or extensive segmentations, limiting their practical application. In this work, we present a fully automatic framework for CCTA-IVUS registration using deep learning-based feature detection and a differentiable image registration module. Our approach leverages a convolutional neural network trained to identify key anatomical features from polar-transformed multiplanar reformatted CCTA or IVUS data. These detected anatomical featuers subsequently guide a differentiable registration module to optimize transformation parameters of an automatically extracted coronary artery centerline. The method does not require landmark selection or segmentations as input, while accounting for the presence of IVUS guidewire artifacts. Evaluated on 48 clinical cases with reference CCTA centerlines corresponding to IVUS pullback, our method achieved successful registration in 83.3\% of cases, with a median centerline overlap F$_1$-score of 0.982 and median cosine similarities of 0.940 and 0.944 for cross-sectional plane orientation. Our results demonstrate that automatically detected anatomical features can be leveraged for accurate registration. The fully automatic nature of the approach represents a significant step toward streamlined multimodal coronary analysis, potentially facilitating large-scale studies of coronary plaque characteristics across modalities.
RFUDS -- A Brain Metastases Imaging Dataset of Radiotherapy Follow-Up
Authors: Margarida Fernandes, José Soares, Matheus Silva, Crystian Saraiva, Victor Alves
Date: 2024-12-21 00:00:00
Tasks: Computed Tomography (CT)

Brain metastases are a common diagnosis that affects between 20% and 40% of cancer patients. Subsequent to radiation therapy, patients with brain metastases undergo follow-up sessions during which the response to treatment is monitored. In this study, a dataset of medical images from 44 patients with at least one brain metastasis and different primary tumor locations was collected and processed. Each patient was treated with either a linear accelerator or a gamma knife. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans were collected at various time points, including before treatment and during follow-up sessions. The CT datasets were processed using windowing and artifact reduction techniques, while the MRI datasets were subjected to CLAHE. The NifTI files corresponding to the CT and MRI images were made public available. In order to align the datasets of each patient, a multimodal registration was performed between the CT and MRI datasets, with different software options being tested. The fusion matrices were provided together with the dataset. The aforementioned steps resulted in the creation of an optimized dataset, prepared for use in a range of studies related to brain metastases. RFUds is publicity available at zenodo under the DOI 10.5281/zenodo.14524784.
Federal Learning Framework for Quality Evaluation of Blastomere Cleavage
Authors: Jung-Hua Wang, Huai-Wen Chang, Rong-Yu Wu, Ming-Jer Chen, Yu-Chiao Yi
Date: 2024-12-21 00:00:00
Tasks: Federated Learning

This study addresses the issue of leveraging federated learning to improve data privacy and performance in IVF embryo selection. The EM (Expectation-Maximization) algorithm is incorporated into deep learning models to form a federated learning framework for quality evaluation of blastomere cleavage using two-dimensional images. The framework comprises a server site and several client sites characterized in that each is locally trained with an EM algorithm. Upon the completion of the local EM training, a separate 5-mode mixture distribution is generated for each client, the clients' distribution statics are then uploaded to the server site and aggregated therein to produce a global (sharing) 5-mode distribution. During the inference phase, each client uses image classifiers and an instance segmentor, assisted by the global 5-mode distribution acting as a calibrator to (1) identify the absolute cleavage timing of blastomere, i.e., tPNa, tPNf, t2, t3, t4, t5, t6, t7, and t8, (2) track the cleavage process of blastomeres to detect the irregular cleavage patterns, and (3) assess the symmetry degree of blastomeres. Experimental results show that the proposed method outperforms commercial Time-Lapse Incubators in reducing the average error of timing prediction by twofold. The proposed facilitate frameworks the adaptability and scalability of classifiers and segmentor to data variability associated with patients in different locations or countries.
Localization Accuracy Improvement in Multistatic ISAC with LoS/NLoS Condition using 5G NR Signals
Authors: Keivan Khosroshahi, Philippe Sehier, Sami Mekki, Michael Suppa
Date: 2024-12-23 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is anticipated to play a crucial role in sixth-generation (6G) mobile communication networks. A significant challenge in ISAC systems is the degradation of localization accuracy due to poor propagation conditions, such as multipath effects and non-line-of-sight (NLoS) scenarios. These conditions result in outlier measurements that can severely impact localization performance. This paper investigates the enhancement of target localization accuracy in multistatic ISAC systems under both line-of-sight (LoS) and NLoS conditions. We leverage positioning reference signal (PRS), which is currently employed in fifth-generation (5G) new radio (NR) for user equipment (UE) positioning, as the sensing signal. We introduce a novel algorithm to improve localization accuracy by mitigating the impact of outliers in range measurements, while also accounting for errors due to PRS range resolution. Eventually, through simulation results, we demonstrate the superiority of the proposed method over previous approaches. Indeed, we achieve up to 28% and 20% improvements in average localization error over least squares (LS) and iteratively reweighted least squares (IRLS) methods, respectively. Additionally, we observe up to 16% and 13% enhancements in the 90th percentile of localization error compared to LS and IRLS, respectively. Our simulation is based on 3rd Generation Partnership Project (3GPP) standards, ensuring the applicability of our results across diverse environments, including urban and indoor areas.
Tri-Spectral PPG: Robust Reflective Photoplethysmography by Fusing Multiple Wavelengths for Cardiac Monitoring
Authors: Manuel Meier, Berken Utku Demirel, Christian Holz
Date: 2024-12-23 00:00:00
Tasks: Heart Rate Variability, Photoplethysmography (PPG)

Multi-channel photoplethysmography (PPG) sensors have found widespread adoption in wearable devices for monitoring cardiac health. Channels thereby serve different functions -- whereas green is commonly used for metrics such as heart rate and heart rate variability, red and infrared are commonly used for pulse oximetry. In this paper, we introduce a novel method that simultaneously fuses multi-channel PPG signals into a single recovered PPG signal that can be input to further processing. Via signal fusion, our learning-based method compensates for the artifacts that affect wavelengths to different extents, such as motion and ambient light changes. We evaluate our method on a novel dataset of multi-channel PPG recordings and electrocardiogram recordings for reference from 10 participants over the course of 13 hours during real-world activities outside the laboratory. Using the fusion PPG signal our method recovered, participants' heart rates can be calculated with a mean error of 4.5\,bpm (23\% lower than from green PPG signals at 5.9\,bpm).
Robust Heart Rate Detection via Multi-Site Photoplethysmography
Authors: Manuel Meier, Christian Holz
Date: 2024-12-23 00:00:00
Tasks: Photoplethysmography (PPG), Sensor Fusion

Smartwatches have become popular for monitoring physiological parameters outside clinical settings. Using reflective photoplethysmography (PPG) sensors, such watches can non-invasively estimate heart rate (HR) in everyday environments and throughout a patient's day. However, achieving consistently high accuracy remains challenging, particularly during moments of increased motion or due to varying device placement. In this paper, we introduce a novel sensor fusion method for estimating HR that flexibly combines samples from multiple PPG sensors placed across the patient's body, including wrist, ankle, head, and sternum (chest). Our method first estimates signal quality across all inputs to dynamically integrate them into a joint and robust PPG signal for HR estimation. We evaluate our method on a novel dataset of PPG and ECG recordings from 14 participants who engaged in real-world activities outside the laboratory over the course of a whole day. Our method achieves a mean HR error of 2.4\,bpm, which is 46\% lower than the mean error of the best-performing single device (4.4\,bpm, head).
FPGA Implementation of Low-Power Multiplierless Pre-Processing Free Chromatic Dispersion Equalizer
Authors: Geraldo Gomes, Pedro Freire, Jaroslaw E. Prilepsky, Sergei K. Turitsyn
Date: 2024-12-23 00:00:00

We present a novel time-domain chromatic dispersion equalizer, implemented on FPGA, eliminating pre-processing and multipliers, achieving up to 54.3% energy savings over 80-1280 km with a simple, low-power design.
Assessment of Deep-Learning Methods for the Enhancement of Experimental Low Dose Dental CBCT Volumes
Authors: Louise Friot--Giroux, Françoise Peyrin, Voichiţa Maxim
Date: 2024-12-23 00:00:00

Cone-beam tomography enables rapid 3D acquisitions, making it a suitable imaging modality for dental imaging. However, as with all X-ray techniques, the main challenge is to reduce the dose while maintaining good image quality. Moreover, dental reconstructions face a series of issues stemming from truncated projections as well as metal and cone beam artifacts. The aim here is to investigate the ability of neural networks to improve the quality of 3D CBCT dental images at low doses. We test different configurations of convolutional neural networks, trained in a supervised way to reduce artifacts and noise present in analytically reconstructed volumes. In a study on 32 experimental cone beam volumes, we show their capacity to preserve and enhance details while still reducing the artifacts. The best results are obtained with a 3D U-Net which compares advantageously with a TV regularized iterative method and is considerably faster.
Cech Complex Generation with Homotopy Equivalence Framework for Myocardial Infarction Diagnosis using Electrocardiogram Signals
Authors: Srikireddy Dhanunjay Reddy, Pujayita Deb, Tharun Kumar Reddy Bollu
Date: 2024-12-23 00:00:00
Tasks: Anomaly Detection, Rhythm

Early and optimal identification of cardiac anomalies, especially Myocardial infarction (MCI) can aid the individual in obtaining prompt medical attention to mitigate the severity. Electrocardiogram (ECG) is a simple non-invasive physiological signal modality, that can be used to examine the electrical activity of heart tissue. Existing methods for MCI detection mostly rely on the temporal, frequency, and spatial domain analysis of the ECG signals. These conventional techniques lack in effective identification of cardiac cycle inter-dependency during diagnosis. Hence, there is an emerging need for incorporating the underlying connectivity of the intra-sessional cardiac cycles for improved anomaly detection. This article proposes a novel framework for ECG signal analysis and classification using persistent homological features through Cech Complex generation with homotopy equivalence check, by taking the above-mentioned emerging needs into account. Homological features like persistent birth-death rates, betti curves, and persistent entropy provide transparency of the regional and cardiac cycle connectivity when combined with Machine Learning (ML) models. The proposed framework is assessed using publicly available datasets (MIT-BIH and PTB), and the performance metrics of machine learning models indicate its efficacy in classifying Normal Sinus Rhythm (NSR), MCI, and non-MCI subjects, achieving a 2.8% mean improvement in AUC (area under the ROC curve) over existing approaches.
Energy-Efficient RIS-Aided Cell-Free Massive MIMO Systems: Application, Opportunities, and Challenges
Authors: Yu Lu, Jiayi Zhang, Enyu Shi, Peng Zhang, Derrick Wing Kwan Ng, Dusit Niyato, Bo Ai
Date: 2024-12-23 00:00:00

Reconfigurable intelligent surfaces (RIS)-assisted cell-free massive multiple-input multiple-output (CF mMIMO) systems have emerged as a promising technology for sixth-generation communication systems. These systems capitalize on RIS to minimize power consumption, thereby achieving consistent performance and enhancing communication quality through the establishment and shaping of auxiliary signal propagation pathways between access points (APs) and users. However, integrating RIS into existing CF mMIMO infrastructures presents several technical challenges. This study delves into the signal transmission scheme and deployment architecture of RIS-aided CF mMIMO systems, addressing inherent challenges such as interference induced by RIS and the increased complexity in beam alignment. Furthermore, we address the complexities arising from the joint optimization of the reflection phase of RIS and beamforming technology at the APs, intending to fully exploit the reflection capabilities of RISs and beamforming technology to maximize the energy efficiency (EE) of the system. To overcome these challenges, we propose cooperation communication to suppress RIS-induced interference, beam tracking, and joint optimization to improve system EE. We also present specific examples of cooperative communication under the constraint of electromagnetic interference and the beam tracking of a mobile system. Additionally, we emphasize important research directions for RIS-aided CF mMIMO systems, aiming to inspire future investigations.
Joint Multitarget Detection and Tracking with mmWave Radar
Authors: Jiang Zhu, Menghuai Xu, Ruohai Guo, Fangyong Wang, Guangying Zheng, Fengzhong Qu
Date: 2024-12-23 00:00:00
Tasks: Super-Resolution

Accurate targets detection and tracking with mmWave radar is a key sensing capability that will enable more intelligent systems, create smart, efficient, automated system. This paper proposes an end-to-end detection-estimation-track framework named MNOMP-SPA-KF consisting of the target detection and estimation module, the data association (DA) module and the target tracking module. In the target estimation and detection module, a low complexity, super-resolution and constant false alarm rate (CFAR) based two dimensional multisnapshot Newtonalized orthogonal matching pursuit (2D-MNOMP) is designed to extract the multitarget's radial distances and velocities, followed by the conventional (Bartlett) beamformer to extract the multitarget's azimuths. In the DA module, a sum product algorithm (SPA) is adopted to obtain the association probabilities of the existed targets and measurements by incorporating the radial velocity information. The Kalman filter (KF) is implemented to perform target tracking in the target tracking module by exploiting the asymptotic distribution of the estimators. To improve the detection probability of the weak targets, extrapolation is also coupled into the MNOMP-SPA-KF. Numerical and real data experiments demonstrate the effectiveness of the MNOMP-SPA-KF algorithm, compared to other benchmark algorithms.
Multi-classification of High-Frequency Oscillations Using iEEG Signals and Deep Learning Models
Authors: Zayneb Sadek, Abir Hadriche, Rahma Maalej, Nawel Jmail
Date: 2024-12-22 00:00:00
Tasks: Seizure prediction

Over the past decade, high-frequency oscillations (HFOs) have been studied as a promising biomarker for localizing epileptogenic areas in drug-resistant patients requiring pre-surgical intervention, while exploiting intracranial electroencephalographic iEEG. Consequently, it's important to develop accurate methods for predicting epileptic seizures. Seizure prediction involves classifying appropriate indicators, which is a difficult classification problem. Deep learning techniques, such as convolutional neural networks (CNN), have shown great promise in analyzing and classifying epilepsy-related iEEG signals. In this study, we proposed three global methods, multiclass SVM, multiple architecture CNN, and CNN-SVM, which are evaluated on a simulated iEEG dataset and then on a real iEEG signal. Our best results for the three models yield high accuracy rates, GoogLeNet-SVM achieves approximately 99.63% and 94.07% for simulated data (1) and real data (2), respectively, SVM multiclass achieves 98.14% and 88.51% for (1) and (2), respectively, and GoogLeNet achieves 98.52% and 91.85% for (1) and (2), respectively. Furthermore, the proposed model performs better than other current techniques. These results suggest that deep learning models could be a successful strategy for classifying epilepsy indicators and could potentially improve seizure prediction methods, thus improving the life quality of epileptic patients.
6DMA-Aided Hybrid Beamforming with Joint Antenna Position and Orientation Optimization
Authors: Yichi Zhang, Yuchen Zhang, Lipeng Zhu, Sa Xiao, Wanbin Tang, Yonina C. Eldar, Rui Zhang
Date: 2024-12-22 00:00:00
Tasks: Position

This paper studies a sub-connected six-dimensional movable antenna (6DMA)-aided multi-user communication system. In this system, each sub-array is connected to a dedicated radio frequency chain and collectively moves and rotates as a unit within specific local regions. The movement and rotation capabilities of 6DMAs enhance design flexibility, facilitating the capture of spatial variations for improved communication performance. To fully characterize the effect of antenna position and orientation on wireless channels between the base station (BS) and users, we develop a field-response-based 6DMA channel model to account for the antenna radiation pattern and polarization. We then maximize the sum rate of multiple users, by jointly optimizing the digital and unit-modulus analog beamformers given the transmit power budget as well as the positions and orientations of sub-arrays within given movable and rotatable ranges at the BS. Due to the highly coupled variables, the formulated optimization problem is non-convex and thus challenging to solve. We develop a fractional programming-aided alternating optimization framework that integrates the Lagrange multiplier method, manifold optimization, and gradient descent to solve the problem. Numerical results demonstrate that the proposed 6DMA-aided sub-connected structure achieves a substantial sum-rate improvement over various benchmark schemes with less flexibility in antenna movement and can even outperform fully-digital beamforming systems that employ antenna position or orientation adjustments only. The results also highlight the necessity of considering antenna polarization for optimally adjusting antenna orientation.
Design of Frequency Index Modulated Waveforms for Integrated SAR and Communication on High-Altitude Platforms (HAPs)
Authors: Bang Huang, Sajid Ahmed, Mohamed-Slim Alouini
Date: 2024-12-22 00:00:00

This paper, addressing the integration requirements of radar imaging and communication for High-Altitude Platform Stations (HAPs) platforms, designs a waveform based on linear frequency modulated (LFM) frequency-hopping signals that combines synthetic aperture radar (SAR) and communication functionalities. Specifically, each pulse of an LFM signal is segmented into multiple parts, forming a sequence of sub-pulses. Each sub-pulse can adopt a different carrier frequency, leading to frequency hops between sub-pulses. This design is termed frequency index modulation (FIM), enabling the embedding of communication information into different carrier frequencies for transmission. To further enhance the data transmission rate at the communication end, this paper incorporates quadrature amplitude modulation (QAM) into waveform design. %For the SAR portion, this approach reduces the ADC sampling requirements while maintaining range resolution. The paper derives the ambiguity function of the proposed waveform and analyzes its Doppler and range resolution, establishing upper and lower bounds for the range resolution. In processing SAR signals, the receiver first removes QAM symbols, and to address phase discontinuities between sub-pulses, a phase compensation algorithm is proposed to achieve coherent processing. For the communication receiver, the user first performs de-chirp processing and then demodulates QAM symbols and FIM index symbols using a two-step maximum likelihood (ML) algorithm. Numerical simulations further confirm the theoretical validity of the proposed approach.
Amplifier-Enhanced Memristive Massive MIMO Linear Detector Circuit: An Ultra-Energy-Efficient and Robust-to-Conductance-Error Design
Authors: Jia-Hui Bi, Shaoshi Yang, Ping Zhang, Sheng Chen
Date: 2024-12-22 00:00:00

The emerging analog matrix computing technology based on memristive crossbar array (MCA) constitutes a revolutionary new computational paradigm applicable to a wide range of domains. Despite the proven applicability of MCA for massive multiple-input multiple-output (MIMO) detection, existing schemes do not take into account the unique characteristics of massive MIMO channel matrix. This oversight makes their computational accuracy highly sensitive to conductance errors of memristive devices, which is unacceptable for massive MIMO receivers. In this paper, we propose an MCA-based circuit design for massive MIMO zero forcing and minimum mean-square error detectors. Unlike the existing MCA-based detectors, we decompose the channel matrix into the product of small-scale and large-scale fading coefficient matrices, thus employing an MCA-based matrix computing module and amplifier circuits to process the two matrices separately. We present two conductance mapping schemes which are crucial but have been overlooked in all prior studies on MCA-based detector circuits. The proposed detector circuit exhibits significantly superior performance to the conventional MCA-based detector circuit, while only incurring negligible additional power consumption. Our proposed detector circuit maintains its advantage in energy efficiency over traditional digital approach by tens to hundreds of times.