pwc-archive/papers-with-abstracts huggingface dataset:
Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites
Authors: Hanliang Xu, Nancy R. Newlin, Michael E. Kim, Chenyu Gao, Praitayini Kanakaraj, Aravind R. Krishnan, Lucas W. Remedios, Nazirah Mohd Khairi, Kimberly Pechman, Derek Archer, Timothy J. Hohman, Angela L. Jefferson, The BIOCARD Study Team, Ivana Isgum, Yuankai Huo, Daniel Moyer, Kurt G. Schilling, Bennett A. Landman
Date: 2024-01-09 00:00:00
Tasks: Diffusion  MRI

Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis across datasets to improve robustness and reproducibility of results. To evaluate different harmonization approaches on connectivity matrices, we compared graph measures derived from these matrices before and after applying three harmonization techniques: mean shift, ComBat, and CycleGAN. The sample comprises 168 age-matched, sex-matched normal subjects from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we plotted the graph measures and used coefficient of variation (CoV) and the Mann-Whitney U test to evaluate different methods' effectiveness in removing site effects on the matrices and the derived graph measures. ComBat effectively eliminated site effects for global efficiency and modularity and outperformed the other two methods. However, all methods exhibited poor performance when harmonizing average betweenness centrality. Second, we tested whether our harmonization methods preserved correlations between age and graph measures. All methods except for CycleGAN in one direction improved correlations between age and global efficiency and between age and modularity from insignificant to significant with p-values less than 0.05.
HA-HI: Synergising fMRI and DTI through Hierarchical Alignments and Hierarchical Interactions for Mild Cognitive Impairment Diagnosis
Authors: Xiongri Shen, Zhenxi Song, Linling Li, Min Zhang, Lingyan Liang Honghai Liu, Demao Deng, Zhiguo Zhang
Date: 2024-01-02 00:00:00

Early diagnosis of mild cognitive impairment (MCI) and subjective cognitive decline (SCD) utilizing multi-modal magnetic resonance imaging (MRI) is a pivotal area of research. While various regional and connectivity features from functional MRI (fMRI) and diffusion tensor imaging (DTI) have been employed to develop diagnosis models, most studies integrate these features without adequately addressing their alignment and interactions. This limits the potential to fully exploit the synergistic contributions of combined features and modalities. To solve this gap, our study introduces a novel Hierarchical Alignments and Hierarchical Interactions (HA-HI) method for MCI and SCD classification, leveraging the combined strengths of fMRI and DTI. HA-HI efficiently learns significant MCI- or SCD- related regional and connectivity features by aligning various feature types and hierarchically maximizing their interactions. Furthermore, to enhance the interpretability of our approach, we have developed the Synergistic Activation Map (SAM) technique, revealing the critical brain regions and connections that are indicative of MCI/SCD. Comprehensive evaluations on the ADNI dataset and our self-collected data demonstrate that HA-HI outperforms other existing methods in diagnosing MCI and SCD, making it a potentially vital and interpretable tool for early detection. The implementation of this method is publicly accessible at https://github.com/ICI-BCI/Dual-MRI-HA-HI.git.
A Hypernetwork Based Framework for Non-Stationary Channel Prediction
Authors: Guanzhang Liu, Zhengyang Hu, Lei Wang, Hongying Zhang, Jiang Xue, Michail Matthaiou
Date: 2024-01-16 00:00:00
Tasks: Prediction

In order to break through the development bottleneck of modern wireless communication networks, a critical issue is the out-of-date channel state information (CSI) in high mobility scenarios. In general, non-stationary CSI has statistical properties which vary with time, implying that the data distribution changes continuously over time. This temporal distribution shift behavior undermines the accurate channel prediction and it is still an open problem in the related literature. In this paper, a hypernetwork based framework is proposed for non-stationary channel prediction. The framework aims to dynamically update the neural network (NN) parameters as the wireless channel changes to automatically adapt to various input CSI distributions. Based on this framework, we focus on low-complexity hypernetwork design and present a deep learning (DL) based channel prediction method, termed as LPCNet, which improves the CSI prediction accuracy with acceptable complexity. Moreover, to maximize the achievable downlink spectral efficiency (SE), a joint channel prediction and beamforming (BF) method is developed, termed as JLPCNet, which seeks to predict the BF vector. Our numerical results showcase the effectiveness and flexibility of the proposed framework, and demonstrate the superior performance of LPCNet and JLPCNet in various scenarios for fixed and varying user speeds.
Sparse array design for MIMO radar in multipath scenarios
Authors: Xuchen Li, Ronghao Lin, Hing Cheung So
Date: 2024-01-16 00:00:00

Sparse array designs have focused mostly on angular resolution, peak sidelobe level and directivity factor of virtual arrays for multiple-input multiple-output (MIMO) radar. The notion of the MIMO radar virtual array is based on the direct path assumption in that the direction-of-departure (DOD) and direction-of-arrival (DOA) of the targets are equal. However, the DOD and DOA of targets in multipath scenarios are likely to be very different. The identification of multipath targets requires DOD-DOA imaging using the the transmit and receive arrays, not the virtual array. To improve the imaging of both direct path and multipath targets, we introduce several new criteria for MIMO radar sparse linear array (SLA) designs for multipath scenarios. Under the new criteria, we adopt a cyclic optimization strategy under a coordinate descent framework to design the MIMO SLAs. We present several numerical examples to demonstrate the effectiveness of the proposed approaches.
Spoofing Detection in the Physical Layer with Graph Neural Networks
Authors: Tien Ngoc Ha, Daniel Romero
Date: 2024-01-16 00:00:00
Tasks: Graph Embedding, Graph Neural Network

In a spoofing attack, a malicious actor impersonates a legitimate user to access or manipulate data without authorization. The vulnerability of cryptographic security mechanisms to compromised user credentials motivates spoofing attack detection in the physical layer, which traditionally relied on channel features, such as the received signal strength (RSS) measured by spatially distributed receivers or access points. However, existing methods cannot effectively cope with the dynamic nature of channels, which change over time as a result of user mobility and other factors. To address this limitation, this work builds upon the intuition that the temporal pattern of changes in RSS features can be used to detect the presence of concurrent transmissions from multiple (possibly changing) locations, which in turn indicates the existence of an attack. Since a localization-based approach would require costly data collection and would suffer from low spatial resolution due to multipath, the proposed algorithm employs a deep neural network to construct a graph embedding of a sequence of RSS features that reflects changes in the propagation conditions. A graph neural network then classifies these embeddings to detect spoofing attacks. The effectiveness and robustness of the proposed scheme are corroborated by experiments with real-data.
Simultaneous Coherent and Displacement Compounding for 2D Noninvasive Carotid Strain Imaging: a Proof of Principle Study
Authors: Moein Mozaffarzadeh, Anne E. C. M. Saris, Jan Menssen, Chris L. de Korte
Date: 2024-01-16 00:00:00

Arteriosclerosis results from lipid buildup in artery walls, leading to plaque formation, and is a leading cause of death. Plaque rupture can cause blood clots that might lead to a stroke. Distinguishing plaque types is a challenge, but ultrasound elastography can help by assessing plaque composition based on strain values. Since the artery has a circular structure, an accurate axial and lateral displacement strategy is needed to derive the radial and circumferential strains. A high precision lateral displacement is challenging due to the lack of phase information in the lateral direction of the beamformed RF data. Previously, our group has developed a compounding technique in which the lateral displacement is estimated using tri-angulation of the axial displacement estimated from transmitting and beamforming ultrasound beams at +-20 degree. However, its applicability to in vivo is challenging due to the imaging noise and the low contrast of the arterial wall, caused by a single plane wave transmission. In this paper, we combine our displacement compounding with coherent compounding. Instead of transmitting a single plane wave, multiple plane waves are transmitted at certain angles with respect to the angle of the beamforming grids, and then the backscattered wavefronts are beamformed and coherently compounded on the center of the transmit beams (-20, +20 and 0 degree). ...
Near-Far Field Channel Modeling for Holographic MIMO Using Expectation-Maximization Methods
Authors: Houfeng Chen, Shuhao Zeng, Hao Guo, Tommy Svensson, Hongliang Zhang
Date: 2024-01-15 00:00:00

Holographic Multiple-Input Multiple-Output (HMIMO), which densely integrates numerous antennas into a limited space, is anticipated to provide higher rates for future 6G wireless communications. The increase in antenna aperture size makes the near-field region enlarge, causing some users to be located in the near-field region. Thus, we are facing a hybrid near-field and far-field communication problem, where conventional far-field modeling methods may not work well. In this paper, we propose a near-far field channel model that does not presuppose whether each path is near-field or far-field, different from the existing work requiring the ratio of the number of near-field paths to that of far-field paths as prior knowledge. However, this gives rise to a new challenge for accurately modeling the channel, as conventional methods of obtaining channel model parameters are not applicable to this model. Therefore, we propose a new method, Expectation-Maximization (EM)-based Near-Far Field Channel Modeling, to obtain channel model parameters, which considers whether each path is near-field or far-field as a hidden variable, and optimizes the hidden variables and channel model parameters through an alternating iteration method. Simulation results show that our method is superior to conventional near-field and far-field algorithms in fitting the near-far field channel in terms of outage probability.
Passive Beamforming For Practical RIS-Assisted Communication Systems With Non-Ideal Hardware
Authors: Yiming Liu, Rui Wang, Zhu Han
Date: 2024-01-15 00:00:00

Reconfigurable intelligent surface (RIS) technology is a promising solution to improve the performance of existing wireless communications. To achieve its cost-effectiveness advantage, there inevitably exist certain hardware impairments in the system. Therefore, it is more reasonable to design passive beamforming in this scenario. Some existing research has considered such problems under transceiver impairments. However, their performance still leaves room for improvement, possibly due to their algorithms not properly handling the fractional structure of the objective function. To address this, the passive beamforming is redesigned in this correspondence paper, taking into account both transceiver impairments and the practical phase-shift model. We tackle the fractional structure of the problem by employing the quadratic transform. The remaining sub-problems are addressed utilizing the penalty-based method and the difference-of-convex programming. Since we provide closed-form solutions for all sub-problems, our algorithm is highly efficient. The simulation results demonstrate the superiority of our proposed algorithm.
From OTFS to AFDM: A Comparative Study of Next-Generation Waveforms for ISAC in Doubly-Dispersive Channels
Authors: Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, Junil Choi, David González G., Marios Kountouris, Yong Liang Guan, Osvaldo Gonsa
Date: 2024-01-15 00:00:00
Tasks: ISAC

Next-generation wireless systems will offer integrated sensing and communications (ISAC) functionalities not only in order to enable new applications, but also as a means to mitigate challenges such as doubly-dispersive channels, which arise in high mobility scenarios and/or at millimeter-wave (mmWave) and Terahertz (THz) bands. An emerging approach to accomplish these goals is the design of new waveforms, which draw from the inherent relationship between the doubly-dispersive nature of time-variant (TV) channels and the environmental features of scatterers manifested in the form of multi-path delays and Doppler shifts. Examples of such waveforms are the delay-Doppler domain orthogonal time frequency space (OTFS) and the recently proposed chirp domain affine frequency division multiplexing (AFDM), both of which seek to simultaneously combat the detrimental effects of double selectivity and exploit them for the estimation (or sensing) of environmental information. This article aims to provide a consolidated and comprehensive overview of the signal processing techniques required to support reliable ISAC over doubly-dispersive channels in beyond fifth generation (B5G)/sixth generation (6G) systems, with an emphasis on OTFS and AFDM waveforms, as those, together with the traditional orthogonal frequency division multiplexing (OFDM) waveform, suffice to elaborate on the most relevant properties of the trend. The analysis shows that OTFS and AFDM indeed enable significantly improved robustness against inter-carrier interference (ICI) arising from Doppler shifts compared to OFDM. In addition, the inherent delay-Doppler domain orthogonality of the OTFS and AFDM effective channels is found to provide significant advantages for the design and the performance of integrated sensing functionalities.
Remaining Useful Life Prediction for Aircraft Engines using LSTM
Authors: Anees Peringal, Mohammed Basheer Mohiuddin, Ahmed Hassan
Date: 2024-01-15 00:00:00
Tasks: Prediction, Temporal Sequences, Time Series

This study uses a Long Short-Term Memory (LSTM) network to predict the remaining useful life (RUL) of jet engines from time-series data, crucial for aircraft maintenance and safety. The LSTM model's performance is compared with a Multilayer Perceptron (MLP) on the C-MAPSS dataset from NASA, which contains jet engine run-to-failure events. The LSTM learns from temporal sequences of sensor data, while the MLP learns from static data snapshots. The LSTM model consistently outperforms the MLP in prediction accuracy, demonstrating its superior ability to capture temporal dependencies in jet engine degradation patterns. The software for this project is in https://github.com/AneesPeringal/rul-prediction.git.
Multiperson Detection and Vital-Sign Sensing Empowered by Space-Time-Coding RISs
Authors: Xinyu Li, Jian Wei You, Ze Gu, Qian Ma, Jingyuan Zhang, Long Chen, Tie Jun Cui
Date: 2024-01-15 00:00:00
Tasks: Human Detection

Passive human sensing using wireless signals has attracted increasing attention due to its superiorities of non-contact and robustness in various lighting conditions. However, when multiple human individuals are present, their reflected signals could be intertwined in the time, frequency and spatial domains, making it challenging to separate them. To address this issue, this paper proposes a novel system for multiperson detection and monitoring of vital signs (i.e., respiration and heartbeat) with the assistance of space-time-coding (STC) reconfigurable intelligent metasurfaces (RISs). Specifically, the proposed system scans the area of interest (AoI) for human detection by using the harmonic beams generated by the STC RIS. Simultaneously, frequencyorthogonal beams are assigned to each detected person for accurate estimation of their respiration rate (RR) and heartbeat rate (HR). Furthermore, to efficiently extract the respiration signal and the much weaker heartbeat signal, we propose an improved variational mode decomposition (VMD) algorithm to accurately decompose the complex reflected signals into a smaller number of intrinsic mode functions (IMFs). We build a prototype to validate the proposed multiperson detection and vital-sign monitoring system. Experimental results demonstrate that the proposed system can simultaneously monitor the vital signs of up to four persons. The errors of RR and HR estimation using the improved VMD algorithm are below 1 RPM (respiration per minute) and 5 BPM (beats per minute), respectively. Further analysis reveals that the flexible beam controlling mechanism empowered by the STC RIS can reduce the noise reflected from other irrelative objects on the physical layer, and improve the signal-to-noise ratio of echoes from the human chest.
Inkjet printed intelligent reflecting surface (IRS) for indoor applications
Authors: Kairi Takimoto, Kazutomo Nakamura, Peter Njogu, Kota Suzuki, Masato Sugimoto, Ashif Fathnan, Takashi Kondo, Masayuki Mori, Daisuke Anzai, Hiroki Wakatsuchi
Date: 2024-01-14 00:00:00

A passive, low-cost, paper-based intelligent reflecting surface (IRS) is designed to reflect a signal in a desired direction to overcome non-line-of-sight scenarios in indoor environments. The IRS is fabricated using conductive silver ink printed on a paper with a specific nanoparticle arrangement, yielding a cost effective paper-based IRS that can easily be mass-produced. Full-wave numerical simulation results were consistent with measurements results, demonstrating the IRS's ability to reflect incident wave into a desired nonspecular direction based on the inkjet-printed design and materials.
Diagnosing epilepsy using entropy measures and embedding parameters of EEG signals
Authors: Fatemeh Valipour, Zahra Valipour, Mani Garousi, Ali Khadem
Date: 2024-01-14 00:00:00
Tasks: EEG

Epilepsy is a neurological disorder that affects normal neural activity. These electrical activities can be recorded as signals containing information about the brain known as Electroencephalography (EEG) signals. Analysis of the EEG signals by individuals for epilepsy diagnosis is subjective and time-consuming. So, an automatic classification system with high detection accuracy is required to overcome possible errors. In this study, the discrete wavelet transform has been applied to EEG signals. Then, entropy measures and embedding parameters have been extracted. These features have been investigated individually to find the most discriminating ones. The significance level of each feature was evaluated by statistical analysis. Consequently, LDA and SVM algorithms have been employed to categorize the EEG signals. The results have indicated that the features of Embedding parameters, PermutationEntropy, FuzzyEntropy, SampleEntropy, NormEntropy, SureEntropy, LogEntropy, and ThresholdEntropy have the potential to discriminate epileptic patients from healthy subjects significantly. Also, SVM classifier has achieved the highest classification accuracy. In this study, we could find effective embedding-based and entropy-based features as appropriate single measures for identifying abnormal activities that can efficiently discriminate the EEG signals of epileptics from healthy individuals. According to the results, they can be used for automatic classification of epileptic EEG signals that are difficult to examine visually.
Importance of array redundancy pattern in active sensing
Authors: Robin Rajamäki, Piya Pal
Date: 2024-01-13 00:00:00

This paper further investigates the role of the array geometry and redundancy in active sensing. We are interested in the fundamental question of how many point scatterers can be identified (in the angular domain) by a given array geometry using a certain number of linearly independent transmit waveforms. We consider redundant array configurations (with repeated virtual transmit-receive sensors), which we have recently shown to be able to achieve their maximal identifiability while transmitting fewer independent waveforms than transmitters. Reducing waveform rank in this manner can be beneficial in various ways. For example, it may free up spatial resources for transmit beamforming. In this paper, we show that two array geometries with identical sum co-arrays, and the same number of physical and virtual sensors, need not achieve equal identifiability, regardless of the choice of waveform of a fixed reduced rank. This surprising result establishes the important role the pattern (not just the number) of repeated virtual sensors has in governing identifiability, and reveals the limits of compensating for unfavorable array geometries via waveform design.
Malicious RIS versus Massive MIMO: Securing Multiple Access against RIS-based Jamming Attacks
Authors: Arthur Sousa de Sena, Jacek Kibilda, Nurul Huda Mahmood, André Gomes, Matti Latva-aho
Date: 2024-01-13 00:00:00

In this letter, we study an attack that leverages a reconfigurable intelligent surface (RIS) to induce harmful interference toward multiple users in massive multiple-input multiple-output (mMIMO) systems during the data transmission phase. We propose an efficient and flexible weighted-sum projected gradient-based algorithm for the attacker to optimize the RIS reflection coefficients without knowing legitimate user channels. To counter such a threat, we propose two reception strategies. Simulation results demonstrate that our malicious algorithm outperforms baseline strategies while offering adaptability for targeting specific users. At the same time, our results show that our mitigation strategies are effective even if only an imperfect estimate of the cascade RIS channel is available.
Near-Field Channel Estimation for XL-RIS Assisted Multi-User XL-MIMO Systems: Hybrid Beamforming Architectures
Authors: Jeongjae Lee, Hyeongjin Chung, Yunseong Cho, Sunwoo Kim, SongNam Hong
Date: 2024-01-13 00:00:00

Channel estimation is one of the key challenges for the deployment of extremely large-scale reconfigurable intelligent surface (XL-RIS) assisted multiple-input multiple-output (MIMO) systems. In this paper, we study the channel estimation problem for XL-RIS assisted multi-user XL-MIMO systems with hybrid beamforming structures. For this system, we propose an {\em unified} channel estimation method that yields a notable estimation accuracy in the near-field BS-RIS and near-field RIS-User channels (in short, near-near field channels), far-near field channels, and far-far field channels. Our key idea is that the effective (or cascaded) channels to be estimated can be each factorized as the product of low-rank matrices (i.e., the product of the common (or user-independent) matrix and the user-specific coefficient matrix). The common matrix whose columns are the basis of the column space of the BS-RIS channel matrix is efficiently estimated via a {\em collaborative} low-rank approximation (CLRA). Leveraging the hybrid beamforming structures, we develop an efficient iterative algorithm that jointly optimizes the user-specific coefficient matrices. Via experiments and complexity analysis, we verify the effectiveness of the proposed channel estimation method (named CLRA-JO) in the aforementioned three classes of wireless channels.