pwc-archive/papers-with-abstracts huggingface dataset:
Wireless vs. Traditional Ultrasound Assessed Knee Cartilage Outcomes Utilizing Automated Gain and Normalization Techniques
Authors: Arjun Parmar, Corey D Grozier, Robert Dima, Jessica E Tolzman, Ilker Hacihaliloglu, Kenneth L Cameron, Ryan Fajardo, Matthew S Harkey
Date: 2024-05-20 00:00:00

Advancements in wireless ultrasound technology allow for point of care cartilage imaging, yet validation against traditional ultrasound units remains to be established for knee cartilage outcomes. Therefore, the purpose of our study was to establish the agreement of articular cartilage thickness and echo-intensity measures between traditional and wireless ultrasound units utilizing automatic-gain and normalization techniques. We used traditional and wireless ultrasound to assess the femoral cartilage via transverse suprapatellar scans with the knee in maximum flexion in 71 female NCAA Division I athletes (age: 20.0$\pm$1.3 years, height: 171.7$\pm$8.7 cm, mass: 69.4$\pm$11.0 kg). Wireless ultrasound images (auto-gain and standard gain) were compared to traditional ultrasound images (standard gain) before and after normalization. Ultrasound image pixel values were algebraically scaled to normalize imaging parameter differences between units. Mean thickness and echo-intensity of the global and sub-regions of interest were measured for unnormalized and normalized images. Intraclass correlation coefficients ($ICC_{2,k}$) for absolute agreement, standard error of the measurement, and minimum detectable difference were calculated between the traditional and wireless ultrasound units across both gain parameters and normalization. Cartilage thickness demonstrated good to excellent agreement for all regions ($ICC_{2,k} = 0.83 {\text -} 0.95$) regardless of gain and normalization. However, mean echo-intensity demonstrated poor to moderate agreement in all regions regardless of gain and normalization ($ICC_{2,k} = 0.23 {\text -} 0.68 $). While there was a high level of agreement between units when assessing cartilage thickness, further research in ultrasound beam forming may lead to improvements in agreement of cartilage echo-intensity between ultrasound units.
Verification technology for finger vein biometric
Authors: George Kumi Kyeremeh, M. Abdul-Al, R. Qahwaji, R. A. Abd-Alhameed
Date: 2024-05-19 00:00:00
Tasks: image-classification, Image Classification, Image Enhancement

Finger vein biometrics is an approach to identifying individuals based on the unique patterns of blood vessels in their fingers, and the technology is advanced in image capture and processing techniques, which is leading to more efficient, accurate, and reliable systems. This article focuses on a verification system that compares the matrices of an efficient finger vein verification system on different databases to test its strength and efficiency. Contrast Limited Adaptive Histogram Equalization (CLAHE) has been examined as an image enhancement and processing method to improve contrast and render details in an image easier to detect. A random forest classifier is deployed with a comparison between two pretrained systems, VGG16 and ResNet50, which are types of convolutional neural networks. VGG-16 and ResNet-50 models are implemented on three different datasets, and fine-tuning these models enabled the harnessing of their powerful capabilities and achieving superior performance on the specific image classification task.
Confidence Estimation in Unsupervised Deep Change Vector Analysis
Authors: Sudipan Saha
Date: 2024-05-16 00:00:00
Tasks: Change Detection, Earth Observation, Transfer Learning

Unsupervised transfer learning-based change detection methods exploit the feature extraction capability of pre-trained networks to distinguish changed pixels from the unchanged ones. However, their performance may vary significantly depending on several geographical and model-related aspects. In many applications, it is of utmost importance to provide trustworthy or confident results, even if over a subset of pixels. The core challenge in this problem is to identify changed pixels and confident pixels in an unsupervised manner. To address this, we propose a two-network model - one tasked with mere change detection and the other with confidence estimation. While the change detection network can be used in conjunction with popular transfer learning-based change detection methods such as Deep Change Vector Analysis, the confidence estimation network operates similarly to a randomized smoothing model. By ingesting ensembles of inputs perturbed by noise, it creates a distribution over the output and assigns confidence to each pixel's outcome. We tested the proposed method on three different Earth observation sensors: optical, Synthetic Aperture Radar, and hyperspectral sensors.
Column Bound for Orthogonal Matrix Factorization
Authors: Anirudh Dash
Date: 2024-05-21 00:00:00

This article explores the intersection of the Coupon Collector's Problem and the Orthogonal Matrix Factorization (OMF) problem. Specifically, we derive bounds on the minimum number of columns $p$ (in $\mathbf{X}$) required for the OMF problem to be tractable, using insights from the Coupon Collector's Problem. Specifically, we establish a theorem outlining the relationship between the sparsity of the matrix $\mathbf{X}$ and the number of columns $p$ required to recover the matrices $\mathbf{V}$ and $\mathbf{X}$ in the OMF problem. We show that the minimum number of columns $p$ required is given by $p = \Omega \left(\max \left\{ \frac{n}{1 - (1 - \theta)^n}, \frac{1}{\theta} \log n \right\} \right)$, where $\theta$ is the i.i.d Bernoulli parameter from which the sparsity model of the matrix $\mathbf{X}$ is derived.
An Experimental Study of C-Band Channel Model in Integrated LEO Satellite and Terrestrial Systems
Authors: Hung Nguyen-Kha, Vu Nguyen Ha, Eva Lagunas, Symeon Chatzinotas, Joel Grotz
Date: 2024-05-21 00:00:00

This paper studies the channel model for the integrated satellite-terrestrial networks operating at C-band under deployment in dense urban and rural areas. Particularly, the interference channel from the low-earth-orbit (LEO) satellite to the dense urban area is analyzed carefully under the impact of the environment's characteristics, i.e., the building density, building height, and the elevation angle. Subsequently, the experimental results show the strong relationships between these characteristics and the channel gain loss. Especially, the functions of channel gain loss are obtained by utilizing the model-fitting approach that can be used as the basis for studying future works of integration of satellite and terrestrial networks (ISTNs).
LPD-Aware Uplink CSI-based 5G NR Downlink Synchronization for Tactical Networks
Authors: Karthik Upadhya, Akshay Jain, Mikko A. Uusitalo, Harish Viswanathan
Date: 2024-05-21 00:00:00

5G NR is touted to be an attractive candidate for tactical networks owing to its versatility, scalability, and low cost. However, tactical networks need to be stealthy, where an adversary is not able to detect or intercept the tactical communication. In this paper, we investigate the stealthiness of 5G NR by looking at the probability with which an adversary that monitors the downlink synchronization signals can detect the presence of the network. We simulate a single-cell single-eavesdropper scenario and evaluate the probability with which the eavesdropper can detect the synchronization signal block when using either a correlator or an energy detector. We show that this probability is close to $ 100\% $ suggesting that 5G out-of-the-box is not suitable for a tactical network. We then propose utilizing the uplink channel-state-information to beamform the downlink synchronization-signals towards the tactical user-equipment (UE) to lower the eavesdropper detection probability while not compromising the performance of the legitimate tactical UE.
Comprehensive mm-Wave FMCW Radar Dataset for Vital Sign Monitoring: Embracing Extreme Physiological Scenarios
Authors: Ehsan Sadeghi, Karina Skurule, Alessandro Chiumento, Paul Havinga
Date: 2024-05-21 00:00:00

Recent advancements in non-invasive health monitoring technologies underscore the potential of mm-Wave Frequency-Modulated Continuous Wave (FMCW) radar in real-time vital sign detection. This paper introduces a novel dataset, the first of its kind, derived from mm-Wave FMCW radar, meticulously capturing heart rate and respiratory rate under various conditions. Comprising data from ten participants, including scenarios with elevated heart rates and participants with diverse physiological profiles such as asthma and meditation practitioners, this dataset is validated against the Polar H10 sensor, ensuring its reliability for scientific research. This dataset can offer a significant resource for developing and testing algorithms aimed at non-invasive health monitoring, promising to facilitate advancements in remote health monitoring technologies.
3D Reconfigurable Intelligent Surfaces for Satellite-Terrestrial Networks
Authors: Islam M. Tanash, Risto Wichman
Date: 2024-05-20 00:00:00

This paper proposes a three-dimensional (3D) satellite-terrestrial communication network assisted with reconfigurable intelligent surfaces (RISs). Using stochastic geometry models, we present an original framework to derive tractable yet accurate closed-form expressions for coverage probability and ergodic capacity in the presence of fading. A homogeneous Poisson point process models the satellites on a sphere, while RISs are randomly deployed in a 3D cylindrical region. We consider nonidentical channels that correspond to different RISs and follow the {\kappa}-{\mu} fading distribution. We verify the high accuracy of the adopted approach through Monte Carlo simulations and demonstrate the significant improvement in system performance due to using RISs. Furthermore, we comprehensively study the effect of the different system parameters on its performance using the derived analytical expressions, which enable system engineers to predict and optimize the expected downlink coverage and capacity performance analytically.
TypeII-CsiNet: CSI Feedback with TypeII Codebook
Authors: Yiliang Sang, Ke Ma, Yang Ming, Jin Lian, Zhaocheng Wang
Date: 2024-05-21 00:00:00
Tasks: Decoder

The latest TypeII codebook selects partial strongest angular-delay ports for the feedback of downlink channel state information (CSI), whereas its performance is limited due to the deficiency of utilizing the correlations among the port coefficients. To tackle this issue, we propose a tailored autoencoder named TypeII-CsiNet to effectively integrate the TypeII codebook with deep learning, wherein three novel designs are developed for sufficiently boosting the sum rate performance. Firstly, a dedicated pre-processing module is designed to sort the selected ports for reserving the correlations of their corresponding coefficients. Secondly, a position-filling layer is developed in the decoder to fill the feedback coefficients into their ports in the recovered CSI matrix, so that the corresponding angular-delay-domain structure is adequately leveraged to enhance the reconstruction accuracy. Thirdly, a two-stage loss function is proposed to improve the sum rate performance while avoiding the trapping in local optimums during model training. Simulation results verify that our proposed TypeII-CsiNet outperforms the TypeII codebook and existing deep learning benchmarks.
Approximating Multi-Dimensional and Multiband Signals
Authors: Yuhan Li, Tianyao Huang, Yimin Liu, Xiqin Wang
Date: 2024-05-20 00:00:00

We study the problem of representing a discrete tensor that comes from finite uniform samplings of a multi-dimensional and multiband analog signal. Particularly, we consider two typical cases in which the shape of the subbands is cubic or parallelepipedic. For the cubic case, by examining the spectrum of its corresponding time- and band-limited operators, we obtain a low-dimensional optimal dictionary to represent the original tensor. We further prove that the optimal dictionary can be approximated by the famous \ac{dpss} with certain modulation, leading to an efficient constructing method. For the parallelepipedic case, we show that there also exists a low-dimensional dictionary to represent the original tensor. We present rigorous proof that the numbers of atoms in both dictionaries are approximately equal to the dot of the total number of samplings and the total volume of the subbands. Our derivations are mainly focused on the \ac{2d} scenarios but can be naturally extended to high dimensions.
Complex Principle Kurtosis Analysis
Authors: Liangliang Zhu, Zhebin Song, Xuesen Zhang, Meibin Qi
Date: 2024-05-20 00:00:00

Independent component analysis (ICA) is a fundamental problem in the field of signal processing, and numerous algorithms have been developed to address this issue. The core principle of these algorithms is to find a transformation matrix that maximizes the non-Gaussianity of the separated signals. Most algorithms typically assume that the source signals are mutually independent (orthogonal to each other), thereby imposing an orthogonal constraint on the transformation matrix. However, this assumption is not always valid in practical scenarios, where the orthogonal constraint can lead to inaccurate results. Recently, tensor-based algorithms have attracted much attention due to their ability to reduce computational complexity and enhance separation performance. In these algorithms, ICA is reformulated as an eigenpair problem of a statistical tensor. Importantly, the eigenpairs of a tensor are not inherently orthogonal, making tensor-based algorithms more suitable for nonorthogonal cases. Despite this advantage, finding exact solutions to the tensor's eigenpair problem remains a challenging task. In this paper, we introduce a non-zero volume constraint and a Riemannian gradient-based algorithm to solve the tensor's eigenpair problem. The proposed algorithm can find exact solutions under nonorthogonal conditions, making it more effective for separating nonorthogonal sources. Additionally, existing tensor-based algorithms typically rely on third-order statistics and are limited to real-valued data. To overcome this limitation, we extend tensor-based algorithms to the complex domain by constructing a fourth-order statistical tensor. Experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of the proposed algorithm.
Non-Invasive Monitoring of Vital Signs in Calves Using Thermal Imaging Technology
Authors: Ehsan Sadeghi, Zinan Guo, Alessandro Chiumento, Paul Havinga
Date: 2024-05-19 00:00:00
Tasks: Management

This study presents a non-invasive method using thermal imaging to estimate heart and respiration rates in calves, avoiding the stress from wearables. Using Kernelised Correlation Filters (KCF) for movement tracking and advanced signal processing, we targeted one ROI for respiration and four for heart rate based on their thermal correlation. Achieving Mean Absolute Percentage Errors (MAPE) of 3.08% for respiration and 3.15% for heart rate validates the efficacy of thermal imaging in vital signs monitoring, offering a practical, less intrusive tool for Precision Livestock Farming (PLF), improving animal welfare and management.
Meta Reinforcement Learning for Resource Allocation in Multi-Antenna UAV Network with Rate Splitting Multiple Access
Authors: Hosein Zarini, Maryam Farajzadeh Dehkordi, Armin Farhadi, Mohammad Robat Mili, Ali Movaghar, Mehdi Rasti, Yonghui Li, Kai-Kit Wong
Date: 2024-05-18 00:00:00
Tasks: Meta-Learning, Meta Reinforcement Learning

Unmanned aerial vehicles (UAVs) with multiple antennas have recently been explored to improve capacity in wireless networks. However, the strict energy constraint of UAVs, given their simultaneous flying and communication tasks, renders the exploration of energy-efficient multi-antenna techniques indispensable for UAVs. Meanwhile, lens antenna subarray (LAS) emerges as a promising energy-efficient solution that has not been previously harnessed for this purpose. In this paper, we propose a LAS-aided multi-antenna UAV to serve ground users in the downlink transmission of the terahertz (THz) band, utilizing rate splitting multiple access (RSMA) for effective beam division multiplexing. We formulate an optimization problem of maximizing the total system spectral efficiency (SE). This involves optimizing the UAV's transmit beamforming and the common rate of RSMA. By recasting the optimization problem into a Markov decision process (MDP), we propose a deep deterministic policy gradient (DDPG)-based resource allocation mechanism tailored to capture problem dynamics and optimize its variables. Moreover, given the UAV's frequent mobility and consequential system reconfigurations, we fortify the trained DDPG model with a meta-learning strategy, enhancing its adaptability to system variations. Numerically, more than 20\% energy efficiency gain is achieved by our proposed LAS-aided multi-antenna UAV equipped with 4 lenses, compared to a single-lens UAV. Simulations also demonstrate that at a signal-to-noise (SNR) of 10 dB, the incorporation of RSMA results in a 22\% SE enhancement over conventional orthogonal beam division multiple access. Furthermore, the overall system SE improves by 27\%, when meta-learning is employed for fine-tuning the conventional DDPG method in literature.
MAMCA -- Optimal on Accuracy and Efficiency for Automatic Modulation Classification with Extended Signal Length
Authors: Yezhuo Zhang, Zinan Zhou, Yichao Cao, Guangyu Li, Xuanpeng Li
Date: 2024-05-18 00:00:00
Tasks: Denoising, GPU, Mamba

With the rapid growth of the Internet of Things ecosystem, Automatic Modulation Classification (AMC) has become increasingly paramount. However, extended signal lengths offer a bounty of information, yet impede the model's adaptability, introduce more noise interference, extend the training and inference time, and increase storage overhead. To bridge the gap between these requisites, we propose a novel AMC framework, designated as the Mamba-based Automatic Modulation ClassificAtion (MAMCA). Our method adeptly addresses the accuracy and efficiency requirements for long-sequence AMC. Specifically, we introduce the Selective State Space Model as the backbone, enhancing the model efficiency by reducing the dimensions of the state matrices and diminishing the frequency of information exchange across GPU memories. We design a denoising-capable unit to elevate the network's performance under low signal-to-noise radio. Rigorous experimental evaluations on the publicly available dataset RML2016.10, along with our synthetic dataset within multiple quadrature amplitude modulations and lengths, affirm that MAMCA delivers superior recognition accuracy while necessitating minimal computational time and memory occupancy. Codes are available on https://github.com/ZhangYezhuo/MAMCA.
Learning-based Block-wise Planar Channel Estimation for Time-Varying MIMO OFDM
Authors: ChenChen Liu, Wenjun Jiang, Xiaojun Yuan
Date: 2024-05-18 00:00:00

In this paper, we propose a learning-based block-wise planar channel estimator (LBPCE) with high accuracy and low complexity to estimate the time-varying frequency-selective channel of a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system. First, we establish a block-wise planar channel model (BPCM) to characterize the correlation of the channel across subcarriers and OFDM symbols. Specifically, adjacent subcarriers and OFDM symbols are divided into several sub-blocks, and an affine function (i.e., a plane) with only three variables (namely, mean, time-domain slope, and frequency-domain slope) is used to approximate the channel in each sub-block, which significantly reduces the number of variables to be determined in channel estimation. Second, we design a 3D dilated residual convolutional network (3D-DRCN) that leverages the time-frequency-space-domain correlations of the channel to further improve the channel estimates of each user. Numerical results demonstrate that the proposed significantly outperforms the state-of-the-art estimators and maintains a relatively low computational complexity.
On the Performance of Unmanned Aerial Vehicles with MIMO VLC
Authors: Hosein Zarini, Amir Mohammadisarab, Maryam Farajzadeh Dehkordi, Mohammad Robat Mili, Bardia Safaei, Ali Movaghar, Sinem Coleri, Eduard Jorswieck
Date: 2024-05-18 00:00:00
Tasks: Meta-Learning, Meta Reinforcement Learning

This paper centers around a multiple-input-multiple-output (MIMO) visible light communication (VLC) system, where an unmanned aerial vehicle (UAV) benefits from a light emitting diode (LED) array to serve photo-diode (PD)-equipped users for illumination and communication simultaneously. Concerning the battery limitation of the UAV and considerable energy consumption of the LED array, a hybrid dimming control scheme is devised at the UAV that effectively controls the number of glared LEDs and thereby mitigates the overall energy consumption. To assess the performance of this system, a radio resource allocation problem is accordingly formulated for jointly optimizing the motion trajectory, transmit beamforming and LED selection at the UAV, assuming that channel state information (CSI) is partially available. By reformulating the optimization problem in Markov decision process (MDP) form, we propose a soft actor-critic (SAC) mechanism that captures the dynamics of the problem and optimizes its parameters. Additionally, regarding the high mobility of the UAV and thus remarkable rearrangement of the system, we enhance the trained SAC model by integrating a meta-learning strategy that enables more adaptation to system variations. By defining energy efficiency as a trade-off between the data rate and power consumption, simulations verify that upgrading a single-LED UAV by an array of 10 LEDs, exhibits 47% and 34% improvements in data rate and energy efficiency, albeit at the expense of 8% more power consumption.
Carrier Aggregation Enabled MIMO-OFDM Integrated Sensing and Communication
Authors: Haotian Liu, Zhiqing Wei, Jinghui Piao, Huici Wu, Xingwang Li, Zhiyong Feng
Date: 2024-05-17 00:00:00
Tasks: Integrated sensing and communication, ISAC

In the evolution towards the forthcoming era of sixth-generation (6G) mobile communication systems characterized by ubiquitous intelligence, integrated sensing and communication (ISAC) is in a phase of burgeoning development. However, the capabilities of communication and sensing within single frequency band fall short of meeting the escalating demands. To this end, this paper introduces a carrier aggregation (CA)-enabled multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system fusing the sensing data on high and low-frequency bands by symbol-level fusion for ultimate communication experience and high-accuracy sensing. The challenges in sensing signal processing introduced by CA include the initial phase misalignment of the echo signals on high and low-frequency bands due to attenuation and radar cross section, and the fusion of the sensing data on high and low-frequency bands with different physical-layer parameters. To this end, the sensing signal processing is decomposed into two stages. In the first stage, the problem of initial phase misalignment of the echo signals on high and low-frequency bands is solved by the angle compensation, spatial filtering and cyclic cross-correlation operations. In the second stage, this paper realizes symbol-level fusion of the sensing data on high and low-frequency bands through sensing vector rearrangement and cyclic prefix adjustment operations, thereby obtaining high-precision sensing performance. Then, the closed-form communication mutual information (MI) and sensing Cram\'er-Rao lower bound (CRLB) for the proposed ISAC system are derived to explore the theoretical performance bound with CA. Simulation results validate the feasibility and superiority of the proposed ISAC system.