pwc-archive/papers-with-abstracts huggingface dataset:
Blockage Prediction for Mobile UE in RIS-assisted Wireless Networks: A Deep Learning Approach
Authors: Shakil Ahmed, Ibrahim Abdelmawla, Ahmed E. Kamal, Mohamed Y. Selim
Date: 2022-09-22 00:00:00

Due to significant blockage conditions in wireless networks, transmitted signals may considerably degrade before reaching the receiver. The reliability of the transmitted signals, therefore, may be critically problematic due to blockages between the communicating nodes. Thanks to the ability of Reconfigurable Intelligent Surfaces (RISs) to reflect the incident signals with different reflection angles, this may counter the blockage effect by optimally reflecting the transmit signals to receiving nodes, hence, improving the wireless network's performance. With this motivation, this paper formulates a RIS-aided wireless communication problem from a base station (BS) to a mobile user equipment (UE). The BS is equipped with an RGB camera. We use the RGB camera at the BS and the RIS panel to improve the system's performance while considering signal propagating through multiple paths and the Doppler spread for the mobile UE. First, the RGB camera is used to detect the presence of the UE with no blockage. When unsuccessful, the RIS-assisted gain takes over and is then used to detect if the UE is either "present but blocked" or "absent". The problem is determined as a ternary classification problem with the goal of maximizing the probability of UE communication blockage detection. We find the optimal solution for the probability of predicting the blockage status for a given RGB image and RIS-assisted data rate using a deep neural learning model. We employ the residual network 18-layer neural network model to find this optimal probability of blockage prediction. Extensive simulation results reveal that our proposed RIS panel-assisted model enhances the accuracy of maximization of the blockage prediction probability problem by over 38\% compared to the baseline scheme.
Stochastic Optimization of 3D Non-Cartesian Sampling Trajectory (SNOPY)
Authors: Guanhua Wang, Jon-Fredrik Nielsen, Jeffrey A. Fessler, Douglas C. Noll
Date: 2022-09-22 00:00:00
Tasks: Stochastic Optimization

Optimizing 3D k-space sampling trajectories for efficient MRI is important yet challenging. This work proposes a generalized framework for optimizing 3D non-Cartesian sampling patterns via data-driven optimization. We built a differentiable MRI system model to enable gradient-based methods for sampling trajectory optimization. By combining training losses, the algorithm can simultaneously optimize multiple properties of sampling patterns, including image quality, hardware constraints (maximum slew rate and gradient strength), reduced peripheral nerve stimulation (PNS), and parameter-weighted contrast. The proposed method can either optimize the gradient waveform (spline-based freeform optimization) or optimize properties of given sampling trajectories (such as the rotation angle of radial trajectories). Notably, the method optimizes sampling trajectories synergistically with either model-based or learning-based reconstruction methods. We proposed several strategies to alleviate the severe non-convexity and huge computation demand posed by the high-dimensional optimization. The corresponding code is organized as an open-source, easy-to-use toolbox. We applied the optimized trajectory to multiple applications including structural and functional imaging. In the simulation studies, the reconstruction PSNR of a 3D kooshball trajectory was increased by 4 dB with SNOPY optimization. In the prospective studies, by optimizing the rotation angles of a stack-of-stars (SOS) trajectory, SNOPY improved the PSNR by 1.4dB compared to the best empirical method. Optimizing the gradient waveform of a rotational EPI trajectory improved subjects' rating of the PNS effect from 'strong' to 'mild.' In short, SNOPY provides an efficient data-driven and optimization-based method to tailor non-Cartesian sampling trajectories.
Phase Aberration Correction for in vivo Ultrasound Localization Microscopy Using a Spatiotemporal Complex-Valued Neural Network
Authors: Paul Xing, Jonathan Porée, Brice Rauby, Antoine Malescot, Éric Martineau, Vincent Perrot, Ravi L. Rungta, Jean Provost
Date: 2022-09-21 00:00:00

Ultrasound Localization Microscopy (ULM) can map microvessels at a resolution of a few micrometers (\mu m). Transcranial ULM remains challenging in presence of aberrations caused by the skull, which lead to localization errors. Herein, we propose a deep learning approach based on complex-valued convolutional neural networks (CV-CNNs) to retrieve the aberration function, which can then be used to form enhanced images using standard delay-and-sum beamforming. CV-CNNs were selected as they can apply time delays through multiplication with in-phase quadrature input data. Predicting the aberration function rather than corrected images also confers enhanced explainability to the network. In addition, 3D spatiotemporal convolutions were used for the network to leverage entire microbubble tracks. For training and validation, we used an anatomically and hemodynamically realistic mouse brain microvascular network model to simulate the flow of microbubbles in presence of aberration. The proposed CV-CNN performance was compared to the coherence-based method by using microbubble tracks. We then confirmed the capability of the proposed network to generalize to transcranial \textit{in vivo} data in the mouse brain (n=3). Vascular reconstructions using a locally predicted aberration function included additional and sharper vessels. The CV-CNN was more robust than the coherence-based method and could perform aberration correction in a 6-month-old mouse. After correction, we measured a resolution of 15.6 \mu m for younger mice, representing an improvement of 25.8 $\%$, while the resolution was improved by 13.9 $\%$ for the 6-month-old mouse. This work leads to different applications for complex-valued convolutions in biomedical imaging and strategies to perform transcranial ULM.
Matrix Adaptive Synthesis Filter for Uniform Filter Bank
Authors: Sandeep Patel, Ravindra Dhuli, Brejesh lall
Date: 2022-09-22 00:00:00

In this paper, we use a matrix adaptive filter as the synthesis stage of a Uniform Filter Bank (UFB) to reconstruct the input signal. We first develop the mathematical theory behind it by applying the model of optimal filtering at the synthesis stage of the UFB and obtaining an expression for the matrix Wiener filter. We have developed a theorem which we use to simplify the expression further. In the absence of required information about the analysis stage, we use adaptive filtering to arrive at the Wiener solution. We use the Least Mean Square (LMS) algorithm to update the filter coefficients. Through experimental results, we find that the adaptive filter is convergent for a stable Wiener filter.
Joint Communication, Sensing and Localization for Airborne Applications
Authors: Nils L. Johannsen, Max Schurwanz, Lukas Grundmann, Jan Mietzner, Dirk Manteuffel, Peter A. Hoeher
Date: 2022-09-22 00:00:00
Tasks: Autonomous Driving

With the upcoming trends in autonomous driving and urban air mobility, the number of self-navigating vehicles will increase, since they are foreseen for deliveries as well as autonomous taxis among other applications. To this end, a multitude of on-board systems for wireless communication, environment sensing, and localization will become mandatory. This is particularly true for unmanned aerial vehicles (UAVs), since participation in the airspace requires compatibility to and safe interaction with established users. A certain number of systems are already in-use and occupy defined spectra as well as installation space, which limits the freedom in the design of new systems. The miniaturization of aerial vehicles like drones for delivery services further reduces the degrees of freedom, especially in terms of size and weight of any additional equipment. Hence, in this paper a joint approach of the design of joint communication, sensing and localization for UAVs is discussed. Towards this goal, multi-mode multi-port antennas and joint waveform design are proposed as a part of the solution, when elevating autonomous driving to the third dimension.
Improving UAV Communication in Cell Free MIMO Using a Reconfigurable Intelligent Surface
Authors: Bayan Al-Nahhas, Anas Chaaban, Md. Jahangir Hossain
Date: 2022-09-21 00:00:00

Communication with unmanned aerial vehicles (UAVs) in current terrestrial networks suffers from poor signal strength due to the down-tilt of the access points (APs) that are optimized to serve ground users ends (GUEs). To solve this, one could tilt the AP antenna upwards or allocate more power to serve the UAV. However, this negatively affects GUE downlink (DL) rates. In this paper, we propose to solve this challenge using a reconfigurable intelligent surface (RIS) to enhance the UAV communication while preserving the 3GPP- prescribed downwards antenna tilt and potentially improving the DL performance of the GUE. We show that under conjugate beamforming (CB) precoding and proper power split between GUEs and the UAV at the APs, an RIS with phase-shifts configured to reflect radio signals towards the UAV can significantly improve the UAV DL throughput while simultaneously benefiting the GUEs. The presented numerical results show that the RIS- aided system can serve a UAV with a required data rate while improving the GUEs DL performance relative to that in a CF- MIMO system without a UAV and an RIS. We support this conclusion through simulations under a varying numbers of RIS reflecting elements, UAV heights, and power split factor.
3D Super-Resolution Imaging Method for Distributed Millimeter-wave Automotive Radar System
Authors: Yanqin Xu, Xiaoling Zhang, Shunjun Wei, Jun Shi, Xu Zhan, Tianwen Zhang
Date: 2022-09-21 00:00:00
Tasks: Super-Resolution

Millimeter-wave (mmW) radar is widely applied to advanced autopilot assistance systems. However, its small antenna aperture causes a low imaging resolution. In this paper, a new distributed mmW radar system is designed to solve this problem. It forms a large sparse virtual planar array to enlarge the aperture, using multiple-input and multiple-output (MIMO) processing. However, in this system, traditional imaging methods cannot apply to the sparse array. Therefore, we also propose a 3D super-resolution imaging method specifically for this system in this paper. The proposed method consists of three steps: (1) using range FFT to get range imaging, (2) using 2D adaptive diagonal loading iterative adaptive approach (ADL-IAA) to acquire 2D super-resolution imaging, which can satisfy this sparsity under single-measurement, (3) using constant false alarm (CFAR) processing to gain final 3D super-resolution imaging. The simulation results show the proposed method can significantly improve imaging resolution under the sparse array and single-measurement.
AETomo-Net: A Novel Deep Learning Network for Tomographic SAR Imaging Based on Multi-dimensional Features
Authors: Yu Ren, Xiaoling Zhang, Yunqiao Hu, Xu Zhan
Date: 2022-09-21 00:00:00
Tasks: Rolling Shutter Correction

Tomographic synthetic aperture radar (TomoSAR) imaging algorithms based on deep learning can effectively reduce computational costs. The idea of existing researches is to reconstruct the elevation for each range-azimuth cell in one-dimensional using a deep-unfolding network. However, since these methods are commonly sensitive to signal sparsity level, it usually leads to some drawbacks like continuous surface fractures, too many outliers, \textit{et al}. To address them, in this paper, a novel imaging network (AETomo-Net) based on multi-dimensional features is proposed. By adding a U-Net-like structure, AETomo-Net performs reconstruction by each azimuth-elevation slice and adds 2D features extraction and fusion capabilities to the original deep unrolling network. In this way, each azimuth-elevation slice can be reconstructed with richer features and the quality of the imaging results will be improved. Experiments show that the proposed method can effectively solve the above defects while ensuring imaging accuracy and computation speed compared with the traditional ISTA-based method and CV-LISTA.
Constant-Time-Delay Interferences In Near-Field SAR: Analysis And Suppression In Image Domain
Authors: Xu Zhan, Xiaoling Zhang, Jun Shi, Shunjun Wei
Date: 2022-09-21 00:00:00

Inevitable interferences exist for the SAR system, adversely affecting the imaging quality. However, current analysis and suppression methods mainly focus on the far-field situation. Due to different sources and characteristics of interferences, they are not applicable in the near field. To bridge this gap, in the first time, analysis and the suppression method of interferences in near-field SAR are presented in this work. We find that echoes from both the nadir points and the antenna coupling are the main causes, which have the constant-time-delay feature. To characterize this, we further establish an analytical model. It reveals that their patterns in 1D, 2D and 3D imaging results are all comb-like, while those of targets are point-like. Utilizing these features, a suppression method in image domain is proposed based on low-rank reconstruction. Measured data are used to validate the correctness of our analysis and the effectiveness of the suppression method.
U-Sleep's resilience to AASM guidelines
Authors: Luigi Fiorillo, Giuliana Monachino, Julia van der Meer, Marco Pesce, Jan D. Warncke, Markus H. Schmidt, Claudio L. A. Bassetti, Athina Tzovara, Paolo Favaro, Francesca D. Faraci
Date: 2022-09-19 00:00:00
Tasks: Clinical Knowledge, EEG, Electroencephalogram (EEG), valid

AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications,e.g., recommended EEG derivations, to detailed sleep scoring rules accordingly to age. Automated sleep scoring systems have always largely exploited the standards as fundamental guidelines. In this context, deep learning has demonstrated better performance compared to classical machine learning. Our present work shows that a deep learning based sleep scoring algorithm may not need to fully exploit the clinical knowledge or to strictly adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects. We finally strengthen a well-known finding that using data from multiple data centers always results in a better performing model compared with training on a single cohort. Indeed, we show that this latter statement is still valid even by increasing the size and the heterogeneity of the single data cohort. In all our experiments we used 28528 polysomnography studies from 13 different clinical studies.
Fault Detection in Ball Bearings
Authors: Joshua Pickard, Sarah Moll
Date: 2022-09-19 00:00:00
Tasks: Fault Detection

Ball bearing joints are a critical component in all rotating machinery, and detecting and locating faults in these joints is a significant problem in industry and research. Intelligent fault detection (IFD) is the process of applying machine learning and other statistical methods to monitor the health states of machines. This paper explores the construction of vibration images, a preprocessing technique that has been previously used to train convolutional neural networks for ball bearing joint IFD. The main results demonstrate the robustness of this technique by applying it to a larger dataset than previously used and exploring the hyperparameters used in constructing the vibration images.
Option pricing in Sandwiched Volterra Volatility model
Authors: Giulia Di Nunno, Yuliya Mishura, Anton Yurchenko-Tytarenko
Date: 2022-09-21 00:00:00
Tasks: model

We introduce a new model of financial market with stochastic volatility driven by an arbitrary H\"older continuous Gaussian Volterra process. The distinguishing feature of the model is the form of the volatility equation which ensures the solution to be ``sandwiched'' between two arbitrary H\"older continuous functions chosen in advance. We discuss the structure of local martingale measures on this market, investigate integrability and Malliavin differentiability of prices and volatilities as well as study absolute continuity of the corresponding probability laws. Additionally, we utilize Malliavin calculus to develop an algorithm of pricing options with discontinuous payoffs.
On the unknown proteins of eukaryotic proteomes
Authors: Yves-Henri Sanejouand
Date: 2022-09-22 00:00:00

In order to study unknown proteins on a large scale, a reference system has been set up for the three major eukaryotic lineages, built with 36 proteomes as taxonomically diverse as possible. Proteins from 362 eukaryotic proteomes with no known homologue in this set were then analyzed, focusing noteworthy on singletons, that is, on unknown proteins with no known homologue in their own proteome. Consistently, according to Uniprot, for a given species, no more than 12% of the singletons thus found are known at the protein level. Also, since they rely on the information found in the alignment of homologous sequences, predictions of AlphaFold2 for their tridimensional structure are usually poor. In the case of metazoan species, the number of singletons seems to increase as a function of the evolutionary distance from the reference system. Interestingly, no such trend is found in the cases of viridiplantae and fungi, as if the timescale on which singletons are added to proteomes were different in metazoa and in other eukaryotic kingdoms. In order to confirm this phenomenon, further studies of proteomes closer to those of the reference system are however needed.
Stochastic approach to study the properties of the complex patterns observed in cytokine and T-cells interaction process
Authors: Moirangthem Shubhakanta Singh, Mairembam Kelvin Singh, R. K. Brojen Singh
Date: 2022-09-21 00:00:00

Patterns in complex systems store hidden information of the system which is needed to be explored. We present a simple model of cytokine and T-cells interaction and studied the model within stochastic framework by constructing Master equation of the system and solving it. The solved probability distribution function of the model show classical Poisson pattern in the large population limit $M,Z\rightarrow large$ indicating the system has the tendency to attract a large number small-scale random processes of the cytokine population towards the basin of attraction of the system by segregating from nonrandom processes. Further, in the large $\langle Z\rangle$ limit, the pattern transform to classical Normal pattern, where, uncorrelated small-scale fluctuations are wiped out to form a regular but memoryless spatiotemporal aggregated pattern. The estimated noise using Fano factor shows clearly that the cytokine dynamics is noise induced process driving the system far away from equilibrium.
Physics-Informed Convolutional Transformer for Predicting Volatility Surface
Authors: Soohan Kim, Seok-Bae Yun, Hyeong-Ohk Bae, Muhyun Lee, Youngjoon Hong
Date: 2022-09-22 00:00:00

Predicting volatility is important for asset predicting, option pricing and hedging strategies because it cannot be directly observed in the financial market. The Black-Scholes option pricing model is one of the most widely used models by market participants. Notwithstanding, the Black-Scholes model is based on heavily criticized theoretical premises, one of which is the constant volatility assumption. The dynamics of the volatility surface is difficult to estimate. In this paper, we establish a novel architecture based on physics-informed neural networks and convolutional transformers. The performance of the new architecture is directly compared to other well-known deep-learning architectures, such as standard physics-informed neural networks, convolutional long-short term memory (ConvLSTM), and self-attention ConvLSTM. Numerical evidence indicates that the proposed physics-informed convolutional transformer network achieves a superior performance than other methods.
A Real Data-Driven Analytical Model to Predict Information Technology Sector Index Price of S&P 500
Authors: Jayanta K. Pokharel, Erasmus Tetteh-Bator, Chris P. Tsokos
Date: 2022-09-22 00:00:00

S&P 500 Index is one of the most sought after stock indices in the world. In particular, Information Technology Sector of S&P 500 is the number one business segment of the S&P 500 in terms of market capital, annual revenue and the number of companies (75) associated with it, and is one of the most attracting areas for many investors due to high percentage annual returns on investment over the years. A non-linear real data-driven analytical model is built to predict the Weekly Closing Price (WCP) of the Information Technology Sector Index of S&P 500 using six financial, four economic indicators and their two way interactions as the attributable entities that drive the stock returns. We rank the statistically significant indicators and their interactions based on the percentage of contribution to the $WCP$ of the Information Technology Sector Index of the S&P 500 that provides significant information for the beneficiary of the proposed predictive model. The model has the predictive accuracy of 99.4%, and the paper presents some intriguing findings and the model's usefulness.
Siamese Network-based Lightweight Framework for Tomato Leaf Disease Recognition
Authors: Selvarajah Thuseethan, Palanisamy Vigneshwaran, Joseph Charles, Chathrie Wimalasooriya
Date: 2022-09-18 00:00:00

Automatic tomato disease recognition from leaf images is vital to avoid crop losses by applying control measures on time. Even though recent deep learning-based tomato disease recognition methods with classical training procedures showed promising recognition results, they demand large labelled data and involve expensive training. The traditional deep learning models proposed for tomato disease recognition also consume high memory and storage because of a high number of parameters. While lightweight networks overcome some of these issues to a certain extent, they continue to show low performance and struggle to handle imbalanced data. In this paper, a novel Siamese network-based lightweight framework is proposed for automatic tomato leaf disease recognition. This framework achieves the highest accuracy of 96.97% on the tomato subset obtained from the PlantVillage dataset and 95.48% on the Taiwan tomato leaf disease dataset. Experimental results further confirm that the proposed framework is effective with imbalanced and small data. The backbone deep network integrated with this framework is lightweight with approximately 2.9629 million trainable parameters, which is way lower than existing lightweight deep networks.