pwc-archive/papers-with-abstracts huggingface dataset:
Predictive Enforcement
Authors: Yeon-Koo Che, Jinwoo Kim, Konrad Mierendorff
Date: 2024-05-08 00:00:00

We study law enforcement guided by data-informed predictions of "hot spots" for likely criminal offenses. Such "predictive" enforcement could lead to data being selectively and disproportionately collected from neighborhoods targeted for enforcement by the prediction. Predictive enforcement that fails to account for this endogenous "datafication" may lead to the over-policing of traditionally high-crime neighborhoods and performs poorly, in particular, in some cases as poorly as if no data were used. Endogenizing the incentives for criminal offenses identifies additional deterrence benefits from the informationally efficient use of data.
Turning the Ratchet: Dynamic Screening with Multiple Agents
Authors: Mehmet Ekmekci, Lucas Maestri, Dong Wei
Date: 2024-05-07 00:00:00

We study a dynamic contracting problem with multiple agents and limited commitment. A principal seeks to screen efficient agents using one-period contracts, but is tempted to revise contract terms upon knowing an agent's type. Alterations of contracts are observable and, hence, whenever past promises are broken future information revelation stops. We provide necessary and sufficient conditions under which information revelation can be fostered. For sufficiently patient players, private information is either never revealed or fully revealed in a sequential manner. Optimal contracts provide high-powered incentives upon initial disclosure of an agent's type, and rewards for information revelation vanish over time.
Array SAR 3D Sparse Imaging Based on Regularization by Denoising Under Few Observed Data
Authors: Yangyang Wang, Xu Zhan, Jing Gao, Jinjie Yao, Shunjun Wei, JianSheng Bai
Date: 2024-05-09 00:00:00
Tasks: compressed sensing, Denoising

Array synthetic aperture radar (SAR) three-dimensional (3D) imaging can obtain 3D information of the target region, which is widely used in environmental monitoring and scattering information measurement. In recent years, with the development of compressed sensing (CS) theory, sparse signal processing is used in array SAR 3D imaging. Compared with matched filter (MF), sparse SAR imaging can effectively improve image quality. However, sparse imaging based on handcrafted regularization functions suffers from target information loss in few observed SAR data. Therefore, in this article, a general 3D sparse imaging framework based on Regulation by Denoising (RED) and proximal gradient descent type method for array SAR is presented. Firstly, we construct explicit prior terms via state-of-the-art denoising operators instead of regularization functions, which can improve the accuracy of sparse reconstruction and preserve the structure information of the target. Then, different proximal gradient descent type methods are presented, including a generalized alternating projection (GAP) and an alternating direction method of multiplier (ADMM), which is suitable for high-dimensional data processing. Additionally, the proposed method has robust convergence, which can achieve sparse reconstruction of 3D SAR in few observed SAR data. Extensive simulations and real data experiments are conducted to analyze the performance of the proposed method. The experimental results show that the proposed method has superior sparse reconstruction performance.
Quantifying Smooth Muscles Regional Organization in the Rat Bladder Using Immunohistochemistry, Multiphoton Microscopy and Machine Learning
Authors: Alireza Asadbeygi, Yasutaka Tobe, Naoki Yoshimura, Sean D. Stocker, Simon Watkins, Paul Watton, Anne M. Robertson
Date: 2024-05-08 00:00:00

The smooth muscle bundles (SMBs) in the bladder act as contractile elements which enable the bladder to void effectively. In contrast to skeletal muscles, these bundles are not highly aligned, rather they are oriented more heterogeneously throughout the bladder wall. In this work, for the first time, this regional orientation of the SMBs is quantified across the whole bladder, without the need for optical clearing or cryosectioning. Immunohistochemistry staining was utilized to visualize smooth muscle cell actin in multiphoton microscopy (MPM) images of bladder smooth muscle bundles (SMBs). Feature vectors for each pixel were generated using a range of filters, including Gaussian blur, Gaussian gradient magnitude, Laplacian of Gaussian, Hessian eigenvalues, structure tensor eigenvalues, Gabor, and Sobel gradients. A Random Forest classifier was subsequently trained to automate the segmentation of SMBs in the MPM images. Finally, the orientation of SMBs in each bladder region was quantified using the CT-FIRE package. This information is essential for biomechanical models of the bladder that include contractile elements.
Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction
Authors: Tao Hong, Xiaojian Xu, Jason Hu, Jeffrey A. Fessler
Date: 2024-05-06 00:00:00
Tasks: compressed sensing, Denoising, MRI Reconstruction

Model-based methods play a key role in the reconstruction of compressed sensing (CS) MRI. Finding an effective prior to describe the statistical distribution of the image family of interest is crucial for model-based methods. Plug-and-play (PnP) is a general framework that uses denoising algorithms as the prior or regularizer. Recent work showed that PnP methods with denoisers based on pretrained convolutional neural networks outperform other classical regularizers in CS MRI reconstruction. However, the numerical solvers for PnP can be slow for CS MRI reconstruction. This paper proposes a preconditioned PnP (P^2nP) method to accelerate the convergence speed. Moreover, we provide proofs of the fixed-point convergence of the P^2nP iterates. Numerical experiments on CS MRI reconstruction with non-Cartesian sampling trajectories illustrate the effectiveness and efficiency of the P^2nP approach.
Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans
Authors: Hartmut Häntze, Lina Xu, Maximilian Rattunde, Leonhard Donle, Felix J. Dorfner, Alessa Hering, Lisa C. Adams, Keno K. Bressem
Date: 2024-05-04 00:00:00
Tasks: Computed Tomography (CT), MRI segmentation, Segmentation

Computed tomography (CT) segmentation models often contain classes that are not currently supported by magnetic resonance imaging (MRI) segmentation models. In this study, we show that a simple image inversion technique can significantly improve the segmentation quality of CT segmentation models on MRI data. We demonstrate the feasibility for both a general multi-class and a specific renal carcinoma model for segmenting T1-weighted MRI images. Using this technique, we were able to localize and segment clear cell renal cell carcinoma in T1-weighted MRI scans, using a model that was trained on only CT data. Image inversion is straightforward to implement and does not require dedicated graphics processing units, thus providing a quick alternative to complex deep modality-transfer models. Our results demonstrate that existing CT models, including pathology models, might be transferable to the MRI domain with reasonable effort.
supDQN: Supervised Rewarding Strategy Driven Deep Q-Network for sEMG Signal Decontamination
Authors: Ashutosh Jena, Naveen Gehlot, Rajesh Kumar, Ankit Vijayvargiya, Mahipal Bukya
Date: 2024-05-09 00:00:00
Tasks: Denoising

The presence of muscles throughout the active parts of the body such as the upper and lower limbs, makes electromyography-based human-machine interaction prevalent. However, muscle signals are stochastic and noisy. These noises can be regular and irregular. Irregular noises due to movements or electrical switching require dynamic filtering. Conventionally, filters are stacked, which trims and delays the signal unnecessarily. This study introduces a decontamination technique involving a supervised rewarding strategy to drive a deep Q-network-based agent (supDQN). It applies one of three filters to decontaminate a 1sec long surface electromyography signal, which is dynamically contaminated. A machine learning agent identifies whether the signal after filtering is clean or noisy. Accordingly, a reward is generated. The identification accuracy is enhanced by using a local interpretable model-agnostic explanation. The deep Q-network is guided by this reward to select filter optimally while decontaminating a signal. The proposed filtering strategy is tested on four noise levels (-5 dB, -1 dB, +1 dB, +5 dB). supDQN filters the signal desirably when the signal-to-noise ratio (SNR) is between -5 dB to +1 dB. It filters less desirably at high SNR (+5 dB). A normalized root mean square (nRMSE) is formulated to depict the difference of filtered signal from ground truth. This is used to compare supDQN and conventional methods including wavelet denoising with debauchies and symlet wavelet, high order low pass filter, notch filter, and high pass filter. The proposed filtering strategy gives an average value nRMSE of 1.1974, which is lower than the conventional filters.
3D Positioning using a New Diffraction Path Model
Authors: Gaurav Duggal, R. Michael Buehrer, Harpreet S. Dhillon, Jeffrey H. Reed
Date: 2024-05-09 00:00:00
Tasks: model

Enhancing 3D and Z-axis positioning accuracy is crucial for effective rescue in indoor emergencies, ensuring safety for emergency responders and at-risk individuals. Additionally, reducing the dependence of a positioning system on fixed infrastructure is crucial, given its vulnerability to power failures and damage during emergencies. Further challenges from a signal propagation perspective include poor indoor signal coverage, multipath effects and the problem of Non-Line-OfSight (NLOS) measurement bias. In this study, we utilize the mobility provided by a rapidly deployable Uncrewed Aerial Vehicle (UAV) based wireless network to address these challenges. We recognize diffraction from window edges as a crucial signal propagation mechanism and employ the Geometrical Theory of Diffraction (GTD) to introduce a novel NLOS path length model. Using this path length model, we propose two different techniques to improve the indoor positioning performance for emergency scenarios.
A minimal dynamical system and analog circuit for non-associative learning
Authors: Matthew Smart, Stanislav Y. Shvartsman, Martin Mönnigmann
Date: 2024-05-09 00:00:00

Learning in living organisms is typically associated with networks of neurons. The use of large numbers of adjustable units has also been a crucial factor in the continued success of artificial neural networks. In light of the complexity of both living and artificial neural networks, it is surprising to see that very simple organisms -- even unicellular organisms that do not possess a nervous system -- are capable of certain forms of learning. Since in these cases learning may be implemented with much simpler structures than neural networks, it is natural to ask how simple the building blocks required for basic forms of learning may be. The purpose of this study is to discuss the simplest dynamical systems that model a fundamental form of non-associative learning, habituation, and to elucidate technical implementations of such systems, which may be used to implement non-associative learning in neuromorphic computing and related applications.
Shifting the ISAC Trade-Off with Fluid Antenna Systems
Authors: Jiaqi Zou, Hao Xu, Chao Wang, Lvxin Xu, Songlin Sun, Kaitao Meng, Christos Masouros, Kai-Kit Wong
Date: 2024-05-09 00:00:00
Tasks: Diversity, Integrated sensing and communication, ISAC

As an emerging antenna technology, a fluid antenna system (FAS) enhances spatial diversity to improve both sensing and communication performance by shifting the active antennas among available ports. In this letter, we study the potential of shifting the integrated sensing and communication (ISAC) trade- off with FAS. We propose the model for FAS-enabled ISAC and jointly optimize the transmit beamforming and port selection of FAS. In particular, we aim to minimize the transmit power, while satisfying both communication and sensing requirements. An efficient iterative algorithm based on sparse optimization, convex approximation, and a penalty approach is developed. The simulation results show that the proposed scheme can attain 33% reductions in transmit power with guaranteed sensing and communication performance, showing the great potential of the fluid antenna for striking a flexible tradeoff between sensing and communication in ISAC systems.
Maximum Correntropy Polynomial Chaos Kalman Filter for Underwater Navigation
Authors: Rohit Kumar Singh, Joydeb Saha, Shovan Bhaumik
Date: 2024-05-09 00:00:00
Tasks: Position

This paper develops an underwater navigation solution that utilizes a strapdown inertial navigation system (SINS) and fuses a set of auxiliary sensors such as an acoustic positioning system, Doppler velocity log, depth meter, attitude meter, and magnetometer to accurately estimate an underwater vessel's position and orientation. The conventional integrated navigation system assumes Gaussian measurement noise, while in reality, the noises are non-Gaussian, particularly contaminated by heavy-tailed impulsive noises. To address this issue, and to fuse the system model with the acquired sensor measurements efficiently, we develop a square root polynomial chaos Kalman filter based on maximum correntropy criteria. The filter is initialized using acoustic beaconing to accurately locate the initial position of the vehicle. The computational complexity of the proposed filter is calculated in terms of flops count. The proposed method is compared with the existing maximum correntropy sigma point filters in terms of estimation accuracy and computational complexity. The simulation results demonstrate an improved accuracy compared to the conventional deterministic sample point filters.
End-to-End Waveform and Beamforming Optimization for RF Wireless Power Transfer
Authors: Abdul Basit Khattak, Onel L. A. López, Amirhossein Azarbahram, Deepak Kumar, Matti Latva-aho
Date: 2024-05-09 00:00:00

Radio frequency (RF) wireless power transfer (WPT) is a key technology for future low-power wireless systems. However, the inherently low end-to-end power transfer efficiency (PTE) is challenging for practical applications. The main factors contributing to it are the channel losses, transceivers' power consumption, and losses related, e.g., to the digital-to-analog converter (DAC), high-power amplifier, and rectenna. Optimizing PTE requires careful consideration of these factors, motivating the current work. Herein, we consider an analog multi-antenna power transmitter that aims to charge a single energy harvester. We first provide a mathematical framework to calculate the harvested power from multi-tone signal transmissions and the system power consumption. Then, we formulate the joint waveform and analog beamforming design problem to minimize power consumption and meet the charging requirements. Finally, we propose an optimization approach relying on swarm intelligence to solve the specified problem. Simulation results quantify the power consumption reduction as the DAC, phase shifters resolution, and antenna length are increased, while it is seen that increasing system frequency results in higher power consumption.
Deep Learning for CSI Feedback: One-Sided Model and Joint Multi-Module Learning Perspectives
Authors: Yiran Guo, Wei Chen, Feifei Sun, Jiaming Cheng, Michail Matthaiou, Bo Ai
Date: 2024-05-09 00:00:00

The use of deep learning (DL) for channel state information (CSI) feedback has garnered widespread attention across academia and industry. The mainstream DL architectures, e.g., CsiNet, deploy DL models on the base station (BS) side and the user equipment (UE) side, which are highly coupled and need to be trained jointly. However, two-sided DL models require collaborations between different network vendors and UE vendors, which entails considerable challenges in order to achieve consensus, e.g., model maintenance and responsibility. Furthermore, DL-based CSI feedback design invokes DL to reduce only the CSI feedback error, whereas jointly optimizing several modules at the transceivers would provide more significant gains. This article presents DL-based CSI feedback from the perspectives of one-sided model and joint multi-module learning. We herein introduce various novel one-sided CSI feedback architectures. In particular, the recently proposed CSI-PPPNet provides a one-sided one-for-all framework, which allows a DL model to deal with arbitrary CSI compression ratios. We review different joint multi-module learning methods, where the CSI feedback module is learned jointly with other modules including channel coding, channel estimation, pilot design and precoding design. Finally, future directions and challenges for DL-based CSI feedback are discussed, from the perspectives of inherent limitations of artificial intelligence (AI) and practical deployment issues.
Communications under Bursty Mixed Gaussian-impulsive Noise: Demodulation and Performance Analysis
Authors: Tianfu Qi, Jun Wang, Zexue Zhao
Date: 2024-05-09 00:00:00
Tasks: parameter estimation

This is the second part of the two-part paper considering the communications under the bursty mixed noise composed of white Gaussian noise and colored non-Gaussian impulsive noise. In the first part, based on Gaussian distribution and student distribution, we proposed a multivariate bursty mixed noise model and designed model parameter estimation algorithms. However, the performance of a communication system will significantly deteriorate under the bursty mixed noise if a conventional signal detection algorithm with respect to Gaussian noise is applied. To address this issue, in the second part, we leverage the probability density function (PDF) to derive the maximum likelihood (ML) demodulation methods for both linear and nonlinear modulations, including M-array PSK (M-PSK) and MSK modulation schemes. We analyze the theoretical bit error rate (BER) performance of M-PSK and present close-form BER expressions. For the MSK demodulation based on the Viterbi algorithm, we derive a lower and upper bound of BER. Simulation results showcase that the proposed demodulation methods outperform baselines by more than 2.5dB when the BER performance reaches the order of magnitude of $10^{-3}$, and the theoretical analysis matches the simulated results well.
Performance Bounds for Velocity Estimation with Large Antenna Arrays
Authors: Caterina Giovannetti, Nicolò Decarli, Davide Dardari
Date: 2024-05-08 00:00:00
Tasks: Position

Joint communication and sensing (JCS) is envisioned as an enabler of future 6G networks. One of the key features of these networks will be the use of extremely large aperture arrays (ELAAs) and high operating frequencies, which will result in significant near-field propagation effects. This unique property can be harnessed to improve sensing capabilities. In this paper, we focus on velocity sensing, as using ELAAs allows the estimation of not just the radial component but also the transverse component. We derive analytical performance bounds for both velocity components, demonstrating how they are affected by the different system parameters and geometries. These insights offer a foundational understanding of how near-field effects play in velocity sensing differently from the far field and from position estimate.
Filtering and smoothing estimation algorithms from uncertain nonlinear observations with time-correlated additive noise and random deception attacks
Authors: R. Caballero-Águila, J. Hu, J. Linares-Pérez
Date: 2024-05-08 00:00:00

This paper discusses the problem of estimating a stochastic signal from nonlinear uncertain observations with time-correlated additive noise described by a first-order Markov process. Random deception attacks are assumed to be launched by an adversary, and both this phenomenon and the uncertainty in the observations are modelled by two sets of Bernoulli random variables. Under the assumption that the evolution model generating the signal to be estimated is unknown and only the mean and covariance functions of the processes involved in the observation equation are available, recursive algorithms based on linear approximations of the real observations are proposed for the least-squares filtering and fixed-point smoothing problems. Finally, the feasibility and effectiveness of the developed estimation algorithms are verified by a numerical simulation example, where the impact of uncertain observation and deception attack probabilities on estimation accuracy is evaluated.
Bistatic OFDM-based ISAC with Over-the-Air Synchronization: System Concept and Performance Analysis
Authors: David Brunner, Lucas Giroto de Oliveira, Charlotte Muth, Silvio Mandelli, Marcus Henninger, Axel Diewald, Yueheng Li, Mohamad Basim Alabd, Laurent Schmalen, Thomas Zwick, Benjamin Nuss
Date: 2024-05-08 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has been defined as one goal for 6G mobile communication systems. In this context, this article introduces a bistatic ISAC system based on orthogonal frequency-division multiplexing (OFDM). While the bistatic architecture brings advantages such as not demanding full duplex operation with respect to the monostatic one, the need for synchronizing transmitter and receiver is imposed. In this context, this article introuces a bistatic ISAC signal processing framework where an incoming OFDM-based ISAC signal undergoes over-the-air synchronization based on preamble symbols and pilots. Afterwards, bistatic radar processing is performed using either only pilot subcarriers or the full OFDM frame. The latter approach requires estimation of the originally transmitted frame based on communication processing and therefore error-free communication, which can be achieved via appropriate channel coding. The performance and limitations of the introduced system based on both aforementioned approaches are assessed via an analysis of the impact of residual synchronization mismatches and data decoding failures on both communication and radar performances. Finally, the performed analyses are validated by proof-of-concept measurement results.
System Identification of the Upgraded LHPOST6 Reaction Mass at the University of California San Diego
Authors: Andres Rodriguez-Burneo, Jose I. Restrepo, Joel P. Conte
Date: 2024-05-07 00:00:00

Upon completing the upgrade from one to six degrees of freedom of the Outdoor Shake Table at UCSD in 2019, forced vibration tests were carried out to identify the dynamic characteristics of the reaction mass and soil system. This report describes the motivation, execution, and results from such tests, which independently excited the reaction mass in four degrees of freedom: longitudinal, transverse, yaw, and vertical. The report discusses the frequency response curves and deformation patterns from which the natural frequencies, damping ratio, mode shapes, and rigid body motion were determined. The first objective of the study was to investigate if the dynamic properties of the system had dramatically changed after the upgrade by comparing the results to those from forced vibration tests performed 20 years ago, during the construction of the facility. In addition, most recent tests also contributed with results from the vertical degree of freedom, which had never been tested. The second objective was to obtain high-quality response data of the system that will be used to develop a high-fidelity computational model of the reaction mass in future research. A comparison of results showed a slight difference of 0.5Hz in the natural frequency of 2 degrees of freedom. Moreover, maximum displacements in the recent tests were overall larger than the previous ones with few exceptions. The report thoroughly discusses the several sources of discrepancy between the past and most recent results. Finally, test results allowed us to estimate the system's response if the shake table actuators were to be used at their maximum nominal capacity. Small displacement and high damping results were consistent with those of previous tests and further validated the design of the reaction mass.