pwc-archive/papers-with-abstracts huggingface dataset:
Crowd Size Estimation for Non-Uniform Spatial Distributions with mmWave Radar
Authors: Anurag Pallaprolu, Aaditya Prakash Kattekola, Winston Hurst, Upamanyu Madhow, Ashutosh Sabharwal, Yasamin Mostofi
Date: 2024-10-11 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we present a novel methodology for crowd size estimation using monostatic mmWave radar. Our aim is to accurately count large crowds that follow a non-uniform spatial distribution. Our estimation approach relies on the rigorous derivation of occlusion probabilities, which are then used to mathematically characterize the probability distributions that describe the number of agents visible to the radar as a function of the crowd size. We then estimate the true crowd size by comparing these derived mathematical models to the empirical distribution of the number of visible agents detected by the radar. This method requires minimal sensing capabilities (e.g., angle-of-arrival information is not needed), thus being well suited for either a dedicated mmWave radar or an integrated sensing and communication (ISAC) system. Extensive numerical simulations validate our methodology, demonstrating strong performance across diverse spatial distributions and for crowd sizes of up to (and including) 30 agents. We achieve a mean absolute error (MAE) of 0.48 agents, significantly outperforming a baseline which assumes that the agents are uniformly distributed in the area. Overall, our approach holds significant promise for a variety of applications including network resource allocation, crowd management, and urban planning.
Multi-Functional RIS for a Multi-Functional System: Integrating Sensing, Communication, and Wireless Power Transfer
Authors: Ahmed Magbool, Vaibhav Kumar, Ahmad Bazzi, Mark F. Flanagan, Marwa Chafii
Date: 2024-10-11 00:00:00

Communication networks are evolving from solely emphasizing communication to facilitating multiple functionalities. In this regard, integrated sensing, communication, and powering (ISCAP) provides an efficient way of enabling data transmission, radar sensing, and wireless power transfer simultaneously. Such a multi-functional network requires a multi-functional architectural solution. Toward this end, sensor-aided zero-energy reconfigurable intelligent surfaces (SAZE-RISs) offer an energy-efficient solution for ISCAP by meeting the requirements of the end users as well as supplying power for the RIS. This paper explores the use of SAZE-RIS within the ISCAP framework. First, we present the general system architecture, operational protocols, and main application scenarios for employing SAZE-RIS in ISCAP. Next, we discuss methods for managing the conflicting requirements of communication, sensing, and powering within ISCAP and the role of SAZE-RIS in this process. We then provide a detailed case study complete with simulation results, offering valuable insights into the design choices and tradeoffs that come into play when adopting this technology. Furthermore, we discuss the related challenges and open research avenues, highlighting areas that require further exploration to fully realize the potential of SAZE-RIS within this ISCAP framework.
FMCW Radar Principles and Human Activity Recognition Systems: Foundations, Techniques, and Applications
Authors: Ziqian Bi, Jiawei Xu, Ming Liu
Date: 2024-10-11 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

This book introduces the theoretical foundations of FMCW radar systems, including range and velocity estimation, signal processing techniques, and the generation of radar point clouds. A detailed discussion of Python and MATLAB as the primary programming tools for radar signal processing is provided, including the integration of libraries like NumPy, Matplotlib, and SciPy for data analysis and visualization. In addition, the book covers advanced techniques such as deep learning applications for radar signal processing, focusing on Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformers for analyzing radar data. Furthermore, it highlights state-of-the-art methods for human activity recognition using radar, leveraging a combination of traditional signal processing techniques and machine learning models. The book is designed to cater to both beginners and experts in radar signal processing, offering practical examples, code implementations, and insights into the future of radar technology in various domains, including autonomous systems and security applications.
High-Efficient Near-Field Channel Characteristics Analysis for Large-Scale MIMO Communication Systems
Authors: Hao Jiang, Wangqi Shi, Xiao Chen, Qiuming Zhu, Zhen Chen
Date: 2024-10-11 00:00:00

Large-scale multiple-input multiple-output (MIMO) holds great promise for the fifth-generation (5G) and future communication systems. In near-field scenarios, the spherical wavefront model is commonly utilized to accurately depict the propagation characteristics of large-scale MIMO communication channels. However, employing this modeling method necessitates the computation of angle and distance parameters for each antenna element, resulting in challenges regarding computational complexity. To solve this problem, we introduce a subarray decomposition scheme with the purpose of dividing the whole large-scale antenna array into several smaller subarrays. This scheme is implemented in the near-field channel modeling for large-scale MIMO communications between the base stations (BS) and the mobile receiver (MR). Essential channel propagation statistics, such as spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (CFs), and channel capacities, are derived and discussed. A comprehensive analysis is conducted to investigate the influence of the height of the BS, motion characteristics of the MR, and antenna configurations on the channel statistics. The proposed channel model criterions, such as the modeling precision and computational complexity, are also theoretically compared. Numerical results demonstrate the effectiveness of the presented communication model in obtaining a good tradeoff between modeling precision and computational complexity.
Designing Unimodular Waveforms for MIMO Radar Based on Manifold Optimization Method
Authors: Xuyang Zhao, Jiangtao Wang, Shihao Yan, Yongchao Wang
Date: 2024-10-10 00:00:00

In this paper, we design unimodular waveforms with good correlation properties for multi-input multi-output (MIMO) radar systems. Specifically, first, we analyze the geometric properties of the unimodular constraint in the fourth-order polynomial minimization problem using Riemannian geometry theory. By embedding it into the search space, we transform the original non-convex optimization problem into an unconstrained problem on a Riemannian manifold. Then, we construct the manifold corresponding to the search space and the operators required for the customized optimization algorithm. Second, we develop a customized low-complexity unimodular manifold gradient descent (UM-GD) algorithm on the constructed manifold to solve the optimization problem in the normal-scale case, and propose its acceleration version unimodular manifold accelerated gradient descent (UM-AGD) algorithm, to speed up the convergence. In the large-scale case, we transform the objective function into the form of a summation of a large but finite number of loss functions and develop a customized unimodular manifold stochastic variance reduced gradient (UM-SVRG) algorithm to solve this problem. Compared to the existing bechmark method, which has a computational complexity of roughly $\mathcal{O}(M^4+3M^2N+3|\mathcal{D}| |\hat{\Theta}|^2MN)$, UM-SVRG algorithm effectively reduces the computational complexity of each iteration to roughly $\mathcal{O}(|\mathcal{D}||\hat{\Theta}|^2MN)$. Thirdly, we provide theoretical guarantees of convergence for both the UM-GD and UM-SVRG algorithms through appropriate parameter selection, and prove that the proposed algorithms can converge to a stationary point. Finally, numerical examples demonstrate the effectiveness of the proposed UM-GD and UM-SVRG algorithms.
Unique Word-Based Frame Design for Bistatic Integrated Sensing and Communication
Authors: Roberto Bomfin, Marwa Chafii
Date: 2024-10-10 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) aims at enhancing the network functionalities and enabling new applications in the upcoming communications networks. In this paper, we propose two unique word (UW)-based frame designs for bistatic ISAC. The approach consists of replacing the cyclic prefix (CP) with a Zadoff-Chu (ZC)-based sequence. With this approach, the radar receiver does not need to know the data symbols to perform sensing and the data rate is not compromised by the addition of extra pilots. The sensing performance of the UW-based frames is compared with that of orthogonal frequency division multiplexing (OFDM) as well as the pilot-symbol (PS) based radar processing. We derive the Cram\'er-Rao bound (CRB) considering a band-limited system with raised-cosine filtering. Furthermore, we provide low-complexity fast Fourier transform (FFT)-based radar receivers that perform integer and fine grid multi-target delay-Doppler (DD) estimations. For the integer FFT-based receiver, an upper bound for the outlier probability is derived when the true DD falls outside the integer grid. The results demonstrate that the UW frames exhibit competitive radar performance with PS while having a 16.67% higher data rate for the cases investigated.
Adaptive Hopping for Bluetooth Backscatter using Commodity Edges
Authors: Maoran Jiang, Wei Gong
Date: 2024-10-10 00:00:00
Tasks: TAG

Channel hopping is essential to BLE backscatter as commodity BLE switches channels frequently during transmission to overcome interferences in busy radio environments. Existing Bluetooth backscatter systems, however, suffer from slow responses to excitation change and poor control of the target channel. To address these issues, this paper presents ChannelDance, a BLE backscatter system that utilizes a low-latency edge server to achieve fast and accurate hopping. Specifically, we show that the backscattered channel relies on the excitation channel and tag toggling frequency. By identifying excitation frequency, the tag can achieve accurate hopping with a dynamically configured clock. Further, we introduce a low-latency architecture, which is centralized, asynchronous, and equipped with high-speed interfaces. This architecture supports the tag to respond to excitation changes fastly. We prototype the ChannelDance tag with FPGA and build the low latency edge server with commodity MCU and off-the-shelf BLE and WiFi radios. Experimental results show that ChannelDance can realize 40 to 40 channel mapping with a median success rate of 93% and achieve 3.5x goodput gain with channel optimization. Moreover, with adaptive hopping, the ChannelDance tag successfully establishes a connection with commodity BLE.
Robustness and Security Enhancement of Radio Frequency Fingerprint Identification in Time-Varying Channels
Authors: Lu Yang, Seyit Camtepe, Yansong Gao, Vicky Liu, Dhammika Jayalath
Date: 2024-10-10 00:00:00
Tasks: Transfer Learning

Radio frequency fingerprint identification (RFFI) is becoming increasingly popular, especially in applications with constrained power, such as the Internet of Things (IoT). Due to subtle manufacturing variations, wireless devices have unique radio frequency fingerprints (RFFs). These RFFs can be used with pattern recognition algorithms to classify wireless devices. However, Implementing reliable RFFI in time-varying channels is challenging because RFFs are often distorted by channel effects, reducing the classification accuracy. This paper introduces a new channel-robust RFF, and leverages transfer learning to enhance RFFI in the time-varying channels. Experimental results show that the proposed RFFI system achieved an average classification accuracy improvement of 33.3 % in indoor environments and 34.5 % in outdoor environments. This paper also analyzes the security of the proposed RFFI system to address the security flaw in formalized impersonation attacks. Since RFF collection is being carried out in uncontrolled deployment environments, RFFI systems can be targeted with false RFFs sent by rogue devices. The resulting classifiers may classify the rogue devices as legitimate, effectively replacing their true identities. To defend against impersonation attacks, a novel keyless countermeasure is proposed, which exploits the intrinsic output of the softmax function after classifier training without sacrificing the lightweight nature of RFFI. Experimental results demonstrate an average increase of 0.3 in the area under the receiver operating characteristic curve (AUC), with a 40.0 % improvement in attack detection rate in indoor and outdoor environments.
Phenotype structuring in collective cell migration:a tutorial of mathematical models and methods
Authors: Tommaso Lorenzi, Kevin J Painter, Chiara Villa
Date: 2024-10-17 00:00:00

Populations are heterogeneous, deviating in numerous ways. Phenotypic diversity refers to the range of traits or characteristics across a population, where for cells this could be the levels of signalling, movement and growth activity, etc. Clearly, the phenotypic distribution -- and how this changes over time and space -- could be a major determinant of population-level dynamics. For instance, across a cancerous population, variations in movement, growth, and ability to evade death may determine its growth trajectory and response to therapy. In this review, we discuss how classical partial differential equation (PDE) approaches for modelling cellular systems and collective cell migration can be extended to include phenotypic structuring. The resulting non-local models -- which we refer to as phenotype-structured partial integro-differential equations (PS-PIDEs) -- form a sophisticated class of models with rich dynamics. We set the scene through a brief history of structured population modelling, and then review the extension of several classic movement models -- including the Fisher-KPP and Keller-Segel equations -- into a PS-PIDE form. We proceed with a tutorial-style section on derivation, analysis, and simulation techniques. First, we show a method to formally derive these models from underlying agent-based models. Second, we recount travelling waves in PDE models of spatial spread dynamics and concentration phenomena in non-local PDE models of evolutionary dynamics, and combine the two to deduce phenotypic structuring across travelling waves in PS-PIDE models. Third, we discuss numerical methods to simulate PS-PIDEs, illustrating with a simple scheme based on the method of lines and noting the finer points of consideration. We conclude with a discussion of future modelling and mathematical challenges.
Mathematically tractable models of random phylogenetic networks: an overview of some recent developments
Authors: François Bienvenu
Date: 2024-10-17 00:00:00

Models of random phylogenetic networks have been used since the inception of the field, but the introduction and rigorous study of mathematically tractable models is a much more recent topic that has gained momentum in the last 5 years. This manuscript discusses some recent developments in the field through a selection of examples. The emphasis is on the techniques rather than on the results themselves, and on probabilistic tools rather than on combinatorial ones.
Parameters estimation and uncertainty assessment in the transmission dynamics of rabies in humans and dogs
Authors: Mfano Charles, Sayoki G. Mfinanga, G. A. Lyakurwa, Delfim F. M. Torres, Verdiana G. Masanja
Date: 2024-10-15 00:00:00

Rabies remains a pressing global public health issue, demanding effective modeling and control strategies. This study focused on developing a mathematical model using ordinary differential equations (ODEs) to estimate parameters and assess uncertainties related to the transmission dynamics of rabies in humans and dogs. To determine model parameters and address uncertainties, next-generation matrices were utilized to calculate the basic reproduction number ${\cal R}_{0}$. Furthermore, the Partial Rank Correlation Coefficient was used to identify parameters that significantly influence model outputs. The analysis of equilibrium states revealed that the rabies-free equilibrium is globally asymptotically stable when $R_0<1$, whereas the endemic equilibrium is globally asymptotically stable when ${\cal R}_{0}\geq 1$. To reduce the severity of rabies and align with the Global Rabies Control (GRC) initiative by 2030, the study recommends implementing control strategies targeting indoor domestic dogs.
Examining the Link Between Peroxiredoxin Proteins and Mutually Exclusive Transcription Factor Activation With a Mathematical Model
Authors: Zachary Schlamowitz, Andrew L. Paek, Kevin K. Lin
Date: 2024-10-14 00:00:00

Oxidative stress is a fundamental stimulus to which eukaryotic cells respond via many channels. Among these channels are both protein systems that process oxidative stress, such as the 2-Cys peroxiredoxin-thioredoxin system (PTRS), as well as changes in transcriptional activity that target outcomes such as growth, damage control and repair, or cell death. Recent work has revealed connections between the PTRS and temporal phases of transcriptional activity involving famous transcription factors like p53 and FOXO1. To examine potential mechanisms for these connections, we implement an existing dynamical systems mathematical model for the PTRS. We hypothesize that dose-dependent hyperoxidation events enact ultrasensitive switches by which the PTRS can categorize stress severity and activate appropriate transcriptional responses. Using numerical simulations of the PTRS in human cells, we provide a proof of principle for staggered, switchlike hyperoxidation of peroxiredoxins (Prx) as well as an underlying mechanism requiring simultaneous signaling by Prx I and II. Then, we use our model to make testable predictions about individual Prx knockouts as well as the affinity for hydrogen peroxide of Prx across oxidation states. This study provides direction for future experimental work and sheds light into the mechanisms underlying oxidative stress response in human cells.
A New Perspective on Determining Disease Invasion and Population Persistence in Heterogeneous Environments
Authors: Poroshat Yazdanbakhsh, Mark Anderson, Zhisheng Shuai
Date: 2024-10-11 00:00:00
Tasks: Epidemiology

We introduce a new quantity known as the network heterogeneity index, denoted by $\mathcal{H}$, which facilitates the investigation of disease propagation and population persistence in heterogeneous environments. Our mathematical analysis reveals that this index embodies the structure of such networks, the disease or population dynamics of patches, and the dispersal between patches. We present multiple representations of the network heterogeneity index and demonstrate that $\mathcal{H}\geq 0$. Moreover, we explore the applications of $\mathcal{H}$ in epidemiology and ecology across various heterogeneous environments, highlighting its effectiveness in determining disease invasibility and population persistence.
Delegated portfolio management with random default
Authors: Alberto Gennaro, Thibaut Mastrolia
Date: 2024-10-17 00:00:00
Tasks: Management

We are considering the problem of optimal portfolio delegation between an investor and a portfolio manager under a random default time. We focus on a novel variation of the Principal-Agent problem adapted to this framework. We address the challenge of an uncertain investment horizon caused by an exogenous random default time, after which neither the agent nor the principal can access the market. This uncertainty introduces significant complexities in analyzing the problem, requiring distinct mathematical approaches for two cases: when the random default time falls within the initial time frame [0,T] and when it extends beyond this period. We develop a theoretical framework to model the stochastic dynamics of the investment process, incorporating the random default time. We then analyze the portfolio manager's investment decisions and compensation mechanisms for both scenarios. In the first case, where the default time could be unbounded, we apply traditional results from Backward Stochastic Differential Equations (BSDEs) and control theory to address the agent problem. In the second case, where the default time is within the interval [0,T], the problem becomes more intricate due to the degeneracy of the BSDE's driver. For both scenarios, we demonstrate that the contracting problem can be resolved by examining the existence of solutions to integro-partial Hamilton-Jacobi-Bellman (HJB) equations in both situations. We develop a deep-learning algorithm to solve the problem in high-dimension with no access to the optimizer of the Hamiltonian function.
Multiple Gaussian process models based global sensitivity analysis and efficient optimization of in vitro mRNA transcription process
Authors: Min Tao, Adithya Nair, Ioanna Kalospyrou, Robert A Milton, Mabrouka Maamra, Zoltan Kis, Joan Cordiner, Solomon F Brown
Date: 2024-10-15 00:00:00
Tasks: Sensitivity

The in vitro transcription (IVT) process is a critical step in RNA production. To ensure the efficiency of RNA manufacturing, it is essential to optimize and identify its key influencing factors. In this study, multiple Gaussian Process (GP) models are used to perform efficient optimization and global sensitivity analysis (GSA). Firstly, multiple GP models were constructed using the data from multiple experimental replicates, accurately capturing the complexities of the IVT process. Then GSA was conducted to determine the dominant reaction factors, specifically the concentrations of reactants NTP and Mg across all data-driven models. Concurrently, a multi-start optimization algorithm was applied to these GP models to identify optimal operational conditions that maximize RNA yields across all surrogate models. These optimized conditions are subsequently validated through additional experimental data.
Unbiased estimation of second-order parameter sensitivities for stochastic reaction networks
Authors: Quentin Badolle, Ankit Gupta, Mustafa Khammash
Date: 2024-10-15 00:00:00

Stochastic models for chemical reaction networks are increasingly popular in systems and synthetic biology. These models formulate the reaction dynamics as Continuous-Time Markov Chains (CTMCs) whose propensities are parameterized by a vector $\theta$ and parameter sensitivities are introduced as derivatives of their expected outputs with respect to components of the parameter vector. Sensitivities characterise key properties of the output like robustness and are also at the heart of numerically efficient optimisation routines like Newton-type algorithms used in parameter inference and the design of of control mechanisms. Currently the only unbiased estimator for second-order sensitivities is based on the Girsanov transform and it often suffers from high estimator variance. We develop a novel estimator for second-order sensitivities by first rigorously deriving an integral representation of these sensitivities. We call the resulting method the Double Bernoulli Path Algorithm and illustrate its efficiency through numerical examples.