pwc-archive/papers-with-abstracts huggingface dataset:
Improved Generalizability of CNN Based Lane Detection in Challenging Weather Using Adaptive Preprocessing Parameter Tuning
Authors: I-Chen Sang, William R. Norris
Date: 2024-02-09 00:00:00
Tasks: Autonomous Vehicles, Lane Detection

Ensuring the robustness of lane detection systems is essential for the reliability of autonomous vehicles, particularly in the face of diverse weather conditions. While numerous algorithms have been proposed, addressing challenges posed by varying weather remains an ongoing issue. Geometric-based lane detection methods, rooted in the inherent properties of road geometry, provide enhanced generalizability. However, these methods often require manual parameter tuning to accommodate it fluctuating illumination and weather conditions. Conversely, learning-based approaches, trained on pre-labeled datasets, excel in localizing intricate and curved lane configurations but grapple with the absence of diverse weather datasets. This paper introduces a promising hybrid approach that merges the strengths of both methodologies. A novel adaptive preprocessing method is proposed in this work. Utilizing a fuzzy inference system (FIS), the algorithm dynamically adjusts parameters in geometric-based image processing functions and enhances adaptability to diverse weather conditions. Notably, this preprocessing algorithm is designed to seamlessly integrate with all learning-based lane detection models. When implemented in conjunction with CNN-based models, the hybrid approach demonstrates commendable generalizability across weather conditions and adaptability to complex lane configurations. Rigorous testing on datasets featuring challenging weather conditions showcases the proposed method's significant improvements over existing models, underscoring its efficacy in addressing the persistent challenges associated with lane detection in adverse weather scenarios.
SC-FDMA as a Delay-Doppler Domain Modulation Technique
Authors: Arman Farhang, Mohsen Bayat
Date: 2024-02-12 00:00:00

This paper compares orthogonal time frequency space (OTFS) modulation and single-carrier frequency division multiple access (SC-FDMA). It shows that these are equivalent except for a set of linear phase shifts, applied to the transmit/receive data symbols, which can be absorbed into the channel. Through mathematical and numerical analysis, it is confirmed that SC-FDMA is in fact a delay-Doppler domain multiplexing technique that can achieve the same performance gains as those of OTFS in time-varying wireless environments. This is a promising result as SC-FDMA is already a part of the current wireless standards. The derivations in this paper also shed light on the time-frequency resources used by the delay-Doppler domain data symbols with the fine granularity of delay and Doppler spacings. While comparing the detection performance of the two waveforms, a timing offset (TO) estimation technique with orders of magnitude higher accuracy than the existing solutions in the literature is proposed. From multiple access viewpoint, the underlying tile structures in the time-frequency domain for OTFS and SC-FDMA are discussed. Finally, multiuser input-output relationships for both waveforms in the uplink are derived.
Localization in Massive MIMO Networks: From Near-Field to Far-Field
Authors: Parisa Ramezani, Özlem Tuğfe Demir, Emil Björnson
Date: 2024-02-12 00:00:00

Source localization is the process of estimating the location of signal sources based on the signals received at different antennas of an antenna array. It has diverse applications, ranging from radar systems and underwater acoustics to wireless communication networks. Subspace-based approaches are among the most effective techniques for source localization due to their high accuracy, with Multiple SIgnal Classification (MUSIC) and Estimation of Signal Parameters by Rotational Invariance Techniques (ESPRIT) being two prominent methods in this category. These techniques leverage the fact that the space spanned by the eigenvectors of the covariance matrix of the received signals can be divided into signal and noise subspaces, which are mutually orthogonal. Originally designed for far-field source localization, these methods have undergone several modifications to accommodate near-field scenarios as well. This chapter aims to present the foundations of MUSIC and ESPRIT algorithms and introduce some of their variations for both far-field and near-field localization by a single array of antennas. We further provide numerical examples to demonstrate the performance of the presented methods.
Passive detection of a random signal common to multi-sensor reference and surveillance arrays
Authors: David Ramírez, Ignacio Santamaria, Louis L. Scharf
Date: 2024-02-12 00:00:00

This paper addresses the passive detection of a common rank-one subspace signal received in two multi-sensor arrays. We consider the case of a one-antenna transmitter sending a common Gaussian signal, independent Gaussian noises with arbitrary spatial covariance, and known channel subspaces. The detector derived in this paper is a generalized likelihood ratio (GLR) test. For all but one of the unknown parameters, it is possible to find closed-form maximum likelihood (ML) estimator functions. We can further compress the likelihood to only an unknown vector whose ML estimate requires maximizing a product of ratios in quadratic forms, which is carried out using a trust-region algorithm. We propose two approximations of the GLR that do not require any numerical optimization: one based on a sample-based estimator of the unknown parameter whose ML estimate cannot be obtained in closed-form, and one derived under low-SNR conditions. Notably, all the detectors are scale-invariant, and the approximations are functions of beamformed data. However, they are not GLRTs for data that has been pre-processed with a beamformer, a point that is elaborated in the paper. These detectors outperform previously published correlation detectors on simulated data, in many cases quite significantly. Moreover, performance results quantify the performance gains over detectors that assume only the dimension of the subspace to be known.
Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach
Authors: Emilio Calvanese Strinati, Paolo Di Lorenzo, Vincenzo Sciancalepore, Adnan Aijaz, Marios Kountouris, Deniz Gündüz, Petar Popovski, Mohamed Sana, Photios A. Stavrou, Beatriz Soret, Nicola Cordeschi, Simone Scardapane, Mattia Merluzzi, Lanfranco Zanzi, Mauro Boldi Renato, Tony Quek, Nicola di Pietro, Olivier Forceville, Francesca Costanzo, Peizheng Li
Date: 2024-02-12 00:00:00
Tasks: Semantic Communication

Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents. These advancements impose new requirements on future 6G mobile network architectures. To meet these demands, it is essential to transcend classical boundaries and integrate communication, computation, control, and intelligence. This paper presents the 6G-GOALS approach to goal-oriented and semantic communications for AI-Native 6G Networks. The proposed approach incorporates semantic, pragmatic, and goal-oriented communication into AI-native technologies, aiming to facilitate information exchange between intelligent agents in a more relevant, effective, and timely manner, thereby optimizing bandwidth, latency, energy, and electromagnetic field (EMF) radiation. The focus is on distilling data to its most relevant form and terse representation, aligning with the source's intent or the destination's objectives and context, or serving a specific goal. 6G-GOALS builds on three fundamental pillars: i) AI-enhanced semantic data representation, sensing, compression, and communication, ii) foundational AI reasoning and causal semantic data representation, contextual relevance, and value for goal-oriented effectiveness, and iii) sustainability enabled by more efficient wireless services. Finally, we illustrate two proof-of-concepts implementing semantic, goal-oriented, and pragmatic communication principles in near-future use cases. Our study covers the project's vision, methodologies, and potential impact.
You can monitor your hydration level using your smartphone camera
Authors: Rose Alaslani, Levina Perzhilla, Muhammad Mahboob Ur Rahman, Taous-Meriem Laleg-Kirati, Tareq Y. Al-Naffouri
Date: 2024-02-12 00:00:00
Tasks: Binary Classification, Dimensionality Reduction, Explainable artificial intelligence, feature selection, Photoplethysmography (PPG)

This work proposes for the first time to utilize the regular smartphone -- a popular assistive gadget -- to design a novel, non-invasive method for self-monitoring of one's hydration level on a scale of 1 to 4. The proposed method involves recording a small video of a fingertip using the smartphone camera. Subsequently, a photoplethysmography (PPG) signal is extracted from the video data, capturing the fluctuations in peripheral blood volume as a reflection of a person's hydration level changes over time. To train and evaluate the artificial intelligence models, a custom multi-session labeled dataset was constructed by collecting video-PPG data from 25 fasting subjects during the month of Ramadan in 2023. With this, we solve two distinct problems: 1) binary classification (whether a person is hydrated or not), 2) four-class classification (whether a person is fully hydrated, mildly dehydrated, moderately dehydrated, or extremely dehydrated). For both classification problems, we feed the pre-processed and augmented PPG data to a number of machine learning, deep learning and transformer models which models provide a very high accuracy, i.e., in the range of 95% to 99%. We also propose an alternate method where we feed high-dimensional PPG time-series data to a DL model for feature extraction, followed by t-SNE method for feature selection and dimensionality reduction, followed by a number of ML classifiers that do dehydration level classification. Finally, we interpret the decisions by the developed deep learning model under the SHAP-based explainable artificial intelligence framework. The proposed method allows rapid, do-it-yourself, at-home testing of one's hydration level, is cost-effective and thus inline with the sustainable development goals 3 & 10 of the United Nations, and a step-forward to patient-centric healthcare systems, smart homes, and smart cities of future.
Planar near-field antenna measurements with a uniform step larger than half-wavelength
Authors: Raffaele Moretta, Fabio Pascariello, Giovanni Petraglia, Maurizio Feo, Maria Antonia Maisto
Date: 2024-02-11 00:00:00

In this paper, a new sampling scheme of the near field radiated by a planar source is proposed and assessed. More in detail, the paper shows a uniform sampling criterion that allows representing the near field over a plane with a number of measurements lower than the classical half-wavelength sampling. At first, a discretization strategy of the near field based on the warping method is recalled from the literature. The latter requires to collect a non-redundant number of field measurements that are non-uniformly arranged over the observation domain. Despite this, the warping sampling scheme works well only if the measurement plane does not overcome the source. When the observation domain is larger, it does not predict the exact positions of the field samples at the edges of the measurement plane; accordingly, in these regions it is not possible to recover the near field behavior by the collected samples. To overcome this drawback, a spatially varying oversampling is exploited. The latter is chosen in such a way that the resulting sampling becomes uniform. Such choice also ensures a growth of the sampling rate only at the edges of the observation domain permitting the retrieval of the near field by its samples. Finally, numerical simulations based on experimental data corroborate the effectiveness of the approach in recovering both the near and the far field.
RIS-Augmented Millimeter-Wave MIMO Systems for Passive Drone Detection
Authors: Jiguang He, Aymen Fakhreddine, George C. Alexandropoulos
Date: 2024-02-11 00:00:00

In the past decade, the number of amateur drones is increasing, and this trend is expected to continue in the future. The security issues brought by abuse and misconduct of drones become more and more severe and may incur a negative impact to the society. In this paper, we leverage existing cellular multiple-input multiple-output (MIMO) base station (BS) infrastructure, operating at millimeter wave (mmWave) frequency bands, for drone detection in a device-free manner with the aid of one reconfigurable intelligent surface (RIS), deployed in the proximity of the BS. We theoretically examine the feasibility of drone detection with the aid of the generalized likelihood ratio test (GLRT) and validate via simulations that, the optimized deployment of an RIS can bring added benefits compared to RIS-free systems. In addition, the effect of RIS training beams, training overhead, and radar cross section, is investigated in order to offer theoretical design guidance for the proposed cellular RIS-based passive drone detection system.
Optimization of Super-Directive Linear Arrays with Differential Evolution for High Realized Gain
Authors: Ihsan Kanbaz, Okan Yurduseven, Michail Matthaiou
Date: 2024-02-10 00:00:00

Due to the low impedance and high feeding currents, it is naturally challenging to design super-directive antenna arrays that perfectly match the feed line, and this becomes almost impossible as the number of elements increases. In this paper, we assert that it is crucial to consider the trade-off between directivity and overall efficiency (to achieve high realized gain) before employing super-directive arrays in real-world applications. Given this trade-off (high directivity and low mismatch for high realized gain), a 4-element dipole array (unit array) is optimized using the differential evolution (DE) algorithm. Then, the performance of the unit array in subarray configuration scenarios is analyzed. Finally, the obtained parameters are verified using the CST full-wave simulation software. The results clearly indicate that the proposed unit array is a strong candidate for dense array applications, particularly in the context of massive multiple-input multiple-output (MIMO), thanks to its notable high gain and efficiency.
RIS-Enhanced Cognitive Integrated Sensing and Communication: Joint Beamforming and Spectrum Sensing
Authors: Yongqing Xu, Yong Li, Tony Q. S. Quek
Date: 2024-02-10 00:00:00
Tasks: Integrated sensing and communication, ISAC

Cognitive radio (CR) and integrated sensing and communication (ISAC) are both critical technologies for the sixth generation (6G) wireless networks. However, their interplay has yet to be explored. To obtain the mutual benefits between CR and ISAC, we focus on a reconfigurable intelligent surface (RIS)-enhanced cognitive ISAC system and explore using the additional degrees-of-freedom brought by the RIS to improve the performance of the cognitive ISAC system. Specifically, we formulate an optimization problem of maximizing the signal-to-noise-plus-interference ratios (SINRs) of the mobile sensors (MSs) while ensuring the requirements of the spectrum sensing (SS) and the secondary transmissions by jointly designing the SS time, the secondary base station (SBS) beamforming, and the RIS beamforming. The formulated non-convex problem can be solved by the proposed block coordinate descent (BCD) algorithm based on the Dinkelbach's transform and the successive convex approximation (SCA) methods. Simulation results demonstrate that the proposed scheme exhibits good convergence performance and can effectively reduce the position error bounds (PEBs) of the MSs, thereby improving the radio environment map (REM) accuracy of CR networks. Additionally, we reveal the impact of RIS deployment locations on the performance of cognitive ISAC systems.
Existence of an equilibrium with limited stock market participation and power utilities
Authors: Paolo Guasoni, Kasper Larsen, Giovanni Leoni
Date: 2024-02-11 00:00:00

For constants $\gamma \in (0,1)$ and $A\in (1,\infty)$, we prove existence and uniqueness of a solution to the singular and path-dependent Riccati-type ODE \begin{align*} \begin{cases} h'(y) = \frac{1+\gamma}{y}\big( \gamma - h(y)\big)+h(y)\frac{\gamma + \big((A-\gamma)e^{\int_y^1 \frac{1-h(q)}{1-q}dq}-A\big)h(y)}{1-y},\quad y\in(0,1), h(0) = \gamma, \quad h(1) = 1. \end{cases} \end{align*} As an application, we use the ODE solution to prove existence of a Radner equilibrium with homogenous power-utility investors in the limited participation model from Basak and Cuoco (1998).
A mathematical model for fibrous dysplasia: The role of the flow of mutant cells
Authors: Mariia Soloviova, Juan Carlos Beltran Vargas, Luis Fernandez de Castro, Juan Belmonte-Beitia, Víctor M. Pérez-García, Magdalena Caballero
Date: 2024-02-12 00:00:00

Fibrous dysplasia (FD) is a mosaic non-inheritable genetic disorder of the skeleton in which normal bone is replaced by structurally unsound fibro-osseous tissue. There is no curative treatment for FD, partly because its pathophysiology is not yet fully known. We present a simple mathematical model of the disease incorporating its basic known biology, to gain insight on the dynamics of the involved bone-cell populations, and shed light on its pathophysiology. Our mathematical models account for the dynamic evolution over time of several interacting populations of bone cells averaged over a volume of bone of sufficient size in order to obtain consistent results. We develop an analytical study of the model and study its basic properties. The existence and stability of steady states are studied, an analysis of sensitivity on the model parameters is done, and different numerical simulations provide findings in agreement with the analytical results. We discuss the model dynamics match with known facts on the disease, and how some open questions could be addressed using the model.
Combination Therapy for Chronic Hepatitis B Using Capsid Recycling Inhibitor
Authors: Rupchand Sutradhar, D C Dalal
Date: 2024-02-12 00:00:00

In this paper, we investigate the dynamics of hepatitis B virus infection taking into account the implementation of combination therapy through mathematical modeling. This model is established considering the interplay between uninfected cells, infected cells, capsids, and viruses. Three drugs are considered for specific roles (i) pegylated interferon (PEG IFN) for immune modulation, (ii) lamivudine (LMV) as a reverse-transcriptase inhibitor, and (iii) entecavir (ETV) to block capsid recycling. Using these drugs, three combination therapies are introduced, specifically CT PEG IFN plus LMV, CT PEG IFN plus ETV, and CT PEG IFN plus LMV plus ETV. As a result, when LMV is used in combination therapy with PEG IFN and ETV, the impacts of ETV become insignificant. In conclusion, if the appropriate drug effectively inhibits reverse transcription, there is no need for an additional inhibitor to block capsid recycling.
Oja's Algorithm for Streaming Sparse PCA
Authors: Syamantak Kumar, Purnamrita Sarkar
Date: 2024-02-11 00:00:00

Oja's algorithm for Streaming Principal Component Analysis (PCA) for $n$ data-points in a $d$ dimensional space achieves the same sin-squared error $O(r_{\mathsf{eff}}/n)$ as the offline algorithm in $O(d)$ space and $O(nd)$ time and a single pass through the datapoints. Here $r_{\mathsf{eff}}$ is the effective rank (ratio of the trace and the principal eigenvalue of the population covariance matrix $\Sigma$). Under this computational budget, we consider the problem of sparse PCA, where the principal eigenvector of $\Sigma$ is $s$-sparse, and $r_{\mathsf{eff}}$ can be large. In this setting, to our knowledge, \textit{there are no known single-pass algorithms} that achieve the minimax error bound in $O(d)$ space and $O(nd)$ time without either requiring strong initialization conditions or assuming further structure (e.g., spiked) of the covariance matrix. We show that a simple single-pass procedure that thresholds the output of Oja's algorithm (the Oja vector) can achieve the minimax error bound under some regularity conditions in $O(d)$ space and $O(nd)$ time. We present a nontrivial and novel analysis of the entries of the unnormalized Oja vector, which involves the projection of a product of independent random matrices on a random initial vector. This is completely different from previous analyses of Oja's algorithm and matrix products, which have been done when the $r_{\mathsf{eff}}$ is bounded.
Cost optimisation of individual-based institutional reward incentives for promoting cooperation in finite populations
Authors: M. H. Duong, C. M. Durbac, T. A. Han
Date: 2024-02-12 00:00:00

In this paper, we study the problem of cost optimisation of individual-based institutional incentives (reward, punishment, and hybrid) for guaranteeing a certain minimal level of cooperative behaviour in a well-mixed, finite population. In this scheme, the individuals in the population interact via cooperation dilemmas (Donation Game or Public Goods Game) in which institutional reward is carried out only if cooperation is not abundant enough (i.e., the number of cooperators is below a threshold $1\leq t\leq N-1$, where $N$ is the population size); and similarly, institutional punishment is carried out only when defection is too abundant. We study analytically the cases $t=1$ for the reward incentive under the small mutation limit assumption and two different initial states, showing that the cost function is always non-decreasing. We derive the neutral drift and strong selection limits when the intensity of selection tends to zero and infinity, respectively. We numerically investigate the problem for other values of $t$ and for population dynamics with arbitrary mutation rates.
Eco-evolutionary dynamics of adapting pathogens and host immunity
Authors: Pierre Barrat-Charlaix, Richard A. Neher
Date: 2024-02-11 00:00:00

As pathogens spread in a population of hosts, immunity is built up and the pool of susceptible individuals is depleted. This generates selective pressure, to which many human RNA viruses, such as influenza virus or SARS-CoV-2, respond with rapid antigenic evolution and frequent emergence of immune evasive variants. However, the host's immune systems adapt and older immune responses wane, such that escape variants only enjoy a growth advantage for a limited time. If variant growth dynamics and reshaping of host-immunity operate on comparable time scales, viral adaptation is determined by eco-evolutionary interactions that are not captured by models of rapid evolution in a fixed environment. Here, we use a Susceptible/Infected model to describe the interaction between an evolving viral population in a dynamic but immunologically diverse host population. We show that depending on strain cross-immunity, heterogeneity of the host population, and durability of immune responses, escape variants initially grow exponentially, but lose their growth advantage before reaching high frequencies. Their subsequent dynamics follows an anomalous random walk determined by future escape variants and results in variant trajectories that are unpredictable. This model can explain the apparent contradiction between the clearly adaptive nature of antigenic evolution and the quasi-neutral dynamics of high frequency variants observed for influenza viruses.