pwc-archive/papers-with-abstracts huggingface dataset:
Joint Channel Estimation and Cooperative Localization for Near-Field Ultra-Massive MIMO
Authors: Ruoxiao Cao, Hengtao He, Xianghao Yu, Shenghui Song, Kaibin Huang, Jun Zhang, Yi Gong, Khaled B. Letaief
Date: 2023-12-21 00:00:00

The next-generation (6G) wireless networks are expected to provide not only seamless and high data-rate communications, but also ubiquitous sensing services. By providing vast spatial degrees of freedom (DoFs), ultra-massive multiple-input multiple-output (UM-MIMO) technology is a key enabler for both sensing and communications in 6G. However, the adoption of UM-MIMO leads to a shift from the far field to the near field in terms of the electromagnetic propagation, which poses novel challenges in system design. Specifically, near-field effects introduce highly non-linear spherical wave models that render existing designs based on plane wave assumptions ineffective. In this paper, we focus on two crucial tasks in sensing and communications, respectively, i.e., localization and channel estimation, and investigate their joint design by exploring the near-field propagation characteristics, achieving mutual benefits between two tasks. In addition, multiple base stations (BSs) are leveraged to collaboratively facilitate a cooperative localization framework. To address the joint channel estimation and cooperative localization problem for near-field UM-MIMO systems, we propose a variational Newtonized near-field channel estimation (VNNCE) algorithm and a Gaussian fusion cooperative localization (GFCL) algorithm. The VNNCE algorithm exploits the spatial DoFs provided by the near-field channel to obtain position-related soft information, while the GFCL algorithm fuses this soft information to achieve more accurate localization. Additionally, we introduce a joint architecture that seamlessly integrates channel estimation and cooperative localization.
Optical Integrated Sensing and Communication: Architectures, Potentials and Challenges
Authors: Yunfeng Wen, Fang Yang, Jian Song, Zhu Han
Date: 2023-12-21 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is viewed as a crucial component of future mobile networks and has gained much interest in both academia and industry. Similar to the emergence of radio-frequency (RF) ISAC, the integration of free space optical communication and optical sensing yields optical ISAC (O-ISAC), which is regarded as a powerful complement to its RF counterpart. In this article, we first introduce the generalized system structure of O-ISAC, and then elaborate on three advantages of O-ISAC, i.e., increasing communication rate, enhancing sensing precision, and reducing interference. Next, waveform design and resource allocation of O-ISAC are discussed based on pulsed waveform, constant-modulus waveform, and multi-carrier waveform. Furthermore, we put forward future trends and challenges of O-ISAC, which are expected to provide some valuable directions for future research.
Jointly Optimal RIS Placement and Power Allocation for Underlay D2D Communications: An Outage Probability Minimization Approach
Authors: Sarbani Ghose, Deepak Mishra, Santi P. Maity, George C. Alexandropoulos
Date: 2023-12-21 00:00:00

In this paper, we study underlay device-to-device (D2D) communication systems empowered by a reconfigurable intelligent surface (RIS) for cognitive cellular networks. Considering Rayleigh fading channels and the general case where there exist both the direct and RIS-enabled D2D channels, the outage probability (OP) of the D2D communication link is presented in closed-form. Next, for the considered RIS-empowered underlaid D2D system, we frame an OP minimization problem. We target the joint optimization of the transmit power at the D2D source and the RIS placement, under constraints on the transmit power at the D2D source and on the limited interference imposed on the cellular user for two RIS deployment topologies. Due to the coupled optimization variables, the formulated optimization problem is extremely intractable. We propose an equivalent transformation which we are able to solve analytically. In the transformed problem, an expression for the average value of the signal-to-interference-noise ratio (SINR) at the D2D receiver is derived in closed-form. Our theoretical derivations are corroborated through simulation results, and various system design insights are deduced. It is indicatively showcased that the proposed RIS-empowered underlaid D2D system design outperforms the benchmark semi-adaptive optimal power and optimal distance schemes, offering $44\%$ and $20\%$ performance improvement, respectively.
Approximating reproduction numbers: a general numerical method for age-structured models
Authors: Simone De Reggi, Francesca Scarabel, Rossana Vermiglio
Date: 2023-12-20 00:00:00

In this paper, we introduce a general numerical method to approximate the reproduction numbers of a large class of multi-group, age-structured, population models with a finite age span. To provide complete flexibility in the definition of the birth and transition processes, we propose an equivalent formulation for the age-integrated state within the extended space framework. Then, we discretize the birth and transition operators via pseudospectral collocation. We discuss applications to epidemic models with continuous and piecewise continuous rates, with different interpretations of the age variable (e.g., demographic age, infection age and disease age) and the transmission terms (e.g., horizontal and vertical transmission). The tests illustrate that the method can compute different reproduction numbers, including the basic and type reproduction numbers as special cases.
Blind Localization of Room Reflections with Application to Spatial Audio
Authors: Yogev Hadadi, Vladimir Tourbabin, Paul Calamia, Boaz Rafaely
Date: 2023-12-21 00:00:00

Blind estimation of early room reflections, without knowledge of the room impulse response, holds substantial value. The FF-PHALCOR (Frequency Focusing PHase ALigned CORrelation), method was recently developed for this objective, extending the original PHALCOR method from spherical to arbitrary arrays. However, previous studies only compared the two methods under limited conditions without presenting a comprehensive performance analysis. This study presents an advance by evaluating the performance of the algorithm in a wider range of conditions. Additionally, performance in terms of perception is investigated through a listening test. This test involves synthesizing room impulse responses from known room acoustics parameters and replacing the early reflections with the estimated ones. The importance of the estimated reflections for spatial perception is demonstrated through this test.
Self-supervised Complex Network for Machine Sound Anomaly Detection
Authors: Miseul Kim, Minh Tri Ho, Hong-Goo Kang
Date: 2023-12-21 00:00:00
Tasks: Anomaly Detection, Time Series

In this paper, we propose an anomaly detection algorithm for machine sounds with a deep complex network trained by self-supervision. Using the fact that phase continuity information is crucial for detecting abnormalities in time-series signals, our proposed algorithm utilizes the complex spectrum as an input and performs complex number arithmetic throughout the entire process. Since the usefulness of phase information can vary depending on the type of machine sound, we also apply an attention mechanism to control the weights of the complex and magnitude spectrum bottleneck features depending on the machine type. We train our network to perform a self-supervised task that classifies the machine identifier (id) of normal input sounds among multiple classes. At test time, an input signal is detected as anomalous if the trained model is unable to correctly classify the id. In other words, we determine the presence of an anomality when the output cross-entropy score of the multiclass identification task is lower than a pre-defined threshold. Experiments with the MIMII dataset show that the proposed algorithm has a much higher area under the curve (AUC) score than conventional magnitude spectrum-based algorithms.
Style Modeling for Multi-Speaker Articulation-to-Speech
Authors: Miseul Kim, Zhenyu Piao, Jihyun Lee, Hong-Goo Kang
Date: 2023-12-21 00:00:00

In this paper, we propose a neural articulation-to-speech (ATS) framework that synthesizes high-quality speech from articulatory signal in a multi-speaker situation. Most conventional ATS approaches only focus on modeling contextual information of speech from a single speaker's articulatory features. To explicitly represent each speaker's speaking style as well as the contextual information, our proposed model estimates style embeddings, guided from the essential speech style attributes such as pitch and energy. We adopt convolutional layers and transformer-based attention layers for our model to fully utilize both local and global information of articulatory signals, measured by electromagnetic articulography (EMA). Our model significantly improves the quality of synthesized speech compared to the baseline in terms of objective and subjective measurements in the Haskins dataset.
MHE under parametric uncertainty -- Robust state estimation without informative data
Authors: Simon Muntwiler, Johannes Köhler, Melanie N. Zeilinger
Date: 2023-12-21 00:00:00
Tasks: Friction, State Estimation

In this paper, we study state estimation for general nonlinear systems with unknown parameters and persistent process and measurement noise. In particular, we are interested in stability properties of the state estimate in the absence of persistency of excitation (PE). With a simple academic example, we show that existing moving horizon estimation (MHE) approaches as well as classical adaptive observers can result in diverging state estimates in the absence of PE, even if the noise is small. We propose a novel MHE formulation involving a regularization based on a constant prior estimate of the unknown system parameters. Only assuming the existence of a stable estimator, we prove that the proposed MHE results in practically robustly stable state estimates even in the absence of PE. We discuss the relation of the proposed MHE formulation to state-of-the-art results from MHE, adaptive estimation, and functional estimation. The properties of the proposed MHE approach are illustrated with a numerical example of a car with unknown tire friction parameters.
Nonlinear Functional Estimation: Functional Detectability and Full Information Estimation
Authors: Simon Muntwiler, Johannes Köhler, Melanie N. Zeilinger
Date: 2023-12-21 00:00:00

We consider the design of functional estimators, i.e., approaches to compute an estimate of a nonlinear function of the state of a general nonlinear dynamical system subject to process noise based on noisy output measurements. To this end, we introduce a novel functional detectability notion in the form of incremental input/output-to-output stability ($\delta$-IOOS). We show that $\delta$-IOOS is a necessary condition for the existence of a functional estimator satisfying an input-to-output type stability property. Additionally, we prove that a system is functional detectable if and only if it admits a corresponding $\delta$-IOOS Lyapunov function. Furthermore, $\delta$-IOOS is shown to be a sufficient condition for the design of a stable functional estimator by introducing the design of a full information estimation (FIE) approach for functional estimation. Together, we present a unified framework to study functional estimation with a detectability condition, which is necessary and sufficient for the existence of a stable functional estimator, and a corresponding functional estimator design. The practical need for and applicability of the proposed functional estimator design is illustrated with a numerical example of a power system.
Modelling of Networked Measuring Systems -- From White-Box Models to Data Based Approaches
Authors: Klaus-Dieter Sommer, Peter Harris, Sascha Eichstädt, Roland Füssl, Tanja Dorst, Andreas Schütze, Michael Heizmann, Nadine Schiering, Andreas Maier, Yuhui Luo, Christos Tachtatzis, Ivan Andonovic, Gordon Gourlay
Date: 2023-12-21 00:00:00

Mathematical modelling is at the core of metrology as it transforms raw measured data into useful measurement results. A model captures the relationship between the measurand and all relevant quantities on which the measurand depends, and is used to design measuring systems, analyse measured data, make inferences and predictions, and is the basis for evaluating measurement uncertainties. Traditional modelling approaches are typically analytical, for example, based on principles of physics. But with the increasing use of digital technologies, large sensor networks and powerful computing hardware, these traditional approaches are being replaced more and more by data-driven methods. This paradigm shift holds true in particular for the digital future of measurement in all spheres of our lives and the environment, where data provided by large and complex interconnected systems of sensors are to be analysed. Additionally, there is a requirement for existing guidelines and standards in metrology to take the paradigm shift into account. In this paper we lay the foundation for the development from traditional to data-driven modelling approaches. We identify key aspects from traditional modelling approaches and discuss their transformation to data-driven modelling.
Sample-based nonlinear detectability for discrete-time systems
Authors: Isabelle Krauss, Victor G. Lopez, Matthias A. Müller
Date: 2023-12-21 00:00:00

This paper introduces two sample-based formulations of incremental input/output-to-state stability (i-IOSS), a suitable detectability notion for general nonlinear systems. In this work we consider the case of limited output information, i.e., measurements are only infrequently and/or irregularly available. The output-dependent term of the sample-based i-IOSS bound is properly modified to yield a characterization for detectability in presence of incomplete output sequences. We provide both a non-timediscounted and a time-discounted formulation of samplebased i-IOSS. Furthermore, conditions for an i-IOSS system to be also sample-based i-IOSS are given and the relation between the two formulations of sample-based i-IOSS is shown.
Adaptive Decision-Objective Loss for Forecast-then-Optimize in Power Systems
Authors: Haipeng Zhang, Ran Li, Mingyang Sun, Teng Fei
Date: 2023-12-21 00:00:00
Tasks: Decision Making

Forecast-then-optimize is a widely-used framework for decision-making problems in power systems. Traditionally, statistical losses have been employed to train forecasting models, but recent research demonstrated that improved decision utility in downstream optimization tasks can be achieved by using decision loss as an alternative. However, the implementation of decision loss in power systems faces challenges in 1) accommodating multi-stage decision-making problems where upstream optimality cannot guarantee final optimality; 2) adapting to dynamic environments such as changing parameters and nature of the problem like continuous or discrete optimization tasks. To this end, this paper proposes a novel adaptive decision-objective loss (ADOL) to address the above challenges. Specifically, ADOL first redefines the decision loss as objective utilities rather than objective loss to eliminate the need to manually set the optimal decision, thus ensuring the globally optimal decision. ADOL enables one-off training in a dynamic environment by introducing additional variables. The differentiability and convexity of ADOL provide useful gradients for forecasting model training in conjunction with continuous and discrete optimization tasks. Experiments are conducted for both linear programming-based and mixed integer linear programming-based power system two-stage dispatching cases with changing costs, and the results show that the proposed ADOL is capable of achieving globally optimal decision-making and adaptability to dynamic environments. The method can be extended to other multi-stage tasks in complex systems.
Support Vector Machine For Transient Stability Assessment: A Review
Authors: Umair Shahzad
Date: 2023-12-21 00:00:00

Accurate transient stability assessment is a crucial prerequisite for proper power system operation and planning with various operational constraints. Transient stability assessment of modern power systems is becoming very challenging due to rising uncertainty and continuous integration of renewable energy generation. The stringent requirements of very high accuracy and fast computation speed has further necessitated accurate transient stability assessment for power system planning and operation. The traditional approaches are unable to fulfil these requirements due to their shortcomings. In this regard, the popularity of prospective approaches based on big data and machine learning, such as support vector machine, is constantly on the rise as they have all the features required to fulfil important criteria for real-time TSA. Therefore, this paper aims to review the application of support vector machine for transient stability assessment of power systems. It is believed that this work will provide a solid foundation for researchers in the domain of machine learning and computational intelligence-based applications to power system stability and operation.
Front stability of infinitely steep travelling waves in population biology
Authors: Matthew J Simpson, Nizhum Rahman, Alexander KY Tam
Date: 2023-12-21 00:00:00

Reaction-diffusion models are often used to describe biological invasion, where populations of individuals that undergo random motility and proliferation lead to moving fronts. Many models of biological invasion are extensions of the Fisher-KPP model that describes the evolution of a 1D population density as a result of linear diffusion and logistic growth. In 2020 Fadai introduced a new model of biological invasion that was formulated as a moving boundary problem with a nonlinear degenerate diffusive flux. Fadai's model leads to travelling wave solutions with infinitely steep, well-defined fronts at the moving boundary, and the model has the mathematical advantage of being analytically tractable in certain parameter limits. We aim to provide general insight by first presenting two key extensions by considering: (i) generalised nonlinear degenerate diffusion with flux; and, (ii) solutions describing both biological invasion, and biological recession. We establish the existence of travelling wave solutions for these two extensions, and then consider stability of the travelling wave solutions by introducing a lateral perturbation of the travelling wavefront. Full 2D time-dependent level-set numerical solutions indicate that invasive travelling waves are stable to small lateral perturbations, whereas receding travelling waves are unstable. These preliminary numerical observations are corroborated through a linear stability analysis that gives more formal insight into short time growth/decay of wavefront perturbation amplitude.
The neuron as a temporal electroacoustic medium
Authors: Francisco Meseguer, Fernando Ramiro-Manzano
Date: 2023-12-21 00:00:00

The human brain is one of the most complex and intriguing scientific topics. The most established theory on neuronal communication is a pure electrical model based on the propagation of intracell cationic charges along the neurons. Here we propose a complementary model based on two properties of brain communication: A) The Coulomb interaction associated to the Action Potential (AP) pulse induces a deformation of the neuron membrane which travels as an acoustic signal, i.e.: The ions play an essential role and the electric and acoustic signals, composing the AP, are strongly correlated. B) As brain communication is stablished through a periodic train of AP pulses it induces a time periodic modulation of the acoustic parameters. In this framework we propose envisaging the neuron as a temporal electro-acoustic medium. The temporal varying media framework could help understanding brain conundrums such as propagation routes involved in the neuronal plasticity in the consolidation of the memory, as well as on the generation of the signals associated to the brain field theory.
Bayesian optimized physics-informed neural network for estimating wave propagation velocities
Authors: Mahindra Rautela, S. Gopalakrishnan, J. Senthilnath
Date: 2023-12-21 00:00:00
Tasks: Bayesian Optimization, parameter estimation

In this paper, we propose a novel inverse parameter estimation approach called Bayesian optimized physics-informed neural network (BOPINN). In this study, a PINN solves the partial differential equation (PDE), whereas Bayesian optimization (BO) estimates its parameter. The proposed BOPINN estimates wave velocity associated with wave propagation PDE using a single snapshot observation. An objective function for BO is defined as the mean squared error (MSE) between the surrogate displacement field and snapshot observation. The inverse estimation capability of the proposed approach is tested in three different isotropic media with different wave velocities. From the obtained results, we have observed that BOPINN can accurately estimate wave velocities with lower MSE, even in the presence of noisy conditions. The proposed algorithm shows robust predictions in limited iterations across different runs.
Binary Endogenous Treatment in Stochastic Frontier Models with an Application to Soil Conservation in El Salvador
Authors: Samuele Centorrino, Maria Pérez-Urdiales, Boris Bravo-Ureta, Alan J. Wall
Date: 2023-12-21 00:00:00

Improving the productivity of the agricultural sector is part of one of the Sustainable Development Goals set by the United Nations. To this end, many international organizations have funded training and technology transfer programs that aim to promote productivity and income growth, fight poverty and enhance food security among smallholder farmers in developing countries. Stochastic production frontier analysis can be a useful tool when evaluating the effectiveness of these programs. However, accounting for treatment endogeneity, often intrinsic to these interventions, only recently has received any attention in the stochastic frontier literature. In this work, we extend the classical maximum likelihood estimation of stochastic production frontier models by allowing both the production frontier and inefficiency to depend on a potentially endogenous binary treatment. We use instrumental variables to define an assignment mechanism for the treatment, and we explicitly model the density of the first and second-stage composite error terms. We provide empirical evidence of the importance of controlling for endogeneity in this setting using farm-level data from a soil conservation program in El Salvador.