pwc-archive/papers-with-abstracts huggingface dataset:
On the Reliability and Security of Ambient Backscatter Uplink NOMA Networks
Authors: Athanasios P. Chrysologou, Nestor D. Chatzidiamantis, Alexandros-Apostolos A. Boulogeorgos, Zhiguo Ding
Date: 2024-05-11 00:00:00

A fundamental objective of the forthcoming sixth-generation wireless networks is to concurrently serve a vast array of devices many of which, such as Internet-of-Things (IoT) sensors, are projected to have low power requirements or even operate in a battery-free manner. To achieve this goal, non-orthogonal multiple access (NOMA) and ambient backscatter communications (AmBC) are regarded as two pivotal and promising technologies. In this work, we present a novel analytical framework for studying the reliability and security of uplink NOMA-based AmBC systems. Specifically, closed-form analytical expressions for both NOMA-users' and IoT backscatter device's (BD's) outage probabilities (OPs) are derived for both cases of perfect and imperfect successive interference cancellation (SIC). In addition, assuming that one NOMA-user transmits an artificial noise in order to enhance system's security, the physical layer security (PLS) of the system is investigated by extracting analytical expressions for NOMA-users' and BD's intercept probabilities (IPs). To gain insightful understandings, an asymptotic analysis is carried out by focusing on the high signal-to-noise (SNR) regime, which reveals that NOMA-users and BDs face outage floors in the high SNR regime as well as that IPs reach constant values at high SNR. Additionally, practical insights regarding how different system parameters affect these OP floors and IP constant values are extracted. Numerical results verify the accuracy of othe developed theoretical framework, offer performance comparisons between the presented NOMA-based AmBC system and a conventional orthogonal multiple access-based AmBC system, and reveal the impact of different system parameters on the reliability and security of NOMA-based AmBC networks.
A Statistical Evaluation of Coherence Time for Non-Terrestrial Communications
Authors: Pinjun Zheng, Anas Chaaban, Md. Jahangir Hossain, Tareq Y. Al-Naffouri
Date: 2024-05-11 00:00:00

Non-terrestrial networks (NTNs) present significant challenges for reliable communication due to the dynamic nature of their channels. Studying channel coherence time is crucial, since it directly impacts the design of robust transmission schemes (e.g., channel estimation and precoding strategies). This paper evaluates the coherence time of non-terrestrial channels theoretically, revealing that the rapid mobility of non-terrestrial base stations (BSs) substantially reduces channel coherence time. Our results demonstrate that the presence and enhancement of the line-of-sight (LoS) channel play a crucial role in extending coherence time, thereby improving the stability of NTN links. Furthermore, unlike terrestrial networks, where beamwidth adjustments can effectively influence coherence time, our findings indicate that in NTNs, receiver beamwidth has a negligible effect under high-speed BS motion. These insights provide valuable guidelines for designing robust transmission schemes and adaptive signal processing techniques in future NTN deployments.
Two-Timescale Design for Reconfigurable Intelligent Surface-Aided URLLC
Authors: Qihao Peng, Hong Ren, Cunhua Pan, Maged Elkashlan, Ana Garcia Armada, Petar Popovski
Date: 2024-05-11 00:00:00

In this paper, to tackle the blockage issue in massive multiple-input-multiple-output (mMIMO) systems, a reconfigurable intelligent surface (RIS) is seamlessly deployed to support devices with ultra-reliable and low-latency communications (URLLC). The transmission power of the base station and the phase shifts of the RIS are jointly devised to maximize the weighted sum rate while considering the spatially correlation and channel estimation errors. Firstly, \textcolor{black}{the relationship between the channel estimation error and spatially correlated RIS's elements is revealed by using the linear minimum mean square error}. Secondly, based on the maximum-ratio transmission precoding, a tight lower bound of the rate under short packet transmission is derived. Finally, the NP-hard problem is decomposed into two optimization problems, where the transmission power is obtained by geometric programming and phase shifts are designed by using gradient ascent method. Besides, we have rigorously proved that the proposed algorithm can rapidly converge to a sub-optimal solution with low complexity. Simulation results confirm the tightness between the analytic results and Monte Carlo simulations. Furthermore, the two-timescale scheme provides a practical solution for the short packet transmission.
Model-Free Unsupervised Anomaly Detection Framework in Multivariate Time-Series of Industrial Dynamical Systems
Authors: Mazen Alamir, Raphaël Dion
Date: 2024-05-14 00:00:00
Tasks: Anomaly Detection, Incremental Learning, Time Series, Unsupervised Anomaly Detection

In this paper, a new model-free anomaly detection framework is proposed for time-series induced by industrial dynamical systems.The framework lies in the category of conventional approaches which enable appealing features such as a learning with reduced amount of training data, a high potential for explainability as well as a compatibility with incremental learning mechanism to incorporate operator feedback after an alarm is raised and analyzed. Although these are crucial features towards acceptance of data-driven solutions by industry, they are rarely considered in the comparisons that generally almost exclusively focus on performance metrics. Moreover, the features engineering step involved in the proposed framework is inspired by the time-series being implicitly governed by physical laws as it is generally the case in industrial time-series. Two examples are given to assess the efficiency of the proposed approach.
AI-driven, Model-Free Current Control: A Deep Symbolic Approach for Optimal Induction Machine Performance
Authors: Muhammad Usama, Yunkyung Hwang, Jaehong Kim
Date: 2024-05-14 00:00:00
Tasks: Symbolic Regression

This paper proposed a straightforward and efficient current control solution for induction machines employing deep symbolic regression (DSR). The proposed DSR-based control design offers a simple yet highly effective approach by creating an optimal control model through training and fitting, resulting in an analytical dynamic numerical expression that characterizes the data. Notably, this approach not only produces an understandable model but also demonstrates the capacity to extrapolate and estimate data points outside its training dataset, showcasing its adaptability and resilience. In contrast to conventional state-of-the-art proportional-integral (PI) current controllers, which heavily rely on specific system models, the proposed DSR-based approach stands out for its model independence. Simulation and experimental tests validate its effectiveness, highlighting its superior extrapolation capabilities compared to conventional methods. These findings pave the way for the integration of deep learning methods in power conversion applications, promising improved performance and adaptability in the control of induction machines. The simulation and experimental test results are provided with a 3.7 kw induction machine to verify the efficacy of the proposed control solution.
Minimum-Variance Recursive State Estimation for 2-D Systems: When Asynchronous Multi-Channel Delays meet Energy Harvesting Constraints
Authors: Yu Chen, Wei Wang
Date: 2024-05-13 00:00:00
Tasks: State Estimation

This paper is concerned with the state estimation problem for two-dimensional systems with asynchronous multichannel delays and energy harvesting constraints. In the system, each smart sensor has a certain probability of harvesting energy from the external environment, the authorized transmission between the sensor and the remote filter is contingent upon the current energy level of the sensor, which results in intermittent transmission of observation information. Addressing the issue of incomplete observation information due to asynchronous multi-channel delays, a novel approach for observation partition reconstruction is proposed to convert the delayed activated observation sequences into equivalent delay-free activated observation sequences. Through generating spatial equivalency validation, it is found that the reconstructed delay-free activated observation sequences contain the same information as the original delayed activated observation sequences. Based on the reconstructed activated observation sequence and activated probability, a novel unbiased h+1-step recursive estimator is constructed. Then, the evolution of the probability distribution of the energy level is discussed. The estimation gains are obtained by minimizing the filtering error covariance. Subsequently, through parameter assumptions, a uniform lower bound and a recursive upper bound for the filtering error covariance are presented. And the monotonicity analysis of activated probability on estimation performance is given. Finally, the effectiveness of the proposed estimation scheme is verified through a numerical simulation example.
Slow Inter-area Electro-mechanical Oscillations Revisited: Structural Property of Complex Multi-area Electric Power Systems
Authors: Hiya Akhil Gada, Marija D. Ilic
Date: 2024-05-13 00:00:00

This paper introduces a physically-intuitive notion of inter-area dynamics in systems comprising multiple interconnected energy conversion modules. The idea builds on an earlier general approach of setting their structural properties by modeling internal dynamics in stand-alone modules (components, areas) using the fundamental conservation laws between energy stored and generated, and then constraining explicitly their Tellegen's quantities (power and rate of change of power). In this paper we derive, by following the same principles, a transformed state-space model for a general nonlinear system. Using this model we show the existence of an area-level interaction variable, intVar, whose rate of change depends solely on the area internal power imbalance and is independent of the model complexity used for representing individual module dynamics in the area. Given these structural properties of stand-alone modules, we define in this paper for the first time an inter-area variable as the difference of power wave incident to tie-line from Area I and the power reflected into tie-lie from Area II. Notably, these power waves represent the interaction variables associated with the two respective interconnected areas. We illustrate these notions using a linearized case of two lossless inter-connected areas, and show the existence of a new inter-area mode when the areas get connected. We suggest that lessons learned in this paper open possibilities for computationally-efficient modeling and control of inter-area oscillations, and offer further the basis for modeling and control of dynamics in changing systems comprising faster energy conversion processes.
A Second-Order Audio VCO-ADC with 103-dB-A Dynamic Range and Binary-Weighted Internal Architecture
Authors: Victor Medina, Ruben Garvi, Javier Granizo, Pedro Amaral, Luis Hernandez
Date: 2024-05-13 00:00:00

One of the limitations of conventional VCO-ADCs is the restriction to first-order noise shaping. True-VCO architectures have been proposed to increase the noise-shaping order by cascading several VCO integrators, but without requiring analog feedback loops. A high noise shaping order allows to reduce the input VCO frequency compared to a conventional VCO-ADC with similar dynamic range, which improves power consumption. Prior-art True-VCO architectures represent state variables either with a thermometer code or with a single-bit. Thermometer encoding is a natural choice when ring oscillators are selected as loop filter integrators. However, chip area restrictions force thermometer-encoded state variables to have few levels. A reduced number of levels in the state variables limits the dynamic range of True VCO-ADCs. In this paper, we show experimentally a second-order audio VCO-based ADC which uses ring oscillators as integrators but employs Gray and binary encoding for state variables. As a consequence, the complexity and area of the True-VCO architecture is reduced, breaking the barrier that limits the dynamic range of prior designs. The implemented chip shows a dynamic range of 103~dB achieving a peak SNDR of 76.5 dB-A with a power of 250 $\mu$W occupying 0.095 $\text{mm}^2$ in 130 nm CMOS.
Contract-Based Design for Hybrid Dynamical Systems and Invariance Properties
Authors: Sadek Belamfedel Alaoui, Adnane Saoud
Date: 2024-05-13 00:00:00

This work establishes fundamental principles for verifying contract for interconnected hybrid systems. When system's hybrid arcs conform to the contract for a certain duration but subsequently violate it, the composition of hybrid dynamical systems becomes challenging. The objective of this work is to analyze the temporal satisfaction of the contract, allowing us to reason about the compositions that do not violate the contract up to a certain point in a hybrid time. Notions of weak and strong satisfaction of an assume-guarantee contract are introduced. These semantics permits the compositional reasoning on hybrid systems of varying complexity depending on the interconnection's type, feedback or cascade. The results show that both semantics are compatible with cascade composition, while strong semantic is required for feedback composition. Moreover, we have shown how one can go from weak to strong contract satisfaction. Finally, we have studied a particular class of hybrid systems and we have shown that the concept of forward (pre-)invariant relative to a contract makes it possible to deal with feedback compositions. These results are demonstrated throughout the paper with simple numerical examples.
Edge Computing for IoT: Novel Insights from a Comparative Analysis of Access Control Models
Authors: Tao Xue, Ying Zhang, Yanbin Wang, Wenbo Wang, Shuailou Li, Haibin Zhang
Date: 2024-05-13 00:00:00
Tasks: Autonomous Vehicles, Cloud Computing, Edge-computing

IoT edge computing positions computing resources closer to the data sources to reduce the latency, relieve the bandwidth pressure on the cloud, and enhance data security. Nevertheless, data security in IoT edge computing still faces critical threats (e.g., data breaches). Access control is fundamental for mitigating these threats. However, IoT edge computing introduces notable challenges for achieving resource-conserving, low-latency, flexible, and scalable access control. To review recent access control measures, we novelly organize them according to different data lifecycles--data collection, storage, and usage--and, meanwhile, review blockchain technology in this novel organization. In this way, we provide novel insights and envisage several potential research directions. This survey can help readers find gaps systematically and prompt the development of access control techniques in IoT edge computing under the intricacy of innovations in access control.
Structure-Preserving Model Order Reduction for Nonlinear DAE Models of Power Networks
Authors: Muhammad Nadeem, Ahmad F. Taha
Date: 2024-05-13 00:00:00

This paper deals with the joint reduction of the number of dynamic and algebraic states of a nonlinear differential-algebraic equation (NDAE) model of a power network. The dynamic states depict the internal states of generators, loads, renewables, whereas the algebraic ones define network states such as voltages and phase angles. In the current literature of power system model order reduction (MOR), the algebraic constraints are usually neglected and the power network is commonly modeled via a set of ordinary differential equations (ODEs) instead of NDAEs. Thus, reduction is usually carried out for the dynamic states only and the algebraic variables are kept intact. This leaves a significant part of the system's size and complexity unreduced. This paper addresses this aforementioned limitation by jointly reducing both dynamic and algebraic variables. As compared to the literature the proposed MOR techniques are endowed with the following features: (i) no system linearization is required, (ii) require no transformation to an equivalent or approximate ODE representation, (iii) guarantee that the reduced order model to be NDAE-structured and thus preserves the differential-algebraic structure of original power system model, and (iv) can seamlessly reduce both dynamic and algebraic variables while maintaining high accuracy. Case studies performed on a 2000-bus power system reveal that the proposed MOR techniques are able to reduce system order while maintaining accuracy.
Coded Event-triggered Control for Nonlinear Systems
Authors: Ruihang Ji, Shuzhi Sam Ge, Kai Zhao
Date: 2024-05-13 00:00:00

This paper studies a Coded Event-triggered Control (CEC) for a class of nonlinear systems under any initial condition. To reduce communication burden, the CEC is designed from the encoding-decoding viewpoint by which only $m$-length string is transmitted for each communication between CEC and actuator. If a more general Entry Capture Problem is encountered, such control design will be rather complicated yet challenging where the performance constraints are satisfied some time after (rather than from the beginning of) system operation, rendering normally employed prescribed performance control invalid because they may be not defined in the initial interval. By introducing auxiliary functions, we develop a Self-adjustable Prescribed Performance (SPP) mechanism which can flexibly adjust the symmetric or asymmetric performance boundaries to accommodate different initial conditions, providing an effective solution for the underlying tracking problem. In this way, the resulted CEC can not only consume less communication resources but also regulate the tracking error under any initial condition into an allowable set before a given time in a bounded and customizable manner. Simulation results verify and clarify the theoretical findings.
Advocating Feedback Control for Human-Earth System Applications
Authors: Guido Cavraro
Date: 2024-05-12 00:00:00

This paper proposes a feedback control perspective for Human-Earth Systems (HESs) which essentially are complex systems that capture the interactions between humans and nature. Recent attention in HES research has been directed towards devising strategies for climate change mitigation and adaptation, aimed at achieving environmental and societal objectives. However, existing approaches heavily rely on HES models, which inherently suffer from inaccuracies due to the complexity of the system. Moreover, overly detailed models often prove impractical for optimization tasks. We propose a framework inheriting from feedback control strategies the robustness against model errors, because inaccuracies are mitigated using measurements retrieved from the field. The framework comprises two nested control loops. The outer loop computes the optimal inputs to the HES, which are then implemented by actuators controlled in the inner loop. Potential fields of applications are also identified.
Machine Learning-Based Protection and Fault Identification of 100% Inverter-Based Microgrids
Authors: Milad Beikbabaei, Michael Lindemann, Mohammad Heidari Kapourchali, Ali Mehrizi-Sani
Date: 2024-05-12 00:00:00
Tasks: Fault Detection

100% inverter-based renewable units are becoming more prevalent, introducing new challenges in the protection of microgrids that incorporate these resources. This is particularly due to low fault currents and bidirectional flows. Previous work has studied the protection of microgrids with high penetration of inverter-interfaced distributed generators; however, very few have studied the protection of a 100% inverter-based microgrid. This work proposes machine learning (ML)-based protection solutions using local electrical measurements that consider implementation challenges and effectively combine short-circuit fault detection and type identification. A decision tree method is used to analyze a wide range of fault scenarios. PSCAD/EMTDC simulation environment is used to create a dataset for training and testing the proposed method. The effectiveness of the proposed methods is examined under seven distinct fault types, each featuring varying fault resistance, in a 100% inverter-based microgrid consisting of four inverters.
Timely Offloading in Mobile Edge Cloud Systems
Authors: Nitya Sathyavageeswaran, Roy D. Yates, Anand D. Sarwate, Narayan Mandayam
Date: 2024-05-12 00:00:00
Tasks: Scheduling

Future real-time applications like smart cities will use complex Machine Learning (ML) models for a variety of tasks. Timely status information is required for these applications to be reliable. Offloading computation to a mobile edge cloud (MEC) can reduce the completion time of these tasks. However, using the MEC may come at a cost such as related to use of a cloud service or privacy. In this paper, we consider a source that generates time-stamped status updates for delivery to a monitor after processing by the mobile device or MEC. We study how a scheduler must forward these updates to achieve timely updates at the monitor but also limit MEC usage. We measure timeliness at the monitor using the age of information (AoI) metric. We formulate this problem as an infinite horizon Markov decision process (MDP) with an average cost criterion. We prove that an optimal scheduling policy has an age-threshold structure that depends on how long an update has been in service.
Competition in the nutrient-driven self-cycling fermentation process
Authors: Stacey R. Smith?, Tyler Meadows, Gail S. K. Wolkowicz
Date: 2024-05-14 00:00:00

Self-cycling fermentation is an automated process used for culturing microorganisms. We consider a model of $n$ distinct species competing for a single non-reproducing nutrient in a self-cycling fermentor in which the nutrient level is used as the decanting condition. The model is formulated in terms of impulsive ordinary differential equations. We prove that two species are able to coexist in the fermentor under certain conditions. We also provide numerical simulations that suggest coexistence of three species is possible and that competitor-mediated coexistence can occur in this case. These results are in contrast to the chemostat, the continuous analogue, where multiple species cannot coexist on a single nonreproducing nutrient.
A Dynamical Systems Approach to Predicting Patient Outcome after Cardiac Arrest
Authors: Richard J Povinelli, Mathew Dupont
Date: 2024-05-13 00:00:00
Tasks: EEG

Aim: Approximately six million people suffer cardiac arrests worldwide per year with very low survival rates (<1%). Thus, the aim of this study is to estimate the probability of a poor outcome after cardiac arrest. Accurate outcome predictions avoid removing care too soon for patients with potentially good outcomes or continuing care for patients with likely poor outcomes. Method: The method is based on dynamical systems embedding theorems that show that a reconstructed phase space (RPS) topologically equivalent to an underlying system can be constructed from measured signals. Here the underlying system is the human brain after a cardiac arrest, and the signals are the EEG channels. We model the RPS with a Gaussian mixture model (GMM) and ensemble the output of the RPS-GMM with clinical data via XGBoost. Results: As team Blue and Gold in the Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023, our RPS-GMM-XGBoost method obtained a test set competition score of 0.426 and rank of 24/36.