pwc-archive/papers-with-abstracts huggingface dataset:
Analyzing the Scalability of Bi-static Backscatter Networks for Large Scale Applications
Authors: Kartik Patel, Junbo Zhang, John Kimionis, Lefteris Kampianakis, Michael S. Eggleston, Jinfeng Du
Date: 2024-12-09 00:00:00
Tasks: TAG

Backscatter radio is a promising technology for low-cost and low-power Internet-of-Things (IoT) networks. The conventional monostatic backscatter radio is constrained by its limited communication range, which restricts its utility in wide-area applications. An alternative bi-static backscatter radio architecture, characterized by a dis-aggregated illuminator and receiver, can provide enhanced coverage and, thus, can support wide-area applications. In this paper, we analyze the scalability of the bi-static backscatter radio for large-scale wide-area IoT networks consisting of a large number of unsynchronized, receiver-less tags. We introduce the Tag Drop Rate (TDR) as a measure of reliability and develop a theoretical framework to estimate TDR in terms of the network parameters. We show that under certain approximations, a small-scale prototype can emulate a large-scale network. We then use the measurements from experimental prototypes of bi-static backscatter networks (BNs) to refine the theoretical model. Finally, based on the insights derived from the theoretical model and the experimental measurements, we describe a systematic methodology for tuning the network parameters and identifying the physical layer design requirements for the reliable operation of large-scale bi-static BNs. Our analysis shows that even with a modest physical layer requirement of bit error rate (BER) 0.2, 1000 receiver-less tags can be supported with 99.9% reliability. This demonstrates the feasibility of bi-static BNs for large-scale wide-area IoT applications.
Harmonic Mean Density Fusion in Distributed Tracking: Performance and Comparison
Authors: Nikhil Sharma, Ratnasingham Tharmarasa, Thiagalingam Kirubarajan
Date: 2024-12-09 00:00:00
Tasks: Sensor Fusion

A distributed sensor fusion architecture is preferred in a real target-tracking scenario as compared to a centralized scheme since it provides many practical advantages in terms of computation load, communication bandwidth, fault-tolerance, and scalability. In multi-sensor target-tracking literature, such systems are better known by the pseudonym - track fusion, since processed tracks are fused instead of raw measurements. A fundamental problem, however, in such systems is the presence of unknown correlations between the tracks, which renders a standard Kalman filter (naive fusion) useless. A widely accepted solution is covariance intersection (CI) which provides near-optimal estimates but at the cost of a conservative covariance. Thus, the estimates are pessimistic, which might result in a delayed error convergence. Also, fusion of Gaussian mixture densities is an active area of research where standard methods of track fusion cannot be used. In this article, harmonic mean density (HMD) based fusion is discussed, which seems to handle both of these issues. We present insights on HMD fusion and prove that the method is a result of minimizing average Pearson divergence. This article also provides an alternative and easy implementation based on an importance-sampling-like method without the requirement of a proposal density. Similarity of HMD with inverse covariance intersection is an interesting find, and has been discussed in detail. Results based on a real-world multi-target multi-sensor scenario show that the proposed approach converges quickly than existing track fusion algorithms while also being consistent, as evident from the normalized estimation-error squared (NEES) plots.
On Pooling-Based Track Fusion Strategies : Harmonic Mean Density
Authors: Nikhil Sharma, Shovan Bhaumik, Ratnasingham Tharmarasa, Thiagalingam Kirubarajan
Date: 2024-12-09 00:00:00
Tasks: Sensor Fusion

In a distributed sensor fusion architecture, using standard Kalman filter (naive fusion) can lead to degraded results as track correlations are ignored and conservative fusion strategies are employed as a sub-optimal alternative to the problem. Since, Gaussian mixtures provide a flexible means of modeling any density, therefore fusion strategies suitable for use with Gaussian mixtures are needed. While the generalized covariance intersection (CI) provides a means to fuse Gaussian mixtures, the procedure is cumbersome and requires evaluating a non-integer power of the mixture density. In this paper, we develop a pooling-based fusion strategy using the harmonic mean density (HMD) interpolation of local densities and show that the proposed method can handle both Gaussian and mixture densities without much changes to the framework. Mathematical properties of the proposed fusion strategy are studied and simulated on 2D and 3D maneuvering target tracking scenarios. The simulations suggest that the proposed HMD fusion performs better than other conservative strategies in terms of root-mean-squared error while being consistent.
Physical Layer Security in AmBC-NOMA Networks with Random Eavesdroppers
Authors: Xinyue Pei, Xingwei Wang, Min Huang, Yingyang Chen, Xiaofan Li, Theodoros A. Tsiftsis
Date: 2024-12-09 00:00:00

In this work, we investigate the physical layer security (PLS) of ambient backscatter communication non-orthogonal multiple access (AmBC-NOMA) networks where non-colluding eavesdroppers (Eves) are randomly distributed. In the proposed system, a base station (BS) transmits a superimposed signal to a typical NOMA user pair, while a backscatter device~(BD) simultaneously transmits its unique signal by reflecting and modulating the BS's signal. Meanwhile, Eves passively attempt to wiretap the ongoing transmissions. Notably, the number and locations of Eves are unknown, posing a substantial security threat to the system. To address this challenge, the BS injects artificial noise (AN) to mislead the Eves, and a protected zone is employed to create an Eve-exclusion area around the BS. Theoretical expressions for outage probability (OP) and intercept probability (IP) are provided to evaluate the system's reliability-security trade-off. Asymptotic behavior at high signal-to-noise ratio (SNR) is further explored, including the derivation of diversity orders for the OP. Numerical results validate the analytical findings through extensive simulations, demonstrating that both the AN injection and protected zone can effectively enhance PLS. Furthermore, analysis and insights of different key parameters, including transmit SNR, reflection efficiency at the BD, power allocation coefficient, power fraction allocated to desired signal, Eve-exclusion area radius, Eve distribution density, and backscattered AN cancellation efficiency, on OP and IP are also provided.
Memory-Based Control with Event-Triggered Protocol for interval type-2 fuzzy network system under fading channel
Authors: Sen Kong, Meng Wang
Date: 2024-12-09 00:00:00

To address the challenges in networked environments and control problems associated with complex nonlinear uncertain systems, this paper investigates the design of a membership-function-dependent (MFD) memory output-feedback (MOF) controller for interval type-2 (IT2) fuzzy systems under fading channels, leveraging a memory dynamic event-triggering mechanism (MDETM). To conserve communication resources, MDETM reduces the frequency of data transmission. For mitigating design conservatism, a MOF controller is employed. A stochastic process models the fading channel, accounting for phenomena such as reflection, refraction, and diffraction that occur during data packet transmission through networks. An actuator failure model addresses potential faults and inaccuracies in practical applications. Considering the impacts of channel fading and actuator failures, the non-parallel distributed compensation (non-PDC) strategy enhances the robustness and anti-interference capability of the MDETM MOF controller. By fully exploiting membership function information, novel MFD control design results ensure mean-square exponential stability and $\mathscr H_{\infty}$ performance $\gamma$ for the closed-loop system. Simulation studies validate the effectiveness of the proposed approach.
Robust Output Tracking for an Uncertain and Nonlinear 3D PDE-ODE System: Preventing Induced Seismicity in Underground Reservoirs
Authors: Diego Gutiérrez-Oribio, Ioannis Stefanou
Date: 2024-12-09 00:00:00
Tasks: continuous-control, Continuous Control

This paper presents a robust control strategy for output tracking of a nonlinear 3D PDE-ODE system, where the ODE has logistic-like dynamics. The output feedback control was developed by bounding the solution and its time derivative for both the infinite-dimensional system and the nonlinear ODE. These bounds were then leveraged to ensure the boundedness of the control coefficient and the perturbations in the error dynamics. The mathematical framework proves the controller's ability to manage two output types within the system, overcoming model uncertainties and heterogeneities, using minimal system information, and a continuous control signal. A case study addressing induced seismicity mitigation while ensuring energy production in the Groningen gas reservoir highlights the control's effectiveness. The strategy guarantees precise tracking of target seismicity rates and pressures across reservoir regions, even under parameter uncertainties. Numerical simulations validate the approach in two scenarios: gas extraction while not exceeding the intrinsic seismicity of the region and the addition of CO2 injections, achieving net-zero environmental impact.
Reconfigurable Holographic Surface-aided Distributed MIMO Radar Systems
Authors: Qian Li, Ziang Yang, Dou Li, Hongliang Zhang
Date: 2024-12-09 00:00:00
Tasks: Diversity

Distributed phased Multiple-Input Multiple-Output (phased-MIMO) radar systems have attracted wide attention in target detection and tracking. However, the phase-shifting circuits in phased subarrays contribute to high power consumption and hardware cost. To address this issue, an energy-efficient and cost-efficient metamaterial antenna array, i.e., reconfigurable holographic surface (RHS), has been developed. In this letter, we propose RHS-aided distributed MIMO radar systems to achieve more accurate multi-target detection under equivalent power consumption and hardware cost as that of distributed phased-MIMO radar systems. Different from phased arrays, the RHS achieves beam steering by regulating the radiation amplitude of its elements, and thus conventional beamforming schemes designed for phased arrays are no longer applicable. Aiming to maximize detection accuracy, we design an amplitude-controlled beamforming scheme for multiple RHS transceiver subarrays. The simulations validate the superiority of the proposed scheme over the distributed phased-MIMO radar scheme and reveal the optimal allocation of spatial diversity and coherent processing gain that leads to the best system performance when hardware resources are fixed.
Input-to-State Stability of Newton Methods in Nash Equilibrium Problems with Applications to Game-Theoretic Model Predictive Control
Authors: Mushuang Liu, Ilya Kolmanovsky
Date: 2024-12-09 00:00:00
Tasks: Model Predictive Control

We prove input-to-state stability (ISS) of perturbed Newton methods for generalized equations arising from Nash equilibrium (NE) and generalized NE (GNE) problems. This ISS property allows the use of inexact computation in equilibrium-seeking to enable fast solution tracking in dynamic systems and plays a critical role in the analysis of interconnected plant-optimizer feedback systems such as model predictive control (MPC) with iterative optimization algorithms. For NE problems, we address the local convergence of perturbed Newton methods from the variational inequality (VI) stability analysis and point out that the ISS of the perturbed Josephy-Newton can be established under less restrictive stability/regularity conditions compared to the existing results established for nonlinear optimizations. Agent-distributed Josephy-Newton methods are then developed to enable agent-wise local computations. For GNE problems, since they cannot be reduced to VI problems in general, we use semismooth Newton methods to solve the semismooth equations arising from the Karush-Kuhn-Tucker (KKT) systems of the GNE problem and show that the ISS of the perturbed semismooth Newton methods can be established under a quasi-regularity condition. Agent-distributed updates are also developed for the GNE-seeking. To illustrate the use of the ISS in dynamic systems, applications to constrained game-theoretic MPC (CG-MPC) are studied. A real-time CG-MPC solver with both time- and agent- distributed computations is developed and is proven to have bounded solution tracking errors.
A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis
Authors: Yiyan Li, Zhenghao Zhou, Jian Ping, Xiaoyuan Xu, Zheng Yan, Jianzhong Wu
Date: 2024-12-08 00:00:00
Tasks: Representation Learning

Graph motif, defined as the microstructure that appears repeatedly in a large graph, reveals important topological characteristics of the large graph and has gained increasing attention in power system analysis regarding reliability, vulnerability and resiliency. However, searching motifs within the large-scale power system is extremely computationally challenging and even infeasible, which undermines the value of motif analysis in practice. In this paper, we introduce a two-stage AI-powered motif mining method to enable efficient and wide-range motif analysis in power systems. In the first stage, a representation learning method with specially designed network structure and loss function is proposed to achieve ordered embedding for the power system topology, simplifying the subgraph isomorphic problem into a vector comparison problem. In the second stage, under the guidance of the ordered embedding space, a greedy-search-based motif growing algorithm is introduced to quickly obtain the motifs without traversal searching. A case study based on a power system database including 61 circuit models demonstrates the effectiveness of the proposed method.
Uncertainty-Aware Capacity Expansion for Real-World DER Deployment via End-to-End Network Integration
Authors: Yiyuan Pan, Yiheng Xie, Steven Low
Date: 2024-12-08 00:00:00
Tasks: Decision Making

The deployment of distributed energy resource (DER) devices plays a critical role in distribution grids, offering multiple value streams, including decarbonization, provision of ancillary services, non-wire alternatives, and enhanced grid flexibility. However, existing research on capacity expansion suffers from two major limitations that undermine the realistic accuracy of the proposed models: (i) the lack of modeling of three-phase unbalanced AC distribution networks, and (ii) the absence of explicit treatment of model uncertainty. To address these challenges, we develop a two-stage robust optimization model that incorporates a 3-phase unbalanced power flow model for solving the capacity expansion problem. Furthermore, we integrate a predictive neural network with the optimization model in an end-to-end training framework to handle uncertain variables with provable guarantees. Finally, we validate the proposed framework using real-world power grid data collected from our partner distribution system operators. The experimental results demonstrate that our hybrid framework, which combines the strengths of optimization models and neural networks, provides tractable decision-making support for DER deployments in real-world scenarios.
Geomagnetic and Inertial Combined Navigation Approach Based on Flexible Correction-Model Predictive Control Algorithm
Authors: Xiaohui Zhang, Xingming Li, Songnan Yang, Wenqi Bai, Yirong Lan
Date: 2024-12-08 00:00:00
Tasks: Model Predictive Control

This paper proposes a geomagnetic and inertial combined navigation approach based on the flexible correction-model predictive control algorithm (Fc-MPC). This approach aims to overcome the limitations of existing combined navigation methods that require prior geomagnetic maps and the inertial navigation drift of long-range missions. The proposed method uses geomagnetic gradient information and the model predictive control (MPC) algorithm with heading control and state constraints, eliminating the dependence on prior geomagnetic maps. Instead, the proposed method achieves real-time measurements of the geomagnetic declination, geomagnetic inclination, and inertial navigation data and introduces uniform compensation conditions to adjust and correct the predictive results in real-time. Simulation and real experiment results demonstrate that the proposed Fc-MPC algorithm significantly improves the precision, efficiency, and stability of the geomagnetic and inertial combined navigation system.
f-P vs P-f based Grid-forming Control under RoCoF Event Considering Power and Energy Limits
Authors: Chu Sun
Date: 2024-12-08 00:00:00

Grid-forming (GFM) converter is deemed as one enabler for high penetration of renewable energy resources in power system. However, as will be pointed out in this letter, the conventional power-to-frequency (P-f) GFM control will face a dilemma in keeping power limit and grid synchronization when the energy resource of the converter reaches the limit. To address this challenge, a f-P and Q-V hybrid control is proposed, which exhibits similar GFM performance, particularly under weak grid condition, but is superior in power-limiting and grid synchronization as demonstrated by comparative studies.
Constant di/dz Scanning Tunneling Microscopy: Atomic Precision Imaging and Hydrogen Depassivation Lithography on a Si(100) - 2 x 1 : H Surface
Authors: Richa Mishra, S. O. Reza Moheimani
Date: 2024-12-07 00:00:00

We introduce a novel control mode for Scanning Tunneling Microscopy (STM) that leverages di/dz feedback. By superimposing a high-frequency sinusoidal modulation on the control signal, we extract the amplitude of the resulting tunneling current to obtain a di/dz measurement as the tip is scanned over the surface. A feedback control loop is then closed to maintain a constant di/dz, enhancing the sensitivity of the tip to subtle surface variations throughout a scan. This approach offers distinct advantages over conventional constant-current imaging. We demonstrate the effectiveness of this technique through high-resolution imaging and lithographic experiments on several Si(100)-2x1:H surfaces. Our findings, validated across multiple STM systems and imaging conditions, pave the way for a new paradigm in STM control, imaging, and lithography.
Error Feedback Approach for Quantization Noise Reduction of Distributed Graph Filters
Authors: Xue Xian Zheng, Tareq Al-Naffouri
Date: 2024-12-07 00:00:00
Tasks: Quantization

This work introduces an error feedback approach for reducing quantization noise of distributed graph filters. It comes from error spectrum shaping techniques from state-space digital filters, and therefore establishes connections between quantized filtering processes over different domains. Quantization noise expression incorporating error feedback for finite impulse response (FIR) and autoregressive moving average (ARMA) graph filters are both derived with regard to time-invariant and time-varying graph topologies. Theoretical analysis is provided, and closed-form error weight coefficients are found. Numerical experiments demonstrate the effectiveness of the proposed method in noise reduction for the graph filters regardless of the deterministic and random graph topologies.
A signal discovery step in interstellar communication
Authors: William J. Crilly Jr
Date: 2024-12-09 00:00:00

Prior work using synchronized, geographically spaced radio telescopes, and a radio interferometer, suggests that narrow-bandwidth polarized pulse pair measurements repeatedly falsify a noise-cause hypothesis, given a prior celestial direction of interest. A four-step method was proposed, tested, and reported, using interferometer phase measurements, to seek common celestial directions among pulse pair components, during 92 days of observation. In the work reported here, the proposed four-step signal discovery method is simplified to have a single step. A 123.8 day interferometer experiment provides measurement evidence supporting a hypothesis that the prior direction of interest, and a second direction of interest, are associated with celestial coordinates. Each pointing direction measures statistical power at greater than six standard deviations, with some indications of associated interferometer-induced Right Ascension aliasing. Explanations are proposed and discussed.
The transition to speciation in the finite genome Derrida-Higgs model: a heuristic solution
Authors: Vitor M. Marquioni, Marcus A. M. de Aguiar
Date: 2024-12-09 00:00:00

The process of speciation, where an ancestral species divides in two or more new species, involves several geographic, environmental and genetic components that interact in a complex way. Understanding all these elements at once is challenging and simple models can help unveiling the role of each factor separately. The Derrida-Higgs model describes the evolution of a sexually reproducing population subjected to mutations in a well mixed population. Individuals are characterized by a string with entries $\pm1$ representing a haploid genome with biallelic genes. If mating is restricted by genetic similarity, so that only individuals that are sufficiently similar can mate, sympatric speciation, i.e. the emergence of species without geographic isolation, can occur. Only four parameters rule the dynamics: population size $N$, mutation rate $\mu$, minimum similarity for mating $q_{min}$ and genome size $B$. In the limit $B\rightarrow\infty$, speciation occurs if the simple condition $q_{min}>(1+4\mu N)^{-1}$ is satisfied. However, this condition fails for finite genomes, and speciation does not occur if the genome size is too small. This indicates the existence of a critical genome size for speciation. In this work, we develop an analytical theory of the distribution of similarities between individuals, a quantity that defines how tight or spread out is the genetic content of the population. This theory is carried out in the absence of mating restrictions, where evolution equations for the mean and variance of the similarity distribution can be derived. We then propose a heuristic description of the speciation transition which allows us to numerically calculate the critical genome size for speciation as a function of the other model parameters. The result is in good agreement with the simulations of the model and may guide further investigations on theoretical conditions for species formation.
Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture
Authors: Subash Katel, Haoyang Li, Zihan Zhao, Raghav Kansal, Farouk Mokhtar, Javier Duarte
Date: 2024-12-05 00:00:00
Tasks: Jet Tagging, Self-Supervised Learning

In high energy physics, self-supervised learning (SSL) methods have the potential to aid in the creation of machine learning models without the need for labeled datasets for a variety of tasks, including those related to jets -- narrow sprays of particles produced by quarks and gluons in high energy particle collisions. This study introduces an approach to learning jet representations without hand-crafted augmentations using a jet-based joint embedding predictive architecture (J-JEPA), which aims to predict various physical targets from an informative context. As our method does not require hand-crafted augmentation like other common SSL techniques, J-JEPA avoids introducing biases that could harm downstream tasks. Since different tasks generally require invariance under different augmentations, this training without hand-crafted augmentation enables versatile applications, offering a pathway toward a cross-task foundation model. We finetune the representations learned by J-JEPA for jet tagging and benchmark them against task-specific representations.