Scholarly Works, Electrical and Computer Engineering

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https://hdl.handle.net/10919/23145

Research articles, presentations, and other scholarship

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Wireless Mobile Distributed-MIMO for 6G
Bondada, Kumar Sai; Jakubisin, Daniel J.; Said, Karim; Buehrer, R. Michael; Liu, Lingjia (IEEE, 2024-01-01)
The paper proposes a new architecture for Distributed MIMO (D-MIMO) in which the base station (BS) jointly transmits with wireless mobile nodes to serve users (UEs) within a cell for 6G communication systems. The novelty of the architecture lies in the wireless mobile nodes participating in joint D-MIMO transmission with the BS (referred to as D-MIMO nodes), which are themselves users on the network. The D-MIMO nodes establish wireless connections with the BS, are generally near the BS, and ideally benefit from higher SNR links and better connections with edge-located UEs. These D-MIMO nodes can be existing handset UEs, Unmanned Aerial Vehicles (UAVs), or Vehicular UEs. Since the D-MIMO nodes are users sharing the access channel, the proposed architecture operates in two phases. First, the BS communicates with the D-MIMO nodes to forward data for the joint transmission, and then the BS and D-MIMO nodes jointly serve the UEs through coherent D-MIMO operation. Capacity analysis of this architecture is studied based on realistic 3GPP channel models, and the paper demonstrates that despite the two-phase operation, the proposed architecture enhances the system's capacity compared to the baseline where the BS communicates directly with the UEs.
Acoustic Sensing Fiber Coupled with Highly Magnetostrictive Ribbon for Small-Scale Magnetic-Field Detection
Dejneka, Zach; Homa, Daniel; Theis, Logan; Wang, Anbo; Pickrell, Gary (MDPI, 2025-01-30)
Fiber-optic sensing has shown promising development for use in detecting magnetic fields for downhole and biomedical applications. Coupling existing fiber-based strain sensors with highly magnetostrictive materials allows for a new method of magnetic characterization capable of distributed and high-sensitivity field measurements. This study investigates the strain response of the highly magnetostrictive alloys Metglas^® 2605SC and Vitrovac^® 7600 T70 using Fiber Bragg Grating (FBG) acoustic sensors and an applied AC magnetic field. Sentek Instrument’s picoDAS interrogated the distributed FBG sensors set atop a ribbon of magnetostrictive material, and the corresponding strain response transferred to the fiber was analyzed. Using the Vitrovac^® ribbon, a minimal detectable field amplitude of 60 nT was achieved. Using Metglas^®, an even better sensitivity was demonstrated, where detected field amplitudes as low as 3 nT were measured via the strain response imparted to the FBG sensor. Distributed FBG sensors are readily available commercially, easily integrated into existing interrogation systems, and require no bonding to the magnetostrictive material for field detection. The simple sensor configuration with nanotesla-level sensitivity lends itself as a promising means of magnetic characterization and demonstrates the potential of fiber-optic acoustic sensors for distributed measurements.
A Fully Polynomial Time Approximation Scheme for Adaptive Variable Rate Task Demand
Willcock, Aaron; Fisher, Nathan; Chantem, Thidapat (Tam) (ACM, 2024-11-06)
The Adaptive Variable Rate (AVR) task model defines a task where job WCET and period are a function of engine speed. Motivated by a lack of tractable AVR task demand methods, this work uses predefined job sequences for the Bounded Precedence Constraint Knapsack Problem inherent in AVR task demand calculation instead of enumerating all considered speeds as in existing work. A new, exact approach is proposed and approximated, enabling the derivation of a Fully Polynomial Time Approximation Scheme that outperforms the state-of-the-art in runtime (7,800x improvement) and RAM use (99% reduction) with less than 8% demand overestimate.
On Extending Incorrectness Logic with Backwards Reasoning
Verbeek, Freek; Sefat, Md Syadus; Fu, Zhoulai; Ravindran, Binoy (ACM, 2025-01-07)
This paper studies an extension of O'Hearn's incorrectness logic (IL) that allows backwards reasoning. IL in its current form does not generically permit backwards reasoning. We show that this can be mitigated by extending IL with underspecification. The resulting logic combines underspecification (the result, or postcondition, only needs to formulate constraints over relevant variables) with underapproximation (it allows to focus on fewer than all the paths). We prove soundness of the proof system, as well as completeness for a defined subset of presumptions. We discuss proof strategies that allow one to derive a presumption from a given result. Notably, we show that the existing concept of loop summaries -- closed-form symbolic representations that summarize the effects of executing an entire loop at once -- is highly useful. The logic, the proof system and all theorems have been formalized in the Isabelle/HOL theorem prover.
Flexible Reconfiguration for Optimal Operation of Distribution Network Under Renewable Generation and Load Uncertainty
Esmaeilnezhad, Behzad; Amini, Hossein; Noroozian, Reza; Jalilzadeh, Saeid (MDPI, 2025-01-09)
The primary objective when operating a distribution network is to minimize operating costs while taking technical constraints into account. Minimizing the operational costs is difficult when there is a high penetration of renewable resources and variability of loads, which introduces uncertainty. In this paper, a flexible, dynamic reconfiguration model is developed that enables a distribution network to minimize operating costs on an hourly basis. The model fitness function is to minimize the system costs, including power loss, voltage deviation, purchased power from the upstream network, renewable generation, and switching costs. The uncertainty of the load and generation from renewable energies is planned to use their probability density functions via a scenario-based approach. The suggested optimization problem is solved using a metaheuristic approach based on the coati optimization algorithm (COA) due to the nonlinearity and non-convexity of the problem. To evaluate the performance of the presented approach, it is validated on the IEEE 33-bus radial system and TPC 83-bus real system. The simulation results show the impact of dynamic reconfiguration on reducing operation costs. It is found that dynamic reconfiguration is an efficient solution for reducing power losses and total energy drawn from the upstream network by increasing the number of switching operations.
Model-Free Resilient Grid-Forming and Grid-Following Inverter Control Against Cyberattacks Using Reinforcement Learning
Beikbabaei, Milad; Kwiatkowski, Brian Michael; Mehrizi-Sani, Ali (MDPI, 2025-01-13)
The U.S. movement toward clean energy generation has increased the number of installed inverter-based resources (IBR) in the grid, introducing new challenges in IBR control and cybersecurity. IBRs receive their set point through the communication link, which may expose them to cyber threats. Previous work has developed various techniques to detect and mitigate cyberattacks on IBRs, developing schemes for new inverters being installed in the grid. This work focuses on developing model-free control techniques for already installed IBR in the grid without the need to access IBR internal control parameters. The proposed method is tested for both the grid-forming and grid-following inverter control. Different detection and mitigation algorithms are used to enhance the accuracy of the proposed method. The proposed method is tested using the modified CIGRE 14-bus North American grid with seven IBRs in PSCAD/EMTDC. Finally, the performance of the detection algorithm is tested under grid normal transients, such as set point change, load change, and short-circuit fault, to make sure the proposed detection method does not provide false positives.
Eavesdropper Avoidance through Adaptive Beam Management in SDR-Based MmWave Communications
Baron-Hyppolite, Adrian; Santos, Joao F.; DaSilva, Luiz A.; Kibiłda, Jacek (IEEE, 2024-01-01)
High-frequency systems use beamforming to mitigate the increased path loss. As the resulting beams become highly directional, Millimeter Wave (mmWave) radios conduct a beam sweep to probe all possible angular directions to locate each other and establish communication. In this paper, we propose an adaptive beam management strategy that leverages beam sweeping to avoid eavesdroppers and other potential attackers. Our solution employs Deep Reinforcement Learning (DRL) to dynamically select a subset of beams in the transmitter codebook. We evaluate this solution through a proof-of-concept implementation using a combination of Software-Defined Radios (SDRs) and commercial mmWave equipment, and show the improvements in the secrecy capacity.
Experimental Validation of a 3GPP compliant 5G-based Positioning System
Dhungel, Sarik; Duggal, Gaurav; Ron, Dara; Tripathi, Nishith; Buehrer, R. Michael; Reed, Jeffrey H.; Shah, Vijay K. (ACM, 2024-12-04)
The advent of 5G positioning techniques by 3GPP has unlocked possibilities for applications in public safety, vehicular systems, and location-based services. However, these applications demand accurate and reliable positioning performance, which has led to the proposal of newer positioning techniques. To further advance the research on these techniques, in this paper, we develop a 3GPP-compliant 5G positioning testbed, incorporating gNodeBs (gNBs) and User Equipment (UE). The testbed uses New Radio (NR) Positioning Reference Signals (PRS) transmitted by the gNB to generate Time of Arrival (TOA) estimates at the UE. We mathematically model the inter-gNB and UE-gNB time offsets affecting the TOA estimates and examine their impact on positioning performance. Additionally, we propose a calibration method for estimating these time offsets. Furthermore, we investigate the environmental impact on the TOA estimates. Our findings are based on our mathematical model and supported by experimental results.
Automated and Blind Detection of Low Probability of Intercept RF Anomaly Signals
Gusain, Kuanl; Hassan, Zoheb; Couto, David; Malek, Mai Abdel; Shah, Vijay K; Zheng, Lizhong; Reed, Jeffrey H. (ACM, 2024-12-04)
Automated spectrum monitoring necessitates the accurate detection of low probability of intercept (LPI) radio frequency (RF) anomaly signals to identify unwanted interference in wireless networks. However, detecting these unforeseen low-power RF signals is fundamentally challenging due to the scarcity of labeled RF anomaly data. In this paper, we introduce WANDA (Wireless ANomaly Detection Algorithm), an automated framework designed to detect LPI RF anomaly signals in low signal-to-interference ratio (SIR) environments without relying on labeled data. WANDA operates through a two-step process: (i) Information extraction, where a convolutional neural network (CNN) utilizing soft Hirschfeld-Gebelein-Rényi correlation (HGR) as the loss function extracts informative features from RF spectrograms; and (ii) Anomaly detection, where the extracted features are applied to a one-class support vector machine (SVM) classifier to infer RF anomalies. To validate the effectiveness of WANDA, we present a case study focused on detecting unknown Bluetooth signals within the WiFi spectrum using a practical dataset. Experimental results demonstrate that WANDA outperforms other methods in detecting anomaly signals across a range of SIR values (-10 dB to 20 dB).
sMVX: Multi-Variant Execution on Selected Code Paths
Yeoh, Sengming; Wang, Xiaoguang; Jang, Jae-Won; Ravindran, Binoy (ACM, 2024-12-02)
Multi-Variant Execution (MVX) is an effective way to detect memory corruption vulnerabilities, intrusions, or live software updates. A traditional MVX system concurrently runs multiple copies of functionally identical, layout-different program variants. Therefore, a typical memory corruption attack that forges pointers can succeed on at most one variant, leading the other variant(s) to crash. The replicated execution adds software security and reliability but also brings multiple times of CPU and memory usage. This paper presents sMVX, a flexible multi-variant execution system replicating variants only on the selected code paths. sMVX allows end-users to annotate a target program and indicate sensitive code regions for multi-variant execution. Such code regions can be authentication-related code or sensitive functions that handle potentially malicious input data. An sMVX runtime only replicates the sensitive functions and executes them in lockstep. We have implemented a prototype of sMVX using an in-process code monitor. The sMVX monitor supports the selected code paths MVX from within the target program’s address space, but the monitor is isolated from the target’s code by the Intel Memory Protection Keys (MPK). We evaluated the sMVX using a benchmark suite and two server applications. The evaluation demonstrates that sMVX exhibits a comparable performance overhead to state-of-the-art MVX systems but requires 20% fewer CPU cycles and 49% less memory consumption on server applications.
Verifiably Correct Lifting of Position-Independent x86-64 Binaries to Symbolized Assembly
Verbeek, Freek; Naus, Nico; Ravindran, Binoy (ACM, 2024-12-02)
We present an approach to lift position-independent x86-64 binaries to symbolized NASM. Symbolization is a decompilation step that enables binary patching: functions can be modified, and instructions can be interspersed. Moreover, it is the first abstraction step in a larger decompilation chain. The produced NASM is recompilable, and we extensively test the recompiled binaries to see if they exhibit the same behavior as the original ones. In addition to testing, the produced NASM is accompanied with a certificate, constructed in such a way that if all theorems in the certificate hold, symbolization has occurred correctly. The original and recompiled binary are lifted again with a third-party decompiler (Ghidra). These representations, as well as the certificate, are loaded into the Isabelle/HOL theorem prover, where proof scripts ensure that correctness can be proven automatically. We have applied symbolization to various stripped binaries from various sources, from various compilers, and ranging over various optimization levels.We show how symbolization enables binary-level patching, by tackling challenges originating from industry.
Machine Learning-Driven Optimization of Livestock Management: Classification of Cattle Behaviors for Enhanced Monitoring Efficiency
Zhao, Zhuqing; Shehada, Halah; Ha, Dong; Dos Reis, Barbara; White, Robin; Shin, Sook (ACM, 2024-08-02)
Monitoring cattle health in remote and expansive pastures poses significant challenges that necessitate automated, continuous, and real-time behavior monitoring. This paper investigates the effectiveness and reliability sensor-based cattle behavior classification for such monitoring, emphasizing the impact of intelligent feature selection in enhancing classification performance. To achieve this, we developed Wireless Sensor Nodes (WSN) affixed to individual cattle, enabling the capture of 3-axis acceleration data from five cows across varying seasons, spanning from summer to winter. Initially, we extracted a comprehensive set of 52 features, representing a broad spectrum of cow behaviors alongside statistical attributes. To enhance computational efficiency, we employed the Recursive Feature Elimination (RFE) method to distill 30 critical features by discarding redundant or less significant ones. Subsequently, these optimized features were utilized to train four machine learning (ML) models: Support Vector Machine (SVM), k-Nearest Neighbors (k- NN), Random Forest (RF), and Histogram-based Gradient Boosted Decision Trees (HGBDT). Notably, the HGBDT model demonstrated superior performance, achieving remarkable F1-scores of 99.01% for ’grazing’, 98.74% for ’ruminating’, 89.62% for ’lying’, 84.06% for ’standing’, and 91.87% for ’walking’. These findings underscore the potential of our approach to serve as a robust framework for precision livestock farming, offering valuable insights into enhancing cattle health monitoring in remote environments.
SegIt: Empowering Sensor Data Labeling with Enhanced Efficiency and Security
Zhang, Zhen; Abraham, Samuel; Lee, Alex; Li, Yichen; Morota, Gota; Ha, Dong; Shin, Sook (ACM, 2024-08-02)
SegIt is a novel, user-friendly, and highly efficient sensor data labeling tool designed to tackle critical challenges such as data privacy, synchronization accuracy, and memory efficiency inherent in existing labeling tools. While many current sensor data labeling tools provide free online services, they typically necessitate users to upload unlabeled sensor data, alongside video or audio references, to cloud storage for labeling. Nevertheless, such third-party storage exposes user data to potential security risks. SegIt, an innovative open-source tool, provides a software solution for tagging unlabeled sensor data directly on a local computer, ensuring enhanced accuracy, convenience, and, most importantly, data security.
Energy Backflow in Unidirectional Monochromatic and Space–Time Waves
Saari, Peeter; Besieris, Ioannis M. (MDPI, 2024-11-29)
Backflow, or retropropagation, is a counterintuitive phenomenon whereby for a forward-propagating wave the energy locally propagates backward. In the context of backflow, physically most interesting are the so-called unidirectional waves, which contain only forward-propagating plane wave constituents. Yet, very few such waves possessing closed-form analytic expressions for evaluation of the Poynting vector are known. In this study, we examine energy backflow in a novel (2+time)-dimensional unidirectional monochromatic wave and in a (2+1)D spatiotemporal wavepacket, analytic expressions which we succeeded to find. We also present a detailed study of the backflow in the “needle” pulse. This is an interesting model object because well-known superluminal non-diffracting space–time wave packets can be derived from its simple factored wave function. Finally, we study the backflow in an unidirectional version of the so-called focus wave mode—a pulse propagating luminally and without spread, which is the first and most studied representative of the (3+1)D non-diffracting space–time wave packets (also referred to as spatiotemporally localized waves).
Understanding User Behavior for Enhancing Cybersecurity Training with Immersive Gamified Platforms
Donekal Chandrashekar, Nikitha; Lee, Anthony; Azab, Mohamed; Gracanin, Denis (MDPI, 2024-12-18)
In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure.
Modeling and Analysis of Dispersive Propagation of Structural Waves for Vibro-Localization
Ambarkutuk, Murat; Plassmann, Paul E. (MDPI, 2024-12-04)
The dispersion of structural waves, where wave speed varies with frequency, introduces significant challenges in accurately localizing occupants in a building based on vibrations caused by their movements. This study presents a novel multi-sensor vibro-localization technique that accounts for dispersion effects, enhancing the accuracy and robustness of occupant localization. The proposed method utilizes a model-based approach to parameterize key propagation phenomena, including wave dispersion and attenuation, which are fitted to observed waveforms. The localization is achieved by maximizing the joint likelihood of the occupant’s location based on sensor measurements. The effectiveness of the proposed technique is validated using two experimental datasets: one from a controlled environment involving an aluminum plate and the other from a building-scale experiment conducted at Goodwin Hall, Virginia Tech. Results for the proposed algorithm demonstrates a significant improvement in localization accuracy compared to benchmark algorithms. Specifically, in the aluminum plate experiments, the proposed technique reduced the average localization precision from 7.77 cm to 1.97 cm, representing a ∼74% improvement. Similarly, in the Goodwin Hall experiments, the average localization error decreased from 0.67 m to 0.3 m, with a ∼55% enhancement in accuracy. These findings indicate that the proposed approach outperforms existing methods in accurately determining occupant locations, even in the presence of dispersive wave propagation.
Global Sensitivity Analysis for Integrated Heat and Electricity Energy System
Li, Yibo; Xu, Yijun; Yao, Shuai; Lu, Shuai; Gu, Wei; Mili, Lamine M.; Korkali, Mert (IEEE, 2024-11-18)
Although global sensitivity analysis (GSA) is gaining increasing popularity in power systems due to its ability to measure the importance of uncertain inputs, it has not been explored in the integrated energy system (IES) in the existing literature. Indeed, when coupled multi-energy systems (e.g., heating networks) are considered, the power system operation states are inevitably altered. Accordingly, its associated GSA, which relies on Monte Carlo simulations (MCS), becomes even more computationally prohibitive since it not only increases the model complexity but also faces large uncertainties. To address these issues, this paper proposes a double-loop generalized unscented transform (GenUT)-based strategy that, for the first time, explores the GSA in the IES while simultaneously achieving high computing efficiency and accuracy. More specifically, we first propose a GenUT method that can propagate the moment information of correlated input variables following different types of probability distributions in the IES. We further design a double-loop sampling scheme for GenUT to evaluate the GSA for correlated uncertainties in a cost-effective manner. The simulations of multiple heat- and power-coupled IESs reveal the excellent performance of the proposed method
Robust Constant Curvature Curve Communications with Complex and Quaternion Neural Networks
Buvarp, Anders M.; Mili, Lamine M.; Zaghloul, Amir I. (IEEE, 2024-06-25)
The concept of Digital Twin has recently emerged, which requires the transmission of a massive amount of sensor data with low latency and high reliability. Analog error correction is an attractive method for low-latency communications; hence, in this paper, we propose the use of complex-valued neural networks and Quaternionic Neural Networks (QNNs) to decode analog codes. Furthermore, we propose mapping our codes to the baseband of the frequency domain to enable easy time and frequency synchronization as well as to mitigate frequency-selective fading using robust estimation theory. This is accomplished by applying inverse Discrete Fourier Transform (DFT) modulation, which achieves a significant reduction in hardware complexity, power, and cost as compared to our previously proposed analog coding scheme. Additionally, we introduce a scaled version of our previous analog codes that enables statistical signal processing, something we have not been able to achieve until now. This achieves significant noise immunity with drastic performance improvements at low Signal-to-Noise Ratios (SNR) and a small loss at high SNR.
A Low-Rank Tensor Train Approach for Electric Vehicle Load Data Reconstruction Using Real Industrial Data
Sun, Bo; Xu, Yijun; Gu, Wei; Cai, Huihuang; Lu, Shuai; Mili, Lamine M.; Yu, Wenwu; Wu, Zhi (IEEE, 2024-09-30)
As electric vehicles (EVs) gain popularity, their interaction with the power system cannot be overlooked. Therefore, there is a growing need for accurate EV load data to facilitate precise operation and control in power systems. However, in practice, due to the high cost of high-frequency measurement devices and limited data storage capacity, only low-resolution metered EV data are available. To address this, this paper proposed a tensor completion-based method for EV load data reconstruction. More specifically, we first reformulate the load data as high-dimensional tensors and consider unknown data to be recovered as missing entries. Subsequently, we leverage the low-rank properties of high-dimensional data to perform tensor completion. To achieve this, two optimization formulations are proposed: a nuclear norm minimization algorithm based on singular value thresholding (SVT) and a tensor rank approximation algorithm via parallel matrix factorization. Both approaches are based on the tensor train (TT) rank, thanks to its well-balanced matricization scheme. This enables us to cost-effectively reconstruct high-resolution EV data using only low-resolution measurements. Simulation results using real industrial data reveal the excellent performance of the proposed methods.
Enforcing C/C++ Type and Scope at Runtime for Control-Flow and Data-Flow Integrity
Ismail, Mohannad; Jelesnianski, Christopher; Jang, Yeongjin; Min, Changwoo; Xiong, Wenjie (ACM, 2024-04-27)
Control-flow hijacking and data-oriented attacks are becoming more sophisticated. These attacks, especially dataoriented attacks, can result in critical security threats, such as leaking an SSL key. Data-oriented attacks are hard to defend against with acceptable performance due to the sheer amount of data pointers present. The root cause of such attacks is using pointers in unintended ways; fundamentally, these attacks rely on abusing pointers to violate the original scope they were used in or the original types that they were declared as. This paper proposes Scope Type Integrity (STI), a new defense policy that enforces all pointers (both code and data pointers) to conform to the original programmer’s intent, as well as Runtime Scope Type Integrity (RSTI) mechanisms to enforce STI at runtime leveraging ARM Pointer Authentication. STI gathers information about the scope, type, and permissions of pointers. This information is then leveraged by RSTI to ensure pointers are legitimately utilized at runtime. We implemented three defense mechanisms of RSTI, with varying levels of security and performance tradeoffs to showcase the versatility of RSTI. We employ these three variants on a variety of benchmarks and real-world applications for a full security and performance evaluation of these mechanisms. Our results show that they have overheads of 5.29%, 2.97%, and 11.12%, respectively.

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