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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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.
Designing Technology to Support the Hospital Classroom: Preliminary Findings
Rasberry, Nadra; Essandoh, Joshua; Do, Ethan; Ogbonnaya-Ogburu, Ihudiya (ACM, 2024-11-11)
Hospital teachers are state-employed educators who provide K-12 instruction to children in the hospital. We conducted research to understand how technology is used in hospital classrooms, an area which has been relatively underexplored. We conducted semistructured interviews with five hospital teachers to understand their experience of using technology in and outside the classroom. Our findings revealed that hospital teachers often rely on older curricula given the changing education atmosphere; learning is often assessed through in-classroom observations of mastery; and technology and internet use by students is often restricted, which may inhibit opportunities to use AI and other technical resources in the classroom.We contribute a deeper understanding of technology use in the hospital classroom.
Practical Fault Injection Attacks on Constant Time CSIDH and Mitigation Techniques
Chiu, Tinghung; LeGrow, Jason; Xiong, Wenjie (ACM, 2024-11-19)
Commutative Supersingular Isogeny Diffie-Hellman (CSIDH) is an isogeny-based key exchange protocol which is believed to be secure even when parties use long-lived secret keys. To secure CSIDH against side-channel attacks, constant-time implementations with additional dummy isogeny computations are employed. In this study, we demonstrate a fault injection attack on the constant-time real-then-dummy CSIDH to recover the full static secret key. We prototype the attack using voltage glitches on the victim STM32 microcontroller. The attack scheme, which is based on existing research which has yet to be practically implemented, involves getting the faulty output by injecting the fault in a binary search fashion. Our attack reveals many practical factors that were not considered in the previous theoretical fault injection attack analysis, e.g., the probability of a failed fault injection. We bring the practice to theory and developed new complexity analysis of the attack. Further, to mitigate the possible binary search attack on real-then-dummy CSIDH, dynamic random vector CSIDH was proposed previously to randomize the order of real and dummy isogeny operations. We explore fault injection attacks on dynamic random vector CSIDH and evaluate the security level of the mitigation. Our analysis and experimental results demonstrate that it is infeasible to attack dynamic random vector CSIDH in a reasonable amount of time when the success rate of fault injection is not consistent over time.
Hermes: Boosting the Performance of Machine-Learning-Based Intrusion Detection System through Geometric Feature Learning
Zhang, Chaoyu; Shi, Shanghao; Wang, Ning; Xu, Xiangxiang; Li, Shaoyu; Zheng, Lizhong; Marchany, Randy; Gardner, Mark; Hou, Y. Thomas; Lou, Wenjing (ACM, 2024-10-14)
Anomaly-Based Intrusion Detection Systems (IDSs) have been extensively researched for their ability to detect zero-day attacks. These systems establish a baseline of normal behavior using benign traffic data and flag deviations from this norm as potential threats. They generally experience higher false alarm rates than signature-based IDSs. Unlike image data, where the observed features provide immediate utility, raw network traffic necessitates additional processing for effective detection. It is challenging to learn useful patterns directly from raw traffic data or simple traffic statistics (e.g., connection duration, package inter-arrival time) as the complex relationships are difficult to distinguish. Therefore, some feature engineering becomes imperative to extract and transform raw data into new feature representations that can directly improve the detection capability and reduce the false positive rate. We propose a geometric feature learning method to optimize the feature extraction process. We employ contrastive feature learning to learn a feature space where normal traffic instances reside in a compact cluster. We further utilize H-Score feature learning to maximize the compactness of the cluster representing the normal behavior, enhancing the subsequent anomaly detection performance. Our evaluations using the NSL-KDD and N-BaloT datasets demonstrate that the proposed IDS powered by feature learning can consistently outperform state-of-the-art anomaly-based IDS methods by significantly lowering the false positive rate. Furthermore, we deploy the proposed IDS on a Raspberry Pi 4 and demonstrate its applicability on resource-constrained Internet of Things (IoT) devices, highlighting its versatility for diverse application scenarios.
libLISA: Instruction Discovery and Analysis on x86-64
Craaijo, Jos; Verbeek, Freek; Ravindran, Binoy (ACM, 2024-10-08)
Even though heavily researched, a full formal model of the x86-64 instruction set is still not available. We present libLISA, a tool for automated discovery and analysis of the ISA of a CPU. This produces the most extensive formal x86-64 model to date, with over 118000 different instruction groups. The process requires as little human specification as possible: specifically, we do not rely on a human-written (dis)assembler to dictate which instructions are executable on a given CPU, or what their in- and outputs are. The generated model is CPU-specific: behavior that is "undefined" is synthesized for the current machine. Producing models for five different x86-64 machines, we mutually compare them, discover undocumented instructions, and generate instruction sequences that are CPU-specific. Experimental evaluation shows that we enumerate virtually all instructions within scope, that the instructions' semantics are correct w.r.t. existing work, and that we improve existing work by exposing bugs in their handwritten models.
Octopus-Inspired Adhesives with Switchable Attachment to Challenging Underwater Surfaces
Lee, Chanhong; Via, Austin C.; Heredia, Aldo; Adjei, Daniel A.; Bartlett, Michael D. (Wiley-VCH, 2024-10-09)
Adhesives that excel in wet or underwater environments are critical for applications ranging from healthcare and underwater robotics to infrastructure repair. However, achieving strong attachment and controlled release on difficult substrates, such as those that are curved, rough, or located in diverse fluid environments, remains a major challenge. Here, an octopus-inspired adhesive with strong attachment and rapid release in challenging underwater environments is presented. Inspired by the octopus’s infundibulum structure, a compliant, curved stalk, and an active deformable membrane for multi-surface adhesion are utilized. The stalk’s curved shape enhances conformal contact on large-scale curvatures and increases contact stress for adaptability to small-scale roughness. These synergistic mechanisms improve contact across multiple length scales, resulting in switching ratios of over 1000 within ≈30 ms with consistent attachment strength of over 60 kPa on diverse surfaces and conditions. These adhesives are demonstrated through the robust attachment and precise manipulation of rough underwater objects.
Offloading Datacenter Jobs to RISC-V Hardware for Improved Performance and Power Efficiency
Heerekar, Balvansh; Philippidis, Cesar; Chuang, Ho-Ren; Olivier, Pierre; Barbalace, Antonio; Ravindran, Binoy (ACM, 2024-09-16)
The end of Moore’s Law has brought significant changes in the architecture of servers used in data centers, increasingly incorporating new ISAs beyond x86-64 as well as diverse accelerators. Further, single-board computers have become increasingly efficient and can run certain Linux applications at significantly lower equipment and energy costs compared to traditional servers. Past research has demonstrated that offloading applications at runtime from x86-based servers to ARM-based single-board computers can result in increases in throughput and energy efficiency. The RISC-V architecture has recently gained significant commercial interest, and OS-capable single-board computers with RISC-V cores are increasingly available at the commodity scale. In this paper we propose a system that offloads jobs from an x86 server to a RISC-V single-board computer at runtime, with the goals of improving job throughput and energy saved. Towards this, we port the Popcorn Linux multi-ISA toolchain and runtime framework to RISC-V, enabling the live migration of applications between an x86 Xeon server and a SiFive HiFive RISC-V board. We further propose a scheduling policy, Lowest Slowdown First (LSF) that drives the offloading of long-running and stateful datacenter background jobs from the server to the board, to alleviate workload congestion on the server. LSF’s policy relies on monitoring jobs’ performance on the server, predicting the slowdown they would suffer if running on the board, and migrating the jobs with the lowest estimated slowdown. Our evaluation shows that LSF yields up to 20% increase in throughput while also gaining 16% more energy efficiency for compute-intensive workloads.
Assessing the Value of Transfer Learning Metrics for Radio Frequency Domain Adaptation
Wong, Lauren J.; Muller, Braeden P.; McPherson, Sean; Michaels, Alan J. (MDPI, 2024-07-25)
The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge gained from data with different distributions, TL offers higher performance and reduced training time, but has yet to be fully utilized in applications of machine learning (ML) and deep learning (DL) techniques and applications related to wireless communications, a field loosely termed radio frequency machine learning (RFML). This work examines whether existing transferability metrics, used in other modalities, might be useful in the context of RFML. Results show that the two existing metrics tested, Log Expected Empirical Prediction (LEEP) and Logarithm of Maximum Evidence (LogME), correlate well with post-transfer accuracy and can therefore be used to select source models for radio frequency (RF) domain adaptation and to predict post-transfer accuracy.
Asymmetric Optical Scanning Holography Encryption with Elgamal Algorithm
Wu, Chunying; Ding, Yinggang; Yan, Aimin; Poon, Ting-Chung; Tsang, Peter Wai Ming (MDPI, 2024-09-19)
This paper proposes an asymmetric scanning holography cryptosystem based on the Elgamal algorithm. The method encodes images with sine and cosine holograms. Subsequently, each hologram is divided into a signed bit matrix and an unsigned hologram matrix, both encrypted using the sender’s private key and the receiver’s public key. The resulting ciphertext matrices are then transmitted to the receiver. Upon receipt, the receiver decrypts the ciphertext matrices using their private key and the sender’s public key. We employ an asymmetric single-image encryption method for key management and dispatch for securing imaging and transmission. Furthermore, we conducted a sensitivity analysis of the encryption system. The image encryption metrics, including histograms of holograms, adjacent pixel correlation, image correlation, the peak signal-to-noise ratio, and the structural similarity index, were also examined. The results demonstrate the security and stability of the proposed method.
Epigenomic tomography for probing spatially defined chromatin state in the brain
Liu, Zhengzhi; Deng, Chengyu; Zhou, Zirui; Ya, Xiao; Jiang, Shan; Zhu, Bohan; Naler, Lynette B.; Jia, Xiaoting; Yao, Danfeng (Daphne); Lu, Chang (Cell Press, 2024-03-25)
Spatially resolved epigenomic profiling is critical for understanding biology in the mammalian brain. Singlecell spatial epigenomic assays were developed recently for this purpose, but they remain costly and labor intensive for examining brain tissues across substantial dimensions and surveying a collection of brain samples. Here, we demonstrate an approach, epigenomic tomography, that maps spatial epigenomes of mouse brain at the scale of centimeters. We individually profiled neuronal and glial fractions of mouse neocortex slices with 0.5 mm thickness. Tri-methylation of histone 3 at lysine 27 (H3K27me3) or acetylation of histone 3 at lysine 27 (H3K27ac) features across these slices were grouped into clusters based on their spatial variation patterns to form epigenomic brain maps. As a proof of principle, our approach reveals striking dynamics in the frontal cortex due to kainic-acid-induced seizure, linked with transmembrane ion transporters, exocytosis of synaptic vesicles, and secretion of neurotransmitters. Epigenomic tomography provides a powerful and cost-effective tool for characterizing brain disorders based on the spatial epigenome.
Passive Islanding Detection of Inverter-Based Resources in a Noisy Environment
Amini, Hossein; Mehrizi-Sani, Ali; Noroozian, Reza (MDPI, 2024-09-03)
Islanding occurs when a load is energized solely by local generators and can result in frequency and voltage instability, changes in current, and poor power quality. Poor power quality can interrupt industrial operations, damage sensitive electrical equipment, and induce outages upon the resynchronization of the island with the grid. This study proposes an islanding detection method employing a Duffing oscillator to analyze voltage fluctuations at the point of common coupling (PCC) under a high-noise environment. Unlike existing methods, which overlook the noise effect, this paper mitigates noise impact on islanding detection. Power system noise in PCC measurements arises from switching transients, harmonics, grounding issues, voltage sags and swells, electromagnetic interference, and power quality issues that affect islanding detection. Transient events like lightning-induced traveling waves to the PCC can also introduce noise levels exceeding the voltage amplitude by more than seven times, thus disturbing conventional detection techniques. The noise interferes with measurements and increases the nondetection zone (NDZ), causing failed or delayed islanding detection. The Duffing oscillator nonlinear dynamics enable detection capabilities at a high noise level. The proposed method is designed to detect the PCC voltage fluctuations based on the IEEE standard 1547 through the Duffing oscillator. For the voltages beyond the threshold, the Duffing oscillator phase trajectory changes from periodic to chaotic mode and sends an islanded operation command to the inverter. The proposed islanding detection method distinguishes switching transients and faults from an islanded operation. Experimental validation of the method is conducted using a 3.6 kW PV setup.
Multi-Hop User Equipment (UE) to UE Relays for MANET/Mesh Leveraging 5G NR Sidelink
Shyy, DJ; Luu, Cuong; Xu, John D.; Liu, Lingjia; Erpek, Tugba; Gabay, David; Bate, David (ACM, 2023-12-06)
This paper provides use cases to adapt 5G sidelink technology to enable multi-hop User Equipment (UE)-to-UE (U2U) and UE-to- Network relaying in 3GPP standards. Such a capability could enable groups of users to communicate with each other when operating at the periphery or outside a network’s coverage area, with commercial and public safety benefits. This paper compares routing protocols to enable sidelink with U2U relay to support a Mobile Ad hoc Network (MANET). A gap analysis of current 3rd Generation Partnership Project (3GPP) Release 18 (R-18) specifications is performed to determine the missing procedures to enable multi-hop U2U relaying, along with a proposed candidate protocol to fill the gap. The candidate protocol can be submitted as a contribution to 3GPP TSG Service and System Aspects (SA) Working Group 2 (WG2) as proposed changes to the 5G architecture in 3GPP Release 19 (R-19).
Spiking Neural Encoding Schemes and STDP Training Algorithms for Edge Computing
Zheng, Honghao; Yi, Yang (ACM, 2023-12-06)
To enhance real-time data processing, edge computing is utilized in a wider and wider range of applications. For the areas that require large bandwidth and low latency, edge computing even becomes a must. For instance, in the communication area, spectrum sharing within multiple users requires high accuracy of spectrum using prediction as well as low latency. For such tasks, neuromorphic computing, especially spiking neural networks (SNNs), can be a potential method because of its power and silicon area efficiency. In this paper, we have discussed various kinds of spiking neural encoding schemes and their integrated circuit (IC) implementations. We have also summarized the pair-based STDP and the triplet-based STDP learning rule, their mathematical models, and the triplet-based reconfigurable circuit implementation. The Pytorch simulation of different encoding schemes working with two STDP rules for the MNIST and a dynamic spectrum sensing dataset is also presented. It shows that multiplexing ISI-phase encoder can achieve at most 8.9% higher accuracy than other encoders, and TSTDP provides 2.7% higher accuracy than PSTDP for the MNIST dataset. What’s more, for the task of spectrum sensing for edge computing, the multiplexing encoding is also 4.3% more accurate, and TSTDP is 0.3% more accurate for the spectrum utilization prediction.
T-DOpE probes reveal sensitivity of hippocampal oscillations to cannabinoids in behaving mice
Kim, Jongwoon; Huang, Hengji; Gilbert, Earl T.; Kaiser C., Arndt; English, Daniel Fine; Jia, Xiaoting (Nature Research, 2024-02-24)
Understanding the neural basis of behavior requires monitoring and manipulating combinations of physiological elements and their interactions in behaving animals. We developed a thermal tapering process enabling fabrication of low-cost, flexible probes combining ultrafine features: dense electrodes, optical waveguides, and microfluidic channels. Furthermore, we developed a semi-automated backend connection allowing scalable assembly. We demonstrate T-DOpE (Tapered Drug delivery, Optical stimulation, and Electrophysiology) probes achieve in single neuron-scale devices (1) highfidelity electrophysiological recording (2) focal drug delivery and (3) optical stimulation. The device tip can beminiaturized (as small as 50 μm) tominimize tissue damage while the ~20 times larger backend allows for industrial-scale connectorization. T-DOpE probes implanted in mouse hippocampus revealed canonical neuronal activity at the level of local field potentials (LFP) and neural spiking. Taking advantage of the triple-functionality of these probes, we monitored LFP while manipulating cannabinoid receptors (CB1R; microfluidic agonist delivery) and CA1 neuronal activity (optogenetics). Focal infusion of CB1R agonist downregulated theta and sharp wave-ripple oscillations (SPWRs). Furthermore, we found that CB1R activation reduces sharp wave-ripples by impairing the innate SPW-R-generating ability of the CA1 circuit.
Implementation and Testing of GBDI Memory Compression Algorithm
Panja, Promit; Chiu, TingHung (2023-05-15)
This project aims to implement and test a lossless memory compression technique called GBDI (Global Base-Delta-Immediate) using C. GBDI compresses data by only storing the difference (deltas) between the global base value and the actual values in the memory block and is an extension of the BDI memory compression algorithm. GBDI enables high compression ratios and low decompression latencies, which can improve memory bandwidth and performance. The project involves implementing the GBDI compressor and decompressor, evaluating their performance on C++ and Java benchmarks and comparing them to the results the authors have shown.

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