Scholarly Works, Electrical and Computer Engineering

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Research articles, presentations, and other scholarship

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Quantifying water effluent violations and enforcement impacts using causal AI
Wang, Yingjie; Sobien, Dan; Kulkarni, Ajay; Batarseh, Feras A. (Wiley, 2024-06-01)
In the landscape of environmental governance, controlling water pollution through the regulation of point sources is vital as it preserves ecosystems, protects human health, ensures legal compliance, and fulfills global environmental responsibilities. Under the Clean Water Act, the integrated compliance information system monitors the compliance and enforcement status of facilities regulated by the National Pollutant Discharge Elimination System (NPDES) permit program. This study assesses temporal and geographic trends for effluent violations within the United States and introduces a novel metric for quantifying violation trends at the facility level. Furthermore, we utilize a linear parametric approach for Conditional Average Treatment Effect (CATE) causal analysis to quantify the heterogeneous effects of EPA and state enforcement actions on effluent violation trends at facilities with NPDES permits. Our research reveals insights into national pollutant discharge trends, regional clustering of all pollutant violation types in Ohio (G(i)* Z-score of 2.15), and priority pollutants in West Virginia (G(i)* Z-score of 3.07). The trend metric identifies regulated facilities that struggle with severe and recurring violations. The causal model highlights variations in state compliance and enforcement effectiveness, underscoring the successful moderation of violation trends by states such as Montana and Maryland, among others.
Exploring novel methods: Acid treatment, metal nanoparticle doping, and graphene insertion for enhanced electrical conductivity of nm thin PEDOT: PSS films
Chakraborty, Amrita; DiFilippo, Aaron; Deivasigamani, Sheena; Hong, Calvin; Madwesh, Anshu; Orlowski, Marius (Elsevier, 2024-10-01)
This study builds upon our previous research aimed at enhancing the electrical conductivity of Poly(3,4-ethylenedioxythiophene) Polystyrene Sulfonate (PEDOT:PSS). We investigate a range of techniques, including acid treatments, doping with metal nanoparticles (Cu and Ag), deposition of multiple PEDOT:PSS layers, and incorporation of mono/multiatomic layer graphene. Our investigations reveal that optimizing the deposition of PEDOT:PSS multilayers and treating them with nitric acid yields superior results compared to alternative methods employing metal nanoparticles and graphene. This optimized process not only enhances the electrical conductivity of PEDOT:PSS but also offers advantages in terms of reduced errors, increased stability, and cost-effectiveness when compared to the use of graphene layers and metal nanoparticles. Optimization parameters such as spinning speed, etchant concentration, and etching time are crucial factors in achieving these outcomes. Compared to single-layer PEDOT:PSS films of the same thickness, the optimized nine-layer PEDOT:PSS treated with nitric acid demonstrates a significant enhancement of conductivity from 0.18 S/cm to 15,699 S/cm. Furthermore, we address film aging to mitigate reliability issues induced by ambient conditions.
reInstruct: Toward OS-aware CPU microcode reprogramming
Wang, Yubo; Nikolaev, Ruslan; Ravindran, Binoy (ACM, 2025-10-13)
Historically, the microcode layer has been a proprietary technology which is tightly controlled by the CPU vendors. The microcode layer enables a great flexibility for translating ISAvisible instructions into internal hardware micro-operations. In x86-64, many system-level instructions are microcoded, which enables a great untapped opportunity for OS developers, who want to experiment with future ISA extensions. Recent research work has identified hidden CPU instructions, which are enabled via a firmware exploit, and also partially reverse-engineered and decrypted Intel Goldmont microcode. We go a step further and design an experimental framework for Linux, which allows to transparently modify existing microcoded instructions directly from an OS at runtime. We show how microcode alterations can be used to defeat normal root-privilege isolation in Linux almost without any trace. We also show our new approach which relies on ISA modification via microcode patching to improve performance of commonly-used lightweight Linux system calls. Our approach, effectively, adjusts the CPU ISA to better serve a specific OS kernel and applications, an idea which has been out of reach for commodity hardware previously.
Observation of Quiet-Time Mid-Latitude Joule Heating and Comparisons With the TIEGCM Simulation
Day, E. K.; Grocott, A.; Walach, M. -T.; Wild, J. A.; Lu, G.; Ruohoniemi, J. Michael; Coster, A. J. (American Geophysical Union, 2024-09-01)
Joule heating is a major energy sink in the solar wind-magnetosphere-ionosphere system and modeling it is key to understanding the impact of space weather on the neutral atmosphere. Ion drifts and neutral wind velocities are key parameters when modeling Joule heating, however there is limited validation of the modeled ion and neutral velocities at mid-latitudes. We use the Blackstone Super Dual Auroral Radar Network radar and the Michigan North American Thermosphere Ionosphere Observing Network Fabry-Perot interferometer to obtain the local nightside ion and neutral velocities at similar to 40 degrees geographic latitude during the nighttime of 16 July 2014. Despite being a geomagnetically quiet period, we observe significant sub-auroral ion flows in excess of 200 ms(-1). We calculate an enhancement to the local Joule heating rate due to these ion flows and find that the neutrals impart a significant increase or decrease to the total Joule heating rate of >75% depending on their direction. We compare our observations to outputs from the Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM). At such a low geomagnetic activity however, TIEGCM was not able to model significant sub-auroral ion flows and any resulting Joule heating enhancements equivalent to our observations. We found that the neutral winds were the primary contributor to the Joule heating rates modeled by TIEGCM rather than the ions as suggested by our observations.
Comprehensive Analysis of Maximum Power Association Policy for Cellular Networks Using Distance and Angular Coordinates
Armeniakos, Harris K.; Kanatas, Athanasios G.; Dhillon, Harpreet S. (IEEE, 2024-09-01)
A novel stochastic geometry framework is proposed in this paper to study the downlink coverage performance in a millimeter wave (mmWave) cellular network by jointly considering the polar coordinates of the Base Stations (BSs) with respect to the typical user located at the origin. Specifically, both the Euclidean and the angular distances of the BSs in a maximum power-based association policy for the user equipment (UE) are considered to account for realistic beam management considerations, which have been largely ignored in the literature, especially in the cell association phase. For completeness, two other association schemes are considered and exact-form expressions for the coverage probability are derived. Subsequently, the key role of angular distances is highlighted by defining the dominant interferer using angular distance-based criteria instead of Euclidean distance-based, and conducting a dominant interferer-based coverage probability analysis. Among others, the numerical results reveal that considering angular distance-based criteria for determining both the serving and the dominant interfering BS, can approximate the coverage performance more accurately as compared to utilizing Euclidean distance-based criteria. To the best of the authors' knowledge, this is the first work that rigorously explores the role of angular distances in the association policy and analysis of cellular networks.
DRL-Assisted Dynamic Subconnected Hybrid Precoding for Multi-Layer THz mMIMO-NOMA System
Shahjalal, Md.; Rahman, Md. Habibur; Alam, Md Morshed; Chowdhury, Mostafa Zaman; Jang, Yeong Min (IEEE, 2024-09-01)
Massive multiple-input multiple-output (mMIMO) techniques can be combined with the non-orthogonal multiple access (NOMA) scheme in terahertz (THz) communication to achieve multiplexing gains and satisfy the ultra-high capacity and massive connectivity requirements. However, the development of a near-optimal solution for energy and spectral efficiency problems in a dynamic wireless cellular environment remains challenging. In this paper, a cooperative THz mMIMO-NOMA enabled base station is established to optimize the power consumption and maximize the spectral efficiency. A multi-layer mMIMO antenna architecture is used to perform dynamic sub-connected hybrid precoding in each layer. The fuzzy c-means clustering algorithm is used to group densely located users into clusters to efficiently use the power coefficients. To optimize the power distribution constraints and coordination of the hybrid precoding structure, a multi-agent deep reinforcement learning algorithm is developed, which operates in a distributive manner. Each base station layer involves an agent that trains a deep Q-network, and optimal actions are executed by sharing exchangeable network parameters among layers. The simulation results indicate that the proposed scheme is able to learn the trade-off between maximization of the energy efficiency and overall system capacity.
Dynamic State Estimation for Inverter-Based Resources: A Control-Physics Dual Estimation Framework
Huang, Heqing; Lin, Yuzhang; Lu, Xiaonan; Zhao, Yue; Kumar, Avinash (IEEE, 2024-09-01)
As Inverter-Based Resources (IBRs) gradually replace conventional synchronous generators (SGs), Dynamic State Estimation (DSE) techniques must be extended for the monitoring of IBRs. The key difference between IBRs and SGs is that the dynamics of IBRs comprise a heavy mix of physical processes and digital controller computations. This paper develops a generic framework of Control-Physics Dynamic State Estimation (CPDSE) for IBRs. First, a control-physics state-space representation of IBRs is presented. Noting the symmetry of the control and physical state spaces, it is proposed to use two dual estimators to track the states of the physical inverter subsystem and the digital controller subsystem, respectively. The CPDSE framework has the capability of suppressing errors in both measurement signals and control signals flowing between the two subsystems and the potential to distinguish between cyber and physical events. The advantages and versatility of the proposed CPDSE framework are validated on a variety of IBR systems (solar, wind, and storage), control strategies (grid-following and grid-forming), and both transmission and distribution systems.
Cost-effective reliability level in 100% renewables-based standalone microgrids considering investment and expected energy not served costs
Sakthivelnathan, Nallainathan; Arefi, Ali; Lund, Christopher; Mehrizi-Sani, Ali; Muyeen, S. M. (Pergamon-Elsevier, 2024-12-01)
Loss of load probability (LOLP) and expected energy not served (EENS) are commonly used in electrical power systems to evaluate reliability. LOLP defined as the probability that available generation capacity will be inadequate to supply customer demand. EENS defined as the expected amount of energy not being served to consumers by the system during the period considered due to system capacity shortages or unexpected power outages. Loss of Load Frequency (LOLF) is referred to a number of loss of load (LOL) event happened in the operation life span of the SMG. Loss of Load Reduction (LOLR) is defined as the required reduction in LOLF to obtain a specific reliability level. While power systems are designed to minimize LOLP and EENS, this is constrained by the total cost: investment cost, operation and maintenance cost, and cost of customer interruption (CCI). This research considers Standalone Microgrid (SMG), also known as Autonomous Microgrid which only operates in off-grid mode and cannot be connected to wider electrical power system. When designing a 100 % renewable energy integrated SMGs, it is crucial to determine the cost-effective reliability level (CERL). The CERL occurs when the total cost is minimum. This research proposes an approach to calculate the CERL for a fully renewable SMG. An analytical formulation is proposed to represent the LOLR needed to obtain a specific reliability level as a function of the required size of reliability improvement alternatives. The CCI is evaluated using LOLF and EENS indices. Finally, the total cost of the SMG system is evaluated for each reliability level. Consequently, the total cost of the SMG system is expressed as a function of reliability levels, and the minimum value of total cost and the corresponding reliability level are evaluated. In this research, a Monte Carlo Simulation (MCS) approach is used to find hourly LOLF, considering 25 years (219,000 h) of SMG lifespan, regression analysis is used for an analytical formulation, and mixed integer linear programming (MILP) is used for the investment decision making based on a cost minimisation approach. The result demonstrates that the CERL of the SMG system evaluated in the case study is 98.71 %.
Ultrafast Optically Controlled Power Switch: A General Design and Demonstration With 3.3 kV SiC MOSFET
Yang, Xin; Shi, Guannan; Jin, Liyang; Qin, Yuan; Porter, Matthew; Jia, Xiaoting; Dong, Dong; Shao, Linbo; Zhang, Yuhao (IEEE, 2024-12-01)
Optically controlled high-voltage power devices are desirable for grid and renewable energy applications. This work proposes a hybrid device consisting of a high-voltage, high-power transistor, and two low-voltage, low-power photodiodes (PDs) to achieve the optically controlled power switching. This hybrid device is driven by complementary optical signals, which are applied to two PDs to charge and discharge the capacitances of the power device in the turn-OFF and turn-ON transients. This design can fast switch unipolar devices with an ultralow optical power, as only the driver signals are optically modulated but the device current is not photogenerated. We experimentally demonstrate this design using two InGaAs PDs to switch a 3.3 kV SiC MOSFET, the highest-voltage industrial unipolar device available. Under an optical power of 21.7 mW applied on each PD, 1500 V/3 A hard-switching is demonstrated with a rise time and fall time of 152 and 215 ns, respectively. This represents the highest switching voltage, fastest switching speed, and highest ratio between the power capacity and optical power reported in optically controlled unipolar power switches. The switching dynamics are also modeled to project the frequency scalability of this hybrid device. In addition to achieving a record performance, this general device design is also applicable to the future development of integrated optics for power electronics.
Levenberg-Marquardt algorithm-based solar PV energy integrated internet of home energy management system
Rokonuzzaman, Md.; Rahman, Saifur; Hannan, M. A.; Mishu, Mahmuda Khatun; Tan, Wen-Shan; Rahman, Kazi Sajedur; Pasupuleti, Jagadeesh; Amin, Nowshad (Elsevier, 2025-01-15)
With the emergence of smart grids, the home energy management system (HEMS) has immense prospective optimize energy usage and reduce costs in the residential sector. However, the challenges persist in effectively controlling power consumption, reducing energy expenses, enhancing resident comfort, and optimizing coordination of renewable energy sources (RESs). In this study, a Levenberg-Marquardt (LM) algorithm-based solar PV integrated internet of home energy management system (IoHEMS) is developed. The LM algorithm has been chosen as it outperforms the other two artificial intelligence (AI) algorithms: Bayesian regularization (BR) and scaled conjugate gradient (SCG). With the setup of using 70% of data for training, 15 % for validation, and 15 % for testing, the LM algorithm shows the regression of 0.999999, gradient of 7.8e(-5), performance 2.7133e(-9), and the momentum parameter of 1e(-7). When the trained data set converges to the optimal training results, the best validation performance is achieved after 1000 epochs with approximately zero mean squared error (MSE). The proposed system transforms a conventional home into a smart home by effectively managing four household appliances: Air conditioner (AC), water heater (WH), washing machine (WM), and refrigerator (ref.). The proposed model enables accurate switching functions of appliances and efficient grid-to-battery utilization, resulting in reduced peak-hour electricity tariffs. The proposed system incorporates internet of things (IoT) functionality with the HEMS, utilizing smart plug socket (SPS) and wireless sensor network (WSN) nodes. The proposed model also supports Bluetooth low energy (BLE) connectivity for offline operation. A customized android application, 'MQTT dashboard', allows consumers to monitor power usage, room temperature, humidity, moisture and home appliance status every 60 s intervals.
Total Electron Content Variations During an HSS/SIR-Driven Geomagnetic Storm at High and Mid Latitudes
Geethakumari, G. P.; Aikio, A. T.; Cai, L.; Vanhamaki, H.; Virtanen, I. I.; Coster, A.; Marchaudon, A.; Blelly, P. -l.; Maute, A.; Norberg, J.; Oyama, S.; Zhang, Y.; Kunduri, B. S. R. (American Geophysical Union, 2024-12-01)
Two interacting high-speed solar wind streams (HSSs) and associated stream interaction regions (SIR) caused a moderate geomagnetic storm during 14-20 March 2016. The spatio-temporal evolution of the total electron content (TEC) during the storm is studied by using Global Navigation Satellite System (GNSS) data. The moderate storm caused significant and long-lasting changes on TEC within the polar cap (70 degrees ${}<^>{circ}$-90 degrees ${}<^>{circ}$ MLAT), at auroral and sub-auroral latitudes (60 degrees ${}<^>{circ}$-70 degrees ${}<^>{circ}$ MLAT), and at mid-latitudes (40 degrees ${}<^>{circ}$-60 degrees ${}<^>{circ}$ MLAT). A 25%-50% depletion in TEC was observed for six days in the day, dusk and dawn sectors in the polar cap region and in the day and dusk sectors at the auroral and sub-auroral latitudes. Sub-auroral polarization streams observed by the Defense Meteorological Satellite Program satellite contributed to the sub-auroral dusk TEC decreases. At mid-latitudes, TEC depletion was observed in all local time sectors 21 hr after the storm onset. It is suggested that ion-neutral frictional heating causes the TEC depletions, which is further supported by the observed spatial correlation between TEC depletions and & sum; $sum $O/N2 decreases at mid-latitudes observed by TIMED/GUVI. The storm induced a prolonged positive phase at mid-latitudes lasting 9 hr. In the polar cap, enhancements of TEC up to 200% were caused by polar cap patches. TEC increases were the dominant feature in the night and morning sectors within the auroral oval because of particle precipitation and resulted up to regionally averaged 6 TECU (200%) increases.
Federated quantum long short-term memory (FedQLSTM)
Chehimi, Mahdi; Chen, Samuel Yen-Chi; Saad, Walid; Yoo, Shinjae (Springernature, 2024-12-01)
Quantum federated learning (QFL) can facilitate collaborative learning across multiple clients using quantum machine learning (QML) models, while preserving data privacy. Although recent advances in QFL span different tasks like classification while leveraging several data types, no prior work has focused on developing a QFL framework that utilizes temporal data to approximate functions useful to analyze the performance of distributed quantum sensing networks. In this paper, a novel QFL framework that is the first to integrate quantum long short-term memory (QLSTM) models with temporal data is proposed. The proposed federated QLSTM (FedQLSTM) framework is exploited for performing the task of function approximation. In this regard, three key use cases are presented: Bessel function approximation, sinusoidal delayed quantum feedback control function approximation, and Struve function approximation. Simulation results confirm that, for all considered use cases, the proposed FedQLSTM framework achieves a faster convergence rate under one local training epoch, minimizing the overall computations, and saving 25-33% of the number of communication rounds needed until convergence compared to an FL framework with classical LSTM models.
Thermally Drawn Shape and Stiffness Programmable Fibers for Medical Devices
Choi, Jiwoo; Zheng, Qindong; Abdelaziz, Mohamed E. M. K.; Dysli, Thomas; Bautista-Salinas, Daniel; Leber, Andreas; Jiang, Shan; Zhang, Jianan; Demircali, Ali Anil; Zhao, Jinshi; Liu, Yue; Linton, Nick W. F.; Sorin, Fabien; Jia, Xiaoting; Yeatman, Eric M.; Yang, Guang-Zhong; Temelkuran, Burak (Wiley, 2025-04)
Despite the significant advantages of Shape Memory Polymers (SMPs), material processing and production challenges have limited their applications. Recent advances in fiber manufacturing offer a novel approach to processing polymers, broadening the functions of fibers beyond optical applications. In this study, a thermal drawing technique for SMPs to fabricate Shape Memory Polymer Fibers (SMPFs) tailored for medical applications, featuring programmable stiffness and shape control is developed. Rheological and differential scanning calorimetry analyses are conducted to assess SMP's compatibility with the proposed thermal drawing process and applications, leading to the production of multilumen, multimaterial SMPFs activated at body temperature. Different properties of SMPFs are investigated in three medical devices: stiffness-adjustable catheters, softening neural interface, and shape-programmable cochlear implants. Comprehensive characterization of these devices demonstrates the potential of thermally drawn SMPs to be employed in a wide range of applications demanding programmable mechanical properties.
Hardware Validation for Semi-Coherent Transmission Security
Fletcher, Michael; McGinthy, Jason; Michaels, Alan J. (MDPI, 2025-09-05)
The rapid growth of Internet-connected devices integrating into our everyday lives has no end in sight. As more devices and sensor networks are manufactured, security tends to be a low priority. However, the security of these devices is critical, and many current research topics are looking at the composition of simpler techniques to increase overall security in these low-power commercial devices. Transmission security (TRANSEC) methods are one option for physical-layer security and are a critical area of research with the increasing reliance on the Internet of Things (IoT); most such devices use standard low-power Time-division multiple access (TDMA) or frequency-division multiple access (FDMA) protocols susceptible to reverse engineering. This paper provides a hardware validation of previously proposed techniques for the intentional injection of noise into the phase mapping process of a spread spectrum signal used within a receiver-assigned code division multiple access (RA-CDMA) framework, which decreases an eavesdropper’s ability to directly observe the true phase and reverse engineer the associated PRNG output or key and thus the spreading sequence, even at high SNRs. This technique trades a conscious reduction in signal correlation processing for enhanced obfuscation, with a slight hardware resource utilization increase of less than 2% of Adaptive Logic Modules (ALMs), solidifying this work as a low-power technique. This paper presents the candidate method, quantifies the expected performance impact, and incorporates a hardware-based validation on field-programmable gate array (FPGA) platforms using arbitrary-phase phase-shift keying (PSK)-based spread spectrum signals.
A Research Focused Approach to Customer Discovery
Makowski, William J.; Martin, Thomas L.; Schaudt, W. Andy (IEEE, 2022)
Head injuries in football and bicycling highlight the severe disconnect between impact scenarios, injury biomechanics, standards and solutions for personal protective equipment. They also demonstrate the broader need for better methods in product development. This need is further illustrated in entrepreneurship, as the failure rate for product development is anywhere between 30 to 75%. To improve performance and reduce the failure rates of start-ups, new venture teams and nationally funded programs such as I-Corps adopted product development methods such as Lean Canvas and Customer Discovery. However, there are still challenges with these methodologies. Other than 'listen,' there is limited training for developing questions and conducting interviews. As a result, shortcomings in both the personal protective equipment industry and product development in entrepreneurship demonstrate the need to understand the foundational elements of each, in a way that is grounded in research. The purpose of this case study is to address those shortcomings, identify areas for innovation and improve safety for athletes. The case study is grounded in qualitative research methods and is executed by implementing Customer Discovery. The application of the methodologies is expected to create a product development framework for entrepreneurs, implementing it with individuals that use bicycle helmets and bike industry personnel.
From Transients to Flips: Hardware-level Bit Manipulation of In-Vehicle Serial Communication
Mohammed, Abdullah Zubair; Gerdes, Ryan M. (ACM, 2025-08)
In a modern automobile, the in-vehicle communication network interconnects multiple subsystems, including those that perform safety-critical functions such as engine control, anti-lock braking, and airbag deployment, among many others. Therefore, the loss of data integrity in the network can have serious consequences for the safety of the vehicle. To that extent, CAN protocol, the most common in-vehicle communication standard, employs error-handling mechanisms such as bit-monitoring and cyclic-redundancy check to detect intentional or unintentional data manipulation. In this work, we exploit the transmission line nature of the CAN physical layer (a twisted pair cable) to induce voltage transients that result in bit manipulations. Specifically, we demonstrate bidirectional bit flip attacks, recessive to dominant (R→D) and dominant to recessive (D→R) with the aid of multiple compromised nodes (electronic control units) in the network. In addition, both the attacks, the simpler R→D, and the complex D→R are designed to be undetectable to the aforementioned error-handling mechanisms. The attacks become effective for distances ≥ 4m for D→R and ≥ 1m for R→D between the transmitter and receiver nodes. By demonstrating these bit flips, we challenge two fundamental physical layer assumptions of CAN: the impossibility of turning a dominant bit to recessive without an external current source, and having nonidentical signals on two nodes at the same time. The theory behind the attacks is presented, backed by circuit simulations, in-lab validations, and real-world demonstrations in a vehicle. These bit-level attacks, designed at the physical layer, circumvent software-based CAN defenses and lay the groundwork for a broader spectrum of potential attacks, including the manipulation of a data frame that we demonstrate.
Detecting Presence Of Malicious Hub in MIMI Protocol for Cross-Platform Messaging Interoperability
Sarvaiya, Harditya; Burger, Eric W. (IEEE, 2025-10-08)
The IETF More Instant Messaging Interoperability (MIMI) protocol enables interoperable group messaging across otherwise isolated services such as WhatsApp, Signal, and Telegram. It routes every Messaging Layer Security (MLS) ciphertext through a central hub that timestamps the message and broadcasts it to all group participants. If the hub is compromised, it can silently drop, delay, or reorder messages, undermining order integrity while leaving end-to-end encryption intact. We introduce a lightweight, Merkle-tree-based audit layer that allows clients to detect such misbehavior. Each client stores every received message together with its hub-assigned timestamp in an ordered list. Clients periodically generate a Merkle proof from this list and broadcast it by embedding the proof in an encrypted application message. Because the hub cannot predict which messages carry proofs, it cannot selectively discard them. Upon receiving a proof, other clients verify it and broadcast their own proofs. Any inconsistency is then propagated to the entire room, creating a non-repudiable record of hub misconduct. A Rust prototype built on OpenMLS was evaluated on a 100-node emulated network. With a client sampling rate of 5%, and a hub attack probability of 10%, the scheme detected 95% of message-drop or reordering attacks within the first 40 messages, consumed only 3 kB of additional memory per client, and required less than 1 ms of client-side processing per proof. The audit’s memory requirement grows linearly with room size and requires no changes to the hub protocol, providing a practical, low-overhead path to verifiable message-order integrity in large interoperable messaging systems.
Safe Autonomous UAV Target-Tracking Under External Disturbance, Through Learned Control Barrier Functions
Panja, Promit; Rayguru, Madan Mohan; Baidya, Sabur (MDPI, 2025-08-03)
Ensuring the safe operation of Unmanned Aerial Vehicles (UAVs) is crucial for both mission-critical and safety-critical tasks. In scenarios where UAVs must track airborne targets, they need to follow the target’s path while maintaining a safe distance, even in the presence of unmodeled dynamics and environmental disturbances. This paper presents a novel collision avoidance strategy for dynamic quadrotor UAVs during target-tracking missions. We propose a safety controller that combines a learning-based Control Barrier Function (CBF) with standard sliding mode feedback. Our approach employs a neural network that learns the true CBF constraint, accounting for wind disturbances, while the sliding mode controller addresses unmodeled dynamics. This unified control law ensures safe leader-following behavior and precise trajectory tracking. By leveraging a learned CBF, the controller offers improved adaptability to complex and unpredictable environments, enhancing both the safety and robustness of the system. The effectiveness of our proposed method is demonstrated through the AirSim platform using the PX4 flight controller.
Systematic Use of Random Self-Reducibility in Cryptographic Code against Physical Attacks
Erata, Ferhat; Chiu, TingHung; Etim, Anthony; Nampally, Srilalith; Raju, Tejas; Ramu, Rajashree; Piskac, Ruzica; Antonopoulos, Timos; Xiong, Wenjie; Szefer, Jakub (ACM, 2024-10-27)
This work presents a novel, black-box software-based countermeasure against physical attacks including power side-channel and fault-injection attacks. The approach uses the concept of random self-reducibility and self-correctness to add randomness and redundancy in the execution for protection. Our approach is at the operation level, is not algorithm-specific, and thus, can be applied for protecting a wide range of algorithms. The countermeasure is empirically evaluated against attacks over operations like modular exponentiation, modular multiplication, polynomial multiplication, and number theoretic transforms. An end-to-end implementation of this countermeasure is demonstrated for RSA-CRT signature algorithm and Kyber Key Generation public key cryptosystems. The countermeasure reduced the power side-channel leakage by two orders of magnitude, to an acceptably secure level in TVLA analysis. For fault injection, the countermeasure reduces the number of faults to 95.4% in average.
Uncertainty Quantification and Data Provenance for Data Pipeline Security Analysis
Dadeboe, Alberta O.; Mansourifard, Farzaneh; Sugrim, Shridatt (ACM, 2025-05-06)
Ensuring data integrity and reliability is essential for real-world applications, especially in automated decision-making and anomaly detection systems. In this study, we introduce a data pipeline augmentation tool that combines Uncertainty Quantification (UQ) techniques with Data Provenance Tracking to detect anomalies and shifts. By leveraging a task runner for pipeline orchestration, our approach ensures scalable, fault-tolerant execution while maintaining full traceability and monitoring at each processing stage. To validate our framework, we conduct two experiments using the Lawrence Berkeley National Laboratory (LBNL) Fault Detection and Diagnostics (FDD) datasets, focusing on Fan Coil Unit (FCU) operations in HVAC systems. Our experiments assess the pipeline’s ability to detect anomalies under different corruption scenarios: (1) Detecting corruption in a single pipeline stage, (2) Capturing inline data corruption. We integrate statistical tests, such as the Kolmogorov-Smirnov (KS) test, to identify distributional shifts between sequential data batches. Additionally, we apply UQ techniques to quantify uncertainty, enhancing confidence in detected anomalies. The results demonstrate that our work effectively identifies computational corruption, providing a robust and scalable solution for anomaly detection in real-world data pipelines.

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