Scholarly Works, Electrical and Computer Engineering
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- Implementation of Numerical Model for Prediction of Temperature Distribution for Metallic-Coated Firefighter Protective ClothingNaeem, Jawad; Mazari, Adnan; Kus, Zdenek; Havelka, Antonin; Abdelkader, Mohamed (MDPI, 2024-05-21)The aim of this study is to predict the distribution of temperature at various positions on silver-coated firefighter protective clothing when subjected to external radiant heat flux. This will be helpful in the determination of thermal protective performance. Firefighter clothing consists of three layers, i.e., the outer shell, moisture barrier and thermal liner. The outer shell is the exposed surface, which was coated with silver particles through a physical vapor deposition process called magnetron sputtering. Afterwards, these uncoated and silver-coated samples were exposed to radiant heat transmission equipment at 10 kW/m2 as per the ISO 6942 standard. Silver-coated samples displayed better thermal protective performance as the rate of temperature rise in silver-coated samples slowed. Later, a numerical approach was employed, contemplating the impact of metallic coating on the exterior shell. The finite difference method was utilized for solving partial differential equations and the implicit method was employed to discretize the partial differential equations. The numerical model displayed a good prediction of the distribution of temperature at different nodes with respect to time. The comparison of time vs. temperature graphs at different nodes for uncoated and silver-coated samples acquired from numerical solutions showed similar patterns, as witnessed in the experimental results.
- An Analysis of Radio Frequency Transfer Learning BehaviorWong, Lauren J.; Muller, Braeden; McPherson, Sean; Michaels, Alan J. (MDPI, 2024-06-03)Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML). This work systematically evaluates how the training domain and task, characterized by the transmitter (Tx)/receiver (Rx) hardware and channel environment, impact radio frequency (RF) TL performance for example automatic modulation classification (AMC) and specific emitter identification (SEI) use-cases. Through exhaustive experimentation using carefully curated synthetic and captured datasets with varying signal types, channel types, signal to noise ratios (SNRs), carrier/center frequencys (CFs), frequency offsets (FOs), and Tx and Rx devices, actionable and generalized conclusions are drawn regarding how best to use RF TL techniques for domain adaptation and sequential learning. Consistent with trends identified in other modalities, our results show that RF TL performance is highly dependent on the similarity between the source and target domains/tasks, but also on the relative difficulty of the source and target domains/tasks. Results also discuss the impacts of channel environment and hardware variations on RF TL performance and compare RF TL performance using head re-training and model fine-tuning methods.
- Granular retrosplenial cortex layer 2/3 generates high-frequency oscillations dynamically coupled with hippocampal rhythms across brain statesArndt, Kaiser C.; Gilbert, Earl T.; Klaver, Lianne M.F.; Kim, Jongwoon; Buhler, Chelsea M.; Basso, Julia C.; McKenzie, Sam; English, Daniel Fine (CellPress, 2024-03-26)The granular retrosplenial cortex (gRSC) exhibits high-frequency oscillations (HFOs; ~150 Hz), which can be driven by a hippocampus-subiculum pathway. How the cellular-synaptic and laminar organization of gRSC facilitates HFOs is unknown. Here, we probe gRSC HFO generation and coupling with hippocampal rhythms using focal optogenetics and silicon-probe recordings in behaving mice. ChR2-mediated excitation of CaMKII-expressing cells in L2/3 or L5 induces HFOs, but spontaneous HFOs are found only in L2/3, where HFO power is highest. HFOs couple to CA1 sharp wave-ripples (SPW-Rs) during rest and the descending phase of theta. gRSC HFO current sources and sinks are the same for events during both SPW-Rs and theta oscillations. Independent component analysis shows that high gamma (50–100 Hz) in CA1 stratum lacunosum moleculare is comodulated with HFO power. HFOs may thus facilitate interregional communication of a multisynaptic loop between the gRSC, hippocampus, and medial entorhinal cortex during distinct brain and behavioral states.
- Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to ExerciseSmith, Alana J.; Tasnim, Noor; Psaras, Zach; Gyamfi, Daphne; Makani, Krishna; Suzuki, Wendy A.; Basso, Julia C. (MyJove Corporation, 2024-01-26)Spatial navigation (SN) is the ability to locomote through the environment, which requires an understanding of where one is located in time and space. This capacity is known to rely on the sequential firing of place cells within the hippocampus. SN is an important behavior to investigate as this process deteriorates with age, especially in neurodegenerative disorders. However, the investigation of SN is limited by the lack of sophisticated behavioral techniques to assess this hippocampal-dependent task. Therefore, the goal of this protocol was to develop a novel, real-world approach to studying SN in humans. Specifically, an active virtual SN task was developed using a cross-platform game engine. During the encoding phase, participants navigated their way through a virtual city to locate landmarks. During the remembering phase, participants remembered where these reward locations were and delivered items to these locations. Time to find each location was captured and episodic memory was assessed by a free recall phase, including aspects of place, order, item, and association. Movement behavior (x, y, and z coordinates) was assessed through an asset available in the game engine. Importantly, results from this task demonstrate that it accurately captures both spatial learning and memory abilities as well as episodic memory. Further, findings indicate that this task is sensitive to exercise, which improves hippocampal functioning. Overall, the findings suggest a novel way to track human hippocampal functioning over the course of time, with this behavior being sensitive to physical activity training paradigms.
- Wrist-bound Guanxi, Jiazu, and Kuolie: Unpacking Chinese Adolescent Smartwatch-Mediated SocializationLiu, Lanjing; Zhang, Chao; Lu, Zhicong (ACM, 2024-05-11)Adolescent peer relationships, essential for their development, are increasingly mediated by digital technologies. As this trend continues, wearable devices, especially smartwatches tailored for adolescents, is reshaping their socialization. In China, smartwatches like XTC have gained wide popularity, introducing unique features such as “Bump-to-Connect” and exclusive social platforms. Nonetheless, how these devices infuence adolescents’ peer experience remains unknown. Addressing this, we interviewed 18 Chinese adolescents (age: 11—16), discovering a smartwatch-mediated social ecosystem. Our fndings highlight the ice-breaking role of smartwatches in friendship initiation and their use for secret messaging with local peers. Within the online smartwatch community, peer status is determined by likes and visibility, leading to diverse pursuit activities (i.e., chu guanxi, jiazu, kuolie) and negative social dynamics. We discuss the core afordances of smartwatches and Chinese cultural factors that infuence adolescent social behavior, and ofer implications for designing future wearables that responsibly and safely support adolescent socialization.
- GPU-based Private Information Retrieval for On-Device Machine Learning InferenceLam, Maximilian; Johnson, Jeff; Xiong, Wenjie; Maeng, Kiwan; Gupta, Udit; Li, Yang; Lai, Liangzhen; Leontiadis, Ilias; Rhu, Minsoo; Lee, Hsien-Hsin S.; Reddi, Vijay Janapa; Wei, Gu-Yeon; Brooks, David; Suh, Edward (ACM, 2024-04-27)On-device machine learning (ML) inference can enable the use of private user data on user devices without revealing them to remote servers. However, a pure on-device solution to private ML inference is impractical for many applications that rely on embedding tables that are too large to be stored on-device. In particular, recommendation models typically use multiple embedding tables each on the order of 1-10 GBs of data, making them impractical to store on-device. To overcome this barrier, we propose the use of private information retrieval (PIR) to efficiently and privately retrieve embeddings from servers without sharing any private information. As off-the-shelf PIR algorithms are usually too computationally intensive to directly use for latency-sensitive inference tasks, we 1) propose novel GPU-based acceleration of PIR, and 2) co-design PIR with the downstream ML application to obtain further speedup. Our GPU acceleration strategy improves system throughput by more than 20× over an optimized CPU PIR implementation, and our PIR-ML co-design provides an over 5× additional throughput improvement at fixed model quality. Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to 100, 000 queries per second—a > 100× throughput improvement over a CPU-based baseline—while maintaining model accuracy.
- Enforcing C/C++ Type and Scope at Runtime for Control-Flow and Data-Flow IntegrityIsmail, 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.
- Save the Bruised Striver: A Reliable Live Patching Framework for Protecting Real-World PLCsZhou, Ming; Wang, Haining; Li, Ke; Zhu, Hongsong; Sun, Limin (ACM, 2024-04-22)Industrial Control Systems (ICS), particularly programmable logic controllers (PLCs) responsible for managing underlying physical infrastructures, often operate for extended periods without interruption. Thus, it is challenging to patch security vulnerabilities of ICS in a timely manner after disclosure because it often necessitates waiting for a rare downtime window. While live patching has been introduced to avoid downtime and maintenance costs, conventional live patching methods are not viable for closed-source PLCs. Without the source code, it is difficult to understand the system behaviors and determine binary patch equivalence. To address these challenges, we present a Reliable Live Patching framework called RLPatch for applying live patches to third-party binary without source code.We design RLPatch to capture real-time conditions and dynamic behaviors of PLCs, which enables DevOps engineers to identify major non-recoverable fault (MNRF) vulnerabilities and generate hot patches. The core of RLPatch is an update agent that inserts breakpoints over the original MNRF code and then directs execution to the patches. To ensure system reliability, we use the unique constraints of PLCs to integrate the update processes with the scan cycle. We leverage RLPatch to patch 20 real vulnerabilities in three widely used Rockwell PLCs. We evaluate RLPatch in a real-world gas pipeline, demonstrating its reliability and effectiveness in practice.
- Seneca: Taint-Based Call Graph Construction for Java Object DeserializationSantos, Joanna C. S.; Mirakhorli, Mehdi; Shokri, Ali (ACM, 2024-04-29)Object serialization and deserialization are widely used for storing and preserving objects in files, memory, or database as well as for transporting them across machines, enabling remote interaction among processes and many more. This mechanism relies on reflection, a dynamic language that introduces serious challenges for static analyses. Current state-of-the-art call graph construction algorithms do not fully support object serialization/deserialization, i.e., they are unable to uncover the callback methods that are invoked when objects are serialized and deserialized. Since call graphs are a core data structure for multiple types of analysis (e.g., vulnerability detection), an appropriate analysis cannot be performed since the call graph does not capture hidden (vulnerable) paths that occur via callback methods. In this paper, we present Seneca, an approach for handling serialization with improved soundness in the context of call graph construction. Our approach relies on taint analysis and API modeling to construct sound call graphs. We evaluated our approach with respect to soundness, precision, performance, and usefulness in detecting untrusted object deserialization vulnerabilities. Our results show that Seneca can create sound call graphs with respect to serialization features. The resulting call graphs do not incur significant runtime overhead and were shown to be useful for performing identification of vulnerable paths caused by untrusted object deserialization.
- Magnetic Field Sensing via Acoustic Sensing Fiber with Metglas® 2605SC Cladding WiresDejneka, Zach; Homa, Daniel; Buontempo, Joshua; Crawford, Gideon; Martin, Eileen; Theis, Logan; Wang, Anbo; Pickrell, Gary R. (MDPI, 2024-04-10)Magnetic field sensing has the potential to become necessary as a critical tool for long-term subsurface geophysical monitoring. The success of distributed fiber optic sensing for geophysical characterization provides a template for the development of next generation downhole magnetic sensors. In this study, Sentek Instrument’s picoDAS is coupled with a multi-material single mode optical fiber with Metglas® 2605SC cladding wire inclusions for magnetic field detection. The response of acoustic sensing fibers with one and two Metglas® 2605SC cladding wires was evaluated upon exposure to lateral AC magnetic fields. An improved response was demonstrated for a sensing fiber with in-cladding wire following thermal magnetic annealing (~400 °C) under a constant static transverse magnetic field (~200 μT). A minimal detectable magnetic field of ~500 nT was confirmed for a sensing fiber with two 10 μm cladding wires. The successful demonstration of a magnetic field sensing fiber with Metglas® cladding wires fabricated via traditional draw processes sets the stage for distributed measurements and joint inversion as a compliment to distributed fiber optic acoustic sensors.
- Securing Your Airspace: Detection of Drones Trespassing Protected AreasFamili, Alireza; Stavrou, Angelos; Wang, Haining; Park, Jung-Min (Jerry); Gerdes, Ryan (MDPI, 2024-03-22)Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications. While the popularity of drones has grown lately, the associated intentional and unintentional security threats require adequate consideration. Thus, there is an urgent need for real-time accurate detection and classification of drones. This article provides an overview of drone detection approaches, highlighting their benefits and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost under different operating conditions. We conclude that multi-sensor detection systems offer more compelling results, but further research is required.
- A Logical Circuit Optimization in Balancing Delay and Energy ConsumptionShan, Qihang (ACM, 2023-11-03)The fast-developing chip manufacturing technique and scaling of transistors allow us to fit more transistors on a small chip. The scaling down process, however, is facing a challenge. The smaller transistors are, the more influential quantum channeling and silicon atom size limit become. To improve efficiency, the solution of scaling down is no longer an option. Therefore, to further improve the efficiency of a chip without scaling down transistors, this paper presents a combinational circuit and focuses on an optimization approach where energy consumption is reduced in exchange for increasing delay. By adjusting the size of transistors, energy is saved while maintaining delay to an acceptable range. This approach manages to reduce energy consumption by about 56% while increasing delay by 50%. This paper represents one of many possible approaches that researchers had and has been working on and this tradeoff can benefit some circuit designs depending on the circuit’s purpose and hope to bring some insights on further optimization.
- Application of Distributed Ledger Technology in Distribution NetworksZhou, Yue; Manea, Andrei Nicolas; Hua, Weiqi; Wu, Jianzhong; Zhou, Wei; Yu, James; Rahman, Saifur (IEEE, 2022-06-24)In the transition to a society with net-zero carbon emissions, high penetration of distributed renewable power generation and large-scale electrification of transportation and heat are driving the conventional distribution network operators (DNOs) to evolve into distribution system operators (DSOs) that manage distribution networks in a more active and flexible way. As a radical decentralized data management technology, distributed ledger technology (DLT) has the potential to support a trustworthy digital infrastructure facilitating the DNO-DSO transition. Based on a comprehensive review of worldwide research and practice, as well as the engagement of relevant industrial experts, the application of DLT in distribution networks is identified and analyzed in this article. The DLT features and DSO needs are first summarized, and the mapping relationship between them is identified. Detailed DSO functions are identified and classified into five categories (i.e., 'planning,' 'operation,' 'market,' 'asset,' and 'connection') with the potential of applying DLT to various DSO functions assessed. Finally, the development of seven key DSO functions with high DLT potential is analyzed and discussed from the technical, legal, and social perspectives, including peer-to-peer energy trading, flexibility market facilitation, electric vehicle charging, network pricing, distributed generation register, data access, and investment planning.
- Optimal Scheduling of Integrated Energy Systems With Multiple CCHPs for High Efficiency and Low EmissionsXie, Haimin; Liu, Hui; Wan, Can; Goh, Hui Hwang; Rahman, Saifur (IEEE, 2023-08-14)In order to reach carbon neutrality, there is growing interest in reducing greenhouse gas (GHG) and improving energy efficiency. One way to address this issue is the optimal scheduling of the integrated energy system (IES) with multiple combined cooling heating and power (CCHP) systems as proposed in this article. We model IES as a device with multiple input/output ports by the energy hub (EH) framework and propose a multiobjective optimal model to improve energy efficiency and reduce GHG emissions. The proposed model is constructed as a mixed-integer nonlinear programming (MINLP) due to considering nonlinear couplings of multiple energy flows and the unit commitment of multiple CCHP systems. To improve the computational efficiency, the proposed MINLP model is transformed into a nonlinear programming (NLP) model by a fast unit commitment technique based on the approximation of the aggregated online capacity. Finally, simulation results show the effectiveness of the proposed approach in reducing GHG emissions and improving energy efficiency as well as computational efficiency.
- A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV SystemRoy, Rajib Baran; Rokonuzzaman, Md; Amin, Nowshad; Mishu, Mahmuda Khatun; Alahakoon, Sanath; Rahman, Saifur; Mithulananthan, Nadarajah; Rahman, Kazi Sajedur; Shakeri, Mohammad; Pasupuleti, Jagadeesh (IEEE, 2021-07-13)In this paper, artificial neural network (ANN) based Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in maximum power point tracking (MPPT) energy harvesting in solar photovoltaic (PV) system to forge a comparative performance analysis of the three different algorithms. A comparative analysis among the algorithms in terms of the performance of handling the trained dataset is presented. The MATLAB/Simulink environment is used to design the maximum power point tracking energy harvesting system and the artificial neural network toolbox is utilized to analyze the developed model. The proposed model is trained with 1000 dataset of solar irradiance, temperature, and voltages. Seventy percent data is used for training, while 15% data is employed for validation, and 15% data is utilized for testing. The trained datasets error histogram represents zero error in the training, validation, and test phase of data matching. The best validation performance is attained at 1000 epochs with nearly zero mean squared error where the trained data set is converged to the best training results. According to the results, the regression and gradient are 1, 1, 0.99 and 0.000078, 0.0000015739 and 0.26139 for Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient algorithms, respectively. The momentum parameters are 0.0000001 and 50000 for Levenberg-Marquardt and Bayesian Regularization algorithms, respectively, while the Scaled Conjugate Gradient algorithm does not have any momentum parameter. The Scaled Conjugate Gradient algorithm exhibit better performance compared to Levenberg-Marquardt and Bayesian Regularization algorithms. However, considering the dataset training, the correlation between input-output and error, the Levenberg-Marquardt algorithm performs better.
- An Assessment of Multistage Reward Function Design for Deep Reinforcement Learning-Based Microgrid Energy ManagementGoh, Hui Hwang; Huang, Yifeng; Lim, Chee Shen; Zhang, Dongdong; Liu, Hui; Dai, Wei; Kurniawan, Tonni Agustiono; Rahman, Saifur (IEEE, 2022-06-01)Reinforcement learning based energy management strategy has been an active research subject in the past few years. Different from the baseline reward function (BRF), the work proposes and investigates a multi-stage reward mechanism (MSRM) that scores the agent's step and final performance during training and returns it to the agent in real time as a reward. MSRM will also improve the agent's training through expert intervention which aims to prevent the agent from being trapped in sub-optimal strategies. The energy management performance considered by MSRM-based algorithm includes the energy balance, economic cost, and reliability. The reward function is assessed in conjunction with two deep reinforcement learning algorithms: double deep Q-learning network (DDQN) and policy gradient (PG). Upon benchmarking with BRF, the numerical simulation shows that MSRM tends to improve the convergence characteristic, reduce the explained variance, and reduce the tendency of the agent being trapped in suboptimal strategies. In addition, the methods have been assessed with MPC-based energy management strategies in terms of relative cost, self-balancing rate, and computational time. The assessment concludes that, in the given context, PG-MSRM has the best overall performance.
- Celebrate the 10th Anniversary of IEEE Electrification Magazine and Embrace a New EraRahman, Saifur (IEEE, 2023-09)
- OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of DronesFamili, Alireza; Stavrou, Angelos; Wang, Haining; Park, Jung-Min (Jerry) (MDPI, 2024-03-14)For many applications, drones are required to operate entirely or partially autonomously. In order to fly completely or partially on their own, drones need to access location services for navigation commands. While using the Global Positioning System (GPS) is an obvious choice, GPS is not always available, can be spoofed or jammed, and is highly error-prone for indoor and underground environments. The ranging method using beacons is one of the most popular methods for localization, especially for indoor environments. In general, the localization error in this class is due to two factors: the ranging error, and the error induced by the relative geometry between the beacons and the target object to be localized. This paper proposes OPTILOD (Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones), an optimization algorithm for the optimal placement of beacons deployed in three-dimensional indoor environments. OPTILOD leverages advances in evolutionary algorithms to compute the minimum number of beacons and their optimal placement, thereby minimizing the localization error. These problems belong to the Mixed Integer Programming (MIP) class and are both considered NP-hard. Despite this, OPTILOD can provide multiple optimal beacon configurations that minimize the localization error and the number of deployed beacons concurrently and efficiently.
- Optimizing dynamic electric ferry loads with intelligent power managementRoy, Rajib Baran; Alahakoon, Sanath; Arachchillag, Shantha Jayasinghe; Rahman, Saifur (Elsevier, 2023-12)In recent years, there has been an increasing shift towards using environmentally friendly renewable resources in marine vessels, replacing traditional diesel generators. However, one of the main challenges faced in renewable energy-driven marine vessels is dynamic load management. The feasibility of a renewable-powered electric marine vessel largely depends on the optimal utilization of renewable resources, and storage is an essential component of the marine electric vessel. This paper proposes a two-stage power management system (PMS) for an electric ferry powered by the fuel cell and battery energy storage systems (BESS). The primary objective of the proposed PMS is to ensure a balance between the generated power and the ferry load by minimizing the consumption of hydrogen (H2) fuel. The first stage of the PMS employs particle swarm optimization (PSO), bacterial foraging optimization (BFO), and a hybrid PSO-BFO algorithm to optimize the fuel cell and battery capacity. This is done so that the generated power can follow the load demand. The second stage of the PMS utilizes the Mamdani rule-based fuzzy logic system (FLS) to match the load demand with the generated power. The hybrid PSO-BFO algorithm optimizes the fuzzy control parameters to meet the dynamic load by ensuring optimal H2 fuel consumption and battery state of charge (SOC). To obtain optimal values, the load profile of a conventional ferry is used for the proposed PMS. Based on the optimization results, the optimal capacities are found to be 318 kWh and 317.64 kWh for the fuel cell and BESS, respectively, which are obtained using the hybrid PSO-BFO algorithm. The optimal value of H2 fuel consumption during cruising is found to be 18 kg. A simulated model-based approach validates the operation of the proposed PMS. The proposed PMS ensures optimal H2 fuel consumption and battery SOC while meeting the dynamic load demands of the ferry. The results obtained demonstrate the effectiveness of the proposed PMS in optimizing the renewable energy-driven marine vessel power system.