Scholarly Works, Electrical and Computer Engineering

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3D printed graphene-based self-powered strain sensors for smart tires in autonomous vehicles
Maurya, Deepam; Khaleghian, Seyedmeysam; Sriramdas, Rammohan; Kumar, Prashant; Kishore, Ravi Anant; Kang, Min-Gyu; Kumar, Vireshwar; Song, Hyun-Cheol; Lee, Seul-Yi; Yan, Yongke; Park, Jung-Min (Jerry); Taheri, Saied; Priya, Shashank (2020-10-26)
The transition of autonomous vehicles into fleets requires an advanced control system design that relies on continuous feedback from the tires. Smart tires enable continuous monitoring of dynamic parameters by combining strain sensing with traditional tire functions. Here, we provide breakthrough in this direction by demonstrating tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis. Ink of graphene based material was designed to directly print strain sensor for measuring tire-road interactions under varying driving speeds, normal load, and tire pressure. A secure wireless data transfer hardware powered by a piezoelectric patch is implemented to demonstrate self-powered sensing and wireless communication capability. Combined, this study significantly advances the design and fabrication of cost-effective smart tires by demonstrating practical self-powered wireless strain sensing capability. Designing efficient sensors for smart tires for autonomous vehicles remains a challenge. Here, the authors present a tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis.
6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap
Imoize, Agbotiname Lucky; Adedeji, Oluwadara; Tandiya, Nistha; Shetty, Sachin (MDPI, 2021-03-02)
The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to have a proper insight into the vision and requirements of 6G. Second, we provide a panoramic view of the enabling technologies proposed to facilitate 6G and introduce emerging 6G applications such as multi-sensory–extended reality, digital replica, and more. Next, the technology-driven challenges, social, psychological, health and commercialization issues posed to actualizing 6G, and the probable solutions to tackle these challenges are discussed extensively. Additionally, we present new use cases of the 6G technology in agriculture, education, media and entertainment, logistics and transportation, and tourism. Furthermore, we discuss the multi-faceted communication capabilities of 6G that will contribute significantly to global sustainability and how 6G will bring about a dramatic change in the business arena. Finally, we highlight the research trends, open research issues, and key take-away lessons for future research exploration in 6G wireless communication.
Aberrant Calcium Signaling in Astrocytes Inhibits Neuronal Excitability in a Human Down Syndrome Stem Cell Model
Mizuno, Grace O.; Wang, Yinxue; Shi, Guilai; Wang, Yizhi; Sun, Junqing; Papadopoulos, Stelios; Broussard, Gerard J.; Unger, Elizabeth K.; Deng, Wenbin; Weick, Jason; Bhattacharyya, Anita; Chen, Chao-Yin; Yu, Guoqiang; Looger, Loren L.; Tian, Lin (Elsevier, 2018-07-10)
Down syndrome (DS) is a genetic disorder that causes cognitive impairment. The staggering effects associated with an extra copy of human chromosome 21 (HSA21) complicates mechanistic understanding of DS pathophysiology. We examined the neuronastrocyte interplay in a fully recapitulated HSA21 trisomy cellular model differentiated from DS-patientderived induced pluripotent stem cells (iPSCs). By combining calciumimaging with genetic approaches, we discovered the functional defects of DS astroglia and their effects on neuronal excitability. Compared with control isogenic astroglia, DS astroglia exhibited more-frequent spontaneous calcium fluctuations, which reduced the excitability of co-cultured neurons. Furthermore, suppressed neuronal activity could be rescued by abolishing astrocytic spontaneous calcium activity either chemically by blocking adenosine-mediated signaling or genetically by knockdown of inositol triphosphate (IP3) receptors or S100B, a calcium binding protein coded on HSA21. Our results suggest a mechanism by which DS alters the function of astrocytes, which subsequently disturbs neuronal excitability.
Abnormal Behavior Detection Based on Traffic Pattern Categorization in Mobile Cellular Networks
De Almeida, J. M.; Pontes, C. F. T.; DaSilva, Luiz A.; Both, C. B.; Gondim, J. J. C.; Ralha, Celia G.; Marotta, M. A. (IEEE, 2021-01-01)
Abnormal behavior in mobile cellular networks can cause network faults and consequent cell outages, a major reason for operational cost increase and revenue loss for operators. Nonetheless, network faults and cell outages can be avoided by monitoring abnormal situations in the network and acting accordingly. Thus, anomaly detection is an important component of self-healing control and network management. Network operators may use the detected abnormal behavior to quantify numerically their intensity. The quantification of abnormal behavior assists the characterization of potential regions for infrastructure updates and to support the creation of public policies for local connectivity enhancements. We propose an unsupervised learning solution for anomaly detection in mobile networks using Call Detail Records (CDR) data. We evaluate our solution using a real CDR data set provided by an Italian operator and compare it against other state-of-the-art solutions, showing a performance improvement of around 35%. We also demonstrate the relevance of considering the distinct traffic patterns of diverging geographic areas for anomaly detection in mobile networks, an aspect often ignored in the literature.
Acoustic Energy Harvesting and Sensing via Electrospun PVDF Nanofiber Membrane
Shehata, Nader; Hassanin, Ahmed H.; Elnabawy, Eman; Nair, Remya; Bhat, Sameer A.; Kandas, Ishac (MDPI, 2020-05-31)
This paper introduces a new usage of piezoelectric poly (vinylidene fluoride) (PVDF) electrospun nanofiber (NF) membrane as a sensing unit for acoustic signals. In this work, an NF mat has been used as a transducer to convert acoustic signals into electric voltage outcomes. The detected voltage has been analyzed as a function of both frequency and amplitude of the excitation acoustic signal. Additionally, the detected AC signal can be retraced as a function of both frequency and amplitude with some wave distortion at relatively higher amplitudes and within a certain acoustic spectrum region. Meanwhile, the NFs have been characterized through piezoelectric responses, beta sheet calculations and surface morphology. This work is promising as a low-cost and innovative solution to harvest acoustic signals coming from wide resources of sound and noise.
Acoustic X-wave reflection and transmission at a planar interface: Spectral analysis
Shaarawi, Amr M.; Besieris, Ioannis M.; Attiya, Ahmed M.; El-Diwany, Essam (Acoustical Society of America, 2000-01-01)
The spectral structure of a three-dimensional X-wave pulse incident on a planar surface of discontinuity is examined. Introducing a novel superposition of azimuthally dependent pulsed plane waves, it is shown for oblique incidence that the reflected pulse has a localized wave structure. On the other hand, the transmitted field maintains its localization up to a certain distance from the interface, beyond which it starts disintegrating. An estimate of the localization range of the transmitted pulse is established; also, the parameters affecting the localization range are identified. The reflected and transmitted fields are deduced for X-waves incident from either a slower medium or a faster one. For the former case the evanescent fields in the second medium are calculated and their explicit time dependence is deduced for a normally incident X-wave. Furthermore, at near-critical incidence the transmitted pulse exhibits significant pulse compression and focusing.
Acousto-optic modulators integrated on-chip
Beller, Jared; Shao, Linbo (Springer Nature, 2022-07-29)
Acousto-optic devices that use radio frequency mechanical waves to manipulate light are critical components in many optical systems. Here, the researchers bring acousto-optic devices on-chip and make them more efficient for integrated photonic circuits.
Acousto-Optics: introduction to the feature issue
Poon, Ting-Chung; Tsai, C. S.; Voloshinov, V. B.; Chatterjee, M. R. (Optical Society of America, 2009-03-01)
This Acousto-Optics feature celebrates the scientific careers of two remarkable scientists, Antoni Sliwinski and Adrian Korpel. The feature includes original papers based on a representative selection of topics that were presented at the Tenth Spring School on Acousto-Optics held in Poland in May 2008. (C) 2009 Optical Society of America
Active suppression of acoustic radiation from impulsively excited structures
Baumann, William T.; Saunders, William R.; Robertshaw, Harry H. (Acoustical Society of America, 1991-12-01)
The objective is to use active control to suppress the acoustic energy that is radiated to the far field from a structure that has been excited by a short-duration pulse. The problem is constrained by the assumption that the far-field pressure cannot be directly measured. Therefore, a method is developed for estimating the total radiated energy from measurements on the structure. Using this estimate as a cost function, a feedback controller is designed using linear quadratic regulator theory to minimize the cost. Computer simulations of a clamped-clamped beam show that there is appreciable difference in the total radiated energy between a system with a controller designed to suppress vibrations of the structure and a system with a controller that takes into account the coupling of these vibrations to the surrounding fluid. The results of this work provide a framework for a general, model-based method for actively suppressing transient structural acoustic radiation that can also be applied to steady, narrow, or broadband disturbances.
Adaptive Height Optimization for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach
Fonseca, Erika; Galkin, Boris; Amer, Ramy; DaSilva, Luiz A.; Dusparic, Ivana (IEEE, 2023-01-19)
Providing reliable connectivity to cellular-connected Unmanned Aerial Vehicles (UAVs) can be very challenging; their performance highly depends on the nature of the surrounding environment, such as density and heights of the ground Base Stations (BSs). On the other hand, tall buildings might block undesired interference signals from ground BSs, thereby improving the connectivity between the UAVs and their serving BSs. To address the connectivity of UAVs in such environments, this paper proposes a Reinforcement Learning (RL) algorithm to dynamically optimise the height of a UAV as it moves through the environment, with the goal of increasing the throughput or spectrum ef ciency that it experiences. The proposed solution is evaluated in two settings: using a series of generated environments where we vary the number of BS and building densities, and in a scenario using real-world data obtained from an experiment in Dublin, Ireland. Results show that our proposed RL-based solution improves UAV Quality of Service (QoS) by 6% to 41%, depending on the scenario. We also conclude that, when ying at heights higher than the buildings, building density variation has no impact on UAV QoS. On the other hand, BS density can negatively impact UAV QoS, with higher numbers of BSs generating more interference and deteriorating UAV performance.
Adaptive Optical Scanning Holography
Tsang, P. W. M.; Poon, Ting-Chung; Liu, J. Ping (Springer Nature, 2016-02-26)
Optical Scanning Holography (OSH) is a powerful technique that employs a single-pixel sensor and a row-by-row scanning mechanism to capture the hologram of a wide-view, three-dimensional object. However, the time required to acquire a hologram with OSH is rather lengthy. In this paper, we propose an enhanced framework, which is referred to as Adaptive OSH (AOSH), to shorten the holographic recording process. We have demonstrated that the AOSH method is capable of decreasing the acquisition time by up to an order of magnitude, while preserving the content of the hologram favorably.
"Adaptive Pilot Patterns for CA-OFDM Systems in Non-stationary Wireless Channels"
Rao, Raghunandan M.; Marojevic, Vuk; Reed, Jeffrey H. (IEEE, 2017-09-12)
In this paper, we investigate the performance gains of adapting pilot spacing and power for Carrier Aggregation (CA)-OFDM systems in nonstationary wireless channels. In current multi-band CAOFDM wireless networks, all component carriers use the same pilot density, which is designed for poor channel environments. This leads to unnecessary pilot overhead in good channel conditions and performance degradation in the worst channel conditions. We propose adaptation of pilot spacing and power using a codebook-based approach, where the transmitter and receiver exchange information about the fading characteristics of the channel over a short period of time, which are stored as entries in a channel profile codebook. We present a heuristic algorithm that maximizes the achievable rate by finding the optimal pilot spacing and power, from a set of candidate pilot configurations. We also analyze the computational complexity of our proposed algorithm and the feedback overhead. We describe methods to minimize the computation and feedback requirements for our algorithm in multi-band CA scenarios and present simulation results in typical terrestrial and air-to ground/ air-to-air nonstationary channels. Our results show that significant performance gains can be achieved when adopting adaptive pilot spacing and power allocation in nonstationary channels. We also discuss important practical considerations and provide guidelines to implement adaptive pilot spacing in CAOFDM systems.
An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System
Xu, Yijun; Valinejad, Jaber; Korkali, Mert; Mili, Lamine M.; Wang, Yajun; Chen, Xiao; Zheng, Zongsheng (IEEE, 2021-10-20)
The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, this paper proposes a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)-sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.
Adelie: Continuous Address Space Layout Re-randomization for Linux Drivers
Nikolaev, Ruslan; Nadeem, Hassan; Stone, Cathlyn; Ravindran, Binoy (ACM, 2022-02-28)
While address space layout randomization (ASLR) has been extensively studied for user-space programs, the corresponding OS kernel’s KASLR support remains very limited, making the kernel vulnerable to just-in-time (JIT) return-oriented programming (ROP) attacks. Furthermore, commodity OSs such as Linux restrict their KASLR range to 32 bits due to architectural constraints (e.g., x86-64 only supports 32-bit immediate operands for most instructions), which makes them vulnerable to even unsophisticated brute-force ROP attacks due to low entropy. Most in-kernel pointers remain static, exacerbating the problem when pointers are leaked. Adelie, our kernel defense mechanism, overcomes KASLR limitations, increases KASLR entropy, and makes successful ROP attacks on the Linux kernel much harder to achieve. First, Adelie enables the position-independent code (PIC) model so that the kernel and its modules can be placed anywhere in the 64-bit virtual address space, at any distance apart from each other. Second, Adelie implements stack re-randomization and address encryption on modules. Finally, Adelie enables efficient continuous KASLR for modules by using the PIC model to make it (almost) impossible to inject ROP gadgets through these modules regardless of gadget’s origin. Since device drivers (typically compiled as modules) are often developed by third parties and are typically less tested than core OS parts, they are also often more vulnerable. By fully re-randomizing device drivers, the last two contributions together prevent most JIT ROP attacks since vulnerable modules are very likely to be a starting point of an attack. Furthermore, some OS instances in virtualized environments are specifically designated to run device drivers, where drivers are the primary target of JIT ROP attacks. Using a GCC plugin that we developed, we automatically modify different kinds of kernel modules. Since the prior art tackles only user-space programs, we solve many challenges unique to the kernel code. Our evaluation shows high efficiency of Adelie’s approach: the overhead of the PIC model is completely negligible and re-randomization cost remains reasonable for typical use cases.
Adhesive bondline interrogation using Stoneley wave methods
Claus, Richard O.; Kline, R. A. (American Institute of Physics, 1979)
In this work, a new technique for analyzing interfacial conditions in completed adhesive bonds is discussed. This method is based on the sensitivity of Stoneley waves, which propagate along the boundary between dissimilar solid media, to changes in the material properties of the interface region. Stoneley wave attenuation measured after processing was found to increase as a function of increasing surface roughness in specimens of borosilicate crown glass bonded with an aerobic cement to a substrate of 7740 Pyrex mirrorglass. Possible extensions of these results to high_strength structural adhesively bonded composites are discussed.
Adversarial Unlearning of Backdoors via Implicit Hypergradient
Zeng, Yi; Chen, Si; Park, Won; Mao, Morley; Jin, Ming; Jia, Ruoxi (2022)
We propose a minimax formulation for removing backdoors from a given poisoned model based on a small set of clean data. This formulation encompasses much of prior work on backdoor removal. We propose the Implicit Bacdoor Adversarial Unlearning (I-BAU) algorithm to solve the minimax. Unlike previous work, which breaks down the minimax into separate inner and outer problems, our algorithm utilizes the implicit hypergradient to account for the interdependence between inner and outer optimization. We theoretically analyze its convergence and the generalizability of the robustness gained by solving minimax on clean data to unseen test data. In our evaluation, we compare I-BAU with six stateof- art backdoor defenses on seven backdoor attacks over two datasets and various attack settings, including the common setting where the attacker targets one class as well as important but underexplored settings where multiple classes are targeted. I-BAU’s performance is comparable to and most often significantly better than the best baseline. Particularly, its performance is more robust to the variation on triggers, attack settings, poison ratio, and clean data size. Moreover, I-BAU requires less computation to take effect; particularly, it is more than 13X faster than the most efficient baseline in the single-target attack setting. Furthermore, it can remain effective in the extreme case where the defender can only access 100 clean samples—a setting where all the baselines fail to produce acceptable results.
Aerial high-throughput phenotyping of peanut leaf area index and lateral growth
Sarkar, Sayantan; Cazenave, Alexandre-Brice; Oakes, Joseph C.; McCall, David S.; Thomason, Wade E.; Abbott, A. Lynn; Balota, Maria (Springer Nature, 2021-11-04)
Leaf area index (LAI) is the ratio of the total one-sided leaf area to the ground area, whereas lateral growth (LG) is the measure of canopy expansion. They are indicators for light capture, plant growth, and yield. Although LAI and LG can be directly measured, this is time consuming. Healthy leaves absorb in the blue and red, and reflect in the green regions of the electromagnetic spectrum. Aerial high-throughput phenotyping (HTP) may enable rapid acquisition of LAI and LG from leaf reflectance in these regions. In this paper, we report novel models to estimate peanut (Arachis hypogaea L.) LAI and LG from vegetation indices (VIs) derived relatively fast and inexpensively from the red, green, and blue (RGB) leaf reflectance collected with an unmanned aerial vehicle (UAV). In addition, we evaluate the models’ suitability to identify phenotypic variation for LAI and LG and predict pod yield from early season estimated LAI and LG. The study included 18 peanut genotypes for model training in 2017, and 8 genotypes for model validation in 2019. The VIs included the blue green index (BGI), red-green ratio (RGR), normalized plant pigment ratio (NPPR), normalized green red difference index (NGRDI), normalized chlorophyll pigment index (NCPI), and plant pigment ratio (PPR). The models used multiple linear and artificial neural network (ANN) regression, and their predictive accuracy ranged from 84 to 97%, depending on the VIs combinations used in the models. The results concluded that the new models were time- and cost-effective for estimation of LAI and LG, and accessible for use in phenotypic selection of peanuts with desirable LAI, LG and pod yield.
Aggregate VM: Why Reduce or Evict VM's Resources When You Can Borrow Them From Other Nodes?
Chuang, Ho-Ren; Manaouil, Karim; Xing, Tong; Barbalace, Antonio; Olivier, Pierre; Heerekar, Balvansh; Ravindran, Binoy (ACM, 2023-05-08)
Hardware resource fragmentation is a common issue in data centers. Traditional solutions based on migration or overcommitment are unacceptably slow, and modern commercial or research solutions like Spot VM may reduce or evict VM’s resources anytime.We propose an alternative solution that does not suffer from these drawbacks, the Aggregate VM.We introduce a new distributed hypervisor design, the resource-borrowing hypervisor, which creates Aggregate VMs: distributed VMs that temporarily aggregate fragmented resources belonging to different host machines, which require mobility of virtual CPUs, memory and IO devices.We implement a prototype, FragVisor, which runs guest software transparently.We also propose minimal modifications to the guest OS that can enable significant performance gains. We evaluate FragVisor over a set of microbenchmarks and IaaS-style real applications. Although Aggregate VMs are not a perfect fit for every type of applications, some workloads enjoy significant speedups compared to overcommitted scenarios (up to 3.9x with 4 distributed vCPUs).We further demonstrate that FragVisor is faster than a state-of-the-art competitor, GiantVM (up to 2.5x).
Aging into Perceptual Control: A Dynamic Causal Modeling for fMRI Study of Bistable Perception
Dowlati, Ehsan; Adams, Sarah E.; Stiles, Alexandra; Moran, Rosalyn J. (Frontiers, 2016-03-31)
Aging is accompanied by stereotyped changes in functional brain activations, for example a cortical shift in activity patterns from posterior to anterior regions is one hallmark revealed by functional magnetic resonance imaging (fMRI) of aging cognition. Whether these neuronal effects of aging could potentially contribute to an amelioration of or resistance to the cognitive symptoms associated with psychopathology remains to be explored. We used a visual illusion paradigm to address whether aging affects the cortical control of perceptual beliefs and biases. Our aim was to understand the effective connectivity associated with volitional control of ambiguous visual stimuli and to test whether greater top-down control of early visual networks emerged with advancing age. Using a bias training paradigm for ambiguous images we found that older participants (n = 16) resisted experimenter-induced visual bias compared to a younger cohort (n = 14) and that this resistance was associated with greater activity in prefrontal and temporal cortices. By applying Dynamic Causal Models for fMRI we uncovered a selective recruitment of top-down connections from the middle temporal to Lingual gyrus (LIN) by the older cohort during the perceptual switch decision following bias training. In contrast, our younger cohort did not exhibit any consistent connectivity effects but instead showed a loss of driving inputs to orbitofrontal sources following training. These findings suggest that perceptual beliefs are more readily controlled by top-down strategies in older adults and introduce age-dependent neural mechanisms that may be important for understanding aberrant belief states associated with psychopathology.
AI-driven F-RANs: Integrating Decision Making Considering Different Time Granularities
DeAlmeida, Jonathan M.; DaSilva, Luiz A.; Both, Cristiano Bonato; Ralha, Celia G.; Marotta, Marcelo A. (IEEE, 2021-06-07)
Cloud and fog-based networks are promising paradigms for vehicular and mobile networks. Fog Radio Access Networks (F-RANs), in particular, can offload computation tasks to the network edge and reduce the latency. Artificial Intelligence (AI) techniques can be used in F-RANs to achieve, for example, enhanced energy efficiency and increased throughput. Nonetheless, the appropriate technique selection must consider the different time granularities at which decision-making occurs in F-RANs. We discuss the benefits and challenges of implementing an AI-driven F-RAN considering different timescales, highlighting key Machine Learning (ML) techniques for each granularity. Finally, we discuss the challenges and opportunities to integrate different ML solutions in F-RANs.

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