Digital Commons Network^™

Mobility Management In Industrial Iot Environments, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The Internet Engineering Task Force (IETF) has defined the 6TiSCH architecture to enable the Industrial Inter-net of Things (IIoT). Unfortunately, 6TiSCH does not provide mechanisms to manage node mobility, while many industrial applications involve mobile devices (e.g., mobile robots or wearable devices carried by workers). In this paper, we consider the Synchronized Single-hop Multiple Gateway framework to manage mobility in 6TiSCH networks. For this framework, we address the problem of positioning Border Routers in a deployment area, which is similar to the "Art Gallery"problem, proposing an efficient deployment policy for Border Routers based on geometrical rules. Moreover, we define a …

Mdz: An Efficient Error-Bounded Lossy Compressor For Molecular Dynamics, Kai Zhao, Sheng Di, Danny Perez, Xin Liang, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

Molecular dynamics (MD) has been widely used in today's scientific research across multiple domains including materials science, biochemistry, biophysics, and structural biology. MD simulations can produce extremely large amounts of data in that each simulation could involve a large number of atoms (up to trillions) for a large number of timesteps (up to hundreds of millions). In this paper, we perform an in-depth analysis of a number of MD simulation datasets and then develop an efficient error-bounded lossy compressor that can significantly improve the compression ratios. The contributions are fourfold. (1) We characterize a number of MD datasets and summarize …

Spade: Multi-Stage Spam Account Detection For Online Social Networks, Federico Concone, Giuseppe Lo Re, Marco Morana, Sajal K. Das

Computer Science Faculty Research & Creative Works

In recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest …

Locality-Aware Qubit Routing For The Grid Architecture, Avah Banerjee, Xin Liang, R. Tohid

Computer Science Faculty Research & Creative Works

Due to the short decohorence time of qubits available in the NISQ-era, it is essential to pack (minimize the size and or the depth of) a logical quantum circuit as efficiently as possible given a sparsely coupled physical architecture. In this work we introduce a locality-aware qubit routing algorithm based on a graph theoretic framework. Our algorithm is designed for the grid and certain 'grid-like' architectures. We experimentally show the competitiveness of algorithm by comparing it against the approximate token swapping algorithm, which is used as a primitive in many state-of-the-art quantum trans pilers. Our algorithm produces circuits of comparable …

Privacy-Preserving Data Falsification Detection In Smart Grids Using Elliptic Curve Cryptography And Homomorphic Encryption, Sanskruti Joshi, Ruixiao Li, Shameek Bhattacharjee, Sajal K. Das, Hayato Yamana

Computer Science Faculty Research & Creative Works

In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers' fine-grained power consumption data; however, analyzing customers' private data violates the customers' privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt …

Look-Up Table Based Fhe System For Privacy Preserving Anomaly Detection In Smart Grids, Ruixiao Li, Shameek Bhattacharjee, Sajal K. Das, Hayato Yamana

Computer Science Faculty Research & Creative Works

In advanced metering infrastructure (AMI), the customers' power consumption data is considered private but needs to be revealed to data-driven attack detection frameworks. In this paper, we present a system for privacy-preserving anomaly-based data falsification attack detection over fully homomorphic encrypted (FHE) data, which enables computations required for the attack detection over encrypted individual customer smart meter's data. Specifically, we propose a homomorphic look-up table (LUT) based FHE approach that supports privacy preserving anomaly detection between the utility, customer, and multiple partied providing security services. In the LUTs, the data pairs of input and output values for each function required …

Message From The Bits 2022 Workshop Co-Chairs, Sajal K. Das, Hayato Yamana, Keiichi Yasumoto, Shameek Bhattacharjee

Computer Science Faculty Research & Creative Works

No abstract provided.

Anomaly Based Incident Detection In Large Scale Smart Transportation Systems, Jaminur Islam, Jose Paolo Talusan, Shameek Bhattacharjee, Francis Tiausas, Sayyed Mohsen Vazirizade, Abhishek Dubey, Keiichi Yasumoto, Sajal K. Das

Computer Science Faculty Research & Creative Works

Modern smart cities are focusing on smart transportation solutions to detect and mitigate the effects of various traffic incidents in the city. To materialize this, roadside units and ambient trans-portation sensors are being deployed to collect vehicular data that provides real-time traffic monitoring. In this paper, we first propose a real-time data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a …

Sum-Rate Optimization For Visible-Light-Band Uav Networks Based On Particle Swarm Optimization, Yuwei Long, Nan Cen

Computer Science Faculty Research & Creative Works

The mobility nature of unmanned aerial vehicles (UAVs) takes them into high consideration in military, public, and civilian applications in recent years. However, scaling out millions of UAVs in the air will inevitably lead to a more crowded radio frequency (RF) spectrum. Therefore, researchers have been focused on new technologies such as millimeter-wave, Terahertz, and visible light communications (VLCs) to alleviate the spectrum crunch problem. VLC has shown its great potential for UAV networking because of its high data rate, interference-free to legacy RF spectrum, and low-complex frontends. While the physical layer design of the VLC system has been extensively …

Leveraging Spanning Tree To Detect Colluding Attackers In Federated Learning, Priyesh Ranjan, Federico Coro, Ashish Gupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated learning distributes model training among multiple clients who, driven by privacy concerns, perform training using their local data and only share model weights for iterative aggregation on the server. In this work, we explore the threat of collusion attacks from multiple malicious clients who pose targeted attacks (e.g., label flipping) in a federated learning configuration. By leveraging client weights and the correlation among them, we develop a graph-based algorithm to detect malicious clients. Finally, we validate the effectiveness of our algorithm in presence of varying number of attackers on a classification task using a well-known Fashion-MNIST dataset.

More To Less (M2l): Enhanced Health Recognition In The Wild With Reduced Modality Of Wearable Sensors, Huiyuan Yang, Han Yu, Kusha Sridhar, Thomas Vaessen, Inez Myin-Germeys, Akane Sano

Computer Science Faculty Research & Creative Works

Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effectively fuse information from multiple sensors. Fusing multiple sensors is a common choice in many applications but may not always be feasible in real-world scenarios. For example, although combining bio signals from multiple sensors (i.e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context. To solve the challenges, we propose an effective more to less (M2L) learning framework …

Extensive Thiol Profiling For Assessment Of Intracellular Redox Status In Cultured Cells By Hplc-Ms/Ms, Jiandong Wu, Anna Chernatynskaya, Annalise Pfaff, Huari Kou, Nan Cen, Nuran Ercal, Honglan Shi

Computer Science Faculty Research & Creative Works

Oxidative stress may contribute to the pathology of many diseases, and endogenous thiols, especially glutathione (GSH) and its metabolites, play essential roles in the maintenance of normal redox status. Understanding how these metabolites change in response to oxidative insult can provide key insights into potential methods of prevention and treatment. Most existing methodologies focus only on the GSH/GSH disulfide (GSSG) redox couple, but GSH regulation is highly complex and depends on several pathways with multiple redox-active sulfur-containing species. In order to more fully characterize thiol redox status in response to oxidative insult, a high-performance liquid chromatography with tandem mass spectrometry …

Bcmnet: Cross-Layer Extraction Structure And Multiscale Downsampling Network With Bidirectional Transpose Fpn For Fast Detection Of Wildfire Smoke, Jiayong Li, Guoxiong Zhou, Aibin Chen, Chao Lu, Liujun Li

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

At present, the wildfire smoke detection algorithm based on YOLOv3 has problems, such as low accuracy and slow detection speed. In this article, we propose a cross-layer extraction structure and multiscale downsampling network with bidirectional transpose FPN (BCMNet) for fast detection of wildfire smoke. First, a cross-layer extraction module, which combines linear feature multiplexing and receptive field amplification, is designed. It can improve the speed and accuracy of wildfire smoke detection. Second, a multiscale downsampling module with different convolution kernels and maximum pooling operation is designed to preserve the details of the image while downsampling. Then, a bidirectional transposed FPN …

Am Surface Roughness And Its Impact On Drag, Jackson Landry Chandler

Honors Academy

“The main goal of this thesis is to quantify additive manufacturing (AM) surface roughness, as a precursor to identifying its impact on unsteady transient flow properties. Experimental data using the Hirox optical microscopes and SEM imaging collected from the nylon and stainless-steel lattice structures contained an inherent level of surface roughness at the finer scale. Surface Roughness have a negative effect on additively manufactured materials and as such should be mitigated whenever possible. Surface roughness increases unwanted features such as the level of drag, skin friction, negative heat transfer/mass transfer properties, and other unwanted unsteady transitional flow characteristics. Though exceptions …

Self-Vernier Effect-Assisted Optical Fiber Sensor Based On Microwave Photonics And Its Machine Learning Analysis, Chen Zhu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Optical Vernier Effect Has Been Recently Demonstrated as a Tool to Enhance the Sensitivity of Optical Fiber Interferometric Sensors and Has Become a Hot Topic in the Last Few Years. the Generation of the Vernier Effect Relies on the Superposition of Interferograms of Two Interferometers (A Sensing One and a Reference One) with Marginally Different Optical Path Differences (OPDs), Where an Amplitude Modulation-Like Signal is Sustained in the Output Spectrum. the Vernier Modulation Envelope Exhibits Significantly Magnified Sensitivity in Response to External Perturbations, compared to the Individual Sensing Interferometer, Providing a New Route to New Generations of Ultra-Sensitive Optical Fiber …

Spatial Transformation Of A Layer-To-Layer Control Model For Selective Laser Melting, Xin Wang, Robert G. Landers, Douglas A. Bristow

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Selective Laser Melting (SLM) is an Additive Manufacturing (AM) technique with challenges in its complexity of process parameters and lack of control schemes. Traditionally, people tried time-domain or frequency-domain control methods, but the complexity of the process goes beyond these methods. In this paper, a novel spatial transformation of SLM models is proposed, which transforms the time-domain process into a spatial domain model and, thus, allows for state-space layer-to-layer control methods. In a space domain, this also provides the convenience of modelling laser path changes. Finally, a layer-to-layer Iterative Learning Control (ILC) method is designed and demonstrates the methodology of …

Lithofacies Characterization Of Marine Shelf Shale And Implicaitons On Depositional Conditions And Processes On The Continental Shelf — A Case Study Of The Upper-Cretaceous Tuscaloosa Marine Shale In Louisiana And Mississippi, U.S.A., Efren Mendez Jr

Masters Theses

"The Upper Cretaceous Tuscaloosa Marine Shale (TMS) is an unconventional shale reservoir deposited on the continental shelf of the northern Gulf of Mexico Basin. Previous studies have focused on sequence stratigraphy, thermal modeling, and well-log correlations. However, a limited understanding of the stratigraphic heterogeneity of the TMS is yet to be studied. This study aims to characterize the stratigraphic heterogeneity of the TMS using core, petrographic, and well-log analyses to better understand the depositional conditions and processes that occur on the continental shelf. The TMS has been classified into four lithofacies of very fine sands – coarse silts (LF1), medium-fine …

Automatic Sparse Esm Scan Using Gaussian Process Regression, Jiangshuai Li

Masters Theses

“Emission source microscopy (ESM) technique can be utilized for localization of electromagnetic interference sources in complex and large systems. In this work a Gaussian process regression (GPR) method is applied in real-time to select sampling points for the sparse ESM imaging using a motorized scanner. The Gaussian process regression is used to estimate the complex amplitude of the scanned field and its uncertainty allowing to select the most relevant areas for scanning. Compared with the randomly selected samples the proposed method allows to reduce the number of samples needed to achieve a certain dynamic range of the image, reducing the …

Man-In-The-Middle Attacks On Mqtt Based Iot Networks, Henry C. Wong

Masters Theses

“The use of Internet-of-Things (IoT) devices has increased a considerable amount in recent years due to decreasing cost and increasing availability of transistors, semiconductor, and other components. Examples can be found in daily life through smart cities, consumer security cameras, agriculture sensors, and more. However, Cyber Security in these IoT devices are often an afterthought making these devices susceptible to easy attacks. This can be due to multiple factors. An IoT device is often in a smaller form factor and must be affordable to buy in large quantities; as a result, IoT devices have less resources than a typical computer. …

Theoretical Determination Of The Yield Curve For Unsaturated Soils, Sara Fayek

Doctoral Dissertations

"An important step to developing a good understanding of the behavior of unsaturated soils is to adopt a formal and simple constitutive framework and to conduct high-quality and short-time experiments. Barcelona Basic Model (BBM) was the first elastoplastic constitutive model for unsaturated soils. The yield curve, which separates the elastic region from the elastoplastic region, is an important aspect of constitutive modeling for unsaturated soils. Most concepts for unsaturated soils were developed based upon the results from suction-controlled triaxial (SCTX) tests. Though, the SCTX tests on unsaturated soils are costly, time-consuming, and laborious, and their results are questionable. This study …

Graphical Revamping Of A Crude Distillation Unit Under Two Variable Operational Scenarios - Naphtha Stabilizer And Reformer Operated, Amany G. Abo-Mousa, Dina A. Kamel, Hany A. Elazab, Mamdouh A. Gadalla, Mai K. Fouad

Chemical and Biochemical Engineering Faculty Research & Creative Works

Energy costs represent significant parts of the total operating costs of crude refining industries. Energy integration is a typical solution to reduce heating and cooling utilities in crude refining plants through maximizing the target temperature of crude oil streams before entering the furnace. Over the past few decades, a significant progress has been made in energy integration methods including Pinch technology and mathematical programming approaches. Example of these is a graphical technique which plots Thot versus Tcold for energy analysis and revamping studies. The current research employs the Thot - Tcold diagrams in an algorithm to retrofit an existing crude …

Information Systems Analysis And Design: Past Revolutions, Present Challenges, And Future Research Directions, Keng Siau, Carson Woo, Veda C. Storey, Roger H.L. Chiang, Cecil Eng Huang Chua, Jon W. Beard

Business and Information Technology Faculty Research & Creative Works

Systems Analysis and Design (SAND) is Undoubtedly a Pillar in the Field of Information Systems (IS). Some Researchers Have Even Claimed that SAND is the Field that Defines the Information Systems Discipline and is the Core of Information Systems. the Past Decades Have Seen the Development of Structured SAND Methodologies and Object-Oriented Methodologies. in the Early 1990s, Key Players in the Field Collaborated to Develop the Unified Modeling Language and the Unified Process. Agile Approaches Followed, as Did Other Dynamic Methods. These Approaches Remain Heavily Employed in the Development of Contemporary Information Systems. at the Same Time, New Approaches Such …

Creation Of A Neural Network For The American Sign Language To Russian Translation App, John T. Simmons

Capstone Projects

1. A large population of people utilize American Sign Language for their primary method of communication.
2. No commercially available product is available for these people for when they need to communicate with speakers of a foreign language.
3. We must investigate methods to make communication between these two parties easier and more accessible.
4. By using a neural network to classify images of American Sign Language letters, we can build a service to make translation of American Sign Language into foreign languages possible.

Incorporating Complex Sampling Weights In Multilevel Analyses Of Education Data, Ting Shen, Spyros Konstantopoulos

Psychological Science Faculty Research & Creative Works

Large-scale assessment survey (LSAS) data are collected via complex sampling designs with special features (e.g., clustering and unequal probability of selection). Multilevel models have been utilized to account for clustering effects whereas the probability weighting approach (PWA) has been used to deal with design informativeness derived from the unequal probability selection. However, the difficulty of applying PWA in multilevel models (MLM) has been generally underestimated and practical guidance is scarce. This study utilizes an empirical as well as a Monte Carlo simulation investigation to examine the performance of the multilevel pseudo maximum likelihood (MPML) estimation based on information derived from …

Proton Radius: A Puzzle Or A Solution!?, Ulrich D. Jentschura

Physics Faculty Research & Creative Works

The proton radius puzzle is known as the discrepancy of the proton radius, obtained from muonic hydrogen spectroscopy (obtained as being roughly equal to 0.84 fm), and the proton radius obtained from (ordinary) hydrogen spectroscopy where a number of measurements involving highly excited states have traditionally favored a value of about 0.88 fm. Recently, a number of measurements of hydrogen transitions by the Munich (Garching) groups (notably, several hyperfine-resolved sublevels of the 2S-4P) and by the group at the University of Toronto (2S-2P 1/2) have led to transition frequency data consistent with the smaller proton radius of about 0.84 fm. …

Removal Of Methylene Blue By Adsorption Of Water Hyacinth Derived Active Carbon Embedded With Cobalt Nanoparticles, Hany A. Elazab, A. O. Okasha, M. A. Radwan, M. A. Sadek, Amin Firouzi, Tamer T. El-Idreesy

Chemical and Biochemical Engineering Faculty Research & Creative Works

In this research, active carbon-based catalyst synthesis and characterization were tested for potential catalysts to be used in dye removal of methylene blue (MB). Water hyacinth is one of the major problems that is facing humankind and especially here in Egypt. One of the implications of industrial activities is environmental pollution. Dyes used in the production of textiles, paper, and clothes are one of the major pollutants. The waste of those dyes discharged into water supplies without treatment or with ineffective treatment harmfully impacts the environment. In this research, the treatment is implemented using active carbon-based catalysts using embedded nanoparticles. …

Discovery Of An Olivine-Type Lithium Manganese Thiophosphate, Limnps4,: Via A Building Block Approach, Srikanth Balijapelly, Kartik Ghosh, Aleksandr V. Chernatynskiy, Amitava Choudhury

Physics Faculty Research & Creative Works

An olivine-type orthothiophospate LiMnPS4 has been synthesized for the first time through a building block approach by reacting preformed ternary lithium thiophospate with MnCl2. Diffuse reflectance measurements show an optical band gap of 2.36 eV, which is further confirmed by DFT calculations. Irreversible weak ferromagnetic ordering and metamagnetism are verified through preliminary magnetic measurements.

Solvation Of Isoelectronic Halide And Alkali Metal Ions By Argon Atoms, Carly A. Rock, Sarah N. Arradondo, Gregory S. Tschumper

Chemistry Faculty Research & Creative Works

This work systematically examines the interactions of alkali metal cations and their isoelectronic halide counterparts with up to six solvating Ar atoms (M+Arn and X–Arn, where M = Li, Na, K, and Rb; X = H, F, Cl, and Br; and n = 1–6) via full geometry optimizations with the MP2 method and robust, correlation-consistent quadruple-ζ (QZ) basis sets. 116 unique M+Arn and X–Arn stationary points have been characterized on the MP2/QZ potential energy surface. To the best of our knowledge, approximately two dozen of these stationary points have been reported here for the first time. Some of these new …

Modeling Iterative Reconstruction And Displacement Field In The Large Scale Structure, Atsuhisa Ota, Hee Jong Seo, Shun Saito, Florian Beutler

Physics Faculty Research & Creative Works

The next generation of galaxy surveys like the Dark Energy Spectroscopic Instrument and Euclid will provide datasets orders of magnitude larger than anything available to date. Our ability to model nonlinear effects in late time matter perturbations will be a key to unlock the full potential of these datasets, and the area of initial condition reconstruction is attracting growing attention. Iterative reconstruction developed by Marcel Schmittfull et al. [Phys. Rev. D 96, 023505 (2017)PRVDAQ2470-001010.1103/PhysRevD.96.023505] is a technique designed to reconstruct the displacement field from the observed galaxy distribution. The nonlinear displacement field and initial linear density field are highly correlated. …

Signatures Of A Quantum Griffiths Phase Close To An Electronic Nematic Quantum Phase Transition, Pascal Reiss, David Graf, Amir A. Haghighirad, Thomas Vojta, Amalia I. Coldea

Physics Faculty Research & Creative Works

In the vicinity of a quantum critical point, quenched disorder can lead to a quantum Griffiths phase, accompanied by an exotic power-law scaling with a continuously varying dynamical exponent that diverges in the zero-temperature limit. Here, we investigate a nematic quantum critical point in the iron-based superconductor FeSe0.89S0.11 using applied hydrostatic pressure. We report an unusual crossing of the magneto resistivity isotherms in the Non superconducting normal state that features a continuously varying dynamical exponent over a large temperature range. We interpret our results in terms of a quantum Griffiths phase caused by nematic islands that result from the local …