Engineering Commons^™

A Novel Approach For Enhancing Routing In Wireless Sensor Networks Using Aco Algorithm, Sihem Goumiri, Maohamed Amine Riahla, M'Hamed Hamadouche

Emirates Journal for Engineering Research

Wireless Sensors Network (WSN) is an emergent technology that aims to offer innovative capacities. In the last decade, the use of these networks increased in various fields like military, science, and health due to their fast and inexpressive deployment and installation. However, the limited sensor battery lifetime poses many technical challenges and affects essential services like routing. This issue is a hot topic of search, many researchers have proposed various routing protocols aimed at reducing the energy consumption in WSNs. The focus of this work is to investigate the effectiveness of integrating ACO algorithm with routing protocols in WSNs. Moreover, …

Laser-Direct-Drive Fusion Target Design With A High-Z Gradient-Density Pusher Shell, S. S. Hu, L. Ceurvorst, J. L. Peebles, A. Mao, P. Li, Yongfeng Lu, A. Shvydky, V. N. Goncharov, R. Epstein, K. A. Nichols, R. M. N. Goshadze, M. Ghosh, J. Hinz, V. V. Karasiev, S. Zhang, N. R. Shaffer, D. I. Mihaylov, J. Cappelletti, D. R. Harding, C. K. Li, E. M. Campbell, R. C. Shah, T. J. B. Collins, S. P. Regan, C. Deeney

Department of Electrical and Computer Engineering: Faculty Publications

Laser-direct-drive fusion target designs with solid deuterium-tritium (DT) fuel, a high-Z gradient-density pusher shell (GDPS), and a Au-coated foam layer have been investigated through both 1D and 2D radiationhydrodynamic simulations. Compared with conventional low-Z ablators and DT-push-on-DT targets, these GDPS targets possess certain advantages of being instability-resistant implosions that can be high adiabat (α ≽ 8) and low hot-spot and pusher-shell convergence (CR_hs ≈22 and CR_PS ≈17), and have a low implosion velocity (v_imp < 3 × 10⁷ cm/s). Using symmetric drive with laser energies of 1.9 to 2.5 MJ, 1D LILAC simulations of these GDPS implosions can result …

Aptamer-Based Proteomics Measuring Preoperative Cerebrospinal Fluid Protein Alterations Associated With Postoperative Delirium, Simon T. Dillon, Sarinnapha M. Vasunilashorn, Hasan H. Otu, Long Ngo, Tamara Fong, Xuesong Gu, Michele Cavallari, Alexandra Touroutoglou, Mouhsin Shafi, Sharon K. Inouye, Zhongcong Xie, Edward R. Marcantonio, Towia A. Libermann

Department of Electrical and Computer Engineering: Faculty Publications

Delirium is a common postoperative complication among older patients with many adverse outcomes. Due to a lack of validated biomarkers, prediction and monitoring of delirium by biological testing is not currently feasible. Circulating proteins in cerebrospinal fluid (CSF) may reflect biological processes causing delirium. Our goal was to discover and investigate candidate protein biomarkers in preoperative CSF that were associated with the development of postoperative delirium in older surgical patients. We employed a nested case–control study design coupled with high multiplex affinity proteomics analysis to measure 1305 proteins in preoperative CSF. Twenty-four matched delirium cases and non-delirium controls were selected …

Trumpet Directivity From A Rotating Semicircular Array, Samuel D. Bellows, Joseph E. Avila, Timothy W. Leishman

Directivity

The directivity function of a played musical instrument describes the angular dependence of its acoustic radiation and diffraction about the instrument, musician, and musician’s chair. Directivity influences sound in rehearsal, performance, and recording environments and signals in audio systems. Because high-resolution, spherically comprehensive measurements of played musical instruments have been unavailable in the past, the authors have undertaken research to produce and share such data for studies of musical instruments, simulations of acoustical environments, optimizations of microphone placements, and other applications. The authors acquired the data from repeated chromatic scales produced by a trumpet played at mezzo-forte in an anechoic …

Better Models For High-Stakes Tasks, Jacob Ryan Epifano

Theses and Dissertations

The intersection of machine learning and healthcare has the potential to transform medical diagnosis, treatment, and research. Machine learning models can analyze vast amounts of medical data and identify patterns that may be too complex for human analysis. However, one of the major challenges in this field is building trust between users and the model. Due to things like high false alarm rate and the black box nature of machine learning models, patients and medical professionals need to understand how the model arrives at its recommendations. In this work, we present several methods that aim to improve machine learning models …

Investigations On Potassium Penta Borate Based Semi-Organic Materials For Nonlinear Optical Applications, Kamatchi K

Theses and Dissertations

Crystal growth has attained the pinnacle of a point in the current research owing to the requirement of the single crystals in the frontier field of nonlinear optics. Organic crystals exhibit high transparency, high laser damage threshold and de-localized π electron cloud. But they possess weak mechanical stability. In contrast, the inorganic crystals exhibit magnanimous mechanical and thermal properties but have modest nonlinear optical property. If we combine these two materials, then we can obtain the semi-organic materials.

Among the organic materials, the amino acids are chosen and they are mixed with inorganic material potassium penta borate octa hydrate. They …

Reducing Brain Kynurenic Acid Synthesis Precludes Kynurenine-Induced Sleep Disturbances, Katherine M. Rentschler, Snezana Milosavljevic, Annalisa M. Baratta, Courtney J. Wright, Maria V. Piroli, Zachary Tentor, Homayoun Valafar, Christian O'Reilly, Ana Pocivavsek

Publications

Patients with neurocognitive disorders often battle sleep disturbances. Kynurenic acid is a tryptophan metabolite of the kynurenine pathway implicated in the pathology of these illnesses. Modest increases in kynurenic acid, an antagonist at glutamatergic and cholinergic receptors, result in cognitive impairments and sleep dysfunction. We explored the hypothesis that inhibition of the kynurenic acid synthesising enzyme, kynurenine aminotransferase II, may alleviate sleep disturbances. At the start of the light phase, adult male and female Wistar rats received systemic injections of either: (i) vehicle; (ii) kynurenine (100 mg kg−1; i.p.); (iii) the kynurenine aminotransferase II inhibitor, PF-04859989 (30 mg kg−1; s.c.); …

Comparison Of A Bottom-Up Gnss Radio Occultation Method To Measure D- And E-Region Electron Densities With Ionosondes And Firi, Dylan J. Shaver, Dong L. Wu, Nimalan Swarnalingam, Anthony L. Franz, Eugene V. Dao, Daniel J. Emmons

Faculty Publications

High-frequency skywave propagation can be heavily impacted by D- and E-region dynamics requiring accurate global measurements to optimize performance. A standard measurement technique is to use ionosondes, but they are unable to measure below 1 MHz and are only available at a limited number of land-locked sites around the globe. In contrast, the Global Navigation Satellite System radio occultation (GNSS-RO) bottom-up method is a new approach specifically designed to generate electron density profiles in the D- and E- region ionosphere. It takes advantage of satellite constellations that currently provide over 20,000 daily measurements and global coverage. In this paper, GNSS-RO …

Integrating Glycolysis, Citric Acid Cycle, Pentose Phosphate Pathway, And Fatty Acid Beta‑Oxidation Into A Single Computational Model, Sylwester M. Kloska, Krzysztof Pałczyński, Tomasz Marciniak, Tomasz Talaśka, Beata J. Wysocki, Paul H. Davis, Tadeusz A. Wysocki

Department of Electrical and Computer Engineering: Faculty Publications

The metabolic network of a living cell is highly intricate and involves complex interactions between various pathways. In this study, we propose a computational model that integrates glycolysis, the pentose phosphate pathway (PPP), the fatty acids beta-oxidation, and the tricarboxylic acid cycle (TCA cycle) using queueing theory. The model utilizes literature data on metabolite concentrations and enzyme kinetic constants to calculate the probabilities of individual reactions occurring on a microscopic scale, which can be viewed as the reaction rates on a macroscopic scale. However, it should be noted that the model has some limitations, including not accounting for all the …

Qc-Odkla: Quantized And Communication-Censored Online Decentralized Kernel Learning Via Linearized Admm, Ping Xu, Yue Wang, Xiang Chen, Zhi Tian

Electrical and Computer Engineering Faculty Publications

This article focuses on online kernel learning over a decentralized network. Each agent in the network receives online streaming data and collaboratively learns a globally optimal nonlinear prediction function in the reproducing kernel Hilbert space (RKHS). To overcome the curse of dimensionality issue in traditional online kernel learning, we utilize random feature (RF) mapping to convert the nonparametric kernel learning problem into a fixed-length parametric one in the RF space. We then propose a novel learning framework, named online decentralized kernel learning via linearized ADMM (ODKLA), to efficiently solve the online decentralized kernel learning problem. To enhance communication efficiency, we …

Qc-Sane: Robust Control In Drl Using Quantile Critic With Spiking Actor And Normalized Ensemble, Surbhi Gupta, Gaurav Singal, Deepak Garg, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Recently Introduced Deep Reinforcement Learning (DRL) Techniques in Discrete-Time Have Resulted in Significant Advances in Online Games, Robotics, and So On. Inspired from Recent Developments, We Have Proposed an Approach Referred to as Quantile Critic with Spiking Actor and Normalized Ensemble (QC-SANE) for Continuous Control Problems, Which Uses Quantile Loss to Train Critic and a Spiking Neural Network (NN) to Train an Ensemble of Actors. the NN Does an Internal Normalization using a Scaled Exponential Linear Unit (SELU) Activation Function and Ensures Robustness. the Empirical Study on Multijoint Dynamics with Contact (MuJoCo)-Based Environments Shows Improved Training and Test Results Than …

Fs-Laser Fabricated Miniature Fabry–Perot Interferometer In A No-Core Fiber For High-Temperature Applications †, Chen Zhu, Osamah Alsalman, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This Paper Reports a Fiber In-Line Fabry–Perot Interferometer (FPI) Fabricated in a No-Core Fiber using the Direct Femtosecond Laser Writing Technique for High-Temperature Sensing Applications. Two In-Line Reflectors Are Directly Inscribed in a No-Core Fiber to Construct a Low-Finesse FPI. Fringe Visibility Greater Than 10 DB is Obtained from the Reflection Spectra of the Fabricated No-Core Fiber FPIs. Temperature Responses of a Prototype No-Core Fiber FPI Are Characterized Up to 1000 °C. the Proposed Configuration is Compact and Easy to Fabricate, Making It Attractive for Sensing Applications in High-Temperature Harsh Environments.

Multifunctional Organic Monolayer-Based Coatings For Implantable Biosensors And Bioelectronic Devices: Review And Perspectives, Taral Patel, Jie Huang, Katarzyna Krukiewicz

Electrical and Computer Engineering Faculty Research & Creative Works

The Emerging Field of Biosensors and Bioelectronics Seeks to Exploit Biology in Combination with the Recent Advances in Engineering. Even Though Biomedical Implants Can Significantly Improve Quality of Life, Problems Following the Process of Implantation Are Still an Issue. in This Review, We Point Out Organic Monolayer-Based Coatings as a Potential Solution to the Major Limitations of Implantable Biomaterials, Including Limited Biocompatibility, the Risk of Biofouling, Bacterial Colonization, and Stability under in Vivo Conditions. at First, Selected Current and Perspective Biomaterials Are Discussed, with the Focus on their Use as Implantable Biosensors and Biomedical Electrodes. Surface Modification Strategies for Implantable …

In Situ Monitoring Of The Hydration Of Calcium Silicate Minerals In Cement With A Remote Fiber-Optic Raman Probe, Bohong Zhang, Wenyu Liao, Hongyan Ma, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This study utilized a novel in situ fiber-optic Raman probe to continuously monitor the hydration progress of tricalcium silicate (C3S) and dicalcium silicate (C2S) without the need for sampling, from early hydration stage to later stages, and from fresh to hardened states of paste samples. by virtue of the remarkable ability of this technique in characterizing either dry or wet and crystalline or amorphous samples, the hydration processes of C3S and C2S pastes with different water-to-solid (w/s) ratios could be monitored from the start of the hydration reaction. the main hydration products, calcium silicate hydrate (C–S–H) and portlandite/calcium hydroxide (CH), …

In Situ Monitoring Of The Hydration Of Calcium Silicate Minerals In Cement With A Remote Fiber-Optic Raman Probe, Bohong Zhang, Wenyu Liao, Hongyan Ma, Jie Huang

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This study utilized a novel in situ fiber-optic Raman probe to continuously monitor the hydration progress of tricalcium silicate (C₃S) and dicalcium silicate (C₂S) without the need for sampling, from early hydration stage to later stages, and from fresh to hardened states of paste samples. By virtue of the remarkable ability of this technique in characterizing either dry or wet and crystalline or amorphous samples, the hydration processes of C₃S and C₂S pastes with different water-to-solid (w/s) ratios could be monitored from the start of the hydration reaction. The main hydration products, …

Analysis Of Coronal Magnetic Field Parameters During X- And M-Class Solar Flares, Seth H. Garland

Theses and Dissertations

Using Non-Linear Force Free Field (NLFFF) extrapolation, 3D magnetic fields were modeled from the 12-minute cadence Helioseismic and Magnetic Imager (HMI) photospheric vector magnetograms, spanning a time period of one hour before through one hour after the start of 18 X-class flares and 12 M-class flares. Several magnetic field parameters were calculated from the modeled fields directly – as well as from the power spectrum of surface maps generated by summing the fields along the vertical axis – for two different regions: areas with photospheric Bz ≥ 300 G (Active Region – AR) and areas above the photosphere with the …

Atmospheric Propagation Of Qubits: Laboratory Experiments To Field Demonstrations, Keith A. Wyman

Theses and Dissertations

Free-space quantum networks can enable global-scale quantum communication via satellite-based nodes and quantum ground transceivers. To enable building of robust global quantum networks, it is critical to learn how the state of the qubit is transformed while propagating through the atmosphere. With such an overarching goal, we built a laboratory based atmospheric turbulence simulator (ATS) to characterize the effects of atmospheric turbulence on an entangled pair of photons as a function of statistical quantities such as the Fried parameter or scintillation index for long-distance communication. Specifically, the changes in the statistical properties associated with a quantum source was investigated using …

Brdf Measurements And Physical Optics Modeling Applied To Anisotropic Satellite Solar Cells, Madilynn E. Compean

Theses and Dissertations

Light curve analysis is often used to discern information about satellites in geosynchronous orbits, and since solar panels comprise relatively large surface areas, their reflected energy can contribute greatly to observed light curves. Data was collected using a space qualified solar cell interrogated by a green HeNe laser. The data validated certain aspects from previous work, but also identified multi-slit behavior, showed that the specular term was a second diffraction pattern, and diagnosed the out-of-plane diffraction curvature as the conical diffraction phenomenon. Two physical-optics-based models were developed to incorporate these new features and better predict solar cell BRDF solely from …

Quantifying Atmospheric Turbulence Effects On Image Quality Using A Deconvolution Algorithm, Ahmad M. Almalki

Theses and Dissertations

Imaging through turbulence is affected by several factors including imaging system specifications, imaging system setup and more importantly the atmospheric turbulence as it is uncontrollable. One important parameter which is used to quantify the atmospheric turbulence severity is the atmospheric coherence diameter (��₀ ), known as Fried's parameter. This thesis explores ways to characterize the atmospheric turbulence effects on image quality using simulated and laboratory generated turbulence where ��₀ is estimated using a maximum a posteriori (MAP) estimator and frequency domain analysis algorithms. Furthermore, image quality metrics such as Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM) …

An Analytical Framework For Photonic Storage And Retrieval: Implications For Quantum Optical Networks, Billie V. Deluca

Theses and Dissertations

Quantum information and communication is the next revolution in information technology. However, communication through quantum networks will require the ability to transmit, store, and interact with photonic quantum bits (qubits). The focus of this dissertation is a theoretical description of a promising technology for storage of photonic states employing electromagnetically induced transparency (EIT). A general semiclassical model for EIT photonic storage will be developed, a key capability required for understanding the effect of storage that is independent of the photonic state being stored. This dissertation demonstrates the capability of this model to predict light pulses retrieved from EIT storage as …

Open Source Framework For A Broadly Expandable And Reconfigurable Data Acquisition And Automation Device (Bread), Shane Oberloier, Nicholas G. Whisman, Finn Hafting, Joshua M. Pearce

Michigan Tech Publications

Though open source data acquisition (DAQ) systems have been published, closed source proprietary systems are the standard despite often being prohibitively expensive. High costs, however, limit access to high-quality DAQ in low-resource settings. In many cases the functions executed by the closed source and proprietary DAQ cards could be carried out by an open source alternative; however, as desired function count increases, the simplicity of integrating the designs decreases substantially. Although the global library of open source electronic designs is expanding rapidly, and there is clear evidence they can reduce costs for scientists one device at a time, they are …

Uncertainties In Retrieval Of Remote Sensing Reflectance From Ocean Color Satellite Observations, Eder I. Herrera Estrella

Dissertations, Theses, and Capstone Projects

Ocean Color radiometry uses remote sensing to interpret ocean dynamics by retrieving remote sensing reflectance (������) from satellite imagery at different scales and over different time periods. ������ spectrum characterizes the ocean color that we observe, and from which we can discern concentrations of chlorophyll, organic and inorganic particles, and carbon fluxes in the ocean and atmosphere. ������ is derived from the total radiance at the top of the atmosphere (TOA). However, it only represents up to ten percent of the total signal. Hence, the retrieval of ������ from the total radiance at TOA involves the application of atmospheric correction …

Optimally Distributed Receiver Placements Versus An Environmentally Aware Source: New England Shelf Break Acoustics Signals And Noise Experiment, William K. Stevens, Martin Siderius, Matthew J. Carrier, Drew Wendeborn

Electrical and Computer Engineering Faculty Publications and Presentations

This article describes the results of the Spring of 2021 New England Shelf Break Acoustics (NESBA) Signals and Noise experiment as they pertain to the optimization of a field of passive receivers versus an environmentally aware source with end-state goals. A discrete optimization has been designed and used to demonstrate providing an acoustic system operator with actionable guidance relating to optimally distributed receiver locations and depths and likely mean source detection times and associated uncertainties as a function of source and receiver levels of environmental awareness. The uncertainties considered here are those due to the imperfect spatial and temporal sensing …

A Study On Rapidly Exploring Random Tree Algorithms For Robot Path Planning, Sahil Sharma

Master's Theses

Robot path planning is a critical feature of autonomous systems. Rapidly-exploring Random Trees (RRT) is a path planning technique that randomly samples the robot configuration space to find a path between the start and end point. This thesis studies and compares the performance of four important RRT algorithms, namely, the original RRT, the optimal RRT (also termed RRT*), RRT*-Smart, and Informed RRT* for six different environments. The performance measures include the final path length (which is also the shortest path length found by each algorithm), time to find the first path, run time (of 1000 iterations) for each algorithm, total …

Pymaivar: An Open-Source Python Suit For Audio-Image Representation In Human Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems.

Harnessing The Power Of Neural Networks For The Investigation Of Solar-Driven Membrane Distillation Systems Under The Dynamic Operation Mode, Pooria Behnam, Masoumeh Zargar, Abdellah Shafieian, Amir Razmjou, Mehdi Khiadani

Research outputs 2022 to 2026

Accurate modeling of solar-driven direct contact membrane distillation systems (DCMD) can enhance the commercialization of these promising systems. However, the existing dynamic mathematical models for predicting the performance of these systems are complex and computationally expensive. This is due to the intermittent nature of solar energy and complex heat/mass transfer of different components of solar-driven DCMD systems (solar collectors, MD modules and storage tanks). This study applies a machine learning-based approach to model the dynamic nature of a solar-driven DCMD system for the first time. A small-scale rig was designed and fabricated to experimentally assess the performance of the system …

System Strength Shortfall Challenges For Renewable Energy-Based Power Systems: A Review, Md O. Qays, Iftekhar Ahmad, Daryoush Habibi, Asma Aziz, Thair Mahmoud

Research outputs 2022 to 2026

Renewable energy sources such as wind farms and solar power plants are replacing conventional coal-based synchronous generators (SGs) to achieve net-zero carbon emissions worldwide. SGs play an important role in enhancing system strength in a power system to make it more stable during voltage/frequency disruptions. However, traditional coal-fired SGs are being decommissioned in many parts of the world, owing to stringent environmental regulations and low levelized cost of energy of renewables. Consequently, maintaining system strength in a renewable energy-dominated power system has become a major challenge, and without adequate mitigation techniques, low system strength can potentially cause widespread power outages. …

Exploring Topological Phonons In Different Length Scales: Microtubules And Acoustic Metamaterials, Ssu-Ying Chen

Dissertations

The topological concepts of electronic states have been extended to phononic systems, leading to the prediction of topological phonons in a variety of materials. These phonons play a crucial role in determining material properties such as thermal conductivity, thermoelectricity, superconductivity, and specific heat. The objective of this dissertation is to investigate the role of topological phonons at different length scales.

Firstly, the acoustic resonator properties of tubulin proteins, which form microtubules, will be explored The microtubule has been proposed as an analog of a topological phononic insulator due to its unique properties. One key characteristic of topological materials is the …

Boundary Integral Equation Methods For Superhydrophobic Flow And Integrated Photonics, Kosuke Sugita

Dissertations

This dissertation presents fast integral equation methods (FIEMs) for solving two important problems encountered in practical engineering applications.

The first problem involves the mixed boundary value problem in two-dimensional Stokes flow, which appears commonly in computational fluid mechanics. This problem is particularly relevant to the design of microfluidic devices, especially those involving superhydrophobic (SH) flows over surfaces made of composite solid materials with alternating solid portions, grooves, or air pockets, leading to enhanced slip.

The second problem addresses waveguide devices in two dimensions, governed by the Helmholtz equation with Dirichlet conditions imposed on the boundary. This problem serves as a …

On Explainability Of Neural Networks, Cem Benar

Dissertations

It is widely reported that deep neural networks outperform most competitors for a range of applications. The state-of-the-art neural networks have built-in inductive bias of architectural choices, regularizations, optimizer types, and initialization methods. Using inductive bias is intuitive to enhance the model approximation. Deep neural networks are mostly dense and heavily overparameterized. They tend to be biased towards low-rank solutions to reduce complexity and improve generalization performance, known as implicit regularization. The implicit regularization as observed in specific architectures and various real-world data sets suggests to overparameterize neural networks judiciously and learn compressed representations (lower rank approximation) with improved performance. …