Electrical and Computer Engineering Commons^™

Aquaculture In Hydropower Reservoirs, Racheal Macharia

Tanzania Journal of Engineering and Technology (TJET)

There is an undoubtedly existing relationship between water, energy, and food and their interaction with the natural environment and mankind. Climate change is the talk of the day in global development discussions as it intensifies water shortages, energy, poverty, food shortages, and environmental degradation. Currently, most of the hydropower dams constructed in Africa are majorly built to store water for agricultural purposes, control flooding, and generate electricity. Notable projects include the seven forks hydropower stations located on the Tana River in Kenya; Aswan high dam in Egypt; Grand Renaissance dam in Ethiopia; Kariba dam in Zimbabwe and Akosombo in Ghana. …

A Multitask Learning Framework For Pilot Decontamination In 5g Massive Mimo, Crallet Victor

Tanzania Journal of Engineering and Technology (TJET)

Reference signals enable the acquisition of channel state information (CSI) for purposes such as channel estimation, beam selection, precoding, and symbol detection in 5G massive multiple-input multiple output (MAMIMO) systems. Eventually, as more and more users and cells are added, orthogonal reference signals become few which leads to pilot contamination. Pilot contamination limits the performance and occurs when non-orthogonal reference signals occupy time-frequency resources that are alike. Learning-based techniques have been proposed to alleviate it. However, each can only learn to perform a single task namely pilot assignment, power allocation, pilot design, or de-noising for pilot decontamination. In addition, each …

Stepwise Dynamic Nearest Neighbor (Sdnn): A New Algorithm For Classification, Deni̇z Karabaş, Derya Bi̇rant, Peli̇n Yildirim Taşer

Turkish Journal of Electrical Engineering and Computer Sciences

Although the standard k-nearest neighbor (KNN) algorithm has been used widely for classification in many different fields, it suffers from various limitations that abate its classification ability, such as being influenced by the distribution of instances, ignoring distances between the test instance and its neighbors during classification, and building a single/weak learner. This paper proposes a novel algorithm, called stepwise dynamic nearest neighbor (SDNN), which can effectively handle these problems. Instead of using a fixed parameter k like KNN, it uses a dynamic neighborhood strategy according to the data distribution and implements a new voting mechanism, called stepwise voting. Experimental …

Cognitive Load Detection Using Ci-Ssa For Eeg Signal Decomposition And Nature-Inspired Feature Selection, Jammisetty Yedukondalu, Lakhan Dev Sharma

Turkish Journal of Electrical Engineering and Computer Sciences

Cognitive load detection is eminent during the mental assignment of neural activity because it indicates how the brain reacts to stimuli. The level of cognitive load experienced during mental arithmetic tasks can be determined using an electroencephalogram (EEG). The EEG data were collected from publicly available datasets, namely, mental arithmetic task (MAT) and simultaneous task workload (STEW). The first phase comprises decomposing the electroencephalogram (EEG) signal into intrinsic mode functions (IMFs) using circulant singular spectrum analysis (Ci-SSA). In the second phase, entropy-based features were evaluated using IMFs. After that, the extracted features were fed to nature-inspired feature selection algorithms: genetic …

Using T-Distributed Stochastic Neighbor Embedding For Visualization And Segmentation Of 3d Point Clouds Of Plants, Heli̇n Dutağaci

Turkish Journal of Electrical Engineering and Computer Sciences

In this work, the use of t-SNE is proposed to embed 3D point clouds of plants into 2D space for plant characterization. It is demonstrated that t-SNE operates as a practical tool to flatten and visualize a complete 3D plant model in 2D space. The perplexity parameter of t-SNE allows 2D rendering of plant structures at various organizational levels. Aside from the promise of serving as a visualization tool for plant scientists, t-SNE also provides a gateway for processing 3D point clouds of plants using their embedded counterparts in 2D. In this paper, simple methods were proposed to perform semantic …

Direct Pore-Based Identification For Fingerprint Matching Process, Vedat Delican, Behçet Uğur Töreyi̇n, Ege Çeti̇n, Ayli̇n Yalçin Saribey

Turkish Journal of Electrical Engineering and Computer Sciences

Fingerprints are one of the most important scientific proof instruments in solving forensic cases. Identification in fingerprints consists of three levels based on the flow direction of the papillary lines at the first level, the minutiae points at the second level, and the pores at the third level. The inadequacy of existing imaging systems in detecting fingerprints and the lack of pore details at the desired level limit the widespread use of third-level identification. The fact that fingerprints with images based on pores in the unsolved database are not subjected to any evaluation criteria and remain in the database reveals …

Dynamic Deep Neural Network Inference Via Adaptive Channel Skipping, Meixia Zou, Xiuwen Li, Jinzheng Fang, Hong Wen, Weiwei Fang

Turkish Journal of Electrical Engineering and Computer Sciences

Deep neural networks have recently made remarkable achievements in computer vision applications. However, the high computational requirements needed to achieve accurate inference results can be a significant barrier to deploying DNNs on resource-constrained computing devices, such as those found in the Internet-of-things. In this work, we propose a fresh approach called adaptive channel skipping (ACS) that prioritizes the identification of the most suitable channels for skipping and implements an efficient skipping mechanism during inference. We begin with the development of a new gating network model, ACS-GN, which employs fine-grained channel-wise skipping to enable input-dependent inference and achieve a desirable balance …

Joint Intent Detection And Slot Filling For Turkish Natural Language Understanding, Osman Büyük

Turkish Journal of Electrical Engineering and Computer Sciences

Intent detection and slot filling are two crucial subtasks of a text-based goal-oriented dialogue system. In a goal-oriented dialogue system, users interact with the system to complete a goal (or to fulfill their intent) and provide the necessary information (slot values) to achieve that goal. Therefore, a user?s text input includes information about the user?s intent and contains required slot values. Recently, joint models that simultaneously detect the intent and extract the slots are proposed to benefit from the interaction between the two tasks. The proposed methods are usually tested using benchmark data sets in English such as ATIS and …

Recognizing Handwritten Digits Using Spiking Neural Networks With Learning Algorithms Based On Sliding Mode Control Theory, Yeşi̇m Öni̇z, Mehmet Ayyildiz

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a spiking neural network (SNN) has been proposed for recognizing the digits written on the LCD screen of an experimental setup. The convergence of the learning algorithm has been ensured by using sliding mode control (SMC) theory and the Lyapunov stability method for the adaptation of the network parameters. The spike response model (SRM) has been utilized in the design of the SNN. The performance of the proposed learning scheme has been evaluated both on the experimental data and on the MNIST dataset. The simulated and experimental results of the SNN structure have been compared with the …

Transforming Temporal-Dynamic Graphs Into Time-Series Data For Solving Event Detection Problems, Kutay Taşci, Fuat Akal

Turkish Journal of Electrical Engineering and Computer Sciences

Event detection on temporal-dynamic graphs aims at detecting significant events based on deviations from the normal behavior of the graphs. With the widespread use of social media, many real-world events manifest as social media interactions, making them suitable for modeling as temporal-dynamic graphs. This paper presents a workflow for event detection on temporal-dynamic graphs using graph representation learning. Our workflow leverages generated embeddings of a temporal-dynamic graph to reframe the problem as an unsupervised time-series anomaly detection task. We evaluated our workflow on four distinct real-world social media datasets and compared our results with the related work. The results show …

A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu

Turkish Journal of Electrical Engineering and Computer Sciences

Dyslexia is a learning disorder, characterized by impairment in the ability to read, spell, and decode letters. It is vital to detect dyslexia in earlier stages to reduce its effects. However, diagnosing dyslexia is a time-consuming and costly process. In this paper, we propose a machine-learning model that predicts whether a Turkish-speaking child has dyslexia using his/her audio records. Therefore, our model can be easily used by smart phones and work as a warning system such that children who are likely to be dyslexic according to our model can seek an examination by experts. In order to train and evaluate, …

Hydrophobicity Optimization Of Cathode Catalyst Layer For Proton Exchange Membrane Fuel Cell, Hao-Jie Chen, Mei-Hua Tang, Sheng-Li Chen

Journal of Electrochemistry

Hydrophobicity of the cathode catalyst layers (CCLs) crucially determines the performance of proton exchange membrane fuel cells (PEMFCs) by affecting the transports of oxygen and liquid water. In this regard, polytetrafluoroethylene (PTFE) is usually used as a hydrophobic additive to facilitate the oxygen and water transports in CCLs. So far, there remains lacking systematic effort to optimize the addition methods of PTFE in CCLs and the mechanisms behind. In this work, the effects of the approaches for PTFE addition and the distribution of PTFE on the mass transport of oxygen and the proton conduction in CCLs were studied by using …

Design And Characterization Of 2.4ghz Antennas For Wireless Sensor Applications, Luis Zogby Brennan

Dissertations and Theses

Wireless sensor networks benefit greatly from advances in circuit integration and miniaturization. However, antennas are bound to specific geometries both in terms of fundamental limits and their response to immediate surroundings. As such, various successful techniques for antenna miniaturization must still be carefully tested and characterized as part of the design process of a sensor system. The work presented here fully characterizes a number of miniaturized 2.4GHz antennas and successfully integrates designs into a sensor system using the highly integrated Single Chip Micro Mote (SCµM), which uses a crystal-less radio design resulting in the requirement of only a …

Asymmetric Electrode-Electrolyte Interfaces For High-Performance Rechargeable Lithium-Sulfur Batteries, Jia Chou, Ya-Hui Wang, Wen-Peng Wang, Sen Xin, Yu-Guo Guo

Journal of Electrochemistry

With a high cell-level specific energy and a low cost, lithium-sulfur (Li-S) battery has been intensively studied as one of the most promising candidates for competing the next-generation energy storage campaign. Currently, the practical use of Li-S battery is hindered by the rapidly declined storage performance during battery operation, as caused by irreversible loss of electroactive sulfide species at the cathode, dendrite formation at the anode and parasitic reactions at the electrode-electrolyte interface due to unfavorable cathode-anode crosstalk. In this perspective, we propose to stabilize the Li-S electrochemistry, and improve the storage performance of battery by designing asymmetric electrode-electrolyte interfaces …

Detection Of Cancer-Associated Gene Mutations In Urinary Cell-Free Dna Among Prostate Cancer Patients In South Africa, Dada Oluwaseyi Temilola, Martha Wium, Juliano Paccez, Azola Samkele Salukazana, Solomon O. Rotimi, Hasan H. Otu, Giuseppina M. Carbone, Lisa Kaestner, Stefano Cacciatore, Luiz Fernando Zerbini

Department of Electrical and Computer Engineering: Faculty Publications

Prostate cancer (PCa) is the most common cause of cancer death among African men. The presence of tumor-specific variations in cell-free DNA (cfDNA), such as mutations, microsatellite instability, and DNA methylation, has been explored as a source of biomarkers for cancer diagnosis. In this study, we investigated the diagnostic role of cfDNA among South African PCa patients. We performed whole exome sequencing (WES) of urinary cfDNA. We identified a novel panel of 31 significantly deregulated somatic mutated genes between PCa and benign prostatic hyperplasia (BPH). Additionally, we performed whole-genome sequencing (WGS) on matching PCa and normal prostate tissue in an …

Dual-Site Photoplethysmography Sensing For Noninvasive Continuous-Time Blood Pressure Monitoring Using Artificial Neural Network, Anas Mohmmad Rabab’Ah

Thesis/ Dissertation Defenses

Millions of people worldwide struggle from high blood pressure, often known as hypertension, and it is a major health concern that can lead to serious cardiovascular diseases, including heart attacks and many other consequences. Blood pressure monitoring that is reliable and accurate is crucial to the detection and management of hypertension. Although invasive techniques, such arterial catheterization, are considered to be the most accurate means of evaluating blood pressure, they can be painful, time-consuming and carry a risk of complications.
This thesis presents the development of a real time non-invasive blood pressure monitoring system based on commercially available microcontroller unit …

Spin-Acoustic Control Of Silicon Vacancies In 4h Silicon Carbide, Jonathan R. Dietz, Boyang Jiang, Aaron M. Day, Sunil A. Bhave, Evelyn L. Hu

Purdue University Libraries Open Access Publishing Fund

Bulk acoustic resonators can be fabricated on the same substrate as other components and can operate at various frequencies with high quality factors. Mechanical dynamic metrology of these devices is challenging as the surface information available through laser Doppler vibrometry lacks information about the acoustic energy stored in the bulk of the resonator. Here we report the spin-acoustic control of naturally occurring negatively charged silicon monovacancies in a lateral overtone bulk acoustic resonator that is based on 4H silicon carbide. We show that acoustic driving can be used at room temperature to induce coherent population oscillations. Spin-acoustic resonance is shown …

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 …

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 …

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.

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 …