Computer Engineering Commons^™

Hybrid Scenario Generation Method For Stochastic Virtual Bidding In Electricity Market, Dongliang Xiao,, Wei Qiao

Department of Electrical and Computer Engineering: Faculty Publications

Stochastic optimization can be used to generate optimal bidding strategies for virtual bidders in which the uncertain electricity prices are represented by using scenarios. This paper proposes a hybrid scenario generation method for electricity price using a seasonal autoregressive integrated moving average (SARIMA) model and historical data. The electricity price spikes are first identified by using an outlier detection method. Then, the historical data are decomposed into base and spike components. Next, the base and spike component scenarios are generated by using the SARIMA- and historical data-based methods, respectively. Finally, the electricity price scenarios are obtained by combining the base …

Unconventional Van Der Waals Heterostructures Beyond Stacking, Peter Sutter, Eli A. Sutter

Department of Electrical and Computer Engineering: Faculty Publications

Two-dimensional crystals provide exceptional opportunities for integrating dissimilar materials and forming interfaces where distinct properties and phenomena emerge. To date, research has focused on two basic heterostructure types: vertical van der Waals stacks and laterally joined monolayer crystals with in-plane line interfaces. Much more diverse architectures and interface configurations can be realized in the few-layer and multilayer regime, and if mechanical stacking and single-layer growth are replaced by processes taking advantage of self-organization, conversions between polymorphs, phase separation, strain effects, and shaping into the third dimension. Here, we highlight such opportunities for engineering heterostructures, focusing on group IV chalcogenides, a …

Human-Machine Communication: Complete Volume. Volume 3. Diffusion Of Human-Machine Communication During And After The Covid-19 Pandemic

Human-Machine Communication

This is the complete volume of HMC Volume 3. Diffusion of Human-Machine Communication During and After the COVID-19 Pandemic

What Will Affect The Diffusion Of Ai Agents?, James W. Dearing

Human-Machine Communication

For billions of people, the threat of the Novel Coronavirus SARS-CoV-2 and its variants has precipitated the adoption of new behaviors. Pandemics are radical events that disrupt the gradual course of societal change, offering the possibility that some rapidly adopted innovations will persist in use past the time period of the event and, thus, diffuse more rapidly than in the absence of such an event. Human-machine communication includes a range of technologies with which many of us have quickly become more familiar due to stay-athome orders, distancing, workplace closures, remote instruction, home-bound entertainment, fear of contracting COVID-19, and boredom. In …

The Role Of Vidura Chatbot In The Diffusion Of Knowcovid-19 Gateway, Kerk F. Kee, Prasad P. Calyam, Hariharan Regunath

Human-Machine Communication

The COVID-19 pandemic is an unprecedented global emergency. Clinicians and medical researchers are suddenly thrown into a situation where they need to keep up with the latest and best evidence for decision-making at work in order to save lives and develop solutions for COVID-19 treatments and preventions. However, a challenge is the overwhelming numbers of online publications with a wide range of quality. We explain a science gateway platform designed to help users to filter the overwhelming amount of literature efficiently (with speed) and effectively (with quality), to find answers to their scientific questions. It is equipped with a chatbot …

Communicative Development And Diffusion Of Humanoid Ai Robots For The Post-Pandemic Health Care System, Do Kyun David Kim, Gary Kreps, Rukhsana Ahmed

Human-Machine Communication

As humanoid robot technology, anthropomorphized by artificial intelligence (AI), has rapidly advanced to introduce more human-resembling automated robots that can communicate, interact, and work like humans, we have begun to expect active interactions with Humanoid AI Robots (HAIRs) in the near future. Coupled with the HAIR technology development, the COVID-19 pandemic triggered our interest in using health care robots with many substantial advantages that overcome critical human vulnerabilities against the strong infectious COVID-19 virus. Recognizing the tremendous potential for the active application of HAIRs, this article explores feasible ways to implement HAIRs in health care and patient services and suggests …

Our Future Arrived: Diffusion Of Human-Machine Communication And Transformation Of The World For The Post-Pandemic Era, Do Kyun David Kim, Gary Kreps, Rukhsana Ahmed

Human-Machine Communication

The world is getting into a new phase in history. For the first time, humans are verbally communicating and developing meaningful relationships with non-living objects. AI is a wormhole to open a gateway to the new world, and the COVID-19 pandemic prepared the world to transform its system to be an open system that responds to, communicates with, and utilizes the remnants coming out of the wormhole of the new world. Now, we urgently need to create a holistic discourse on how we can recognize, develop, or shape the identities of communicable machines as people develop a partnership with them. …

On-Site/In Situ Continuous Detecting Ppb-Level Metal Ions In Drinking Water Using Block Loop-Gap Resonators And Machine Learning, Sangmin Oh, Imtiaz Hossen, Juan R. Luglio, Gusphyl Justin, James Richie, Henry Medeiros, Chung-Hoon Lee

Electrical and Computer Engineering Faculty Research and Publications

Microwave measurements and machine learning algorithms are presented to estimate metal ion concentrations in drinking water. A novel block loop gap resonator (BLGR) as a microwave probe is designed and fabricated to estimate Pb ion concentrations in city water as low as 1 ppb with an rms error of 0.18 ppb. No physical contact between the BLGR probe and the water sample allows on-site/in situ continuous detection of ppb-level metal ion concentrations. The S₁₁ raw data (amplitude and phase) from the BLGR are used to classify and estimate metal ion concentrations using a support vector regression algorithm. The performance …

Metasurface Cloaks To Decouple Closely Spaced Printed Dipole Antenna Arrays Fed By A Microstrip-To-Balanced Transmission-Line Transition, Doojin Lee, Alexander B. Yakovlev

Faculty and Student Publications

In this work, we present a numerical study of 1D and 2D closely spaced antenna arrays of microstrip dipole antennas covered by a metasurface in order to properly cloak and decouple the antenna arrays operating at neighboring frequencies. We show that the two strongly coupled arrays fed by a microstrip-to-balanced transmission-line transition are effectively decoupled in 1D and 2D array scenarios by covering the dipole antenna elements with an elliptically shaped metasurface. The metasurface comprises sub-wavelength periodic metallic strips printed on an elliptically shaped dielectric cover around the dipole antennas and integrated with the substrate. We present a practical design …

Precision Grasp Using An Arm-Hand System As A Hybrid Parallel-Serial System: A Novel Inverse Kinematics Solution, Shuwei Qiu, Shuwei Qiu Ph.D., P.Eng.

Electrical and Computer Engineering Publications

In this letter, we present a novel inverse kinematics (IK) solution for a robotic arm-hand system to achieve precision grasp. This problem is kinematically over-constrained and to address the issue and to solve the problem, we propose a new approach with three key insights. First, we propose a human-inspired thumb-first strategy and consider one finger of the robotic hand as the “thumb” to narrow down the search space and increase the success rate of our algorithm. Second, we formulate the arm-thumb serial chain as a closed chain such that the entire arm-hand system is controlled as a hybrid parallel-serial system. …

Reinforcement Learning Algorithms: An Overview And Classification, Fadi Almahamid, Katarina Grolinger

Electrical and Computer Engineering Publications

The desire to make applications and machines more intelligent and the aspiration to enable their operation without human interaction have been driving innovations in neural networks, deep learning, and other machine learning techniques. Although reinforcement learning has been primarily used in video games, recent advancements and the development of diverse and powerful reinforcement algorithms have enabled the reinforcement learning community to move from playing video games to solving complex real-life problems in autonomous systems such as self-driving cars, delivery drones, and automated robotics. Understanding the environment of an application and the algorithms’ limitations plays a vital role in selecting the …

Evaluating Testing Procedures For Openflow Controller Network Re-Provisioning Time, Steven J. Jensen

Theses and Dissertations

Software-Defined Networking promises several advantages over traditional networking architectures, but has seen little adoption. Recently codified controller evaluation methodologies have seen little validation without strong statistical analysis of the results. The research developed an environment testing implementing a published Network Re-provisioning Time methodology to evaluate five OpenFlow controllers. The methodology is strong with required parameters but had issues with some edge cases. Further refinement and adding a convergence metric may close the gaps.

A Cdzntese Gamma Spectrometer Trained By Deep Convolutional Neural Network For Radioisotope Identification, Sandeep K. Chaudhuri, Joshua W. Kleppinger, Ritwik Nag, Kaushik Roy, Rojina Panta, Forest Agostinelli, Amit Sheth, Utpal N. Roy, Ralph B. James, Krishna C. Mandal

Publications

We report the implementation of a deep convolutional neural network to train a high-resolution room-temperature CdZnTeSe based gamma ray spectrometer for accurate and precise determination of gamma ray energies for radioisotope identification. The prototype learned spectrometer consists of a NI PCI 5122 fast digitizer connected to a pre-amplifier to recognize spectral features in a sequence of data. We used simulated preamplifier pulses that resemble actual data for various gamma photon energies to train a CNN on the equivalent of 90 seconds worth of data and validated it on 10 seconds worth of simulated data.

Modeling The Effect Of The Covid-19 Pandemic On Azithromycin Prescription In General Practices Across The Uk, Oluwasegun Isaac Daramola

altREU Projects

In the early months of the COVID-19 pandemic, it was reported that some antibiotics were prescribed as a remedy for viral treatment and prophylaxis based on non-randomized, uncontrolled short clinical trials. A major antibiotic consulted being Azithromycin; a broad-spectrum macrolide selected based on its immunomodulatory effects in chronic inflammatory lung diseases, with a seasonal prescription increase of 21.5% in March 2020 compared to March 2019.

To analyze the effect and possible antimicrobial resistance impact of the pandemic on Azithromycin prescription across general practices in the United Kingdom (UK), this study uses a time series decomposition modeling method to compare a …

Developing A Strategy For Creating Affordable Student Housing Solutions, Juan D. Campolargo

altREU Projects

College is becoming more and more expensive, and students are graduating with more and more debt. In 2021, we have almost 1.6 trillion dollars of student loan debt. While students are in college, they need a place to live, and this project will be about how to develop a strategy for creating affordable student housing solutions.

Housing should not be another cause of concern to students or really anyone.

The way it’s done is that people get more loans to pay for housing, or they work as much as they can when they’re not studying to pay for rent. Affordable …

Forensic Artifact Finder (Forensicaf): An Approach & Tool For Leveraging Crowd-Sourced Curated Forensic Artifacts, Tyler Balon, Krikor Herlopian, Ibrahim Baggili, Cinthya Grajeda-Mendez

Electrical & Computer Engineering and Computer Science Faculty Publications

Current methods for artifact analysis and understanding depend on investigator expertise. Experienced and technically savvy examiners spend a lot of time reverse engineering applications while attempting to find crumbs they leave behind on systems. This takes away valuable time from the investigative process, and slows down forensic examination. Furthermore, when specific artifact knowledge is gained, it stays within the respective forensic units. To combat these challenges, we present ForensicAF, an approach for leveraging curated, crowd-sourced artifacts from the Artifact Genome Project (AGP). The approach has the overarching goal of uncovering forensically relevant artifacts from storage media. We explain our approach …

Forensicast: A Non-Intrusive Approach & Tool For Logical Forensic Acquisition & Analysis Of The Google Chromecast Tv, Alex Sitterer, Nicholas Dubois, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

The era of traditional cable Television (TV) is swiftly coming to an end. People today subscribe to a multitude of streaming services. Smart TVs have enabled a new generation of entertainment, not only limited to constant on-demand streaming as they now offer other features such as web browsing, communication, gaming etc. These functions have recently been embedded into a small IoT device that can connect to any TV with High Definition Multimedia Interface (HDMI) input known as Google Chromecast TV. Its wide adoption makes it a treasure trove for potential digital evidence. Our work is the primary source on forensically …

Machine Learning For Analog/Mixed-Signal Integrated Circuit Design Automation, Weidong Cao

McKelvey School of Engineering Graduate Student Theses & Dissertations

Analog/mixed-signal (AMS) integrated circuits (ICs) play an essential role in electronic systems by processing analog signals and performing data conversion to bridge the analog physical world and our digital information world.Their ubiquitousness powers diverse applications ranging from smart devices and autonomous cars to crucial infrastructures. Despite such critical importance, conventional design strategies of AMS circuits still follow an expensive and time-consuming manual process and are unable to meet the exponentially-growing productivity demands from industry and satisfy the rapidly-changing design specifications from many emerging applications. Design automation of AMS IC is thus the key to tackling these challenges and has been …

Duck Hunt: Memory Forensics Of Usb Attack Platforms, Tyler Thomas, Mathew Piscitelli, Bhavik Ashok Nahar, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

To explore the memory forensic artifacts generated by USB-based attack platforms, we analyzed two of the most popular commercially available devices, Hak5's USB Rubber Ducky and Bash Bunny. We present two open source Volatility plugins, usbhunt and dhcphunt, which extract artifacts generated by these USB attacks from Windows 10 system memory images. Such artifacts include driver-related diagnostic events, unique device identifiers, and DHCP client logs. Our tools are capable of extracting metadata-rich Windows diagnostic events generated by any USB device. The device identifiers presented in this work may also be used to definitively detect device usage. Likewise, the DHCP logs …

Another Brick In The Wall: An Exploratory Analysis Of Digital Forensics Programs In The United States, Syria Mccullough, Stella Abudu, Ebere Onwubuariri, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

We present a comprehensive review of digital forensics programs offered by universities across the United States (U.S.). While numerous studies on digital forensics standards and curriculum exist, few, if any, have examined digital forensics courses offered across the nation. Since digital forensics courses vary from university to university, online course catalogs for academic institutions were evaluated to curate a dataset. Universities were selected based on online searches, similar to those that would be made by prospective students. Ninety-seven (n = 97) degree programs in the U.S. were evaluated. Overall, results showed that advanced technical courses are missing from curricula. We …

Comparison Of Concrete And Steel Jacket Methods For Reinforcing A Concrete Bridge Pier By Numerical And Experimental Studies, Hadi Faghihmaleki

Makara Journal of Technology

Various rehabilitation and strengthening procedures have been developed in recent decades. A study to determine which methods can be implemented to increase the useful life of the bridge before strengthening must be performed. In this study, the seismic behavior of a reinforced concrete bridge pier with dimension 3.5 × 3.5 m, which was reinforced by two steel and a concrete jacket, was investigated. Nonlinear geometric models and materials were analyzed to estimate the seismic parameters of the pier. Results show an increase in energy absorption, ultimate strength, and ductility for the steel jacket, as well as a greater increase in …

Effect Of Arc Plasma Sintering On The Structural And Microstructural Properties Of Fe-Cr-Ni Austenitic Stainless Steels, Parikin Parikin, M. Dani, A. Dimyati, N. D. Purnamasari, B. Sugeng, M. Panitra, A. Insani, T. H. Priyanto, S. Mustofa, Syahbuddin Syahbuddin, A. Huang

Makara Journal of Technology

X-ray diffraction techniques were performed to determine the actual crystal structure of A2 austenitic stainless steel (ASS) as-cast and A2 ASS after arc plasma sintering (APS) for 2 s. Computations were conducted on the basis of the Bragg arithmetic formula by comparing the S2 arithmetic with the interplanar spacing. The Bragg arithmetic formula is a simple series for the determination of the crystalline phase of materials based on the Miller indices of cubic shapes or other shapes. A2 ASS as-cast was identified to have a crystal structure of face-centered cubic with lattice parameter a = 3.58 Å. A similar crystal …

Trend Significance Levels Of Rain Onset And Cessation And Lengths Of The Wet And Dry Seasons In Epe, Lagos State, Nigeria, Adekunle Adedeji Alli, Olorunwa Eric Omofunmi

Makara Journal of Technology

This study aims to reduce crop failure resulting from a dry spell, which is the first occurrence of unsustainable rainfall that deceives farmers into planting. Thus, trends need to be tested using hypothesis, kurtosis, and other statistics to analyze the risks associated with unstable planting calendars and their possible mitigation strategies. The daily rainfall data from 1982 to 2018 were obtained from the archive of NASA/POWER SRB/FLASH, and the research location is Epe, Lagos State, Nigeria, which lies at latitude 6.585° N and longitude 3.962° E. Microsoft Excel was used to perform the tests. Results show that the null hypothesis …

Kinetic Modeling Study Of Laminar Burning Velocity Of Gasoline–Ethanol–Methanol Blends At Elevated Temperature And Pressure, Ahmad Syihan Auzani, Cahyo Setyo Wibowo, Riesta Anggarani, Yulianto Sulistyo Nugroho, Bambang Sugiarto

Makara Journal of Technology

Gasoline–ethanol–methanol (GEM) blends have been considered to replace pure gasoline as spark ignition engine fuel. Their physical and chemical properties and performance and emission measurements from real engines have been reported previously. However, a fundamental study that can explain the unique results of GEM compared with those of pure gasoline is lacking. This study aims to compare the laminar burning velocity of GEM blends at different mixtures, equivalence ratios, temperatures, and pressures with that of pure gasoline. A laminar flame propagation model and reaction mechanisms from the literature were were for a numerical simulation. In this study, the chemical components …

Improvement Of Electrical Properties Of Cmc-Pva Doped With Various Contents Of Lino3 As An Application For Hybrid Polymer Electrolytes, Amalia Zulkifli, Norfatihah Mazuki, Ahmad Salihin Samsudin

Makara Journal of Technology

The present work was carried out with the development of hybrid polymer electrolytes (HPEs) by using carboxymethyl cellulose (CMC)–polyvinyl alcohol (PVA) doped with different contents of lithium nitrate (LiNO3) for the determination of their structural and conduction properties. The structural analysis was conducted by using Fourier transform infra-red spectroscopy and showed the interaction between the blend host polymer and ionic dopant, which formed via the coordinating site of CMC-PVA and Li+-NO3−. The complexes of CMC-PVA doped LiNO3 led to an increment in ionic conductivity, as observed by electrical impedance spectroscopy, and the sample containing 20 wt% LiNO3 obtained the highest …

Music Information Retrieval Based On Active Frequency, Hardianto Wibowo, Wildan Suharso, Yufis Azhar, Galih Wasis Wicaksono, Agus Eko Minarno, Dani Harmanto

Makara Journal of Technology

Music is the art of combining frequencies. A balance of frequencies gives rise to a harmonious tone. Several features of music can be analyzed, and they include sociocultural background, lyrics, mood, tempo, rhythm, harmony, melody, timbre, and instrumentation. In this study, we use the frequency of instrumentation as a feature for classification because each instrument has a frequency range. To test this frequency range, we use five music genres and one music playing skill. The five genres are dangdut, electronic dance music (EDM), metal, pop/rock, and reggae. The music playing skill is acoustic. Active frequencies are tested using the k-nearest …

Predictive Delivery Man Assignment Problem Using Deep Learning, Rahmadini Payla Juarsa, Taufik Djatna

Makara Journal of Technology

Dispatching is a critical part in current online shopping. It relates to how the delivery man assignment should minimize cost along with the service from a source to an end customer with an appropriate scheduled time. The problem arises as neither enough products to deliver nor delivery men are available for dispatch, resulting in suboptimal service and a waste of money. The study aimed to formulate the cost of restaurant dispatching for inducing a deep learning-based solution with the gated recurrent unit recurrent neural network to receive hourly order data and to engage the result for near feature delivery man …

Hardware For Quantized Mixed-Precision Deep Neural Networks, Andres Rios

Open Access Theses & Dissertations

Recently, there has been a push to perform deep learning (DL) computations on the edge rather than the cloud due to latency, network connectivity, energy consumption, and privacy issues. However, state-of-the-art deep neural networks (DNNs) require vast amounts of computational power, data, and energyâ??resources that are limited on edge devices. This limitation has brought the need to design domain-specific architectures (DSAs) that implement DL-specific hardware optimizations. Traditionally DNNs have run on 32-bit floating-point numbers; however, a body of research has shown that DNNs are surprisingly robust and do not require all 32 bits. Instead, using quantization, networks can run on …

Signal Fingerprinting And Machine Learning Framework For Uav Detection And Identification., Olusiji Oloruntobi Medaiyese

Electronic Theses and Dissertations

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into …

Flight Trajectory Prediction For Aeronautical Communications., Nathan T Schimpf

Electronic Theses and Dissertations

The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing a limited spectrum among aircraft and ground systems. Emerging approaches to this spectrum allocation task mostly consider machine learning techniques reliant on aircraft and Air Traffic Control (ATC) sector data. Much of this data, however, is not directly available. This thesis considers the development of two such data products: the 4D trajectory (latitude, longitude, altitude, and time) of aircraft, and the anticipated airspace utilization and communication demand within an ATC sector. Data predictions are treated as a time …