Computer Engineering Commons^™

A Density Functional Theory Study On Using Montmorillonite To Reduce Air Pollution, Triati Dewi Kencana Wungu, Meqorry Yusfi, Suprijadi Suprijadi

Makara Journal of Technology

In this study, density functional theory (DFT) method is used to investigate the possibility of using a smectite clay mineral called montmorillonite (MMT) in reducing heavy metals, such as Cd, through Cd adsorption. The mechanism of Cd adsorption in MMT is observed theoretically, and the tetrahedrally isomorphic substitution on the upper layer of MMT is considered to observe the role of Al and Fe in strengthening Cd adsorption. Two types of MMT are modeled in this study: Al-MMT and Fe-MMT. The Al-MMT means that Al substitutes one atom in the upper tetrahedral layer of MMT, while Fe-MMT means that Fe …

Influence Of Lithium Bromide On Electrical Properties In Bio-Based Polymer Electrolytes, Ahmad Faizrin Fuzlin, Bouchta Sahraoui, Ahmad Salihin Samsudin

Makara Journal of Technology

This research presents the influence of lithium bromide (LiBr) on the electrical properties of alginate in bio-based polymer electrolytes (BBPEs) system. Bio-based alginate was prepared using the solution casting technique with various LiBr compositions. The ionic conductivity and electrical properties of the prepared BBPEs samples were studied using electrical impedance spectroscopy over a frequency range of 50 Hz–1 MHz. A maximum ionic conductivity of 7.46 × 10−5 S cm-1 was obtained for a sample containing 15 wt. % lithium bromide-doped alginate BBPEs at ambient temperature (303 K). The electrical analysis revealed that the most conductive sample based on alginate-LiBr BBPEs …

Use Of Viscoplastic Damper For Improving The Resistance Of Steel Frames To Blast Loading, Gholamreza Abdollahzadeh, Hadi Faghihmaleki, Hamed Hamidi Jamnani, Atefeh Ebrahimi Bardar

Makara Journal of Technology

In this paper, we evaluated the effect of viscoplastic dampers on the response of steel frames under blast loading. We used SAP2000 software and link elements to investigate the responses of nine-story steel frames with and without dampers. The proposed viscoplastic damper is a new type of viscous damper. The application of this damper is based on the availability of its constituent materials. The damper acts as a viscoelastic damper at low levels of vibration, but it acts as a combination of viscoelastic operator and metal-yielding device at extreme levels of vibration. With respect to the height of the structure, …

Object Recognition And Voice Assistant With Augmented Reality, Juan Estrella

Publications and Research

Our research project aims to provide a visually impaired person with a superimposed map that will guide the individual to the desired destination through a voice controlled virtual assistant application that integrates Augmented Reality (AR) with Artificial Intelligence (AI) Computer Vision and Natural Language Processing (subfields of AI) will be combined to identify the spatial environment and then create a graphic enhancement that provides the most direct route to the specific destination These technologies will be incorporated into the Microsoft Hololens which will be controlled by the user.

Representational Learning Approach For Predicting Developer Expertise Using Eye Movements, Sumeet Maan

School of Computing: Dissertations, Theses, and Student Research

The thesis analyzes an existing eye-tracking dataset collected while software developers were solving bug fixing tasks in an open-source system. The analysis is performed using a representational learning approach namely, Multi-layer Perceptron (MLP). The novel aspect of the analysis is the introduction of a new feature engineering method based on the eye-tracking data. This is then used to predict developer expertise on the data. The dataset used in this thesis is inherently more complex because it is collected in a very dynamic environment i.e., the Eclipse IDE using an eye-tracking plugin, iTrace. Previous work in this area only worked on …

Bibliometric Analysis Of The Literature In The Field Of Information Technology Relatedness, Ilham M, Anis Eliyana, Praptini Yulianti

Library Philosophy and Practice (e-journal)

This bibliometric describe Information Technology Relateness is defined as the use of information technology infrastructure and information technology management processes betweeWas this submission previously published in a journal? Bepress will automatically create an OpenURL for published articles. Learn more about OpenURLsn business units together. There is not much research on Information Technology Relateness by providing a big picture that is visualized from year to year. This study aims to map research in the field of Information Technology Relateness with data from all international research publications. This study performs a bibliometric method and analyzes research data using the Services Analyze …

Improve The Prototype Of Low-Cost Near-Infrared Diffuse Optical Imaging System, Chen Xu, Mohammed Z. Shakil

Publications and Research

Diffuse Optical Tomography (DOT) and Optical Spectroscopy using near-infrared (NIR) diffused light has demonstrated great potential for the initial diagnosis of tumors and in the assessment of tumor vasculature response to neoadjuvant chemotherapy. The aims of this project are 1) to test the different types of LEDs in the near-infrared range, and design the driving circuit, and test the modulation of LEDs at different frequencies; 2) to test the APDs as a detector, and build the receiver system and compare efficiency with pre-built systems. In this project, we are focusing on creating a low-cost infrared transmission system for tumor and …

Evaluation Of Characteristics Of Wireless Sensor Networks With Analytical Modeling, Halim Khujamatov, Ernazar Reypnazarov, Doston Hasanov, Elaman Nurullaev, Shahzod Sobirov

Bulletin of TUIT: Management and Communication Technologies

In this paper, a mathematical model of the operation process of the ZigBee standard wireless network for remote monitoring systems based on the Markov chain apparatus was developed. Using the model was evaluated of the operation process of the CSMA/CA algorithm of the MAC level of the IEEE 802.15.4 ZigBee standard. The peculiarity of this mathematical model is that it takes into account the level of loading of network elements and potential distortions in the transmitted packets as a result of the influence of interference. Using the developed mathematical model, the main characteristics of the network operation process, such as …

Predicting Residential Energy Consumption Using Wavelet Decomposition With Deep Neural Network, Dagimawi Eneyew, Miriam A M Capretz, Girma Bitsuamlak, London Hydro

Electrical and Computer Engineering Publications

Electricity consumption is accelerating due to economic and population growth. Hence, energy consumption prediction is becoming vital for overall consumption management and infrastructure planning. Recent advances in smart electric meter technology are making high-resolution energy consumption data available. However, many parameters influencing energy consumption are not typically monitored for residential buildings. Therefore, this study’s main objective is to develop a data-driven energy consumption forecasting model (next-hour consumption) for residential houses solely based on analyzing electricity consumption data. This research proposes a deep neural network architecture that combines stationary wavelet transform features and convolutional neural networks. The proposed approach utilizes automatically …

Deep Neural Network For Load Forecasting Centred On Architecture Evolution, Santiago Gomez-Rosero, Miriam A M Capretz, London Hydro

Electrical and Computer Engineering Publications

Nowadays, electricity demand forecasting is critical for electric utility companies. Accurate residential load forecasting plays an essential role as an individual component for integrated areas such as neighborhood load consumption. Short-term load forecasting can help electric utility companies reduce waste because electric power is expensive to store. This paper proposes a novel method to evolve deep neural networks for time series forecasting applied to residential load forecasting. The approach centres its efforts on the neural network architecture during the evolution. Then, the model weights are adjusted using an evolutionary optimization technique to tune the model performance automatically. Experimental results on …

Transfer-To-Transfer Learning Approach For Computer Aided Detection Of Covid-19 In Chest Radiographs, Barath Narayanan Narayanan, Russell C. Hardie, Vignesh Krishnaraja, Christina Karam, Venkata Salini Priyamvada Davuluru

Electrical and Computer Engineering Faculty Publications

The coronavirus disease 2019 (COVID-19) global pandemic has severely impacted lives across the globe. Respiratory disorders in COVID-19 patients are caused by lung opacities similar to viral pneumonia. A Computer-Aided Detection (CAD) system for the detection of COVID-19 using chest radiographs would provide a second opinion for radiologists. For this research, we utilize publicly available datasets that have been marked by radiologists into two-classes (COVID-19 and non-COVID-19). We address the class imbalance problem associated with the training dataset by proposing a novel transfer-to-transfer learning approach, where we break a highly imbalanced training dataset into a group of balanced mini-sets and …

Proportional Voting Based Semi-Unsupervised Machine Learning Intrusion Detection System, Yang G. Kim, Ohbong Kwon, John Yoon

Publications and Research

Feature selection of NSL-KDD data set is usually done by finding co-relationships among features, irrespective of target prediction. We aim to determine the relationship between features and target goals to facilitate different target detection goals regardless of the correlated feature selection. The unbalanced data structure in NSL-KDD data can be relaxed by Proportional Representation (PR). However, adopting PR would deny the notion of winner-take-all by attracting a majority of the vote and also provide a fairly proportional share for any grouping of like-minded data. Furthermore, minorities and majorities would get a fair share of power and representation in data structure …

Using Agile Methodology To Create A Working Software Application, Aaron Cuadras

Honors Theses

This thesis explains the importance of Agile in the process of writing a web application. A team of three developers worked for sixteen weeks to create a booking site able to hold important information related to customers and hotel owners. The framework used for the project is Ruby on Rails and we stress the importance of the Models-View-Controller pattern and its relation with the Agile methodology, the problem of creating a new application is the pace in which everything should be written as well as the ability to write features independent from one another. The results that we expect are …

Deep Reinforcement Learning For Collaborative Edge Computing In Vehicular Networks, Mushu Li, Jie Gao, Lian Zhao, Xuemin Shen

Electrical and Computer Engineering Faculty Research and Publications

Mobile edge computing (MEC) is a promising technology to support mission-critical vehicular applications, such as intelligent path planning and safety applications. In this paper, a collaborative edge computing framework is developed to reduce the computing service latency and improve service reliability for vehicular networks. First, a task partition and scheduling algorithm (TPSA) is proposed to decide the workload allocation and schedule the execution order of the tasks offloaded to the edge servers given a computation offloading strategy. Second, an artificial intelligence (AI) based collaborative computing approach is developed to determine the task offloading, computing, and result delivery policy for vehicles. …

Imparting 3d Representations To Artificial Intelligence For A Full Assessment Of Pressure Injuries., Sofia Zahia

Electronic Theses and Dissertations

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning …

Authentication Based On Blockchain, Norah Alilwit

Doctoral Dissertations and Master's Theses

Across past decade online services have enabled individuals and organizations to perform different types of transactions such as banking, government transactions etc. The online services have also enabled more developments of applications, at cheap cost with elastic and scalable, fault tolerant system. These online services are offered by services providers which are use authentication, authorization and accounting framework based on client-server model. Though this model has been used over decades, study shows it is vulnerable to different hacks and it is also inconvenient to use for the end users. In addition, the services provider has total control over user data …

A Circle Hough Transform Implementation Using High-Level Synthesis, Carlos Lemus

UNLV Theses, Dissertations, Professional Papers, and Capstones

Circle Hough Transform (CHT) has found applications in biometrics, robotics, and imageanalysis. In this work, the focus is the development of a Field Programmable Gate Array (FPGA) based accelerator that performs a series of procedures and results in circle detection. The design is performed using Vivado High-Level Synthesis (HLS) tools and targeted for a Zynq UltraScale+ ZCU106. The implementation includes the following procedures: Gaussian filter, Sobel edge operator, thresholding, and finally the CHT algorithm. The performance is evaluated based on the execution time as compared to the software (Python code) execution and the analysis tools provided by Vivado HLS tool. …

A Multi-Channel Mcp-Pmt Based Readout Integrated Circuit For Lidar Applications, Sachin Purushothaman Namboodiri

UNLV Theses, Dissertations, Professional Papers, and Capstones

Photon counting techniques are becoming more critical in fields such as LiDAR, high energy physics (HEP), and positron emission tomography (PET). For space-based aerosol-cloud-ocean (ACO) LiDAR, the total aggregate photon flux signal has a very high dynamic range, from a single-photon up to giga-photons per second for a single channel. This dissertation focuses on the design of a multichannel, photon counting readout circuit that can interface with MCP-PMTs for high dynamic range, space-based LiDAR applications. Chapter 2 presents the conventional current mode approach that has been employed to realize a photon counting circuit. A transimpedance amplifier, a 6-bit delay line …

Towards Sensorimotor Coupling Of A Spiking Neural Network And Deep Reinforcement Learning For Robotics Application, Kashu Yamazaki

Mechanical Engineering Undergraduate Honors Theses

Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the great achievements of deep reinforcement learning in various domains including finance,medicine, healthcare, video games, robotics and computer vision.Deep neural network was started with multi-layer perceptron (1stgeneration) and developed to deep neural networks (2ndgeneration)and it is moving forward to spiking neural networks which are knownas3rdgeneration of neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically-realistic models of neurons to carry out computation. In this thesis, we first provide a comprehensive review …

Global Privacy Concerns Of Facial Recognition Big Data, Myranda Westbrook

Honors Theses

Facial recognition technology is a system of automatic acknowledgement that recognizes individuals by categorizing specific features of their facial structure to link the scanned information to stored data. Within the past few decades facial recognition technology has been implemented on a large scale to increase the security measures needed to access personal information. This has been specifically used in surveillance systems, social media platforms, and mobile device access control. The extensive use of facial recognition systems has created challenges as it relates to biometric information control and privacy concerns. This concern raises the cost and benefit analysis of an individual’s …

Cloud Computing Service Interoperability And Architectural Concepts, Anmar Salih

Theses and Dissertations

Today’s market with a vast number of Cloud-computing providers creates a challenge for practical cooperation between the various provider cloud service platforms. Not only does Cloud interoperability provide this needed cooperation, but it also avoids vendor lock-in and, additionally, saves time and cost. Although there is no established definition for Cloud interoperability, most researchers agree on the purposes of Cloud interoperability. Research and literature have attempted to explain interoperability as transferring data, moving workloads, and migrating virtual machines between Cloud platforms. Transferring data between Clouds refers to objects migrating between provider-specific domains. In comparison, data migration is the most commonly …

Docs_On_Blocks – A Defense In Depth Strategy For E-Healthcare, Saad Mohammed

Electronic Theses, Projects, and Dissertations

With the increase in the data breaches and cyber hacks, organizations have come to realize that cyber security alone would not help as the attacks are becoming more sophisticated and complex than ever. E-Healthcare industry has shown a promising improvement in terms of security over the past, but the threat remains. Thus, the E-Healthcare industries are aiming towards a Defense in Depth Strategy approach.

The project here describes how a Defense in Depth Strategy for E-Healthcare system can provide an environment for better security of the data and peer-to-peer interaction with stakeholders. The legacy systems have at some point failed …

Emergency Landing And Guidance System, Joseph Alarid

Master's Theses

Every year there are thousands of aviation accidents along with hundreds of human deaths that happen around the world. While the data is sparse, it is well documented that many of these happen from emergency landings gone awry. While pilots can generally make great landings in clear daytime conditions, they are significantly handicapped when it comes to landing at night or amongst poor visibility conditions.

Due to the nature of this problem and some of the large scale advances in software technology we propose a solution that provides a significant improvement from the status quo. Using transfer learning on neural …

Analysing User Experience Of Mobile Banking Applications In Nigeria: A Text Mining Approach, Omotosho Babatunde S.

CBN Journal of Applied Statistics (JAS)

This paper analyses textual data mined from 37,460 reviews written by mobile banking application users in Nigeria over the period November 2012 – July 2020. On a scale of 1 to 5 (5 being the best), the average user rating for the twenty-two apps included in our sample is 3.5; with the apps deployed by non-interest banks having the highest average rating of 4.0 and those by commercial banks with national authorisation having the least rating of 3.4. Results from the sentiment analysis reveal that the share of positive sentiment words (17.8%) in the corpus more than double that of …

Decentralized Event-Triggered Control In The Presence Of Adversaries, Paul Griffioen, Raffaele Romagnoli, Bruce H. Krogh, Bruce Sinopoli

Faculty Work Comprehensive List

Decentralized control systems are widely used in a number of situations and applications. In order for these systems to function properly and achieve their desired goals, information must be propagated between agents. However, communication between agents entails connecting to a network, potentially allowing adversaries to infiltrate the system through the network and attack multiple agents. To increase resiliency against these attacks, it is desirable for agents to operate disconnected from the network as much as possible, only communicating with other agents when it is absolutely necessary to achieve their goal or to maintain the safety of the overall system. This …

Onboard Autonomous Controllability Assessment For Fixed Wing Suavs, Brian Edward Duvall

Mechanical & Aerospace Engineering Theses & Dissertations

Traditionally fixed-wing small Unmanned Arial Vehicles (sUAV) are flown while in direct line of sight with commands from a remote operator. However, this is changing with the increased popularity and ready availability of low-cost flight controllers. Flight controllers provide fixed-wing sUAVs with functions that either minimize or eliminate the need for a remote operator. Since the remote operator is no longer controlling the sUAV, it is impossible to determine if the fixed-wing sUAV has proper control authority. In this work, a controllability detection system was designed, built, and flight-tested using COTS hardware. The method features in-situ measurement and analysis of …

Efficient Edge Analytics: Addressing Cyber-Physical Masint With Machine Learning On Audio At The Edge, David Elliott

Theses and Dissertations

With the growth of the Internet of Things and the rise of Big Data, data processing and machine learning applications are being moved to cheap and low size, weight, and power (SWaP) devices at the edge, often in the form of mobile phones, embedded systems, or microcontrollers. The field of Cyber-Physical Measurements and Signature Intelligence (MASINT) makes use of these devices to analyze and exploit data in ways not otherwise possible, which results in increased data quality, increased security, and decreased bandwidth. However, methods to train and deploy models at the edge are limited, and models with sufficient accuracy are …

Framework For Reasoning With Speech Processing, Ahmed Laarfi

Theses and Dissertations

It is known that programming languages are textual. We try here to take the advantages of Speech Recognition (SR) and employ them in creating a verbal Language, which takes its instruction from the voice. Because this work is a novel approach in the programming world, we could not find any resources. This dissertation aims to make essential developments in Speech Recognition (SR)and Artificial Intelligence by constructing a new compiler that receives commands verbally and executes them. That means entering data into the Computer by voice commands. This method of input means that we link several major computer topics with several …

Modern Standard Arabic Speech Recognition: Using Formants Measurements To Extract Vowels From Arabic Words’ Consonant-Vowel-Consonant-Vowel Structure, Mohamed Ali Alshaari

Theses and Dissertations

Arabic texts suffer from missing diacritics (short vowels) which become obstacles for new learners. Speech Recognition is the translation of words spoken to text through intelligent computer programs. As of today, it has been integrated into many computer systems. Arabic Speech Recognition has made progress over the years, but it is still not as good as English speech recognition due to the problem of short vowels not being recognized. This is mainly because the Arabic language is unlike the English language in the nature because it is a Semitic language. This is reflected in different characteristics such as grammar, morphology, …

Medical Knowledge-Enriched Textual Entailment Framework, Shweta Yadav, Vishal Pallagani, Amit P. Sheth

Publications

One of the cardinal tasks in achieving robust medical question answering systems is textual entailment. The existing approaches make use of an ensemble of pre-trained language models or data augmentation, often to clock higher numbers on the validation metrics. However, two major shortcomings impede higher success in identifying entailment: (1) understanding the focus/intent of the question and (2) ability to utilize the real-world background knowledge to capture the context beyond the sentence. In this paper, we present a novel Medical Knowledge-Enriched Textual Entailment framework that allows the model to acquire a semantic and global representation of the input medical text …