Other Electrical and Computer Engineering Commons^™

Virtual Network Function Embedding Under Nodal Outage Using Deep Q-Learning, Swarna Bindu Chetty, Hamed Ahmadi, Sachin Sharma, Avishek Nag

Articles

With the emergence of various types of applications such as delay-sensitive applications, future communication networks are expected to be increasingly complex and dynamic. Network Function Virtualization (NFV) provides the necessary support towards efficient management of such complex networks, by virtualizing network functions and placing them on shared commodity servers. However, one of the critical issues in NFV is the resource allocation for the highly complex services; moreover, this problem is classified as an NP-Hard problem. To solve this problem, our work investigates the potential of Deep Reinforcement Learning (DRL) as a swift yet accurate approach (as compared to integer linear ...

Households Electricity Consumption Analysis: A Bibliometric Approach, Gaikwad Sachin Ramnath, Harikrishnan R

Library Philosophy and Practice (e-journal)

The household electricity consumption ranks in the second position after the industrial electricity consumption, across the globe. This is because of the factors, which include, higher consumer income, electrification, digitalization and advancement in technologies. Moreover, the electricity demand is also depending on household characteristics, non-household characteristics and occupant’s behavior. The aim behind this study is to provide insights to researchers on household electricity consumption areas with different aspects, which includes factors affecting them, need of data collection, its approaches and techniques. To know about the research dedicated to the above-mentioned aspects, it is crucial to explore the Scopus database ...

Technique Design Of Filter On Surface-Acoustic Waves, Dilmurod Abdujalilovich Davronbekov, Zafar Tulyaganovich Khakimov

Acta of Turin Polytechnic University in Tashkent

The article describes the design of filters on surface acoustic waves (SAWs), which are selective elements and are widely used in blocks, devices and signal processing systems in telecommunications, including wireless communication systems such as cellular, trunking and others. Designs of filters for surfactants are considered, which are divided into two large groups - by functional purpose and by a systematic approach to the synthesis of performance characteristics of filters for surfactants. Optimization algorithms and methods for synthesizing surfactant filters have been developed and improved. An improved technique for the synthesis of a filter based on surface acoustic waves is proposed ...

Solar Photovoltaic Performance Monitoring: A Bibliometric Review, Research Gaps And Opportunities, Javed Sayyad, Paresh Nasikkar

Library Philosophy and Practice (e-journal)

Electrical power generation has been revolutionized by growing demand and use of Renewable Energy (RE) sources such as Solar Photovoltaic (SPV) as the main electricity source in modern times. The main objective of this bibliometric analysis is to understand the scope of the literature available for SPV performance characterization. This detailed reviewed was performed on the documents related to SPV research considering all the subject categories from Scopus and Web of Science (WoS) databases. The patterns for the particular set of keywords were broke down with the recuperated outcomes from Scopus database in the language, publication type, year of publication ...

Protecting Health Data In A Pandemic: A Systematic Adversarial Threat Analysis Of Contact Tracing Apps, Leah Krehling

Electronic Thesis and Dissertation Repository

In this thesis centralized, decentralized, Bluetooth, and GPS based applications of digital contact tracing were reviewed and assessed. Using privacy principles created by a contingent of security and privacy experts from across Canada, a metric of assessing an application’s privacy was created. An attack tree was built to assess the security of the contact tracing applications. Eighteen attacks were theorized against contact tracing applications currently in use. An application’s vulnerability to the attacks was measured using a scoring system developed for this purpose. The results of the security scores were used to create a metric for assessing the ...

Longitudinal Partitioning Waveform Relaxation Methods For The Analysis Of Transmission Line Circuits, Tarik Menkad

Electronic Thesis and Dissertation Repository

Three research projects are presented in this manuscript. Projects one and two describe two waveform relaxation algorithms (WR) with longitudinal partitioning for the time-domain analysis of transmission line circuits. Project three presents theoretical results about the convergence of WR for chains of general circuits.

The first WR algorithm uses a assignment-partition procedure that relies on inserting external series combinations of positive and negative resistances into the circuit to control the speed of convergence of the algorithm. The convergence of the subsequent WR method is examined, and fast convergence is cast as a generic optimization problem in the frequency-domain. An automatic ...

Novel Memristor Based True Random Number Generator, Scott Stoller

Boise State University Theses and Dissertations

Random numbers are an important, but often overlooked part of the modern computing environment. They are used everywhere around us for a variety of purposes, from simple decision making in video games such as a coin toss, to securing financial transactions and encrypting confidential communications. They are even useful for gambling and the lottery.

Random numbers are generated in many ways. Pseudo random number generators (PRNGs) generate numbers based on a formula. True random number generators (TRNGs) capture entropy from the environment to generate randomness. As our society and our devices become more connected in the digital world, it is ...

Challenges To Adopting Hybrid Methodology: Addressing Organizational Culture And Change Control Problems In Enterprise It Infrastructure Projects, Harishankar Krishnakumar

Dissertations and Theses

IT infrastructure projects have long been an overlooked field superseded by the more popular software development silos and cross-functional project teams when it comes to enterprise Agile transformations. This paper presents a systematic literature review by leveraging a qualitative research methodology based on empirical evidence provided in contemporary scholarly research articles to explore how certain variables such as organizational culture- including team structure, leadership hierarchy, geolocation, etc. along with an organization’s change management processes affect the adoption of a Hybrid/Agile project management methodology, focusing on reported challenges and critical success factors that define such large-scale enterprise transformations. The ...

Efficient Step-Merged Quantum Imaginary Time Evolution Algorithm For Quantum Chemistry, Niladri Gomes, Feng Zhang, Noah F. Berthusen, Cai-Zhuang Wang, Kai-Ming Ho, Peter P. Orth, Yongxin Yao

Physics and Astronomy Publications

We develop a resource-efficient step-merged quantum imaginary time evolution approach (smQITE) to solve for the ground state of a Hamiltonian on quantum computers. This heuristic method features a fixed shallow quantum circuit depth along the state evolution path. We use this algorithm to determine the binding energy curves of a set of molecules, including H2, H4, H6, LiH, HF, H2O, and BeH2, and find highly accurate results. The required quantum resources of smQITE calculations can be further reduced by adopting the circuit form of the variational quantum eigensolver (VQE) technique, such as the unitary coupled cluster ansatz. We demonstrate that ...

Forecasting Vegetation Health In The Mena Region By Predicting Vegetation Indicators With Machine Learning Models, Sachi Perera, Wenzhao Li, Erik Linstead, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the ...

High Wind Alerts: A System Created With Observations From The X-Band Teaching And Research Radar, Lauren Warner

The Journal of Purdue Undergraduate Research

Following the August 13, 2011, Indiana State Fair stage collapse tragedy, caused by a wind gust from an approaching thunderstorm, Purdue University enforced a wind speed restriction of 30 mph (13 m s-1) for tents at outdoor events. During these events, volunteers stand outside with handheld anemometers, measuring and reporting when the wind speeds exceed this limit. In this study, we report testing of a new system to automate high-wind alerts based on observations from a Doppler radar, the X-band Teaching and Research Radar (XTRRA), near Purdue’s campus. XTRRA scans over campus at low elevations approximately every 5 minutes ...

A New Approach For Homomorphic Encryption With Secure Function Evaluation On Genomic Data, Mounika Pratapa

Electronic Thesis and Dissertation Repository

Additively homomorphic encryption is a public-key primitive allowing a sum to be computed on encrypted values. Although limited in functionality, additive schemes have been an essential tool in the private function evaluation toolbox for decades. They are typically faster and more straightforward to implement relative to their fully homomorphic counterparts, and more efficient than garbled circuits in certain applications. This thesis presents a novel method for extending the functionality of additively homomorphic encryption to allow the private evaluation of functions of restricted domain. Provided the encrypted sum falls within the restricted domain, the function can be homomorphically evaluated “for free ...

A 2.56 Gbps Serial Wireline Transceiver That Supports An Auxiliary Channel And A Hybrid Line Driver To Compensate Large Channel Loss, Xiaoran Wang

Electrical Engineering Theses and Dissertations

Serial transceiver links are widely used for high-speed point-to-point communications. This dissertation describes two transceiver link designs for two different applications.

In serial wireline communications, security is an increasingly important factor to concern. Securing an information processing system at the application and system software layers is regarded as a necessary but incomplete defense against the cyber security threats. In this dissertation, an asynchronous serial transceiver that is capable of transmitting and receiving an auxiliary data stream concurrently with the primary data stream is described. The transceiver instantiates the auxiliary data stream by modulating the phase of the primary data without ...

Enhanced Control Algorithms In Permanent Magnet Synchronous Machines, Haibo Li

Theses, Dissertations, and Student Research from Electrical & Computer Engineering

Permanent magnet synchronous machines (PMSMs) are gaining increasing popularity in various applications due to their advantages, such as high efficiency, high power density, and superior control performance. A well-designed machine control algorithm is indispensable for a PMSM system to secure its good performance.

In this work, enhanced control algorithms in PMSMs are developed. Online machine current trajectory tracking, source power management, hardware overcurrent regulation, and machine current sensor fault detection and isolation (FDI) are included in the developed algorithms. The online machine current trajectory tracking ensures the maximum torque per ampere (MTPA) or maximum torque per voltage (MTPV) control in ...

The Effects Of Radiation On Memristor-Based Electronic Spiking Neural Networks, Sumedha Gandharava Dahl

Boise State University Theses and Dissertations

In this dissertation, memristor-based spiking neural networks (SNNs) are used to analyze the effect of radiation on the spatio-temporal pattern recognition (STPR) capability of the networks. Two-terminal resistive memory devices (memristors) are used as synapses to manipulate conductivity paths in the network. Spike-timing-dependent plasticity (STDP) learning behavior results in pattern learning and is achieved using biphasic shaped pre- and post-synaptic spikes. A TiO₂ based non-linear drift memristor model designed in Verilog-A implements synaptic behavior and is modified to include experimentally observed effects of state-altering, ionizing, and off-state degradation radiation on the device. The impact of neuron “death” (disabled neuron ...

Intelligent Algorithm For Trapezoidal Interval Valued Neutrosophic Network Analysis, Florentin Smarandache, Said Broumi, Deivanayagampillai Nagarajan, Malayalan Lathamaheswari, Mohamed Talea, Assia Bakali

Mathematics and Statistics Faculty and Staff Publications

The shortest path problem has been one of the most fundamental practical problems in network analysis. One of the good algorithms is Bellman-Ford, which has been applied in network, for the last some years. Due to complexity in the decision-making process, the decision makers face complications to express their view and judgment with an exact number for single valued membership degrees under neutrosophic environment. Though the interval number is a special situation of the neutrosophic, it did not solve the shortest path problems in an absolute manner. Hence, in this work, the authors have introduced the score function and accuracy ...

An End-To-End Trainable Method For Generating And Detecting Fiducial Markers, J Brennan Peace

Theses, Dissertations, and Student Research from Electrical & Computer Engineering

Existing fiducial markers are designed for efficient detection and decoding. The methods are computationally efficient and capable of demonstrating impressive results, however, the markers are not explicitly designed to stand out in natural environments and their robustness is difficult to infer from relatively limited analysis. Worsening performance in challenging image capture scenarios - such as poorly exposed images, motion blur, and off-axis viewing - sheds light on their limitations. The method introduced in this work is an end-to-end trainable method for designing fiducial markers and a complimentary detector. By introducing back-propagatable marker augmentation and superimposition into training, the method learns to generate ...

A Novel Path Loss Forecast Model To Support Digital Twins For High Frequency Communications Networks, James Marvin Taylor Jr

Theses, Dissertations, and Student Research from Electrical & Computer Engineering

The need for long-distance High Frequency (HF) communications in the 3-30 MHz frequency range seemed to diminish at the end of the 20th century with the advent of space-based communications and the spread of fiber optic-connected digital networks. Renewed interest in HF has emerged as an enabler for operations in austere locations and for its ability to serve as a redundant link when space-based and terrestrial communication channels fail. Communications system designers can create a “digital twin” system to explore the operational advantages and constraints of the new capability. Existing wireless channel models can adequately simulate communication channel conditions with ...

Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat

Electronic Thesis and Dissertation Repository

The rapid growth of the Internet and related technologies has led to the collection of large amounts of data by individuals, organizations, and society in general [1]. However, this often leads to information overload which occurs when the amount of input (e.g. data) a human is trying to process exceeds their cognitive capacities [2]. Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data [1]. This thesis focuses on two applications. The first is education, namely e-Learning environments. Within this field, this thesis proposes different optimized ML ensemble models ...

A Machine Learning Approach To Delineating Neighborhoods From Geocoded Appraisal Data, Rao Hamza Ali, Josh Graves, Stanley Wu, Jenny Lee, Erik Linstead

Engineering Faculty Articles and Research

Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries are static and are inflexible to the shift in the real estate market and fail to represent its dynamics, such as in the case of an up-and-coming residential project. Delineated neighborhoods are also used in socioeconomic and demographic analyses where statistics are computed at a neighborhood level. Current practices of delineating neighborhoods have mostly ignored the information ...