Engineering Commons^™

Milp Modeling Of Matrix Multiplication: Cryptanalysis Of Klein And Prince, Murat Burhan İlter, Ali Aydın Selçuk

Turkish Journal of Electrical Engineering and Computer Sciences

Mixed-integer linear programming (MILP) techniques are widely used in cryptanalysis, aiding in the discovery of optimal linear and differential characteristics. This paper delves into the analysis of block ciphers KLEIN and PRINCE using MILP, specifically calculating the best linear and differential characteristics for reduced-round versions. Both ciphers employ matrix multiplication in their diffusion layers, which we model using multiple XOR operations. To this end, we propose two novel MILP models for multiple XOR operations, which use fewer variables and constraints, proving to be more efficient than standard methods for XOR modeling. For differential cryptanalysis, we identify characteristics with a probability …

Transforming Large-Scale Virtualized Networks: Advancements In Latency Reduction, Availability Enhancement, And Security Fortification, Ibrahim Tamim

Electronic Thesis and Dissertation Repository

In today’s digital age, the increasing demand for networks, driven by the proliferation of connected devices, data-intensive applications, and transformative technologies, necessitates robust and efficient network infrastructure. This thesis addresses the challenges posed by virtualization in 5G networking and focuses on enhancing next-generation Radio Access Networks (RANs), particularly Open-RAN (O-RAN). The objective is to transform virtualized networks into highly reliable, secure, and latency-aware systems. To achieve this, the thesis proposes novel strategies for virtual function placement, traffic steering, and virtual function security within O-RAN. These solutions utilize optimization techniques such as binary integer programming, mixed integer binary programming, column generation, …

Optimizing High-Performance Computing Design: The Impacts Of Bandwidth And Topology Across Workloads For Distributed Shared Memory Systems, Jonathan A. Milton

Electrical and Computer Engineering ETDs

With the complexity of high-performance computing designs continuously increasing, the importance of evaluating with simulation also grows. One of the key design aspects is the network architecture; topology and bandwidth greatly influence the overall performance and should be optimized. This work uses simulations written to run in the Structural Simulation Toolkit software framework to evaluate a variety of architecture configurations, identify the optimal design point based on expected workload, and evaluate the changes with increased scale. The results show that advanced topologies outperform legacy architectures justifying the additional design complexity; and that after a certain point increasing the bandwidth provides …

Network Economics-Based Crowdsourcing In Online Social Networks, Natasha S. Kubiak

Electrical and Computer Engineering ETDs

This thesis addresses the challenge of user recruitment by various competing marketing agencies (MAs) in Online Social Networks. A labor economics approach, following the principles of contract theory, is devised to enable MAs to reveal the potential of each participating user to contribute a personalized level of quality and quantity of information to the crowdsourcing process. The MAs objective is to maximize their personal benefit, i.e., total utility obtained, given its budget. The latter optimization problem is formulated as a Generalized Colonel Blotto (GCB) game among the MAs, where each MA aims at incentivizing each user to report its information. …

A Novel Covid-19 Herd Immunity-Based Optimizer For Optimal Accommodation Of Solar Pv With Battery Energy Storage Systems Including Variation In Load And Generation, Sumanth Pemmada, Nita Patne, Divyesh Kumar, Ashwini Manchalwar

Turkish Journal of Electrical Engineering and Computer Sciences

The world has now looked towards installing more renewable energy sources type distributed generation (DG), such as solar photovoltaic DG (SPVDG), because of its advantages to the environment and the quality of power supply it produces. However, these sources' optimal placement and size are determined before their accommodation in the power distribution system (PDS). This is to avoid an increase in power loss and deviations in the voltage profile. Furthermore, in this article, solar PV is integrated with battery energy storage systems (BESS) to compensate for the shortcomings of SPVDG as well as the reduction in peak demand. This paper …

Scheduling Electric Vehicle Charging For Grid Load Balancing, Zhixin Han, Katarina Grolinger, Miriam Capretz, Syed Mir

Electrical and Computer Engineering Publications

In recent years, electric vehicles (EVs) have been widely adopted because of their environmental benefits. However, the increasing volume of EVs poses capacity issues for grid operators as simultaneously charging many EVs may result in grid instabilities. Scheduling EV charging for grid load balancing has a potential to prevent load peaks caused by simultaneous EV charging and contribute to balance of supply and demand. This paper proposes a user-preference-based scheduling approach to minimize costs for the user while balancing grid loads. The EV owners benefit by charging when the electricity cost is lower, but still within the user-defined preferred charging …

Quantum Computing And Its Applications In Healthcare, Vu Giang

OUR Journal: ODU Undergraduate Research Journal

This paper serves as a review of the state of quantum computing and its application in healthcare. The various avenues for how quantum computing can be applied to healthcare is discussed here along with the conversation about the limitations of the technology. With more and more efforts put into the development of these computers, its future is promising with the endeavors of furthering healthcare and various other industries.

Tutorial - Shodhguru Labs: Optimization And Hyperparameter Tuning For Neural Networks, Kaushik Roy

Publications

Neural networks have emerged as a powerful and versatile class of machine learning models, revolutionizing various fields with their ability to learn complex patterns and make accurate predictions. The performance of neural networks depends significantly on the appropriate choice of hyperparameters, which are critical factors governing their architecture, regularization, and optimization techniques. As the demand for high-performance neural networks grows across diverse applications, the need for efficient optimization and hyperparameter tuning methods becomes paramount. This paper presents a comprehensive exploration of optimization strategies and hyperparameter tuning techniques for neural networks. Neural networks have emerged as a powerful and versatile class …

Applying Hls To Fpga Data Preprocessing In The Advanced Particle-Astrophysics Telescope, Meagan Konst

McKelvey School of Engineering Theses & Dissertations

The Advanced Particle-astrophysics Telescope (APT) and its preliminary iteration the Antarctic Demonstrator for APT (ADAPT) are highly collaborative projects that seek to capture gamma-ray emissions. Along with dark matter and ultra-heavy cosmic ray nuclei measurements, APT will provide sub-degree localization and polarization measurements for gamma-ray transients. This will allow for devices on Earth to point to the direction from which the gamma-ray transients originated in order to collect additional data. The data collection process is as follows. A scintillation occurs and is detected by the wavelength-shifting fibers. This signal is then read by an ASIC and stored in an ADC …

Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu

Research Collection School Of Computing and Information Systems

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient …

Model-Based Deep Learning For Computational Imaging, Xiaojian Xu

McKelvey School of Engineering Theses & Dissertations

This dissertation addresses model-based deep learning for computational imaging. The motivation of our work is driven by the increasing interests in the combination of imaging model, which provides data-consistency guarantees to the observed measurements, and deep learning, which provides advanced prior modeling driven by data. Following this idea, we develop multiple algorithms by integrating the classical model-based optimization and modern deep learning to enable efficient and reliable imaging. We demonstrate the performance of our algorithms by validating their performance on various imaging applications and providing rigorous theoretical analysis.

The dissertation evaluates and extends three general frameworks, plug-and-play priors (PnP), regularized …

Development Of A Hybrid System Based On Abc Algorithm For Selection Of Appropriate Parameters For Disease Diagnosis From Ecg Signals, Ersi̇n Ersoy, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel

Turkish Journal of Electrical Engineering and Computer Sciences

The number of people who die due to cardiovascular diseases is quite high. In our study, ECG (electrocar-diogram) signals were divided into segments and waves based on temporal boundaries. Signal similarity methods such as convolution, correlation, covariance, signal peak to noise ratio (PNRS), structural similarity index (SSIM), one of the basic statistical parameters, arithmetic mean and entropy were applied to each of these sections. In addition, a square error-based new approach was applied and the difference of the signs from the mean sign was taken and used as a feature vector. The obtained feature vectors are used in the artificial …

Design And Control Of Next-Generation Uavs For Effectively Interacting With Environments, Caiwu Ding

Dissertations

In this dissertation, the design and control of a novel multirotor for aerial manipulation is studied, with the aim of endowing the aerial vehicle with more degrees of freedom of motion and stability when interacting with the environments. Firstly, it presents an energy-efficient adaptive robust tracking control method for a class of fully actuated, thrust vectoring unmanned aerial vehicles (UAVs) with parametric uncertainties including unknown moment of inertia, mass and center of mass, which would occur in aerial maneuvering and manipulation. The effectiveness of this method is demonstrated through simulation. Secondly, a humanoid robot arm is adopted to serve as …

An Adaptive Search Equation-Based Artificial Bee Colony Algorithm For Transportation Energy Demand Forecasting, Durmuş Özdemi̇r, Safa Dörterler

Turkish Journal of Electrical Engineering and Computer Sciences

This study aimed to develop a new adaptive artificial bee colony (A-ABC) algorithm that can adaptively select an appropriate search equation to more accurately estimate transport energy demand (TED). Also, A-ABC and canonical artificial bee colony (C-ABC) algorithms were compared in terms of efficiency and performance. The input parameters used in the proposed TED model were the official economic indicators of Turkey, including gross domestic product (GDP), population, and total vehicle kilometer per year (TKM). Three mathematical models, linear (A-ABCL), exponential (A-ABCE), and quadratic (A-ABCQ) were developed and tested. Also, economic variables were generated using the "curve fitting" technique to …

Optimized Cancer Detection On Various Magnified Histopathological Colon Imagesbased On Dwt Features And Fcm Clustering, Tina Babu, Tripty Singh, Deepa Gupta, Shahin Hameed

Turkish Journal of Electrical Engineering and Computer Sciences

Due to the morphological characteristics and other biological aspects in histopathological images, the computerized diagnosis of colon cancer in histopathology images has gained popularity. The images acquired using the histopathology microscope may differ for greater visibility by magnifications. This causes a change in morphological traits leading to intra and inter-observer variability. An automatic colon cancer diagnosis system for various magnification is therefore crucial. This work proposes a magnification independent segmentation approach based on the connected component area and double density dual tree DWT (discrete wavelet transform) coefficients are derived from the segmented region. The derived features are reduced further shortened …

Distributed Wireless Sensor Node Localization Based On Penguin Searchoptimization, Md Al Shayokh, Soo Young Shin

Turkish Journal of Electrical Engineering and Computer Sciences

Wireless sensor networks (WSNs) have become popular for sensing areas-of-interest and performing assigned tasks based on information on the location of sensor devices. Localization in WSNs is aimed at designating distinct geographical information to the inordinate nodes within a search area. Biologically inspired algorithms are being applied extensively in WSN localization to determine inordinate nodes more precisely while consuming minimal computation time. An optimization algorithm belonging to the metaheuristic class and named penguin search optimization (PeSOA) is presented in this paper. It utilizes the hunting approaches in a collaborative manner to determine the inordinate nodes within an area of interest. …

Evaluating The Performance Impact Of Fine-Tuning Optimization Strategies On Pre-Trained Distilbert Models Towards Hate Speech Detection In Social Media, Aidan Mcgovern

Dissertations

Hate speech can be defined as forms of expression that incite hatred or encourage violence towards a person or group based on race, religion, gender, or sexual orientation. Hate speech has gravitated towards social media as its primary platform, and its propagation represents profound risks to both the mental well-being and physical safety of targeted groups. Countermeasures to moderate hate speech face challenges due to the volumes of data generated in social media, leading companies, and the research community to evaluate methods to automate its detection. The emergence of BERT and other pre-trained transformer-based models for transfer learning in the …

Design, Analysis, And Optimization Of Traffic Engineering For Software Defined Networks, Mohammed Ibrahim Salman

Browse all Theses and Dissertations

Network traffic has been growing exponentially due to the rapid development of applications and communications technologies. Conventional routing protocols, such as Open-Shortest Path First (OSPF), do not provide optimal routing and result in weak network resources. Optimal traffic engineering (TE) is not applicable in practice due to operational constraints such as limited memory on the forwarding devices and routes oscillation. Recently, a new way of centralized management of networks enabled by Software-Defined Networking (SDN) made it easy to apply most traffic engineering ideas in practice. \par Toward creating an applicable traffic engineering system, we created a TE simulator for experimenting …

Yard Layout Optimization For General Cargo Terminal, Zhixiong Liu, Dong Yu, Chunjun Zhang

Journal of System Simulation

Abstract: Yard layout is an important component of the port yard allocation decision which affects the cargo storage capacity and through capacity for the port yard. As to the general cargo yard, combined with the cargo type and the yard storage strategy, the yard layout optimization model for the general cargo terminal is presented based on the statistical analysis for the production data when the optimization aim is minimizing the total horizontal transport distance of the trailer. The yard layout optimization results are employed by the mathematical tool Gurobi for different storage strategies, and the yard layout optimization results are …

Impact Assessment, Detection, And Mitigation Of False Data Attacks In Electrical Power Systems, Sagnik Basumallik

Dissertations - ALL

The global energy market has seen a massive increase in investment and capital flow in the last few decades. This has completely transformed the way power grids operate - legacy systems are now being replaced by advanced smart grid infrastructures that attest to better connectivity and increased reliability. One popular example is the extensive deployment of phasor measurement units, which is referred to PMUs, that constantly provide time-synchronized phasor measurements at a high resolution compared to conventional meters. This enables system operators to monitor in real-time the vast electrical network spanning thousands of miles. However, a targeted cyber attack on …

A Study Of Deep Reinforcement Learning In Autonomous Racing Using Deepracer Car, Mukesh Ghimire

Honors Theses

Reinforcement learning is thought to be a promising branch of machine learning that has the potential to help us develop an Artificial General Intelligence (AGI) machine. Among the machine learning algorithms, primarily, supervised, semi supervised, unsupervised and reinforcement learning, reinforcement learning is different in a sense that it explores the environment without prior knowledge, and determines the optimal action. This study attempts to understand the concept behind reinforcement learning, the mathematics behind it and see it in action by deploying the trained model in Amazon's DeepRacer car. DeepRacer, a 1/18^th scaled autonomous car, is the agent which is trained …

A Survey Of Edge Computing Resource Allocation And Task Scheduling Optimization, Wang Ling, Chuge Wu, Wenhui Fan

Journal of System Simulation

Abstract: With the rapid development of Internet of Things (IoT) and mobile terminals, the concept of edge computing arises. By moving the computation and storage capacity to the edge of network, edge computing is able to deal with a large amount of data produced by IoT devices and the responsive request from IoT application. To improve the utility of edge resource, the quality of service and quality of user experience, resource allocation and task scheduling optimization problems under edge computing attract wide attention. It becomes more difficult due to the geographic separated and heterogeneous features of edge computing resource as …

Green Underwater Wireless Communications Using Hybrid Optical-Acoustic Technologies, Kazi Y. Islam, Iftekhar Ahmad, Daryoush Habibi, M. Ishtiaque A. Zahed, Joarder Kamruzzaman

Research outputs 2014 to 2021

Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology – underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe …

A Linear Programming Approach To Multiple Instance Learning, Emel Şeyma Küçükaşci, Mustafa Gökçe Baydoğan, Zeki̇ Caner Taşkin

Turkish Journal of Electrical Engineering and Computer Sciences

Multiple instance learning (MIL) aims to classify objects with complex structures and covers a wide range of real-world data mining applications. In MIL, objects are represented by a bag of instances instead of a single instance, and class labels are provided only for the bags. Some of the earlier MIL methods focus on solving MIL problem under the standard MIL assumption, which requires at least one positive instance in positive bags and all remaining instances are negative. This study proposes a linear programming framework to learn instance level contributions to bag label without emposing the standart assumption. Each instance of …

Optimal Planning Dg And Bes Units In Distribution System Consideringuncertainty Of Power Generation And Time-Varying Load, Mansur Khasanov, Salah Kamel, Ayman Awad, Francisco Jurado

Turkish Journal of Electrical Engineering and Computer Sciences

Global environmental problems associated with traditional energy generation have led to a rapid increasein the use of renewable energy sources (RES) in power systems. The integration of renewable energy technologiesis commercially available nowadays, and the most common of such RES technology is photovoltaic (PV). This paperproposes an application of hybrid teaching-learning and artificial bee colony (TLABC) technique for determining theoptimal allocation of PV based distributed generation (DG) and battery energy storage (BES) units in the distributionsystem (DS) with the aim of minimizing the total power losses. Besides, some potential nodes identified by the powerloss sensitivity factor (PLSF). Thereupon TLABC is …

Evaluation Of Il-17a And Il-17f Gene Expression In Peripheral Blood Mononuclear Cells In Different Clinical Stages Of Chronic Hepatitis B Infection In An Iranian Population, Tannaz Akbari Kolagar, Seyed Reza Mohebbi, Fatemeh Ashrafi, Shahrzad Shoraka, Hamid Asadzadeh Aghdaei, Mohammad Reza Zali

Makara Journal of Technology

Hepatitis B virus (HBV) infection is one of the main causes of liver damage, which can also lead to chronic hepatitis B (CHB) infection. More than 240 million individuals worldwide are chronic carriers of HBV. Among individuals with CHB who are untreated, approximately 15% – 40% will progress to liver cirrhosis or cancer. The interactions between HBV and host immune response play significant roles in the progression of CHB. CHB can be generally divided into four different clinical phases: immune tolerance (IT), immune clearance, inactive carrier, and Hepatitis B surface antigen (HBsAg)-negative reactivation phase (ENEG). Many studies showed that interleukins …

Microstrip Filters: A Review Of Different Filter Designs Used In Ultrawide Band Technology, Hussain Bohra, Giriraj Prajapati

Makara Journal of Technology

In this study, optimization techniques applied in designing microstrip bandpass ultra-wideband (UWB) filters are presented. Optimization based on various defected ground structure techniques, resonator designs, and type of dielectric materials is discussed. Microstrip bandpass filters implemented at UWB frequency bands used in wireless communication systems have key features to control frequency response in passband and stopband. Optimization techniques are studied to attain optimum performance of bandpass microstrip filters to ensure minimum insertion loss, high selectivity, compactness, sharp transitions at cut-off frequencies, high return loss, and excellent linearity. Extensive study shows that proper selection of fabrication techniques and type of material …

Optimizing The Performance Of Multi-Threaded Linear Algebra Libraries Based On Task Granularity, Shahrzad Shirzad

LSU Doctoral Dissertations

Linear algebra libraries play a very important role in many HPC applications. As larger datasets are created everyday, it also becomes crucial for the multi-threaded linear algebra libraries to utilize the compute resources properly. Moving toward exascale computing, the current programming models would not be able to fully take advantage of the advances in memory hierarchies, computer architectures, and networks. Asynchronous Many-Task(AMT) Runtime systems would be the solution to help the developers to manage the available parallelism. In this Dissertation we propose an adaptive solution to improve the performance of a linear algebra library based on a set of compile-time …

Simulation Research On Armored Equipment Maintenance Support Resource Optimization, Huiqi Zhang, Chunliang Chen, Junyan Liu, Liu Shuai, Yongqing Zhang

Journal of System Simulation

Abstract: Contraposing the problem of armored equipment maintenance resource computation and optimization, the armored equipment maintenance support process was analyzed. The armored equipment maintenance support process concept model and the mathematic model were constructed based on discrete system modeling theory; while the simulation model based on net discrete event was given in apply of Anylogic software. The armored equipment maintenance support task fulfilling degree, the resource utilization degree, and the mean maintenance time were got by simulation computation. As a result that maintenance support resource was evaluated based on index weight, and the optimized values of the maintenance support resource …

Quadratic Rational Trigonometric Spline Curves With Shape Controlling, Xinru Liu, Manman Wei, Shengjun Liu, Dangfu Yang

Journal of System Simulation

Abstract: A new quadratic rational trigonometric spline curve with a shape parameter was proposed. The value control and the inflection-point control of the interpolation scheme were discussed in theory. And the optimal methods for calculating the desired inflection-points was proposed, by using optimization theory. Numerical experiments show the interpolation spline and the optimization method can be used in modeling design.