Systems and Communications Commons^™

Impact Of Large-Scale Correlated Failures On Multilevel Virtualized Networks, Max G. Medina, Mohammed J.F. Alenazi, Egemen K. Cetinkaya

Electrical and Computer Engineering Faculty Research & Creative Works

Communication network architectures have been evolving with increased programmability and virtualization of physical infrastructures. While this evolution enables a faster introduction of new services and efficient utilization of resources, the complexities and interdependencies among the physical and virtual infrastructures, as well as the control and management planes, hinder a complete understanding of the system at hand. It is imperative, therefore, to model and study these evolving virtualized infrastructures. We propose a multilevel virtualized network embedding model and study its performance against correlated failures. Our analysis of multiple synthetic network models identified that grid-like infrastructures capture the realistic Internet2 structure the …

Low Latency Anomaly Detection With Imperfect Models, Samrat Nath

Graduate Theses and Dissertations

The problem of anomaly detection deals with detecting abrupt changes/anomalies in the distribution of sequentially observed data in a stochastic system. This problem applies to many applications, such as signal processing, intrusion detection, quality control, medical diagnosis, etc. A low latency anomaly detection algorithm, which is based on the framework of quickest change detection (QCD), aims at minimizing the detection delay of anomalies in the sequentially observed data while ensuring satisfactory detection accuracy. Moreover, in many practical applications, complete knowledge of the post-change distribution model might not be available due to the unexpected nature of the change. Hence, the objective …

Wideband And Flat-Gain Optical Amplifier With Hafnia-Bismuth Erbium Co-Doped Fiber, Abduljabbar Al-Azzawi Alabbas Ahmed

Student Works (2020-2029)

A compact optical amplifier with a flat-gain characteristic is demonstrated using hafnia bismuth-erbium co-doped fiber (HB-EDF) as the gain medium. The HB-EDF was recently fabricated using a modified chemical vapor deposition (MCVD) process in conjunction with solution doping (SD) technique. The fiber has an Erbium ions concentration of 12500 wt. ppm, which was realized due to the co-doping with Hafnium and Aluminum ions. Firstly, the proposed amplifier was investigated for both single and double-pass configurations. It is found that the 0.5m long HB-EDF is the optimum length for the C-band region. For double-pass HB-EDFA, at input signal power of -10 …

Energy-Efficient Data Transmission With Clustering And Compressive Sensing In Wireless Sensor Networks, Alagirisamy Mukil

Student Works (2020-2029)

One of the most important application of wireless sensor network is environmental monitoring. The application involves lifetime of sensor nodes for longer duration associating its energy module. Wireless sensor nodes deployed in sensing field aggregate enormous amount of sensed data and transfer them to the sink. The inherent limitation of energy carried within the battery of sensor nodes fetches extreme difficulty to acquire adequate network lifetime, becoming a bottleneck in forwarding data to sink. Hence the motivation is to reduce the amount of data transfer and attain energy efficiency. This is achieved by clustering and compressive sensing techniques. First objective …

Ensemble Malware Classification System Using Deep Neural Networks, Barath Narayanan Narayanan, Venkata Salini Priyamvada Davuluru

Electrical and Computer Engineering Faculty Publications

With the advancement of technology, there is a growing need of classifying malware programs that could potentially harm any computer system and/or smaller devices. In this research, an ensemble classification system comprising convolutional and recurrent neural networks is proposed to distinguish malware programs. Microsoft's Malware Classification Challenge (BIG 2015) dataset with nine distinct classes is utilized for this study. This dataset contains an assembly file and a compiled file for each malware program. Compiled files are visualized as images and are classified using Convolutional Neural Networks (CNNs). Assembly files consist of machine language opcodes that are distinguished among classes using …

On-Site And External Energy Harvesting In Underground Wireless, Usman Raza, Abdul Salam

Computer Information Technology Faculty Publications

Energy efficiency is vital for uninterrupted long-term operation of wireless underground communication nodes in the field of decision agriculture. In this paper, energy harvesting and wireless power transfer techniques are discussed with applications in underground wireless communications (UWC). Various external wireless power transfer techniques are explored. Moreover, key energy harvesting technologies are presented that utilize available energy sources in the field such as vibration, solar, and wind. In this regard, the Electromagnetic(EM)- and Magnetic Induction(MI)-based approaches are explained. Furthermore, the vibration-based energy harvesting models are reviewed as well. These energy harvesting approaches lead to design of an efficient wireless underground …

Nonlinear Least Squares 3-D Geolocation Solutions Using Time Differences Of Arrival, Michael V. Bredemann

Mathematics & Statistics ETDs

This thesis uses a geometric approach to derive and solve nonlinear least squares minimization problems to geolocate a signal source in three dimensions using time differences of arrival at multiple sensor locations. There is no restriction on the maximum number of sensors used. Residual errors reach the numerical limits of machine precision. Symmetric sensor orientations are found that prevent closed form solutions of source locations lying within the null space. Maximum uncertainties in relative sensor positions and time difference of arrivals, required to locate a source within a maximum specified error, are found from these results. Examples illustrate potential requirements …

A System Dynamic Model For Application Of Blockchain In The United States Electricity Sector, Oluwafemi Ezekiel Oyeniran

Morehead State Theses and Dissertations

A thesis presented to the faculty of the College of Business and Technology at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Oluwafemi Ezekiel Oyeniran on April 14, 2020.

Sunseeker Display And Driver Controller, Alec Kwapis

Honors Theses

Digital dashboard displays with critical driver information are found in all modern vehicles. Examples of such information available to the driver include a speedometer, odometer, engine RPM, fuel gauge and more. The current 2016 Sunseeker solar car already has numerous displays that can show critical information to the driver, however, there are several problems that exist. Each display itself is less than two inches in size, the text on the screens is difficult to read, and the measurements have no units. Furthermore, these displays were made by a company that no longer exists, thus preventing the solar car team from …

Study Of Fundamental Tradeoff Between Deliverable And Private Information In Statistical Inference, Farhang Bayat

LSU Doctoral Dissertations

My primary objective in this dissertation is to establish a framework under which I launch a systematic study of the fundamental tradeoff between deliverable and private information in statistical inference. My research was partly motivated by arising and prevailing privacy concerns of users in many machine learning problems.

In this dissertation, I begin by introducing examples where I am concerned of privacy leakage versus decision utility in statistical inference problems. I then go into further details about what I have achieved in formulating and solving such problems using information theory related metrics in a variety of settings. Both related works …

Virtual Satcom, Long Range Broadband Digital Communications, Dennis George Watson

Electrical & Computer Engineering Theses & Dissertations

The current naval strategy is based on a distributed force, networked together with high-speed communications that enable operations as an intelligent, fast maneuvering force. Satellites, the existing network connector, are weak and vulnerable to attack. HF is an alternative, but it does not have the information throughput to meet the distributed warfighting need. The US Navy does not have a solution to reduce dependency on space-based communication systems while providing the warfighter with the required information speed.

Virtual SATCOM is a solution that can match satellite communications (SATCOM) data speed without the vulnerable satellite. It is wireless communication on a …

Innovation In Pedagogy And Technology Symposium, 2019: Selected Conference Proceedings, University Of Nebraska Online, University Of Nebraska Information Technology Services

Zea E-Books Collection

Advancing Technology in Education at the University of Nebraska, May 7, 2019

Welcome Address • Susan Fritz, Ph.D., Executive Vice President and Provost, University of Nebraska 6

Opening Remarks • Mary Niemiec, Associate Vice President for Digital Education, Director of University of Nebraska Online 6

Keynote Presentation: Shaping the Next Generation of Higher Education • Bryan Alexander, Ph.D. 6

Featured Extended Presentation: Redesigning Courses & Determining Effectiveness Through Research • Tanya Joosten, University of Wisconsin-Milwaukee (UWM), Erin Blankenship, Ph.D. (UNL), Ella Burnham (UNL), Nate Eidem, Ph.D. (UNK), Marnie Imhoff (UNMC), Linsey Donner (UNMC), Ellie Miller (UNMC) 7

5 Ways to …

Deal: Differentially Private Auction For Blockchain Based Microgrids Energy Trading, Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen

Publications

Modern smart homes are being equipped with certain renewable energy resources that can produce their own electric energy. From time to time, these smart homes or microgrids are also capable of supplying energy to other houses, buildings, or energy grid in the time of available self-produced renewable energy. Therefore, researches have been carried out to develop optimal trading strategies, and many recent technologies are also being used in combination with microgrids. One such technology is blockchain, which works over decentralized distributed ledger. In this paper, we develop a blockchain based approach for microgrid energy auction. To make this auction more …

Cyber-Physical Security With Rf Fingerprint Classification Through Distance Measure Extensions Of Generalized Relevance Learning Vector Quantization, Trevor J. Bihl, Todd J. Paciencia, Kenneth W. Bauer Jr., Michael A. Temple

Faculty Publications

Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and …

Evaluation Of Capacitive Deionization Desalination Technology For Irrigation, Ziad Khalifa, Moustafa Elshafei Prof, Abdalrahman Amer, Eng, Ashraf Seleym Dr, Tamer Samir Eng

Chemical Engineering

Desalination of brackish groundwater has great potential to alleviate the problem of the limited water resources in Egypt. In this paper, we studied the desalination of brackish water for irrigation purposes using capacitive deionization (CDI) technology. We investigated a modular unit for use in greenhouses (GH). A GH for the production of tomato requires about 3.2 m3/d of water. The target CDI unit has a production capacity of 32 m3/d for irrigation of 10 greenhouses from brackish water. The paper provides an extensive simulation study to illustrate the influence of various design parameters and to unveil the CDI technology pros …

Wireless Underground Communications In Sewer And Stormwater Overflow Monitoring: Radio Waves Through Soil And Asphalt Medium, Usman Raza, Abdul Salam

Computer Information Technology Faculty Publications

Storm drains and sanitary sewers are prone to backups and overflows due to extra amount wastewater entering the pipes. To prevent that, it is imperative to efficiently monitor the urban underground infrastructure. The combination of sensors system and wireless underground communication system can be used to realize urban underground IoT applications, e.g., storm water and wastewater overflow monitoring systems. The aim of this article is to establish a feasibility of the use of wireless underground communications techniques, and wave propagation through the subsurface soil and asphalt layers, in an underground pavement system for storm water and sewer overflow monitoring application. …

Iot For Smart Parking, Mohammad Abdellatif, Noura Elshabasy

Electrical Engineering

Nowadays, technology has become more reliable that allowed human interaction to decrease while the machines have started to take over in many fields of interest. The Internet of things (IoT) represents the idea of machines ability to control entire systems without any human interactions. This paper proposes an IoT system that takes a picture of parked cars in a garage, extract the plate number and the location of the car, and places these information in a database. At the garage entrance, a terminal linked to the system allows the user to locate the parked car using the license plate no.. …

Short-Term Electricity Price Forecasting In Deregulated Electricity Market Based On Enhanced Artificial Intelligence Techniques, Pourdaryaei Alireza

Student Works (2020-2029)

Electricity price forecasting is considered as one of prime factors for operation, planning and scheduling of price-setter market participants. However, possessing time variant, non-linear and non-stationary behaviors make the electricity price a complex signal. The main challenge in this area is providing highly accurate and efficient day-ahead price forecasting. A suitable feature selection technique, which is able to model the interacting features and nonlinearities of the forecast processes, is still required although researches have been performed for day-ahead forecasting. In this research, a hybrid electricity price forecasting methodology is proposed using two-stage feature selection method and optimization using adaptive neuro-fuzzy …

Improved Estimation For Saleh Model And Predistortion Of Power Amplifiers Using 1-Db Compression Point, Haider Al Kanan, Xianzhen Yang, Fu Li

Electrical and Computer Engineering Faculty Publications and Presentations

This paper proposes an improved estimation approach for modelling RF power amplifiers (PAs) using the Saleh behavioural model. The proposed approach is appropriate for solid-state PA technologies. The 1-dB compression point of the PA is included in the estimation approach to improve the estimation of the Saleh coefficients. Thus, expressions are derived to describe the relationship between the parameters of the Saleh model and the manufacturing specifications of PAs: gain (G), third-order intercept point (IP₃) and 1-dB compression point (P_1dB). This method is a simple estimation of a memoryless amplitude-to-amplitude (AM/AM) nonlinearity to benefit RF designers …

Ensemble Lung Segmentation System Using Deep Neural Networks, Redha A. Ali, Russell C. Hardie, Hussin K. Ragb

Electrical and Computer Engineering Faculty Publications

Lung segmentation is a significant step in developing computer-aided diagnosis (CAD) using Chest Radiographs (CRs). CRs are used for diagnosis of the 2019 novel coronavirus disease (COVID-19), lung cancer, tuberculosis, and pneumonia. Hence, developing a Computer-Aided Detection (CAD) system would provide a second opinion to help radiologists in the reading process, increase objectivity, and reduce the workload. In this paper, we present the implementation of our ensemble deep learning model for lung segmentation. This model is based on the original DeepLabV3+, which is the extended model of DeepLabV3. Our model utilizes various architectures as a backbone of DeepLabV3+, such as …