Systems and Communications Commons^™

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 …

A Power Constrained 433-Mhz Low Noise Amplifier, Pablo Alvarez Arellano

Graduate Theses and Dissertations

Within wireless communication systems, low noise amplifiers are critical for the performance of receivers. They are primarily responsible for providing enough gain while adding little noise to overcome the noise of the subsequent stages. The LNA presented here is part of a battery-powered transceiver meant to measure crop nutrient data and relay the information. Therefore, power consumption and area become import considerations. To design for a specific power level, a power-constrained noise optimization method is used. The method sizes the amplifying transistor for a fixed source impedance, power dissipation, technology, and operating frequency. The chosen topology is the cascode stage …

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

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

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 …

Calibration To Mitigate Near-Field Antennas Effects For A Mimo Radar Imaging System, Ha Hoang, Matthias John, Patrick Mcevoy, Max Ammann

Articles

A calibration method for a high-resolution hybrid MIMO turntable radar imaging system is presented. A line of small metal spheres is employed as a test pattern in the calibration process to measure the position shift caused by undesired antenna effects. The unwanted effects in the antenna near-field responses are analysed, modelled and significantly mitigated based on the symmetry and differences in the responses of the MIMO configuration.

Enhancing The Performance Of 433 Mhz Underwater Wsn Using Handover Mechanisms., Mohammad Abdellatif, Sameh Abdellatif, Salma M. Maher, Ziad M. Ali

Electrical Engineering

Wireless technology has taken part of our daily life which paved the way to underwater wireless communication to flourish as a research area. This research field is important for exploration of the seabed as oceanographers stated that there are still unexplored ocean perceptions. Latency and distance are considered to be the main factors that confronts this particular field and degrades the overall performance. This paper investigates the performance of RF signals in varying water depths. A point to point system was first considered with 2 nodes, the results of this experiment along with our previous work aroused the idea of …

Cognitive Resource Management In 5g Networks, Kelvin M. Franco-Argueta

OUR Journal: ODU Undergraduate Research Journal

The 4G LTE network offers a high speed connectivity that is predicated on the construction platform of the 3G network and relies on an Internet Protocol (IP) for data transmission and reception. This platform’s utility is quickly becoming exhausted as the frequency spectrum approaches maximum device connectivity capacity. To improve network capacity, we must expand the bandwidth that our devices operate on. To effectively carry out this task, a self-configurable network must be employed in the development of the 5g network. This article aims to explore the technologies which form the platform for the 5G network and the cognitive resource …

Corrections To ‘‘Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping’’, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

In the above article [1], Figure 2 was incorrect. Unfortunately, we mixed the color label of "CONV $\to $ BN $\to $ ReLu" and "Unpooling" in the CNN structure section of Figure 2. The color label of "CONV $\to $ BN $\to $ ReLu" should be orange while the color label of "Unpooling" should be green. Also, the word "Decoder" is misspelled. That same figure with the same error is also used for the graphic abstract. The corrected figure is given here. None of the sections in the figure is modified. The only change is in the color label of …

A Four-Port Dc-Dc Converter For A Hybrid Wind/Solar Energy System, Jiahong Ning

All Graduate Theses, Dissertations, and Other Capstone Projects

With the increasing demand for clean and hybrid energy in the last decades, more and more attentions are paid to the renewable energy, e.g., wind and solar energy. According to the International Energy Agency, solar power was growing-fastest source in the world. In a hybrid wind/solar system, power converters play an important role in in power management and the voltage regulation. Gradually, power systems have undergone a paradigm shift from centralized generation to distributed generation. Smart grid, which is a combination of power systems and communication networks, was proposed to allow power systems to fit the future system challenge. However, …

Virtual Impedance Based Decentralized Control For A Microgrid System, Ramkrishna Mishan

All Graduate Theses, Dissertations, and Other Capstone Projects

As greenhouse gases produced from the conventional power plant causes global climate change, using renewable energy sources (RESs) in the future power system is inevitable. To minimize greenhouse gases emission, the interest in renewable grid or micro-grid is growing nowadays. In the microgrid, especially in AC microgrids, converters play an important role in many areas, including microgrid integration, uninterrupted power supply, and flexible alternating current transmission systems. Inverter plays an essential role in grid integration because it serves the interface between the energy source and the power grid. The important aspect of the inverter is control. This thesis studies several …

Accurate Range-Based Indoor Localization Using Pso-Kalman Filter Fusion, Paul Bupe Jr

College of Graduate Studies: Theses & Dissertations

Accurate indoor localization often depends on infrastructure support for distance estimation in range-based techniques. One can also trade off accuracy to reduce infrastructure investment by using relative positions of other nodes, as in range-free localization. Even for range-based methods where accurate Ultra-WideBand (UWB) signals are used, non line-of-sight (NLOS) conditions pose significant difficulty in accurate indoor localization. Existing solutions rely on additional measurements from sensors and typically correct the noise using a Kalman filter (KF). Solutions can also be customized to specific environments through extensive profiling. In this work, a range-based indoor localization algorithm called PSO - Kalman Filter Fusion …

Coverage Guided Differential Adversarial Testing Of Deep Learning Systems, Jianmin Guo, Houbing Song, Yue Zhao, Yu Jiang

Publications

Deep learning is increasingly applied to safety-critical application domains such as autonomous cars and medical devices. It is of significant importance to ensure their reliability and robustness. In this paper, we propose DLFuzz, the coverage guided differential adversarial testing framework to guide deep learing systems exposing incorrect behaviors. DLFuzz keeps minutely mutating the input to maximize the neuron coverage and the prediction difference between the original input and the mutated input, without manual labeling effort or cross-referencing oracles from other systems with the same functionality. We also design multiple novel strategies for neuron selection to improve the neuron coverage. The …

Ev Charging Behavior Analysis Using Hybrid Intelligence For 5g Smart Grid, Yi Shen, Wei Fang, Feng Ye, Michel Kadoch

Electrical and Computer Engineering Faculty Publications

With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select …

Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

Rising global temperatures over the past decades is directly affecting glacier dynamics. To understand glacier fluctuations and document regional glacier-state trends, glacier-boundary detection is necessary. Debris-covered glacier (DCG) mapping, however, is notoriously difficult using conventional geospatial technology methods. Therefore, in this research for automated DCG mapping, we evaluate the utility of a convolutional neural network (CNN), which is a deep learning feed-forward neural network. The CNN inputs include Landsat satellite images, an Advanced Land Observation Satellite (ALOS) digital elevation model (DEM) and DEM-derived land-surface parameters. Our CNN based deep-learning approach named GlacierNet was designed by appropriately choosing the type, number …

Mitosisnet: End-To-End Mitotic Cell Detection By Multi-Task Learning, Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Tj Bowen, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Mitotic cell detection is one of the challenging problems in the field of computational pathology. Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis. The clinical visual inspection on histology slides is tedious, error prone, and time consuming for the pathologist. Thus, automatic mitotic cell detection approaches are highly demanded in clinical practice. In this paper, we propose an end-to-end multi-task learning system for mitosis detection from pathological images which is named"MitosisNet". MitosisNet consist of segmentation, detection, and classification models where the segmentation, and detection models are used for mitosis reference region …

The Picture Fuzzy Distance Measure In Controlling Network Power Consumption, Florentin Smarandache, Ngan Thi Roan, Salvador Coll Arnau, Marina Alonso Diaz, Juan Miguel Martinez Rubio, Pedro Lopez, Fran Andujar, Son Hoang Lee, Manh Van Vu

Branch Mathematics and Statistics Faculty and Staff Publications

In order to solve the complex decision making problems, there are many approaches and systems based on fuzzy theory were proposed.