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Articles 1 - 30 of 841
Full-Text Articles in Computer Engineering
Optimized Learning Using Fuzzy-Inference-Assisted Algorithms For Deep Learning, Miroslava Barua
Optimized Learning Using Fuzzy-Inference-Assisted Algorithms For Deep Learning, Miroslava Barua
Open Access Theses & Dissertations
For years, researchers in Artificial Intelligence (AI) and Deep Learning (DL) observed that performance of a Deep Learning Network (DLN) could be improved by using larger and larger datasets coupled with complex network architectures. Although these strategies yield remarkable results, they have limits, dictated by data quantity and quality, rising costs by the increased computational power, or, more frequently, by long training times on networks that are very large. Training DLN requires laborious work involving multiple layers of densely connected neurons, updates to millions of network parameters, while potentially iterating thousands of times through millions of entries in a big …
Online/Incremental Learning To Mitigate Concept Drift In Network Traffic Classification, Alberto R. De La Rosa
Online/Incremental Learning To Mitigate Concept Drift In Network Traffic Classification, Alberto R. De La Rosa
Open Access Theses & Dissertations
Communication networks play a large role in our everyday lives. COVID19 pandemic in 2020 highlighted their importance as most jobs had to be moved to remote work environments. It is possible that the spread of the virus, the death toll, and the economic consequences would have been much worse without communication networks. To remove sole dependence on one equipment vendor, networks are heterogeneous by design. Due to this, as well as their increasing size, network management has become overwhelming for network managers. For this reason, automating network management will have a significant positive impact. Machine learning and software defined networking …
Productivity And Quality Evaluation In Assembly Using Collaborative Robots, Carlos F. Manzanares Vega
Productivity And Quality Evaluation In Assembly Using Collaborative Robots, Carlos F. Manzanares Vega
Open Access Theses & Dissertations
In Industry 4.0, various technologies have been applied to achieve automation for traditional manufacturing and practices. For this reason, Smart Manufacturing (SM) environments utilize collaborative robots for process optimization by integrating the Internet of Things (IoT). Cobots are equipped with sensors and/or other devices to be able to transmit data in real-time while performing their tasks. Consequently, such SM implementations improves the decision making and business development, such as supply chain and operations, by sharing real-time data from a plant operational level. The collaborative robots are also designed to safely interact and collaborate with humans to perform tasks and optimize …
Intelligent Autonomous Inspections Using Deep Learning And Detection Markers, Alejandro Martinez Acosta
Intelligent Autonomous Inspections Using Deep Learning And Detection Markers, Alejandro Martinez Acosta
Open Access Theses & Dissertations
Inspection of industrial and scientific facilities is a crucial task that must be performed regularly. These inspections tasks ensure that the facilityâ??s structure is in safe operational conditions for humans. Furthermore,the safe operation of industrial machinery, is dependent on the conditions of the environment. For safety reasons, inspections for both structural integrity and equipment is often manually performed by operators or technicians. Naturally, this is often a tedious and laborious task. Additionally, buildings and structures frequently contain hard to reach or dangerous areas, which leads to the harm, injury or death of humans. Autonomous robotic systems offer an attractive solution …
Security Analysis And Implementation Of Dnp3 Multilayer Protocol For Secure And Safe Communication In Scada Systems, Isaac Monroy
Security Analysis And Implementation Of Dnp3 Multilayer Protocol For Secure And Safe Communication In Scada Systems, Isaac Monroy
Open Access Theses & Dissertations
When SCADA systems were first introduced into society, a lot of manpower was required for monitoring and controlling devices within critical infrastructures. With the increasing demand for services and growing systems, a need arose to automate the monitoring and controlling tasks. This led to introduction of networks into SCADA systems to enhance monitoring and control capabilities, that can scale with system size and requirements. But this introduction of network layer along with its advantages, also introduced a new threat surface which exposed multiple vulnerabilities within the system that can exploited to launch attacks, that led to the integration of security …
Miner-Town: Self-Driving Robotics Testbed For Vehicle-To-Grid Simulation, Carlos Adolfo Cortes Pliego
Miner-Town: Self-Driving Robotics Testbed For Vehicle-To-Grid Simulation, Carlos Adolfo Cortes Pliego
Open Access Theses & Dissertations
Autonomous vehicles and Vehicle-to-Grid (V2G) technology bring promising implications in boosting energy efficiency, helping the environment, improving our productivity, and have the potential to stabilize the grid during peak times and reduce car accidents. However, implementing and testing these complex novel technologies in the real world comes with high risks and investment. For these reasons, there is the need to research, test, and validate these theories in a compact and controlled environment at minimal cost. This thesis presents a modular autonomous vehicle testbed for the exploration of Vehicle-to-Grid and charging activities in pedestrian filled environments such as a University campus. …
Efficient Approaches To Steady State Detection In Multivariate Systems, Honglun Xu
Efficient Approaches To Steady State Detection In Multivariate Systems, Honglun Xu
Open Access Theses & Dissertations
Steady state detection is critically important in many engineering fields such as fault detection and diagnosis, process monitoring and control. However, most of the existing methods are designed for univariate signals. In this dissertation, we proposed an efficient online steady state detection method for multivariate systems through a sequential Bayesian partitioning approach. The signal is modeled by a Bayesian piecewise constant mean and covariance model, and a recursive updating method is developed to calculate the posterior distributions analytically. The duration of the current segment is utilized to test the steady state. Insightful guidance is provided for hyperparameter selection. The effectiveness …
Development Of An Automated Electronic Prototyping System, Cesar Yahir Sanchez Zambrano
Development Of An Automated Electronic Prototyping System, Cesar Yahir Sanchez Zambrano
Open Access Theses & Dissertations
Prototyping systems with interconnected components can be a time and resource expensive process. The process consists of three main phases (design, build and analysis) with each having their own associated cost. For the case of electronic circuits, the building phase is the costliest phase among the three, being prone to human errors which causes the circuit to fail. All three phases of the prototyping process are important. However, often a disproportionate amount of time is spent on the build phase due to the difficulty of making and troubleshooting circuits by hand. In this thesis we will discuss a system that …
Addressing Security And Privacy Issues By Analyzing Vulnerabilities In Iot Applications, Francsico Javier Candelario Burgoa
Addressing Security And Privacy Issues By Analyzing Vulnerabilities In Iot Applications, Francsico Javier Candelario Burgoa
Open Access Theses & Dissertations
The Internet of Things (IoT) environment has been expanding rapidly for the past few years into several areas of our lives, from factories, to stores and even into our own homes. All these new devices in our homes make our day-to-day lives easier and more comfortable with less effort on our part, converting our simple houses into smart homes. This increase in inter-connectivity brings multiple benefits including the improvement in energy efficiency in our homes, however it also brings with it some potential dangers since more points of connection mean more potential vulnerabilities in our grid. These vulnerabilities bring security …
The Network Link Outlier Factor (Nlof) For Localizing Network Faults, Christopher Mendoza
The Network Link Outlier Factor (Nlof) For Localizing Network Faults, Christopher Mendoza
Open Access Theses & Dissertations
This work presents the Network Link Outlier Factor (NLOF), a data analytics pipeline for network fault detection and localization solution that consists of four stages. In the first stage, flow record throughput values are clustered in two sub-stages: using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and then a novel domain-specific ThroughPut Cluster (TPCluster) technique. In the second stage, Flow Outlier Factor (FOF) scores are computed for each flow. In the third stage, flows are traced onto the network. Finally, in the fourth stage, each link is given a Network Link Outlier Factor (NLOF) score which is the ratio …
Hardware For Quantized Mixed-Precision Deep Neural Networks, Andres Rios
Hardware For Quantized Mixed-Precision Deep Neural Networks, Andres Rios
Open Access Theses & Dissertations
Recently, there has been a push to perform deep learning (DL) computations on the edge rather than the cloud due to latency, network connectivity, energy consumption, and privacy issues. However, state-of-the-art deep neural networks (DNNs) require vast amounts of computational power, data, and energyâ??resources that are limited on edge devices. This limitation has brought the need to design domain-specific architectures (DSAs) that implement DL-specific hardware optimizations. Traditionally DNNs have run on 32-bit floating-point numbers; however, a body of research has shown that DNNs are surprisingly robust and do not require all 32 bits. Instead, using quantization, networks can run on …
Geometric Analysis Leads To Adversarial Teaching Of Cybersecurity, Christian Servin, Olga Kosheleva, Vladik Kreinovich
Geometric Analysis Leads To Adversarial Teaching Of Cybersecurity, Christian Servin, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
As time goes, our civilization becomes more and more dependent on computers and therefore, more and more vulnerable to cyberattacks. Because of this threat, it is very important to make sure that computer science students -- tomorrow's computer professionals -- are sufficiently skilled in cybersecurity. In this paper, we analyze the need for teaching cybersecurity from the geometric viewpoint. We show that the corresponding geometric analysis leads to adversarial teaching -- an empirically effective but not-well-theoretically-understood approach, when the class is divided into sparring mini-teams that try their best to attack each other and defend from each other. Thus, our …
We Need Fuzzy Techniques To Design Successful Human-Like Robots, Vladik Kreinovich, Olga Kosheleva, Laxman Bokati
We Need Fuzzy Techniques To Design Successful Human-Like Robots, Vladik Kreinovich, Olga Kosheleva, Laxman Bokati
Departmental Technical Reports (CS)
In this chapter, we argue that to make sure that human-like robots exhibit human-like behavior, we need to use fuzzy techniques -- and we also provide details of this usage. The chapter is intended both for researchers and practitioners who are very familiar with fuzzy techniques and also for researchers and practitioners who do not know these techniques -- but who are interested in designing human-like robots.
When Can We Be Sure That Measurement Results Are Consistent: 1-D Interval Case And Beyond, Hani Dbouk, Steffen Schön, Ingo Neumann, Vladik Kreinovich
When Can We Be Sure That Measurement Results Are Consistent: 1-D Interval Case And Beyond, Hani Dbouk, Steffen Schön, Ingo Neumann, Vladik Kreinovich
Departmental Technical Reports (CS)
In many practical situations, measurements are characterized by interval uncertainty -- namely, based on each measurement result, the only information that we have about the actual value of the measured quantity is that this value belongs to some interval. If several such intervals -- corresponding to measuring the same quantity -- have an empty intersection, this means that at least one of the corresponding measurement results is an outlier, caused by a malfunction of the measuring instrument. From the purely mathematical viewpoint, if the intersection is non-empty, there is no reason to be suspicious, but from the practical viewpoint, if …
Why Lasso, Ridge Regression, And En: Explanation Based On Soft Computing, Woraphon Yamaka, Hamza Alkhatib, Ingo Neumann, Vladik Kreinovich
Why Lasso, Ridge Regression, And En: Explanation Based On Soft Computing, Woraphon Yamaka, Hamza Alkhatib, Ingo Neumann, Vladik Kreinovich
Departmental Technical Reports (CS)
In many practical situations, observations and measurement results are consistent with many different models -- i.e., the corresponding problem is ill-posed. In such situations, a reasonable idea is to take into account that the values of the corresponding parameters should not be too large; this idea is known as {\it regularization}. Several different regularization techniques have been proposed; empirically the most successful are LASSO method, when we bound the sum of absolute values of the parameters, ridge regression method, when we bound the sum of the squares, and a EN method in which these two approaches are combined. In this …
How To Train A-To-B And B-To-A Neural Networks So That The Resulting Transformations Are (Almost) Exact Inverses, Paravee Maneejuk, Torben Peters, Claus Brenner, Vladik Kreinovich
How To Train A-To-B And B-To-A Neural Networks So That The Resulting Transformations Are (Almost) Exact Inverses, Paravee Maneejuk, Torben Peters, Claus Brenner, Vladik Kreinovich
Departmental Technical Reports (CS)
In many practical situations, there exist several representations, each of which is convenient for some operations, and many data processing algorithms involve transforming back and forth between these representations. Many such transformations are computationally time-consuming when performed exactly. So, taking into account that input data is usually only 1-10% accurate anyway, it makes sense to replace time-consuming exact transformations with faster approximate ones. One of the natural ways to get a fast-computing approximation to a transformation is to train the corresponding neural network. The problem is that if we train A-to-B and B-to-A networks separately, the resulting approximate transformations are …
Lexicographic-Type Extension Of Min-Max Logic Is Not Uniquely Determined, Olga Kosheleva, Vladik Kreinovich
Lexicographic-Type Extension Of Min-Max Logic Is Not Uniquely Determined, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
Since in a computer, "true" is usually represented as 1 and ``false'' as 0, it is natural to represent intermediate degrees of confidence by numbers intermediate between 0 and 1; this is one of the main ideas behind fuzzy logic -- a technique that has led to many useful applications. In many such applications, the degree of confidence in A & B is estimated as the minimum of the degrees of confidence corresponding to A and B, and the degree of confidence in A \/ B is estimated as the maximum; for example, 0.5 \/ 0.3 = 0.5. It is …
A Fully Lexicographic Extension Of Min Or Max Operation Cannot Be Associative, Olga Kosheleva, Vladik Kreinovich
A Fully Lexicographic Extension Of Min Or Max Operation Cannot Be Associative, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
In many applications of fuzzy logic, to estimate the degree of confidence in a statement A&B, we take the minimum min(a,b) of the expert's degrees of confidence in the two statements A and B. When a < b, then an increase in b does not change this estimate, while from the commonsense viewpoint, our degree of confidence in A&B should increase. To take this commonsense idea into account, Ildar Batyrshin and colleagues proposed to extend the original order in the interval [0,1] to a lexicographic order on a larger set. This idea works for expressions of the type A&B, so maybe we can extend it to more general expressions? In this paper, we show that such an extension, while theoretically possible, would violate another commonsense requirement -- associativity of the "and"-operation. A similar negative result is proven for lexicographic extensions of the maximum operation -- that estimates the expert's degree of confidence in a statement A\/B.
What Is The Optimal Annealing Schedule In Quantum Annealing, Oscar Galindo, Vladik Kreinovich
What Is The Optimal Annealing Schedule In Quantum Annealing, Oscar Galindo, Vladik Kreinovich
Departmental Technical Reports (CS)
In many real-life situations in engineering (and in other disciplines), we need to solve an optimization problem: we want an optimal design, we want an optimal control, etc. One of the main problems in optimization is avoiding local maxima (or minima). One of the techniques that helps with solving this problem is annealing: whenever we find ourselves in a possibly local maximum, we jump out with some probability and continue search for the true optimum. A natural way to organize such a probabilistic perturbation of the deterministic optimization is to use quantum effects. It turns out that often, quantum annealing …
Physical Randomness Can Help In Computations, Olga Kosheleva, Vladik Kreinovich
Physical Randomness Can Help In Computations, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
Can we use some so-far-unused physical phenomena to compute something that usual computers cannot? Researchers have been proposing many schemes that may lead to such computations. These schemes use different physical phenomena ranging from quantum-related to gravity-related to using hypothetical time machines. In this paper, we show that, in principle, there is no need to look into state-of-the-art physics to develop such a scheme: computability beyond the usual computations naturally appears if we consider such a basic notion as randomness.
Evaluating Flow Features For Network Application Classification, Carlos Alcantara
Evaluating Flow Features For Network Application Classification, Carlos Alcantara
Open Access Theses & Dissertations
Communication networks provide the foundational services on which our modern economy depends. These services include data storage and transfer, video and voice telephony, gaming, multimedia streaming, remote invocation, and the world wide web. Communication networks are large-scale distributed systems composed of heterogeneous equipment. As a result of scale and heterogeneity, communication networks are cumbersome to manage (e.g., configure, assess performance, detect faults) by human operators. With the emergence of easily accessible network data and machine learning algorithms, there is a great opportunity to move network management towards increasing automation. Network management automation will allow for a reduced likelihood of human …
Development Of The Payload System And Obc Microcontroller Coding For A Cubic Satellite Performing An Additive Self-Repair Experiment In Space, Eduardo Macias-Zugasti
Development Of The Payload System And Obc Microcontroller Coding For A Cubic Satellite Performing An Additive Self-Repair Experiment In Space, Eduardo Macias-Zugasti
Open Access Theses & Dissertations
Additive manufacturing, which is also known as three-dimensional printing, in space is one of the most promising technologies advancing current capabilities for in-orbit space manufacturing and assembly. Additive manufacturing contributes to the reduction of cost per kilogram and number of launches, thus facilitating extraterrestrial colonization and deep-space exploration. The state of the art includes advancing efforts inside the International Space Station (ISS). However, the ISS is a controlled environment and, to the best of our knowledge, no spacecraft or satellite has performed additive manufacturing tasks in the extreme environment of outer space. In this work a 1U CubeSat named Orbital …
Compound Vision Approach For Autonomous Vehicles Navigation, Michael Mikhael
Compound Vision Approach For Autonomous Vehicles Navigation, Michael Mikhael
Open Access Theses & Dissertations
An analogy can be made between the sensing that occurs in simple robots and drones and that in insects and crustaceans, especially in basic navigation requirements. Thus, an approach in robots/drones based on compound eye vision could be useful. In this research, several image processing algorithms were used to detect and track moving objects starting with images upon which a grid (compound eye image) was superimposed, including contours detection, the second moments of those contours along with the grid applied to the original image, and Fourier Transforms and inverse Fourier Transforms. The latter also provide information about scene or camera …
Tram System Automation For Environmental Spectroscopy And Vegetation Monitoring, Enrique Anguiano Chavez
Tram System Automation For Environmental Spectroscopy And Vegetation Monitoring, Enrique Anguiano Chavez
Open Access Theses & Dissertations
Spectroscopy is the science of studying the interactions of matter and electromagnetic radiation (EMR). In particular, field spectroscopy takes place in a natural environment with a natural source of EMR. The paper presents progress towards the development and automation of a tram cart system. The new system in development collects high resolution, hyperspectral images and data from a spectrometer. Alternatives for a sensor cover mechanism to provide cover for the sensors mounted while the system is not operating are discussed, analyzing and comparing the benefits and disadvantages. An implementation for a charging station in an environment isolated from the electric …
A Comprehensive And Modular Robotic Control Framework For Model-Less Control Law Development Using Reinforcement Learning For Soft Robotics, Charles Sullivan
A Comprehensive And Modular Robotic Control Framework For Model-Less Control Law Development Using Reinforcement Learning For Soft Robotics, Charles Sullivan
Open Access Theses & Dissertations
Soft robotics is a growing field in robotics research. Heavily inspired by biological systems, these robots are made of softer, non-linear, materials such as elastomers and are actuated using several novel methods, from fluidic actuation channels to shape changing materials such as electro-active polymers. Highly non-linear materials make modeling difficult, and sensors are still an area of active research. These issues have rendered typical control and modeling techniques often inadequate for soft robotics. Reinforcement learning is a branch of machine learning that focuses on model-less control by mapping states to actions that maximize a specific reward signal. Reinforcement learning has …
Deep Learning (Partly) Demystified, Vladik Kreinovich, Olga Kosheleva
Deep Learning (Partly) Demystified, Vladik Kreinovich, Olga Kosheleva
Departmental Technical Reports (CS)
Successes of deep learning are partly due to appropriate selection of activation function, pooling functions, etc. Most of these choices have been made based on empirical comparison and heuristic ideas. In this paper, we show that many of these choices -- and the surprising success of deep learning in the first place -- can be explained by reasonably simple and natural mathematics.
Computing Without Computing: Dna Version, Vladik Kreinovich, Julio C. Urenda
Computing Without Computing: Dna Version, Vladik Kreinovich, Julio C. Urenda
Departmental Technical Reports (CS)
The traditional DNA computing schemes are based on using or simulating DNA-related activity. This is similar to how quantum computers use quantum activities to perform computations. Interestingly, in quantum computing, there is another phenomenon known as computing without computing, when, somewhat surprisingly, the result of the computation appears without invoking the actual quantum processes. In this chapter, we show that similar phenomenon is possible for DNA computing: in addition to the more traditional way of using or simulating DNA activity, we can also use DNA inactivity to solve complex problems. We also show that while DNA computing without …
Why Deep Learning Is More Efficient Than Support Vector Machines, And How It Is Related To Sparsity Techniques In Signal Processing, Laxman Bokati, Olga Kosheleva, Vladik Kreinovich
Why Deep Learning Is More Efficient Than Support Vector Machines, And How It Is Related To Sparsity Techniques In Signal Processing, Laxman Bokati, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
Several decades ago, traditional neural networks were the most efficient machine learning technique. Then it turned out that, in general, a different technique called support vector machines is more efficient. Reasonably recently, a new technique called deep learning has been shown to be the most efficient one. These are empirical observations, but how we explain them -- thus making the corresponding conclusions more reliable? In this paper, we provide a possible theoretical explanation for the above-described empirical comparisons. This explanation enables us to explain yet another empirical fact -- that sparsity techniques turned out to be very efficient in signal …
Adaptive Microphone Array Systems With Neural Network Applications, Jazmine Marisol Covarrubias
Adaptive Microphone Array Systems With Neural Network Applications, Jazmine Marisol Covarrubias
Open Access Theses & Dissertations
A microphone array integrated with a neural network framework is proposed to enhance and optimize speech signals derived from environments prone to noise and room reflections that cause reverberation. Microphone arrays provide a way to capture spatial acoustic information for extracting voice input from ambient noise. In this study, we utilize and analyze established signal processing methods combined with different neural network architectures to achieve denoised and dereverberated speech signal results that are comparable with their clean, anechoic versions. The first stage of the proposed system involves using datasets containing anechoic speech recordings of speech utterances and convolving them with …
Artificial Intelligence In The Assessment Of Transmission And Distribution Systems Under Natural Disasters Using Machine Learning And Deep Learning Techniques In A Knowledge Discovery Framework, Rossana Villegas
Open Access Theses & Dissertations
Warming trends and increasing temperatures have been observed and reported by federal agencies, such as the National Oceanic and Atmospheric Administration (NOAA). Extreme-weather events, especially hurricanes, tornadoes and winter storms, are among the highly devastating natural disasters responsible for massive and prolonged power outages in Electrical Transmission and Distribution Systems (ETDS). Moreover, the failure rate probability of any system component under extreme-weather tends to increase in the impacted geographic area. This Dissertation proposes an Artificial Intelligence (AI) Decision Support System that can predict damage in the ETDS and allow operators to mitigate disastrous extreme weather events. The document reports the …