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Full-Text Articles in Computer Engineering

A Fully Lexicographic Extension Of Min Or Max Operation Cannot Be Associative, Olga Kosheleva, Vladik Kreinovich Jun 2020

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 Jun 2020

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 ...


Lexicographic-Type Extension Of Min-Max Logic Is Not Uniquely Determined, Olga Kosheleva, Vladik Kreinovich Jun 2020

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 intuitively OK that, e.g., 0.5 \/ 0.3 < 0.51 and, more generally, that 0.5 \/ 0.3 < 0.5 + ε for all ε > 0. However, intuitively, an additional argument in favor of the statement should increase our degree of confidence, i.e., we should have ...


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 Jun 2020

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 ...


Why Lasso, Ridge Regression, And En: Explanation Based On Soft Computing, Woraphon Yamaka, Hamza Alkhatib, Ingo Neumann, Vladik Kreinovich Jun 2020

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 ...


When Can We Be Sure That Measurement Results Are Consistent: 1-D Interval Case And Beyond, Hani Dbouk, Steffen Schön, Ingo Neumann, Vladik Kreinovich Jun 2020

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 the intersection is ...


Development Of The Payload System And Obc Microcontroller Coding For A Cubic Satellite Performing An Additive Self-Repair Experiment In Space, Eduardo Macias-Zugasti Jan 2020

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 Jan 2020

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 ...


Evaluating Flow Features For Network Application Classification, Carlos Alcantara Jan 2020

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 ...


Physical Randomness Can Help In Computations, Olga Kosheleva, Vladik Kreinovich Jan 2020

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.


Tram System Automation For Environmental Spectroscopy And Vegetation Monitoring, Enrique Anguiano Chavez Jan 2020

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 ...


Computing Without Computing: Dna Version, Vladik Kreinovich, Julio C. Urenda Nov 2019

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 computing is ...


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 Nov 2019

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 processing.


Deep Learning (Partly) Demystified, Vladik Kreinovich, Olga Kosheleva Nov 2019

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.


Adaptive Microphone Array Systems With Neural Network Applications, Jazmine Marisol Covarrubias Jan 2019

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 ...


Dedicated Hardware For Machine/Deep Learning: Domain Specific Architectures, Angel Izael Solis Jan 2019

Dedicated Hardware For Machine/Deep Learning: Domain Specific Architectures, Angel Izael Solis

Open Access Theses & Dissertations

Artificial intelligence has come a very long way from being a mere spectacle on the silver screen in the 1920s [Hml18]. As artificial intelligence continues to evolve, and we begin to develop more sophisticated Artificial Neural Networks, the need for specialized and more efficient machines (less computational strain while maintaining the same performance results) becomes increasingly evident. Though these “new” techniques, such as Multilayer Perceptron’s, Convolutional Neural Networks and Recurrent Neural Networks, may seem as if they are on the cutting edge of technology, many of these ideas are over 60 years old! However, many of these earlier models ...


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 Jan 2019

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 ...


The Effect Of Data Marshalling On Computation Offloading Decisions, Julio Alberto Reyes Muñoz Jan 2018

The Effect Of Data Marshalling On Computation Offloading Decisions, Julio Alberto Reyes Muñoz

Open Access Theses & Dissertations

Computation offloading consists in allowing resource constrained computers, such as smartphones and other mobile devices, to use the network for the remote execution of resource intensive computing tasks in powerful computers. However, deciding whether to offload or not is not a trivial problem, and it depends in several variables related to the environment conditions, the computing devices involved in the process, and the nature of the task to be remotely executed. Furthermore, it comprises the optimal solution to some questions, like how to partition the application and where to execute the tasks.

The computation offloading decision problem has been widely ...


Development Of A Desktop Material Extrusion 3d Printer With Wire Embedding Capabilities, Jose Francisco Motta Jan 2018

Development Of A Desktop Material Extrusion 3d Printer With Wire Embedding Capabilities, Jose Francisco Motta

Open Access Theses & Dissertations

Printed circuit boards (PCB) have been widely used as a permanent solution for generating complex circuitries to power electronic devices. Over the years, PCB boards have proved to be reliable when powering electronic devices. However, when fabricating a printed circuit board, one must outsource to fabricate the boards when in prototype phase. Therefore, the risk of intellectual property theft and long lead time is an issue. The objective of this Thesis is to develop a hybrid multi-tool desktop material extrusion 3D printer that allows for easy integration (modularity) of tools to generate multi-functional 3D printed components.

The addition of an ...


An Efficient Method For Online Identification Of Steady State For Multivariate System, Honglun None Xu Jan 2018

An Efficient Method For Online Identification Of Steady State For Multivariate System, Honglun None Xu

Open Access Theses & Dissertations

Most of the existing steady state detection approaches are designed for univariate signals. For multivariate signals, the univariate approach is often applied to each process variable and the system is claimed to be steady once all signals are steady, which is computationally inefficient and also not accurate. The article proposes an efficient online method for multivariate steady state detection. It estimates the covariance matrices using two different approaches, namely, the mean-squared-deviation and mean-squared-successive-difference. To avoid the usage of a moving window, the process means and the two covariance matrices are calculated recursively through exponentially weighted moving average. A likelihood ratio ...


A New Approach To Multiplanar, Real-Time Simulation Of Physiological Knee Loads And Synthetic Knee Components Augmented By Local Composition Control In Fused Filament Fabrication, Joshua Taylor Green Jan 2018

A New Approach To Multiplanar, Real-Time Simulation Of Physiological Knee Loads And Synthetic Knee Components Augmented By Local Composition Control In Fused Filament Fabrication, Joshua Taylor Green

Open Access Theses & Dissertations

Despite numerous advances in biomedical engineering, few developments in surgical simulation have been made outside of computational models. Cadavers remain the primary media on which surgical research and simulation is conducted. Most attempts to quantify the effects of orthopedic surgical methods fail to achieve statistical significance due to limited quantities of cadaver specimen, large variations among the cadaver population, and a lack of repeatability among measurement techniques. The general purpose of the research covered in this dissertation is to develop repeatable simulation of physiological loads and develop techniques to fabricate a synthetic-based replacement of cadaver specimens. Future work applying this ...


Detecting Contaminated Fiber Connectors Using Sfp Optical Power Data, Christopher A. Mendoza Jan 2018

Detecting Contaminated Fiber Connectors Using Sfp Optical Power Data, Christopher A. Mendoza

Open Access Theses & Dissertations

Fiber optic technology is an important part of communication networks enabling high-bandwidth transmissions over long and short distances. They do have their fair share of problems though, contamination being the biggest culprit. Contamination of fiber optic connectors can lead to serious performance degradation or even loss of signal. Detecting contaminated fiber connectors can take weeks or even months using traditional practices. There are standard cleanliness practices when dealing with optical connectors but still the problem seems to persist. This work presents an inequality to solve the detection portion of this problem. The proposed inequality uses power readings from the Small ...


Improving Time-Of-Flight And Other Depth Images: Super-Resolution And Denoising Using Variational Methods, Salvador Canales Andrade Jan 2018

Improving Time-Of-Flight And Other Depth Images: Super-Resolution And Denoising Using Variational Methods, Salvador Canales Andrade

Open Access Theses & Dissertations

Depth information is a new important source of perception for machines, which allow them to have a better representation of the surroundings. The depth information provides a more precise map of the location of every object and surfaces in a space of interest in comparison with conventional cameras. Time of flight (ToF) cameras provide one of the techniques to acquire depth maps, however they produce low spatial resolution and noisy maps. This research proposes a framework to enhance and up-scale depth maps by using two different regularization terms: Total Generalized Variation (TGV) and Total Generalized Variation with a Structure Tensor ...


Decision Making For Dynamic Systems Under Uncertainty: Predictions And Parameter Recomputations, Leobardo Valera Jan 2018

Decision Making For Dynamic Systems Under Uncertainty: Predictions And Parameter Recomputations, Leobardo Valera

Open Access Theses & Dissertations

In this Thesis, we are interested in making decision over a model of a dynamic system. We want to know, on one hand, how the corresponding dynamic phenomenon unfolds under different input parameters (simulations). These simulations might help researchers to design devices with a better performance than the actual ones. On the other hand, we are also interested in predicting the behavior of the dynamic system based on knowledge of the phenomenon in order to prevent undesired outcomes. Finally, this Thesis is concerned with the identification of parameters of dynamic systems that ensure a specific performance or behavior.

Understanding the ...


Probabilistic Graphical Models Follow Directly From Maximum Entropy, Anh H. Ly, Francisco Zapata, Olac Fuentes, Vladik Kreinovich Sep 2017

Probabilistic Graphical Models Follow Directly From Maximum Entropy, Anh H. Ly, Francisco Zapata, Olac Fuentes, Vladik Kreinovich

Departmental Technical Reports (CS)

Probabilistic graphical models are a very efficient machine learning technique. However, their only known justification is based on heuristic ideas, ideas that do not explain why exactly these models are empirically successful. It is therefore desirable to come up with a theoretical explanation for these models' empirical efficiency. At present, the only such explanation is that these models naturally emerge if we maximize the relative entropy; however, why the relative entropy should be maximized is not clear. In this paper, we show that these models can also be obtained from a more natural -- and well-justified -- idea of maximizing (absolute) entropy.


Simplest Polynomial For Which Naive (Straightforward) Interval Computations Cannot Be Exact, Olga Kosheleva, Vladik Kreinovich, Songsak Sriboonchitta Jun 2017

Simplest Polynomial For Which Naive (Straightforward) Interval Computations Cannot Be Exact, Olga Kosheleva, Vladik Kreinovich, Songsak Sriboonchitta

Departmental Technical Reports (CS)

One of the main problem of interval computations is computing the range of a given function over given intervals. It is known that naive interval computations always provide an enclosure for the desired range. Sometimes -- e.g., for single use expressions -- naive interval computations compute the exact range. Sometimes, we do not get the exact range when we apply naive interval computations to the original expression, but we get the exact range if we apply naive interval computations to an equivalent reformulation of the original expression. For some other functions -- including some polynomials -- we do not get the exact range ...


How To Gauge The Accuracy Of Fuzzy Control Recommendations: A Simple Idea, Patricia Melin, Oscar Castillo, Andrzej Pownuk, Olga Kosheleva, Vladik Kreinovich Jun 2017

How To Gauge The Accuracy Of Fuzzy Control Recommendations: A Simple Idea, Patricia Melin, Oscar Castillo, Andrzej Pownuk, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Fuzzy control is based on approximate expert information, so its recommendations are also approximate. However, the traditional fuzzy control algorithms do not tell us how accurate are these recommendations. In contrast, for the probabilistic uncertainty, there is a natural measure of accuracy: namely, the standard deviation. In this paper, we show how to extend this idea from the probabilistic to fuzzy uncertainty and thus, to come up with a reasonable way to gauge the accuracy of fuzzy control recommendations.


Normalization-Invariant Fuzzy Logic Operations Explain Empirical Success Of Student Distributions In Describing Measurement Uncertainty, Hamza Alkhatib, Boris Kargoll, Ingo Neumann, Vladik Kreinovich Jun 2017

Normalization-Invariant Fuzzy Logic Operations Explain Empirical Success Of Student Distributions In Describing Measurement Uncertainty, Hamza Alkhatib, Boris Kargoll, Ingo Neumann, Vladik Kreinovich

Departmental Technical Reports (CS)

In engineering practice, usually measurement errors are described by normal distributions. However, in some cases, the distribution is heavy-tailed and thus, not normal. In such situations, empirical evidence shows that the Student distributions are most adequate. The corresponding recommendation -- based on empirical evidence -- is included in the International Organization for Standardization guide. In this paper, we explain this empirical fact by showing that a natural fuzzy-logic-based formalization of commonsense requirements leads exactly to the Student's distributions.


Safety Airway For Small Unmanned Aerial Vehicles Using A Gas Particles Behavior Analogy, Pablo Rangel Jan 2017

Safety Airway For Small Unmanned Aerial Vehicles Using A Gas Particles Behavior Analogy, Pablo Rangel

Open Access Theses & Dissertations

The United States Federal Aviation Administration (FAA) implemented the Part 107 legislation to allow the flight of Unmanned Aerial Vehicles (UAV) for commercial use (i.e. package deliveries, power transmission line inspections, etc.) in the National Airspace System (NAS). As a consequence of the newly introduced rules, there is an increased risk for accidents involving injured bystanders or damaged to property. The work within this document defines a UAV to UAV safety distance model that acts as a range sensor enabled "elastic bubble". The length of the UAV safety bubble contracts and expands upon changing airway wind speed conditions. It ...


Structural And Electrical Characterization Of Tin Oxide Resistive Switching, Arka Talukdar Jan 2017

Structural And Electrical Characterization Of Tin Oxide Resistive Switching, Arka Talukdar

Open Access Theses & Dissertations

Resistive switching in metal oxide is a phenomenon in which the metal oxide changes its resistance upon application of electric field and thus giving two states; high resistance state (HRS) and low resistance state (LRS). Many metal oxides have been investigated however very little is known about unipolar resistive switching in SnO2 though it has shown excellent resistive switching characteristics. Defects in the material play a vital role in resistive switching of the metal oxides. In this work, the role of defects in resistive switching of SnO2 are investigated in Ti/SnO2/Au structures. Two methods were used to control ...