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Articles 9961 - 9990 of 25752

Full-Text Articles in Computer Engineering

Multi-Evidence Learning For Medical Diagnosis, Tongjai Yampaka Jan 2020

Multi-Evidence Learning For Medical Diagnosis, Tongjai Yampaka

Chulalongkorn University Theses and Dissertations (Chula ETD)

In recent years, a great many approaches for learning from multiple sources by considering the diversity of different views have been proposed. The most interesting field is medical diagnosis. For example, breast cancer screening normally employs two views of mammography (Cranio-Caudal and Medio-Lateral-Oblique) or two modes of ultrasound (B-mode and Doppler mode) breast images. This study proposes a multi-evidence learning model that combines the multiple evidences of breast images to improve diagnosis. Two views mammography and two modes of ultrasound were used. Our proposed model consists of four stages. First, feature extraction using Convolutional Neuron Networks was operated to extract …

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Adaptive Object Detection For Autonomous Vehicles, Christopher Wolfe Jan 2020

Adaptive Object Detection For Autonomous Vehicles, Christopher Wolfe

Graduate Research Theses & Dissertations

Autonomous vehicles are gradually entering our daily lives. The goal of fully autonomous commercially available vehicles is becoming closer to reality each day as the contributions from researchers and various institutions are being added to the overall body of knowledge. Object detection is a critical component of an autonomous or semi-autonomous vehicle and draws extensively on results from many fields such as image processing and statistics. In this thesis, we consider ideas from the study of real-time computing and control systems to present a novel method of real-time adaptive object detection. We present a conceptual framework of the method as …

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A Multi-Constraint Predictive Control System With Auxiliary Emergency Controllerfor Autonomous Vehicles, Farhad Partovi Ebrahimpour Jan 2020

A Multi-Constraint Predictive Control System With Auxiliary Emergency Controllerfor Autonomous Vehicles, Farhad Partovi Ebrahimpour

Graduate Research Theses & Dissertations

In the last few years, several research groups and companies have worked on developing autonomous vehicles. Among the first automation layers, the safety layer plays a significant role in this field. However, considering safety should not diminish the importance of the efficiency of the vehicle in terms of path tracking with the desired speed. This thesis introduces a multi-constraint predictive control algorithm along with a safety layer to guarantee object avoidance in emergency situations. First, there is a quick review of autonomous cars and their functionalities. In the following, a controller switching mechanism is proposed and designed. It switches the …

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Table Of Contents Jitim Vol 29 Issue 2, 2020 Jan 2020

Table Of Contents Jitim Vol 29 Issue 2, 2020

Journal of International Technology and Information Management

Table of contents

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Instructor Activity Recognition Using Smartwatch And Smartphone Sensors, Zayed Uddin Chowdhury Jan 2020

Instructor Activity Recognition Using Smartwatch And Smartphone Sensors, Zayed Uddin Chowdhury

College of Graduate Studies: Theses & Dissertations

During a classroom session, an instructor performs several activities, such as writing on the board, speaking to the students, gestures to explain a concept. A record of the time spent in each of these activities could be valuable information for the instructors to virtually observe their own style of instruction. It can help in identifying activities that engage the students more, thereby enhancing teaching effectiveness and efficiency. In this work, we present a preliminary study on profiling multiple activities of an instructor in the classroom using smartwatch and smartphone sensor data. We use 2 benchmark datasets to test out the …

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Quantitative Performance Assessment Of Lidar-Based Vehicle Contour Estimation Algorithms For Integrated Vehicle Safety Applications, David M. Mothershed Jan 2020

Quantitative Performance Assessment Of Lidar-Based Vehicle Contour Estimation Algorithms For Integrated Vehicle Safety Applications, David M. Mothershed

College of Graduate Studies: Theses & Dissertations

Many nations and organizations are committing to achieving the goal of `Vision Zero' and eliminate road traffic related deaths around the world. Industry continues to develop integrated safety systems to make vehicles safer, smarter and more capable in safety critical scenarios. Passive safety systems are now focusing on pre-crash deployment of restraint systems to better protect vehicle passengers. Current commonly used bounding box methods for shape estimation of crash partners lack the fidelity required for edge case collision detection and advanced crash modeling. This research presents a novel algorithm for robust and accurate contour estimation of opposing vehicles. The presented …

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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 camera …

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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 human …

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Real-Time Urban Weather Observations For Urban Air Mobility, Kevin A. Adkins, Mustafa Akbas, Marc Compere Jan 2020

Real-Time Urban Weather Observations For Urban Air Mobility, Kevin A. Adkins, Mustafa Akbas, Marc Compere

International Journal of Aviation, Aeronautics, and Aerospace

Cities of the future will have to overcome congestion, air pollution and increasing infrastructure cost while moving more people and goods smoothly, efficiently and in an eco-friendly manner. Urban air mobility (UAM) is expected to be an integral component of achieving this new type of city. This is a new environment for sustained aviation operations. The heterogeneity of the urban fabric and the roughness elements within it create a unique environment where flight conditions can change frequently across very short distances. UAM vehicles with their lower mass, more limited thrust and slower speeds are especially sensitive to these conditions. Since …

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Zips Racing Electric Can Communications, Andrew Jordan, Adam Long, Susanah Kowalewski, Rami Nehme Jan 2020

Zips Racing Electric Can Communications, Andrew Jordan, Adam Long, Susanah Kowalewski, Rami Nehme

Williams Honors College, Honors Research Projects

The CAN protocol has been a standard of electronic communication networks of automotive vehicles since the early 2000s due to its robust reliability in harsh environments. For the 2020 competition year, the Zips Racing Electric design team will be building an entirely new, fully-electric vehicle with CAN communication implemented rather than communicating via pure analog signals. Hardware and software can be utilized to read analog electrical signals from a source, such as accelerator and brake sensors, and encode them into a digital message that meets the CAN 2.0B communication protocol standard. Likewise, software can be used to extract data from …

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Smart Collar, Gretchen T. Woodling, Sean Moran, Justen Bischoff, Jacob Sindelar Jan 2020

Smart Collar, Gretchen T. Woodling, Sean Moran, Justen Bischoff, Jacob Sindelar

Williams Honors College, Honors Research Projects

The Smart Collar is a universal pet tracker, designed to be small and exceedingly comfortable for any pet to wear. GPS technology is used to locate the device, allowing the user to track their pet, via a smart phone application. This application can be used to program the device, view maps of their pet’s location and history of travel. Operating primarily on Long Range Wide Area Network (LoRaWAN) for data transfer, the device consumes very little power, allowing for several days of run-time per charge of the battery. Boasting no monthly service fees, The Smart Collar provides pet owner’s an …

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Piezoelectric Energy Harvester Improvement, Nathan Embaugh, Jason Mack, Jeremiah Fitzgerald, Zachary J. Lindsey Jan 2020

Piezoelectric Energy Harvester Improvement, Nathan Embaugh, Jason Mack, Jeremiah Fitzgerald, Zachary J. Lindsey

Williams Honors College, Honors Research Projects

The energy harvester is used to convert a portion of the tire deflection waste energy to power up tire embedded sensors. A piezoelectric energy harvester is designed and some preliminary tests are done on it. So far, it has been shown that this design is sufficient for tire application. The team will need to modify the design of the energy harvester, the measurement setup and add a temperature and a strain senor to the existing setup so that the tire deflection and temperature can be measured and at the same time the energy harvester should be tested to see how …

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Data Science Methods For Standardization, Safety, And Quality Assurance In Radiation Oncology, Khajamoinuddin Syed Jan 2020

Data Science Methods For Standardization, Safety, And Quality Assurance In Radiation Oncology, Khajamoinuddin Syed

Theses and Dissertations

Radiation oncology is the field of medicine that deals with treating cancer patients through ionizing radiation. The clinical modality or technique used to treat the cancer patients in the radiation oncology domain is referred to as radiation therapy. Radiation therapy aims to deliver precisely measured dose irradiation to a defined tumor volume (target) with as minimal damage as possible to surrounding healthy tissue (organs-at-risk), resulting in eradication of the tumor, high quality of life, and prolongation of survival. A typical radiotherapy process requires the use of different clinical systems at various stages of the workflow. The data generated in these …

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Explainable Ai Using Knowledge Graphs, Manas Gaur, Ankit Desai, Keyur Faldu, Amit Sheth Jan 2020

Explainable Ai Using Knowledge Graphs, Manas Gaur, Ankit Desai, Keyur Faldu, Amit Sheth

Publications

During the last decade, traditional data-driven deep learning (DL) has shown remarkable success in essential natural language processing tasks, such as relation extraction. Yet, challenges remain in developing artificial intelligence (AI) methods in real-world cases that require explainability through human interpretable and traceable outcomes. The scarcity of labeled data for downstream supervised tasks and entangled embeddings produced as an outcome of self-supervised pre-training objectives also hinders interpretability and explainability. Additionally, data labeling in multiple unstructured domains, particularly healthcare and education, is computationally expensive as it requires a pool of human expertise. Consider Education Technology, where AI systems fall along a …

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Relational Sequential Decision Making, Kaushik Roy Jan 2020

Relational Sequential Decision Making, Kaushik Roy

Publications

Markov Decision Processes(MDPs) are the standard for sequential decision making. Comprehensive theory and methods have been developed to deal with solving MDPs in the propositional setting. Real world domains however are naturally represented using objects and relationships. To this effect, relational adaptations of algorithms to solve MDPs have been proposed in recent years. This paper presents a study of these techniques both in the model based and model free setting.

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Knowledge-Infused Statistical Learning For Social Good, Kaushik Roy, Manas Gaur Jan 2020

Knowledge-Infused Statistical Learning For Social Good, Kaushik Roy, Manas Gaur

Publications

Humans are able to provide symbolic knowledge in structured form for potential use by an AI system in learning human-desirable concepts. In clinical settings, for instance, prediction of patient outcomes by an AI can be guided by knowledge from patient history. This history contains concepts such as treatment information, observational and drug-related information, mental health conditions, and severity of disease/disorder. Additionally, there is also often a certain graphical structure to the knowledge among the concepts, for example, ”patient symptoms cause certain tests to be taken”, which in turn affects the prescription of medication. This type of structure between human interpretable …

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Exploring The Relationship Between Teamwork Skills And Team Members' Centrality, Francisco Cima, Pilar Pazos, Ana Maria Canto Jan 2020

Exploring The Relationship Between Teamwork Skills And Team Members' Centrality, Francisco Cima, Pilar Pazos, Ana Maria Canto

Engineering Management & Systems Engineering Faculty Publications

The present paper describes an exploratory study of small teams working on a four-month project as part of a graduate engineering program. The research had two primary goals. The first was to utilize the log files from shared repositories used for team collaboration to describe the network structure of the teams. The second was to determine whether the network centrality of any individual team member is associated with their teamwork skills and attitudes towards the collaboration platform. The relationship between teamwork skills, attitudes towards the collaboration technology, and the centrality index was explored using Pearson correlations. A total of 35 …

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Applications Of Artificial Intelligence To Cryptography, Jonathan Blackledge, Napo Mosola Jan 2020

Applications Of Artificial Intelligence To Cryptography, Jonathan Blackledge, Napo Mosola

Articles

This paper considers some recent advances in the field of Cryptography using Artificial Intelligence (AI). It specifically considers the applications of Machine Learning (ML) and Evolutionary Computing (EC) to analyze and encrypt data. A short overview is given on Artificial Neural Networks (ANNs) and the principles of Deep Learning using Deep ANNs. In this context, the paper considers: (i) the implementation of EC and ANNs for generating unique and unclonable ciphers; (ii) ML strategies for detecting the genuine randomness (or otherwise) of finite binary strings for applications in Cryptanalysis. The aim of the paper is to provide an overview on …

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Novel Approaches For Reliable And Efficient Circuit Design, Prashanthi Metku Jan 2020

Novel Approaches For Reliable And Efficient Circuit Design, Prashanthi Metku

Doctoral Dissertations

"In this research work, a suite of approaches are presented to improve reliability of 3D heterogeneous processors (3DHP) and to reduce the area overhead of asynchronous designs. This work is primarily divided into two parts. In the first part, we present an approach for improving reliability in 3DHP. Typically, in 3DHP, thermal hotspots introduce spatial and temporal variability that results in wide bit error variation in DRAM dies. To address this issue multi- path BCH decoder is introduced. Based on the thermal gradient data generated by on-chip temperature sensors, the proposed methodology specializes in adaptively estimating the number of errors …

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A Probabilistic Machine Learning Framework For Cloud Resource Selection On The Cloud, Syeduzzaman Khan Jan 2020

A Probabilistic Machine Learning Framework For Cloud Resource Selection On The Cloud, Syeduzzaman Khan

University of the Pacific Theses and Dissertations

The execution of the scientific applications on the Cloud comes with great flexibility, scalability, cost-effectiveness, and substantial computing power. Market-leading Cloud service providers such as Amazon Web service (AWS), Azure, Google Cloud Platform (GCP) offer various general purposes, memory-intensive, and compute-intensive Cloud instances for the execution of scientific applications. The scientific community, especially small research institutions and undergraduate universities, face many hurdles while conducting high-performance computing research in the absence of large dedicated clusters. The Cloud provides a lucrative alternative to dedicated clusters, however a wide range of Cloud computing choices makes the instance selection for the end-users. This thesis …

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Brexit Election: Forecasting A Conservative Party Victory Through The Pound Using Arima And Facebook's Prophet, James Usher, Pierpaolo Dondio Jan 2020

Brexit Election: Forecasting A Conservative Party Victory Through The Pound Using Arima And Facebook's Prophet, James Usher, Pierpaolo Dondio

Conference papers

On the 30th October, 2019, the markets watched as British Prime Minister, Boris Johnson, took a massive political gamble to call a general election to break the Withdrawal Agreement stalemate in the House of Commons to “Get BREXIT Done”. The pound had been politically sensitive owing to BREXIT uncertainty. With the polls indicating a Conservative win on 4thDecember, 2019, the margin of victory could be observed through increases in the pound. The outcome of a Conservative party victory would benefit the pound by removing the current market turbulence. We look to provide a short-term forecast of the pound. Our approach …

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Brain Disease Detection From Eegs: Comparing Spiking And Recurrent Neural Networks For Non-Stationary Time Series Classification, Hristo Stoev Jan 2020

Brain Disease Detection From Eegs: Comparing Spiking And Recurrent Neural Networks For Non-Stationary Time Series Classification, Hristo Stoev

Dissertations

Modeling non-stationary time series data is a difficult problem area in AI, due to the fact that the statistical properties of the data change as the time series progresses. This complicates the classification of non-stationary time series, which is a method used in the detection of brain diseases from EEGs. Various techniques have been developed in the field of deep learning for tackling this problem, with recurrent neural networks (RNN) approaches utilising Long short-term memory (LSTM) architectures achieving a high degree of success. This study implements a new, spiking neural network-based approach to time series classification for the purpose of …

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Content-Based Filtering Recommendation Approach To Label Irish Legal Judgements, Sandesh Gangadhar Jan 2020

Content-Based Filtering Recommendation Approach To Label Irish Legal Judgements, Sandesh Gangadhar

Dissertations

Machine learning approaches are applied across several domains to either simplify or automate tasks which directly result in saved time or cost. Text document labelling is one such task that requires immense human knowledge about the domain and efforts to review, understand and label the documents. The company Stare Decisis summarises legal judgements and labels them as they are made available on Irish public legal source www.courts.ie. This research presents a recommendation-based approach to reduce the time for solicitors at Stare Decisis by reducing many numbers of available labels to pick from to a concentrated few that potentially contains the …

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A Discrimination Aware Model To Predict Childhood Literacy Levels, Kate Byrne Jan 2020

A Discrimination Aware Model To Predict Childhood Literacy Levels, Kate Byrne

Dissertations

It is illegal in Ireland to discriminate in the provision of education on the basis of multiple characteristics including gender, race and religion. While the increased use of machine learning models can open multiple avenues to identify early intervention strategies in education, caution must be exercised to ensure that any intervention does not discriminate with respect to a protected class. Poor literacy in childhood can have long term effects as the child ages, including on employment and mental health outcomes. Early intervention is key in mitigating this. In this dissertation, a model was created that predicted the outcome of a …

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Optimization Of Home Mortgage Mover Predictive Model Applying Geo-Spatial Analysis And Machine Learning Techniques, Natalia Riscovaia Jan 2020

Optimization Of Home Mortgage Mover Predictive Model Applying Geo-Spatial Analysis And Machine Learning Techniques, Natalia Riscovaia

Dissertations

In the last decade digital innovations and online banking services have significantly changed customers banking preferences and behaviour. Banking industry is going through the changes and developments in the provision of banking services that are affecting the structure and the organization of the bank network. However, private home loan, referred as Home Mortgage hereinafter, continue to remain among the products, that customers prefer to have personal interaction about with professional advisors prior making the decision to apply for the loan with financial institution.

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Synthetic Data Generation Using Wasserstein Conditional Gans With Gradient Penalty (Wcgans-Gp), Manhar Singh Walia Jan 2020

Synthetic Data Generation Using Wasserstein Conditional Gans With Gradient Penalty (Wcgans-Gp), Manhar Singh Walia

Dissertations

With data protection requirements becoming stricter, the data privacy has become increasingly important and more crucial than ever. This has led to restrictions on the availability and dissemination of real-world datasets. Synthetic data offers a viable solution to overcome barriers of data access and sharing. Existing data generation methods require a great deal of user-defined rules, manual interactions and domainspecific knowledge. Moreover, they are not able to balance the trade-off between datausability and privacy. Deep learning based methods like GANs have seen remarkable success in synthesizing images by automatically learning the complicated distributions and patterns of real data. But they …

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Improving Transfer Learning For Use In Multi-Spectral Data, Yuvraj Sharma Jan 2020

Improving Transfer Learning For Use In Multi-Spectral Data, Yuvraj Sharma

Dissertations

Recently Nasa as well as the European Space Agency have made observational satellites images public. The main reason behind opening it to public is to foster research among university students and corporations alike. Sentinel is a program by the European Space Agency which has plans to release a series of seven satellites in lower earth orbit for observing land and sea patterns. Recently huge datasets have been made public by the Sentinel program. Many advancements have been made in the field of computer vision in the last decade. Krizhevsky, Sutskever & Hinton, 2012, revolutionized the field of image analysis by …

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Exploration Of Approaches To Arabic Named Entity Recognition, Husamelddin Balla, Sarah Jane Delany Jan 2020

Exploration Of Approaches To Arabic Named Entity Recognition, Husamelddin Balla, Sarah Jane Delany

Conference papers

Abstract. The Named Entity Recognition (NER) task has attracted significant attention in Natural Language Processing (NLP) as it can enhance the performance of many NLP applications. In this paper, we compare English NER with Arabic NER in an experimental way to investigate the impact of using different classifiers and sets of features including language-independent and language-specific features. We explore the features and classifiers on five different datasets. We compare deep neural network architectures for NER with more traditional machine learning approaches to NER. We discover that most of the techniques and features used for English NER perform well on Arabic …

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Android Compcache Based On Graphics Processing Unit, Muder Almi'ani, Abdu Razaque, Saleh Atiewi, Mohammed Alweshah, Ayman Al-Dmour, Basel Magableh Jan 2020

Android Compcache Based On Graphics Processing Unit, Muder Almi'ani, Abdu Razaque, Saleh Atiewi, Mohammed Alweshah, Ayman Al-Dmour, Basel Magableh

Conference papers

Android systems have been successfully developed to meet the demands of users. The following four methods are used in Android systems for memory management: backing swap, CompCache, traditional Linux swap, and low memory killer. These memory management methods are fully functioning.
However, Android phones cannot swap memory into solid-state drives, thus slowing the processor and reducing storage lifetime. In addition, the compression and decompression processes consume additional energy and latency. Therefore, the CompCache requires an extension. An extended Android CompCache using a graphics processing unit to compress and decompress memory pages on demand and reduce the latency is introduced in …

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A Saliency-Driven Video Magnifier For People With Low Vision, Ali Selman Aydin, Shirin Feiz, Iv Ramakrishnan, Vikas Ashok Jan 2020

A Saliency-Driven Video Magnifier For People With Low Vision, Ali Selman Aydin, Shirin Feiz, Iv Ramakrishnan, Vikas Ashok

Computer Science Faculty Publications

Consuming video content poses significant challenges for many screen magnifier users, which is the “go to” assistive technology for people with low vision. While screen magnifier software could be used to achieve a zoom factor that would make the content of the video visible to low-vision users, it is oftentimes a major challenge for these users to navigate through videos. Towards making videos more accessible for low-vision users, we have developed the SViM video magnifier system [6]. Specifically, SViM consists of three different magnifier interfaces with easy-to-use means of interactions. All three interfaces are driven by visual saliency as a …

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