Computer Engineering Commons^™

Inferring Motion Direction Using Commodity Wi-Fi For Interactive Exergames, Kun Qian, Chenshu Wu, Zimu Zhou, Yue Zheng, Yang Zheng, Yunhao Liu

Research Collection School Of Computing and Information Systems

In-air interaction acts as a key enabler for ambient intelligence and augmented reality. As an increasing popular example, exergames, and the alike gesture recognition applications, have attracted extensive research in designing accurate, pervasive and low-cost user interfaces. Recent advances in wireless sensing show promise for a ubiquitous gesture-based interaction interface with Wi-Fi. In this work, we extract complete information of motion-induced Doppler shifts with only commodity Wi-Fi. The key insight is to harness antenna diversity to carefully eliminate random phase shifts while retaining relevant Doppler shifts. We further correlate Doppler shifts with motion directions, and propose a light-weight pipeline to …

Lightweight Data Aggregation Scheme Against Internal Attackers In Smart Grid Using Elliptic Curve Cryptography, Debiao He, Sherali Zeadally, Huaqun Wang, Qin Liu

Information Science Faculty Publications

Recent advances of Internet and microelectronics technologies have led to the concept of smart grid which has been a widespread concern for industry, governments, and academia. The openness of communications in the smart grid environment makes the system vulnerable to different types of attacks. The implementation of secure communication and the protection of consumers’ privacy have become challenging issues. The data aggregation scheme is an important technique for preserving consumers’ privacy because it can stop the leakage of a specific consumer’s data. To satisfy the security requirements of practical applications, a lot of data aggregation schemes were presented over the …

Classification Of Images Based On Pixels That Represent A Small Part Of The Scene. A Case Applied To Microaneurysms In Fundus Retina Images, Pablo F. Ordonez, Pablo F. Ordonez

Master of Science in Computer Science Theses

Convolutional Neural Networks (CNNs), the state of the art in image classification, have proven to be as effective as an ophthalmologist, when detecting Referable Diabetic Retinopathy (RDR). Having a size of less than 1\% of the total image, microaneurysms are early lesions in DR that are difficult to classify. The purpose of this thesis is to improve the accuracy of detection of microaneurysms using a model that includes two CNNs with different input image sizes, 60x60 and 420x420 pixels. These models were trained using the Kaggle and Messidor datasets and tested independently against the Kaggle dataset, showing a sensitivity of …

A Robotics Framework For Simulation And Control Of A Robotic Arm For Use In Higher Education, Craig Christensen

MS in Computer Science Project Reports

Robotic arms have been in common use for a several decades now in many areas from manufacturing and industrial uses to hobby projects and amusement park rides. However, there have been very few attempts to make an inexpensive robot arm with a software stack for use in higher education. This paper will outline a control and interfacing software stack built on the Robot Operating System (ROS) and a simulation of the 5 degree of freedom (DoF) robotic arm.

Tum: Towards Ubiquitous Multi-Device Localization For Cross-Device Interaction, Han Xu, Zheng Yang, Zimu Zhou, Ke Yi, Chunyi Peng

Research Collection School Of Computing and Information Systems

Cross-device interaction is becoming an increasingly hot topic as we often have multiple devices at our immediate disposal in this era of mobile computing. Various cross-device applications such as file sharing, multi-screen display, and crossdevice authentication have been proposed and investigated. However, one of the most fundamental enablers remains unsolved: How to achieve ubiquitous multi-device localization? Though pioneer efforts have resorted to gesture-assisted or sensing-assisted localization, they either require extensive user participation or impose some strong assumptions on device sensing abilities. This introduces extra costs and constraints, and thus degrades their practicality. To overcome these limitations, we propose TUM, an …

Developing And Implementing An Online Research Data Repository For Your University Or College Campus, Raymond J. Uzwyshyn

Digital Initiatives Symposium

Data-driven research is becoming increasingly important on university and college campuses. Most US federal and many international granting agencies mandatorily require that researchers applying for public grants possess a data management plan and make their research and data publically available through online access. This presentation overviews online research data repositories and implementation strategies for university and college campus libraries. The presentation pragmatically surveys this newer technology landscape and how organizations can begin to think about and implement an online data research repository. This session will survey the landscape but also makes use of practical example from Texas State University and …

Improving Automatic Content Type Identification From A Data Set, Kathy T. Dai

Computer Science and Computer Engineering Undergraduate Honors Theses

Data file layout inference refers to building the structure and determining the metadata of a text file. The text files dealt within this research are personal information records that have a consistent structure. Traditionally, if the layout structure of a text file is unknown, the human user must undergo manual labor of identifying the metadata. This is inefficient and prone to error. Content-based oracles are the current state-of-the-art automation technology that attempts to solve the layout inference problem by using databases of known metadata. This paper builds upon the information and documentation of the content-based oracles, and improves the databases …

Music Feature Matching Using Computer Vision Algorithms, Mason Hollis

Computer Science and Computer Engineering Undergraduate Honors Theses

This paper seeks to establish the validity and potential benefits of using existing computer vision techniques on audio samples rather than traditional images in order to consistently and accurately identify a song of origin from a short audio clip of potentially noisy sound. To do this, the audio sample is first converted to a spectrogram image, which is used to generate SURF features. These features are compared against a database of features, which have been previously generated in a similar fashion, in order to find the best match. This algorithm has been implemented in a system that can run as …

Project Pradio, Trigg T. La Tour

Computer Science and Computer Engineering Undergraduate Honors Theses

This paper examines the design and manufacturing of a device that allows two or more users to share a wireless audio stream. Effectively, this allows a group of people to listen to the same audio in a synchronized manner. The product was unable to be completed in the allotted time. Regardless, significant progress was made and valuable insight into the circuit board design process was gained.

Enhancing An Extensible Interpreter With A Syntax Macro Facility, Xin Wan

Theses and Dissertations from 2017

This thesis builds a syntax macro facility for an extensible interpreter, which enables the developer to extend the base language at run time, without knowing the details of the interpreter. In the enhanced extensible interpreter, each grammar rule is represented by a distinct class that inherits from Instruction class, which contains all the information necessary for scanning, parsing, and interpreting the corresponding construct. The macro facility is implemented by a special class Macro, which also inherits from Instruction class. Each macro rule is associated with a unique Macro instance. With the new extensible interpreter strategy, the syntax macro facility does …

A Study Of Activation Functions For Neural Networks, Meenakshi Manavazhahan

Computer Science and Computer Engineering Undergraduate Honors Theses

Artificial neural networks are function-approximating models that can improve themselves with experience. In order to work effectively, they rely on a nonlinearity, or activation function, to transform the values between each layer. One question that remains unanswered is, “Which non-linearity is optimal for learning with a particular dataset?” This thesis seeks to answer this question with the MNIST dataset, a popular dataset of handwritten digits, and vowel dataset, a dataset of vowel sounds. In order to answer this question effectively, it must simultaneously determine near-optimal values for several other meta-parameters, including the network topology, the optimization algorithm, and the number …

On The Simulation And Mitigation Of Anisoplanatic Optical Turbulence For Long Range Imaging, Russell C. Hardie, Daniel A. Lemaster

Electrical and Computer Engineering Faculty Publications

We describe a numerical wave propagation method for simulating long range imaging of an extended scene under anisoplanatic conditions. Our approach computes an array of point spread functions (PSFs) for a 2D grid on the object plane. The PSFs are then used in a spatially varying weighted sum operation, with an ideal image, to produce a simulated image with realistic optical turbulence degradation. To validate the simulation we compare simulated outputs with the theoretical anisoplanatic tilt correlation and differential tilt variance. This is in addition to comparing the long- and short-exposure PSFs, and isoplanatic angle. Our validation analysis shows an …

Automation In Entertainment: Concept, Design, And Application, Ryan Thally

Undergraduate Honors Theses

The focus of this thesis is to explore the automation technology used in the modern entertainment industry. Upon completion of my thesis, I will deliver a working prototype of the chosen technology and present its capabilities in a choreographed show.

Sparse Coding On Stereo Video For Object Detection, Sheng Y. Lundquist, Melanie Mitchell, Garrett T. Kenyon

Computer Science Faculty Publications and Presentations

Deep Convolutional Neural Networks (DCNN) require millions of labeled training examples for image classification and object detection tasks, which restrict these models to domains where such a dataset is available. We explore the use of unsupervised sparse coding applied to stereo-video data to help alleviate the need for large amounts of labeled data. In this paper, we show that unsupervised sparse coding is able to learn disparity and motion sensitive basis functions when exposed to unlabeled stereo-video data. Additionally, we show that a DCNN that incorporates unsupervised learning exhibits better performance than fully supervised networks. Furthermore, finding a sparse representation …

Models For Pedestrian Trajectory Prediction And Navigation In Dynamic Environments, Jeremy N. Kerfs

Master's Theses

Robots are no longer constrained to cages in factories and are increasingly taking on roles alongside humans. Before robots can accomplish their tasks in these dynamic environments, they must be able to navigate while avoiding collisions with pedestrians or other robots. Humans are able to move through crowds by anticipating the movements of other pedestrians and how their actions will influence others; developing a method for predicting pedestrian trajectories is a critical component of a robust robot navigation system. A current state-of-the-art approach for predicting pedestrian trajectories is Social-LSTM, which is a recurrent neural network that incorporates information about neighboring …

Dsrc Performance Analysis In Foggy Environment For Intelligent Vehicles System, Mostafa El-Said, Samah Mansour, Alexander Arendsen

Peer-Reviewed Publications

Advanced Driver Assistance System (ADAS) is one of the fastest growing areas in the Intelligent Transportation Systems (ITS). Research efforts has focused on developing a driver assistant alert system to warn driver in foggy environment. However, there is a lack of which effective V2V/V2I communication technology would be the best to extend and disseminate this information to nearby vehicles. In this paper, we examine the use of Dedicated Short Range Communications (DSRC) as a V2V communication mechanism to share the foggy conditions to nearby vehicles. The study also investigates the effect of changing the fog/air density on the DSRC performance …

Software Updates To A Multiple Autonomous Quadcopter Search System (Maqss), Jared Speck, Toby Chan

Computer Engineering

A series of performance-based and feature implementation software updates to an existing multiple vehicle autonomous target search system is outlined in this paper. The search system, MAQSS, is designed to address a computational power constraint found on modern autonomous aerial platforms by separating real-time and computationally expensive tasks through delegation to multiple multirotor vehicles. A Ground Control Station (GCS) is also described as part of the MAQSS system to perform the delegation and provide a low workload user interface. Ultimately, the changes to MAQSS noted in this paper helped to improve the performance of the autonomous search mission, the accuracy …

Expierience, Seong Bo Chang, Albert Chen, Emily Nguyen

Computer Engineering

The name of this project is called “exPIERience” and the technological field of focus is virtual reality. We will be working on creating a virtual reality experience using the Oculus Rift for the Center of Coastal Marine Sciences (CCMS). The purpose of this system is to enable people to experience diving at the Cal Poly Pier even during nonoptimal diving conditions which occur frequently. CCMS hopes to attract further interest and possible momentum in future outreach such as a “Live Dive” program, where live videos of divers under the pier can be streamed. A 360° video from the diver’s perspective …

A Hybrid Partially Reconfigurable Overlay Supporting Just-In-Time Assembly Of Custom Accelerators On Fpgas, Zeyad Tariq Aklah

Graduate Theses and Dissertations

The state of the art in design and development flows for FPGAs are not sufficiently mature to allow programmers to implement their applications through traditional software development flows. The stipulation of synthesis as well as the requirement of background knowledge on the FPGAs' low-level physical hardware structure are major challenges that prevent programmers from using FPGAs. The reconfigurable computing community is seeking solutions to raise the level of design abstraction at which programmers must operate, and move the synthesis process out of the programmers' path through the use of overlays. A recent approach, Just-In-Time Assembly (JITA), was proposed that enables …

Investigation Into The Application Of Personality Insights And Language Tone Analysis In Spam Classification, Colm Mcgetrick

Dissertations

Due to its persistence spam remains as one of the biggest problems facing users and suppliers of email communication services. Machine learning techniques have been very successful at preventing many spam mails from arriving in user mailboxes, however they still account for over 50% of all emails sent. Despite this relative success the economic cost of spam has been estimated as high as $50 billion in 2005 and more recently at $20 billion so spam can still be considered a considerable problem. In essence a spam email is a commercial communication trying to entice the receiver to take some positive …

Detection Of Plant Emergence Based On Spatio Temporal Image Sequence Analysis, Bhushit Agarwal

School of Computing: Dissertations, Theses, and Student Research

The phenome of a plant is the sum of all observable phenotypes for that plant. Phenotypes are observable characteristics or traits of a plant. These traits generally reflect a combination of influences from the genotype of the plant and the environment in which the plant has grown and developed. Collecting phenotypic data has traditionally been a slow and costly manual process, albeit one highly necessary for plant breeding and the development of improved agronomic practices. As a result automated methods for plant phenotyping analysis have become an active research field in recent years. Image-based plant phenotyping analysis facilitates extraction of …

Vehicle Make And Model Recognition For Intelligent Transportation Monitoring And Surveillance., Faezeh Tafazzoli

Electronic Theses and Dissertations

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS), such as autonomous navigation, traffic analysis, traffic surveillance and security systems. A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multi-class classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicles makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system. In this dissertation, …

Uncovering Exceptional Predictions Using Exploratory Analysis Of Second Stage Machine Learning., Aneseh Alvanpour

Electronic Theses and Dissertations

Nowadays, algorithmic systems for making decisions are widely used to facilitate decisions in a variety of fields such as medicine, banking, applying for universities or network security. However, many machine learning algorithms are well-known for their complex mathematical internal workings which turn them into black boxes and makes their decision-making process usually difficult to understand even for experts. In this thesis, we try to develop a methodology to explain why a certain exceptional machine learned decision was made incorrectly by using the interpretability of the decision tree classifier. Our approach can provide insights about potential flaws in feature definition or …

Using A Multi Variate Pattern Analysis (Mvpa) Approach To Decode Fmri Responses To Fear And Anxiety., Sajjad Torabian Esfahani

Electronic Theses and Dissertations

This study analyzed fMRI responses to fear and anxiety using a Multi Variate Pattern Analysis (MVPA) approach. Compared to conventional univariate methods which only represent regions of activation, MVPA provides us with more detailed patterns of voxels. We successfully found different patterns for fear and anxiety through separate classification attempts in each subject’s representational space. Further, we transformed all the individual models into a standard space to do group analysis. Results showed that subjects share a more common fear response. Also, the amygdala and hippocampus areas are more important for differentiating fear than anxiety.

Peeking Into The Other Half Of The Glass : Handling Polarization In Recommender Systems., Mahsa Badami

Electronic Theses and Dissertations

This dissertation is about filtering and discovering information online while using recommender systems. In the first part of our research, we study the phenomenon of polarization and its impact on filtering and discovering information. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We study polarization within the context of the users' interactions with a space of items and how this affects recommender systems. We first formalize the concept of polarization based on item ratings and then relate …

Data Driven Discovery Of Materials Properties., Fadoua Khmaissia

Electronic Theses and Dissertations

The high pace of nowadays industrial evolution is creating an urgent need to design new cost efficient materials that can satisfy both current and future demands. However, with the increase of structural and functional complexity of materials, the ability to rationally design new materials with a precise set of properties has become increasingly challenging. This basic observation has triggered the idea of applying machine learning techniques in the field, which was further encouraged by the launch of the Materials Genome Initiative (MGI) by the US government since 2011. In this work, we present a novel approach to apply machine learning …

Evaluating A Personal Stress Monitoring System, Sneha Gogineni

Theses and Dissertations

Now-a-days, Life is generally much more stressful than in the past. "Stress" is the word that we use when we feel that we are overloaded mentally in our thoughts and wonder whether we can really cope with those placed upon us. Sometimes, stress gets us going and they are good for us but at other times, it could be the cause to undermine both our mental and physical health. The way we respond to a challenge can be considered as a kind of stress. Part of our response to a challenge is physiological and affects our own physical state. When …

Repeatability Of Reference Signal Received Power Measurements In Lte Networks, Hayder A Abdulrasool Khzaali

Theses and Dissertations

No abstract provided.

Exploiting Hardware Abstraction For Parallel Programming Framework: Platform And Multitasking, Hongyuan Ding

Graduate Theses and Dissertations

With the help of the parallelism provided by the fine-grained architecture, hardware accelerators on Field Programmable Gate Arrays (FPGAs) can significantly improve the performance of many applications. However, designers are required to have excellent hardware programming skills and unique optimization techniques to explore the potential of FPGA resources fully. Intermediate frameworks above hardware circuits are proposed to improve either performance or productivity by leveraging parallel programming models beyond the multi-core era.

In this work, we propose the PolyPC (Polymorphic Parallel Computing) framework, which targets enhancing productivity without losing performance. It helps designers develop parallelized applications and implement them on FPGAs. …

A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings, Wee Leong Lee, Kar Way Tan, Zui Young Lim

Research Collection School Of Computing and Information Systems

This study proposes adata-driven approach for benchmarking energy efficiency of warehouse buildings.Our proposed approach provides an alternative to the limitation of existingbenchmarking approaches where a theoretical energy-efficient warehouse was usedas a reference. Our approach starts by defining the questions needed to capturethe characteristics of warehouses relating to energy consumption. Using an existingdata set of warehouse building containing various attributes, we first cluster theminto groups by their characteristics. The warehouses characteristics derivedfrom the cluster assignments along with their past annual energy consumptionare subsequently used to train a decision tree model. The decision tree providesa classification of what factors contribute to different …