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Interferometric Detection Of Pinned Interactions In Bismuth-Substituted Iron Garnet, L. Bauer, Neelam G. Prabhu Gaunkar, Mani Mina, J. W. Pritchard 2019 Iowa State University

Interferometric Detection Of Pinned Interactions In Bismuth-Substituted Iron Garnet, L. Bauer, Neelam G. Prabhu Gaunkar, Mani Mina, J. W. Pritchard

Industrial Design Publications

The utilization of a bismuth-substituted iron garnet as a magnetooptic Faraday rotator (MOFR) has been reported for all-optical networking purposes as well as for other applications. Our measurements and observations demonstrate that the MOFR saturates once a significantly large magnetic field (>225 G) is applied. After the applied magnetic field enters the saturation region, the material's magnetic domains can become pinned at intermediate levels of magnetization. Pinning in this form has not been reported nor well studied for this application. In this paper, a method to detect and describe anomalous pinning in terms of Faraday rotation is presented. Measurements ...


Camr: Coded Aggregated Mapreduce, Konstantinos Konstantinidis, Aditya Ramamoorthy 2019 Iowa State University

Camr: Coded Aggregated Mapreduce, Konstantinos Konstantinidis, Aditya Ramamoorthy

Electrical and Computer Engineering Publications

Many big data algorithms executed on MapReduce-like systems have a shuffle phase that often dominates the overall job execution time. Recent work has demonstrated schemes where the communication load in the shuffle phase can be traded off for the computation load in the map phase. In this work, we focus on a class of distributed algorithms, broadly used in deep learning, where intermediate computations of the same task can be combined. Even though prior techniques reduce the communication load significantly, they require a number of jobs that grows exponentially in the system parameters. This limitation is crucial and may diminish ...


Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach, Anindya B. Das, Aditya Ramamoorthy 2019 Iowa State University

Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach, Anindya B. Das, Aditya Ramamoorthy

Electrical and Computer Engineering Publications

Distributed computing systems are well-known to suffer from the problem of slow or failed nodes; these are referred to as stragglers. Straggler mitigation (for distributed matrix computations) has recently been investigated from the standpoint of erasure coding in several works. In this work we present a strategy for distributed matrix-vector multiplication based on convolutional coding. Our scheme can be decoded using a low-complexity peeling decoder. The recovery process enjoys excellent numerical stability as compared to Reed-Solomon coding based approaches (which exhibit significant problems owing their badly conditioned decoding matrices). Finally, our schemes are better matched to the practically important case ...


Universally Decodable Matrices For Distributed Matrix-Vector Multiplication, Aditya Ramamoorthy, Li Tang, Pascal O. Vontobel 2019 Iowa State University

Universally Decodable Matrices For Distributed Matrix-Vector Multiplication, Aditya Ramamoorthy, Li Tang, Pascal O. Vontobel

Electrical and Computer Engineering Publications

Coded computation is an emerging research area that leverages concepts from erasure coding to mitigate the effect of stragglers (slow nodes) in distributed computation clusters, especially for matrix computation problems. In this work, we present a class of distributed matrix-vector multiplication schemes that are based on codes in the Rosenbloom-Tsfasman metric and universally decodable matrices. Our schemes take into account the inherent computation order within a worker node. In particular, they allow us to effectively leverage partial computations performed by stragglers (a feature that many prior works lack). An additional main contribution of our work is a companion matrix-based embedding ...


Sequence Design Via Semidefinite Programming Relaxation And Randomized Projection, Dian Mo 2019 University of Massachusetts Amherst

Sequence Design Via Semidefinite Programming Relaxation And Randomized Projection, Dian Mo

Doctoral Dissertations

Wideband is a booming technology in the field of wireless communications. The receivers in wideband communication systems are expected to cover a very wide spectrum and adaptively extract the parts of interest. The literature has focused on mixing the input spectrum to baseband using a pseudorandom sequence modulation and recovering the received signals from linearly independent measurements by parallel branches to mitigate the pressures from required extreme high sampling frequency. However, a pseudorandom sequence provides no rejection for the strong interferers received together with weak signals from distant sources. The interferers cause significant distortion due to the nonlinearity of the ...


Insar Simulations For Swot And Dual Frequency Processing For Topographic Measurements, Gerard Masalias Huguet 2019 University of Massachusetts Amherst

Insar Simulations For Swot And Dual Frequency Processing For Topographic Measurements, Gerard Masalias Huguet

Masters Theses

In Earth remote sensing precise characterization of the backscatter coefficient is important to extract valuable information about the observed target. A system that eliminates platform motion during near-nadir airborne observations is presented in this thesis, showing an improvement on the accuracy of measurements for a Ka- band scatterometer previously developed at Microwave Remote Sensing Laboratory (MIRSL). These very same results are used to simulate the reflectivity of such targets as seen from a spaceborne radar and estimate height errors based on mission-specific geometry. Finally, data collected from a dual-frequency airborne interferometer com- prised by the Ka-band system and an S-band ...


Weed And Crop Discrimination Through An Offline Computer Vision Algorithm, Phillip J. Putney 2018 Olivet Nazarene University

Weed And Crop Discrimination Through An Offline Computer Vision Algorithm, Phillip J. Putney

ELAIA

With the recent global interest in organic farming and cultivation, many people are turning away from chemical-based herbicides and moving towards alternate methods to extirpate weeds living amongst their crops. Of the methods proposed, robotic weed detection and removal is the most promising because of its possibility to be completely autonomous. Several robust, fully-autonomous robots have been developed, although none have been approved for commercial use. This paper proposes a weed and crop discrimination algorithm that utilizes an excessive green filter paired with principal component analysis to detect specific spatial frequencies within an image corresponding to different types of weeds ...


Accurate Vehicle Detection Using Multi-Camera Data Fusion And Machine Learning, Hao Wu 2018 Southern Methodist University

Accurate Vehicle Detection Using Multi-Camera Data Fusion And Machine Learning, Hao Wu

Electrical Engineering Theses and Dissertations

Computer-vision methods have recently been extensively used in intelligent transportation systems for vehicle detection. However, the detection of severely occluded or partially observed vehicles due to the limited camera fields of view remains a significant challenge. This paper presents a multi-camera vehicle detection system that significantly improves the detection performance under occlusion conditions. The key elements of the proposed method include a novel multi-view region proposal network that localizes the candidate vehicles on the ground plane. We also infer the vehicle occupancies by leveraging multi-view cross-camera context. Experiments are conducted on a dataset captured from a roadway in Richardson, TX ...


Vocal Processing With Spectral Analysis, Bradley J. Fitzgerald 2018 Olivet Nazarene University

Vocal Processing With Spectral Analysis, Bradley J. Fitzgerald

ELAIA

A well-known signal processing issue is that of the “cocktail party problem,” which A well-known signal processing issue is that of the “cocktail party problem,” which refers to the need to be able to separate speakers from a mixture of voices. A solution to this problem could provide insight into signal separation in a variety of signal processing fields. In this study, a method of vocal signal processing was examined to determine if principal component analysis of spectral data could be used to characterize differences between speakers and if these differences could be used to separate mixtures of vocal signals ...


Analysis And Simulation Of Convolution Reverb Using City Tech’S New Auditorium, Tian Leng 2018 CUNY New York City College of Technology

Analysis And Simulation Of Convolution Reverb Using City Tech’S New Auditorium, Tian Leng

Publications and Research

In digital signal processing, convolution reverb can simulate the reverberation of a real acoustic space. The acoustics of different seating areas in an auditorium can vary from each other. To determine the reverberant characteristics of City Tech new building’s auditorium, impulse response (IR) signals are recorded in five key locations of the auditorium.

Directly recorded balloon burst is chosen as the source of impulse source. An omnidirectional and a cardioid microphone with flat frequency response curves are used to record IR signals to 24-bit monophonic wav files. Each IR signal, along with a vocal, is convoluted in MATLAB through ...


3d Signal Strength Mapping Of 2.4ghz Wifi Networks, Brett D. Glidden 2018 California Polytechnic State University, San Luis Obispo

3d Signal Strength Mapping Of 2.4ghz Wifi Networks, Brett D. Glidden

Electrical Engineering

Many commercial businesses operate out of multi-story office buildings. These companies often use many Wi-Fi access points to set up their own wireless network. IT personnel determine proper Wi-Fi access point placement using Wi-Fi strength maps. Conventional Wi-Fi strength maps only provide a two-dimensional view representing the wireless access point's effective range. The signal quality and strength measurements do not include changing vertical elevation. Efficient network layout in a multi-story building requires a system calculating signal quality metrics in three dimensions.

This project involves designing and prototyping a system to achieve 2.4GHz Wi-Fi signal quality measurements in a ...


Distance-Based Cluster Head Election For Mobile Sensing, Ruairí de Fréin, Liam O'Farrell 2018 Technological University Dublin

Distance-Based Cluster Head Election For Mobile Sensing, Ruairí De Fréin, Liam O'Farrell

Conference papers

Energy-efficient, fair, stochastic leader-selection algorithms are designed for mobile sensing scenarios which adapt the sensing strategy depending on the mobile sensing topology. Methods for electing a cluster head are crucially important when optimizing the trade-off between the number of peer-to- peer interactions between mobiles and client-server interactions with a cloud-hosted application server. The battery-life of mobile devices is a crucial constraint facing application developers who are looking to use the convergence of mobile computing and cloud computing to perform environmental sensing. We exploit the mobile network topology, specifically the location of mobiles with respect to the gateway device, to stochastically ...


Study On The Pattern Recognition Enhancement For Matrix Factorizations With Automatic Relevance Determination, hau tao 2018 California State University, San Bernardino

Study On The Pattern Recognition Enhancement For Matrix Factorizations With Automatic Relevance Determination, Hau Tao

Electronic Theses, Projects, and Dissertations

Learning the parts of objects have drawn more attentions in computer science recently, and they have been playing the important role in computer applications such as object recognition, self-driving cars, and image processing, etc… However, the existing research such as traditional non-negative matrix factorization (NMF), principal component analysis (PCA), and vector quantitation (VQ) has not been discovering the ground-truth bases which are basic components representing objects. On this thesis, I am proposed to study on pattern recognition enhancement combined non-negative matrix factorization (NMF) with automatic relevance determination (ARD). The main point of this research is to propose a new technique ...


Transcribing Braille Code: Learning Equations Across Platforms, Deegan Atha, Courtney Balogh 2018 Purdue University

Transcribing Braille Code: Learning Equations Across Platforms, Deegan Atha, Courtney Balogh

Purdue Journal of Service-Learning and International Engagement

Deegan Atha, a graduating senior in electrical engineering and a future engineer, is interested in human-centered design and developing technology that helps students engage and be successful in STEM.

Courtney Balogh, a junior in mechanical engineering, is interested in human-centered design and the importance it plays in product development. Deegan and Courtney are members of the Purdue EPICS project, Learning Equations Across Platforms (LEAP). They partnered with the Indiana School for the Blind and Visually Impaired (ISBVI) to develop a braille transcription device and web application that converts braille to print in real time.


Erasure Coding For Distributed Matrix Multiplication For Matrices With Bounded Entries, Li Tang, Konstantinos Konstantinidis, Aditya Ramamoorthy 2018 Iowa State University

Erasure Coding For Distributed Matrix Multiplication For Matrices With Bounded Entries, Li Tang, Konstantinos Konstantinidis, Aditya Ramamoorthy

Electrical and Computer Engineering Publications

Distributed matrix multiplication is widely used in several scientific domains. It is well recognized that computation times on distributed clusters are often dominated by the slowest workers (called stragglers). Recent work has demonstrated that straggler mitigation can be viewed as a problem of designing erasure codes. For matrices A and B, the technique essentially maps the computation of ATB into the multiplication of smaller (coded) submatrices. The stragglers are treated as erasures in this process. The computation can be completed as long as a certain number of workers (called the recovery threshold) complete their assigned tasks. We present a novel ...


End-To-End Deep Learning Systems For Scene Understanding, Path Planning And Navigation In Fire Fighter Teams, Manish Bhattarai 2018 University of New Mexico

End-To-End Deep Learning Systems For Scene Understanding, Path Planning And Navigation In Fire Fighter Teams, Manish Bhattarai

Shared Knowledge Conference

Firefighting is a dynamic activity with many operations occurring simultaneously. Maintaining situational awareness, defined as knowledge of current conditions and activities at the scene, are critical to accurate decision making. Firefighters often carry various sensors in their personal equipment, namely thermal cameras, gas sensors, and microphones. Improved data processing techniques can mine this data more effectively and be used to improve situational awareness at all times thereby improving real-time decision making and minimizing errors in judgment induced by environmental conditions and anxiety levels. This objective of this research employs state of the art Machine Learning (ML) techniques to create an ...


1 - A Comprehensive Study Of Motor Imagery Eeg-Based Classification Using Computational Analysis, Justin McCorkle, Andrew Kalaani 2018 Georgia Southern University

1 - A Comprehensive Study Of Motor Imagery Eeg-Based Classification Using Computational Analysis, Justin Mccorkle, Andrew Kalaani

Georgia Undergraduate Research Conference (GURC)

Brain computer interfaces (BCI) are systems that integrate a user’s neural features with robotic machines to perform tasks. BCI systems are very unstable still due to Electroencephalography (EEG) having interference from unanticipated noise. Using Independent Component Analysis (ICA), a novel variable threshold model for noise feature extraction. The de-noised EEG data is classified with a high accuracy of more than 94% when using artificial neural networks. The effectiveness of the proposed variable threshold model is validated by the significant reduction in the variance of user classification accuracy across multiple sessions. Nonetheless, based on the variance and classification, subjects are ...


Investigating The Effect Of Detecting And Mitigating A Ring Oscillator-Based Hardware Trojan, Lakshmi Ramakrishnan 2018 Southern Methodist University

Investigating The Effect Of Detecting And Mitigating A Ring Oscillator-Based Hardware Trojan, Lakshmi Ramakrishnan

Electrical Engineering Theses and Dissertations

The outsourcing of the manufacturing process of integrated circuits to fabrications plants all over the world has exposed these chips to several security threats, especially at the hardware level. There have been instances of malicious circuitry, such as backdoors, being added to circuits without the knowledge of the chip designers or vendors. Such threats could be immensely powerful and dangerous against confidentiality, among other vulnerabilities.

Defense mechanisms against such attacks have been probed and defense techniques have been developed. But with the passage of time, attack techniques have improved immensely as well. From directly observing the inputs or outputs, adversaries ...


A Deep Learning-Based Approach For Fault Diagnosis Of Roller Element Bearings, Mohammakazem Sadoughi, Austin Downey, Garrett Bunge, Aditya Ranawat, Chao Hu, Simon Laflamme 2018 Iowa State University

A Deep Learning-Based Approach For Fault Diagnosis Of Roller Element Bearings, Mohammakazem Sadoughi, Austin Downey, Garrett Bunge, Aditya Ranawat, Chao Hu, Simon Laflamme

Civil, Construction and Environmental Engineering Conference Presentations and Proceedings

Condition monitoring and fault detection of roller element bearings is of vital importance to ensuring safe and reliable operation of rotating machinery systems. Over the past few years, convolutional neural network (CNN) has been recognized as a useful tool for fault detection of roller element bearings. Unlike the traditional fault diagnosis approaches, CNN does not require manually extracting the fault-related features from the raw sensor data and most CNN-based fault diagnosis approaches feed the raw or shallowly pre-processed data as the training/testing inputs to a CNN model, thereby avoiding the need for manual feature extraction. As such, these approaches ...


Signal Identification In Discrete-Time Based On Internal-Model-Principle, Jie Chen 2018 The University of Western Ontario

Signal Identification In Discrete-Time Based On Internal-Model-Principle, Jie Chen

Electronic Thesis and Dissertation Repository

This work presents an implementation of a signal identification algorithm which is based on the internal model principle. By using several internal models in feedback with a tuning function, this algorithm can decompose a signal into narrow-band signals and identify the frequencies, amplitudes and relative phases. A desired band-pass filter response can be achieved by selecting appropriate coefficients of the controllers and tuning functions, which can reject the noise and improve the performance. To achieve a result with fast transient characteristics, this system is then modified by adding a low-pass filter. This work is based on the previous work in ...


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