A Deep Learning-Based Approach For Fault Diagnosis Of Roller Element Bearings, 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, 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 ...
Remote Sensing Using I-Band And S-Band Signals Of Opportunity, 2018 Purdue University
Remote Sensing Using I-Band And S-Band Signals Of Opportunity, Kadir Efecik, Benjamin R. Nold, James L. Garrison
The Summer Undergraduate Research Fellowship (SURF) Symposium
Measurement of soil moisture, especially the root zone soil moisture, is important in agriculture, meteorology, and hydrology. Root zone soil moisture is concerned with the first meter down the soil. Active and passive remote sensing methods used today utilizing L-band(1-2GHz) are physically limited to a sensing depth of about 5 cm or less. To remotely sense the soil moisture in the deeper parts of the soil, the frequency should be lowered. Lower frequencies cannot be used in active spaceborne instruments because of their need for larger antennas, radio frequency interference (RFI), and frequency spectrum allocations. Ground-based passive remote sensing ...
Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal
The Summer Undergraduate Research Fellowship (SURF) Symposium
In this work, we investigate the application of Principal Component Analysis to the task of wireless signal modulation recognition using deep neural network architectures. Sampling signals at the Nyquist rate, which is often very high, requires a large amount of energy and space to collect and store the samples. Moreover, the time taken to train neural networks for the task of modulation classification is large due to the large number of samples. These problems can be drastically reduced using Principal Component Analysis, which is a technique that allows us to reduce the dimensionality or number of features of the samples ...
High Dynamic Range Optical Devices And Applications., 2018 University of Louisville
High Dynamic Range Optical Devices And Applications., Elijah Robert Jensen
Electronic Theses and Dissertations
Much of what we know about fundamental physical law and the universe derives from observations and measurements using optical methods. The passive use of the electromagnetic spectrum can be the best way of studying physical phenomenon in general with minimal disturbance of the system in the process. While for many applications ambient visible light is sufficient, light outside of the visible range may convey more information. The signals of interest are also often a small fraction of the background, and their changes occur on time scales so quickly that they are visually imperceptible. This thesis reports techniques and technologies developed ...
Hand Movement Detection In Collaborative Learning Environment Videos, 2018 University of New Mexico
Hand Movement Detection In Collaborative Learning Environment Videos, Callie J. Darsey
Electrical and Computer Engineering ETDs
Human activity detection in digital videos is currently attracting significant research interest. This problem is especially challenging for video datasets that have a lot of human activity, illumination noise, and structural noise. The video dataset associated with the Advancing Out of School Learning in Mathematics and Engineering (AOLME) project has these challenges. ALOME videos have been used in the study of human activities “in the wild”.
This thesis explores detection of hand movement using color and optical flow. Exploratory analysis considered the problem component wise on components created from thresholds applied to motion and color. The proposed approach uses patch ...
New Algorithms For Compressed Sensing Of Mri: Wtwts, Dwts, Wdwts, 2018 Kennesaw State University
New Algorithms For Compressed Sensing Of Mri: Wtwts, Dwts, Wdwts, Srivarna Settisara Janney
Master of Science in Computer Science Theses
Magnetic resonance imaging (MRI) is one of the most accurate imaging techniques that can be used to detect several diseases, where other imaging methodologies fail. MRI data takes a longer time to capture. This is a pain taking process for the patients to remain still while the data is being captured. This is also hard for the doctor as well because if the images are not captured correctly then it will lead to wrong diagnoses of illness that might put the patients lives in danger. Since long scanning time is one of most serious drawback of the MRI modality, reducing ...
"Dual Peaks" Analog Distortion Guitar Effects Pedal, 2018 California Polytechnic State University, San Luis Obispo
"Dual Peaks" Analog Distortion Guitar Effects Pedal, Dave Raul Breuer
No abstract provided.
Fpga-Based Dsp System, 2018 California Polytechnic State University, San Luis Obispo
Fpga-Based Dsp System, Aaron Nguyen
The purpose of this project is to create a modular FPGA-based filtering system for audio in VHDL. The final implementation has a working input, output, and filtering system but the filtering system still must be fine-tuned.
Portable High-Definition Audio Spectrum Analyzer, 2018 California Polytechnic State University, San Luis Obispo
Portable High-Definition Audio Spectrum Analyzer, Alex Zahn, Jamie Corr
The Portable High-definition Audio Spectrum Analyzer (PHASA) allows the user to visualize the audio frequency spectrum of an incoming line-level stereo audio signal. Upon pressing the touch screen spectrum graph, the PHASA displays the corresponding frequency and volume levels as well as crosshairs at the touched location. The PHASA features multiple left/right channel display modes— Left channel only, right channel only, both channels simultaneously, and the average between the two channels. The PHASA features multiple resolution display modes (standard-resolution and high-resolution) and multiple dynamics display modes (standard dynamics, averaging, and peak/hold). The PHASA accepts input audio via a ...
2018 Ieee Signal Processing Cup: Forensic Camera Model Identification Challenge, 2018 Union College
2018 Ieee Signal Processing Cup: Forensic Camera Model Identification Challenge, Michael Geiger
The goal of this Senior Capstone Project was to lead Union College’s first ever Signal Processing Cup Team to compete in IEEE’s 2018 Signal Processing Cup Competition. This year’s competition was a forensic camera model identification challenge and was divided into two separate stages of competition: Open Competition and Final Competition. Participation in the Open Competition was open to any teams of undergraduate students, but the Final Competition was only open to the three finalists from Open Competition and is scheduled to be held at ICASSP 2018 in Calgary, Alberta, Canada. Teams that make it to the ...
Collision Avoidance Smartphone, 2018 California Polytechnic State University, San Luis Obispo
Collision Avoidance Smartphone, Aaron Parisi, Luis Wong, Joey Schnecker, Matt Columbres
There are many instances in day-to-day life where people cannot or would rather not pay full attention to their surroundings. Walking while preoccupied with a smartphone or walking while blind are excellent examples where technology could be used to make the task of avoiding 2collisions reactive, instead of proactive. A device which monitors a user’s surroundings and notifies the user when a potential collision is detected (and, additionally, notifying them as to where the obstacle is with respect to them) could be used to make walking distracted less of a hazard for the user and those around the user ...
Corridor Navigation For Monocular Vision Mobile Robots, 2018 California Polytechnic State University, San Luis Obispo
Corridor Navigation For Monocular Vision Mobile Robots, Matthew James Ng
Master's Theses and Project Reports
Monocular vision robots use a single camera to process information about its environment. By analyzing this scene, the robot can determine the best navigation direction. Many modern approaches to robot hallway navigation involve using a plethora of sensors to detect certain features in the environment. This can be laser range finders, inertial measurement units, motor encoders, and cameras.
By combining all these sensors, there is unused data which could be useful for navigation. To draw back and develop a baseline approach, this thesis explores the reliability and capability of solely using a camera for navigation. The basic navigation structure begins ...
Optimization Of Gpu-Accelerated Iterative Ct Reconstruction Algorithm For Clinical Use, 2018 Washington University in St. Louis
Optimization Of Gpu-Accelerated Iterative Ct Reconstruction Algorithm For Clinical Use, Tao Ge
Engineering and Applied Science Theses & Dissertations
In order to transition the GPU-accelerated CT reconstruction algorithm to a more clinical environment, a graphical user interface is implemented. Some optimization methods on the implementation are presented. We describe the alternating minimization (AM) algorithm as the updating algorithm, and the branchless distance-driven method for the system forward operator. We introduce a version of the Feldkamp-Davis-Kress algorithm to generate the initial image for our alternating minimization algorithm and compare it to a choice of a constant initial image. For the sake of better rate of convergence, we introduce the ordered-subsets method, find the optimal number of ordered subsets, and discuss ...
Hypothesis Testing And Model Estimation With Dependent Observations In Heterogeneous Sensor Networks, 2018 Louisiana State University and Agricultural and Mechanical College
Hypothesis Testing And Model Estimation With Dependent Observations In Heterogeneous Sensor Networks, Sima Sobhiyeh
LSU Doctoral Dissertations
Advances in microelectronics, communication and signal processing have enabled the development of inexpensive sensors that can be networked to collect vital information from their environment to be used in decision-making and inference. The sensors transmit their data to a central processor which integrates the information from the sensors using a so-called fusion algorithm. Many applications of sensor networks (SNs) involve hypothesis testing or the detection of a phenomenon. Many approaches to data fusion for hypothesis testing assume that, given each hypothesis, the sensors' measurements are conditionally independent. However, since the sensors are densely deployed in practice, their field of views ...
Super-Resolution Textured Digital Surface Model Formation Using Aerial Texel Images Taken From A Low-Cost Small Unmanned Aerial System, Bikalpa Khatiwada, Scott Budge
Digital Enhancement Of Analog Measurement Systems For Temperature Compensation Of Strain Gages, 2018 University of Texas at Tyler
Digital Enhancement Of Analog Measurement Systems For Temperature Compensation Of Strain Gages, Islombek Karimov
Electrical Engineering Theses
Generally known temperature compensation techniques for strain gages (like the use of a dummy gage or the implementation of half- and full-bridge configurations) are not applicable to all strain-measurement situations and cannot fully compensate for all sources of error. Digital Enhancement of Analog Measurement Systems presents a universal method of corrections for these effects in which temperature is measured independently of other variables and ex post facto corrections are computed and applied to digitized readings of the analog measurement system.
A single, linear-pattern strain gage, self-temperature-compensated for steel 1018, has been utilized in a quarter-bridge to measure tensile strain in ...
Software Defined Radar For Vital Sign Detection, 2018 University of Tennessee, Knoxville
Software Defined Radar For Vital Sign Detection, Chandler J. Bauder, James Bates, Steven Engel, James S. Tucker, Fangzhou Liu
University of Tennessee Honors Thesis Projects
No abstract provided.
Longitudinal Tracking Of Physiological State With Electromyographic Signals., 2018 University of Louisville
Longitudinal Tracking Of Physiological State With Electromyographic Signals., Robert Warren Stallard
Electronic Theses and Dissertations
Electrophysiological measurements have been used in recent history to classify instantaneous physiological configurations, e.g., hand gestures. This work investigates the feasibility of working with changes in physiological configurations over time (i.e., longitudinally) using a variety of algorithms from the machine learning domain. We demonstrate a high degree of classification accuracy for a binary classification problem derived from electromyography measurements before and after a 35-day bedrest. The problem difficulty is increased with a more dynamic experiment testing for changes in astronaut sensorimotor performance by taking electromyography and force plate measurements before, during, and after a jump from a small ...
Vocal Processing With Spectral Analysis, 2018 Olivet Nazarene University
Vocal Processing With Spectral Analysis, Brad Fitzgerald
Scholar Week 2016 - present
A method of vocal signal processing was examined to determine if principal component analysis of spectral data may be used to characterize differences between speakers and if these differences may be used to separate mixtures of vocal signals. Processing was done on a set of voice recordings from 30 different speakers in order to create a projection matrix which could be used by an algorithm to identify the source of an unknown recording from one of the 30 speakers. Two different identification algorithms were tested, both of which were generally unable to correctly identify the source of a single vocal ...