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Articles 1 - 7 of 7
Full-Text Articles in Engineering
Ppmexplorer: Using Information Retrieval, Computer Vision And Transfer Learning Methods To Index And Explore Images Of Pompeii, Cindy Roullet
Ppmexplorer: Using Information Retrieval, Computer Vision And Transfer Learning Methods To Index And Explore Images Of Pompeii, Cindy Roullet
Graduate Theses and Dissertations
In this dissertation, we present and analyze the technology used in the making of PPMExplorer: Search, Find, and Explore Pompeii. PPMExplorer is a software tool made with data extracted from the Pompei: Pitture e Mosaic (PPM) volumes. PPM is a valuable set of volumes containing 20,000 historical annotated images of the archaeological site of Pompeii, Italy accompanied by extensive captions. We transformed the volumes from paper, to digital, to searchable. PPMExplorer enables archaeologist researchers to conduct and check hypotheses on historical findings. We present a theory that such a concept is possible by leveraging computer generated correlations between artifacts using …
Towards Sensorimotor Coupling Of A Spiking Neural Network And Deep Reinforcement Learning For Robotics Application, Kashu Yamazaki
Towards Sensorimotor Coupling Of A Spiking Neural Network And Deep Reinforcement Learning For Robotics Application, Kashu Yamazaki
Mechanical Engineering Undergraduate Honors Theses
Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the great achievements of deep reinforcement learning in various domains including finance,medicine, healthcare, video games, robotics and computer vision.Deep neural network was started with multi-layer perceptron (1stgeneration) and developed to deep neural networks (2ndgeneration)and it is moving forward to spiking neural networks which are knownas3rdgeneration of neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically-realistic models of neurons to carry out computation. In this thesis, we first provide a comprehensive review …
Lexicon Based Approaches To Sentiment Analysis Of Spanish Tweets: A Comparative Study, Jean Roca
Lexicon Based Approaches To Sentiment Analysis Of Spanish Tweets: A Comparative Study, Jean Roca
Computer Science and Computer Engineering Undergraduate Honors Theses
Sentiment analysis is a natural language processing technique that aims to classify text based on the emotions expressed in them. It is a research area that has been around for almost 20 years and has seen a lot of development. The works presented in this paper attempts to target a less-developed area in sentiment analysis known as multilingual sentiment analysis. More specifically, multilingual sentiment analysis of micro-texts. Using the existing WordNet lexicon and a domain-specific lexicon for a corpus of Spanish tweets, we analyze the effectiveness of these techniques.
An Fpga-Based Hardware Accelerator For The Digital Image Correlation Engine, Keaten Stokke
An Fpga-Based Hardware Accelerator For The Digital Image Correlation Engine, Keaten Stokke
Graduate Theses and Dissertations
The work presented in this thesis was aimed at the development of a hardware accelerator for the Digital Image Correlation engine (DICe) and compare two methods of data access, USB and Ethernet. The original DICe software package was created by Sandia National Laboratories and is written in C++. The software runs on any typical workstation PC and performs image correlation on available frame data produced by a camera. When DICe is introduced to a high volume of frames, the correlation time is on the order of days. The time to process and analyze data with DICe becomes a concern when …
Locating Relay Nodes To Maximize Wireless Sensor Network Lifetime: A Numerical Study, Maria Rene Arandia Jimenez
Locating Relay Nodes To Maximize Wireless Sensor Network Lifetime: A Numerical Study, Maria Rene Arandia Jimenez
Industrial Engineering Undergraduate Honors Theses
A wireless sensor network (WSN) is a group of sensors deployed over an area, which monitor changes in the environment, collects them as data and forwards it between sensors through wireless links. Data is routed, either in a single-hop or multi-hop manner, with the goal of getting this collected data to the sink nodes, which have higher computational capabilities and connects the network with a user interface. Studies have determined that multi-hop WSNs that integrate relay nodes, which function is to only receive and forward data, can maximize lifetime network. A linear programming model, created by Chang and Tassiulas in …
Stay-At-Home Motor Rehabilitation: Optimizing Spatiotemporal Learning On Low-Cost Capacitive Sensor Arrays, Reid Sutherland
Stay-At-Home Motor Rehabilitation: Optimizing Spatiotemporal Learning On Low-Cost Capacitive Sensor Arrays, Reid Sutherland
Graduate Theses and Dissertations
Repeated, consistent, and precise gesture performance is a key part of recovery for stroke and other motor-impaired patients. Close professional supervision to these exercises is also essential to ensure proper neuromotor repair, which consumes a large amount of medical resources. Gesture recognition systems are emerging as stay-at-home solutions to this problem, but the best solutions are expensive, and the inexpensive solutions are not universal enough to tackle patient-to-patient variability. While many methods have been studied and implemented, the gesture recognition system designer does not have a strategy to effectively predict the right method to fit the needs of a patient. …
A Capacitive Sensing Gym Mat For Exercise Classification & Tracking, Adam Goertz
A Capacitive Sensing Gym Mat For Exercise Classification & Tracking, Adam Goertz
Computer Science and Computer Engineering Undergraduate Honors Theses
Effective monitoring of adherence to at-home exercise programs as prescribed by physiotherapy protocols is essential to promoting effective rehabilitation and therapeutic interventions. Currently physical therapists and other health professionals have no reliable means of tracking patients' progress in or adherence to a prescribed regimen. This project aims to develop a low-cost, privacy-conserving means of monitoring at-home exercise activity using a gym mat equipped with an array of capacitive sensors. The ability of the mat to classify different types of exercises was evaluated using several machine learning models trained on an existing dataset of physiotherapy exercises.