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Articles 1 - 8 of 8
Full-Text Articles in Computer Engineering
Purdue Air Sense: A Methodology For Improving The Accuracy Of Ambient Aerosol Mass Concentration And Size Distribution Measurement With Low-Cost Optical Sensing Techniques, Rishabh Ramsisaria, Satya Sundar Patra, Brandon Emil Boor
Purdue Air Sense: A Methodology For Improving The Accuracy Of Ambient Aerosol Mass Concentration And Size Distribution Measurement With Low-Cost Optical Sensing Techniques, Rishabh Ramsisaria, Satya Sundar Patra, Brandon Emil Boor
The Summer Undergraduate Research Fellowship (SURF) Symposium
There is a global lack of a means for monitoring air pollutant levels at a local level due to expensive and bulky instrument requirements. It is important to monitor toxic gas levels, as well as particulate matter levels, in the atmosphere to study their effects on human health and to further develop city- and community-level air pollution solutions. In this study, with the means of a Raspberry Pi, low-cost Alphasense Optical Particle Counter and gas sensors, and methodical calibration techniques, we built a portable 3-D printed module powered by clean electricity generated by an on-board Voltaic solar cell that measures …
Remote Sensing Using I-Band And S-Band Signals Of Opportunity, Kadir Efecik, Benjamin R. Nold, James L. Garrison
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 using …
Micro-Manipulation Using Learned Model, Matthew A. Lyng, Benjamin V. Johnson, David J. Cappelleri
Micro-Manipulation Using Learned Model, Matthew A. Lyng, Benjamin V. Johnson, David J. Cappelleri
The Summer Undergraduate Research Fellowship (SURF) Symposium
Microscale devices can be found in applications ranging from sensors to structural components. The dominance of surface forces at the microscale hinders the assembly processes through nonlinear interactions that are difficult to model for automation, limiting designs of microsystems to primarily monolithic structures. Methods for modeling surface forces must be presented for viable manufacturing of devices consisting of multiple microparts. This paper proposes the implementation of supervised machine learning models to aid in automated micromanipulation tasks for advanced manufacturing applications. The developed models use sets of training data to implicitly model surface interactions and predict end-effector placement and paths that …
Virtual Reality For Baseball Batting, Fengchen Gong, Tianjie Jia, Hong Tan, Casey Kohr
Virtual Reality For Baseball Batting, Fengchen Gong, Tianjie Jia, Hong Tan, Casey Kohr
The Summer Undergraduate Research Fellowship (SURF) Symposium
Nowadays, researchers explore the applications of Virtual Reality in different aspects of people’s lives. A few studies of Virtual Reality focus on applications in sports training. In this research area, one of the most important benefits is that athletes can focus on the training of one specific skill at one time. This SURF project focuses on the development of the virtual reality environment by designing targeted training for baseball batters, with the goal to achieve sufficient realism as judged by the Purdue baseball coaches. With the Virtual Reality training, baseball batters can practice and perfect a specific skill without a …
Investigating Dataset Distinctiveness, Andrew Ulmer, Kent W. Gauen, Yung-Hsiang Lu, Zohar R. Kapach, Daniel P. Merrick
Investigating Dataset Distinctiveness, Andrew Ulmer, Kent W. Gauen, Yung-Hsiang Lu, Zohar R. Kapach, Daniel P. Merrick
The Summer Undergraduate Research Fellowship (SURF) Symposium
Just as a human might struggle to interpret another human’s handwriting, a computer vision program might fail when asked to perform one task in two different domains. To be more specific, visualize a self-driving car as a human driver who had only ever driven on clear, sunny days, during daylight hours. This driver – the self-driving car – would inevitably face a significant challenge when asked to drive when it is violently raining or foggy during the night, putting the safety of its passengers in danger. An extensive understanding of the data we use to teach computer vision models – …
Sort Vs. Hash Join On Knights Landing Architecture, Victor L. Pan, Felix Lin
Sort Vs. Hash Join On Knights Landing Architecture, Victor L. Pan, Felix Lin
The Summer Undergraduate Research Fellowship (SURF) Symposium
With the increasing amount of information stored, there is a need for efficient database algorithms. One of the most important database operations is “join”. This involves combining columns from two tables and grouping common values in the same row in order to minimize redundant data. The two main algorithms used are hash join and sort merge join. Hash join builds a hash table to allow for faster searching. Sort merge join first sorts the two tables to make it more efficient when comparing values. There has been a lot of debate over which approach is superior. At first, hash join …
Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal
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 …
Real-Time Non-Contact Road Defect Detection Using Inexpensive Sensors, Zhao Xing Lim, Mohammad Jahanshahi, Tarutal Ghosh Mondal, Da Cheng, Shutao Wang, Mohammad K. Sweidan, Aanis Ahmad, Omar Hesham Abouhussein, Xi Chen
Real-Time Non-Contact Road Defect Detection Using Inexpensive Sensors, Zhao Xing Lim, Mohammad Jahanshahi, Tarutal Ghosh Mondal, Da Cheng, Shutao Wang, Mohammad K. Sweidan, Aanis Ahmad, Omar Hesham Abouhussein, Xi Chen
The Summer Undergraduate Research Fellowship (SURF) Symposium
Road defects such as potholes, humps, and road cracks have become one of the main concerns for road and traffic safety worldwide. Pavement defect detection is crucial to ensure road safety. However, current solutions to this problem are either too time-consuming or too expensive to be employed large-scale. We propose a novel approach which has the ability to autonomously detect potholes in real-time using cost-effective sensors. Inexpensive sensors are mounted on a vehicle and a deep learning algorithm is used to identify road defects. The detection system is paired with a GPS and positional sensors to map the location of …