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- All Subjects: Signal Processing
- All Subjects: Technology
- Creators: Electrical Engineering Program
This honors thesis explores the potential use of LoRa technology for detecting moisture in a diaper. Tests of both onboard and external humidity sensors coupled with LoRa transmission are incredibly promising. The potential scale of the final device also shows much promise, measuring smaller than a U.S. dime. However, the estimated cost for producing these proof-of-concept units in bulk is $19.41 per unit. While this is believed to be a pessimistic estimate of the price, the cost of production remains too high regardless for large-scale implementation. The thesis concludes by emphasizing the need for further research and development to optimize the design and reduce the cost of production. Despite the limitations imposed by price, the idea of using LoRa in detecting moisture in a diaper remains intriguing and promising, however, RFID technology has many advantages, such as size, cost, and passive power features.
This Honors Thesis is a continuation of Prof. Lauren Hayes’s and Dr. Xin Luo’s research initiative, Haptic Electronic Audio Research into Musical Experience (HEAR-ME), which investigates how to enhance the musical listening experience for CI users using a wearable haptic system. The goals of this Honors Thesis are to adapt Prof. Hayes’s system code from the Max visual programming language into the C++ object-oriented programming language and to study the results of the developed C++ codes. This adaptation allows the system to operate in real-time and independently of a computer.
Towards these goals, two signal processing algorithms were developed and programmed in C++. The first algorithm is a thresholding method, which outputs a pulse of a predefined width when the input signal falls below some threshold in amplitude. The second algorithm is a root-mean-square (RMS) method, which outputs a pulse-width modulation signal with a fixed period and with a duty cycle dependent on the RMS of the input signal. The thresholding method was found to work best with speech, and the RMS method was found to work best with music. Future work entails the design of adaptive signal processing algorithms to allow the system to work more effectively on speech in a noisy environment and to emphasize a variety of elements in music.