| Summary: | 碩士 === 國立東華大學 === 電機工程學系 === 107 === With the improvement of information technologies, the usage of handheld and wearable devices are rapidly increased. However, based on thin, light and multiple functions integrated in a limited space, there must be trade-off between the capacities of the battery Accordingly, low-power hardware are needed for these consumer products. Fourier transform is a popular method to analyze characteristics of signals. That is, the wearable device then, has the good ability to analyze signals, including image recognition, speech recognition, etc. However, Fast Fourier Transform (FFT) is common implementation with high hardware complexity.
Coordinate Rotation Digital Computer (CORDIC) is a digital calculator, it replaces the complex multiplier with low cost design. However both CORDIC and complex multiplier require large amounts of memory based on searching algorithms. This study uses a neural network for deep learning to search angles for angle rotation. By adjusting the complexity of the neural network, including the number of hidden layers and the number of neurons at each level, the network learning performance can be improved. Based on the learning results and tests, a five-layer neural network is selected. The network has one input unit, and the number of hidden layers is four. The number of neurons from the 1st to 4th layer is 10, 12, 15, and15, respectively. Besides, the output layer has two neurons. The neural network is applied to the Extended Elementary-Angle Set (EEAS) CORDIC, which results in an average signal-to-noise ratio (SNR) of 64.44dB, a maximum value of 93.79dB, and a minimum value of 43.12dB.
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