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Stochastic computing (SC) is an alternative computing paradigm that possesses data in the form of long uniform bit-streams rather than conventional compact weighted binary numbers. SC is fault-tolerant and can compute on small, efficient circuits, promising advantages over conventional arithmetic for smaller computer chips. SC has been primarily used in scientific research, not in practical applications. Digital sound source localization (SSL) is a useful signal processing technique that locates speakers using multiple microphones in cell phones, laptops, and other voice-controlled devices. SC has not been integrated into SSL in practice or theory. In this work, for the first time to the best of our knowledge, we implement an SSL algorithm in the stochastic domain and develop a functional SC-based sound source localizer. The developed design can replace the conventional design of the algorithm. The practical part of this work shows that the proposed stochastic circuit does not rely on conventional analog-to-digital conversion and can process data in the form of pulss-width-mudulated (PWM) signals. 

Images are often corrupted with noise. As a result, noise reduction is an important task in image processing. Common noise reduction techniques, such as mean or median filtering, lead to blurring of the edges in the image, while fuzzy filters are able to preserve the edge information. In this work, we implement an efficient hardware design for a well-known fuzzy noise reduction filter based on stochastic computing. The filter consists of two main stages: edge detection and fuzzy smoothing. The fuzzy difference, which is encoded as bit-streams, is used to detect edges. Then, fuzzy smoothing is done to average the pixel value based on eight directions. Our experimental results show a significant reduction in the hardware area and power consumption compared to the conventional binary implementation while preserving the quality of the results.