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Edge detection is a common operation in image/video processing applications. Canny edge detection, which performs well in different conditions, is one of the most popular and widely used of these algorithms. Canny's superior performance is due mainly to its provision of the ability to adjust the output quality by manipulating the edge detection parameters, Sigma and Threshold. Calculating values for these two parameters on-the-fly and based on the application's circumstances requires additional preprocessing, which increases the algorithm's computational complexity. To reduce the complexity, several proposed methods simply employ precalculated, fixed values for the Canny parameters (based on either the worst or typical conditions), which sacrifices the edge detection's performance in favor of the computational complexity. In this paper, an adaptive parameter selection method is proposed that selects values for the Canny parameters from a configuration table (rather than calculating in run-time), based on the estimated noise intensity of the input image and the minimum output performance that can satisfy the application requirements. This adaptive implementation of the Canny algorithm ensures that, while the edge detection performance (noise robustness) is higher than state-of-the-art counterparts in different circumstances, the execution time of the proposed Canny remains lower than those of recent cutting-edge Canny realizations. INDEX TERMS Adaptive systems, Gaussian noise, computational complexity, image edge detection, reconfigurable architectures. HOOMAN NIKMEHR received the B.Sc. degree in electronic engineering and the M.Sc. degree in computer architecture engineering from the University of Tehran, Tehran, Iran, in 1992 and 1997, respectively, and the Ph.D. degree in computer engineering from

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Being an essential issue in digital systems, especially battery-powered devices, energy efficiency has been the subject of intensive research. In this research, a multi-precision FFT module with dynamic runtime reconfigurability is proposed to trade off accuracy with the energy efficiency of OFDM in an SDR-based architecture. To support variable size FFT, a reconfigurable memory-based architecture is investigated. It is revealed that the radix-4 FFT has the minimum computational complexity in this architecture. Regarding implementation constraints such as fixed-width memory, a noise model is exploited to statistically analyze the proposed architecture. The required FFT word-lengths for different criteria-namely BER, modulation scheme, FFT size, and SNR-are computed analytically and confirmed by simulations in AWGN and Rayleigh fading channels. At run-time, the most energyefficient word-length is chosen and the FFT is reconfigured while the required application-specific BER is met. Evaluations show that the implementation area and the number of memory accesses are reduced. The results obtained

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