FPGA Realization of Wavelet Transform for Detection of Electric Power System Disturbances

FPGA implementation of fractal patterns classifier for multiple cardiac arrhythmias detection

“…FPGA is also a programmability device, like programmable ROMs, and its programming techniques include antifuse-based device (Programmed Once) and static-memory-based device (Reprogrammed an Unlimited Number of Times). A typical FPGA consists of configurable logic blocks (CLB), input/output blocks (IOB), and programmable interconnects [4][5][6][7][8][9]. Each CLB can design the logical functions and latching data with combinational logic and sequential logic (AND, OR, Flipflops, and Registers).…”

“…For 50 sampling points (25 points before R-peak and 25 points after R-peak, N = N 1 + N 2 = 50), the remaining map parameters can be solved by Equations (7) and (8) in the Matlab workspace. By using FD between 1 and 2, the fractal patterns are constructed with Equations (5), (6), and (9). The FDTs with fractal dimension D = 1.6 is chosen in this study.…”

“…It has been applied for digital filters design [4,5], signal analysis and classification [6,7], fault detection [8], and Fuzzy logic controller [9]. For signal analysis, transform methods have been used to extract features in the transform domain, such as fast Fourier transform (FFT) and wavelet transform (WT).…”

“…It is a dilated or constricted function by changing dilation and translation coefficients for feature extraction. WT process uses a several-structured filter bank to generalize several frequency bands containing decomposed signals [6,7]. Significant features are obtained at specific wavelet coefficients with a trial procedure of wavelet decomposition and experiences.…”

Enhancement of auxiliary overcurrent protection in transformers using novel feature extraction algorithms

“…To provide a more trustworthy protection, some improved approaches have been suggested, including power differential method [5], artificial intelligence method [6][7][8][9], equivalent instantaneous inductance technique [10], and statistical recognition [11][12]. Recently, with the emergence of wavelet transforms and its effective application [13][14][15][16][17][18], some papers have adopted this technique to identify the overcurrent [19][20]. In Reference [19], the wavelet analysis was performed with pattern recognition to justify the fault, yet its effectiveness is highly relied on the sufficient number of training data.…”