Mehmet Ertugrul Çelebi

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A free-field recording of the Denali fault earthquake was obtained by the Alyeska Pipeline Service Company 3 km from the surface rupture of the De-nali fault. The instrument, part of the monitoring and control system for the trans-Alaska pipeline, was located at Pump Station 10, approximately 85 km east of the epicenter. After correction for the measured(More)
In this paper, we propose efficient maximum-likelihood (ML) decoding for binary Kronecker product-based (KPB) codes. This class of codes, have a matrix defined by the n-fold iterated Kronecker product G<sub>n</sub> = F<sup>&#x2297;n</sup> of a binary upper-triangular kernel matrix F, where some columns are suppressed given a specific puncturing pattern.(More)
—Polar coding is known as the first provably capacity-achieving coding scheme under low-complexity suboptimal successive cancelation decoding (SCD). The large error-correction capability of finite-length polar codes is mostly achieved with relatively long codes. SCD is the conventional decoder for polar codes and exhibits a quasi-linear complexity in terms(More)
This paper introduces the state-of-the-art seismic monitoring system implemented for the 1,206-m-long ͑3,956 ft͒ cable-stayed Bill Emerson Memorial Bridge in Cape Girardeau ͑Missouri͒, a new Mississippi River crossing, approximately 80 km from the epicentral region of the 1811 and 1812 New Madrid earthquakes. The real-time seismic monitoring system for the(More)
The key point in design of radial basis function networks is to specify the number and the locations of the centers. Several heuristic hybrid learning methods, which apply a clustering algorithm for locating the centers and subsequently a linear leastsquares method for the linear weights, have been previously suggested. These hybrid methods can be put into(More)
Polar codes are known as the first provable code construction to achieve Shannon capacity for arbitrary symmetric binary-input channels. Although, there exist efficient sub-optimal decoders with reduced complexity for polar codes, the complexity of the optimum ML decoder increases exponentially. Hence the optimum decoder is infeasible for the practical(More)