-Low-density Parity-check (LDPC) code a very promising near-optimal error correction code(ECC), is being widely considered in next generation industry standards. In this paper, two simple iterative low complexity algorithms for decoding LDPC codes have been explained. These algorithms are implemented using real additions only and also not dependent on power spectral density. The survey paper pr...

With the upcoming trend towards built-in test structures and implicit testing, more and more issues of test design need to be resolved during circuit design. A basic requirement for manual as well as for automatic test generation is to assess how accurately a given test strategy will classify good and faulty circuits. Measurement error plays an important role here and must be taken into account...

The original formula proposed to estimate variable-volume single-pool (VVSP) Kt/V was Kt/V = -In(R - 0.008 * t - f * UF/W), where in the Kt/V range of 0.7 to 1.3, f = 1.0 (* denotes multiplication). This formula tends to overestimate Kt/V as the Kt/V increases above 1.3. Because higher Kt/V values are now commonly delivered, the validity of both the urea generation term (0.008 * f) and correcti...

Rainfall is considered a highly valuable climatologic resource, particularly in arid regions. As one of the primaryinputs that drive watershed dynamics, rainfall has been shown to be crucial for accurate distributed hydrologicmodeling. Precipitation is known only at certain locations; interpolation procedures are needed to predict this variablein other regions. In this study, the ordinary cokri...

 Abstract: Local flux may be distorted in many regions of core, although total flux is usually sinusoidal. When attempting to predict the loss distribution in materials operating under localized distorted flux conditions, which occur in machines and transformer cores, it is essential that proper account of the waveform be taken. Moreover for development of new magnetic materials and generation ...

This demo showcases Thoughtland, an end-to-end system that takes training data and a selected machine learning model, produces a cloud of points via crossvalidation to approximate its error function, then uses model-based clustering to identify interesting components of the error function and natural language generation to produce an English text summarizing the error function.