The trend towards smaller transistor technologies and lower operating voltages stresses the hardware and makes transistors more susceptible to transient errors. In future systems, performance and power gains will come at the cost of unreliable areas on the chip. For this reason, there is an increased need for low-overhead highlyreliable error detection methodologies. In the last years, several ...

The watermark signals are weakly inserted in images due to imperceptibility constraints which makes them prone to errors in the extraction stage. Although the error correcting codes can potentially improve their performance one must pay attention to the fact that the watermarking channel is in general very noisy. We have considered the trade-o of the BCH codes and repetition codes in various co...

In this paper, a numberof ultrawideband (UWB)multiple-inputmultiple-output (MIMO) spatialmultiplexing systems are presented and their error performance is analysed. For both model-based and measured UWB channels, the performance of various MIMO detectors is evaluated under the multiband orthogonal frequency division multiplexing (MB-OFDM) regime. Contrary to expectation, the results demonstrate...

In this paper a new class of lattices called turbo lattices is introduced and established. We use the lattice Construction D to produce turbo lattices. This method needs a set of nested linear codes as its underlying structure. We benefit from turbo codes as our basis codes. Therefore, a set of nested turbo codes based on nested interleavers (block interleavers) and nested convolutional codes i...

Error diffusion is one of the most widely used algorithms for halftoning gray scale and color images. It works by distributing the thresholding error of each pixel to unprocessed neighboring pixels, while maintaining the average value of the image. Error diffusion results in inter-pixel data dependencies that prohibit a straightforward data pipelining processing approach and increase the memory...

Model-free decentralized optimizations and learning are receiving increasing attention from theoretical and practical perspectives. In particular, two fully decentralized learning algorithms, namely Trial and Error (TEL) and Optimal Dynamical Learning (ODL), are very appealing for a broad class of games. In fact, ODL has the property to spend a high proportion of time in an optimum state that m...

A common set of statistical metrics has been used to summarize the performance ofmodels ormeasurements— the most widely used ones being bias, mean square error, and linear correlation coefficient. They assume linear, additive, Gaussian errors, and they are interdependent, incomplete, and incapable of directly quantifying uncertainty. The authors demonstrate that these metrics can be directly de...

Classification in bioinformatics often suffers from small samples in conjunction with large numbers of features, which makes error estimation problematic. When a sample is small, there is insufficient data to split the sample and the same data are used for both classifier design and error estimation. Error estimation can suffer from high variance, bias, or both. The problem of choosing a suitab...