Quantification of the presence of enzymes in gelatin zymography using the Gini index.

Gel zymography quantifies the activity of certain enzymes in tumor processes. These enzymes are widely used in medical diagnosis. In order to analyze them, experts classify the zymography spots into various classes according to their tonalities. This classification is done by visual analysis, which is what makes it a subjective process. This work proposes a methodology to carry out this classifications with a process that involves an unsupervised learning algorithm in the images, denoted as the GI algorithm. With the experiments shown in this paper, this methodology could constitute a tool that bioinformatics scientists can trust to perform the desired classification since it is a quantitative indicator to order the enzymatic activity of the spots in a zymography.

Near-ML detection for MDL-impaired few-mode fiber transmission.

Few-mode fiber transmission systems are typically impaired by mode-dependent loss (MDL). In an MDL-impaired link, maximum-likelihood (ML) detection yields a significant advantage in system performance compared to linear equalizers, such as zero-forcing and minimum-mean square error equalizers. However, the computational effort of the ML detection increases exponentially with the number of modes and the cardinality of the constellation. We present two methods that allow for near-ML performance without being afflicted with the enormous computational complexity of ML detection: improved reduced-search ML detection and sphere decoding. Both algorithms are tested regarding their performance and computational complexity in simulations of three and six spatial modes with QPSK and 16QAM constellations.

12-mode OFDM transmission using reduced-complexity maximum likelihood detection.

We report the transmission of 163-Gb/s MDM-QPSK-OFDM and 245-Gb/s MDM-8QAM-OFDM transmission over 74 km of few-mode fiber supporting 12 spatial and polarization modes. A low-complexity maximum likelihood detector is employed to enhance the performance of a system impaired by mode-dependent loss.

DSP complexity of mode-division multiplexed receivers.

The complexities of common equalizer schemes are analytically analyzed in this paper in terms of complex multiplications per bit. Based on this approach we compare the complexity of mode-division multiplexed digital signal processing algorithms with different numbers of multiplexed modes in terms of modal dispersion and distance. It is found that training symbol based equalizers have significantly lower complexity compared to blind approaches for long-haul transmission. Among the training symbol based schemes, OFDM requires the lowest complexity for crosstalk compensation in a mode-division multiplexed receiver. The main challenge for training symbol based schemes is the additional overhead required to compensate modal crosstalk, which increases the data rate. In order to achieve 2000 km transmission, the effective modal dispersion must therefore be below 6 ps/km when the OFDM specific overhead is limited to 10%. It is concluded that for few mode transmission systems the reduction of modal delay is crucial to enable long-haul performance.

Impact of mode coupling on the mode-dependent loss tolerance in few-mode fiber transmission.

Spatial-division multiplexing in the form of few-mode fibers has captured the attention of researchers since it is an attractive approach to significantly increase the channel capacity. However, the optical components employed in such systems introduce mode-dependent loss or gain (MDL) due to manufacturing imperfections, leading to significant system impairments. In this work the impact of MDL from optical amplifiers in few-mode fibers with either weak or strong mode coupling is analyzed for a 3x136-Gbit/s DP-QPSK mode-division multiplexed transmission system. It is shown that strong mode coupling reduces the impact of MDL in a similar manner as that polarization-dependent loss is reduced in single mode fibers by polarization-mode dispersion.