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Although research is still needed, it is important to define screening strategies to exploit the potential of image-based automatic recognition systems integrated in the daily routine of laboratories along with other analysis methodologies.

The method reaches a high precision in the recognition of five different types of lymphoid cells and could allow for the design of a diagnosis support tool in the future.

The future of the wind energy industry passes through the use of larger and more flexible wind turbines in remote locations, which are increasingly o↵shore to benefit stronger and more uniform wind conditions. The cost of operation and maintenance of o↵shore wind turbines is approximately 15-35% of the total cost. Of this, 80% goes towards unplanned maintenance issues due to di↵erent faults in the wind turbine components. Thus, an auspicious way to contribute to the increasing demands and challenges is by applying low-cost advanced fault detection schemes. This work proposes a new method for detection and classification of wind turbine actuators and sensors faults in variablespeed wind turbines. For this purpose, time domain signals acquired from the operating wind turbine are represented as two-dimensional matrices to obtain grayscale digital images. Then, the image pattern recognition is processed getting texture features under a multichannel representation. In this work, four types of texture characteristics are used: statistical, wavelet, granulometric and Gabor features. Next, the most significant ones are selected using the conditional mutual criterion. Finally, the faults are detected and distinguished between them (classified) using an automatic classification tool. In particular, a 10-fold cross-validation is used to obtain a more generalized model and evaluates the classification performance. Coupled non-linear aero-hydro-servo-elastic simulations of a 5MW o↵shore type wind turbine are carried out in several fault scenarios. The results show a promising methodology able to detect and classify the most common wind turbine faults.

This study reviews current and new quantitative features, which can contribute to optimize morphology through blood cell digital image processing techniques.

The approach has been able to automatically classify with high precision three types of lymphoid cells. The addition of more descriptors and other classification techniques will allow extending the classification to other classes of atypical lymphoid cells.

The excellent automatic screening of the three groups of normal, reactive, and abnormal lymphocytes is useful as it discriminates between malignancy and not malignancy. The discrimination of the five groups of abnormal lymphoid cells is encouraging toward the idea that the system could be an automated image-based screening method to identify blood involvement by a variety of B lymphomas.