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Control chart pattern analysis is a crucial task in statistical process control. There are various types of nonrandom patterns that may appear on the control chart indicating that the process is out of control. The presence of nonrandom patterns manifests that a process is affected by assignable causes, and corrective actions should be taken. From a process control point of view, identification of nonrandom patterns can provide clues to the set of possible causes that must be searched; hence, the troubleshooting time could be reduced in length. In this paper, we discuss two implementation modes of control chart pattern recognition and introduce a new research issue concerning pattern displacement problem in the process of control chart analysis. This paper presents a neural network-based pattern recognizer with selected features as inputs. We propose a novel application of statistical correlation analysis for feature extraction purposes. Unlike previous studies, the proposed features are developed by taking the pattern displacement into account. The superior performance of the feature-based recognizer over the raw data-based one is demonstrated using synthetic pattern data. [ABSTRACT FROM AUTHOR]

Microalgae counting is used to measure biomass quantity. Usually, it is performed in a manual way using a Neubauer chamber and expert criterion, with the risk of a high error rate. Scenedesmus algae can build coenobia consisting of 1, 2, 4 and 8 cells. The amount of algae of each coenobium helps to determine the amount of lipids, proteins, and other substances in a given sample of a algae crop. The knowledge of the quantity of those elements improves the quality of bioprocess applications. This paper addresses the methodology for automatic identification of Scenedesmus microalgae (used in the methane production and food industry) and applies it to images captured by a digital microscope. The use of contrast adaptive histogram equalization for pre-processing, and active contours for segmentation are presented. The calculation of statistical features (histogram of oriented gradients, Hu and Zernike moments) with texture features (Haralick and local binary patterns descriptors) are proposed for algae characterization. Classification of coenobia achieves accuracies of 98.63% and 97.32% with support vector machine and artificial neural network, respectively. According to the results, it is possible to consider the proposed methodology as an alternative to the traditional technique for algae counting. In addition, the database used for the developing of the proposed methodology is publicly available. [ABSTRACT FROM AUTHOR]

The use of computer-readable visual codes became common in our everyday life both in industrial environments and for private use. The reading process of visual codes consists of two steps, namely, localization and data decoding. In this paper we examine the localization step of visual codes using conventional and deep rectifier neural networks. They are also evaluated in the discrete cosine transform domain and shown to be efficient, which makes full decompression unnecessary for setups involving JPEG images. This approach is also efficient from a storage viewpoint and computation cost viewpoint, since camera hardware can provide a JPEG stream as output in many cases. The use of neural networks implemented on graphics processing unit allows real-time automatic code object localization. In our earlier studies, the proposed approach was evaluated on the most popular code type, quick response code, and some other 2D codes as well. Here, we also prove that deep rectifier networks are also suitable for 1D barcode localization and present extensive evaluation and comparison to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of an original hierarchical neural model on a complex sequence - the complete sixteenth fourpart fugue in G minor of the Well-Tempered Clavier (vol 1) of J. S. Bach. The model makes an effective use of context information, through its hierarchical topology and embedded time integrators, and that enables it to keep a very good account of past events. The model performs sequence classification and discrimination efficiently. It has application in domains which require pattern recognition, or particulary, which demand recognising either a set if sequences of vectors in time, or sub-sequences into a unique and large sequence of vectors in time. [ABSTRACT FROM AUTHOR]