Pattern recognition methods in microarray based oncology study

With the development of microarray technology,more and more microarray-based oncology studies have been carried out.Huge amounts of data and the complexity of cancer mechanisms make data analysis methods a much more important part of these studies.In this article,we will mainly focus on the pattern recognition methods used in oncology studies.According to the availability of sample information,the unsupervised methods and supervised methods are reviewed separately.Finally,some possible future directions are proposed.

Recognition Analysis and Simulation Implementation Based on High-Order Cumulants of Wireless Digital Modulation Mode

This paper mainly studies the data characteristics of high order cumulants using digitally modulated signals, and constructs the identification feature parameters that can distinguish the signal modulation type by the high-order cumulants data of the digital modulation signal. Set the identification signal modulation type determination threshold based on the value of the identification feature parameter. The identification feature parameter value of the signal modulation type is compared with the set determination threshold, to realize the recognition of digital modulation signal. This identification method is implemented based on MATLAB design, with a 2ASK (2-ary Amplitude Shift Keying) signal, 4ASK (4-ary Amplitude Shift Keying) signal, 2PSK (2-ary Phase Shift Keying) signal, 4PSK (4-ary Phase Shift Keying) signal, 2FSK (2-ary Frequency Shift Keying) signal, 4FSK (4-ary Frequency Shift Keying) signal. The second, fourth and sixth order cumulants of the six signals were analyzed. Calculate the selected identification feature parameter value and the determination threshold to identify the six signals. The six signals have made MATLAB identification simulation. Simulation results show that this method is feasible and has high recognition rate. Simulation results verify that such recognition methods maintain a high recognition rate under conditions with low signal-to-noise ratio. This identification method can be extended to more MASK (M-ary Amplitude Shift Keying), MPSK (M-ary Phase Shift Keying), MFSK (M-ary Frequency Shift Keying), MQAM (M-ary Quadrature Amplitude Modulation) signal identification.

Recognition results classification and post-processing methods for painted characters on billet surface

Automatic identification of characters marked on billets is very important for steelworks to achieve manu- facturing and logistics informatization management. Due to the presence of adhesions, fractures, blurs, and other problems in characters painted on billets, character recognition accuracy with machine vision is relatively low, and hardly meets practical application requirements. To make the character recognition results more reliable and accu- rate, an identification results classification and post-pro- cessing method has been proposed in this paper. By analyzing issues in the image segmentation and recognition stage, the recognition result classification model, based on character encoding rules and recognition confidence, is built, and the character recognition results can be classified as correct, suspect, or wrong. In the post-processing stage, a human-machine-cooperation mechanism with a post- processing interface is designed to eliminate error infor- mation in suspect and wrong types. The system was developed and experiments conducted with images acquired in an iron and steel factory. The results show the character recognition accuracy to be approximately 89% using the character recognizer. However, this result cannot be directly applied in information management systems. With the proposed post-processing method, a human worker will query the suspect and wrong results classified by the system, determine whether the result is correct or wrong, and then, correct the wrong result through the post-processing interface. Using this method, the character recognition accuracy ultimately improves to 99.4%. Thus, the results will be more reliable applied in a practical system.

Automatic target recognition method for inverse synthetic aperture sonar imaging

To address the randomness of target aspect angle and the incompleteness of observed target in inverse synthetic aperture sonar（ISAS） imaging,a method for target recognition is proposed based on topology vector feature（TVF） of multiple highlights. Analysis of the projection relationship from 3 D space to 2 D imaging plane in ISAS indicates that the distance between two highlights in the cross-range scale calibrated image is determined by the distance between the corresponding physical scattering centers. Then, TVFs of different targets, which remain stable in various possibilities of target aspect angle, can be built. K-means clustering technique is used to effectively alleviate effect of the point missing due to incompleteness of the observed target. A nearest neighbor classifier is used to realize the target recognition. The ISAS experimental results using underwater scaled models are provided to demonstrate the effectiveness of the proposed method. A classification rate of 84.0% is reached.

2-D EAG Method for the Recognition of Hand-Printed Chinese Characters

A method, called Two-Dimensional Extended Attribute Grammars (2-DEAGs). for the recognition of hand-printed Chinese characters is presented. This method uses directly two dimensional information, and pro- vides a scheme for dealing with various kinds of specific cases in a uniform way. In this method, components are drawn in guided and redundant way and reductions are made level by level just in accordance with the com- ponent combination relations of Chinese characters. The method provides also polysemous grammars, coexisting grammars and structure inferrings which constrain redundant recognition by comparison among similar characters or components and greatly increase the tolerance ability to distortion.

Review of rigid fruit and vegetable picking robots

The important indicators to measure the goodness of rigid fruit and vegetable picking robot have two aspects,the first is the reasonable structural design of the end-effector,and the second is having a high precision positioning recognition method.Many researchers have done a lot of work in these two aspects,and a variety of end-effector structures and advanced position recognition methods are constantly appearing in people’s view.The working principle,structural characteristics,advantages and disadvantages of each end-effector are summarized to help us design better fruit and vegetable picking robot.The authors start from the rigid fruit and vegetable picking robot grasping methods,separation methods,and position recognition methods,firstly introduce three different grasping methods and the characteristics of the two separation methods,then introduce the under-driven picking robot developed on the basis of the traditional rigid picking robot,then explain the single special,multi-feature,and deep learning location position recognition methods currently used,and finally make a summary and outlook on the rigid fruit and vegetable picking robot from the structural development and position recognition methods.