Research on the problems and development of software quality in the new era

With the rapid development of information and intelligence, the number of software information projects continues to increase, people’s dependence on information is getting higher and higher, the information system requirements are more and more rich, it is inevitable to the software quality requirements more stringent. For such as software defects, vulnerabilities, performance, security and so on. In view of these software quality problems, we conduct in-depth analysis and understanding of them, so as to improve the level of software quality and promote the high-quality development of software information. This paper fi rst analyzes the defi ciency of software quality under the background of the new era, and then discusses the future development direction in this fi eld, in order to deepen the research on software quality

Recognition of Human Periodic Movements From Unstructured Information Using A Motion-based Frequency Domain Approach

Feature-based motion cues play an important role in biological visual perception. We present a motion-based frequency-domain scheme for human periodic motion recognition. As a baseline study of feature based recognition we use unstructured feature-point kinematic data obtained directly from a marker-based optical motion capture (MoCap) system, rather than accommodate bootstrapping from the low-level image processing of feature detection. Motion power spectral analysis is applied to a set of unidentified trajectories of feature points representing whole body kinematics. Feature power vectors are extracted from motion power spectra and mapped to a low dimensionality of feature space as motion templates that offer frequency domain signatures to characterise different periodic motions. Recognition of a new instance of periodic motion against pre-stored motion templates is carried out by seeking best motion power spectral similarity. We test this method through nine examples of human periodic motion using MoCap data. The recognition results demonstrate that feature-based spectral analysis allows classification of periodic motions from low-level, un-structured interpretation without recovering underlying kinematics. Contrasting with common structure-based spatio-temporal approaches, this motion-based frequency-domain method avoids a time-consuming recovery of underlying kinematic structures in visual analysis and largely reduces the parameter domain in the presence of human motion irregularities

Developmental Learning for Autonomous Robots

Developmental robotics is concerned with the design of algorithms that promote robot adaptation and learning through qualitative growth of behaviour and increasing levels of competence. This paper uses ideas and inspiration from early infant psychology (up to 3 months of age) to examine how robot systems could discover the structure of their local sensory-motor spaces and learn how to coordinate these for the control of action. An experimental learning model is described and results from robotic experiments using the model are presented and discussed

Developmental Robotics from Developmental Psychology

Developmental robotics is concerned with the design of algorithms that promote robot adaptation and learning through qualitative growth of behaviour and increasing levels of competence. This paper uses ideas and inspiration from psychological knowledge of pre-grasping infants (up to 3 months of age) to examine the issues and factors that might produce similar mechanisms for use in robotic systems. The study includes discussion of results from robotic experiments on sensory-motor models and key issues are raised throughout

Automatic citrus canker detection from leaf images captured in field

Citrus canker, a bacterial disease of citrus tree leaves, causes significant damage to citrus production worldwide. Effective and fast disease detection methods must be undertaken to minimize the losses of citrus canker infection. In this paper, we present a new approach based on global features and zone-based local features to detect citrus canker from leaf images collected in field which is more difficult than the leaf images captured in labs. Firstly, an improved AdaBoost algorithm is used to select the most significant features of citrus lesions for the segmentation of the lesions from their background. Then a canker lesion descriptor is proposed which combines both color and local texture distribution of canker lesion zones suggested by plant phytopathologists. A two-level hierarchical detection structure is developed to identify canker lesions. Thirdly, we evaluate the proposed method and its comparison with other approaches, and the experimental results show that the proposed approach achieves similar classification accuracy as human experts