Reversible Information of Encrypted Image Based on Feature Difference Detection and Wavelet Transform

Aiming at the shortcomings of the existing lossless digital watermarking algorithm based on frequency domain in reversibility and embedding capacity, this study proposes a lossless digital image watermarking algorithm based on fractional wavelet transform, which is used for large-capacity reversible information hiding of images. First, the image is transformed by LeGall5/3 fractional wavelet, and then, the watermark is embedded in the high-frequency subband by the histogram shift method. In order to obtain maximum embedding capacity and reduce image distortion, the methods of selecting embedding parameters and stopping parameters are proposed, respectively. At the same time, in order to prevent overflow and reduce additional information, a new method of generating position map is proposed. The experimental results show that Lena is the result of multilayer embedding based on the algorithm in this study. In order to better observe the distortion phenomenon and enlarge the image, the Lena test image is the watermark image obtained after two and three layers of embedding, and its embedding capacity can be 2.7 bpp. It is proved that wavelet transform is suitable for encrypted images to implement covert communication.

May the trophy evaluation variables be utilized as population indices in cervids

The most frequently utilized population quality indices in cervids were a fluctuating asymmetry or a branch length in yearlings. Nevertheless, unlike bovids, there has not been a reliable quality index so far that could also be applied at a later age. Therefore, an application reliability testing pertaining to certain elements of trophy measurement for the sake of difference detection between the cohorts was performed on a sample numbering 225 trophies of red deer stags from 11 cohorts. Although each of the nine trophy variables tested has succeeded in finding certain differences, the most reliable are the overall trophy value and circumferences (circumference of coronets, lower beam circumference and upper beam circumference). Since various trophy evaluation systems have been applied in the world, the implementation of the overall trophy value is limited only to the trophies evaluated pursuant to the CIC system. Hence, the most reliable indices are those based upon circumferences.

Edge color difference detection of color image based on artificial intelligence technology

In order to solve the problems of the traditional methods in detecting color image edge chromatic aberration, such as the poor accuracy of detection and the poor detection effect, a color image edge chromatic aberration detection method based on artificial intelligence technology is proposed. The approximate principal component analysis method is used to segment the color image and smooth the image denoising; The linear gray-scale transformation is applied to the color image to enlarge the smaller gray-scale space to the larger gray-scale space according to the linear relationship and obtain the edge information of the color image; The artificial intelligence technology is used to locate the edge sub-pixel of the image to complete the edge color difference detection of the color image. The experimental results show that the detection accuracy of the proposed method is about 98%, and the detection effect is good, which is feasible.

An Algorithm for Motion Estimation Based on the Interframe Difference Detection Function Model

In this paper, we simulate the estimation of motion through an interframe difference detection function model and investigate the spatial-temporal context information correlation filtering target tracking algorithm, which is complex and computationally intensive. The basic theory of spatiotemporal context information and correlation filtering is studied to construct a fast target tracking method. The different computational schemes are designed for the flow of multiframe target detection from background removal to noise reduction, to single-frame detection, and finally to multiframe detection, respectively. This enables the ground-based telescope to effectively detect spatial targets in dense stellar backgrounds in both modes. The method is validated by simulations and experiments and can meet the requirements of real projects. The interframe bit attitude estimation is optimized by using the beam-parity method to reduce the interframe estimation noise; a global optimization strategy based on the bit attitude map is used in the back end to reduce the system computation amount and make the global bit attitude estimation more accurate; a loop detection based on the word pocket model is added to the system to reduce the cumulative error.

Detection of tower bolt looseness and its influence of wind level

Taking the wind turbine tower as the research object, based on the finite element software, a simplified beam-shell hybrid element model was first established; through the simulation, the phase difference between the loose position and the unloose position was compared to verify the feasibility of the phase difference detection method; Secondly, the influence of the number of loose bolts, the position of loosening, and the magnitude of the wind force on the phase of the flange bolt connection structure and the response characteristics of the system are analyzed. The research results show that the number of loose bolts, the position of loosening, and the magnitude of the wind have certain effects on the phase difference and response characteristics of the flange. With the increase in the number of loose bolts, the connection stiffness of the bolt connection continues to decrease. The linear characteristic is enhanced; the closer the loosening is to the excitation force loading position, the greater the detected phase difference; as the wind increases, the phase of the upper flange of the tower changes, and the phase of the lower flange remains unchanged, and the wind is on the flange The disc connection strength has little effect.

UCDiff: Difference Detection in Use Case Models of Healthcare System

Development of large-scale healthcare software projects essentially need the efficient management of the created software artifacts during software development process. In such projects different versions of an artifact are created at different times. Traditional software configuration management systems, such as Git, Subversion (SVN), etc., are designed for later phases of software development, which mainly handle the source code document. These systems are unable to perform difference detection and version management tasks on models such as unified modeling language diagrams. UML use case model is used for capturing functional requirements at analyses phase. Different versions of the use case model are created during analyses phase. This paper addresses the detection of differences between two versions of a use case model. In order to perform difference detection, we need to perform three main tasks, i. e., extract the contents of the model, comparison of models and difference representation. Most of the existing approaches in literature of model comparison deal with UML class diagrams. To the best of our knowledge, so far no appropriate approach addresses difference computation of use case model. Existing approaches are not applicable on use case model due to different semantics of use case model. In this research, the concept of model-based software configuration management (SCM) for use case difference detection is proposed. The use case models are created in an open source tool, starUML. The proposed difference algorithm is applied on intermediate tree structure representation of models. As a case study, different versions of a patient appointment healthcare system is used to evaluate different evaluation parameters, such as accuracy, domain independence, high conceptual level and tool independence.

UCDiff: Difference Detection in Use Case Models of Healthcare System

Development of large-scale healthcare software projects essentially need the efficient management of the created software artifacts during software development process. In such projects different versions of an artifact are created at different times. Traditional software configuration management systems, such as Git, Subversion (SVN), etc., are designed for later phases of software development, which mainly handle the source code document. These systems are unable to perform difference detection and version management tasks on models such as unified modeling language diagrams. UML use case model is used for capturing functional requirements at analyses phase. Different versions of the use case model are created during analyses phase. This paper addresses the detection of differences between two versions of a use case model. In order to perform difference detection, we need to perform three main tasks, i. e., extract the contents of the model, comparison of models and difference representation. Most of the existing approaches in literature of model comparison deal with UML class diagrams. To the best of our knowledge, so far no appropriate approach addresses difference computation of use case model. Existing approaches are not applicable on use case model due to different semantics of use case model. In this research, the concept of model-based software configuration management (SCM) for use case difference detection is proposed. The use case models are created in an open source tool, starUML. The proposed difference algorithm is applied on intermediate tree structure representation of models. As a case study, different versions of a patient appointment healthcare system is used to evaluate different evaluation parameters, such as accuracy, domain independence, high conceptual level and tool independence.