Modern Classification of the Properties of Papillary Patterns of Human Hands and Feet for the Solution of Identification and Non-Identification Problems in Forensic Expert Activities

The relevance and novelty of the topic of the article is connected with the fact that for the first time the improved classification of the properties of human papillary patterns and their displays and the dynamic trend of expanding the legal base of fingerprint registration in the world are considered. The goal is to systematize the properties of human papillary patterns and their representations. General scientific and special methods were used. The analysis of the literature and the author's practice allow us to identify and consider the following properties of papillary patterns and their mappings, which were divided into three groups for cognitive purposes: their own properties, the properties of mappings, and the properties of the fat-rich substance of the human papillary pattern and its trace. For the first time, the properties of the poly-fat substance of the human papillary pattern and its trace are presented, which is important in view of the trend in the development of complex fingerprint examination. For the first time, the first and second groups are conditionally divided into four subgroups: for solving identification, diagnostic, classification and situational problems. For the first time, the third group is further conditionally divided into four subgroups: according to the interaction of the human body fat with the carrier object; according to the isolation of the human body fat trace; according to the dynamics of the preservation and contrast of the display of the fat-free substance of the human papillary pattern. On the other hand, the properties of papillary patterns themselves are divided into basic properties and properties for solving identification and non-identification problems, respectively. The author's definition of the following concepts is presented: "basic properties of human papillary patterns" and "properties of human papillary patterns", "basic properties of human papillary patterns mappings" and "properties of human papillary patterns mappings", "basic properties of human papillary matter" and "properties of human papillary matter", "basic properties of human papillary matter" and" properties of human papillary matter". This provides an opportunity for specialists and teachers to improve their cognitive and practical activities, and for those responsible and competent for the organization of examinations to consider creating a center for comprehensive fingerprint examination.

POLLEN MORPHOLOGY OF LYCIUM (SOLANACEAE) SPECIES IN IRAN

Lycium (Solanaceae) has more than 100 species in arid and semi-arid parts of the world. In Iran, this medicinal plant is represented by 6 species, on which no palynological study has been conducted. Pollen morphology of 7 taxa are considered for the first time to evaluate the species relationships based on palynological data. Observations were made by use of Scanning electron microscopy on untreated pollen grains. Multivariate statistical analyses were carried out for qualitative and quantitative pollen features in the species studied. Results show that all pollen grains were monad, isopolar, zonotricolporate in prolate and preprolate shape. Exine sculpture was of striate and regulate patterns. Variations in pollen grains are considered to overcome the identification problems in species separation in morphologically similar taxa in this genus in Iran. The importance of pollen features in delimitation in some genera of Solanaceae was previously proven and the present study elucidates that some features as exine sculpture and some quantitative characters are of diagnostic importance in Lycium species native to Iran.

Algorithm of search identification with forced search for extremum

We provide one of possible ways to reduce the time of transitive process of models tuning in the identification problems, which is: forced search for extremum of system state criterion.

Classification of Geometric Forms in Mosaics Using Deep Neural Network

The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that autonomously quantifies the feature map and classifies it. Convolution, pooling and dense layers are three distinct categories of levels that generate attributes from the dataset images by introducing certain specified filters. As a case study, a Roman mosaic is considered, which is digitally reconstructed by close-range photogrammetry based on standard photos. During the digital transformation from a 2D perspective view of the mosaic into an orthophoto, each photo is rectified (i.e., it is an orthogonal projection of the real photo on the plane of the mosaic). Image samples of the geometric forms, e.g., triangles, squares, circles, octagons and leaves, even if they are partially deformed, were extracted from both the original and the rectified photos and originated the dataset for testing the CNN-based approach. The proposed method has proved to be robust enough to analyze the mosaic geometric forms, with an accuracy higher than 97%. Furthermore, the performance of the proposed method was compared with standard deep learning frameworks. Due to the promising results, this method can be applied to many other pattern identification problems related to artworks.

Identification of Linear and Bilinear Systems: A Unified Study

System identification problems are always challenging to address in applications that involve long impulse responses, especially in the framework of multichannel systems. In this context, the main goal of this review paper is to promote some recent developments that exploit decomposition-based approaches to multiple-input/single-output (MISO) system identification problems, which can be efficiently solved as combinations of low-dimension solutions. The basic idea is to reformulate such a high-dimension problem in the framework of bilinear forms, and to then take advantage of the Kronecker product decomposition and low-rank approximation of the spatiotemporal impulse response of the system. The validity of this approach is addressed in terms of the celebrated Wiener filter, by developing an iterative version with improved performance features (related to the accuracy and robustness of the solution). Simulation results support the main theoretical findings and indicate the appealing performance of these developments.