The advent of 3D data has revolutionized various industries, from architecture and engineering to healthcare and entertainment, enabling more precise simulations and realistic visualizations. However, 3D data is susceptible to noise and loss during generation and transmission, making quality assessment crucial for ensuring accuracy and usability. While existing literature addresses quality assessment for 3D point clouds and meshes separately, a gap exists in assessing the quality of 3D colored meshes due to the lack of reference datasets. This paper proposes an approach for No Reference 3D Colored Mesh Visual Quality Assessment (CMVQA), based on previous work related to quality assessment of 3D non colored meshes quality assessment. Our approach combines geometric and color features with spatial domain features extracted from mesh projections. Through extensive experiments and comparisons with full-reference metrics, including image quality metrics, our proposed approach demonstrates superior performance.

Classifying and modeling texture images, especially those with significant rotation, illumination, scale, and view-point variations, is a hot topic in the computer vision field. Inspired by local graph structure (LGS), local ternary patterns (LTP), and their variants, this paper proposes a novel image feature descriptor for texture and material classification, which we call Petersen Graph Multi-Orientation based Multi-Scale Ternary Pattern (PGMO-MSTP). PGMO-MSTP is a histogram representation that efficiently encodes the joint information within an image across feature and scale spaces, exploiting the concepts of both LTP-like and LGS-like descriptors, in order to overcome the shortcomings of these approaches. We first designed two single-scale horizontal and vertical Petersen Graph-based Ternary Pattern descriptors ($PGTP_{h}$and$PGTP_{v}$). The essence of$PGTP_{h}$and$PGTP_{v}$is to encode each$5\times 5$image patch, extending the ideas of the LTP and LGS concepts, according to relationships between pixels sampled in a variety of spatial arrangements (i.e., up, down, left, and right) of Petersen graph-shaped oriented sampling structures. The histograms obtained from the single-scale descriptors$PGTP_{h}$and$PGTP_{v}$are then combined, in order to build the effective multi-scale PGMO-MSTP model. Extensive experiments are conducted on sixteen challenging texture data sets, demonstrating that PGMO-MSTP can outperform state-of-the-art handcrafted texture descriptors and deep learning-based feature extraction approaches. Moreover, a statistical comparison based on the Wilcoxon signed rank test demonstrates that PGMO-MSTP performed the best over all tested data sets.

Writer identification is experiencing a revival of activity in recent years and continues to attract great deal of attention as a challenging and important area of research in the field of forensic and authentication. In this work, we introduce a reliable off-line system for text-independent writer identification of handwritten documents. Feature engineering is an essential component of a pattern recognition system, which can enhance or decrease the classification performance. A well-designed and defined feature extraction method improves the classification task. This paper proposes, for feature extraction, an effective, yet high-quality and conceptually simple feature image descriptor referred to as Cross multi-scale Locally encoded Gradient Patterns (CLGP). The proposed CLGP feature extraction method, which is expected to better represent salient local writing structure, operates at small observation regions (i.e., connected component sub-images) of the writing sample. CLGP histogram feature vectors computed from all these observation regions in all writing samples are considered as classification inputs to identify query writers using the Nearest Neighbor Classifier (1-NN). Our system is evaluated on six standard databases (IFN/ENIT, AHTID/MW, CVL, IAM, Firemaker, and ICDAR2011) including handwritten samples in Arabic, English, French, Greek, German, and Dutch languages. Comparing the identification performance with old and recent state-of-the-art methods, the proposed system achieves the highest performance on IFN/ENIT, AHTID/MW, and ICDAR2011 databases, and demonstrates competitive performance on IAM, CVL, and Firemaker databases.

The development of Internet and social media contributes to multiplying the data produced on the Internet and the connected nodes, but the default installation and the configuration of variety of software systems represent some security holes and shortcomings, while the majority of Internet users have not really set up safety awareness, leading to huge security risks. With the development of network attack techniques, every host on the Internet has become the target of attacks. Therefore, the network information security cannot be ignored as a problem. To deal with 0-day and future attacks, the honeypot technique can be used not only passively as an information system, but also to reinforce the traditional defense systems against future attacks. In this paper, we present an introduction of machine learning and honeypot systems, and based on these technologies, we design a smart agent for cyber-attack prevention and prediction.

This paper presents an efficient handcrafted texture operator for texture modeling and classification. The proposed descriptor, referred to as local directional decoded ternary pattern (LDDTP), consists in encoding both directional pattern features and contrast information in a compact way based on local derivative variations. The proposed operator first computes for each pixel within its 3×3 overlapping square neighborhood, on the one hand, central edge response through the 2ndderivative of Gaussian filter, and on the other hand, eight directional edge responses using the eight Frei-Chen masks to capture more detailed information. Then, spatial relationships among the neighboring pixels through the generated edge responses are exploited independently with the help of the concepts of LTP and LDP operators to enhance the discriminative power. Finally, the produced LDDTP pattern is splitted into two distinct parts: local directional decoded ternary pattern lower ( LDDTPL) and local directional decoded ternary pattern upper ( LDDTPU), which are combined into hybrid distributions to form the final LDDTP feature descriptor. Experimental results on eight publicly available texture datasets showed that the proposed LDDTP descriptor achieves classification performance, which is competitive or better than several old and recent state-of-the-art LBP variants.

In this communication, we present an enhanced gain and directivity UWB elliptical metamaterial antenna based on the Near Zero refractive Index (NZIM). The operating frequency range is from 3.1GHz to 20GHz. The NZIM superstrate metamaterial has a property thats could collimates the incident radiated wave towards the normal direction because the effective permeability and permittivity of the (NZIM) are optimized to synchronously approach zero, this effects leads the NZIM to have an effective wave impedance matching with air and near-zero index simultaneously. The proposed antenna is simulated and developed using CST microwave. Obviously, the antenna gain and directivity are improved by 6.07 and 5.42 respectively.

It is well-known that intrusion detection systems are an effective way to detect malicious connections in a computer network. Different feature extraction techniques have been applied in the field of detection intrusions; the most common one is Principal Component Analysis (PCA). Nevertheless, PCA is restricted to linear principal components and suffers from sensitivity to noise and can be easily affected by outliers. To deal with the drawbacks of PCA many data dimensionality reduction methods have been proposed, among them we found Robust Fuzzy PCA (RFPCA) which employs fuzzy memberships in order to reduce the effect of outliers. Unfortunately, even though RFPCA showed satisfactory results, it still suffers from the influence of outliers. In fact, using an increasing function such as exponential function in the estimation of memberships will assign a big membership values to outliers, consequently, the obtained results can be skewed. In this paper, we suggest a new variant of Robust Fuzzy PCA (RFPCA) method for the purpose of network IDS. Extensive experiments on the two well known datasets i.e. KDDcup99 and NSL-KDD demonstrated that the proposed approach takes the advantage over RFPCA and PCA in terms of network attacks detection and false alarms reduction.

3D scene based objects detection and tracking is a central problem in many intelligent transportation applications. Dynamic stereo vision is the known approach to solve this problem. It consists in detecting and tracking objects from their reconstructed features using stereo images. This paper proposes a new method for detecting and tracking objects using stereo vision with linear cameras. Edge points extracted from the stereo linear images are first matched to reconstruct points that represent the objects in the scene. To detect the objects, a clustering process based on a spectral analysis is then applied to the reconstructed points. The obtained clusters are finally tracked throughout their center of gravity using Kalman filtering and a Nearest Neighbour based data association algorithm. Experimental results using real stereo linear images are shown to demonstrate the effectiveness of the proposed methods for obstacle detection and tracking in front of a vehicle.