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In certain cases, persons with disabilities may be forced to rely on others for the performance of their duties. Blindness is one of the impairments that might be encountered. Up to this point, there has been N number of solutions presented that make life easier for visually impaired individuals. One of the problems they face on a daily basis is making an independent purchase of a product they need. To solve this issue, the approach is to utilize a camera to record a picture, which is then processed using the tesseract method to extract text from the image, which is then transformed into an audio file that can be heard using headphones. Following the implementation of this strategy, during this shopping trolley technology to detect the item put with machine learning and precision location discover a person will be used to locate a person in the shopping trolley.

Pallet recognition is a critical technology for industrial unmanned forklifts, yet accurately locating pallet holes using depth cameras remains challenging due to complex industrial environments. This paper proposes an improved YOLOv5 (named YOLOv5-GE) to recognize and locate the pallet hole position. In the YOLOv5-GE, the ECA (Efficient Channel Attention) module is introduced after the CSP module of the backbone network, and the CBS module of the neck network is replaced by the GSC (Ghost-Shuffle Convolution) module. YOLOv5-GE outperforms the baseline YOLOv5 by 0.71% in mAP@0.5, 8.55% in mAP@0.5:0.95, and 11.27% in FPS. These advancements make YOLOv5-GE particularly suitable for real-time pallet hole recognition in complex industrial settings.

Identifying critical edges in complex networks is a fundamental challenge in the study of complex networks. Traditional approaches tend to rely solely on either global information or local information. However, this dependence on a single information source fails to capture the multi-layered complexity of critical edges, often resulting in incomplete or inaccurate identification. Therefore, it is essential to develop a method that integrates multiple sources of information to enhance critical edge identification and provide a deeper understanding and optimization of the structure and function of complex networks. In this paper, we introduce a Global–Local Hybrid Centrality method which integrates a second-order neighborhood index, a first-order neighborhood index, and an edge betweenness index, thus combining both local and global perspectives. We further employ the edge percolation process to evaluate the significance of edges in maintaining network connectivity. Experimental results on various real-world complex network datasets demonstrate that the proposed method significantly improves the accuracy of critical edge identification, providing theoretical and methodological support for the analysis and optimization of complex networks.

In a connected graph G, the distance function between each pair of two vertices from a set vertex V ( G ) is a shortest distance between them and the vertex degree v , deg v , is the number of edges which are incident to the vertex v. The Schultz and modified Schultz polynomials of G are have defined as : Sc ( G ; x ) = ∑( deg v + deg u ) x d ( u , v ) and Sc ∗ ( G ; x ) = ∑ ( deg v . deg u ) x d ( u , v ), respectively, where the summations are taken over all unordered pairs of distinct vertices in V ( G ) and d ( u , v ) is the distance between u and v in V ( G ). We shall find the general forms of Schultz and modified Schultz polynomials and indices of the edge – identification chain and ring – pentagon and hexagon graphs in the present work.

The optical measurement technology could reduce manual work and improve quality in assembly. The confocal scanning scheme is proposed to measure the narrow gap in-line but it is hard to identify the edges of a gap. This paper proposed an edge definition and presented an edge identification method for gap measurement. The edge profile is defined as a part of a circle, which could be identified by the shutter time feature. The defined edge point could be calculated by fitting the edge profile, and then the width of a gap section is calculated by two edge points. The width of a gap can be evaluated by the average of several gap section. The experimental results indicate that the repeatability of the method is less than 2 µm and the precision of the measurement is higher than ±2.5 µm. The proposed method is feasible for in-line measurement application in assembly.

Normal vector of 3D surface is important differential geometric property over localized neighborhood, and its abrupt change along the surface directly reflects the variation of geometric morphometric. Based on this observation, this paper presents a novel edge detection algorithm in 3D point clouds, which utilizes the change intensity and change direction of adjacent normal vectors and is composed of three steps. First, a two-dimensional grid is constructed according to the inherent data acquisition sequence so as to build up the topology of points. Second, by this topological structure preliminary edge points are retrieved, and the potential directions of edges passing through them are estimated according to the change of normal vectors between adjacent points. Finally, an edge growth strategy is designed to regain the missing edge points and connect them into complete edge lines. The results of experiment in a real scene demonstrate that the proposed algorithm can extract geometric edges from 3D point clouds robustly, and is able to reduce edge quality’s dependence on user defined parameters.

We propose a new automatic method for the interpretation of potential field data, called the RDAS-Euler method, which is based on Euler’s deconvolution and analytic signal methods. The proposed method can estimate the horizontal and vertical extent of geophysical anomalies without prior information of the nature of the anomalies (structural index). It also avoids inversion errors because of the erroneous choice of the structural index N in the conventional Euler deconvolution method. The method was tested using model gravity anomalies. In all cases, the misfit between theoretical values and inversion results is less than 10%. Relative to the conventional Euler deconvolution method, the RDAS-Euler method produces inversion results that are more stable and accurate. Finally, we demonstrate the practicability of the method by applying it to Hulin Basin in Heilongjiang province, where the proposed method produced more accurate data regarding the distribution of faults.

The world's population and the multitude of cars on the road today are both gaining popularity. Most countries in the world are currently experiencing traffic congestion. Ineffective traffic management, which results in regular traffic congestion at major intersections, is one of the major causes of such issues, which includes environmental pollution, traffic injuries, health losses, and time wasted. As a result, to efficiently control traffic congestion on streets, highways, and roads, a successful management system is required. The aim of this study was to compare and contrast various traffic electronic systems and their feature extraction techniques in order to effectively manage pollution level. Based on data from video camera images of roads and image processing techniques used to monitor traffic road traffic congestion, we constructed a framework for a traffic management system.

The goal of this work was to study interactions of chromaticity and luminance in edge identification. Two horizontal spatial sawtooth patterns, one with positive and the other with negative harmonics, were compared in a two-alternative forced-choice (2-AFC) procedure. The observer identified which pattern had sharp upper or lower edges. The fundamental frequency was 2 cycles/deg (cpd), with 5 cycles presented in a 2.5-deg square field. The pattern was presented as a 1-s raised temporal cosine, replacing part of an 8-deg background. Stimuli were specified in a cone troland (l, s, Y) chromaticity space, with correction for individual equiluminance at a nominal 115 td, and individual tritan direction. A preliminary set of interleaved staircases established edge identification for the six directions of the (l, s, Y) space. Three compound stimuli combining two orthogonal directions were chosen and included with the end-points in five randomly interleaved staircases. For combinations of Y with l-chromaticity, or l- with s-chromaticity, probability summation was observed. Combinations of Y with s-chromaticity revealed opponency. Data for +s, +Y and −s, −Y were subadditive; data for +s, −Y and −s, +Y were additive. Control studies using detection rather than edge identification revealed probability summation for all combinations. Luminance edges did not enhance stimuli with l-chromaticities. There was an interaction of luminance edges with s-chromaticities. Dim “blues” and bright “yellows” showed linear summation. Bright “blues” and dim “yellows” showed opponency.

New logical properties and implications are revealed on a subset of pairwise Markov properties satisfied in causal networks. The results obtained characterize a wide class of graphical models including mixed graphs and cyclic digraphs. The following three kinds of separators are defined: minimal, locally minimal, and non-redundant. This article also formulates necessary requirements on members of a non-redundant separator and principles of forming non-redundant separators from elementary (in)dependency facts.