An Ultra Fast Semantic Segmentation Model for AMR’s Path Planning

Computer vision plays a significant role in mobile robot navigation due to the abundance of information extracted from digital images. On the basis of the captured images, mobile robots determine their location and proceed to the desired destination. Obstacle avoidance still requires a complex sensor system with a high computational efficiency requirement due to the complexity of the environment. This research provides a real-time solution to the issue of extracting corridor scenes from a single image. Using an ultra-fast semantic segmentation model to reduce the number of training parameters and the cost of computation. In addition, the mean Intersection over Union (mIoU) is 89%, and the high accuracy is 95%. To demonstrate the viability of the prosed method, the simulation results are contrasted to those of contemporary techniques. Finally, the authors employ the segmented image to construct the frontal view of the mobile robot in order to determine the available free areas for mobile robot path planning tasks

A Secured, Multilevel Face Recognition based on Head Pose Estimation, MTCNN and FaceNet

Artificial Intelligence and IoT have always attracted a lot of attention from scholars and researchers because of their high applicability, which make them a typical technology of the Fourth Industrial Revolution. The hallmark of AI is its self-learning ability, which enables computers to predict and analyze complex data such as bio data (fingerprints, irises, and faces), voice recognition, text processing. Among those application, the face recognition is under intense research due to the demand in users’ identification. This paper proposes a new, secured, two-step solution for an identification system that uses MTCNN and FaceNet networks enhanced with head pose estimation of the users. The model's accuracy ranges from 92% to 95%, which make it competitive with recent research to demonstrate the system's usability

License Plate Recognition Based On Multi-Angle View Model

In the realm of research, the detection/recognition of text within images/videos captured by cameras constitutes a highly challenging problem for researchers. Despite certain advancements achieving high accuracy, current methods still require substantial improvements to be applicable in practical scenarios. Diverging from text detection in images/videos, this paper addresses the issue of text detection within license plates by amalgamating multiple frames of distinct perspectives. For each viewpoint, the proposed method extracts descriptive features characterizing the text components of the license plate, specifically corner points and area. Concretely, we present three viewpoints: view-1, view-2, and view-3, to identify the nearest neighboring components facilitating the restoration of text components from the same license plate line based on estimations of similarity levels and distance metrics. Subsequently, we employ the CnOCR method for text recognition within license plates. Experimental results on the self-collected dataset (PTITPlates), comprising pairs of images in various scenarios, and the publicly available Stanford Cars Dataset, demonstrate the superiority of the proposed method over existing approaches

Determinants of Green Banking Implementation in Emerging Country: Evidence from Vietnam Banks

With the purpose of examining which factors affecting on the implementation of ‘Green Banking’ in transitional countries, the research used in-depth interview and survey questionnaire with statistics analysis with the case of Vietnam banks. The results showed that in emerging country such as Vietnam, the level of ‘Green Banking’ implementation is most affected by Human determinant, followed by Strategy, Product & Services, Culture and Technology determinant respectively. Based on findings, several implications were proposed to enhance the performance of these kind of environment friendly financial products in transitional country. By determining these factors, the banking sector in transitional countries will enhance the implementation of ‘Green Banking’ and achieve sustainable development in the context of escalating environmental contamination. Keywords: Green Banking, Implementation, Emerging Country, Human DOI: 10.7176/EJBM/11-15-04 Publication date:May 31st 201

Fabrication and Magnetic Properties of Mn1 - xZnxFe2O4 (0 ≤ x ≤ 0.8) Nanoparticles

In this paper, we report results on the fabrication and magnetic properties of spinel ferrite Mn1-xZnxFe2O4 (0 ≤ x ≤ 0.8) nanoparticles. The nanoparticles were synthesized by a co-precipitation method. The effects of substituting Zn for Mn on the magnetic properties and particles size were focused. It was found that the phase-formation temperature is 90OC and the average particle size decreases from 40 nm to 10 nm when increased Zn concentration from zero to 0.8. The Curie temperature TC strongly decreases from 585 K (x = 0) to 320 K (x = 0.8) concomitantly with a decrease of the saturation magnetization MS. With a TC of 320 K and MS of 17 emu/g, the x=0.8 sample could be a promising candidate for some biomedical applications

Investigating the Effect of Future Orientation on Saving Intention of Generation Z’s University Students in Vietnam’s Northern Region

Young people are often thought to have a lifestyle focusing on enjoyment, which is why issues related to saving among the youth, especially saving money, receive significant attention from society. This issue has been analyzed in some previous studies; however, there is a new factor that has not been explored in the context of Vietnam, which is future orientation. The purpose of this study is to find out the relationship between future orientation, attitude towards saving and saving intention of generation Z university students in Northern Vietnam. The research method used was the combination of qualitative and quantitative methods. The primary data was collected from 531 respondents who are currently studying at universities in Northern Vietnam and the data was analyzed by using SPSS and AMOS software. The structural equation modeling (SEM) method was used, and the results indicate that both future orientation and attitude towards saving have a positive impact on savings intention. Furthermore, the mediating role of attitude in the relationship between future orientation and saving intention was confirmed. Therefore, the results should be applied by governmental agencies, financial organizations and individuals, especially young people, to encourage saving intention, and furthermore, saving behavior

HỢP CHẤT STEROID VÀ FLAVONE TỪ THÂN RỄ THIÊN NIÊN KIỆN LÁ LỚN (Homalomena pendula)

Phytochemical investigation of the rhizomes of Homalomena pendula resulted in the isolation of one flavone and three steroids. These compounds were determined as tangeretin (1), ergosterol peroxide (2), sitoindoside I (3), and stigmasterol (4) on the basis of 1D and 2D NMR data and in comparison with the available data in the literature. Compounds 1‒3 were found for the first time from the genus Homamomena. The n-hexane and ethyl acetate extracts show NO production inhibitory activity in RAW 264.7 macrophage cells with IC50 values of 46.8 and 75.52 µg·mL–1.Hợp chất flavone, tangeretin (1), và ba hợp chất steroid: ergosterol peroxide (2), sitoindoside I (3) và stigmasterol (4) đã được phân lập từ thân rễ của cây thiên niên kiện lá lớn (Homalomena pendula). Cấu trúc hóa học của chúng được xác định dựa trên phân tích dữ liệu phổ cộng hưởng từ hạt nhân (1D và 2D NMR) và so sánh với các tài liệu đã công bố. Các hợp chất (1-3) được phân lập lần đầu tiên từ chi Homalomena. Cao chiết n-hexane và ethyl acetate của cây này có hoạt tính ức chế sản sinh NO trên đại thực bào RAW 264.7 kích thích bằng lipopolysaccharide với các giá trị IC50 là 46,80 và 75,52 µg·mL–1