Automated Evaluation of Surface Roughness using Machine Vision based Intelligent Systems

Machine vision systems play a vital role in entirely automating the evaluation of surface roughness due to the hitches in the conformist system. Machine vision systems significantly abridged the ideal time and human errors for evaluation of the surface roughness in a nondestructive way. In this work, face milling operations are performed on aluminum and a total of 60 diverse cutting experiments are conducted. Surface images of machined components are captured for the development of machine vision systems. Images captured are processed for texture features namely RGB (Red Green Blue), GLCM (Grey Level Co-occurrence Matrix) and an advanced wavelet known as curvelet transforms. Curvelet transforms are developed to study the curved textured lines present in the captured images and this module is capable to unite the discontinuous curved lines present in images. The CNC machined components consists of visible lay patterns in the curved form, so this novel machine vision technique is developed to identify the texture well over the other two extensively researched methods. Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) intelligent models are developed to evaluate the surface roughness from texture features. The model average error attained using RGB, GLCM, Curvelet transform-based machine vision systems are 12.68, 7.8 and 3.57 respectively. In comparison, the results proved that computer vision system based on curvelet transforms outperformed the other two existing systems. This curvelet based machine vision system can be used for the evaluation of surface roughness. Here, image processing might be crucial in identifying certain information. One crucial issue is that, even as performance improves, cameras continue to get smaller and more affordable. The possibility for new applications in Industry 4.0 is made possible by this technological advancement and the promise of ever-expanding networking

Transition metal saccharide chemistry and biology: syntheses, characterization, solution stability and putative bio-relevant studies of iron-saccharide complexes

A number of Fe(III) complexes of saccharides and their derivatives, and those of ascorbic acid were synthesized, and characterized by a variety of analytical, spectral (FT-IR, UV-Vis, EPR, Mossbauer and EXAFS), magnetic and electrochemical techniques. Results obtained from various methods have shown good correlations. Data obtained from EPR, magnetic susceptibility and EXAFS techniques could be fitted well with the mono-, di- and trinuclear nature of the complexes. The solution stability of these complexes has been established using UV-Vis absorption and cyclic voltammetric techniques as a function of pH of the solution. Mixed valent, Fe(II,III) ascorbate complexes have also been synthesized and characterized. Reductive release of Fe(II) from the complexes using sodium dithionite has been addressed. In vitro absorption of Fe(III)-glucose complex has been studied using everted sacs of rat intestines and the results have been compared with that of simple ferric chloride. Fe(III)-saccharide complexes have shown regular protein synthesis even in hemin-deficient rabbit reticulocyte lysate indicating that these complexes play a role that is equivalent to that played by hemin in order to restore the normal synthesis of protein. These complexes have exhibited enhanced DNA cleavage properties in the presence of hydrogen peroxide with pUC-18 DNA plasmid

A community health worker-led program to improve access to gestational diabetes screening in urban slums of Pune, India: Results from a mixed methods study

The World Health Organization recommends all pregnant women receive screening for gestational diabetes (GDM) with a fasting oral glucose tolerance test (OGTT). However, very few women receive recommended screening in resource-limited countries like India. We implemented a community health worker (CHW)-delivered program to evaluate if home-based, CHW-delivered OGTT would increase GDM screening in a low-resource setting. We conducted a mixed methods study in two urban slum communities in Pune, India. CHWs were trained to deliver home-based, point-of-care fasting OGTT to women in their third trimester of pregnancy. The primary outcome was uptake of CHW-delivered OGTT. Secondary outcomes included GDM prevalence and linkage to GDM care. Individual interviews were conducted with purposively sampled pregnant women, CHWs, and local clinicians to assess barriers and facilitators of this approach. From October 2021-June 2022, 248 eligible pregnant women were identified. Of these, 223 (90%) accepted CHW-delivered OGTT and 31 (14%) were diagnosed with GDM. Thirty (97%) women diagnosed with GDM subsequently sought GDM care; only 10 (33%) received lifestyle counseling or pharmacologic therapy. Qualitative interviews indicated that CHW-delivered testing was considered highly acceptable as home-based testing saved time and was more convenient than clinic-based testing. Inconsistent clinical management of GDM was attributed to providers’ lack of time to deliver counseling, and perceptions that low-income populations are not at risk for GDM. Convenience and trust in a CHW-delivered GDM screening program resulted in high access to gold-standard OGTT screening and identification of a high GDM prevalence among pregnant women in two urban slum communities. Appropriate linkage to care was limited by clinician time constraints and misperceptions of GDM risk. CHW-delivered GDM screening and counseling may improve health education and access to preventive healthcare, offloading busy public clinics in high-need, low-resource settings

Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution

[No abstract available

A Two-stage Flow-based Intrusion Detection Model ForNext-generation Networks

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results

Automated Evaluation of Surface Roughness using Machine Vision based Intelligent Systems

11-25Machine vision systems play a vital role in entirely automating the evaluation of surface roughness due to the hitches in the conformist system. Machine vision systems significantly abridged the ideal time and human errors for evaluation of the surface roughness in a nondestructive way. In this work, face milling operations are performed on aluminum and a total of 60 diverse cutting experiments are conducted. Surface images of machined components are captured for the development of machine vision systems. Images captured are processed for texture features namely RGB (Red Green Blue), GLCM (Grey Level Co-occurrence Matrix) and an advanced wavelet known as curvelet transforms. Curvelet transforms are developed to study the curved textured lines present in the captured images and this module is capable to unite the discontinuous curved lines present in images. The CNC machined components consists of visible lay patterns in the curved form, so this novel machine vision technique is developed to identify the texture well over the other two extensively researched methods. Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) intelligent models are developed to evaluate the surface roughness from texture features. The model average error attained using RGB, GLCM, Curvelet transform-based machine vision systems are 12.68, 7.8 and 3.57 respectively. In comparison, the results proved that computer vision system based on curvelet transforms outperformed the other two existing systems. This curvelet based machine vision system can be used for the evaluation of surface roughness. Here, image processing might be crucial in identifying certain information. One crucial issue is that, even as performance improves, cameras continue to get smaller and more affordable. The possibility for new applications in Industry 4.0 is made possible by this technological advancement and the promise of everexpanding networking

On the Feasibility of the Link Abstraction in Wireless Mesh Networks

Outdoor community mesh networks based on IEEE 802.11 have seen tremendous growth in the recent past. The current understanding is that wireless link performance in these settings is inherently unpredictable, due to multipath delay spread. Consequently, researchers have focused on developing intelligent routing techniques to achieve the best possible performance. In this paper, we are specifically interested in mesh networks in rural locations. We first present detailed measurements to show that the PHY layer in these settings is indeed stable and predictable. There is a strong correlation between the error rate and the received signal strength. We show that interference, and not multipath fading, is the primary cause of unpredictable performance. This is in sharp contrast with current widespread knowledge from prior studies. Furthermore, we corroborate our view with a fresh analysis of data presented in these prior studies. While our initial measurements focus on 802.11b, we then use two different PRY technologies as well, operating in the 2.4-GHz ISM band: 802.11g and 802.15.4. These show similar results too. Based on our results, we argue that outdoor rural mesh networks can indeed be built with the link abstraction being valid. This has several design implications, including at the MAC and routing layers, and opens up a fresh perspective on a wide range of technical issues in this domain