Online tree reconstruction and forest inventory on a mobile robotic system

Terrestrial laser scanning (TLS) is the standard technique used to create accurate point clouds for digital forest inventories. However, the measurement process is demanding, requiring up to two days per hectare for data collection, significant data storage, as well as resource-heavy post-processing of 3D data. In this work, we present a real-time mapping and analysis system that enables online generation of forest inventories using mobile laser scanners that can be mounted e.g. on mobile robots. Given incrementally created and locally accurate submaps—data payloads—our approach extracts tree candidates using a custom, Voronoi-inspired clustering algorithm. Tree candidates are reconstructed using an algorithm based on the Hough transform, which enables robust modeling of the tree stem. Further, we explicitly incorporate the incremental nature of the data collection by consistently updating the database using a pose graph LiDAR SLAM system. This enables us to refine our estimates of the tree traits if an area is revisited later during a mission. We demonstrate competitive accuracy to TLS or manual measurements using laser scanners that we mounted on backpacks or mobile robots operating in conifer, broad-leaf and mixed forests. Our results achieve RMSE of 1.93 cm, a bias of 0.65 cm and a standard deviation of 1.81 cm (averaged across these sequences)—with no post-processing required after the mission is complete

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

DigiForests: a longitudinal LIDAR dataset for forestry robotics

Forests are vital to our ecosystems, acting as carbon sinks, climate stabilizers, biodiversity centers, and wood sources. Due to their scale, monitoring and managing forests takes a lot of work. Forestry robotics offers the potential for enabling efficient and sustainable foresting practices through automation. Despite increasing interest in this field, the scarcity of robotics datasets and benchmarks in forest environments is hampering progress in this domain. In this paper, we present a real-world, longitudinal dataset for forestry robotics that enables the development and comparison of approaches for various relevant applications, ranging from semantic interpretation to estimating traits relevant to forestry management. The dataset consists of multiple recordings of the same plots in a forest in Switzerland during three different growth periods. We recorded the data with a mobile 3D LiDAR scanning setup. Additionally, we provide semantic annotations of trees, shrubs, and ground, instance-level annotations of trees, as well as more fine-grained annotations of tree stems and crowns. Furthermore, we provide reference field measurements of traits relevant to forestry management for a subset of the trees. Together with the data, we also provide open-source baseline panoptic segmentation and tree trait estimation approaches to enable the community to bootstrap further research and simplify comparisons in this domain

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