Commercialisation of precision agriculture technologies in the macadamia industry

A prototype vision-based yield monitor has been developed for the macadamia industry. The system estimates yield for individual trees by detecting nuts and their harvested location. The technology was developed by the National Centre for Engineering in Agriculture, University of Southern Queensland for the purpose of reducing labour and costs in varietal assessment trials where yield for individual trees are required to be measured to indicate tree performance. The project was commissioned by Horticulture Australia Limited

A Depth-Based Computer Vision Approach to Unmanned Aircraft System Landing with Optimal Positioning

High traffic congestion in cities can lead to difficulties in delivering appropriate aid to people in need of emergency services. Developing an autonomous aerial medical evacuation system with the required size to facilitate the need can allow for the mitigation of the constraint. The aerial system must be capable of vertical takeoff and landing to reach highly conjected areas and areas where traditional aircraft cannot access. In general, the most challenging limitation within any proposed solution is the landing sequence. There have been several techniques developed over the years to land aircraft autonomously; however, very little attention has been scoped to operate strictly within highly congested urban-type environments. The goal of this research is to develop a possible solution to achieve autonomous landing based on computer vision-capture systems. For example, by utilizing modern computer vision approaches involving depth estimation through binocular stereo computer vision, a depth map can be developed. If the vision system is mounted to the bottom of an autonomous aerial system, it can represent the area below the aircraft and determine a possible landing zone. In this work, neural networks are used to isolate the ground via the computer vision height map. Then out of the entire visible ground area, a potential landing position can be estimated. An optimization routine is then developed to identify the most optimal landing position within the visible area. The optimization routine identifies the largest identifiable open area near the desired landing location. Web cameras were utilized and processed on a desktop to form a basis for the computer vision system. The algorithms were tested and verified using a simulation effort proving the feasibility of the approach. In addition, the system was tested on a scaled down city scene and was able to determine an optimal landing zone

An architecture for distributed ledger-based M2M auditing for Electric Autonomous Vehicles

Electric Autonomous Vehicles (EAVs) promise to be an effective way to solve transportation issues such as accidents, emissions and congestion, and aim at establishing the foundation of Machine-to-Machine (M2M) economy. For this to be possible, the market should be able to offer appropriate charging services without involving humans. The state-of-the-art mechanisms of charging and billing do not meet this requirement, and often impose service fees for value transactions that may also endanger users and their location privacy. This paper aims at filling this gap and envisions a new charging architecture and a billing framework for EAV which would enable M2M transactions via the use of Distributed Ledger Technology (DLT)

Automated assembly of large space structures using an expert system executive

NASA LaRC has developed a unique testbed for investigating the practical problems associated with the assembly of large space structures using robotic manipulators. The testbed is an interdisciplinary effort which considers the full spectrum of assembly problems from the design of mechanisms to the development of software. This paper will describe the automated structures assembly testbed and its operation, detail the expert system executive and its development, and discuss the planned system evolution. Emphasis will be placed on the expert system development of the program executive. The executive program must be capable of directing and reliably performing complex assembly tasks with the flexibility to recover from realistic system errors. By employing an expert system, information pertaining to the operation of the system was encapsulated concisely within a knowledge base. This lead to a substantial reduction in code, increased flexibility, eased software upgrades, and realized a savings in software maintenance costs

Impact of end effector technology on telemanipulation performance

Generic requirements for end effector design are briefly summarized as derived from generic functional and operational requirements. Included is a brief summary of terms and definitions related to end effector technology. The second part contains a brief overview of end effector technology work as JPL during the past ten years, with emphasis on the evolution of new mechanical, sensing and control capabilities of end effectors. The third and major part is devoted to the description of current end effector technology. The ongoing work addresses mechanical, sensing and control details with emphasis on mechanical ruggedness, increased resolution in sensing, and close electronic and control integration with overall telemanipulator control system

Bayesian graphical models for indoor localization in MTC deployment scenarios

Abstract. Herein, we propose and assess an iterative Bayesian-based indoor localization system to estimate the position of a target device. We describe the Bayesian network and then build graphical models for various measurement metrics, namely Received Signal Strength (RSS), Time Difference of Arrival (TDOA), and Angle of Arrival (AOA) which are collected by the distributed receivers in the network area. The estimations are carried out by Markov chain Monte Carlo (MCMC) methods which approximates the target’s position using the Bayesian network model and measurements collected by the receivers. We employ an iterative method by using previous estimations of the target’s position as prior knowledge to improve the accuracy of the subsequent estimations, where the prior knowledge is used as the prior distributions of our Bayesian model. In our results, we observe that the proposed iterative localization system improves the performance of the Bayesian TDOA-based localization system by increasing the respective estimate accuracy. Furthermore, we show that the number of measurements collected by the receivers and the selected prior distribution also affect the performance of the proposed iterative mechanism. In fact, the number of measurements increases the accuracy of the mechanism, while its benefit diminishes with more iterations as the mechanism progresses. Regarding the prior distribution, we show that it can lead to good or bad estimations of the target’s position, and therefore, needs to be carefully chosen considering the measurement metric and the mobility of the target node

Slit Lamp Ophthalmoscope Redesign

The slit lamp ophthalmoscope is used by ophthalmologists for diagnosis of pathologies in the eye. The device is used for identifying ocular diseases or indications of other possible systemic diseases to treat them before the diseases progress. Redesign of the ergonomics and adjustment mechanisms of the slit lamp ophthalmoscope will attempt to improve the compatibility of the device across a greater variety of patients. The wider headrest tower of 12.75 inches, the wider table cutout of 12 inches, and the 12 inches of extension of the headrest tower creates a better ergonomic fit for the patient and doctor. The wider headrest tower will decrease the proximity of the doctor’s hand when adjusting the device and give more space to the patient, while the wider table cutout and extension will allow the patient to sit comfortably for longer periods than the previous device. For a quality diagnosis, correct positioning of the ophthalmoscope is required. Deflection tests were done to ensure position of the headrest tower was not compromised during loading. All p-values were less than 0.001 meaning the device can withstand loading of a patient\u27s head without a change in eye position in the microscope view. The ergonomic capability was also tested with the headrest tower to make sure sufficient space was available for the patient to measure our specification for greater comfort in the device. The patient needs to be very still without moving their head position, which is why patient comfort is essential for a quality examination by an ophthalmologis

A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis.

Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%