title: Nanoswimmer-oriented Direct Targeting Strategy Inspired by Momentum-based Gradient Optimization

authors: Ali, Muhammad

abstract: This paper considers the advantage of knowledgeaided direct targeting strategy (DTS) over systemic targeting for tumor homing in complex human vasculature. Tumor location in the body can be estimated by closely observing the tumortriggered bio-physical gradients in its vicinity, helping drugloaded magnetic nanoswimmers to accumulate at the disease location. These nanoswimmers are assembled by magnetic nanoparticles (MNPs), which act as contrast agents increasing the diagnostic capability of different medical imaging techniques. We propose a novel DTS inspired by the iterative gradient descent (GD) with momentum optimization for tumor targeting amplification. We show by computational experiments that the MNPs accumulate at the disease location at a faster rate and the probability of tumor detection is higher for the proposed knowledge-aided DTS as compared to the knowledgeless systemic targeting. We believe that our work for tumor targeting amplification based on such nanosystem will open new horizons in the field of diagnosing tumor at its early stage.

text: Nanoswimmer-oriented Direct Targeting Strategy Inspired by Momentum-based Gradient Optimization This paper considers the advantage of knowledgeaided direct targeting strategy (DTS) over systemic targeting for tumor homing in complex human vasculature. Tumor location in the body can be estimated by closely observing the tumortriggered bio-physical gradients in its vicinity, helping drugloaded magnetic nanoswimmers to accumulate at the disease location. These nanoswimmers are assembled by magnetic nanoparticles (MNPs), which act as contrast agents increasing the diagnostic capability of different medical imaging techniques. We propose a novel DTS inspired by the iterative gradient descent (GD) with momentum optimization for tumor targeting amplification. We show by computational experiments that the MNPs accumulate at the disease location at a faster rate and the probability of tumor detection is higher for the proposed knowledge-aided DTS as compared to the knowledgeless systemic targeting. We believe that our work for tumor targeting amplification based on such nanosystem will open new horizons in the field of diagnosing tumor at its early stage.

year: 2019

title: Identification and manipulation of lily bulbs for an automated lily bulb planting system

authors: Venter, Gerhard Meyer

subjects: Agriculture Automation

abstract: Automation in agriculture is growing year by year. The goal of automating processes is to provide inexpensive and more effective solutions for everyday problems present in the industry. Automation in agriculture adds value to the product and in turn, to the farmer's infrastructure. This automation also aims to provide higher skill labour for workers that the automation processes substitute.  Using machine vision as a means of automating processes is very common in factory environments and is being adapted for the external agriculture environments (i.e. automated detection for produce harvesting).

Machine vision and manipulation techniques for a lily bulb plantation were presented. The techniques were investigated to determine the feasibility of using an autonomous, machine vision based approach to manipulate and plant lily bulbs from a provided source, to pre-augered holes produced by a pre-defined autonomous platform.

The machine vision approach involved taking a top down image of the bulbs and identifying the head positions and what orientation they were facing relative to their root structures. This was achieved using various standard machine vision techniques like segmenting using global thresholding and identification of heads using the Hough circular transform. The investigated manipulation method involved applying the above mentioned vision system to a standard ABB IRB-120 universal manipulator with a three bellow suction gripper to pick up the detected bulbs and manipulate the bulbs in the orientation perceived by the vision system.

It was found that the machine vision algorithm provided a 75 per cent success rate when providing an optimal region of interest within the bulbs head. The success rate is a considerably successful result as the detection algorithm not only needed to detect the location of the bulbs, but the centroid of its head and also determine the approximate orientation relative to each samples individual root structure. The manipulation results showed that the engagement of the suction gripper was a significant component of failure during testing. The observed success rate was at 41 per cent. This high failure rate means that further improvements should be made before a successful end effector and manipulation pair would be achieved. Improving suction rate or developing a specialized gripper for the specific amorphous bulbs would have to be investigated further before there is confirmation of a satisfactory solution for the Automated Lily Planter. Further work could be done to improve the algorithm and fine-tune the output provided. Improvements could be made to optimise the detection algorithm like improved lighting and better contrast between the bulbs colour gradient and that of the platform's background. Further development on the manipulators approach should also be conducted for validation.

text: Identification and manipulation of lily bulbs for an automated lily bulb planting system Automation in agriculture is growing year by year. The goal of automating processes is to provide inexpensive and more effective solutions for everyday problems present in the industry. Automation in agriculture adds value to the product and in turn, to the farmer's infrastructure. This automation also aims to provide higher skill labour for workers that the automation processes substitute.  Using machine vision as a means of automating processes is very common in factory environments and is being adapted for the external agriculture environments (i.e. automated detection for produce harvesting).

Machine vision and manipulation techniques for a lily bulb plantation were presented. The techniques were investigated to determine the feasibility of using an autonomous, machine vision based approach to manipulate and plant lily bulbs from a provided source, to pre-augered holes produced by a pre-defined autonomous platform.

The machine vision approach involved taking a top down image of the bulbs and identifying the head positions and what orientation they were facing relative to their root structures. This was achieved using various standard machine vision techniques like segmenting using global thresholding and identification of heads using the Hough circular transform. The investigated manipulation method involved applying the above mentioned vision system to a standard ABB IRB-120 universal manipulator with a three bellow suction gripper to pick up the detected bulbs and manipulate the bulbs in the orientation perceived by the vision system.

It was found that the machine vision algorithm provided a 75 per cent success rate when providing an optimal region of interest within the bulbs head. The success rate is a considerably successful result as the detection algorithm not only needed to detect the location of the bulbs, but the centroid of its head and also determine the approximate orientation relative to each samples individual root structure. The manipulation results showed that the engagement of the suction gripper was a significant component of failure during testing. The observed success rate was at 41 per cent. This high failure rate means that further improvements should be made before a successful end effector and manipulation pair would be achieved. Improving suction rate or developing a specialized gripper for the specific amorphous bulbs would have to be investigated further before there is confirmation of a satisfactory solution for the Automated Lily Planter. Further work could be done to improve the algorithm and fine-tune the output provided. Improvements could be made to optimise the detection algorithm like improved lighting and better contrast between the bulbs colour gradient and that of the platform's background. Further development on the manipulators approach should also be conducted for validation.

year: 2019

title: Racism and intersections with health services

authors: Cormack, Donna

abstract: WERO webinar hosted by Māori and Indigenous Analysis Ltd & AATEA Solutions.

text: Racism and intersections with health services WERO webinar hosted by Māori and Indigenous Analysis Ltd & AATEA Solutions.

year: 2025-10-07