Sunita Chauhan

Vision-based force measurement using neural networks for biological cell microinjection.

This paper presents a vision-based force measurement method using an artificial neural network model. The proposed model is used for measuring the applied load to a spherical biological cell during micromanipulation process. The devised vision-based method is most useful when force measurement capability is required, but it is very challenging or even infeasible to use a force sensor. Artificial neural networks in conjunction with image processing techniques have been used to estimate the applied load to a cell. A bio-micromanipulation system capable of force measurement has also been established in order to collect the training data required for the proposed neural network model. The geometric characterization of zebrafish embryos membranes has been performed during the penetration of the micropipette prior to piercing. The geometric features are extracted from images using image processing techniques. These features have been used to describe the shape and quantify the deformation of the cell at different indentation depths. The neural network is trained by taking the visual data as the input and the measured corresponding force as the output. Once the neural network is trained with sufficient number of data, it can be used as a precise sensor in bio-micromanipulation setups. However, the proposed neural network model is applicable for indentation of any other spherical elastic object. The results demonstrate the capability of the proposed method. The outcomes of this study could be useful for measuring force in biological cell micromanipulation processes such as injection of the mouse oocyte/embryo.

Vision-based robot-assisted biological cell micromanipulation

This paper presents a modular design for a robot-assisted biological cell microinjection system. The proposed design is composed of injection, vision, force measurement and control units that provides sufficient flexibility to observe and control cell microinjection by monitoring and regulating position and force simultaneously. Methodologies have been presented for automation of the laborious tasks associated with microinjection to improve the repeatability and reliability of the process. The system is capable of automatic positioning and focusing of the microcapillary tip as well as automatic realization of the cell piercing during the microinjection process with vision-based approaches. The proposed methods were tested for 100 zebrafish embryos micromanipulation experiments at Blastula stage. 97% success rate was achieved which shows high capability of the proposed design and methods.

Comparision of PID controller & adaptive neuro fuzzy controller for robot manipulator

This paper has been proposed to deal with controlling of robot manipulator. It utilizes method of Adaptive Neuro Fuzzy controlling to control position and trajectory tracking of robotic arm with three degree of freedom. This technique also handles the problems like tuning and uncertainties of system associated with proportional integral differential (PID) controller. It has ability to learn the multi processes related to the each of the links and produces accurate and desire controlling.

Ultrasound-based liver tracking utilizing a hybrid template/optical flow approach

PurposeWith the ongoing shift toward reduced invasiveness in many surgical procedures, methods for tracking moving targets within the body become vital. Non-invasive treatment methods such as stereotactic radiation therapy and high intensity focused ultrasound, in particular, rely on the accurate localization of targets throughout treatment to ensure optimal treatment provision. This work aims at developing a robust, accurate and fast method for target tracking based on ultrasound images.MethodsA method for tracking of targets in real-time ultrasound image data was developed, based on the combination of template matching, dense optical flow and image intensity information. A weighting map is generated from each of these approaches which are then normalized, weighted and combined, with the weighted mean position then calculated to predict the current position. The approach was evaluated on the Challenge for Liver Ultrasound Tracking 2015 dataset, consisting of a total of 24 training and 39 test datasets with a total of 53 and 85 annotated targets throughout the liver, respectively.ResultsThe proposed method was implemented in MATLAB and achieved an accuracy of

Modelling and analysis of breathing-induced lung motion in swimming performance

0.80\pm 0.80

Quantitative Study on the Effect of Abnormalities on Respiration-Induced Kidney Movement

0.80±0.80 (95%: 1.91) mm and

Tracking control of inverted pendulum on a cart with disturbance using pole placement and LQR

0.74\pm 1.03