Segmented capacitance tomography electrodes: a design and experimental verifications

A segmented capacitance tomography system for real-time imaging of multiphase flows is developed and pre-sented in this work. The earlier research shows that the electrical tomography (ECT) system is applicable in flow visualization (image reconstruction). The acquired concentration profile ob-tained from capacitance measurements able to imaged liquid and gas mixture in pipelines meanwhile the system development is designed to attach on a vessel. The electrode plates which act as the sensor previously has been assembled and fixed on the pipeline, thus it causes obscurity for the production to have any new process installation in the future. Therefore, a segmented electrode sensor offers a new design and idea on ECT system which is portable to be assembled in different diameter sizes of pipeline, and it is flexible to apply in any number due to different size of pipeline without the need of redesigning the sensing module. The new ap-proach of this sensing module contains the integration intelligent electrode sensing circuit on every each of electrode sensors. A microcontroller unit and data acquisition (DAQ) system has been integrated on the electrode sensing circuit and USB technology was applied into the data acquisition system making the sensor able to work independently. Other than that the driven guard that usually placed between adjacent measuring electrodes and earth screen has been embedded on the segmented electrode sensor plates. This eliminates the cable noise and the electrode, so the signal conditioning board can be expanded according to pipe diameter

Vision-Based Control of the Robotenis System

In this paper a visual servoing architecture based on a parallel robot for the tracking of faster moving objects with unknown trajectories is proposed. The control strategy is based on the prediction of the future position and velocity of the moving object. The synthesis of the predictive control law is based on the compensation of the delay introduced by the vision system. Demonstrating by experiments, the high-speed parallel robot system has good performance in the implementation of visual control strategies with high temporary requirement

Dynamic Capture Using a Traplike Soft Gripper With Stiffness Anisotropy

Dynamic capture is a common skill that humans have practiced extensively but is a challenging task for robots in which sensing, planning, and actuation must be tightly coordinated to deal with targets of diverse shapes, sizes, and velocity. In particular, the impact force may cause serious damage to a rigid gripper and even its carrier, e.g., a robotic arm. Existing soft grippers suffer from low speed and force to actively respond to capturing dynamic targets. In this article, we propose a soft gripper capable of efficient capture of dynamic targets, taking inspiration from the biological structures of multitentacled animals or plants. The presented gripper uses a cluster of tentacles to achieve an omnidirectional envelope and high tolerance to dynamic target during the capturing process. In addition, a stiffness anisotropy property is implemented to the tentacle structure to form a “trap” making it easy for the targets to enter yet difficult to escape. We also present an analytical model for the tentacle structure to describe its deformation during the collision with a target. In experiments, we construct a robotic prototype and demonstrate its ability to capture dynamic targets

Nutritional Value extraction of food exploiting computer vision and near infrared Spectrometry

The population growth in the last few decades has led to the development of urban areas, which induced an increased difficulty in finding quality food. The difficulty in finding quality nourishment and a growing offer in the fast-food industry due to the fast pace at which life is lived in big cities has caused increasing obesity and sedentary lifestyle. In 2016 more than 1.9 billion adults aged 18 years and older were overweight[1]. However, this tendency has started to reverse, and with the increasing concern for diseases such as obesity and diabetes, people started return to shopping in farmers mar kets and choosing wisely the locals where they eat, which led to the development of more healthy fast food chains. This new tendency has made new technologies appear that were created to help improve customer choices and facilitate choosing the best food items that have the best quality. This dissertation will analyse the different devices and solutions in the market, such as near-infrared sensors and computer vision. The objective of this dissertation is to build a system that can detect which type of food item we choose and obtain nutritional information. The development begins with researching the different options of small devices that already exist in the market and with which a person can take shopping and assist them by obtaining the nutritional information, such as SCIO or Tellspec. This device cannot detect which type of food is being analysed, so human interaction it is still needed to obtain the best results possible. However, it can return the nutritional information necessary for the first part of this dissertation’s development. Besides being small (palm-handed), these sensors are also cheap and faster compared to equivalent laboratory equipment. The second objective of this dissertation was developed to solve the lack of detection of which type of food is present in the module. To solve this problem and taking into account the objective, it was decided to use computer vision and, more specifically, image recognition and deep machine learning applied in food databases. This dissertation’s main objective is to create a module that can classify and obtain the nutritional information of different types of food. It also serves as a helping hand in the kitchen to control the quality and quantity of the food that the user ingests daily. There will be an exhaustive testing session for the near-infrared sensors using different types of fruits to prove the concept. For the computer vision, it will be applied a deep learning algorithm with supervised training to obtain a high accuracy result

The Development of an assistive chair for elderly with sit to stand problems

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyStanding up from a seated position, known as sit-to-stand (STS) movement, is one of the most frequently performed activities of daily living (ADLs). However, the aging generation are often encountered with STS issues owning to their declined motor functions and sensory capacity for postural control. The motivated is rooted from the contemporary market available STS assistive devices that are lack of genuine interaction with elderly users. Prior to the software implementation, the robot chair platform with integrated sensing footmat is developed with STS biomechanical concerns for the elderly. The work has its main emphasis on recognising the personalised behavioural patterns from the elderly users’ STS movements, namely the STS intentions and personalised STS feature prediction. The former is known as intention recognition while the latter is defined as assistance prediction, both achieved by innovative machine learning techniques. The proposed intention recognition performs well in multiple subjects scenarios with different postures involved thanks to its competence of handling these uncertainties. To the provision of providing the assistance needed by the elderly user, a time series prediction model is presented, aiming to configure the personalised ground reaction force (GRF) curve over time which suggests successful movement. This enables the computation of deficits between the predicted oncoming GRF curve and the personalised one. A multiple steps ahead prediction into the future is also implemented so that the completion time of actuation in reality is taken into account

Robot Control and Computer Vision for Automated Test System on Touch Display Products

The goal of this master thesis is to set up a low cost automated robotic test system which can later be reproduced to greatly increase test coverage. Mostly through experimental research this thesis will find good components to use and evaluate these from a performance and cost perspective. It will also develop computer vision algorithms needed and automation software for the final set up. The performance of the robotics will be investigated by comparing with an industrial robot. A modified 6000 SEK 3D-printer was selected and proved to work. The computer vision was developed using OpenCV and a fully automated system with appropriate resulting plots was created. Comparing with the industrial robot the setup using a 3D-printer proved to work better because it allowed for better positioning of the camera. It was also concluded that the selected robot system based on a 3Dprinter was capable enough and would drastically lower the space and cost from a system using an industrial robot

Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes

Current efficient LiDAR-based detection frameworks are lacking in exploitingobject relations, which naturally present in both spatial and temporal manners.To this end, we introduce a simple, efficient, and effective two-stagedetector, termed as Ret3D. At the core of Ret3D is the utilization of novelintra-frame and inter-frame relation modules to capture the spatial andtemporal relations accordingly. More Specifically, intra-frame relation module(IntraRM) encapsulates the intra-frame objects into a sparse graph and thusallows us to refine the object features through efficient message passing. Onthe other hand, inter-frame relation module (InterRM) densely connects eachobject in its corresponding tracked sequences dynamically, and leverages suchtemporal information to further enhance its representations efficiently througha lightweight transformer network. We instantiate our novel designs of IntraRMand InterRM with general center-based or anchor-based detectors and evaluatethem on Waymo Open Dataset (WOD). With negligible extra overhead, Ret3Dachieves the state-of-the-art performance, being 5.5% and 3.2% higher than therecent competitor in terms of the LEVEL 1 and LEVEL 2 mAPH metrics on vehicledetection, respectively.<br