A Framework for Controlling Wheelchair Motion by using Gaze Information

Users with severe motor ability are unable to control their wheelchair using standard joystick and hence an alternative control input is preferred. In this paper a method on how to enable the severe impairment user to control a wheelchair via gaze information is proposed. Since when using such an input, the navigation burden for the user is significantly increased, an assistive navigation platform is also proposed to reduce the user burden. Initially, user information is inferred using a camera and a bite-like switch. Then information from the environment is obtained using combination of laser and Kinect sensors. Eventually, both information from the environment and the user is analyzed to decide the final control operation that according to the user intention and safe from collision. Experimental results demonstrate the feasibility of the proposed approach

Robot creativity: humanlike behaviour in the robot-robot interaction

Artificial Intelligence development is mainly directed toward imitat­ing human reasoning and performing different tasks. For that purpose, related software and program solution where artificial intelligence is used have mostly thinking abilities. However, there are many questions to answer in ongoing AI research, especially when we come to the point which is addressing humanlike behaviour and reasoning triggered by emotions. In this paper, we are presenting an interactive installation Botorikko: Machine Create State, which is part of the Syntropic Counterpoints art/research project. We are exposing AI cyber clones to some of the fundamental questions for humankind and challenge their creativity. The robots are trained by using the publications Machiavelli and Sun Tzu and confronted to the crucial questions related to moral, ethic, strategy, politics, diplo­macy, war etc. We are using a recurrent neural network (RNN) and robot-robot interaction to trigger unsupervised robot creativity and humanlike behaviour on generated machine-made content

Non-contact Heart Rate Monitoring Analysis from Various Distances with different Face Regions

Heart rate (HR) is one of vital biomedical signals for medical diagnosis. Previously, conventional camera is proven to be able to detect small changes in the skin due to the cardiac activity and can be used to measure the HR. However, most of the previous systems operate on near distance mode with a single face patch, thus the feasibility of the remote heart rate for various distances remains vague. This paper tackles this issue by analyzing an optimal framework that capable to works under the mentioned issues. Initially, plausible face landmarks are estimated by employing cascaded of regression mechanism. Next, the region of interest (ROI) was constructed from the landmarks in a face location where non rigid motion is minimal. From the ROI, temporal photoplethysmograph (PPG) signal is calculated based on the average green pixels intensity and environmental illumination is separated using Independent Component Analysis (ICA) filter. Eventually, the PPG signal is further processed using series of temporal filter to exclude frequencies outside the range of interest prior to estimate the HR. As a conclusion, the HR can be detected up to 5 meters range with 94% accuracy using lower part of face region

A Hybrid Approach for Counting Templates in Images

© 2020 ACM. In the research, hybrid algorithm for counting repeated objects in the image is proposed. Proposed algorithm consists of two parts. Template matching sub-algorithm is based on normalized cross correlation function which is widely used in image processing application. Template matching can be used to recognize and/or locate specific objects in an image. Neural network sub-algorithm is needed to filter out false positives that may occur during cross correlation function evaluation. In the last section of the paper experimental evaluation is carried out to estimate the performance of the proposed template matching algorithm for images of blood microscopy and chamomile field image. In the first case, the task is to count erythrocytes in the blood sample. In the second case, it is needed to count the flowers in the field. For all 2 datasets we got precise results that coincides with actual number of objects in image. The reason of such performance is that convolutional neural network sub-algorithm improved initial results of template-matching sub-algorithm based on correlation function

Implementation of Robot Operating System in Beaglebone Black based Mobile Robot for Obstacle Avoidance Application

The Robot Operating System (ROS) is a collection of tools, libraries, and conventions that focus on simplifying the task of creating a complex and advanced robotics system. Its standard framework can be shared with another robotics system that has a similar platform and suitable for being introduced as an educational tool in robotics. However, the problems found out in the current robot platform available in the market are expensive and encapsulated. The development of an open source robot platform is encouraged. Therefore, this research is carried out to design and develop an ROS based obstacle avoidance system for existing differential-wheeled mobile robot. The ROS was installed under Ubuntu 14.04 on a Beaglebone Black embedded computer system. Then, the ROS was implemented together with the obstacle avoidance system to establish the communication between program nodes. The mobile robot was then designed and developed to examine the obstacle avoidance application. The debugging process was carried out to check the obstacle avoidance system application based on the communication between nodes. This process is important in examining the message publishing and subscribing from all nodes. The obstacle avoidance mobile robot has been successfully tested where the communication between nodes was running without any problem

Deep transfer learning application for automated ischemic classification in posterior fossa CT images

Abstract—Computed Tomography (CT) imaging is one of the conventional tools used to diagnose ischemic in Posterior Fossa (PF). Radiologist commonly diagnoses ischemic in PF through CT imaging manually. However, such a procedure could be strenuous and time consuming for large scale images, depending on the expertise and ischemic visibility. With the rapid development of computer technology, automatic image classification based on Machine Learning (ML) is widely been developed as a second opinion to the ischemic diagnosis. The practical performance of ML is challenged by the emergence of deep learning applications in healthcare. In this study, we evaluate the performance of deep transfer learning models of Convolutional Neural Network (CNN); VGG-16, GoogleNet and ResNet-50 to classify the normal and abnormal (ischemic) brain CT images of PF. This is the first study that intensively studies the application of deep transfer learning for automated ischemic classification in the posterior part of brain CT images. The experimental results show that ResNet-50 is capable to achieve the highest accuracy performance in comparison to other proposed models. Overall, this automatic classification provides a convenient and time-saving tool for improving medical diagnosis

Development of Human Fall Detection System using Joint Height, Joint Velocity, and Joint Position from Depth Maps

Human falls are a major health concern in many communities in today’s aging population. There are different approaches used in developing fall detection system such as some sort of wearable, ambient sensor and vision based systems. This paper proposes a vision based human fall detection system using Kinect for Windows. The generated depth stream from the sensor is used in the proposed algorithm to differentiate human fall from other activities based on human Joint height, joint velocity and joint positions. From the experimental results our system was able to achieve an average accuracy of 96.55% with a sensitivity of 100% and specificity of 95

Design and Simulation Study of Excitation Coil System with Different Array Configurations for Magnetic Particle Imaging Application

Magnetic Particle Imaging (MPI) is a tomographic imaging method has been introduced for three-dimensional (3D) imaging of human body with some potential applications such as magnetic hyperthermia and cancer imaging. It involves three important elements; tracer development using magnetic nanoparticles (MNPs), hardware realization (scanner using excitation and pickup coils), and image reconstruction optimization. Their combination will produce a high-quality image taken from any biological tissue in the human body based on the secondary magnetic field signal from the magnetized MNPs that are injected into human body. A homogeneous and adequate magnetic field strength from an excitation coil is needed to enhance the quality of the secondary signal. However, the complex surface topography of human body and physical properties of an excitation coil influence the strength and the homogeneity of the magnetic field generation at the MNPs. Therefore, this paper presents a new concept of excitation coil configuration to improve the magnetic field strength and the homogeneity to obtain better magnetization of MNPs to be detected in MPI. Two designs will be proposed with variation in physical properties and coil arrangement based on simulation study that will be carried out by using ANSYS Maxwell to generate magnetic field strength and homogeneity towards the targeted distance of 10 mm – 50 mm below the coils. The obtained magnetic field from the simulation was validated by the mathematical calculation using Biot-Savart Law equation. As a result, the new concept of excitation coil configuration proposed can be used to improve the MPI scanner system performance for various medical application

Stabilization of inverted pendulum system using discrete sliding mode control

This paper presents the development of Discrete Sliding Mode Control (DSMC) to control an inverted pendulum systems. The mathematical model of inverted pendulum system is linearized using Taylor expansion method. The linear sliding surface was used to design the DSMC using equivalent method. The system is designed with additional matched external disturbance to validate the robustness of the controller. The findings demonstrated that the proposed controller’s capable to track the reference tracking and provide better response compared to Discrete Linear Quadratic Regulator (DLQR