Mohammed Hayyan Alsibai

Bio: Mohammed Hayyan Alsibai is an academic researcher from Universiti Malaysia Pahang. The author has contributed to research in topics: Camera resectioning & Robotic arm. The author has an hindex of 5, co-authored 16 publications receiving 81 citations. Previous affiliations of Mohammed Hayyan Alsibai include University of Tsukuba & International University, Cambodia.

Design and Implementation of IoT-Based Automation System for Smart Home

Abstract: Home Automation System (HAS) gains popularity due to communication technology advancement. Smart home is one of the Internet of Things (IoT) applications that facilitates the control of home appliances over the Internet using automation system. This paper proposes a low-cost Wi-Fi based automation system for Smart Home (SH) in order to monitor and control home appliances remotely using Android-based application. An Arduino Mega microcontroller provided with Wi-Fi module is utilized to build the automation system. In addition, several sensors are used to monitor the temperature, humidity and motion in home. A relay board is exploited to connect the HAS with home under controlled appliances. The proposed automation system, can easily and efficiently control the electrical appliances via Wi-Fi and Virtuino mobile application.

Medical data analysis based on Nao robot: An automated approach towards robotic real-time interaction with human body

Abstract: There is a significant increase of strokes, heart diseases and premature death, people need more than ever to be aware of their vital signs such as blood pressure, heart beats, cholesterol level etc Monitoring and analysing this medical data can help increase the awareness of the risk factor of heart disease However, there is a huge pressure on medical staff and general practitioners (GPs), therefore this research proposes a medical data analysis based on Nao robots to meet these needs and it will serve as an automated approach towards a robotics real-time interaction with the human body The proposed research offers a new way to allow users to understand the meaning of their vital signs using a human robot interaction The developed system has been tested on publicly available data and simulated data It can predict the future risk of heart disease based on some data attributes Based on the risk prediction, it can feedback the result and the required lifestyle changes to avoid any related risk

Grasping and Positioning Tasks for Selective Compliant Articulated Robotic Arm Using Object Detection and Localization: Preliminary Results

Abstract: Vision guided robots have more ability, functionality and adaptivity in industrial assembly lines than normal robots. This research attempts to increase the impact of computer vision on robotic positioning and grasping applications. Therefore, we addressed object detection and localization to perform robotic grasping and positioning using Selective Compliant Assembly Robot Arm (SCARA). The target position of SCARA robot is determined based on information obtained from object detection and position measurement process. This process is implemented on a circular object to simplify the task. For accurate position measurement, the distortion of camera lens is removed using camera calibration technique. In object detection, several methods are compared to detect circular holes in an input image. The most successful methods with 100% Precision, Recall and F-measure are used to detect the circular object. The position of this object is measured in world coordinate unit for pick-and-place operation. Then, the experiment is designed to move SCARA robot to the measured position of the detected circular object. The result showed that the robot is successfully moved to the measured position of the detected object with average positioning error (0.314, 0.155) mm.

Real-Time Robotic Grasping and Localization Using Deep Learning-Based Object Detection Technique

Abstract: This work aims to increase the impact of computer vision on robotic positioning and grasping in industrial assembly lines. Real-time object detection and localization problem is addressed for robotic grasp-and-place operation using Selective Compliant Assembly Robot Arm (SCARA). The movement of SCARA robot is guided by deep learning-based object detection for grasp task and edge detection-based position measurement for place task. Deep Convolutional Neural Network (CNN) model, called KSSnet, is developed for object detection based on CNN Alexnet using transfer learning approach. SCARA training dataset with 4000 images of two object categories associated with 20 different positions is created and labeled to train KSSnet model. The position of the detected object is included in prediction result at the output classification layer. This method achieved the state-of-the-art results at 100% precision of object detection, 100% accuracy for robotic positioning and 100% successful real-time robotic grasping within 0.38 seconds as detection time. A combination of Zerocross and Canny edge detectors is implemented on a circular object to simplify the place task. For accurate position measurement, the distortion of camera lens is removed using camera calibration technique where the measured position represents the desired location to place the grasped object. The result showed that the robot successfully moved to the measured position with positioning Root Mean Square Error (0.361, 0.184) mm and 100% for successful place detection.

Real-Time Recognition of Blue Traffic Signs Designating Directions

Abstract: In this research we propose a real-time system to recognize blue traffic signs designating directions. This research is complementary to the previous work done on six annular red signs. The system consists of several processing steps: We firstly label the blue objects in each frame and segment them from the background. After that we try to verify if the segmented blue object is a sign candidate, and then we segment white objects within the blue object. Finally we classify the white objects by matching them to arrow patterns according to geometrical features, or reject them if no arrow pattern is matched. Classification is done using a decision tree. Processing time is about 110 ms/frame, and recognition rate is about 81%.

The Internet of Things

Abstract: We turn on the lights in our house from a desk in an office miles away. Our refrigerator alerts us to buy milk on the way home. A package of cookies on the supermarket shelf suggests that we buy it, based on past purchases. The cookies themselves are on the shelf because of a "smart" supply chain. When we get home, the thermostat has already adjusted the temperature so that it's toasty or bracing, whichever we prefer. This is the Internet of Things -- a networked world of connected devices, objects, and people. In this book, Samuel Greengard offers a guided tour through this emerging world and how it will change the way we live and work. Greengard explains that the Internet of Things (IoT) is still in its early stages. Smart phones, cloud computing, RFID (radio-frequency identification) technology, sensors, and miniaturization are converging to make possible a new generation of embedded and immersive technology. Greengard traces the origins of the IoT from the early days of personal computers and the Internet and examines how it creates the conceptual and practical framework for a connected world. He explores the industrial Internet and machine-to-machine communication, the basis for smart manufacturing and end-to-end supply chain visibility; the growing array of smart consumer devices and services -- from Fitbit fitness wristbands to mobile apps for banking; the practical and technical challenges of building the IoT; and the risks of a connected world, including a widening digital divide and threats to privacy and security. Finally, he considers the long-term impact of the IoT on society, narrating an eye-opening "Day in the Life" of IoT connections circa 2025.

Design and Fabrication of Smart Home With Internet of Things Enabled Automation System

Abstract: Home automation systems have attracted considerable attention with the advancement of communications technology. A smart home (SH) is an Internet of Things (IoT) application that utilizes the Internet to monitor and control appliances using a home automation system. Lack of IoT technology usage, unfriendly user interface, limited wireless transmission range, and high costs are the limitations of existing home automation systems. Therefore, this study presents a cost-effective and hybrid (local and remote) IoT-based home automation system with a user-friendly interface for smartphones and laptops. A prototype called IoT@HoMe is developed with an algorithm to enable the monitoring of home conditions and automate the control of home appliances over the Internet anytime and anywhere. This system utilizes a node microcontroller unit (NodeMCU) as a Wi-Fi-based gateway to connect different sensors and updates their data to Adafruit IO cloud server. The collected data from several sensors (radio-frequency identification, ultrasonic, temperature, humidity, gas, and motion sensors) can be accessed via If This Then That (IFTTT) on users' devices (smartphones and/or laptops) over the Internet regardless of their location. A set of relays is used to connect the NodeMCU to homes under controlled appliances. The designed system is structured in a portable manner as a control box that can be attached for monitoring and controlling a real house. The proposed IoT-based system for home automation can easily and efficiently control appliances over the Internet and support home safety with autonomous operation. IoT@HoMe is a low cost and reliable automation system that reduces energy consumption and can notably provide convenience, safety, and security for SH residents.

A Smart Voltage and Current Monitoring System for Three Phase Inverters Using an Android Smartphone Application.

Abstract: In this paper, a new smart voltage and current monitoring system (SVCMS) technique is proposed. It monitors a three phase electrical system using an Arduino platform as a microcontroller to read the voltage and current from sensors and then wirelessly send the measured data to monitor the results using a new Android application. The integrated SVCMS design uses an Arduino Nano V3.0 as the microcontroller to measure the results from three voltage and three current sensors and then send this data, after calculation, to the Android smartphone device of an end user using Bluetooth HC-05. The Arduino Nano V3.0 controller and Bluetooth HC-05 are a cheap microcontroller and wireless device, respectively. The new Android smartphone application that monitors the voltage and current measurements uses the open source MIT App Inventor 2 software. It allows for monitoring some elementary fundamental voltage power quality properties. An effort has been made to investigate what is possible using available off-the-shelf components and open source software.