Automatically generated interactive weather reports based on webcam images

Most weather reports are either based on data from dedicated weather stations, satellite images, manual measurements or forecasts. In this paper a system that automatically generates weather reports using the contents on webcam images are proposed. There are thousands of openly available webcams on the Internet that provide images in real time. A webcam image can reveal much about the weather conditions at a particular site and this study demonstrates a strategy for automatically classifying a webcam scene into cloudy, partially cloudy, sunny, foggy and night. The system has been run for several months collecting 60 Gb of image data from webcams across the world. The reports are available through an interactive web-based interface. A selection of benchmark images was manually tagged to assess the accuracy of the weather classification which reached a success rate of 67.3%

DEVELOPMENT OF A FIELD-BASED MOBILE PLATFORM FOR PLANT PHENOTYPING

Design, implementation and performance verification of an affordable field-based high-throughput plant phenotyping platform for monitoring Canola plants, including both data acquisition/visualization software and measurement system, was the main objective of this research. The primary motivation for this research is the fact that breeders need a well-organized approach and efficient tool to monitor and analyze a number of plant traits to achieve a higher yield. At the moment, manual measurement is a conventional approach to gather the required information for plant analysis. Nevertheless, manual measurement has many limitations especially to study a large-scale field. To address this bottleneck, a high-throughput plant phenotyping platform (HTPP) was developed which consists of a data acquisition system, a data storage unit, and a data visualization and analysis software. Such an HTPP will be an essential asset for breeders to conveniently gather a comprehensive database which contains various information such as a plant height, temperature, Normalized Difference Vegetation Index (NDVI), etc. To develop and implement such an HTPP, first, the overall system block diagram and required algorithms were drawn. Then to find the optimum set of equipment according to the requirement of this application, the performance of different sensors and devices were examined using literature search and experimental examinations in the laboratory setting. Then a mechanical boom was attached to the rear of a farm vehicle (a Swather) to carry different sensors, cameras and other measurement equipment (mechanical development of the boom structure was carried out by other members of the research team). A control box containing power supplies, safety fuses, and a data logger unit was attached to the farm vehicle, and a program was developed for data logger to read sensors signals as well as GPS data for data geo-referencing and future retrieval purposes. The efficiency of different system architecture including different data transmission networks was examined by conducting several field tests to minimize existing errors such as delays in synchronizing different steps. Three programs were developed in MATLAB GUI for image acquisition via webcam and DSLR cameras as well as a central program for data processing and interactive data visualization. The indoor tests were performed at the Robotics laboratory, University of Saskatchewan and outdoor experiments were performed on a Canola nursery at Cargill Canada, Aberdeen, SK, throughout spring-summer 2016 and 2017. Finally, the performance and effectiveness of the developed field-based phenotyping platform was validated by various measures such as conducting some manual measurements and comparing the results with the values given by the platform. According to the achieved results, both hardware and software components of the proposed system meet the requirements of a field-based plant phenotyping platform as an essential asset for breeders for comprehensive study of Canola plants or any other cultivars as a result of some minor design modifications. The main contributions of this study to plant phenotyping research are autonomous image acquisition capability, enhancement of the data acquisition cycle to minimize data geo-referencing error, development of a modular program for data visualization in MATLAB, and faster data collection in a high-throughput fashion (almost 125 times faster)

Energy Efficiency Use of Amount of Light in a Room Based on Number of People Using IoT and Image Processing

A room with too humid conditions can cause mold and bacteria to multiply quickly. The humidity level of the room can be influenced by several factors such as the temperature level and the intensity of light entering the room. Even so, the use of lighting as a sterilizer is currently still lacking in reducing the level of humidity in the room. For this reason, it is necessary to develop technology in the form of a control system for the use of led and ultraviolet lamps in the study room by utilizing the IoT system, so that it can turn on the lights and utilize electrical energy. In this study, the Internet of Things system was applied as a control for Web cameras, Gyml 8511, DHT11, and BH1750 sensors. Data from the sensor will be sent from Arduino uno to a local server, namely raspberry pi 4 using NRF24L01. The communication system between nodes is designed with a mesh topology that can determine the fastest route in the process of sending information. The result of this research is monitoring on the web display that displays the condition of the class and the condition of the lights. From the experimental results, it is found that the mesh communication system can work well

IoT-based smart wheelchair system for physically impaired person / Muhammad Afiq Mohd Aizam... [et al.]

Disabled persons usually require an assistant to help them in their daily routines especially for their mobility. The limitation of being physically impaired affects the quality of life in executing their daily routine especially the ones with a wheelchair. Pushing a wheelchair has its own side effects for the user especially the person with hands and arms impairments. This paper aims to develop a smart wheelchair system integrated with home automation. With the advent of the Internet of Things (IoT), a smart wheelchair can be operated using voice command through the Google assistant Software Development Kit (SDK). The smart wheelchair system and the home automation of this study were powered by Raspberry Pi 3 B+ and NodeMCU, respectively. Voice input commands were processed by the Google assistant Artificial Intelligence Yourself (AIY) to steer the movement of wheelchair. Users were able to speak to Google to discover any information from the website. For the safety of the user, a streaming camera was added on the wheelchair. An improvement to the wheelchair system that was added on the wheelchair is its combination with the home automation to help the impaired person to control their home appliances through Blynk application. Observations on three voice tones (low, medium and high) of voice command show that the minimum voice intensity for this smart wheelchair system is 68.2 dB. Besides, the user is also required to produce a clear voice command to increase the system accuracy

Modeling the power consumption of a Wifibot and studying the role of communication cost in operation time

Mobile robots are becoming part of our every day living at home, work or entertainment. Due to their limited power capabilities, the development of new energy consumption models can lead to energy conservation and energy efficient designs. In this paper, we carry out a number of experiments and we focus on the motors power consumption of a specific robot called Wifibot. Based on the experimentation results, we build models for different speed and acceleration levels. We compare the motors power consumption to other robot running modes. We, also, create a simple robot network scenario and we investigate whether forwarding data through a closer node could lead to longer operation times. We assess the effect energy capacity, traveling distance and data rate on the operation time

Intelligent Image Capturing Alarm System Using Raspberry Pi

Home surveillance system assumes an essential part in this present day living style to help recognizing illegal activities. In this proposed paper, an intelligent image capturing alarm system to protect locker was developed. Raspberry Pi 2 is used as the main controller (server). At the point when any conceivable intrusion is identified, a webcam installed to Raspberry Pi 2 will capture the picture of the intruder. In the meantime, the spotlight or light of the house which represented by an LED will be turned "ON" alongside an alarm sound from a buzzer which is fixed as an output. Taking everything into account, this improvement offers reasonable and easy to use surveillance alarm system