Comparative Study of Computer Vision Based Line Followers Using Raspberry Pi and Jetson Nano

The line follower robot is a mobile robot which can navigate and traverse to another place by following a trajectory which is generally in the form of black or white lines. This robot can also assist human in carrying out transportation and industrial automation. However, this robot also has several challenges with regard to the calibration issue, incompatibility on wavy surfaces, and also the light sensor placement due to the line width variation. Robot vision utilizes image processing and computer vision technology for recognizing objects and controlling the robot motion. This study discusses the implementation of vision based line follower robot using a camera as the only sensor used to capture objects. A comparison of robot performance employing different CPU controllers, namely Raspberry Pi and Jetson Nano, is made. The image processing uses an edge detection method which detect the border to discriminate two image areas and mark different parts. This method aims to enable the robot to control its motion based on the object captured by the webcam. The results show that the accuracies of the robot employing the Raspberry Pi and Jetson Nano are 96% and 98%, respectively

Optimized PID-Like Neural Network Controller for Single-Objective Systems

The utilization of intelligent controllers becomes more prevalent as the hype of Industry 4.0 arises. Artificial neural network (ANN) exhibits the mapping ability and can estimate the output by means of either interpolation or extrapolation. These properties are sought to supersede the classical controllers. In this study, the ANN establishment was initiated by collecting dataset from the input and output of a well-known PID controller. The dataset was trained using a set of control factor combinations, including the number of neurons, the number of hidden layers, activation functions, and learning rates. Two kinds of ANN controllers were investigated, including one-input and three-input ANN. The testing was conducted under normal and uncertain conditions. These uncertainties include external disturbances, plant variations, and setpoint variations. The integral absolute error (IAE) was selected as the single objective to assess. The simulation results show that the response of three-input ANN controllers could yield smaller IAE at their best combinations under most kinds of conditions. Besides, the three-input ANN outperforms the one-input ANN both qualitatively and quantitatively. These facts might lead to a broader utilization of ANN as controllers

Peningkatan Gain dengan Teknik Multilayer Parasitic pada Perancangan Antena Mikrostrip Persegi Panjang 2,4 GHz

Microstrip patch antennas offer low profile and low cost fabrication advantages but limited gain and bandwidth. Some methods and techniques have been proposed and developed to improve gain of microstrip antenna, and one of them is multilayer parasitic technique. In this paper, the design and realization of rectangular patch microstrip antenna with multilayer parasitic for enhancing antenna gain that operates at frequency of 2.4 GHz is presented. The designed antenna consists of one rectangular patch as the main antenna on the first layer and the 2x2 configuration of rectangular patches on the second and third layers as the parasitic substrate. The simulation results show that the single element antenna has a gain of 3.224 dB and increases to 8.593 dB by using the parasitic multilayer antenna. The antenna design was then fabricated using an Epoxy FR4 substrate with a dielectric constant of 4.65 and a thickness of 1.6 mm. The fabricated multilayer parasitic microstrip antenna has dimension of 80 mm x 90 mm x 34.8 mm. The measurement results show that the VSWR value is 1,284 and the return loss is -18,091 dB at the center frequency of 2,442 GHz. The gain of the multilayer parasitic microstrip antenna measurement is 9.1 dB. The operation frequency range is 2.32 - 2.565 GHz at VSWR  < 2, the bandwidth of 245 MHz is achieved and unidirectional radiation patterns are obtained

A Fuzzy Logic-Based Automation toward Intelligent Air Conditioning Systems

Most of the energy used in residential buildings originates from air conditioners. Meanwhile, air conditioner manufacturers are addressing this issue by the production of efficient air conditioners. However, the convertible frequency air conditioners are expensive, up to 60% higher than the fixed frequency control air conditioners. Besides the human behavior in determining the temperature, setpoint plays an important role regardless of the air conditioners technology used. This study incorporated intelligence in setting up the temperature by means of specially designed remote control. The Tsukamoto fuzzy reasoning was utilized as a decision making system with two inputs, namely the outdoor temperature and the number of occupants. The device used DHT22 as the temperature sensor and HC-SR04 to detect incoming and outgoing occupants. Furthermore, the fuzzy inference system generated infrared signal associated with the temperature setpoint. This signal was received by the air conditioner receiver to adjust the temperature setpoint accordingly. The result of this study showed that the fuzzy inference system determines the temperature setpoint appropriately under variations of surrounding temperature and the number of occupants. The proposed approach yielded a satisfactory perception of thermal comfort and also a promising approach to energy conservation

Optimized PID-Like Neural Network Controller for Single-Objective Systems

The utilization of intelligent controllers becomes more prevalent as the hype of Industry 4.0 arises. Artificial neural network (ANN) exhibits the mapping ability and can estimate the output by means of either interpolation or extrapolation. These properties are sought to supersede the classical controllers. In this study, the ANN establishment was initiated by collecting dataset from the input and output of a well-known PID controller. The dataset was trained using a set of control factor combinations, including the number of neurons, the number of hidden layers, activation functions, and learning rates. Two kinds of ANN controllers were investigated, including one-input and three-input ANN. The testing was conducted under normal and uncertain conditions. These uncertainties include external disturbances, plant variations, and setpoint variations. The integral absolute error (IAE) was selected as the single objective to assess. The simulation results show that the response of three-input ANN controllers could yield smaller IAE at their best combinations under most kinds of conditions. Besides, the three-input ANN outperforms the one-input ANN both qualitatively and quantitatively. These facts might lead to a broader utilization of ANN as controllers

Optimized PID-Like Neural Network Controller for Single-Objective Systems

The utilization of intelligent controllers becomes more prevalent as the hype of Industry 4.0 arises. Artificial neural network (ANN) exhibits the mapping ability and can estimate the output by means of either interpolation or extrapolation. These properties are sought to supersede the classical controllers. In this study, the ANN establishment was initiated by collecting dataset from the input and output of a well-known PID controller. The dataset was trained using a set of control factor combinations, including the number of neurons, the number of hidden layers, activation functions, and learning rates. Two kinds of ANN controllers were investigated, including one-input and three-input ANN. The testing was conducted under normal and uncertain conditions. These uncertainties include external disturbances, plant variations, and setpoint variations. The integral absolute error (IAE) was selected as the single objective to assess. The simulation results show that the response of three-input ANN controllers could yield smaller IAE at their best combinations under most kinds of conditions. Besides, the three-input ANN outperforms the one-input ANN both qualitatively and quantitatively. These facts might lead to a broader utilization of ANN as controllers