Navigating an auto guided vehicle using rotary encoders and proportional controller

Auto Guided Vehicle (AGV) is commonly used in industry to reduce labour cost and to improve the productivity. A few programmable devices are combined in an AGV to optimize the usage of time and energy. AGV is widely used to transport goods and materials from one place to another place. For the first generation of AGV was used the track to guide the AGV but it was not flexible enough. This study investigates an alternative to control an AGV using two rotary encoders and proportional controller. Arduino Mega 2560 was used as a microcontroller to receive and process the signals from the rotary encoders. Logic controller and proportional controller were implemented to control the AGV, respectively. The coefficient of proportional controller was optimized to improve the performance of the AGV during navigation process. Findings show that AGV with the proportional controller with coefficient 1.5 achieved the best performance during the navigation process

An Integration of Open-Source Resources in Distance Teaching for Real-Time Embedded System Using Arduino Microcontroller and Freertos

Real-Time Embedded System (RTES) is about applying real-time system in an embedded system (e.g. microcontroller) in ways that both logical and temporal requirements are fulfilled. A special attention shall be on the temporal response analysis so that students can have a clear distinction between microcontrollers with and without real-time system. During new norms of learning and teaching from home, an affordable and accessible infrastructure in RTES course is urgently needed. Thus, this study aims to evaluate the feasibility of an integration of open source resources (i.e. Arduino platform and FreeRTOS) in teaching RTES remotely and to provide real-time learning experience about RTES concepts without additional components or wiring out of school. Results show that an obvious context switch could be observed when task pre-emption happened. Finding indicates that the proposed integration was useful for students to understand the complex RTES concepts e.g. task scheduling, pre-emption, and mutually exclusion

A Temperature Control System for Near Infrared Spectroscopic Analysis using Proportional Controller

Near Infrared Spectroscopy (NIRS) has been widely investigated as an alternative to replace conventional chemical analysis. However, NIRS analysis is susceptible to the change of surrounding temperature. This will affect the NIRS analysis and cause the result inconsistent. Therefore, this paper presents a work in designing the temperature control system for NIRS data acquisition. First, the cooling element of the system was designed using a TEC1-12706 Peltier as a cooling element, and a temperature sensor LM35 was used to measure the temperature inside the data acquisition container. The PWM signal of microcontroller was used to control the N-channel MOSFET so that the desired temperature can be achieved. The Arduino Uno was used as the microcontroller of the system. Lastly, a Proportional Controller was applied for forming the temperature control system in the container and the performance of the system was analysed. Results show that the Proportional Controller outperformed to Logic controller. Findings indicate that the proposed control system with optimal Kp value is able to maintain the desired temperature

An Integration of Open-Source Resources in Distance Teaching for Real-Time Embedded System Using Arduino Microcontroller and Freertos

Real-Time Embedded System (RTES) is about applying real-time system in an embedded system (e.g. microcontroller) in ways that both logical and temporal requirements are fulfilled. A special attention shall be on the temporal response analysis so that students can have a clear distinction between microcontrollers with and without real-time system. During new norms of learning and teaching from home, an affordable and accessible infrastructure in RTES course is urgently needed. Thus, this study aims to evaluate the feasibility of an integration of open source resources (i.e. Arduino platform and FreeRTOS) in teaching RTES remotely and to provide real-time learning experience about RTES concepts without additional components or wiring out of school. Results show that an obvious context switch could be observed when task pre-emption happened. Finding indicates that the proposed integration was useful for students to understand the complex RTES concepts e.g. task scheduling, pre-emption, and mutually exclusion

Pineapples internal quality inspection approaches and its potential effects in Malaysia

Pineapple (Ananas Comosus L. Metil) is a tropical plant under Bromeliaceous family that is grown in American tropics, subtropics, and warmer regions (U. Nations 2012). Pineapple is the second harvest tropical fruit after banana in the world (S. Ndungu 2014). Pineapple has spiny skin that will turn into yellowish brown from green when ripe. Pineapple is a non-climatic fruit that will stop ripping once it is harvested. Pineapple is famous not only because its taste but also its nutrients for health (M. Farid Hossain 2015, p.84). It contains nutriet that can supply suffiecient requirement of calcium, sodium, potassium, fibre, vitamin C, and so on. Those nutrients can help to build up healthy and growth of bone, enhance body immune system, and decrease high blood pressure for body system

Investigation of Parameters That Affect the Acquired Near Infrared Diffuse Reflected Signals in Non-Destructive Soluble Solids Content Prediction

Near infrared spectroscopy is a susceptible technique which can be affected by various factors including the surface of samples. According to the Lambertian reflection, the uneven and matte surface of fruits will provide Lambertian light or diffuse reflectance where the light enters the sample tissues and that uniformly reflects out in all orientations. Bunch of researches were carried out using near infrared diffuse reflection mode in non-destructive soluble solids content (SSC) prediction whereas fewer of them studying about the geometrical effects of uneven surface of samples. Thus, this study aims to investigate the parameters that affect the near infrared diffuse reflection signals in non-destructive SSC prediction using intact pineapples. The relationship among the reflectance intensity, measurement positions, and the SSC value was studied. Next, three independent artificial neural networks were separately trained to investigate the geometrical effects on three different measurement positions. Results show that the concave surface of top and bottom parts of pineapples would affect the reflectance of light and consequently deteriorate the predictive model performance. The predictive model of middle part of pineapples achieved the best performance, i.e. root mean square error of prediction (RMSEP) and correlation coefficient of prediction (Rp) of 1.2104 °Brix and 0.7301 respectively

A Supervisory and Control System for Indoor Lettuce Farming

The agriculture-based economy has undergone a significant transformation in the Malaysian economy where the agricultural sector has moved towards the status of an industrialized country rapidly. However, there are several challenges that threatens the ability to achieve this industrial status, namely the level of available technological advancements as well as the production efficiency. Malaysia has a tropical climate and monsoon seasons. With unpredictable weather conditions, it can affect the agricultural sector. Therefore, this study evaluates the performance of a proposed system in monitoring and controlling the variables that influence the growth of vegetables leading to optimal growth in indoor farming. The proposed system used various sensors to monitor temperature, humidity, and soil moisture, and to optimally control these parameters. Besides, a spectral light was used to replace the sunlight source so that indoor farming is feasible. The results show that the proposed system was able to accurately provide enough light that vegetables needed and to control the temperature better compared to a typical outdoor and traditional cultivation. Thus, the proposed system is promising to bring positive impact to the agricultural sector by providing a better environment conditions for growing vegetables

A Comparison between the Post- and Pre-dispersive Near Infrared Spectroscopy in Non-Destructive Brix Prediction Using Artificial Neural Network

Even though near infrared (NIR) spectroscopy have been implemented in determining the Brix of pineapples, no traceable study compares the effects of different acquisition designs. Thus, this study aims to evaluate the prediction performance of both pre- and post-dispersive NIR sensing devices in non-destructive Brix prediction using artificial neural network (ANN). The pre-dispersive device has five narrowband light emitting diodes (LEDs) with different wavelengths and a photodiode detector, whereas the post-dispersive device has a bifurcated fiber optic, a broadband LED, and a spectral sensor. First, the NIR diffuse reflectance was non-destructively collected using both NIR devices. Then, the collected diffuse reflectance was calibrated with the white and dark references, and then pre-processed using normalization and standard normal variate methods. After that, ANNs were built for both devices using the pre-processed data. Results show both devices are suitable for sample screening application with range error ratio (RER) of more than seven. Nevertheless, the ANN that trained using the post-dispersive device outperformed that trained using the pre-dispersive device with an 8.1% improvement of correlation coefficient of prediction (i.e. from 0.6853 to 0.7408), and a 5.7% improvement of root mean square error of prediction (i.e. from 1.3918 to 1.313°Brix)