Speed Limit Traffic Sign Classification Using Multiple Features Matching

This paper presents the method to classify the speed limit traffic sign using multiple features, namely histogram of oriented gradient (HOG) and maximally stable extremal regions (MSER) features. The classification process is divided into the outer circular ring matching and the inner part matching. The HOG feature is employed to match the outer circular ring of the sign, while MSER feature is employed to extract the digit number in the inner part of the sign. Both features are extracted from the grayscale image. The algorithm detects the rotation angle of the sign by analyzing the blobs which is extracted using MSER. In the matching process, tested images are matched with the standard reference images by calculating the Euclidean distance. The experimental results show that the proposed method for matching the outer circular ring works properly to recognize the circular sign. Further, the digit number matching achieves the high classification rate of 93.67% for classifying the normal and rotated speed limit signs. The total execution time for classifying six types of speed limit sign is 10.75 ms. Keywords: Speed limit traffic sign � HOG � MSER � Template matchin

Improving On-Road Vehicle Detection Performance by Combining Detection and Tracking Techniques

This paper presents the combination method of detection and tracking to improve the recall of on-road vehicle detection. The vehicle detection method is a kind of the object detection where the vehicle in front of a car is detected using a camera installed on the car. In the proposed method, the Viola Jones detection and the support vector machine technique are combined complementary. Further the Lucas Kanade tracking technique is introduced to gain the true positive detection when both detection techniques fail to detect the vehicle. The experimental results show that the proposed method achieves the recall of 0.97, the precision of 0.96 and the frame rate of 25.54 fps (frames per second). Keywords-vehicle detection; Viola Jones; support vector machine; Lukas Kanade trackin

A Modified Step Size Perturb and Observe Maximum Power Point Tracking for PV System

The Perturb and Observe (P&O) is an algorithm to find the maximum power point in the photovoltaic (PV) system. This paper presents a new method to modify the step size of P&O algorithm by categorizing the step size into the large value during the sun irradiation change where the drift occurs, the medium value when the operating point is far away from the maximum point, and the small value when the operating point is close to the maximum point. The simulation results show that the proposed method achieves the high performance in tracking the maximum power point in the terms of the fast response, the small oscillation in the steady state condition, and avoiding the drift problem. Further, the method yields the highest generated power compared to the existing techniques

Implementation of Optimization Technique on the Embedded Systems and Wireless Sensor Networks for Home Energy Management in Smart Grid

This paper presents the implementation of optimization technique on the HEMS (Home Energy Management System). The objective of load scheduling optimization problem is to minimize the peak hourly load power consumption. The Raspberry Pi module is employed as the smart controller installed at a home. The smart controller is used to solve the optimization problem using MILP (Mixed Integer Linear Programming). It communicates with the load controllers implemented on the Arduino microcontroller over the ZigBee wireless network. The experiment results show that the proposed system is able to compute the MILP in real-time at 396 ms, very fast compared to the hourly interval used by the optimization technique. Further, the transmission time from smart controller to the local controller, and vice versa is 167 ms and 187 ms respectively. Index Terms—Energy management; linear programming; Raspberry Pi; Arduino; ZigBee

Simulation of Fuzzy Logic Based Energy Management for the Home with Grid Connected PV-Battery System

This paper presents the energy management for the home with the grid connected PV-battery system. The fuzzy logic controller is employed to control the power flow delivered to the loads. The objective is to minimize the electricity cost by managing the energy penetration from the PV-battery system to the grid. The fuzzy logic controller is developed in distributed scheme where each load has its own controller. The fuzzy rule is designed by considering the availability of PV power, the state of charge of the battery and the total loads power consumption. The simulation results show that the proposed system achieves a cost reduction of 13.9% compared to the system without the fuzzy logic controller. Keywords-energy management; grid connected; PV-battery; fuzzy logic controlle

Intelligent Multi Agent System for Energy Management in the Classrooms with Grid Connected PV

This paper presents an application of the Multi Agent System (MAS) in the Building Energy Management System, more specifically to manage the energy in the classrooms of a university. The grid connected photovoltaic (PV) is used as the electrical generation system to supply the loads in the classrooms. The objective is to minimize the electricity cost while maintaining user comfort. The MAS consists of the PV Agent, the Utility Agent, the Load Agent and the Central Control Agent. In addition, the Course Scheduler Unit is employed to inform the utilization or occupancy of the classrooms. The proposed system provides a new method to manage the energy usage from the PV by changing the temperature set-point of the air conditioner system using the Fuzzy Logic Controller. The simulation results show that the proposed system provides the highest performance index of 0.9902 in the optimization of the electricity cost and temperature comfort compared to the conventional method using a fixed temperature set-point. Index Terms – Multi agent, energy management, grid connected PV

Otomatisasi dan Monitoring Parameter Lingkungan Pada Media Tumbuh Budidaya Jamur Tiram Berbasis Internet of Things

Jamur Tiram (Pleurotus Ostreatus) adalah tanaman sejenis fungi yang sering dibudidayakan pada lingkup pertanian Indonesia, dikarenakan tanaman ini memiliki banyak kegunaan dari segi kuliner dan kesehatan. Jamur tersebut dibesarkan dengan cara memanipulasi parameter lingkungan, agar sedemikian rupa sehingga dapat tumbuh didalam suatu wadah/tempat yang telah disediakan. Perlu diketahui bahwa jamur tersebut pertumbuhannya dapat dipengaruhi oleh pH, suhu dan kelembapan yang dapat mempengaruhi tumbuhnya jamur, agar pertumbuhan budidaya jamur tersebut dapat terpantau dan terkontrol, maka diperlukan sebuah alat yang dapat mengontrol parameterr lingkungan tersebut agar sesuai dengan yang seharusnya. Dari hasil pengujian alat keseluruhan, dapat dikatakan bahwa alat tersebut dapat merespon perubahan parameter lingkungan berdasarkan nilai sebenarnya dari media dan lingkungan jamur tersebut, serta mengetahui estimasi panen yang dianjurkan

IoT Based Real-Time Monitoring of Phytoremediation of Wastewater using the Mathematical Model Implemented on the Embedded Systems

This paper presents the Internet of things (IoT) technology for real-time monitoring of wastewater phytoremediation. Phytoremediation is a technique to remove pollutants from the wastewater using the plants. A conventional method to monitor the phytoremediation performance is by taking the samples of the contaminants from a site and measuring them at the laboratory. This method needs many works for data preparation and analysis. A recent development on the IoT technology may eliminate such tasks by a real-time monitoring system. In the proposed realtime monitoring system, several phytoremediation models are implemented on embedded hardware and connected to the Thingspeak IoT platform. The proposed system aims to provide a real-time monitoring system to better model the phytoremediation by examining the monitoring data time interval and fitting techniques. From the experiments, the proposed monitoring system achieves a data transfer reliability of 81.4% when the period of the data transmission is one minute, which is suitable for one-day interval real-time monitoring system. The proposed monitoring system can build a phytoremediation model using a polynomial fitting with a higher fit than the existing methods using an exponential fitting. Further, the proposed method promises a better solution in terms of the best model, the low cost, and the acceptable accuracy

Color Segmentation for Extracting Symbols and Characters of Road Sign Images

Abstract—This paper presents a color segmentation technique based on the normalized RGB chromaticity diagram for extracting symbols and characters of road sign images. The method separates blue color of the sign’s background by utilizing the developed histogram on the RGB chromaticity diagram for selecting threshold automatically. The morphology operators are used to extract symbols and characters. From the experiments using real scene images with varying illumination, the proposed method could extract symbols and characters of road sign images properly. Index Terms—Color segmentation, RGB chromaticity diagram, objects extraction, guidance sign

Fast and Robust Traffic Sign Detection

This paper deals with the fast and robust detection of the traffic sign images. A new technique called geometric fragmentation is proposed to detect the red circular traffic signs. It detects the outer ellipses of the signs by combining the left and right fragments of the ellipse objects. A search based on the geometric fragmentation is used to find the ellipse fragments. This search is somewhat similar to genetic algorithm (GA) in the sense that it employs the terms of individual, population, crossover, and objective function usually used in GA. To increase the accuracy and reduce the computational time, a new objective function is introduced for evaluating the individuals. The algorithm was tested for detecting the red circular traffic signs from the real scene image. The experimental results show that the proposed algorithm has a higher detection rate with a lower computational cost compared with the referential genetic algorithm-based ellipse detection