Smart Computing and Systems Engineering (SCSE)
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Item A solution for reducing electricity in residential sector using image processing(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Ekanayake, D.S.; Samankula, W.G.D.M.Energy saving is a critical issue that should be addressed in a worldwide scale. In the residential sector of Sri Lanka, there are many houses. Each household on average includes four people and has diverse electronic needs to be fulfilled. This paper proposes a solution to reduce the electricity consumption of residential sector. The solution has the ability to manage the use of electricity consumption of households. It identifies each and every household electric item and connects through Wi-Fi. Each household electric item which has the ability to connect to a Wi-Fi network, will be connected to the system via the routers port forwarding function. The user has the ability to check the system and identify which electric item is wasting energy and then the user can switch it off remotely through the system. Furthermore, the proposed solution is equipped with image processing algorithms. Image processing is fast, flexible and opens a whole new world of real time computer vision. A video camera located in several places in the house is used to identify presence of humans and then automatically switch off unnecessary electronic items. The proposed detection process depends on the light condition, camera angle and the efficiency of the real time detection. Matlab’s SVM classifier people detection algorithm was used as the image processing algorithm. One thousand six hundred images were split equally into two data sets as images with humans, and images without humans. The analysis revealed a unique threshold value as 6 220 800 in images to identify humans images in it. In the future, the system is envisaged to connect to an IoT (Internet of Things) platform to derive more benefits to the end user.Item Gender recognition of Luffa flowers using machine learning(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Gunasinghe, H.N.; de Silva, R.Automatic flower gender identification could be introduced to large farmlands to help artificial pollination of imperfect flowers. Incomplete flowers contain either male or female organs but not both. In this paper, we present a computer aided system based on image processing and machine learning to identify the gender of a Luffa flower automatically. A pre-trained machine learning model is used for gender segmentation of flowers. The system is developed using Tensorflow Machine Learning Tool, which is an open-source software library for Machine Intelligence. The network was selected as the Google’s Inception model and a dataset was prepared after capturing flower images from a Sri Lankan Luffa farm. The system was tested using two datasets. The first contained the captured original images and the second was prepared by cropping each image to extract male and female floral organs, stamen and pistil respectively. The prototype system classified the flowers as either male or female at 95% accuracy level. The experimental results indicate that the proposed approach can significantly support an accurate identification of the gender of a Luffa flower with some computational effort.Item Traffic through - An effective right-turn-bay of signalized intersections for busy hours(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Seneviratne, W.Y.H.; Jayasiri, A.Sri Lanka, as a developing country, the traffic congestions are becoming severe day by day because of the population and the economic background. To overcome the congested areas, the traffic controlling light systems were introduced and used in most of the junctions. Existing traffic light system is based on the fixed cycle times for each phase, depending on the environmental conditions, geometry design of the junction and traffic movements of the particular junction. Among the most of the features at the road intersections, the feature called “Right-Turn-Bay” is an extra segment of lane which was introduced to the vehicles which are supposed to proceed right turning movement at the intersection. Because of the limited area of that lane segment, it can be filled easily. The proposed solution is to overcome the overflowing problem in the right-turn-bay by giving an extra cycle time period for the right turnings in busy time. For this real-time process, the image processing techniques were used for a video sequences, captured by a video camera to detect the arrival of vehicles at the bay and its count was gathered using the pixel-wise detection and blob tacking of the vehicle. The traffic light was controlled as usual and the additional functionality was to control the junction considering the maximum count and the current count at the bay. The controller is to decide whether the traffic should leave the bay or not by considering its parameters.Item Pedestrian detection using image processing for an effective traffic light controlling system(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Chathumini, K.G.L.; Kumara, B.T.G.S.Traffic congestion and road pedestrian accident are the two major issues that the Sri Lankan society faced toady. These two issues can be reduced by use of traffic light controlling system in an effective way. This research paper proposed a system to make effective PEdestrian LIght CONtrolled (PELICON) crossing system using image processing. The proposed system consists of three major parts. That is CCTV camera, the system, and pair of poles with standard traffic light system. First the system captures an image of pedestrians who are waiting to cross the road, using CCTV camera. Then the system processes the image to identify and detect the number of pedestrians. Finally, if the number of pedestrians exceeds a given threshold value or pedestrian waiting time is exceeded, then the logical part of the system works and produces a result to control the traffic light system. This system that uses PELICON crossing system could be more effective than a button clicking PELICON Crossing system.Item Smart veggie identification and alerting system for supermarkets using image processing techniques and neural networks(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Hewasinghe, H.H.K.; Pemarathne, W.P.J.The recent advancements in the field of image processing have become a great source of benefit to the development in fields of science and engineering. Image recognition is one of the foremost areas in computer vision as it yields favorable results in many applications. One of the main applications in image recognition is the object recognition; this is a process of identifying a specific object in an image or a video. In this paper, we present the current state of the image processing techniques to identify vegetables and fruits, and is capable of being installed with existing hardware resources. The implemented system can identify vegetables and fruits in a basket that are to be retailed and alert authorities when a basket is near to being empty. The system is based on color and size comparison of the vegetables and fruits in a live video with a reference image and thereby extract similar features by using neural network for identification. Height of the baskets is marked with colored lines, contrasting to the color of the content. The level of the basket is identified through the visible levels of the color lines and then the notifications are sent to the responsible parties. The system is tested with two vegetables egg plants and tomatoes as well as two fruits apple and oranges. Accuracy in identifying egg plants and apples have shown to be high, with the accuracy of the results for tomatoes and oranges being average.Item Intelligent traffic controller using image processing(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Fernando, J.J.R.S.; Jayasiri, A.Traffic congestion has become significant problem in recent years with the ever increasing number of vehicles and poor management of traffic. Traffic patterns are not constant throughout the day. They are changing from time to time. Since present traffic controllers have fixed time intervals for signal lights, they could not provide a better solution. Computer vision can be used to create an intelligent traffic controller which can adapt its time intervals according to the real traffic. Several studies have been carried out based on the concept of real time image processing to manage the traffic. In current traffic controllers, wastage of effective green time is occurred, as many times fixed green time period which is assigned for a phase is larger than it actually needs. Hence the other roads at the intersection have to wait in vain, with more traffic, until that fixed green time period is over. In the proposed method real time traffic image sequences are analysed using image processing, in order to obtain actual traffic area. Then, time for green light is allocated according to that traffic area. Hence, wastage of effective green time is eliminated by the proposed method since it allocates time to green signal that is sufficient to pass the actual traffic presented on the road. Results reveals, effective green time that need to pass the traffic, is proportional to the road area covered by traffic at that time.Item Handwritten signature verification(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Abewardana, H.M.H.P.; Ranathunga, L.A number of biometric methods can be used to authenticate a human identity such as using fingerprint detection, face detection, iris inspection and voice recognition. The verification of the signature of a human is the most prominent and prevalent method among those. The banking and insurance sector manually uses this verification method. It is a critical biometric attribute, which may differ from time to time due to the age and emotional state of the person. Because of the absence of the time feature of the signature, offline signature verification has a risk than online signature verification. The paper introduces six features for an alternate solution. They include scale and rotation invariant such as signature pixel ratio of concentric circles and number of cross points while others are rotation variant such as baseline slant angle, aspect ratio, normalized area and slope of the line connecting center of gravities of left and right halves of the bounding box of the signature. Back-propagation neural network is used to train and test the signature images. Experimentation and results of this methodology presents the possibility of using this system in relevant sectors.