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Conference

International Conference on Industrial Instrumentation and Control 

About: International Conference on Industrial Instrumentation and Control is an academic conference. The conference publishes majorly in the area(s): Image segmentation & Control theory. Over the lifetime, 318 publications have been published by the conference receiving 1895 citations.

Papers published on a yearly basis

Papers
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Proceedings ArticleDOI
28 May 2015
TL;DR: An intellectual classification system to recognize normal and abnormal MRI brain images and the Hybrid classifier SVM-KNN demonstrated the highest classification accuracy rate of 98% among others is proposed.
Abstract: This paper proposes an intellectual classification system to recognize normal and abnormal MRI brain images. Nowadays, decision and treatment of brain tumors is based on symptoms and radiological appearance. Magnetic resonance imaging (MRI) is a most important controlled tool for the anatomical judgment of tumors in brain. In the present investigation, various techniques were used for the classification of brain cancer. Under these techniques, image preprocessing, image feature extraction and subsequent classification of brain cancer is successfully performed. When different machine learning techniques: Support Vector Machine (SVM), K- Nearest Neighbor (KNN) and Hybrid Classifier (SVM-KNN) is used to classify 50 images, it is observed from the results that the Hybrid classifier SVM-KNN demonstrated the highest classification accuracy rate of 98% among others. The main goal of this paper is to give an excellent outcome of MRI brain cancer classification rate using SVM-KNN.

113 citations

Proceedings ArticleDOI
28 May 2015
TL;DR: This paper proposes a context-free grammar based proposed method that gives effective real time performance with great accuracy and robustness for more than four hand gestures and implements the alternate representation method for same gestures i.e. fingertip detection using convex hull algorithm.
Abstract: Gestures are important for communicating information among the human. Nowadays new technologies of Human Computer Interaction (HCI) are being developed to deliver user's command to the robots. Users can interact with machines through hand, head, facial expressions, voice and touch. The objective of this paper is to use one of the important modes of interaction i.e. hand gestures to control the robot or for offices and household applications. Hand gesture detection algorithms are based on various machine learning methods such as neural networks, support vector machine, and Adaptive Boosting (AdaBoost). Among these methods, AdaBoost based hand-pose detectors are trained with a reduced Haar-like feature set to make the detector robust. The corresponding context-free grammar based proposed method gives effective real time performance with great accuracy and robustness for more than four hand gestures. Rectangles are creating some problem due to that we have also implement the alternate representation method for same gestures i.e. fingertip detection using convex hull algorithm.

73 citations

Proceedings ArticleDOI
28 May 2015
TL;DR: The aim of this survey is to informing the progress of human sentient manipulation planner of adaptive path planning and navigation through dynamic environments.
Abstract: Practical realistic environment for path and continuous motion planning problems normally consist of numerous working areas such as in indoor application consist of number of bedrooms, hallways, multiple doorways with many static and dynamic obstacle in between. Disintegration of such environment into small areas, or regions shows impact on the quality of adaptive path planning in dynamic environment. Many algorithms are developed for solving problems involving narrow passages and multiple regions with optimal solution. Autonomous mobile robot system must have sense of balance of its potential, steadfastness and sturdiness issue with task and the final goals while generating and executing an adaptive as well as effective strategy with optimal solution. Navigation algorithms approaching to a certain maturity in the field of autonomous mobile robot, so most of research is now focused more advance task like adaptive path planning and navigation through dynamic environments. Adaptive path planning and navigation needs to set learning rate, rules for classifying spaces and defining proposed library parameters. The aim of this survey is to informing the progress of human sentient manipulation planner.

38 citations

Proceedings ArticleDOI
28 May 2015
TL;DR: AgriBot as mentioned in this paper is a prototype and implemented for performing various agricultural activities like seeding, weeding, spraying of fertilizers, insecticides, and harvesting citrus, cucumber, and other fruits.
Abstract: Robotics in agriculture is not a new concept; in controlled environments (green houses), it has a history of over 20 years. Research has been performed to develop harvesters for cherry tomatoes, cucumbers, mushrooms, and other fruits. In horticulture, robots have been introduced to harvest citrus and apples. In this paper autonomous robot for agriculture (AgriBot) is a prototype and implemented for performing various agricultural activities like seeding, weeding, spraying of fertilizers, insecticides. AgriBot is controlled with a Arduino Mega board having At mega 2560 microcontroller. The powerful Raspberry Pi a mini computer is used to control and monitor the working of the robot. The Arduino Mega is mounted on a robot allowing for access to all of the pins for rapid prototyping. Its hexapod body can autonomously walk in any direction, avoiding objects with its ultrasonic proximity sensor. Its walking algorithms allow it to instantly change direction and walk in any new direction without turning its body. An underbody sensory array allows the robot to know if a seed has been planted in the area at the optimal spacing and depth. AgriBot can then dig a hole, plant a seed in the hole, cover the seed with soil, and apply any pre-emergence fertilizers and/or herbicides along with the marking agent. AgriBot can then signal to other robots in the immediate proximity that it needs help planting in that area or that this area has been planted and to move on by communicating through Wi-Fi.

32 citations

Proceedings ArticleDOI
28 May 2015
TL;DR: In this article, a Bench-Top Test Rigger (BTR) is designed to mimic the operating condition of an actual wind turbine and use it for monitoring its condition so as to diagnose the incipient faults in its critical components using latest machine learning algorithms such as Artificial Neural Network (ANN).
Abstract: Wind energy is an emerging, clean and renewable source of energy. It is estimated that by year 2035, wind energy will be generating more than 25% of the world's electricity according to International Energy Agency (IEA). With the increase in demand for wind energy, its maintenance issues are becoming more prominent. The scheduled maintenance is more economical than unscheduled repair resulting from failure. So a continuous condition monitoring of various critical components like bearings, gearbox, and shafts of wind turbine is essential in order to enable predictive maintenance. 10% of the total failure is contributed by the bearings, shaft and gear box failures, but the downtime is more than 50% of the total downtime. This paper discusses the development of a bench-top test rig which is designed to mimic the operating condition of an actual wind turbine and use it for monitoring its condition so as to diagnose the incipient faults in its critical components using latest machine learning algorithms such as Artificial Neural Network (ANN).

31 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
2015318