Forthcoming articles


International Journal of Intelligent Machines and Robotics


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International Journal of Intelligent Machines and Robotics (6 papers in press)


Regular Issues


  • Robust Industrial Vision System for Mechanical Parts Recognition   Order a copy of this article
    by Tushar Jain, Meenu , H.K. Sardana 
    Abstract: Automated recognition of mechanical parts is a task in manufacturing that has been automated at a comparatively slow pace. Nearly all of the existing object recognition systems, with the exception of very few experimental systems have been designed to recognize a single object. In this paper, this problem is solved in a great manner so that the same process can handle different 2-D recognition applications. Color images are used during object recognition. The Fourier descriptor method has been adopted for recognition of mechanical parts. This method recognizes an object by extraction of features from an object image. The objects may be classified using Artificial Neural Network (ANN). For training and testing in either case, the features are extracted by presenting the object in different orientations. A feed forward neural network structure that learns the characteristics of the training data through the back-propagation learning algorithm is employed. The emphasis is put on the choice of network architecture and setting of different parameters. The study also considers the effects of various user-defined parameters and noting their effect on classification accuracy. The effect of orientation angle of the object and sample size on overall accuracy is also considered on the used classifier.
    Keywords: Automated Recognition; Mechanical parts; Object recognition systems; Image Processing; Artificial Neural Network; Intelligent Machines; and Robotics.

  • Optical dynamic balancing of shaking force and shaking moment for planar mechanisms   Order a copy of this article
    by Samiksha Agarwal, Vikas Bansal 
    Abstract: An optimization technique is applied in dynamic balancing of planner mechanism in which gen etic algorithm is applied. By using genetic algorithm the driving torque, shaking force and shaking moment are minimized. An equivalent (equimomental) system (point masses) are developed which is dynamic equivalent to another system, where some sets of forces and moments can produce the same linear velocity, angular velocity, angular acceleration or linear acceleration. Point masses are represented by the shaking force and shaking moment, choose as design variables. Design variables of an optimization problem are changed into single objective function and apply genetic algorithm. Best results obtained through Genetic algorithm than conventional algorithm. The standard problem of four bar mechanism shows its effectiveness.
    Keywords: Optimization; Equimomental system; Dynamic balancing; Shaking force; Shaking moment; Genetic algorithm.

  • Prediction of Diabetic Retinopathy based on a Committee of Random Forests   Order a copy of this article
    by Hedieh Sajedi 
    Abstract: Diabetic retinopathy is an ocular disease generated by complications of diabetes, and it must be discovered quickly for effective cure. By early diagnosis of retinal fundus disease, ophthalmologists can cure the disease or reduce its deterioration, thereby preventing the patients from vision loss. Using enlarged images, ophthalmologists can diagnose diabetic retinopathy. In this paper, Committee of Random Forests (CRF) for detection of diabetic retinopathy is proposed. In this approach, we use k-means clustering algorithm and random forest classification method to create a new classifier. CRF has been tested on the Diabetic retinopathy Debrecen dataset, in which 94.76% accuracy is reached in a disease or no disease setting.
    Keywords: Diabetic retinopathy; Machine learning; Hybrid method; K-means clustering; Random forest classification.

  • Construction of Inverter Powered Lawn Mower   Order a copy of this article
    by Adefemi Adeyemi Adekunle, Samuel Babatope Adejuyigbe, Oluremilekun Ropo Oyetunji 
    Abstract: The need for the maintenance of lawns through cutting and trimming cannot be overlooked in sporting fields, residential houses, hotel, hospital and others. Some lawns are specially bred for the sake of aesthetic. Homeowners, horticulturist, gardeners are always on the quest for beautifying and making the environment to be attractive. In some places like hospital, special health care and rehabilitation centers, the natural beauty of the environment is believed to contribute to a great deal in aiding the recovery and healing the mentally and physically challenged patients. This contributory effect of the lawn has made lawn care to be involved with a great admiration. In the world of sport, field events are known to be carried out on a well maintained, low cut lawn as in the example of football, javelin, rugby, golf, etc. An inverter powered lawnmower was designed and constructed using the locally available materials such as a 2KVA inverter, prime mower (electric motor) of a rating of 370W, a 60Amphour battery and a 12 Volts battery. It is of advantage over the existing ones because it uses no fuel consumption, it emits no smoke which causes air pollution, and it has no internal combustion engine to drive the motor of the machine. This lawnmower was designed to mow for a period of two (2) hours and the discharged battery can be recharged with the aid of the inverter when connected to an A.C supply. This machine is most suitable in our environments (Nigeria) because no fuel is needed to power this machine. The unit is simple, light and easy to maneuver.
    Keywords: Battery; Frames; Inverter; Motor; Prime; Rotary blade; Supports; Wheels,.

    by Adefemi Adeyemi Adekunle, Samuel Babatope Adejuyigbe, Bukola Olalekan Bolaji, Bayode Julius Olorunfemi 
    Abstract: The development of unmanned aerial vehicles (UAVs) has been growing significantly over the last decade. UAVs have been expanding from military applications into civilian purposes like Aerial photography, field surveillance, and disaster relief. However, most are often found to be expensive and difficult to deploy. To address these issues, this research work sought to design a Light weight solar powered drone that was autonomous, inexpensive, easy to manufacture and capable of performing surveillance while communicating in real time to the user. The drone was designed as a quad-rotor that houses one camera with a wireless transmission system that provides live feed from the camera to the ground station. It was also intended to be able to carry a payload for future developments. The drone consists of the following component parts: 4 propeller shaft, 4 blades, 4 rotors, 4 gears, cushioning foam, 5W solar panel, 7.4V Rechargeable battery, control panel and the transmitter. The drone met cost standard and could successfully localize its position with GPS sensors. The drone was able to operate for twenty five minutes after the improvisation of the solar panel, the solar panel extended the operational time of the drone for additional ten minutes to the normal operation when using battery only which is fifteen minutes. We are able to make significant development towards creating a low-cost, lightweight unmanned aerial vehicle capable of surveillance with the implementation of a camera system.
    Keywords: Battery; Drone; Global Positioning System; Microcontroller; Motors; Propellers; Sensor; Solar panel; Surveillance Camera; Unmanned Aerial Vehicles,.

  • Development of Machine Vision System for Automated Mechanical Objects Recognition   Order a copy of this article
    by Tushar Jain, Meenu , H.K. Sardana 
    Abstract: Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing, and nanotechnology to multimedia databases. In this paper, mechanical objects recognition used in manufacturing industry is taken into consideration. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear, and variations in raw material. This paper considers the problem of recognizing and classifying of such objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The features are extracted using Fourier descriptor technique. Artificial Neural Network (ANN) with back-propagation learning algorithm is used to train the network and for classification of five different objects. This paper shows the effect of learning rate and momentum on the classification accuracy of objects.
    Keywords: Mechanical objects; Machine vision system; Image processing; Fourier descriptor; Object recognition; Artificial neural network; and Learning rate and momentum.