No abstract

The invention of a can crusher machine in this project is to reduce the wasted storage space occupied by the tremendous amount of use aluminium can at the commercial establishment like in the restaurant, cafeteria and bar. Basically, can crusher machine be operated in manual effort and time in the can crushing process. Shrinking the initial volume of empty used-aluminium cans down to 50% in more effective, faster and effortless way, as well as to develop a low-cost device that is suitable for the small-industry usage are mainly the objectives for the Automatic Can Crusher, where an automated process is executed in Automatic Can Crusher due to the automation in the modern world is inevitable and nominal to be used. The Automatic Can Crusher is run by a Programmable Logic Controller (PLC) with the aid of an inductive and capacitive sensor, where it is applied to detect whether the object is metal or non-metal. Overall, the system can be controlled manually through the push start and stop button as well as using the Human Machine Interface (HMI) using NB-Designer, for displaying the total of cans being crushed per day. The average result of empty can could shrink from 31% to 60 % of the original value, by using the attuned and compatible pressure for this system.

No abstract

Handwritten Mathematical Expression recognition and grading system is a challenging task in the field of pattern recognition. A lot of researchers have already worked on Handwritten Mathematical Expression recognition and have used various classifiers. In past, Convolutional Neural Network, also called CNN, has been highly used for recognizing patterns. In this paper, We propose an idea to recognize HME and evaluate offline using CNN for classification. The steps included are, first the worksheet is scanned and is sent to the work-spaces detection module where it will return all the rectangular work-spaces from the given worksheet, then the workspaces are sent to the line extraction module to extract all the lines. The extracted lines are then passed to the character segmentation module where it will segment the character and then characters will be classified using deep learning model DCCNN. Finally, the evaluation module will assess the line and draw a green/red bounding box depending on whether the line is correct or not.