Shrimp farming is a highly profitable business in the aquaculture industry. The farming profitability can be achieved by the implementation of better management practices in conjunction with optimal shrimp feed management and growth monitoring. Manual measurement for shrimp growth on a large population is a tedious and difficult task. Underfeeding results to lower growth rate, and overfeeding results to environmental pollution. Automated, continuous, and non-invasive methods therefore such as computer vision are being increasingly employed. However, existing researches of vision-based measurement of growth parameters are not yet incorporated to shrimp feed management. This paper presented an Artificial Neural Network-based decision support system of classifying feed type whether starter, grower or finisher using area, length and weight derived from image processing techniques. The neural network was trained using scaled conjugate gradient back propagation. The decision support system exhibited promising results in feed type classification.

Braille is one of the major tools for teaching the visually impaired. Sufficient number of teachers engaged in special education involved in Filipino Braille is not available. One of the possible approaches to address this problem is the use of computers in automation of extracting information in Braille that can facilitate teaching. Other countries have taken their initiative to develop similar technology capable of teaching Braille however the Filipino Braille code including its contractions, and the Filipino language per se has features that are distinct to other languages. This research proposes a system that use machine vision in recognizing one-cell Filipino Braille contractions. Scanned Braille images undergo image processing and HOG feature extraction to train the system classifier thru SVM. Performance evaluation results reflect a high accuracy of recognition.

A design of a secondary power supply using Proportional-Integrative-Derivative (PID) controller has been implemented for better compensation, response and output in comparison with a conventional control strategy of a switch mode converter. A boost converter for an automobile battery as a power source with adaptive control has been introduced in the design. The offsetting and joggling of a 3D printer is manifested as electronic load during the testing. An analysis about the modes of controlling a switch mode power supply (SMPS) has been made using: (1) MATLAB for simulation of graphical presentation of the method and for analysis of transient response of the design and (2) NI Multisim for simulation of SMPS circuit including PID control and conventional SMPS circuit with a fundamental compensation circuit. The two approaches are compared in this paper. The simulations have shown that PID compensates for error in output response and decreases the response time of the converter circuit. Overall the entire design scheme is successfully verified through simulations and by prototyping that shown improvement on dynamic performance (response and efficiency) as compared with the conventional scheme. With the use of adaptive controller, the performance of the boost converter has improved mainly with reduced its response time (3.64ms settling time and 467μs peak time) ensuring its effectiveness in adapting to changes in the power demanded by the load.

This paper presents a classification algorithm for air quality index (AQI) using fuzzy logic (FL) system. AQI tells the level of cleanliness of the air and provides a corresponding health warning. In this study, two types of input pollutants are only considered which are the carbon monoxide (CO) and nitrogen dioxide (NO2). Each input is classified into six categories that include very low, low, moderate, high, very high and extremely high. Mamdani fuzzy inference system (FIS) is used to process the FL system giving an output of AQI values expressed in six categories: good, moderate, unhealthy for sensitive groups, unhealthy, very unhealthy and hazardous. Simulation is performed using MATLAB fuzzy logic toolbox, which provides effective and reliable results.