Ramon G. Garcia

Hand tremor analyzer using accelerometry for preliminary diagnosis, classification and monitoring of selected movement disorders

In this paper, a system that is designed to analyze and monitor hand tremor which is to aid medical practitioners in classifying and diagnosing movement disorders in accordance to the frequency of the tremor. The tremor classifications fall into Parkinsons, Essential, and Drug Induced. Diagnosing the underlying cause of tremor does not necessarily lead to curing of the disease itself, but usually works on curing the symptom or lessening it. By maximizing the framework of the system, it can produce useful information about the tremors that is used for emergency and medical diagnosis. The tremors at least 3 Hz, at most 12 Hz can be detected. This device can be useful for the early detection of Parkinsons disease and other pathological tremors, aiding medical practitioners in terms in a time efficient way. The device can be used to monitor the progression of the tremor.

Crowd counting system by facial recognition using Histogram of Oriented Gradients, Completed Local Binary Pattern, Gray-Level Co-Occurrence Matrix and Unmanned Aerial Vehicle

A counting system is a device used for identifying the number of people present in a crowd. It has a wide variety of uses from fields of statistics, business and social sciences. This study introduces a method of a facial recognition counting system through the use of an unmanned aerial vehicle to capture aerial images of the crowd and the use of MATLAB to process those images to count the number of people present in the crowd. The algorithms used in this paper are Histogram of Oriented Gradients (HOG) and Completed Local Binary Pattern (CLBP) for low density and Gray Level Co-Occurrence Matrix (GLCM) for high density. From the data gathered, the program can classify an object as a head if it can see all of the human facial features like e.g. eyes, nose, mouth, etc. Thus, to obtain the best results in counting people in a crowd using this method, the user must take pictures at an angle and height where the features of the face can be seen, in our case, at 15 degrees and 3.2 meters respectively. But, if applied in an actual field, many people will be facing different directions and some faces will be blocked by other people.

Pattern recognition of concrete surface cracks and defects using integrated image processing algorithms

Pattern recognition of concrete surface crack defects is very important in determining stability of structure like building, roads or bridges. Surface crack is one of the subjects in inspection, diagnosis, and maintenance as well as life prediction for the safety of the structures. Traditionally determining defects and cracks on concrete surfaces are done manually by inspection. Moreover, any internal defects on the concrete would require destructive testing for detection. The researchers created an automated surface crack detection for concrete using image processing techniques including Hough transform, LoG weighted, Dilation, Grayscale, Canny Edge Detection and Haar Wavelet Transform. An automatic surface crack detection robot is designed to capture the concrete surface by sectoring method. Surface crack classification was done with the use of Haar trained cascade object detector that uses both positive samples and negative samples which proved that it is possible to effectively identify the surface crack defects.

Support vector machine and mel frequency Cepstral coefficient based algorithm for hand gestures and bidirectional speech to text device

This research is about translating series of hand gesture to form a word and produce its equivalent sound on how it is read and said in Filipino accent using Support Vector Machine and Mel Frequency Cepstral Coefficient analysis. The concept is to detect Filipino speech input and translate the spoken words to their text form in Filipino. This study is trying to help the Filipino deaf community to impart their thoughts through the use of hand gestures and be able to communicate to people who do not know how to read hand gestures. This also helps literate deaf to simply read the spoken words relayed to them using the Filipino speech to text system.

Application specific integrated circuit (ASIC) for Ion Sensitive Field Effect Transistor (ISFET) L-Asparagine biosensor

This work presents the design of application specific integrated circuit (ASIC) intended for L-Asparagine measurement, using ion sensitive field effect transistor (ISFET) as biosensor. The L-Asparagine is an amino acid that promotes the growth of cancer cells in leukemia patients. The designed ASIC is comprised of ISFET bridge-type readout circuit, inverting operational amplifier, and peak detector circuit. The L-Asparagine concentrations are translated into pH levels. SPICE simulations showed a sensitivity of −72.78 mV/pH with a linear correlation of almost 1 across the output of the designed ASIC.

ZigBee and Power Line Communications interconnectivity applied to fuzzy logic controlled automated lighting system

The ongoing trend in technology made home appliances and electronic devices connect to the Internet. However, an issue on energy efficiency is still a big problem to different countries although these technologies were used. Recently, the European Commission introduced the use of Power Line Communications (PLC) in which data is being transmitted through power lines as a preparation for transition from the Electric Grid to a new concept Smart Grid. PLC being a new wired protocol is still under development to communicate with other protocols including wireless protocol for seamless connection. The study focuses on interconnecting ZigBee wireless protocol to the said wired protocol by developing a gateway to convert their protocols into one common protocol. A method used in converting protocols includes varying the PLCs Programmable System on Chip (PSoC) into a computer architecture which enables Universal Asynchronous Receiver/Transmitted (UART) communication to successfully interconnect with ZigBee. The said interconnection is applied on a fuzzy logic controlled light system in which transmission test and power consumption was evaluated. The results of the analysis were presented to which they can be used as a guide for interconnecting other protocols with PLC.

Maximum power point tracking controller IC based on ripple correlation control algorithm

A 5V analog maximum power point tracking (MPPT) controller integrated circuit (IC) based on ripple correlation control was presented in this study to achieve the maximum power from the photovoltaic array or solar panel under partial shading during the first 72 hours following a disaster when power interruptions and inaccessibility to light may be expected. The IC was implemented in TSMC 0.25 micron 2P4M 5V mixed-signal CMOS technology. The simulation results showed that at 1000W/m2 solar irradiance and 25 degrees Celsius temperature, the tracking efficiency is about 97.42%.

Aural and photonic spectrum based digital pest controller for Oryza Sativa (rice)

A research study was conducted in Western Philippines University, Aborlan, Palawan from November 2013 to January 2014 to test a prototype of an alternative digital pest controller for rice which eliminates the use of chemicals. Two modules were developed to ascertain the attraction of pests: the day module operation covered the determination of frequency (60 Hz, 80 Hz, 100 Hz, 120 Hz) and sound intensity (20 dB, 50 dB, 80 dB); the night module operation determines the light color (white, yellow, blue, UV) and the light intensity (10 lux, 40 lux, 80 lux). The results for the day module operation showed that insects responded to 80 Hz at 50 dB. For the night module, the most number of insects were attracted to UV, at 80 lux intensity. These results indicated that insects are diversely attracted on different sound frequencies but are more inclined with modulated sound level and are attracted to lights with short wavelengths on higher light intensities.