Jose Claro Monje

FPGA-Based Digital Signal Processing Trainer

Field programmable gate arrays (FPGAs) have been used in a wide range of applications including the field of digital signal processing (DSP). This paper presents the use of an FPGA in the implementation of a DSP trainer that will serve as an educational tool to effectively teach the fundamental principles of digital signal processing. This trainer is capable of performing a 1024-point discrete Fourier transform, convolution, correlation, and finite impulse response filter, which includes a low pass, high pass, and band pass filter. This paper also describes the capability of an FPGA to internally generate different input signals like a square wave, triangle wave, and a sine wave, to accept an external signal from a microphone, an MP3 player and the like, to output the transformed signal in digital or analog form, and through the use of a VGA port, to visualize the signals in a display device making this trainer low cost.

Post-disaster rescue facility: Human detection and geolocation using aerial drones

The research focuses on the implementation of a human detection and geolocation system using aerial drones to complement search and rescue. The study includes the characterization of the human detection system for thermal and optical imagery by determining the frame accuracy, true and false positive rates. Geolocation was achieved using triangulated-adjusted GPS data and integration of Google Maps.

Towards building a predictive model for remote river quality monitoring for mining sites

Most, if not all, mining sites in the Philippines are not equipped with expensive or modern monitoring tools to check for quality of soil, water and air elements which are relevant to ensure safety and wellness of miners. This study focused on the development of low cost mobile electronic sensors to monitor quality of water from rivers near mining sites. Low cost electronic sensors connected to a smart phone were developed to capture dissolved oxygen (DO2), pH, Turbidity, Temperature, and Salinity. The data for mercury (Hg) and arsenic (As) were obtained through AAS analyses to form baseline data for the model. Data was collected for over a period of one year, with site visits once every two months. A conditional inference tree (ctree) using recursive binary partitioning was used to generate the prediction model using 70 - 30 split on the training and test data set. The multi-feature model returns Good, Not Good or Unknown based on the scores of each element. The results showed a possible three feature model with significant results for site, salinity and pH balance.

Design and Development of a Wireless Sensor Network Framework for Water Quality Remote Monitoring

This study involves the design and development of a wireless sensor network (WSN) that integrates several sensing modules into a fully-functional system. The overall system is composed of a remote server, a controller node, and several sensing modules. The controller node is implemented using an Android mobile phone with Bluetooth and 3G capabilities. Bluetooth is used to communicate with the various sensing modules; while 3G is used to relay data to the remote server. The sensing modules utilize an Arduino Mega 2560 (with the sensor circuits) and a Bluetooth shield. Test results show that this framework is a viable design for WSN systems and can be used for remote installations that can be continuously upgraded over time.

Design of autonomous sensor nodes for remote soil monitoring in tropical banana plantation

Determining the effect of Fusarium oxysporum f. sp. cubense Tropical Race 4 on various soil parameters is essential in modeling and predicting its occurrence in banana plantations. One way to fulfill this is through a sensor network that will continuously and automatically monitor environmental conditions at suspect locations for an extended period of time. A wireless sensor network was developed specifically for this purpose. This sensor network is capable of measuring soil acidity, moisture, temperature, and conductivity. The designed prototype made use of off-the-shelf Parrot Flower Power soil sensor, pH sensor, Bluno Beetle, battery, and 3D-printed materials, catering specifically to the conditions of tropical banana plantations with consideration for sensor node size, communication, and power. Sensor nodes were tested on both simulated tropical environments and on an actual banana plantation in San Jose, General Santos City, Philippines. Challenges were resolved through iterative design and development of prototypes. Several tests including temperature and weather resilience, and structural stress tests were done to validate the design. Findings showed that the WSN nodes developed for this purpose are resilient to high tropical temperatures for up to 12 hours of continuous exposure, are able to withstand compressive forces of up to 8880.6 N, and can reliably collect data automatically from the area 47.96% of the time at an hourly frequency under actual field conditions.