Rosula S. J. Reyes

Wireless power consumption monitoring and analysis system using Winter's forecasting method

The Philippines is one of the rising economies in Southeast Asia. However, it is facing a power crisis where there is a continuous increases in an already huge demand in electricity given the limited and scare supply from different power sectors. Consumers are called to be aware of their power consumption and make necessary efforts towards the smart and efficient use of it. As a response, the proponents developed an appliance-level system that monitors and analyzes power consumption. The monitoring subsystem was implemented through a portable hardware black box which includes the power meter sensor, an Arduino microcontroller clone board and a ZigBee shield for wireless transmission to a microcomputer. The Raspberry Pi microcomputer served as a temporary local server for the sync node and the gateway for the data to be stored in an online database. From this, the analysis subsystem retrieves the consolidated data to undergo both retrospective and Winters forecasting technique. All necessary information, figures and graphs will be presented to the user for interpretation through a simple web application. Overall, the study fulfills its vision of giving people the power over their utility bills by being a tool for raising awareness towards responsibly reducing power consumption to a more efficient use.

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.

Precision agriculture monitoring system using wireless sensor network and Raspberry Pi local server

Precision Agriculture is utilized to improve the productivity and efficiency of limited agricultural resources by monitoring the relevant data in the field. The main objective of this study is to deploy a low-cost sensor system, gather field data, and display the data through a graphical user interface (GUI). Sensors such as humidity, temperature, moisture, luminosity, electrical conductivity, and pH was used for data acquisition and the Raspberry Pi, acting as a local server, was used for data processing and transfer. The data sent was stored in a main server and organized using SQL. A GUI was developed to provide visualization of the data gathered. The trends of data gathered revealed pattern such as the occurrence of a local maximum for humidity right after dawn and the inverse relationship of humidity and temperature. The whole system was tested and proven to work by the application of fertilizer to the soil and seeing its response in the GUI.

Brain-computer interface and voice-controlled 3D printed prosthetic hand

Current designs for 3D printed hand prosthetics are only capable of basic hand gestures given their fully mechanical structure. That said, the purpose of this thesis is to give users greater flexibility and control over 3D printed hand prosthetics by modifying and improving existing open-source prosthetics models. This will be accomplished by integrating two control modules with the 3D printed prosthetic hand - (a) a brain-computer interface, and (b) a voice recognition module - which will serve as its two primary modes of control.

Simulation and FPGA-based implementation of iterative parallel schedulers for optical interconnection networks

High-performance interconnection networks are required for inter-board, intra-board, and on-chip data communication. With the growth of data communication, the requirements for high bandwidth density, high scalability, low latency, and low power consumption are becoming more stringent, making optical solutions appealing. Such requirements should be achieved not only by the hardware architecture but also by the electronic scheduler that is in charge of deciding the packet transmissions and controlling the optical devices. In particular, low-latency schedulers are of paramount importance—especially for optical interconnection networks whose switching capabilities may be constrained by the optical domain. This paper focuses on the hardware implementation and optimization of a scheduler suitable for optical interconnection networks. Parallel, iterative scheduling algorithms are considered for high computational efficiency. More specifically, an iterative parallel implementation of the longestqueue- first algorithm (ipLQF) is proposed and compared to the well-known iSLIP algorithm. Hardware optimization is carried out to improve their implementation efficiency. Although ipLQF achieves better network performance in terms of packet latency, the hardware implementation indicates that iSLIP stands for execution time and resource utilization in commercial field programmable gate array boards.

Appliance recognition system for ILM using AGILASx — Dataset of common appliances in the Philippines

This study presents the development of a system which can automatically recognize home appliances based on a dataset of electric consumption profiles. The authors report the creation of AGILASx, a dataset of 50 common home appliances and devices in the Philippines. The dataset is populated with 100 appliance signatures in .XML format acquired using plug-based sensors. Each appliance signature consists of the following electric characteristics: real power (W), apparent power (VA), reactive power (var), RMS current (A), RMS voltage (V) and Power Factor (PF). A machine learning approach was utilized for the recognition experiment following a set of test protocols — intersession and unseen instances. The baseline recognition algorithm used was the k-Nearest Neighbor (k-NN) for both test protocols and accuracy levels were collected over three different acquisition frequencies. Using results of the confusion matrices, best results were observed at acquisition frequency of 10−1 Hz for intersession (99%) and unseen instance (99%) test protocols. Lastly, to integrate the dataset and the recognition algorithm, a web application was developed adapting a Web-of-Things architecture to present a smart of recognized appliances and their corresponding consumption.

Appliance recognition using Hall Effect sensors and k-nearest neighbors for power management systems

Power management systems employ appliance recognition such that the burden of manually configuring the system for each appliance is lifted from the user. This research then aims to develop an appliance recognition functionality through current readings gathered from a data acquisition (DAQ) device consisting of Hall Effect current sensors, and through a machine learning classification algorithm called k-nearest neighbors. Ten appliances were tested, comprising of 6,500 samples of test data in the four outlets tested. The average accuracy for the trials is 92.73%. In addition, the appliance recognition functionality was embedded to a cloud-based power management system following an Internet of Things (IoT) architecture. In the end, the developed system can gather data from plugged appliances, perform recognition, and carry out various power management functionalities such as monitoring and appliance-level smart-recommendations.

Wireless real-time performance monitoring with fuzzy logic protection capability for power inverter

The increase of the demand in using renewable energy provides a significant increase of the use of the power inverter in the country. This study provides an opportunity to improve the reliability and capability of power inverters. The minimum and maximum values of the input parameters of the power inverter were characterized and were used as reference for the implementation of the fuzzy logic membership function. A wireless real-time monitoring system has been developed to monitor the critical parameters of the power inverter. It is composed of an Arduino compatible board, power sensors, voltage and current sensors, Zigbee wireless modules and a graphical user interface. For the power inverter protection, a fuzzy logic approach was implemented on the input side of the device. A fuzzy logic charge control is implemented to sustain the input parameters during the operation. Overall results of the study show an improvement on capability and reliability of the power inverter.

The efficiency and effectiveness of engineers as full-time and part-time teachers in selected colleges of engineering of higher learning: an assessment

The study assessed the efficiency and effectiveness of engineers as full-time and part-time college of engineering teachers in selected institutions of higher learning in the National Capital Region of the Philippines. Specifically, answers were sought in terms of how efficient/effective are professional engineers as full-time and part-time engineering teachers as perceived by the engineer-teachers themselves and the students; if there is any significant difference in the perception of the engineer-teacher themselves and the students on the efficiency/effectiveness of full-time and part-time engineering teachers; how the efficiency/effectiveness as full-time and part-time engineering teachers relate to the demographic variables; what factors are perceived as constraints to efficient/effective teaching according to the engineer-teachers; how may the efficiency/effectiveness of engineers as full-time and part-time engineering teachers be improved to upgrade the quality of the instructional delivery services at the colleges of engineering.