Every person has different perspective on whether a food is spoiled, and this may lead to wrong assumptions on the food's condition. Misinterpretation on food condition could lead to food-borne illness. The need for electronic nose system for classifying if a food is spoiled is still in demand due to this prediction confusion. This study aims to detect food spoilage of tomato-based Filipino cuisines using an electronic nose. Specifically, this research aims to develop a device with an array of sensors to detect the gases emitted by spoiled tomato-based Filipino cuisines and implement Artificial Neural Network as an algorithm for the classification of the data reading of the sensor. The hardware part of the e-nose system makes use of Gizduino 1281, Raspberry Pi 3 model B, 20×4 LCD Screen, 7 MQ gas sensors, and 1 temperature/humidity sensor. Stochastic Gradient Descent together with Back propagation algorithm is used for training the Artificial Neural Network data. The tomato-based Filipino cuisine is placed below the sensor chamber for easy recognition of the gas sensors emitted by the cuisine itself. The study could be useful in classifying whether food is spoiled. This electronic nose system could determine spoilage level of a specific tomato-based Filipino cuisine. The researchers assigned level 0 to 12 as the spoilage level. These levels correspond to the time when the food is observed every four hours starting 7:00 AM of Day 1. As the experiment continued, it was also that the food often spoiled between levels 5 and 6. Based on the confusion matrix, the error rate of the electronic nose system is 3.85%.

This paper describes the design and implementation of a water level monitoring and flood warning system using light detection and ranging (LiDAR) as a water level sensor. The integration of hybrid solar-wind renewable power supply is also included in this paper. The developed system was installed at Bucayao Bridge in Calapan City, Oriental Mindoro. The system used a floater object, enclosed in a heavy-duty plastic mat, as a reflector of light pulses from the LiDAR sensor. During a 5-day observation period, in which red rainfall warning was raised at the test site in Calapan City, the water level monitoring system measured a maximum height of water at 4.52 m. Whereas, during good weather conditions, the system water level measurements were only around 0.28 m to 0.44 m. The algorithms were embedded in microcontrollers and made it possible for the system to provide early flood warnings via short message service (SMS) and siren. The maximum output power of the wind turbine was detected at around 6:00 PM with 370 W at a wind speed of 5.56 m/s. While the high output power of the solar panel was recorded from noontime until around 3:00 PM with 290 W to 320 W. The hybrid solar-wind power produced a maximum of 454.97 W when the weather was mostly clear and windy. The combined output power of solar panel and wind turbine met the daily power requirement of the water level monitoring and flood warning system.

Smartphones are being used on a daily basis by most people. It assists every persons' needs and wants in terms of communication, entertainment, news, and many more. As times passes by, it provided more ways to help people on their everyday life. The project aims to provide a way for sea vessel's watch-standing officers to monitor the marine traffic, and to provide information to avoid collision within Philippine maritime area of responsibility. The developed systems was also conceptualized to consider monitoring unidentified ship intruders within local territory that can possibly serve as a threat to national security. This research is to create an Automatic Identification System (AIS) localized monitoring application using mobile phone. An AIS monitoring system is used to simulate actual maritime data to check the location and movement of vessels or ships within the surrounding maritime area. After actual data simulation is done in the system, results show that accurate sea vessels information are being tracked using the AIS mobile app.

This study focuses on the modification of the design of an existing “smart backpack” by integrating photoelectric sensors, Radio-Frequency Identification (RFID) and ZigBee technology in order to assist blind people. The photoelectric sensor emits light continuously until an obstacle blocks its line of sight and receives the reflected light. RFID reader and tags are incorporated to help the user identify specific objects. For indoor position tracking, ZigBee modules are mounted on the ceiling of the target area of application. Moreover, an audio output would be heard to inform the user’s specific distance from the obstacle and the direction where the user was detected, the user’s current location and the identified object. The audio notifications were in the form of text-to-speech generated voice messages stored on an Android application needed to be installed on a smartphone. Bit error rate for ZigBee modules and proximity check for photoelectric sensors and RFID tags were rendered to test each component’s performance as it operates with the overall system. With the aid of the tests performed, proper orientation and inclination of the sensors were found to be significant for more accurate results. The power rating of the system’s battery is also important to ensure that can support all the components for higher efficiency.

Upper-air observation uses radiosonde, via weather balloon, to measure atmospheric data in order to make an accurate weather forecast. This observation includes the gathering of air pressure, temperature, and relative humidity from ground level to the above of the lower troposphere. In this study, Long-Range (LoRa) embedded modules are used to provide transmission of atmospheric data from the radiosonde carried by a balloon to the ground system for their low-cost, low-power and long-range characteristics. The data to be transmitted, received and saved in secure digital card includes sample atmospheric data, location coordinates, altitude, timestamps and data for power monitoring. Environmental tests and actual balloon flight test were conducted to determine the performance of the transmission of data. Based on the results of the tests, 433 MHz RFM96W LoRa RF transceiver module successfully transmit data up to approximately 10 kilometers of altitude as a transmitter of the payload. Transmission of every data happens once in a second. The payload has an output power of 300 mW, has a weight of 292 grams, and has 50% cost compared with commercial radiosondes. Supply voltage lasts for almost 57 hours.

Philippines, being a tropical country had two seasons every year such as rainy and dry. These conditions lead the soil moisture is not constant which affected the agricultural crops. This study aims to: (1) prepare a soil moisture sensor using Plaster of Paris material; (2) determine the time drift characteristic of such soil moisture sensor; (3) design a sensor system with time drift compensation; and (4) design a wireless monitoring system for soil moisture measurement. The study had created a system is measured the soil moisture and wirelessly transmit measured values from time to time and identify whether the clay loam soil is in the right amount or in need of water. The wireless soil moisture sensor system had been developed can compensate with time drift. The study focused on clay loam soil used in growing rice plants. The time drift was observed directly proportional to sensitivity. Besides, time drift values with compensation (16.71 mV/hr) were lesser than time drift without compensation (24.47 mV//hr). Time drift compensation is lower in bare sensors (14.7 mV/hr) than sensors with PVC casing (16.71 mV/hr). Longer sensors had lower than the “2” and “3” length with values of 11.75 mV/hr, 13.87 mV/hr respectively. This study could help in the growing farming management concept and precision agriculture.