QUIC is a transport protocol that is gaining adoption among content providers, offering reliability and speed by building an encryption layer similar to TCP's atop a UDP framework. Due to the vulnerabilities of both protocols, the demand for a QUIC-based Network Intrusion Detection System (IDS) continues to grow. QUIC-AID, an adaptive IDS framework designed for QUIC traffic, is proposed. Labeled network traffic flows were collected through a simulated environment for benign traffic and two types of QUIC attacks: flooding and fuzzing. QUIC-AID incorporates (1) dynamic feature selection-OFS and FIRES, (2) an online learning classifier-Adaptive Random Forest (ARF), and (3) drift detection-Adaptive Windowing (ADWIN). Prequential evaluation on the collected QUIC dataset (390,000 flows) showed that QUIC-AID outperformed other benchmarks (KNN and OC-SVM) in terms of true positive detection with a false positive rate below 0.1%. It also yielded the best results for ARF, achieving 99.94% recall. With dynamic feature selection, the rate of byte flow during attacks consistently had the highest weighting feature through FIRES, whereas OFS shifted weights based on the attack type.

In the world of the Internet of Things, testbeds offer the necessary infrastructure in which ideas, technologies, and practices are tested and validated. A smart classroom called Smart-iLAB has been previously developed to showcase advances in electronics laboratory instruction, and is equipped with IoTbased sensing and actuation features. However, the Smart-iLAB did not offer the infrastructure required for users to conduct experiments and tests using the available sensors and actuators. In this work, we established a REST-based API testbed for the Smart-iLAB's IoT platform leveraging on MQTT connections, python scripts, a SQL-based database, and a RESTful API. In addition, a digital twin has been developed to provide immediate visual feedback of the Smart I-LAB environment. The RESTbased API testbed, with its accompanying digital twin, is hosted on a web-based platform that enables users to interact with the sensors and actuators of the Smart-iLAB externally. The RESTbased API testbed can also be used to facilitate the development of scripts, from simple manipulation to complex automation. In addition, the visualization of the layout of the available sensors and actuators serves as a convenient way for users to maximize the capabilities of the smart system remotely. To illustrate its effectiveness, the REST-based API testbed was tested to assess its latency, scalability, and reliability.

Electronic scoring systems in combat sports aim to ensure fair and objective scoring by using wearable electronics to detect impacts on valid body parts, reducing human error. This technology has been successfully integrated into protective gear in sports like Olympic Fencing and Taekwondo. This study focuses on developing a low-cost electronic scoring system for Arnis, the national sport of the Philippines. The proposed system uses a Smartfoam architecture with an RFID-based proximity sensor and a Velostat-based thin film impact sensor. To evaluate the system, a stationary test bed simulated a sparring match between two Arnis athletes. The test, repeated three times, measured the accuracy of hit validation by comparing detected hits to actual valid hits. The system achieved a high accuracy rate of 99%. The few missed hits were attributed to issues with the RFID reader and tags in the Smartfoam and Arnis stick. Thus, the study recommends further experiments to optimize the RFID components for better performance.

Weather Monitoring Systems available in the market are typically closed systems that are designed to work only with other equipment from the same manufacturer. This hinders the collection of information from a variety of weather stations, preventing denser data collection and better observation of weather patterns through collaborative weather sensing among individuals and institutions. We leveraged the programmability of software-defined radios (SDRs) to enable collecting data from multiple, heterogeneous weather stations by a single radio. We developed a weather station gateway equipped with SDRs that are programmed to receive and decode information from weather stations that use different protocols and operate at distinct frequencies. Data from these stations are stored both locally and online, with update intervals being real-time for the local storage and every 15 minutes for the online database. Deployment results show reliable data reception from the heterogeneous weather stations over a 24-hour period.

Harvesting and managing energy is one of the challenges that comes with optimizing the use of renewable energy. IoT (Internet of Things) emerges as one of the current technologies capable of addressing this challenge. In this paper, sensor-converter-microcontroller (SCM) modules are used to monitor energy consumption and to assess the feasibility of using purely renewable energy to power a smart classroom based on the load curve of a 1-hp air conditioning unit, 30W light fixture, and general appliance outlet, monitored through Home Assistant (HA) and ESPHome. The SCM modules successfully allowed remote access to real-time current and voltage data, with the emulated solar system proving that an all-green energy source for smart classrooms is feasible as shown using the LCC analysis, only limited by available space for solar module installation.

Manual attendance tracking in schools is inefficient and error-prone. This project developed an RFID-based attendance system to address these issues. The system uses a WiFi-enabled microcontroller, RFID scanner, MySQL database, and Python scripts to automatically record and manage attendance data. A pilot implementation across six classes demonstrated the system's superiority over manual methods in terms of speed, efficiency, and reliability, with both students and lecturers expressing strong preference for the RFID system. The system presents potential for wider adoption in educational institutions.

Filipino Sign Language (FSL) is an important mode of communication for the deaf community in the Philippines yet the shortage of qualified interpreters creates barriers to effective communication. Thus, in this work, we developed a web application using computer vision and deep learning to recognize Filipino Sign Language (FSL) in real-time. The application extracts key features from hand gestures using Mediapipe and utilizes a CNN-LSTM model, combining strengths of convolutional neural networks and long short-term memory networks, to achieve high accuracy in recognizing both static and dynamic FSL signs. We performed a comparative analysis between five distinct CNN-LSTM models, evaluating them based on their performance on prediction speed, accuracy, precision, recall, and F1-score. The best performing model was CNN-MP-LSTM which achieved 98% accuracy during training, and 86% accuracy in real-world testing. Ultimately, this application has the potential to significantly improve communication accessibility for the deaf community in the Philippines.

Advancement in technology introduced us to various platforms that changed our way of communicating and collaborating. With the sudden demand for technological dependence, the COVID-19 situation exposed the limited internet infrastructure and costly internet service of countries like the Philippines. In this study, the researchers explored the option of real-time offline collaboration in an ad hoc network as a solution to unimpeded collaboration in low bandwidth and poor connectivity environments. The researchers developed on top of CONCLAVE, an open-source collaborative text editor that utilizes WebRTC. Offline functionality was enabled in this collaborative platform along with the implementation of offline peer discovery using Websockets and a peer handling technique inspired by Scalable Content Addressable Networks (CAN). These modifications were evaluated on an ad hoc network using different metrics such as packet delay, jitter, data rate, and peer disconnection resolution time. Results of the testing was favorable to the modified version of CONCLAVE in an ad hoc network. With these results, the project has successfully enabled offline use and continued operation in the event of a disconnected peer, and has successfully implemented a peer handling technique that has improved the P2P operation of the original application.

Mobile vision technologies have paved the way for augmented (AR) and mixed reality (MR) applications to be realizable on mobile devices. Mobile platforms such as Android and iOS have recently demonstrated the early opportunities for AR/MR applications using their devices. Now, while these technologies can still be considered in its infancy, it is opportune to start thinking about privacy and security while their functionalities are slowly being revealed to us. In this work, we present a visual access control mechanism in the form of object-level abstraction. Using readily-available object detection algorithms, we are able to demonstrate a proof-of-concept object-level abstraction for fine-grained access control in a mobile device. Furthermore, aside from the inherent confidentiality and content awareness guarantee of abstraction, reduction in execution times from visual processing resource sharing is another consequential benefit of abstraction without any energy consumption impact.