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Marine Protected Areas (MPA) are conserved areas for reproduction of marine species. Conflicts in the utilization of MPAs had been a challenge for many years. Fishing activities within the MPAs are real threat for the sustainability of marine resources. In the Philippines, direct visual observation of the responsible agencies is the only method to identify the entry of fishing vessels in the MPAs. This paper presents an IoT-based system for monitoring the fishing vessels’ entry in the MPA. The system uses Haversine method for computing the distance between the fishing vessel and the MPA. The distance had been classified to provide different location status to the fishing vessel and Web interface. LoRa communications module was utilized in the study and was able to transmit geographical information from the fishing vessel to the base station within few kilometers. The Web interface with Google Maps API allows the end user to remotely monitor clearly the entry of fishing vessel in the MPA.

the need for better and more reliable security measures is prevalent today especially as the commonly existing security measures are getting less reliable. The study was conducted to create a solution to the said problem using the very versatile and relatively inexpensive Field-Programmable Gate Array (FPGA) board. The system incorporates the unique frequency content of every individual as a more reliable and difficult to replicate, replacing the conventional security keys and passwords. The user's frequency content or voice model was extracted using signal processing in FPGA and stored in the system as the basis for comparison for the different speech signals. Input speech signals were matched to the stored voice model and the authentication system shows a locked or unlocked status through an on-board LEDs. LEDs which lights up indicates unlocked and doesn't otherwise. The system was successfully implemented in Altera DE2-115 board, allowing the correct user in unlocking the system while the other incorrect users can't, with a best system identification accuracy of 86%.