CrOptimize is an integrated agri-tech solution designed to optimize farm lot utilization and resource management by combining geo-tagging, drone surveillance, and soil nutrient monitoring. The system aims to improve agricultural efficiency and sustainability, especially in regions like the Philippines, where food security is a growing concern. Geo-tagging enables accurate measurement of farm areas, ensuring precise seed allocation and minimizing surplus or shortages. Soil nutrient sensors analyze soil composition and guide farmers in selecting suitable crops and planting strategies, enhancing yield potential. Drone surveillance validates seed distribution and continuously monitors crop growth, promoting transparency and accountability in farming practices. This data-driven approach reduces inefficiencies and resource wastage while supporting informed decision-making among farmers and agricultural suppliers. Suppliers also benefit from enhanced supply chain accuracy, enabling better distribution planning and inventory management. Although challenges such as GPS signal limitations, sensor calibration, and drone maintenance may impact deployment, CrOptimize offers a scalable and adaptable framework for modernizing agriculture. By integrating advanced technologies, the system empowers stakeholders to improve productivity and align with global efforts toward sustainable and resilient food systems.

The Detection of Violators of Garbage Disposal Schedule with Monitoring System tackles important waste management issues such as compliance challenges with garbage disposal schedules, illegal dumping, and monitoring problems in Barangay Santiago, Malvar, Batangas. The work relies on rigorous data collection from related studies and literature, which is enriched by the knowledge gained from interviews with local government representatives. Through the use of camera technology, the system proposed here provides a new way to improve monitoring and enforcement. The efficiency of the project clearly exceeds existing solutions, demonstrated through the thorough evaluation of the metric system. This new system will help other areas to have positive results for more efficient waste disposal, safe, clean and healthy environment. More studies can be conducted to determine its scalability, cost-effectiveness, and the potential of adaptations for different geographical areas to make it more effective and sustainable.

Students with disabilities have received inadequate attention in terms of their engagement with technology. Research pertaining to this area is underdeveloped and while generic research often excludes this sector of the student population. Special education gives students with disabilities the opportunity to get quality education in line with their unique needs. As children progress through different stages, guided and comprehensive education becomes a cornerstone for maximizing their potential. Proper education equips them with the tools to navigate the challenges of abstract thinking and cultivates a well-rounded cognitive. Appropriate assistive technology has a direct impact on the well-being of children by supporting their functionality and inclusion into society, thereby increasing the opportunities for education. The project “GestuLearn” is an interactive learning system that utilizes real-time hand detection, tracking, and gesture recognition via computer vision, customized programming, and machine learning algorithms that is designed for assistive teaching to aid students with disabilities particularly the deaf, mute and with learning disabilities.

The COVID-19 pandemic has greatly affected the country, particularly in executing health and safety protocols. In response, the researchers designed and proposed a project which aims to implement a real-time masked face recognition for logging system with health and temperature monitoring. This includes image processing, machine learning model training, masked and unmasked face detection and recognition, facemask classification, body temperature checking, health declaration form verification for health status validation, health data integration, and log entry recording. The project implementation involved MSI-Z97M System Unit, Logitech HD Pro Webcam C920, FLIR Lepton 3.5 with PureThermalV2 smart I/O board, Dual-display Monitor, FaceNet with Single Shot MultiBox Detector training model, and Microsoft Office 365 applications. The results showed that the researchers successfully implemented the system and achieved the objectives of the study. This research offers a promising solution for real-time tracking and management of health protocols, amidst the ongoing concern brought by the pandemic.

This research paper presents an automated system developed using Jetson Nano and the YOLOv5n6 model for efficient and realtime detection and classification of pavement damage. The system offers a promising solution for transportation agencies in countries with extensive road networks, such as the Philippines, by reducing the need for manual inspections and streamlining maintenance efforts. By leveraging deep learning techniques, the proposed system demonstrates high accuracy in identifying various types of pavement damage, including cracks, alligator cracks, and potholes. The system's deployment on Jetson Nano provides efficient processing capabilities, enabling realtime analysis of video feeds from road cameras or mobile devices. The results of comprehensive evaluations indicate the system's adaptability to varying environmental conditions and its potential for large-scale implementation. The automated system contributes to cost savings, improved road safety, and enhanced management of pavement quality.

The expansion of urbanization has resulted in a reduction of available land for agricultural purposes. As a response, aquaponics has emerged as an environmentally friendly solution for local food production. By integrating aquaculture (fish farming) and hydroponics (soilless farming), aquaponics provides an opportunity for individuals without access to land to engage in farming and aquaculture activities. Traditional aquaponic systems typically require substantial space. However, these systems can be upgraded through adequate financial resources and offer enhanced flexibility. This study presents a compact aquaponics system that operates efficiently using solar panels as a sustainable power source. Unlike the traditional aquaponic system, which does not offer any automated monitoring features, this system incorporates automatic monitoring features facilitated by Arduino microcontrollers and sensors, achieving a remarkable accuracy rate of over 90% in maintaining optimal conditions. In conclusion, aquaponics is a viable solution for urban farming in the face of limited available land. The compact aquaponics system proposed in this study, powered by solar panels, and automated monitoring capabilities, exemplifies a scientifically rigorous and sustainable approach to agricultural practices.

This paper presents an Arduino-based monitoring system that measures four physicochemical parameters of water: pH, temperature, turbidity, and electrical conductivity to identify possible water contamination. The system is designed with two nodes: the sensor node and the sink node. The sensor node performs the collection, pre-processing of data, relay of sensed data wirelessly, data storage systems, data display in an LCD and ThingSpeak channel, and SMS notifications. While the sink node performs data reception from the sensor node, data display in an LCD, and alert systems utilizing a buzzer whenever water is determined to be unsafe for drinking.

Rice is the staple food of the Filipinos. According to the Bureau of Agricultural and Fisheries Product Standards, an average Filipino consumes 4-5 servings of rice per day. But because there is no accurate way of detecting rice spoilage before consumption, Filipinos only rely on their senses to know whether the rice is spoiled or not. This makes them at risk of foodborne illness due to rice spoilage. But with the latest technology advancements, machine learning could be used to help lessen the risk and cases of food illness caused by rice spoilage. This study focuses on the implementation of Azure Custom Vision API to detect rice spoilage. Gas sensor readings and images captured during data gathering were correlated with a resulting value of 1 which corresponds to a very strong correlation. The system was tested by the researchers using 20 different rice samples that includes 10 samples of spoiled rice and 10 samples of not spoiled rice which resulted in a detection accuracy of 85%. The system is implemented with its own container using a Raspberry Pi 3B with a camera module through Python programming language.

Efficiency is a requirement when it comes to utilizing a wireless sensor network (WSN) where hydrokinetic energy harvesting through turbine is involved. Thus, not only WSN needs an efficient supply but also the sensors and the storage unit which are powered up by an energy harvesting module, a turbine DC generator. Turbine DC generator produces a low voltage and low voltage means low power. To produce high efficiency output despite the low power it produces, a DC-DC converter is one of the preliminaries. DC-DC converter regulates its input coming from the turbine DC generator and produces a more stable power supply. However, blocks that the DC-DC converter supplies have different voltage requirement. Therefore, the researchers will develop two DC-DC converter topology which are the Buck Converter and the Boost Converter. On the contrary, turbine DC generator produces varying DC supply to the boost converter inducing noises and reducing the efficiency needed. Therefore, to achieve a highly efficient output the device needs to be low noise. To prevent noise from affecting the efficiency of the device, the researchers will use a technique called Hysteretic Control (HC) of DC-DC converter. This research intended to design a high efficiency Direct Current-to-Direct Current Converter for hydroelectric energy harvester in wireless sensor network. Using an Electronic Design Automation (EDA) tool, Synopsys, and ensuing the full custom analog design is practiced, the researchers develop a DC-DC converter that will provide an efficiency of 80% - 95% by reducing the noise by using a switching DC-DC converter.

In the Western Pacific, the Philippines ranks fifth in the number of diagnosed diabetics. According to Philippine Statistics Authority's 2016 report, Diabetes Mellitus ranks sixth as the cause of death in the country comprising of 5.7% of the total deaths. With the increasing urbanization, the number of diabetics in the country is expected to grow, but with the recent technological advancements, artificial intelligence could be used to facilitate and diagnose the people who could potentially suffer from the said disease. This study focuses on the implementation of Multilayer Perceptron with Histogram of Oriented Gradient to classify different fingerprint patterns, namely arch, loops, and whorls and lastly Logistic Regression to predict whether a person has a high or low chance of having a diabetes. The system is implemented using a 64-bit Windows operating system through Python, java programming languages, and U.are.U fingerprint scanner for the input images.

One of the basic sources of life in earth is water and as the population of humans living in it increases, water consumption and contamination also increases. Thus, the researchers aim to develop a sensor platform that monitors the quality and contamination of water bodies. There are commercially available sensor platforms that has sensor nodes which transmit data it gathered wirelessly to a base station and are battery-driven. However, due to lack of recharging capability of batteries, the life of sensor platforms is shortened, and the replacement of these batteries became a burden. Hence, this research aims to develop and implement an energy management unit of a harvesting system in a sensor platform through a 90nm process technology in order to eliminate the burden of replacing the batteries and extend the sensor platform's life by efficiently managing the energy sources given by the environment and the battery.

The research presents about the Design of an Advanced Microcontroller Bus Architecture (AMBA) for 32-bit ARM-based Microprocessor for Wireless Sensor Network. The researchers developed an architecture that is capable of transferring data from one block to another. The researchers' design of AMBA reduces the power consumption of Wireless Sensor Network. ASIC implementation is done by following the semicustom ASIC design flow, from RTL specification written in VERILOG HDL, to functional verification, and synthesis. The AMBA design is developed in a 90nm process technology using Synopsys tools.

The trend in today's technology is continuously evolving and resolving some of the nature's problems through environmental monitoring. Sensor platforms are acquainted to these functions, for it is one of the devices that enables the reading of data from the environment and interprets to a function that allows monitoring. In every sensor platform, a stable clock generator circuit is a necessity to synchronize the input data from the output results and action. A self-correcting clock generator fixes the process, voltage, and temperature variation issues that affect the outputs of almost all clock generator circuit. The Five-stage Prescriptive Model was used to have a proper procedure in designing the clock generator while pairwise comparison chart and decision matrix were used for the evaluation of alternatives. Current starved ring oscillator was the main circuit block that was used as clock generator which produced the desired frequency. Different circuit blocks such as frequency to voltage converter, analog to digital converter, limiter circuit, trimming circuit, and oscillator are put together as the feedback circuit. The clock automatically corrects itself with a frequency variation of ±10% even if the process, voltage, and temperature change. This study focused on having a low power of 1.12mW for the whole circuit, with a voltage supply of 1.2V. It has three different output frequencies, 26MHz, 13MHz, and 1.08MHz, that were obtained using frequency dividers. The design was implemented using Synopsys tool in 90nm CMOS process technology.

Wireless Sensor Network (WSN) for environmental monitoring in a form of a sensor platform is built with individual nodes, called sensors, that accepts analog signals detected from the changes of state in the environment. One of the crucial parts of a WSN is an Analog-to-Digital Converter (ADC) which acts as the interface between the environment and the platform. Since the WSN is for environmental monitoring, it is powered by an energy harvesting unit which demands for low-energy consumption on the constituent blocks including the ADC. Implementing a high-resolution converter can mean higher energy consumption and a much accurate digital result, nonetheless, lower resolution converter consumes less energy but still can give correct digital output. This trade-off between accuracy and energy consumption made impact to the researchers to develop a programmable Successive Approximation Register ADC for WSN. This research study was implemented using Synopsys EDA Tool in 90nm CMOS Process Technology. The SAR ADC was successfully designed with 8-bits and 14-bits resolution, a sampling rate of 100ksps and 62.5ksps, respectively. The energy consumption of the SAR ADC is 234pJ (8-bits) and 366.72pJ (14-bits), and an energy saving of 36.2%.

The paper is about the development of low power Low Dropout (LDO) regulator with additional housekeeping blocks including the Thermal Shutdown (TSD) and Undervoltage Lockout (UVLO) circuits as a temperature and voltage protection schemes for WSN application. The design is implemented using Synopsys Custom Designer tool, and the layout is physically verified using Hercules. The maximum power dissipation of the system is 298uW, with current efficiency of 76.77%.

The aftermath of endless environmental degradation poses a lot of risk among individuals including climate change. Modern technology addresses the need of a more competent environmental monitoring through Internet of Things (IoT) and the use of System-on-Chips (SoCs) such as Environmental Sensor Platform. One of the critical components of SoC is a Random Access Memory (RAM) which presents a sizable fraction of power consumption. This makes memory inappropriate for energy-constrained applications such as environmental monitoring. The purpose of this study is the development of low power full-custom 8T SRAM for sensor platforms. Different SRAM topologies were evaluated to identify which among has the lowest power consumption. Verified through simulations, a fully functioning schematic that can read and write data were designed in 90nm CMOS process technology. Layout of each block were created using Synopsys design tools yielding power consumption of 21.0405mW and static noise margin of 520mV achieved by eliminating sense amplifier and pre- charge circuits. This study significantly reduced power consumption of memory which is suitable for environmental monitoring applications.