The growing need for precise and efficient water consumption monitoring underscores the limitations of traditional meter reading methods, which depend on manual labor and are susceptible to human error, limited accessibility, and delayed billing processes. This research presents a LoRa-based wireless sensor network that automates water meter reading and billing using Optical Character Recognition (OCR) technology. The system employs an ESP32-CAM module to capture images of water meter displays, which are then processed using OCR algorithms to extract numerical data. The extracted readings are transmitted via LoRa communication to a central gateway and stored on a cloud-based platform, which supports a web interface for real-time monitoring. The proposed solution significantly enhances data transmission range, reading accuracy, energy efficiency, and system responsiveness. By enabling continuous and remote monitoring of water usage, the system helps detect leaks early and encourages responsible consumption. This approach benefits both consumers and utility providers by improving operational efficiency, reducing costs, and promoting sustainable water management practices.

The research study addresses the prevalent issue of high energy consumption at FAITH Colleges due to non-adherence to responsible practices, such as neglecting to turn off air conditioning units after use and unauthorized use of vacant rooms for leisure activities, resulting in significant energy wastage. To tackle this challenge, the study proposes the implementation of the ECoSense system, an Internet of Things (IoT)-driven power monitoring and centralized control system. The system utilizes a wireless sensor network to monitor energy consumption, which also includes real-time data collection, data analysis, data processing, automated control operation, database management, and data power visualization. Project implementation involved key components such as the PZEM -004T sensor, ESP8266, mini circuit breaker, MG996r Servo Motor, and Google Drive applications. Notably, ECoSense provides users with real-time data insights and automates air conditioning units' operational status based on user-defined schedules, significantly contributing to energy conservation efforts. The results demonstrate successful ECoSense implementation, highlighting its effectiveness in reducing energy consumption and cutting electricity costs.

During the Taal Volcano eruption in January 2020, which inflicted damage in its 17 km radius danger zone as well as neighboring areas, many infrastructures were destroyed. Ash spread even to Manila, resulting in soil deformation, sulfur dioxide emissions, and other issues. Exposure to extremely high quantities of sulfur dioxide can be fatal, and its effects can harm the eyes, mucous membranes, and respiratory tract. In severe cases, it has proven to be deadly. With that in mind, the researchers proposed a system: a detection, monitoring, and early warning system for sulfur dioxide emissions from volcanic activity. The system consists of a sensor node and utilizes a cloud for storage and data display. The sensor node comprises a sensor module connected to a microcontroller and a Wi-Fi module for sending the data through the cloud.

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.