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.

Environmental monitoring applications often employ an RT-PCR assay step in their methodologies, and often collect samples in the field, far away from a laboratory. In this setting, such fieldwork would benefit greatly from a device that can carry out the reaction. This project involved the construction of a custom RT-PCR thermocycler based on OpenPCR's open source design with the objective of portability, achieved through battery operation. Design changes were made to both scale back the power consumption of the device to 50W, while managing the tradeoffs that come with this miniaturization in order to achieve battery powered operation. A heating element was introduced to improve temperature ramp rate performance, alongside a custom power module to facilitate battery operations. Portability is supplemented by LCD-keypad standalone user interfacing, alongside logging and real time clock features for user accessibility.

In wireless sensor network (WSN) implementations, the potential loss of crucial measurement data is problematic. This happens when the sensor nodes and the base station have a wireless link that does not reach the required received signal strength indicator (RSSI) levels. To prevent data loss, a proactive approach involves the prediction RSSI values within the vicinity of the base station, and positioning sensor nodes in locations with sufficient RSSI levels. Predicting the RSSI level involves channel modelling of radio propagation mechanisms such as ray propagation. The integration of geographic information systems (GIS) into creating 3D environment models would enhance the accuracy of channel modelling, since prediction considers obstructions and reflections. The RSSI prediction was conducted by gathering GIS data to simulate the buildings of UP Diliman, then applying ray propagation models allowed the researcher to determine locations with sufficient RSSI from the proposed base stations. The results of this study will be useful in the instance that a wireless sensor network is implemented in UP Diliman.

Audio-based health monitoring systems have gained traction for its capability in capturing different physiological target signals and holding features relevant in diagnosis and treatment, making it a feasible form of data acquisition in biomedical signal processing and feature extraction. Acquiring audio for both heart and respiratory sounds for rate estimation was accomplished using the bottom microphone of the Google Pixel 6A from the trachea and anterior left. A direct comparison of the filter-Hilbert and wavelet-Shannon algorithms based on literature with modifications catering to the application of this study was done to measure their performance and reliability against the KardiaMobile 6L EKG. Tests done were able to show good accuracy for both algorithms on heart rate calculations on both the trachea and anterior left, satisfying having less than ± 10 BPM difference from control. Tests for respiratory rate only provided promising results of less than ± 5 CPM difference from control for tracheal recordings,

This study contributes to improving heart rate (HR) monitoring by comparing the Peripheral Beat Amplitude (PBA) and Smoothed Z-score Peak Detection (Z-Score) algorithms for HR extraction across different body sites. The Z-Score algorithm, leveraging on edge computing, demonstrated higher accuracy than PBA, with a Mean Absolute Error (MAE) around 4 BPM. The study also explores the suitability of the Perfusion Ratio technique for Blood Oxygen (SpO2) levels and the Welch method for respiratory rate (RR). Testing was conducted simultaneously at four distinct body sites (finger, wrist, arm, and ankle) during sitting and walking activities on six healthy individuals. Results indicate that the wrist is the optimal measurement site for various physiological parameters. Post-processing techniques were deliberately chosen to enhance the system's overall performance. The focus on HR necessitated evaluating two algorithms, given its critical importance. For SpO2and RR, single algorithms were selected to streamline processing while ensuring reliable measurements. The developed system exhibits acceptable power consumption of 51.98 m W and achieves an average coverage of 80% in controlled tests, indicating a significant portion of monitoring time provides accurate physiological data. This study emphasizes the deliberate selection of algorithms and measurement sites, demonstrating the superiority of the Z-Score algorithm and the practicality of integrating HR, SpO2, RR, and PPG monitoring in a single system.

Orthogonal frequency division multiplexing with index modulated (OFDM-IM) is a novel energy efficient variant of OFDM that conveys information through active subcarrier indices and conventional M-ary symbols. OFDM-IM’s potential to be spectrally efficient has been explored extensively in prior work. However, the spectral efficiency (SE) metric is only achievable for very high SNR. For general channel conditions, the achievable rates determined using information-theoretic limits are likely lower than the SE. In this paper, we propose a mapping scheme for an OFDM-IM system that optimizes the mutual information. The proposed scheme uses OFDM-IM symbols with different probabilities of occurring, based on a dyadic probability distribution that best approximates the capacity-achieving input distribution for a peak-power-constrained AWGN channel at a given SNR. The dyadic probability distribution is based on Huffman coding, which is used to create the mapping scheme from OFDM-IM constellation symbols to variable-length bit sequences. The simulation results show that for the same number of subchannels and constellation size, the proposed scheme achieves better mutual information than existing OFDM-based schemes for higher modulation orders.