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Differences in Reliability and Predictability of Harvested Energy from Battery-less Intermittently Powered Systems

Sampath et al. | Apr 29, 2020

Differences in Reliability and Predictability of Harvested Energy from Battery-less Intermittently Powered Systems

Solar and radio frequency harvesters serve as a viable alternative energy source to batteries in many cases where the battery cannot be easily replaced. Using specifically designed circuit models, the authors quantify the reliability of different harvested energy sources to identify the most practical and efficient forms of renewable energy.

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Indoor near-field target detection characteristics under radio and radar joint operation at 2.4 GHz ISM band

Koh et al. | Apr 29, 2022

Indoor near-field target detection characteristics under radio and radar joint operation at 2.4 GHz ISM band

In our modern age, the burgeoning use of radios and radars has resulted in competition for electromagnetic spectrum resources. With recent research highlighting solutions to radio and radar mutual interference, there is a desperate need for a cost-effective configuration that permits a radar-radio joint system. In this study, the authors have set out to determine the feasibility of using single-tone continuous-wave radars in a radar-joint system. With this system, they aim to facilitate cost-effective near-field target detection by way of the popularized 2.4-GHz industrial, scientific, and medical (ISM) band.

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Modeling Energy Produced by Solar Panels

Meister et al. | Jan 13, 2018

Modeling Energy Produced by Solar Panels

In this study, the authors test the effect that the tilt angle of a solar panel has on the amount of energy it generates. This investigation highlights a simple way that people can harvest renewable energy more efficiently and effectively.

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Impact of Kindles4Covid Virtual Reading Buddies Program on reading frequency and social connections

Pandey et al. | Jun 25, 2022

Impact of Kindles4Covid Virtual Reading Buddies Program on reading frequency and social connections

With the COVID-19 pandemic necessitating the transition to remote learning, disruption to daily school routine has impacted educational experiences on a global scale. As a result, it has potentially worsened reading achievement gaps typically exacerbated by long summer months. To address literacy skill retention and pandemic-induced social isolation, the non-profit organization ByKids4Kids has created a reading program, “Kindles4Covid Virtual Reading Buddies Program,” to instill a structure for youth to read together and connect with the convenience of Amazon Kindle devices. In this article, the authors determine the efficacy of their invaluable program by assessing changes in reading frequency and self-reported connectedness among program participants.

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A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring

Vangal et al. | Sep 28, 2020

A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring

Each year, over 100,000 patients die from Sudden Unexpected Death in Epilepsy (SUDEP). A reliable seizure warning system can help patients stay safe. This work presents a comprehensive, comparative analysis of three different signal processing algorithms for automated seizure/ictal detection. The experimental results show that the proposed methods can be effective for accurate automated seizure detection and monitoring in clinical care.

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Towards an Integrated Solution for Renewable Water and Energy

Chen et al. | Jan 09, 2015

Towards an Integrated Solution for Renewable Water and Energy

An integrated plant that would generate energy from solar power and provide clean water would help solve multiple sustainability issues. The feasibility of such a plant was investigated by looking at the efficacy of several different modules of such a plant on a small scale.

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Prediction of molecular energy using Coulomb matrix and Graph Neural Network

Hazra et al. | Feb 01, 2022

Prediction of molecular energy using Coulomb matrix and Graph Neural Network

With molecular energy being an integral element to the study of molecules and molecular interactions, computational methods to determine molecular energy are used for the preservation of time and resources. However, these computational methods have high demand for computer resources, limiting their widespread feasibility. The authors of this study employed machine learning to address this disadvantage, utilizing neural networks trained on different representations of molecules to predict molecular properties without the requirement of computationally-intensive processing. In their findings, the authors determined the Feedforward Neural Network, trained by two separate models, as capable of predicting molecular energy with limited prediction error.

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The Effect of Various Liquid Mediums on the Transport of Photonic Energy and its Impact on the Quantum Efficiency of Photovoltaic Cells

Payra et al. | May 05, 2015

The Effect of Various Liquid Mediums on the Transport of Photonic Energy and its Impact on the Quantum Efficiency of Photovoltaic Cells

A photovoltaic cell (PV cell), or solar cell, converts the energy of light into electricity and is the basis for solar power. In order to increase the efficiency of PV cells, the authors in this study used common household items as photon transmissions mediums and measured their effects on the temperature and voltage output of the PV cells.

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