Design of Wireless Sensors for IoT with Energy Storage and Communication Channel Heterogeneity

Autonomous Wireless Sensors (AWSs) are at the core of every Wireless Sensor Network (WSN). Current AWS technology allows the development of many IoT-based applications, ranging from military to bioengineering and from industry to education. The energy optimization of AWSs depends mainly on: Structural, functional, and application specifications. The holistic design methodology addresses all the factors mentioned above. In this sense, we propose an original solution based on a novel architecture that duplicates the transceivers and also the power source using a hybrid storage system. By identifying the consumption needs of the transceivers, an appropriate methodology for sizing and controlling the power flow for the power source is proposed. The paper emphasizes the fusion between information, communication, and energy consumption of the AWS in terms of spectrum information through a set of transceiver testing scenarios, identifying the main factors that influence the sensor node design and their inter-dependencies. Optimization of the system considers all these factors obtaining an energy efficient AWS, paving the way towards autonomous sensors by adding an energy harvesting element to them

Design of Wireless Sensors for IoT with Energy Storage and Communication Channel Heterogeneity

Autonomous Wireless Sensors (AWSs) are at the core of every Wireless Sensor Network (WSN). Current AWS technology allows the development of many IoT-based applications, ranging from military to bioengineering and from industry to education. The energy optimization of AWSs depends mainly on: Structural, functional, and application specifications. The holistic design methodology addresses all the factors mentioned above. In this sense, we propose an original solution based on a novel architecture that duplicates the transceivers and also the power source using a hybrid storage system. By identifying the consumption needs of the transceivers, an appropriate methodology for sizing and controlling the power flow for the power source is proposed. The paper emphasizes the fusion between information, communication, and energy consumption of the AWS in terms of spectrum information through a set of transceiver testing scenarios, identifying the main factors that influence the sensor node design and their inter-dependencies. Optimization of the system considers all these factors obtaining an energy efficient AWS, paving the way towards autonomous sensors by adding an energy harvesting element to them

Attention Patterns Detection using Brain Computer Interfaces

The human brain provides a range of functions such as expressing emotions, controlling the rate of breathing, etc., and its study has attracted the interest of scientists for many years. As machine learning models become more sophisticated, and bio-metric data becomes more readily available through new non-invasive technologies, it becomes increasingly possible to gain access to interesting biometric data that could revolutionize Human-Computer Interaction. In this research, we propose a method to assess and quantify human attention levels and their effects on learning. In our study, we employ a brain computer interface (BCI) capable of detecting brain wave activity and displaying the corresponding electroencephalograms (EEG). We train recurrent neural networks (RNNS) to identify the type of activity an individual is performing

Electric hybrid storage systems and their applications

Work published in Proceedings of the 4th Intl. Conference “Alternative Energy Sources, Materials & Technologies AESMT’21

X3D Sensor-based Thermal Maps for Residential and Commercial Buildings

There are many building energy simulation systems on the market today; however, most of them rely on theoretical thermal models to make decisions on the building structural design and modifications. Sustainable methods of construction have made tremendous progress in the recent decades. The example of the German Energy-Plus-House technology uses a combination of (almost) zero-carbon passive heating technologies. A web-enabled X3D simulation system coupled with a cost-effective set of temperature/humidity sensors can provide valuable insights into building design, materials and construction that can lead to significant energy savings, an improved thermal comfort for residents and improved efficiency. We propose a cost effective hardware-software prototype system that can provide real data driven 3D thermal maps for residential buildings. The system can easily scale to provide 3D thermal maps for large commercial buildings

Attention Patterns Detection Using Brain Computer Interfaces

This publication was published in the Proceedings of the Annual ACM Southeast Conference (ACMSE 2020). The human brain provides a range of functions such as expressing emotions, controlling the rate of breathing, etc., and its study has attracted the interest of scientists for many years. As machine learning models become more sophisticated, and biometric data becomes more readily available through new non-invasive technologies, it becomes increasingly possible to gain access to interesting biometric data that could revolutionize Human-Computer Interaction. In this research, we propose a method to assess and quantify human attention levels and their effects on learning. In our study, we employ a brain computer interface (BCI) capable of detecting brain wave activity and displaying the corresponding electroencephalograms (EEG). We train recurrent neural networks (RNNS) to identify the type of activity an individual is performing

Rare hereditary COL4A3/COL4A4 variants may be mistaken for familial focal segmental glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS) is a histological lesion with many causes, including inherited genetic defects, with significant proteinuria being the predominant clinical finding at presentation. Mutations in COL4A3 and COL4A4 are known to cause Alport syndrome (AS), thin basement membrane nephropathy, and to result in pathognomonic glomerular basement membrane (GBM) findings. Secondary FSGS is known to develop in classic AS at later stages of the disease. Here, we present seven families with rare or novel variants in COL4A3 or COL4A4 (six with single and one with two heterozygous variants) from a cohort of 70 families with a diagnosis of hereditary FSGS. The predominant clinical finding at diagnosis was proteinuria associated with hematuria. In all seven families, there were individuals with nephrotic-range proteinuria with histologic features of FSGS by light microscopy. In one family, electron microscopy showed thin GBM, but four other families had variable findings inconsistent with classical Alport nephritis. There was no recurrence of disease after kidney transplantation. Families with COL4A3 and COL4A4 variants that segregated with disease represent 10% of our cohort. Thus, COL4A3 and COL4A4 variants should be considered in the interpretation of next-generation sequencing data from such patients. Furthermore, this study illustrates the power of molecular genetic diagnostics in the clarification of renal phenotypes.Kidney International advance online publication, 17 September 2014; doi:10.1038/ki.2014.305