scholarly journals On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3597 ◽  
Author(s):  
Bilal Munir ◽  
Vladimir Dyo
Keyword(s):  
Adaptive Learning ◽  
System Performance ◽  
Learning Algorithm ◽  
Low Energy ◽  
Toxic Waste ◽  
Application Performance ◽  
Sensor Platforms ◽  
Optimal Value ◽  
Energy Harvesting System ◽  
The Impact

The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due to the need to dispose of old batteries. Recently, battery-free sensor platforms have been developed that use supercapacitors as energy storage, promising maintenance-free and perpetual sensor operation. While prior work focused on supercapacitor characterization, modelling and supercapacitor-aware scheduling, the impact of mobility on capacitor charging and overall sensor application performance has been largely ignored. We show that supercapacitor size is critical for mobile system performance and that selecting an optimal value is not trivial: small capacitors charge quickly and enable the node to operate in low energy environments, but cannot support intensive tasks such as communication or reprogramming; increasing the capacitor size, on the other hand, enables the support for energy-intensive tasks, but may prevent the node from booting at all if the node navigates in a low energy area. The paper investigates this problem and proposes a hybrid storage solution that uses an adaptive learning algorithm to predict the amount of available ambient energy and dynamically switch between two capacitors depending on the environment. The evaluation based on extensive simulations and prototype measurements showed up to 40% and 80% improvement compared to a fixed-capacitor approach in terms of the amount of harvested energy and sensor coverage.

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

2019 ◽  
Author(s):  
Andrew Medford ◽  
Shengchun Yang ◽  
Fuzhu Liu
Keyword(s):  
Machine Learning ◽  
Chemical Potential ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Low Energy ◽  
Training Data ◽  
High Coverage ◽  
Metal Compounds ◽  
Adsorption Energies ◽  
The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CH<sub>x</sub>, NH<sub>x</sub> and OH<sub>x</sub> species on the oxygen vacancy and pristine rutile TiO<sub>2</sub>(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N<sup>1.12</sup>) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

scholarly journals IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model

2021 ◽  
Vol 13 (8) ◽  
pp. 195
Author(s):  
Akash Gupta ◽  
Adnan Al-Anbuky
Keyword(s):  
Hip Fracture ◽  
Real Time ◽  
System Performance ◽  
Digital Health ◽  
Time Behavior ◽  
Functional Requirements ◽  
Rehabilitation Process ◽  
Rehabilitation Model ◽  
The Impact ◽  
Movement Monitoring

Hip fracture incidence is life-threatening and has an impact on the person’s physical functionality and their ability to live independently. Proper rehabilitation with a set program can play a significant role in recovering the person’s physical mobility, boosting their quality of life, reducing adverse clinical outcomes, and shortening hospital stays. The Internet of Things (IoT), with advancements in digital health, could be leveraged to enhance the backup intelligence used in the rehabilitation process and provide transparent coordination and information about movement during activities among relevant parties. This paper presents a post-operative hip fracture rehabilitation model that clarifies the involved rehabilitation process, its associated events, and the main physical movements of interest across all stages of care. To support this model, the paper proposes an IoT-enabled movement monitoring system architecture. The architecture reflects the key operational functionalities required to monitor patients in real time and throughout the rehabilitation process. The approach was tested incrementally on ten healthy subjects, particularly for factors relevant to the recognition and tracking of movements of interest. The analysis reflects the significance of personalization and the significance of a one-minute history of data in monitoring the real-time behavior. This paper also looks at the impact of edge computing at the gateway and a wearable sensor edge on system performance. The approach provides a solution for an architecture that balances system performance with remote monitoring functional requirements.

scholarly journals Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films

2021 ◽  
Vol 11 (13) ◽  
pp. 5895
Author(s):  
Kristina Serec ◽  
Sanja Dolanski Babić
Keyword(s):  
Thin Films ◽  
Learning Algorithm ◽  
Principal Component Regression ◽  
Principal Component ◽  
Conformational Transitions ◽  
Cancer Diagnostics ◽  
Dna Conformation ◽  
Support Vector ◽  
Multivariate Statistical ◽  
The Impact

The double-stranded B-form and A-form have long been considered the two most important native forms of DNA, each with its own distinct biological roles and hence the focus of many areas of study, from cellular functions to cancer diagnostics and drug treatment. Due to the heterogeneity and sensitivity of the secondary structure of DNA, there is a need for tools capable of a rapid and reliable quantification of DNA conformation in diverse environments. In this work, the second paper in the series that addresses conformational transitions in DNA thin films utilizing FTIR spectroscopy, we exploit popular chemometric methods: the principal component analysis (PCA), support vector machine (SVM) learning algorithm, and principal component regression (PCR), in order to quantify and categorize DNA conformation in thin films of different hydrated states. By complementing FTIR technique with multivariate statistical methods, we demonstrate the ability of our sample preparation and automated spectral analysis protocol to rapidly and efficiently determine conformation in DNA thin films based on the vibrational signatures in the 1800–935 cm−1 range. Furthermore, we assess the impact of small hydration-related changes in FTIR spectra on automated DNA conformation detection and how to avoid discrepancies by careful sampling.

scholarly journals BeiDou Satellite Unhealthy States and the Impact on System Performance

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4196 ◽  
Author(s):  
Caibo Hu ◽  
Chuang Shi ◽  
Jinping Chen ◽  
Yidong Lou ◽  
Fei Wang
Keyword(s):  
System Performance ◽  
Satellite Orbit ◽  
Service Area ◽  
The Other ◽  
Classification Method ◽  
Positioning Accuracy ◽  
General Classification ◽  
Broadcast Ephemeris ◽  
Beidou System ◽  
The Impact

The BeiDou system satellites may be unhealthy due to many reasons, affecting system performance in different ways. Therefore, it is important to analyze the causes and characteristics of the satellites’ unhealthy states. In this study, these states are classified into five types based on the broadcast ephemeris. Three criteria are presented, based on which a general classification method is proposed. Data from July 2017 to June 2018 are analyzed to validate the method, from which we know that the average unhealthy duration due to satellite maneuvers is much longer than the duration of unhealthy states related to satellite orbit or clock anomalies, and the other unhealthy states may be caused by inbound or outbound satellites. Statistics show that most of the time, the number of unhealthy satellites is no more than two and the average positioning accuracy in the service area will decrease by no more than 0.75 and 1.2 meters when one or two BDS satellites are unhealthy, respectively.

Fourier-Transform Infrared (FTIR) Spectrophotometry: An Ecofriendly Method for the Analysis of Injectable Daptomycin

2017 ◽  
Vol 100 (5) ◽  
pp. 1569-1576 ◽  
Author(s):  
Eliane Gandolpho Tótoli ◽  
Hérida Regina Nunes Salgado
Keyword(s):  
Staphylococcus Aureus ◽  
Methicillin Resistant Staphylococcus Aureus ◽  
Sustainable Consumption ◽  
Toxic Waste ◽  
Vancomycin Resistant Enterococci ◽  
Ir Spectrophotometry ◽  
Vancomycin Resistant ◽  
Ftir Spectrophotometry ◽  
The Impact

Abstract Daptomycin (DPT) is an important antimicrobial agent used in clinical practice because it is very active against several types of medicinally challengingGram-positive bacteria, such as methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococci. In addition to concerns about the quality of the analytical methods used in the QC of drugs, there is also concern about the impact of these methods on the environment. The trend toward sustainable consumption is increasingly evident and has forced the pharmaceutical industry to reduce the generation of toxic waste. Inthis context, IR spectrophotometry stands out because it does not use organic solvents and, although it is formally accepted for the identification of individual compounds, also allows the quantification of substances. Therefore, the aim of this work was to develop and validate a green analytical method for theanalysis of DPT in a lyophilized powder for injection by FTIR spectrophotometry. The method involved absorbance measurements in the spectral region of 1700–1600 cm−1. The method was properly validated and found to be linear, precise, accurate, selective, and robust for the concentrationrange between 0.2 and 0.6 mg/150 mg. The validated method was able to quantify DPT powder for injection and can be used as an environmentally friendly alternative for routine analysis in QC.

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