The aim of this review was to assess the current viable technologies for wireless power delivery and data transmission through metal barriers. Using such technologies sensors enclosed in hermetical metal containers can be powered and communicate through exterior power sources without penetration of the metal wall for wire feed-throughs. In this review, we first discuss the significant and essential requirements for through-metal-wall power delivery and data transmission and then we: (1) describe three electromagnetic coupling based techniques reported in the literature, which include inductive coupling, capacitive coupling, and magnetic resonance coupling; (2) present a detailed review of wireless ultrasonic through-metal-wall power delivery and/or data transmission methods; (3) compare various ultrasonic through-metal-wall systems in modeling, transducer configuration and communication mode with sensors; (4) summarize the characteristics of electromagnetic-based and ultrasound-based systems, evaluate the challenges and development trends. We conclude that electromagnetic coupling methods are suitable for through thin non-ferromagnetic metal wall power delivery and data transmission at a relatively low data rate; piezoelectric transducer-based ultrasonic systems are particularly advantageous in achieving high power transfer efficiency and high data rates; the combination of more than one single technique may provide a more practical and reliable solution for long term operation.
The aim of this study was to prepare biodegradable sustained release magnetite microspheres sized between 1 to 2 μm. The microspheres with or without magnetic materials were prepared by a W/ O/W double emulsion solvent evaporation technique using poly(lactide-co-glycolide) (PLGA) as the biodegradable matrix forming polymer. Effects of manufacturing and formulation variables on particle size were investigated with non-magnetic microspheres. Microsphere size could be controlled by modification of homogenization speed, PLGA concentration in the oil phase, oil phase volume, solvent composition, and polyvinyl alcohol (PVA) concentration in the outer water phase. Most influential were the agitation velocity and all parameters that influence the kinematic viscosity of oil and outer water phase, specifically the type and concentration of the oil phase. The magnetic component yielding homogeneous magnetic microspheres consisted of magnetite nanoparticles of 8 nm diameter stabilized with a polyethylene glycole/polyacrylic acid (PEG/PAA) coating and a saturation magnetization of 47.8 emu/g. Non-magnetic and magnetic microspheres had very similar size, morphology, and size distribution, as shown by scanning electron microscopy. The optimized conditions yielded microspheres with 13.7 weight% of magnetite and an average diameter of 1.37 μm. Such biodegradable magnetic microspheres seem appropriate for vascular administration followed by magnetic drug targeting.
Smartphones have been used for recognizing different transportation states. However, current studies focus on the speed of the object, which only relies on the GPS sensor rather than considering other suitable sensors and actual application factors. In this study, we propose a novel method that considers these factors comprehensively to enhance transportation state recognition. The deep Bi-LSTM (bidirectional long short-term memory) neural network structure, the crowd-sourcing model, and the TensorFlow deep learning system are used to classify the transportation states. Meanwhile, the data captured by the accelerometer and gyroscope sensors of smartphone is used to test and adjust the deep Bi-LSTM neural network model, making it easy to transfer the model into smartphones and conduct real-time recognition. The experimental results show that this study achieves transportation activity classification with an accuracy of up to 92.8%. The model of the deep Bi-LSTM neural network can be used for other time-series fields such as signal recognition and action analysis.
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