RF Energy Harvesting Wireless Communications: RF Environment, Device Hardware and Practical Issues.

Radio frequency (RF) based wireless power transfer provides an attractive solution to extend the lifetime of power-constrained wireless sensor networks. Through harvesting RF energy from surrounding environments or dedicated energy sources, low-power wireless devices can be self-sustaining and environment-friendly. These features make the RF energy harvesting wireless communication (RF-EHWC) technique attractive to a wide range of applications. The objective of this article is to investigate the latest research activities on the practical RF-EHWC design. The distribution of RF energy in the real environment, the hardware design of RF-EHWC devices and the practical issues in the implementation of RF-EHWC networks are discussed. At the end of this article, we introduce several interesting applications that exploit the RF-EHWC technology to provide smart healthcare services for animals, wirelessly charge the wearable devices, and implement 5G-assisted RF-EHWC.

Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks ‡.

In underwater sensor networks (UWSNs), the unique characteristics of acoustic channels have posed great challenges for the design of medium access control (MAC) protocols. The long propagation delay problem has been widely explored in recent literature. However, the long preamble problem with acoustic modems revealed in real experiments brings new challenges to underwater MAC design. The overhead of control messages in handshaking-based protocols becomes significant due to the long preamble in underwater acoustic modems. To address this problem, we advocate the receiver-initiated handshaking method with parallel reservation to improve the handshaking efficiency. Despite some existing works along this direction, the data polling problem is still an open issue. Without knowing the status of senders, the receiver faces two challenges for efficient data polling: when to poll data from the sender and how much data to request. In this paper, we propose a traffic estimation-based receiver-initiated MAC (TERI-MAC) to solve this problem with an adaptive approach. Data polling in TERI-MAC depends on an online approximation of traffic distribution. It estimates the energy efficiency and network latency and starts the data request only when the preferred performance can be achieved. TERI-MAC can achieve a stable energy efficiency with arbitrary network traffic patterns. For traffic estimation, we employ a resampling technique to keep a small computation and memory overhead. The performance of TERI-MAC in terms of energy efficiency, channel utilization, and communication latency is verified in simulations. Our results show that, compared with existing receiver-initiated-based underwater MAC protocols, TERI-MAC can achieve higher energy efficiency at the price of a delay penalty. This confirms the strength of TERI-MAC for delay-tolerant applications.

A numerical study of the effect of varied blood pressure on the stability of carotid atherosclerotic plaque.

BACKGROUND: Blood pressure (BP) is associated with early atherosclerosis and plaque rupture because the BP variability can significantly affect the blood flow velocity and shear stress over the plaque. However, the mechanical response of BP variability to the plaque remains unclear. Therefore, we investigated the correlation between different maximum systolic blood pressure (SBP) and the stress distribution on plaque, as well as the stress over the plaque and blood velocity around the plaque using different BP variations, which are the BP variability in different phases during one cardiac cycle and beat-to-beat BP variability. METHOD: We established a two-dimensional artery model with stenosis at the degree of 62.5%. Eight combinations of pulsatile pressure gradients between the inflow and outflow were implemented at the model. Three levels of fibrous cap thickness were taken into consideration to investigate the additional effect on the BP variability. Wall shear stress and stress/strain distribution over the plaque were derived as well as the oscillation shear index (OSI) to analyze the impact of the changing rate of BP. RESULT: The stresses at diastole were 2.5% ± 1.8% lower than that at systole under the same pressure drop during one cycle. It was also found that elevated SBP might cause the immediate increment of stress in the present cycle (292% ± 72.3%), but slight reduction in the successive cycle (0.48% ± 0.4%). CONCLUSION: The stress/strain distribution over the plaque is sensitive to the BP variability during one cardiac cycle, and the beat-to-beat BP variability could cause considerable impact on the progression of atherosclerosis in long-term.

Simulation of induced electric field distribution based on five-sphere model used in rTMS.

Repetitive Transcranial magnetic stimulation (TMS) is a relatively new technique, which is non-invasive and painless used to stimulate the central and peripheral neural tissues. The principle is generating time-varying magnetic fields to stimulate the cerebral cortex neuron and inducing eddy current inside the tissues. Many researches study on the distributing of magnetic field and electric field induced inside the human brain, whereas the static electric field was neglected roughly in many studies. In this paper, a five-sphere model is established to simulate the human head used in rTMS. According to the different dielectric properties of the head tissues, the Laplace equation of static electric field is deduced by both of Gauss theorem and current's continuity principle. Boundary conditions used in different interface between two adjacent layers in the five-sphere model is proposed in this paper. Simulating study is conducted to calculate the distribution of the electric field in the model. Simulating results suggest that the model is useful to get the parameters of the most focus coil. Therefore this study could be potential to promote the development of rTMS stimulator.

Investigation on cardiovascular risk prediction using genetic information.

Cardiovascular disease (CVD) has become the primary killer worldwide and is expected to cause more deaths in the future. Prediction and prevention of CVD have therefore become important social problems. Many groups have developed prediction models for asymptomatic CVD by classifying its risk based on established risk factors (e.g., age, sex, etc.). More recently, studies have uncovered that many genetic variants are associated with CVD outcomes/traits. If treated as single or multiple risk factors, the genetic information could improve the performance of prediction models as well as promote the development of individually tailored risk models. In this paper, eligible genome-wide association studies for CVD outcomes/traits will be overviewed. Clinical trials on CVD prediction using genetic information will be summarized from overall aspects. As yet, most of the single or multiple genetic markers, which have been evaluated in the follow-up clinical studies, did not significantly improve discrimination of CVD. However, the potential clinical utility of genetic information has been uncovered initially and is expected for further development.

Comparison of median filter and discrete dyadic wavelet transform for noise cancellation in electrocardiogram.

Development of noise cancellation algorithm is essential to facilitate accurate detection of electrocardiogram (ECG) in mobile health and wearable medical devices. In this study, we captured ECG from 20 subjects when they were at rest and during routine activities. The motion artifact in ECG was filtered using two non-linear filters: median filter and discrete dyadic wavelet transform. Signal-to-noise ratio (SNR) and computation time of the filters were determined. We found that median filter showed larger SNR (7.61±1.21 dB) than discrete dyadic wavelet transform did (5.35±1.34 dB). Conversely, discrete dyadic wavelet transform benefited to its short computation time. The algorithms of these non-linear filters should be further investigated to achieve both high SNR and fast computation in wearable and mobile monitoring systems.