https://ieeexplore.ieee.org/document/7279055
Abstract:
Harvesting energy in the human environment has been identified as an effective way to charge the body sensor nodes in wireless body area networks (WBANs). In such networks, the capability of the nodes to detect events is of vital importance and complements the stringent quality of service (QoS) demands in terms of delay, throughput, and packet loss. However, the scarce energy collected by human motions, along with the strict requirements of vital health signals in terms of QoS, raises important challenges for WBANs and stresses the need for new integrated QoS-aware energy management schemes. In this paper, we propose a joint power-QoS (PEH-QoS) control scheme, composed of three modules that interact in order to make optimal use of energy and achieve the best possible QoS. The proposed scheme ensures that a sensor node is able to detect the medical events and transmit the respective data packets efficiently. Extensive simulations, conducted for different human activities (i.e., relaxing, walking, running, and cycling), have shown that the application of PEH-QoS in a medical node increases the detection efficiency, the throughput, and the energy efficiency of the system.
Entropy search and its derivative methods are one class of Bayesian Optimization methods that achieve active exploration of black-box functions. They maximize the information gain about the position in the input space where the black-box function gets the global optimum. However, existing entropy search methods suffer from harassment caused by high dimensional optimization problems. On the one hand, the computation for estimating entropies increases exponentially as dimensions increase, which limits the applicability of entropy search to high dimensional problems. On the other hand, many high-dimensional problems have the property that a large number of dimensions have little influence on the objective function, but currently there is no compress mechanism to exclude these redundant dimensions. In this work, we propose Active Compact Entropy Search (AcCES) to fix the above two defects. Under the guidance of historical evaluation, we bring forward a novel acquisition function that considers the correlation between dimensions in entropy search, which is ignored by existing Bayesian Optimization methods. The correlation term added in the acquisition function will help discover the potential correlation between dimensions. In order to build a more compact input space, redundant dimensions are compressed by exploiting inter-dimensional correlations. We use Pearson Correlation Coefficient and curve fitting to represent the inter-dimensional correlations. Extensive experiments on several benchmarks demonstrate that AcCES achieves higher query efficiency as well as optimal results after convergence than existing entropy search methods.
Published in 2020
“Compact and coherent circularly-polarized (CP) photon source is a critical element for a wide range of applications such as optical communication1, quantum optical information processing2, biomedical diagnosis3, and display systems4. However, the conventional scheme for creating CP waves requires external linear polarizers and quarter-wave plates, which are bulky setups in terms of optical wavelengths. With the external magnetism, it is possible to break the time-reversal (TR) symmetry of light sources so that optical transitions corresponding to a given handedness are selected for CP photon emission5–9. However, the corresponding degree of circular polarization is limited by the Curie temperature of magnetic materials10 and spin relaxation time.... The long-term compatibilities between magnetism and optoelectronic devices also await further examinations.”