Titanium and its alloys have been widely used for orthopedic implants because of their good biocompatibility. We have previously shown that the crystalline titania layers formed on the surface of titanium metal via anodic oxidation can induce apatite formation in simulated body fluid, whereas amorphous titania layers do not possess apatite-forming ability. In this study, hot water and heat treatments were applied to transform the titania layers from an amorphous structure into a crystalline structure after titanium metal had been anodized in acetic acid solution. The apatite-forming ability of titania layers subjected to the above treatments in simulated body fluid was investigated. The XRD and SEM results indicated hot water and/or heat treatment could greatly transform the crystal structure of titania layers from an amorphous structure into anatase, or a mixture of anatase and rutile. The abundance of Ti-OH groups formed by hot water treatment could contribute to apatite formation on the surface of titanium metals, and subsequent heat treatment would enhance the bond strength between the apatite layers and the titanium substrates. Thus, bioactive titanium metals could be prepared via anodic oxidation and subsequent hot water and heat treatment that would be suitable for applications under load-bearing conditions.
Blocking is one of the most important challenges in exploiting millimeterwave for fifth-generation (5G) cellular communication systems. Compared to blockages caused by buildings or terrains, human body blockage exhibits a higher complexity due to the mobility and dynamic statistics of humans. To support development of outdoor millimeter-wave cellular systems, in this paper we present a novel 3D physical model of human body blockage. Based on the proposed model, the impact of human body blockage on frame-based data transmission is discussed, with respect to the system specifications and environment conditions.
Many sensor nodes have been widely deployed in the physical world to gather various environmental information, such as water quality, earthquake, and huge dam safety. Due to the limitation in the batter power, memory, and computational capacity, missing data can occur at arbitrary sensor nodes and time slots. In extreme situations, some sensors may lose readings at consecutive time slots. The successive missing data takes the side effects on the accuracy of real-time monitoring as well as the performance on the data analysis in the wireless sensor networks. Unfortunately, existing solutions to the missing data filling cannot well uncover the complex non-linear spatial and temporal relations. To address these problems, a DNN (Deep Neural Network) multi-view learning method (DNN-MVL) is proposed to fill the successive missing readings. DNN-MVL mainly considers five views: global spatial view, global temporal view, local spatial view, local temporal view, and semantic view. These five views are modeled with inverse distance of weight interpolation, bidirectional simple exponential smoothing, user-based collaborative filtering, mass diffusion-based collaborative filtering with the bipartite graph, and structural embedding, respectively. The results of the five views are aggregated to a final value in a multi-view learning algorithm with DNN model to obtain the final filling readings. Experiments on large-scale real dam deformation data demonstrate that DNN-MVL has a mean absolute error about 6.5%, and mean relative error 21.4%, and mean square error 8.17% for dam deformation data, outperforming all of the baseline methods.
A low-complexity turbo detection scheme is proposed for single-carrier multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications using low-density parity-check (LDPC) channel coding. The low complexity of the proposed detection algorithm is achieved in two aspects: first, the frequency-domain equalization technique is adopted, and it maintains a low complexity irrespective of the highly dispersive UWA channels; second, the computation of the soft equalizer output, in the form of extrinsic log-likelihood ratio, is performed with an approximating method, which further reduces the complexity. Moreover, attributed to the LDPC decoding, the turbo detection converges within only a few iterations. The proposed turbo detection scheme has been used for processing real-world data collected in two different undersea trials: WHOI09 and ACOMM09. Experimental results show that it provides robust detection for MIMO UWA communications with different modulations and different symbol rates, at different transmission ranges. detection capability, attributed to the iterative extrinsic soft information exchanges between a soft-decision equalizer and a soft-decision decoder. In [8], the soft-decision decision-feedback equalizer (DFE), together with the turbo decoder, has been applied to UWA communication. In [9], the turbo linear equalizer was proposed for long-term UWA communication testing. The convolutional decoder implemented with the classic BCJR algorithm [13] has been adopted. In [10], turbo detection using block decisionfeedback equalization (BDFE) has also been proposed for single-carrier UWA communications. The BDFE leads to a better detection performance compared with the conventional DFE. Iterative decoding and turbo detection for orthogonal frequency-division multiplexing (OFDM) UWA systems has also been proposed in [11,12].The equalization methods used in [8,9] are designed in the time domain, and the detection complexity increases with the channel length. On the other hand, frequency-domain equalization (FDE) methods such as OFDM [12] and single-carrier FDE (SC-FDE) [14,15] and Technology. His research focuses on the channel estimation for MIMO wireless communication systems and turbo detection for underwater acoustic communications.Jun Tao received his BS and MS degrees in Electrical Engineering from the
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