Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network

The limited fronthaul capacity imposes a challenge on the uplink of centralized radio access network (C-RAN). We propose to boost the fronthaul capacity of massive multiple-input multiple-output (MIMO) aided C-RAN by globally optimizing the power sharing between channel estimation and data transmission both for the user devices (UDs) and the remote radio units (RRUs). Intuitively, allocating more power to the channel estimation will result in more accurate channel estimates, which increases the achievable throughput. However, increasing the power allocated to the pilot training will reduce the power assigned to data transmission, which reduces the achievable throughput. In order to optimize the powers allocated to the pilot training and to the data transmission of both the UDs and the RRUs, we assign an individual power sharing factor to each of them and derive an asymptotic closed-form expression of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU) links. We then exploit the C-RAN architecture's central computing and control capability for jointly optimizing the UDs' power sharing factors and the RRUs' power sharing factors aiming for maximizing the fronthaul capacity. Our simulation results show that the fronthaul capacity is significantly boosted by the proposed global optimization of the power allocation between channel estimation and data transmission both for the UDs and for their host RRUs. As a specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing factors improves 33\% compared with the one attained without optimizing power sharing factors

Growth Response and Phytoremediation Ability of Reed for Diesel Contaminant

AbstractOil spills may considerably damage sensitive coastal wetlands. In this open-air pot experiment, Reed, a dominant coastal marsh plant, was transplanted into soil contaminated with diesel at concentrations of 1 000, 5 000, 10 000, 15 000 and 20 000mg diesel kg-1 dry soil. In order to better evaluate the phytoremediation potential and restoration of Reed, the chlorophyll content, root vitality, activity of peroxidase (POD), catalase (CAT), ascorbic acid oxidase (AAO) in plant tissue and the dissipation rate of diesel were investigated after 50 days of treatment at the levels mentioned above. The results showed that the activities of POD in root, CAT, and AAO in stem increased first, and declined at higher concentrations. Additionally, the increment of chlorophyll content and root vitality were observed, indicating that Reed was tolerant to diesel, especially when the concentrations of diesel was lower, which was also proved by the highest restoration effectiveness at the lower levels of diesel. Collectively, Reed is a potential plant which can be used for restoring the diesel-contaminated soil

Toxic effects of iron oxide nanoparticles on human umbilical vein endothelial cells

Iron oxide nanoparticles (IONPs) have been employed for hyperthermia treatments, stem cell therapies, cell labeling, and imaging modalities. The biocompatibility and cytotoxic effects of iron oxide nanoparticles when used in biomedical applications, however, are an ongoing concern. Endothelial cells have a critical role in this research dealing with tumors, cardiovascular disease and inflammation. However, there is little information dealing with the biologic effects of IONPs on the endothelial cell. This paper deals with the influence of dextran and citric acid coated IONPs on the behavior and function of human umbilical vein endothelial cells (HUVECs). After exposing endothelial cells to IONPs, dose-dependent effects on HUVECs viability, cytoskeleton and function were determined. Both citric acid and dextran coated particles appeared to be largely internalized by HUVECs through endocytosis and contribute to eventual cell death possibly by apoptosis. Cytoskeletal structures were greatly disrupted, as evidenced by diminished vinculin spots, and disorganized actin fiber and tubulin networks. The capacity of HUVECs to form a vascular network on Matrigel™ diminished after exposure to IONPs. Cell migration/invasion were inhibited significantly even at very low iron concentrations (0.1 mM). The results of this study indicate the great importance of thoroughly understanding nanoparticle-cell interactions, and the potential to exploit this understanding in tumor therapy applications involving IONPs as thermo/chemoembolization agents

Content-sensitive superpixel generation with boundary adjustment.

Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy was used to measure the amount of information in the image, and the amount of information was evenly distributed to each seed. It placed more seeds to achieve the lower under-segmentation in content-dense regions, and placed the fewer seeds to increase computational efficiency in content-sparse regions. Second, the Prim algorithm was adopted to generate uniform superpixels efficiently. Third, a boundary adjustment strategy with the adaptive distance further optimized the superpixels to improve the performance of the superpixel. Experimental results on the Berkeley Segmentation Database show that our method outperforms competing methods under evaluation metrics

Two-stage time-domain pilot contamination elimination in large-scale multiple-antenna aided and TDD based OFDM systems

Pilot contamination (PC) is a major impediment of large-scale multi-cell multiple-input multiple-output (MIMO) systems. Hence we propose an optimal pilot design for timedomain channel estimation, which is capable of completely eliminating PC. More specifically, a sophisticated combination of downlink training and ‘scheduled’ uplink training is designed with the aid of the optimal pilot set. Given the optimal pilot set, every user acquires its unique downlink time-domain channel state information (CSI) through downlink training. The estimated downlink CSIs are then embedded in the uplink training. As a result, PC can be completely eliminated, at the cost of a slight increase in training computational complexity. Our simulation results demonstrate the power of the proposed scheme. Most significantly, our scheme imposes a modest training overhead of (L + 3), training-phase durations corresponding to the number of OFDM symbols, where L is the number of cells, which is substantially lower than that imposed by some of the existing PC elimination schemes. Therefore, it imposes a less stringent requirement on the channel’s coherence time. Finally, our scheme does not need any information exchange between base stations

Weakly supervised conditional random fields model for semantic segmentation with image patches.

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As a result, it is often very time-consuming and labor-intensive, yet still subject to manual errors and subjective inconsistency. To tackle such difficulties, a weakly supervised ISS approach is proposed, in which the challenging problem of label inference from image-level to pixel-level will be particularly addressed, using image patches and conditional random fields (CRF). An improved simple linear iterative cluster (SLIC) algorithm is employed to extract superpixels. for image segmentation. Specifically, it generates various numbers of superpixels according to different images, which can be used to guide the process of image patch extraction based on the image-level labeled information. Based on the extracted image patches, the CRF model is constructed for inferring semantic class labels, which uses the potential energy function to map from the image-level to pixel-level image labels. Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. Experiments conducted on two publicly available benchmark datasets, MSRC and PASCAL VOC 2012, have demonstrated that our proposed algorithm can yield very promising results compared to quite a few state-of-the-art ISS methods, including some deep learning-based models

Design and Development of Water-splitting Electrocatalysts Based on Conjugated Triazine Frameworks

Covalent triazine frameworks (CTFs) with rich nitrogen atoms and permanent porosity have been widely used in the field of opto/electronics as supports. In this study, two CTFs with different pore sizes (single pore and heteropore) were synthesized, after which Cu2+, Co2+, Ni2+, Pd2+, Pt2+, and the corresponding metal cluster were introduced into the CTFs as catalytic active sites through the confinement effect of the pores. Among a series of CTFs-based electrocatalysts, DCP-CTF-Pt2+ displays an outstanding electrocatalytic performance with an overpotential of 46 mV and a Tafel slope of 30.2 mV dec-1. Catalytic kinetics analysis indicates that electrocatalytic performance is closely relevant to hierarchical pore and metal size