Neural network-assisted decision-making for adaptive routing strategy in optical datacenter networks

To improve the blocking probability (BP) performance and enhance the resource utilization, a correct decision of routing strategy which is most adaptable to the network configuration and traffic dynamics is essential for adaptive routing in optical datacenter networks (DCNs). A neural network (NN)-assisted decision-making scheme is proposed to find the optimal routing strategy in optical DCNs by predicting the BP performance for various candidate routing strategies. The features of an optical DCN architecture (i.e., the rack number N, connection degree D, spectral slot number S and optical transceiver number M) and the traffic pattern (i.e., the ratio of requests of various capacities R, and the load of arriving request) are used as the input to the NN to estimate the optimal routing strategy. A case of two-strategy decision in the transparent optical multi-hop interconnected DCN is studied. Three metrics are defined for performance evaluation, which include (a) the ratio of the load range with wrong decision over the whole load range of interest (i.e., decision error E), (b) the maximum BP loss (BPL) and (c) the resource utilization loss (UL) caused by the wrong decision. Numerical results show that the ratio of error-free cases over tested cases always surpasses 83% and the average values of E, BPL and UL are less than 3.0%, 4.0% and 1.2%, respectively, which implies the high accuracy of the proposed scheme. The results validate the feasibility of the proposed scheme which facilitates the autonomous implementation of adaptive routing in optical DCNs

Demonstration of three‐dimensional indoor visible light positioning with multiple photodiodes and reinforcement learning

To provide high‐quality location‐based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi‐photodiodes (multi‐PDs) three‐dimensional (3D) indoor VLP system enhanced by reinforcement learning (RL), which can realize accurate positioning in the 3D space without any off-line training. The basic 3D positioning model is introduced, where without height information of the receiver, the initial height value is first estimated by exploring its relationship with the received signal strength (RSS), and then, the coordinates of the other two dimensions (i.e., X and Y in the horizontal plane) are calculated via trilateration based on the RSS. Two different RL processes, namely RL1 and RL2, are devised to form two methods that further improve horizontal and vertical positioning accuracy, respectively. A combination of RL1 and RL2 as the third proposed method enhances the overall 3D positioning accuracy. The positioning performance of the four presented 3D positioning methods, including the basic model without RL (i.e., Benchmark) and three RL based methods that run on top of the basic model, is evaluated experimentally. Experimental results verify that obviously higher 3D positioning accuracy is achieved by implementing any proposed RL based methods compared with the benchmark. The best performance is obtained when using the third RL based method that runs RL2 and RL1 sequentially. For the testbed that emulates a typical office environment with a height difference between the receiver and the transmitter ranging from 140 cm to 200 cm, an average 3D positioning error of 2.6 cm is reached by the best RL method, demonstrating at least 20% improvement compared to the basic model without performing RL

A Multi-Floor Arrayed Waveguide Grating Based Architecture with Grid Topology for Datacenter Networks

This paper proposes a grid topology based passive optical interconnect (POI) architecture that is composed of multiple floors of arrayed waveguide grating routers (AWGRs) to offer high connectivity and scalability for datacenter networks. In the proposed POI signal only needs to pass one AWGR, and thus can avoid the crosstalk accumulation and cascaded filtering effects, which exist in many existing POI architectures based on cascaded AWGRs. Meanwhile, due to high connectivity, the proposed grid topology based POI also has the potential advantage of high reliability. Simulation results validate the network performance. With a proper node degree, the proposed grid topology can achieve acceptable blocking probability. Besides, steady performance is kept when the number of floors increases, indicating good scalability of the proposed POI

Iterative point-wise reinforcement learning for highly accurate indoor visible light positioning

Iterative point-wise reinforcement learning (IPWRL) is proposed for highly accurate indoor visible light positioning (VLP). By properly updating the height information in an iterative fashion, the IPWRL not only effectively mitigates the impact of non-deterministic noise but also exhibits excellent tolerance to deterministic errors caused by the inaccurate a priori height information. The principle of the IPWRL is explained, and the performance of the IPWRL is experimentally evaluated in a received signal strength (RSS) based VLP system and compared with other positioning algorithms, including the conventional RSS algorithm, the k-nearest neighbors (KNN) algorithm and the PWRL algorithm where iterations exclude. Unlike the supervised machine learning method, e.g., the KNN, whose performance is highly dependent on the training process, the proposed IPWRL does not require training and demonstrates robust positioning performance for the entire tested area. Experimental results also show that when a large height information mismatch occurs, the IPWRL is able to first correct the height information and then offers robust positioning results with a rather low positioning error, while the positioning errors caused by the other algorithms are significantly higher

Real-Time Ozone Detection Based on a Microfabricated Quartz Crystal Tuning Fork Sensor

A chemical sensor for ozone based on an array of microfabricated tuning forks is described. The tuning forks are highly sensitive and stable, with low power consumption and cost. The selective detection is based on the specific reaction of the polymer with ozone. With a mass detection limit of ∼2 pg/mm2 and response time of 1 second, the sensor coated with a polymer sensing material can detect ppb-level ozone in air. The sensor is integrated into a miniaturized wearable device containing a detection circuit, filtration, battery and wireless communication chip, which is ideal for personal and microenvironmental chemical exposure monitoring

Multi-channel collision-free reception for optical interconnects

A multi-channel reception scheme that allows each node to receive an arbitrary set of wavelengths simultaneously (i.e., collision-free) is proposed for optical interconnects. The proposed scheme only needs to use a few receivers and fixed-wavelength filters that are designed based on error-control coding theory. Experiments with up to four channel collision-free reception units are carried out to demonstrate the feasibility of the proposed scheme

Engrailed 1 coordinates cytoskeletal reorganization to induce myofibroblast differentiation

Transforming growth factor-β (TGFβ) is a key mediator of fibroblast activation in fibrotic diseases, including systemic sclerosis. Here we show that Engrailed 1 (EN1) is reexpressed in multiple fibroblast subpopulations in the skin of SSc patients. We characterize EN1 as a molecular amplifier of TGFβ signaling in myofibroblast differentiation: TGFβ induces EN1 expression in a SMAD3-dependent manner, and in turn, EN1 mediates the profibrotic effects of TGFβ. RNA sequencing demonstrates that EN1 induces a profibrotic gene expression profile functionally related to cytoskeleton organization and ROCK activation. EN1 regulates gene expression by modulating the activity of SP1 and other SP transcription factors, as confirmed by ChIP-seq experiments for EN1 and SP1. Functional experiments confirm the coordinating role of EN1 on ROCK activity and the reorganization of cytoskeleton during myofibroblast differentiation, in both standard fibroblast culture systems and in vitro skin models. Consistently, mice with fibroblast-specific knockout of En1 demonstrate impaired fibroblast-to-myofibroblast transition and are partially protected from experimental skin fibrosis

Embryonic diapause due to high glucose is related to changes in glycolysis and oxidative phosphorylation, as well as abnormalities in the TCA cycle and amino acid metabolism

IntroductionThe adverse effects of high glucose on embryos can be traced to the preimplantation stage. This study aimed to observe the effect of high glucose on early-stage embryos. Methods and resultsSeven-week-old ICR female mice were superovulated and mated, and the zygotes were collected. The zygotes were randomly cultured in 5 different glucose concentrations (control, 20mM, 40mM, 60mM and 80mM glucose). The cleavage rate, blastocyst rate and total cell number of blastocyst were used to assess the embryo quality. 40 mM glucose was selected to model high glucose levels in this study. 40mM glucose arrested early embryonic development, and the blastocyst rate and total cell number of the blastocyst decreased significantly as glucose concentration was increased. The reduction in the total cell number of blastocysts in the high glucose group was attributed to decreased proliferation and increased cell apoptosis, which is associated with the diminished expression of GLUTs (GLUT1, GLUT2, GLUT3). Furthermore, the metabolic characterization of blastocyst culture was observed in the high-glucose environment. DiscussionThe balance of glycolysis and oxidative phosphorylation at the blastocyst stage was disrupted. And embryo development arrest due to high glucose is associated with changes in glycolysis and oxidative phosphorylation, as well as abnormalities in the TCA cycle and amino acid metabolism

Neural network-assisted routing strategy selection for optical datacenter networks

This paper proposes the neural network-assisted routing strategy selection for the optical datacenter networks. Results reveal the high accuracy of strategy selection within the range of interested traffic load, validating feasibility of the proposed scheme