BER performance analysis of 100 and 200 Gbit/s all-optical OTDM node using symmetric Mach-Zehnder switches

New insight to the feasibility of all-optical ultra speed switching up to 200 Gb/s. The technique will reduce the dependency and bottleneck on the electronic-to-optical-to-electronic conversion. Current conversion speed is up to 40 Gb/s in laboratories. The novel clock extraction technique proposed shows the potential of an all-optical switch. The research results are directly relevant to the access technology where optical fibre and RF is competing to be the solution. The growing demands of bandwidth will exceed RF capability while the optical fibre will be the optimum solution. A PhD project (Le-Minh) funded by the University Studentship, completed in 2007

All-optical flip flop based on a symmetric Mach-Zehnder switch with a feed-back loop and multiple forward set/reset signals

The paper proposed an improved performance for optical flip-flop using symmetric Mach-Zehnder interformeter with a feedback and multiple forward configurations. At the optimum operating condition for the optical flip-flop, high contrast ratio of 22 dB can be achieved. The findings in the paper will have an impact on the design of future optical flip-flop and other optical logic gates such as exclusive OR and NAND gates. A PhD research (Le-Minh) funded by the University Studentship, completed in 200

Simulation of an all-optical 1 x 2 SMZ switch with a high contrast ratio

Abstract — An all-optical 1×2 high contrast ratio (CR) switch based on the symmetric Mach-Zehnder (SMZ) interferometers is presented. Simulation results show a remarkable improvement of the inter-output CR (~25 dB) between the two outputs compared with an existing SMZ switch. It is shown that the proposed switch offers high values of inter-output CR (> 32dB) over a wide range of input powers using appropriate power of the control pulses. I

1 x M packet-switched router based on the PPM header address for all-optical WDM networks

This paper presents an all-optical 1xM router architecture for simultaneous multiple-wavelength packet routing, without the need for wavelength conversion. The packet header address is based on the pulse position modulation (PPM) format, which allows the use of only a single-bitwise optical AND gate for fast packet header address correlation. The proposed scheme offers both multicast and broadcast capabilities. We’ve demonstrated a high speed packet routing at 160 Gb/s in simulation, with a low channel crosstalk (CXT) of ~ -27 dB with a channel spacing of > 0.4 THz and a demultiplexer bandwidth of 500 GHz. The output transfer function of the PPM header processing (PPM-HP) module is also investigated in this paper

Improving the Knowledge Gradient Algorithm

The knowledge gradient (KG) algorithm is a popular policy for the best arm identification (BAI) problem. It is built on the simple idea of always choosing the measurement that yields the greatest expected one-step improvement in the estimate of the best mean of the arms. In this research, we show that this policy has limitations, causing the algorithm not asymptotically optimal. We next provide a remedy for it, by following the manner of one-step look ahead of KG, but instead choosing the measurement that yields the greatest one-step improvement in the probability of selecting the best arm. The new policy is called improved knowledge gradient (iKG). iKG can be shown to be asymptotically optimal. In addition, we show that compared to KG, it is easier to extend iKG to variant problems of BAI, with the $\epsilon$-good arm identification and feasible arm identification as two examples. The superior performances of iKG on these problems are further demonstrated using numerical examples.Comment: 32 pages, 42 figure

MnasNet: Platform-Aware Neural Architecture Search for Mobile

Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8x faster than MobileNetV2 [29] with 0.5% higher accuracy and 2.3x faster than NASNet [36] with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection. Code is at https://github.com/tensorflow/tpu/tree/master/models/official/mnasnetComment: Published in CVPR 201