The capacity of optical transport networks has been increasing steadily and the networks are becoming more dynamic, complex, and transparent. Though it is common to use worst case assumptions for estimating the quality of transmission (QoT) in the physical layer, over provisioning results in high margin requirements. Accurate estimation on the QoT for to-be-established lightpaths is crucial for reducing provisioning margins. Machine learning (ML) is regarded as one of the most powerful methodological approaches to perform network data analysis and enable automated network self-configuration. In this paper, an artificial neural network (ANN) framework, a branch of ML, to estimate the optical signal-to-noise ratio (OSNR) of to-be-established lightpaths is proposed. It takes account of both nonlinear interference between spectrum neighboring channels and optical monitoring uncertainties. The link information vector of the lightpath is used as input and the OSNR of the lightpath is the target for output of the ANN. The nonlinear interference impact of the number of neighboring channels on the estimation accuracy is considered. Extensive simulation results show that the proposed OSNR estimation scheme can work with any RWA algorithm. High estimation accuracy of over 98% with estimation errors of less than 0.5dB can be achieved given enough training data. ANN model with R=4 neighboring channels should be used to achieve more accurate OSNR estimates. Based on the results, it is expected that the proposed ANN-based OSNR estimation for new lightpath provisioning can be a promising tool for margin reduction and low-cost operation of future optical transport networks.

There are several optimization techniques available for improving rendering speed of direct volume rendering. An acceleration method using the hierarchical min-max map requires little preprocessing and data storage while preserving image quality. However, this method introduces computational overhead because of unnecessary comparison and level shift between blocks. In this paper, we propose an efficient space-leaping method using optimal-sized blocks. To determine the size of blocks, our method partitions an image plane into several uniform grids and computes the minimum and the maximum depth values for each grid. We acquire optimal block sets suitable for individual rays from these values. Experimental results show that our method reduces rendering time when compared with the previous min-max octree method.

The emerging three degree of freedom plus (3DoF+) video provides more interactive and deep immersive visual experience. 3DoF+ video introduces motion parallax to 360 video providing omnidirectional view with limited changes of the view position. A large set of views are required to support such 3DoF+ visual experience, hence it is essential to compress a tremendous amount of 3DoF+ video. Recently, MPEG is developing a standard for efficient coding of 3DoF+ video that consists of multiple videos, and its test model named Test Model for Immersive Video (TMIV). In the TMIV, the redundancy between the input source views is removed as much as possible by selecting one or several basic views and predicting the remaining views from the basic views. Each unpredicted region is cropped to a bounding box called patch, and then a large number of patches are packed into atlases together with the selected basic views. As a result, multiple source views are converted into one or more atlas sequences to be compressed. In this letter, we present an improved clustering method using patch merging in the atlas construction in the TMIV. The proposed method achieves significant BD-rate reduction in terms of various end-to-end evaluation metrics in the experiment, and was adopted in TMIV6.0.

An adaptive hybrid acquisition system is presented. The proposed system combines an antenna diversity technique and the cell-averaging constant false alarm rate (CA-CFAR) algorithm to acquire a pseudo-noise (PN) sequence for code-division multiple-access (CDMA) systems. Since conventional acquisition systems have a fixed threshold value, they are unable to adapt to varying mobile communications environments, resulting in a high false-alarm rate or low detection probability. Accordingly, an antenna diversity technique and adaptively varying threshold scheme using the CA-CFAR algorithm are applied to improve the detection performance. Based on deriving the detection probability, false alarm rate, miss detection probability, and mean acquisition time in Rayleigh fading channels, the performance of the proposed system is compared to that of a conventional system with a fixed threshold.

A blind symbol synchronization scheme for MIMO and Multi-User OFDM systems is proposed, which utilizes short-time Fourier Transformation (STFT) to obtain 2D (time and frequency) timing information from the received signals. By analyzing the obtained 2D time-frequency amplitude spectrum, intervals where no inter-symbol interference (ISI) exists are checked out for symbol synchronization, and samples during these intervals are used to carry out carrier frequency offset estimation. Theoretical analysis and simulation results show that the proposed method is more robust and provides more accurate carrier frequency offset estimation than traditional schemes.

This paper presents a salient method to find an optimal bandwidth for low noise phase-locked loop (PLL) applications by analyzing a discrete-time model of charge-pump PLLs based on ring oscillator VCOs. The analysis shows that the timing jitter of the PLL system depends on the jitter in the ring oscillator and an accumulation factor which is inversely proportional to the bandwidth of the PLL. Further analysis shows that the timing jitter of the PLL system, however, proportionally depends on the bandwidth of the PLL when an external jitter source is applied. The analysis of the PLL timing jitter of both cases gives the clue to the optimal bandwidth design for low noise PLL applications, Simulation results using a C-language PLL model are compared with the theoretical predictions and show good agreement.

Image dehazing is of great significance in computer vision and other fields. The performance of dehazing mainly relies on the precise computation of transmission map. However, the computation of the existing transmission map still does not work well in the sky area and is easily influenced by noise. Hence, the dark channel prior (DCP) and luminance model are used to estimate the coarse transmission in this work, which can deal with the problem of transmission estimation in the sky area. Then a novel weighted variational regularization model is proposed to refine the transmission. Specifically, the proposed model can simultaneously refine the transmittance and restore clear images, yielding a haze-free image. More importantly, the proposed model can preserve the important image details and suppress image noise in the dehazing process. In addition, a new Gaussian Adaptive Weighted function is defined to smooth the contextual areas while preserving the depth discontinuity edges. Experiments on real-world and synthetic images illustrate that our method has a rival advantage with the state-of-art algorithms in different hazy environments.