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Polar codes has a better bit error rate (BER) performance than low-density parity-check (LDPC) and turbo codes with lower coding and decoding complexity. The polar-coded cooperation can provide significant enhancement of data rate and link reliability. However, simply binary coded cooperation cannot meet the requirements of spectral efficiency. In order to improve the spectral efficiency, this paper...
In this paper, we propose an advanced polar encoding scheme with successive cancellation list (SCL) decoder for visible light communication (VLC). As much high-profile channel coding methods, polar codes have achieved the symmetric capacity of binary-input discrete memoryless channels (B-DMCs) based on channel polarization. By taking advantages of simple recursive encoding structure and confirmed...
This paper studies an uplink simultaneous wireless information and power transfer (SWIPT) system with non-orthogonal multiple access (NOMA), consisting of two users and one energy-harvesting access point (AP). The users transmit their independent information simultaneously to the AP which harvests energy and decodes information at the same time by dynamic power splitting (DPS). We focus on the ergodic...
The conventional infinite-length extrinsic information transfer (EXIT) charts would not be accurate for short-length coded systems, because short-length coded sequences do not possess ergodicity as infinite-or very-long length coded sequences. In this paper, we concern with the finite-length EXIT analysis, which is developed for protograph low-density parity-check (PG-LDPC) codes over underwater acoustic...
This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for contentaware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outpues a retargeted image. Retargeting is performed through a shift reap, which is a pixet-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which...
The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby restricting its applications to relatively simple phenomena. In this work, we propose hierarchical non-parametric...
In this work we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. To this end, we combine a convolutional encoder network with an expert-designed generative model that serves as decoder. The core innovation is the differentiable parametric decoder that encapsulates image...
This paper focuses on the task of room layout estimation from a monocular RGB image. Prior works break the problem into two sub-tasks: semantic segmentation of floor, walls, ceiling to produce layout hypotheses, followed by an iterative optimization step to rank these hypotheses. In contrast, we adopt a more direct formulation of this problem as one of estimating an ordered set of room layout keypoints...
Human face exhibits an inherent hierarchy in its representations (i.e., holistic facial expressions can be encoded via a set of facial action units (AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown great results in unsupervised extraction of hierarchical latent representations from large amounts of image data, while being robust to noise and other undesired artifacts. Potentially,...
In this paper Barnes-Wall lattices for the K-user symmetric interference channel are considered. This paper is inspired by the work of Jafar [1], where a deterministic model is used in order to build a scheme to obtain the generalized degrees of freedom for each type of interference. The scheme uses a base Q expression to build the transmitted signals. This is an inspiration for [2] where, for some...
The Gaussian fading channel is studied, in which the channel from the transmitter to the receiver is corrupted by a multiplicative fading coefficient H and an additive Gaussian random noise. It is assumed that the channel is experiencing block fading, and the transmitter does not know the channel state information (CSI). The receiver is assumed to have full knowledge of the CSI. If the channel state...
Inspired by recent work in Optical Character Recognition (OCR) and image captioning, an end-to-end system is utilized which implements the recognition of image formulas. An attention based two-way encoder-decoder structure has been proposed to normal image captioning systems, and it achieves good performance on the recognition of image formulas task. This structure together with a new training method...
The mobile underwater acoustic channel has brought a great challenge on the encoding and decoding process. The recent development of underwater acoustic processing method aims to get a lower bit error rate(BER). However, the controdiction between lower BER and real-time transmission are not well solved. In this paper, we reach a compromise between them, we combine the turbo code with the adaptive...
Scene text recognition has been a hot research topic in computer vision due to its various applications. The state of the art is the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. However, we observe that existing attention-based methods perform poorly on complicated and/or low-quality images. One major reason...
This paper is on active learning where the goal is to reduce the data annotation burden by interacting with a (human) oracle during training. Standard active learning methods ask the oracle to annotate data samples. Instead, we take a profoundly different approach: we ask for annotations of the decision boundary. We achieve this using a deep generative model to create novel instances along a 1d line...
The characteristics of underwater acoustic channel is strong multipath, severe Doppler and limited bandwidth. OFDM modulation has good ability in anti multipath and Turbo Product Codes(TPC) has excellent performance in a high code rate, We make full use of their advantages and designed an OFDM underwater acoustic communication system based on TPC. Using the BELLHOP simulation model to get the approximate...
A direct spread spectrum (DSS) communication system is presented, which uses optimized spread spectrum sequence (SSS) and adaptive soft-iteration (Turbo) equalization (ASIE). The scheme using multiple receiver is similar to MIMO systems. The average cross-correlation of SSS is regarded as a critical factor to search optimized sequences used in under water acoustic (UWA) multi-access burst communication...
We propose a novel framework for multistep predictive current control for induction machines and 3 Level-Neutral Point Clamped Inverter with DC-link balancing. In order to achieve those goals, a nonlinear cost function that weighs different control goals against each other is proposed. The main contribution of this work is the solution of the nonlinear minimization problem of the cost function directly...
Shape reconstruction techniques using structured light have been widely researched and developed due to their robustness, high precision, and density. Because the techniques are based on decoding a pattern to find correspondences, it implicitly requires that the projected patterns be clearly captured by an image sensor, i.e., to avoid defocus and motion blur of the projected pattern. Although intensive...
We present a deep convolutional decoder architecture that can generate volumetric 3D outputs in a compute- and memory-efficient manner by using an octree representation. The network learns to predict both the structure of the octree, and the occupancy values of individual cells. This makes it a particularly valuable technique for generating 3D shapes. In contrast to standard decoders acting on regular...
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