Secure polar coding for a joint source-channel model

This paper investigates a joint source-channel model where Alice,Bob,and Eve,observe compo-nents of a discrete memoryless source and communicate over a discrete...     

Digital image self-recovery algorithm based on improved joint source-channel coding optimizer

Multimedia Tools and Applications - The purpose of the digital image self-recovery is to restore high quality images as much as possible when the image is tampered. Existing algorithms can only...     

一种改进的联合信源信道编码算法

分析了信源信道率失真模型及端到端的总失真模型。在此基础上,提出了一种改进的联合信源信道失真估计模型。     

Attention-based convolutional neural network for deep face recognition

Multimedia Tools and Applications - Discriminative feature embedding is of essential importance in the field of large scale face recognition. In this paper, we propose an attention-based...     

基于LDPC码和物理层网络编码的联合信道编码技术

将LDPC信道编码与物理层网络编码结合起来,通过码字叠加的原理,提出了一种新的联合信道编码技术。该技术通过将中继节点处经过网络编码的信号作为冗余码,与原编码信号共同组成联合编码信号,有效降低了通信系统的误比特率,提高了系统传输性能。     

Iterative joint source-channel decoding of speech spectrum parameters over an additive white Gaussian noise channel

In this paper, we show how the Gaussian mixture modeling framework used to develop efficient source encoding schemes can be further exploited to model source statistics during channel decoding in an iterative framework to develop an effective joint source-channel decoding scheme. The joint probability density function (PDF) of successive source frames is modeled as a Gaussian mixture model (GMM). Based on previous work, the marginal source statistics provided by the GMM is used at the encoder to design a low-complexity memoryless source encoding scheme. The source encoding scheme has the specific advantage of providing good estimates to the probability of occurrence of a given source code-point based on the GMM. The proposed iterative decoding procedure works with any channel code whose decoder can implement the soft-output Viterbi algorithm that uses a priori information (APRI-SOVA) or the BCJR algorithm to provide extrinsic information on each source encoded bit. The source decoder uses the GMM model and the channel decoder output to provide a priori information back to the channel decoder. Decoding is done in an iterative manner by trading extrinsic information between the source and channel decoders. Experimental results showing improved decoding performance are provided in the application of speech spectrum parameter compression and communication.     

Bottom-up broadcast neural network for music genre classification

Music genre classification based on visual representation has been successfully explored over the last years. Recently, there has been increasing interest in attempting convolutional neural networks (CNNs) to achieve the task. However, most of the existing methods employ the mature CNN structures proposed in image recognition without any modification, which results in the learning features that are not adequate for music genre classification. Faced with the challenge of this issue, we fully exploit the low-level information from spectrograms of audio and develop a novel CNN architecture in this paper. The proposed CNN architecture takes the multi-scale time-frequency information into considerations, which transfers more suitable semantic features for the decision-making layer to discriminate the genre of the unknown music clip. The experiments are evaluated on the benchmark datasets including GTZAN, Ballroom, and Extended Ballroom. The experimental results show that the proposed method can achieve 93.9%, 96.7%, 97.2% classification accuracies respectively, which to the best of our knowledge, are the best results on these public datasets so far. It is notable that the trained model by our proposed network possesses tiny size, only 0.18M, which can be applied in mobile phones or other devices with limited computational resources. Codes and model will be available at https://github.com/CaifengLiu/music-genre-classification.

     

On-demand chaotic neural network for broadcast scheduling problem

This paper presents a novel approach to optimizing network packet transfer scheme through introducing a new method for on-demand chaotic noise injection strategy for the Broadcast Scheduling Problem (BSP). Packet radio networks have many applications, while finding an optimized scheduling to transmit data is proven to be a NP-hard problem. The objective of the proposed method is to find an optimal time division multiple access (TDMA) frame, based on maximizing the channel utilization. The proposed method benefits from an on-demand noise injection policy, which injects noise based on the status of neuron and its neighborhoods. The method is superior to other Noise Chaotic Neural Networks (NCNN) that suffer from blind injection policy. The experimental result shows that, in most cases, the proposed on-demand noise injection algorithm finds the best solution with minimal average time delay and maximum channel utilization.     

Estelle中的通道扩展及广播通道

广播通道（点到多点通道）是一种普遍适用于计算机通信网络的结构概念。而作为形式化描述技术（FDT）标准之一的Estelle,其本身并不支持广播通道的描述。对Estelle中的通道概念进行扩展,使其能够描述广播通道。通过分析Estelle通道的3种扩展方案,引入了虚拟转发接口模块（VFNM）的概念。在尽可能保持与原版本的兼容性和一致性的基础上,对新的基于VFNM的广播通道扩展模型的语法和语义进行修订说明,从而扩展了Estelle对广播通道的描述能力。     

Convolutional neural network with joint stepwise character/word modeling based system for scene text recognition

Multimedia Tools and Applications - Text recognition in the wild is a challenging task in the field of computer vision and machine learning. Existing optical character recognition engines cannot...     

A network coding based hybrid ARQ algorithm for wireless video broadcast

This paper proposes a network coding (NC) based hybrid ARQ (HARQ) algorithm for video broad- cast over wireless networks. The sender applies NC technique to combine the lost packets of different terminals, so that multiple terminals can recover their lost packets via per transmission from the sender. The proposed algorithm combines the advantage of FEC scheme and NC based ARQ scheme so as to maximize not only wireless throughput but also video quality for broadcast communication. Simulation results show tha...     

Active neural learners for text with dual supervision

Neural Computing and Applications - Dual supervision for text classification and information retrieval, which involves training the machine with class labels augmented with text annotations that...     

Studies and advances on joint source-channel encoding/decoding techniques in flow media communications

Joint source-channel coding/decoding (JSCC/JSCD) techniques in flow media communications have become a state-of-the-art and one of the challenging research subjects in the spatial communication area. They have great application prospective and deep impact in various manned space flights, satellite missions, mobile radio communications and deep-space explorations. In the last few years, there have been influential achievements in JSCC/JSCD studies. This paper aims at an introduction to the basic principles o...     

A novel offline handwritten text recognition technique to convert ruled-line text into digital text through deep neural networks

Multimedia Tools and Applications - Offline Handwritten Text Recognition (HTR) has been an active area of research due to its wide range of applications and challenges. Recently, many offline HTR...     

A generic scheme for progressive point cloud coding

In this paper, we propose a generic point cloud encoder that provides a unified framework for compressing different attributes of point samples corresponding to 3D objects with arbitrary topology. In the proposed scheme, the coding process is led by an iterative octree cell subdivision of the object space. At each level of subdivision, positions of point samples are approximated by the geometry centers of all tree-front cells while normals and colors are approximated by their statistical average within each of tree-front cells. With this framework, we employ attribute-dependent encoding techniques to exploit different characteristics of various attributes. All of these have led to significant improvement in the rate-distortion (R-D) performance and a computational advantage over the state of the art. Furthermore, given sufficient levels of octree expansion, normal space partitioning and resolution of color quantization, the proposed point cloud encoder can be potentially used for lossless coding of 3D point clouds.     

An efficient adaptive artificial neural network based text to speech synthesizer for Hindi language

Multimedia Tools and Applications - Speech recognition is one of the major research regions these days under speech processing. This paper depends on developing a whole process that takes the input...     

ADI与凌讯建立联合实验室联手打造移动电视前端解决方案

<正>随着数字时代的到来,数字地面电视广播(DTTB)无疑是一个热门话题。2006年8月18日,国家正式公布了中国地面数字电视传输标准——GB2006(业界习惯称为DMB-TH),这标志着电视从模拟向数字转换的时代已经来临,移动电视也更加火热。特别是随着     

Learning neural networks for text classification by exploiting label relations

Multimedia Tools and Applications - Text classification has been a fundamental problem in the realm of Natural Language Processing (NLP), and a variety of approaches have been proposed with the...