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。     

一种多参数优化的联合信源信道编码方法*

针对JPEG2000码流的渐进传输特性,提出了一种多参数优化的联合信源信道编码方法,即MPO-JSCC。在码率分配的基础上,动态地选择Turbo码交织长度、迭代译码次数,通过优化编码器多个参数更好地执行不等差错保护策略,同时加入JPEG2000容错工具,在接收端利用错误掩藏机制以提高图像解码成功率。将该方法应用于噪声信道的图像传输系统中,仿真结果表明,MPO-JSCC既能在不增加系统复杂度及延迟时间的同时提高重建图像的质量,又能节省系统发射功率,具有一定的现实指导意义。     

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.     

联合H.264和多码率Turbo码的无线视频传输

提出了一种无线视频流联合信源信道编码传输的具体方案,结合H.264视频编码,对其进行合理数据分割后采用UEP技术,并联合信道设计了一个支持信道UEP的多码率Turbo编解码器,同时把Turbo编码与type-Ⅲ HARQ混合自动重传请求机制结合以达到更可靠的数据传输保证。通过仿真实验证明,这种设计方法可进一步优化系统在恶劣的无线信道下的链路吞吐量和取得更好的视频重建质量。     

不等长信源的符号约束MAP联合译码算法

由于不等长编码信源（VLCs）所固有的易于误码扩散的弱点,传统的纠错编码并不能高效地解决其差错控制问题。提出了一种新的针对不等长编码信源的符号约束MAP联合译码算法,利用VLCs双树信源的构造方法,有效地抑制了VLCs的错误扩散;同时,通过将“符号约束”的思想应用于最大后验概率（MAP）译码算法,不仅充分利用了信源先验信息,而且极大地降低了误符号率。实验结果表明,在误符号率为1%时,对于在高斯加性白噪声（AWGN）信道传输情况下的JPEG信源,提出的算法比传统的“比特约束”MAP译码算法有将近0.8 dB的性能增益。     

融合注意力机制和CNN-GRNN模型的读者情绪预测

针对主流面向文本的读者情绪预测算法难以捕捉文本中复杂的语义和语法信息,以及局限于使用多标签分类方法的问题,提出一种融合注意力机制和卷积门限循环神经网络的读者情绪预测方法。该方法将文本划分为多个句子,利用卷积神经网络从每个句子中提取不同粒度的n-gram信息,构建句子级别的特征表示;然后通过门限循环神经网络顺序地集成这些句子特征,并利用注意力机制自适应地感知上下文信息提取影响读者情绪的文本特征;最后利用softmax回归进行细粒度的读者情绪分布预测。在雅虎新闻读者情感分析数据集上的实验结果证明了该方法的有效性。     

Attention-based convolutional neural network deep learning approach for robust malware classification

Recently, transforming windows files into images and its analysis using machine learning and deep learning have been considered as a state-of-the art works for malware detection and classification. This is mainly due to the fact that image-based malware detection and classification is platform independent, and the recent surge of success of deep learning model performance in image classification. Literature survey shows that convolutional neural network (CNN) deep learning methods are successfully employed for image-based windows malware classification. However, the malwares were embedded in a tiny portion in the overall image representation. Identifying and locating these affected tiny portions is important to achieve a good malware classification accuracy. In this work, a multi-headed attention based approach is integrated to a CNN to locate and identify the tiny infected regions in the overall image. A detailed investigation and analysis of the proposed method was done on a malware image dataset. The performance of the proposed multi-headed attention-based CNN approach was compared with various non-attention-CNN-based approaches on various data splits of training and testing malware image benchmark dataset. In all the data-splits, the attention-based CNN method outperformed non-attention-based CNN methods while ensuring computational efficiency. Most importantly, most of the methods show consistent performance on all the data splits of training and testing and that illuminates multi-headed attention with CNN model's generalizability to perform on the diverse datasets. With less number of trainable parameters, the proposed method has achieved an accuracy of 99% to classify the 25 malware families and performed better than the existing non-attention based methods. The proposed method can be applied on any operating system and it has the capability to detect packed malware, metamorphic malware, obfuscated malware, malware family variants, and polymorphic malware. In addition, the proposed method is malware file agnostic and avoids usual methods such as disassembly, de-compiling, de-obfuscation, or execution of the malware binary in a virtual environment in detecting malware and classifying malware into their malware family.     

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.     

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.

     

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...     

Attention-based neural network with Generalized Mean Pooling for cross-view geo-localization between UAV and satellite

Artificial Life and Robotics - Cross-view geo-localization is finding images containing the same geographic target in multi-views. For example, given a query image from UAV view, a proposed...     

基于动态学习率深度神经网络的抗干扰信道编码算法

针对电子战条件下,通信信号易受压制干扰的问题,提出了一种基于动态学习率深度自编码器（dynamic learning rate deep AutoEncoder,DLr-DAE）的信道编码算法来提高系统抗压制干扰性能。首先对输入未编码信号进行预处理,将原始输入信号转换为单热矢量,随后使用训练数据样本集,用非监督学习方法训练深度自编码器,基于随机梯度下降法（SGD）更新网络参数,利用指数衰减函数,在迭代次数和网络损失函数值变化过程中动态微调学习率,减少网络迭代循环次数,避免收敛结果陷入局部最优点,从而获得面向电子战环境的信道编码深度学习网络。仿真结果表明,相比现有深度学习编码算法,该算法在取得同等误码率时,抗噪声压制干扰性能最大可提升0.74 dB。     

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...