Evaluation of Codebook Design Using SCMA Scheme Based on A_(n) and D_(n) Lattices

The sparse code multiple access(SCMA)scheme is a Non-Orthogonal Multiple Access(NOMA)type of scheme that is used to handle the uplink com-ponent of mobile communication in the current generation.A need of the 5G mobile network is the ability to handle more users.To accommodate this,the SCMA allows each user to deploy a variety of sub-carrier broadcasts,and several consumers may contribute to the same frequency using superposition coding.The SCMA approach,together with codebook design for each user,is used to improve channel efficiency through better management of the available spectrum.How-ever,developing a codebook with a greater number of value sets is still another challenge.With enhanced techniques of encoding and decoding for 5G networks,mapping the multidimensional constellations in the SCMA system plays a signif-icant role in improving the system performance and enhancing the overall system performance.The creation of a codebook utilizing the SCMA approach in con-junction with the lattice theory is suggested in this study.The prototype is shaped using a popular lattice,such as A n and D n,as the basis.Afterward,from the primary lattice constellation,the multidimensional complex mother constellation with the most noticeable variance in power is discovered.The lattice-based cod-ing is generated by combining the codebooks with the mother constellation,and the codes in the matrices are mapped by rotating the constellations in this context.The suggested technique,in conjunction with the investigation of novel SCMA codebook sets,provides improved performance in terms of Bit Error Rate(BER)and complexity with regard to Signal to Noise Ratio(SNR).Finally,the bit error rate is reduced for various SNRs during transmission in the channel.

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

At the present time,the Industrial Internet of Things(IIoT)has swiftly evolved and emerged,and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data.The use of image sensors as an automa-tion tool for the IIoT is increasingly becoming more common.Due to the fact that this organisation transfers an enormous number of photographs at any one time,one of the most significant issues that it has is reducing the total quantity of data that is sent and,as a result,the available bandwidth,without compromising the image quality.Image compression in the sensor,on the other hand,expedites the transfer of data while simultaneously reducing bandwidth use.The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties.The image encryp-tion model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system.This helps to ensure that image datasets are kept safe.The Linde–Buzo–Gray(LBG)methodology is an example of a vector quantization algorithm that is extensively used and a rela-tively new form of picture reduction known as vector quantization(VQ).As a result,the purpose of this research is to create an artificial humming bird optimi-zation approach that combines LBG-enabled codebook creation and encryption(AHBO-LBGCCE)for use in an IIoT setting.In the beginning,the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ.The Burrows-Wheeler Transform(BWT)model is used in order to accomplish codebook compression.In addition,the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained.A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms.The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.

一种基于主成分分析的Codebook背景建模算法

混合高斯(Mixture of Gaussian,MOG)背景建模算法和Codebook背景建模算法被广泛应用于监控视频的运动目标检测问题,但混合高斯的球体模型通常假设RGB三个分量是独立的,Codebook的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点,这些假设使得模型对背景的描述能力下降.本文提出了一种椭球体背景模型,该模型克服了混合高斯球体模型和Codebook圆柱体模型假设的局限性,同时利用主成分分析(Principal components analysis,PCA)方法来刻画椭球体背景模型,提出了一种基于主成分分析的Codebook背景建模算法.实验表明,本文算法不仅能够更准确地描述背景像素值在RGB空间中的分布特征,而且具有良好的鲁棒性.

结合背景更新和亮度范围的改进Codebook模型算法

传统的Codebook背景模型算法是在RGB颜色空间下建立的,该算法计算复杂、分割质量不高且抗扰动能力较差。首先利用YUV颜色空间下亮度与色度分离的特性,建立Codebook背景模型,其次针对背景模型的亮度范围定义不合理的问题,采用码本中码字的Y分量均值重新定义亮度范围,最后在运动目标检测过程中使用双层码本与短时滑动窗口相结合的背景更新方法提高抗干扰能力。实验表明,改进算法的亮度范围能够随背景变化不断更新。与传统的Codebook背景模型算法相比,该算法能够有效更新背景,同时提高检测的准确性。

基于改进codebook算法的运动目标检测

为解决运动目标检测算法鲁棒性和实时性差的问题,在原始codebook算法的基础上提出一种改进的codebook算法。在匹配码字时将最近更新的码字调整至码本列表的最前端,加快码字匹配的速度;以适应光照变化且运算简单的局部二值模式(local binary patterns,LBP)直方图向量代替原有的RGB向量,采用码本记录局部区域的纹理特性,并通过实验比较原始的codebook、混合高斯算法及改进后的codebook。结果表明:改进后的codebook算法较其他2种算法具有更快的处理速度和更好的检测效果,且增强对场景中光照变化的适应力。

基于Codebook的视频火焰识别算法

为了提高视频火焰识别的准确度,提出了一种基于Codebook的火焰识别方法,创新地在YUV空间使用Codebook背景模型检测火焰区域,定期更新背景,综合火焰的动静态多特征进行火焰识别。首先,提取视频中的每一帧图像,利用原始图像中R、G、B三个分量间存在的线性关系作为颜色模型,初步提取火焰颜色区域;然后,为了利用YUV颜色空间的有利特性,将颜色空间从RGB转化到YUV,使用Codebook背景模型进行背景学习、背景差分,提取出具有火焰颜色的动态前景;最后,利用火焰面积变化率、区域重叠率、质心位移这3个特征来训练反向传播(BP)神经网络,通过训练好的神经网络判断视频图像是否存在火焰。选取相机位置以及方向固定的视频进行实验,所提算法在复杂的视频场景中的识别准确度达到96%以上。实验结果表明,所提算法有效提高识别的准确度,同时降低多种干扰物场景的误判率。

基于Codebook背景建模的视频行人检测

针对视频序列,Codebook背景建模算法能检测出其中的运动物体,但却无法识别行人。而大部分基于支持向量机(SVM)训练的行人分类器,需要通过滑动窗口遍历图像检测行人。为加快行人检测的速度,提出将传统的行人分类器融入到Codebook背景建模算法中,通过背景建模算法为行人检测提供候选区域,减少搜索范围,降低了行人误检率;并根据行人的特点,构建临时块模型定期将满足条件的前景区域更新到背景模型中,解决了Codebook背景建模算法不能应对光照突变的问题。实验结果表明:所提算法能应对光照突变所带来的干扰,实现视频行人实时检测。

基于Codebook的运动补偿内插方法

为提高帧率转换的有效性,提出一种基于Codebook的视频运动补偿内插方法。该方法应用传统块匹配方法得到运动矢量,利用Codebook模型实现背景区域和前景区域的分割并对前景区域的运动矢量进行可变块处理,同时使用块合并算法保证前景区域物体边缘结构信息的完整性,通过矢量中值滤波器和矢量平滑对前景区域进行处理以消除重影现象和方块效应。实验结果表明,与线性插帧、矢量中值滤波、轻决策运动估计方法相比,该方法能得到更加精确和鲁棒的前景区域,满足人们对视觉效果的高质量需求。

融合五帧差分和codebook模型的运动目标检测

针对传统codebook算法在复杂背景下抗干扰能力差以及传统帧差法易出现目标空洞的问题,提出了基于codebook模型的背景差法同五帧差法相结合的运动目标检测提取算法。该方法首先建立YUV颜色空间下codebook背景模型,将获得的背景帧与当前帧进行对比,得到前景图像;随后对连续的五帧图像进行预处理、差分二值化和形态学处理后,得到相应的前景图像;最后将基于codebook模型的背景差法和五帧差法所得到的前景图像进行像素的或运算,得到运动目标的精确区域。实验结果表明,该算法有效减少了目标图像中的噪声和阴影,可精确地检测出运动目标,具有良好的鲁棒性。

基于背景Codebook模型的前景检测算法

很多背景场景都包括复杂的运动目标,解决这种问题的较好方法是获取每个像素或者一组像素的时间序列模型,这类模型可以很好的处理时间起伏。但是,计算复杂度高而且耗时。为了获得与自适应滤波相当接近的性能。采用Codebook来建模场景中感兴趣的状态,选择RGB颜色空间模型,学习一个覆盖组成图像像素三个通道上的Codebook,可以有效的解决像素剧烈变化的问题,从而鲁棒探测出场景的前景目标。通过实验结果表明,提出的基于Codebook背景模型的目标检测方法比传统的目标检测算法具有更高的精确度和鲁棒性。

Codebook Design and Beam Training for Extremely Large-Scale RIS:Far-Field or Near-Field?

Reconfigurable intelligent surface(RIS)is more likely to develop into extremely large-scale RIS(XL-RIS)to efficiently boost the system capacity for future 6 G communications.Beam training is an effective way to acquire channel state information(CSI)for XL-RIS.Existing beam training schemes rely on the far-field codebook.However,due to the large aperture of XL-RIS,the scatters are more likely to be in the near-field region of XL-RIS.The far-field codebook mismatches the near-field channel model.Thus,the existing far-field beam training scheme will cause severe performance loss in the XL-RIS assisted nearfield communications.To solve this problem,we propose the efficient near-field beam training schemes by designing the near-field codebook to match the nearfield channel model.Specifically,we firstly design the near-field codebook by considering the near-field cascaded array steering vector of XL-RIS.Then,the optimal codeword for XL-RIS is obtained by the exhausted training procedure.To reduce the beam training overhead,we further design a hierarchical nearfield codebook and propose the corresponding hierarchical near-field beam training scheme,where different levels of sub-codebooks are searched in turn with reduced codebook size.Simulation results show the proposed near-field beam training schemes outperform the existing far-field beam training scheme.

Codebook design using improved particle swarm optimization based on selection probability of artificial bee colony algorithm

In the paper, a new selection probability inspired by artificial bee colony algorithm is introduced into standard particle swarm optimization by improving the global extremum updating condition to enhance the capability of its overall situation search. The experiment result shows that the new scheme is more valuable and effective than other schemes in the convergence of codebook design and the performance of codebook, and it can avoid the premature phenomenon of the particles.

Angle-Based Beamforming in mmWave Massive MIMO Systems with Low Feedback Overhead Using Multi-Pattern Codebooks

The integration of millimeter-wave(mmWave)communications and massive multiple input multiple output(MIMO)techniques is a promising solution to dramatically increase the 5G network throughput.By using large antenna arrays,beamforming can be adopted to improve the 5G capacity by employing spatial domain resources.In a frequency division duplexing(FDD)based 5G mmWave MIMO system,beamforming operation requires timely downlink channel state information(CSI)feedback.However,the rapid channel variations caused by short wavelength of mmWave band,and the high-level feedback information required due to the large number of antennas in massive MIMO system lead to the significantly increased beamforming overhead.In this paper,by exploiting the higher angular stability of such channels,we propose an angle-based beamforming scheme to reduce the feedback frequency and the number of feedback bits.To facilitate this approach users are initially selected to reduce the intra-zone interference before beamforming.Besides,location related feedback,which is not affected by the number of antennas,is adopted to reduce overhead.The simulation results show that two proposed user selection algorithms can adapt to scenarios with diverse requirements,while the feedback overhead of proposed angle-based beamforming algorithm is sharply reduce compared with that of CSIbased beamforming algorithm.

基于codebook的事件关联分析

为关联系统事件,给出最根本故障原因,采用codebook与原因池相结合的方法,建立了一种新的事件分析模型。模型通过不断地缩小原因池中原因的个数,最终锁定系统故障原因,适用于分布式系统的事件关联分析应用。针对codebook中记录的事件和原因规模扩大的情况进行了模型的性能测试,并对查全率、差误率、重报率等指标进行了模型的功能测试。测试结果表明,模型对丢失事件和事件逆序具有抗干扰性,可独立进行多系统并发事件分析且不会互相影响。

A Codebook Design Method for Robust VQ-Based Face Recognition Algorithm

In this paper, we present a theoretical codebook design method for VQ-based fast face recognition algorithm to im-prove recognition accuracy. Based on the systematic analysis and classification of code patterns, firstly we theoretically create a systematically organized codebook. Combined with another codebook created by Kohonen’s Self-Organizing Maps (SOM) method, an optimized codebook consisted of 2×2 codevectors for facial images is generated. Experimental results show face recognition using such a codebook is more efficient than the codebook consisted of 4×4 codevector used in conventional algorithm. The highest average recognition rate of 98.6% is obtained for 40 persons’ 400 images of publicly available face database of AT&T Laboratories Cambridge containing variations in lighting, posing, and expressions. A table look-up (TLU) method is also proposed for the speed up of the recognition processing. By applying this method in the quantization step, the total recognition processing time achieves only 28 msec, enabling real-time face recognition.

Vehicle detection algorithm based on codebook and local binary patterns algorithms

Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns(LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy.

Towards an Efficient Hierarchical Codebooks for Content-Based Images Retrieval

In space feature quantization, the most important problem is designing an efficient and compact codebook. The hierarchical clustering approach successfully solves the problem of quantifying the feature space in a large vocabulary size. In this paper we propose to use a tree structure of hierarchical self-organizing-map(H-SOM) with the depth length equal to two and a high size of branch factors(50, 100, 200, 400, and 500). Moreover, an incremental learning process of H-SOM is used to overcome the problem of the curse of the dimensionality of space. The method is evaluated on three public datasets. Results exceed the current state-of-art retrieval performance on Kentucky and Oxford5k dataset. However, it is with less performance on the Holidays dataset. The experiment results indicate that the proposed tree structure shows significant improvement with a large number of branch factors.

A New Codebook Design Scheme for Fast Beam Searching in Millimeter-Wave Communications

To overcome imperfection of exhaustive based beam searching scheme in IEEE 802.15.3c and IEEE 802.11 ad and accelerate the beam training process, combined with the fast beam searching algorithm previously proposed, this paper proposed a beam codebook design scheme for phased array to not only satisfy the fast beam searching algorithm's demand, but also make good use of the advantage of the searching algorithm. The simulation results prove that the proposed scheme not only performs well on flexibility and searching time complexity, but also has high success ratio.

A CODEBOOK COMPENSATIVE VOICE MORPHING ALGORITHM BASED ON MAXIMUM LIKELIHOOD ESTIMATION

This paper presents an improved voice morphing algorithm based on Gaussian Mixture Model(GMM) which overcomes the traditional one in the terms of overly smoothed problems of the converted spectral and discontinuities between frames.Firstly, a maximum likelihood estimation for the model is introduced for the alleviation of the inversion of high dimension matrixes caused by traditional conversion function.Then, in order to resolve the two problems associated with the baseline, a codebook compensation technique and a time domain medial filter are applied.The results of listening evaluations show that the quality of the speech converted by the proposed method is significantly better than that by the traditional GMM method, and the Mean Opinion Score(MOS) of the converted speech is improved from 2.5 to 3.1 and ABX score from 38% to 75%.

Two-Codebook-Based Cooperative Precoding for TDD-CoMP in 5G Ultra-Dense Networks

In ultra-dense networks(UDN), the local precoding scheme for time-division duplex coordinated multiple point transmission(TDDCo MP) can have a good performance with no feedback by using reciprocity between uplink and downlink. However, if channel is time-varying, the channel difference would cause codeword mismatch between transmitter and receiver, which leads to performance degradation. In this paper, a linear interpolation method is proposed for TDD-Co MP system to estimate the uplink channel at the receiver, which would reduce the channel difference caused by time delay and decrease the probability of codeword mismatch between both sides. Moreover, to mitigate severe inter-cell interference and increase the coverage and throughput of celledge users in UDN, a two-codebook scheme is used to strengthen cooperation between base stations(BSs), which can outperform the global precoding scheme with less overhead. Simulations show that the proposed scheme can significantly improve the link performance compared to the global precoding scheme.