Self-Encoded Multiple Access Multiuser Convolutional Codes in Uplink and Downlink Cellular Systems

Self-encoded spread spectrum eliminates the need for traditional pseudo noise (PN) code generators. In a self-encoded multiple access (SEMA) system, the number of users is not limited by the number of available sequences, unlike code division multiple access (CDMA) systems that employ PN codes such as m-, Gold or Kassami sequences. SEMA provides a convenient way of supporting multi-rate, multi-level grades of service in multimedia communications and prioritized heterogeneous networking systems. In this paper, we propose multiuser convolutional channel coding in SEMA that provides fewer cross-correlations among users and thereby reducing multiple access interference (MAI). We analyze SEMA multiuser convolutional coding in additive white Gaussian noise (AWGN) channels as well as fading channels. Our analysis includes downlink synchronous system as well as asynchronous system such as uplink mobile-to-base station communication.

Improved Design and SOVA Algorithm for Serial Concatenated Convolutional Code

To improve the performance of the short interleaved serial concatenated convolutional code(SCCC) with low decoding iterative times, the structure of Log MAP algorithm is introduced into the conventional SOVA decoder to improve its performance at short interleaving delay. The combination of Log MAP and SOVA avoids updating the matrices of the maximum path, and also makes a contribution to the requirement of short delay. The simulation results of several SCCCs show that the improved decoder can obtain satisfied performance with short frame interleaver and it is suitable to the high bit rate low delay communication systems.

Blind recognition of k/n rate convolutional encoders from noisy observation

Blind recognition of convolutional codes is not only essential for cognitive radio, but also for non-cooperative context. This paper is dedicated to the blind identification of rate k/n convolutional encoders in a noisy context based on Walsh-Hadamard transformation and block matrix (WHT-BM). The proposed algorithm constructs a system of noisy linear equations and utilizes all its coefficients to recover parity check matrix. It is able to make use of fault-tolerant feature of WHT, thus providing more accurate results and achieving better error performance in high raw bit error rate (BER) regions. Moreover, it is more computationally efficient with the use of the block matrix (BM) method. © 2017 Beijing Institute of Aerospace Information.

Blind reconstruction of convolutional code based on segmented Walsh-Hadamard transform

Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WriT. In order to solve this problem, a method based on segmented WriT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WriT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.

Construction of Regular Rate-Compatible LDPC Convolutional Codes

In this paper, we propose a new method to derive a family of regular rate-compatible low-density parity-check（RC-LDPC） convolutional codes from RC-LDPC block codes. In the RC-LDPC convolutional family, each extended sub-matrix of each extended code is obtained by choosing specified elements from two fixed matrices HE1K and HE1K, which are derived by modifying the extended matrices HE1 and HE2 of a systematic RC-LDPC block code. The proposed method which is based on graph extension simplifies the design, and prevent the defects caused by the puncturing method. It can be used to generate both regular and irregular RC-LDPC convolutional codes. All resulted codes in the family are systematic which simplify the encoder structure and have maximum encoding memories which ensure the property. Simulation results show the family collectively offer a steady improvement in performance with code compatibility over binary-input additive white Gaussian noise channel（BI-AWGNC）.

Determination of quantum toric error correction code threshold using convolutional neural network decoders

Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum error correction,we need to find a fast and close to the optimal threshold decoder.In this work,we build a convolutional neural network(CNN)decoder to correct errors in the toric code based on the system research of machine learning.We analyze and optimize various conditions that affect CNN,and use the RestNet network architecture to reduce the running time.It is shortened by 30%-40%,and we finally design an optimized algorithm for CNN decoder.In this way,the threshold accuracy of the neural network decoder is made to reach 10.8%,which is closer to the optimal threshold of about 11%.The previous threshold of 8.9%-10.3%has been slightly improved,and there is no need to verify the basic noise.

Ensemble of High Performance Structured Binary Convolutional LDPC Codes with Moderate Rates

An algebraic construction methodology is proposed to design binary time-invariant convolutional low-density parity-check(LDPC)codes.Assisted by a proposed partial search algorithm,the polynomialform parity-check matrix of the time-invariant convolutional LDPC code is derived by combining some special codewords of an(n,2,n−1)code.The achieved convolutional LDPC codes possess the characteristics of comparatively large girth and given syndrome former memory.The objective of our design is to enable the time-invariant convolutional LDPC codes the advantages of excellent error performance and fast encoding.In particular,the error performance of the proposed convolutional LDPC code with small constraint length is superior to most existing convolutional LDPC codes.

Turbo Codes―A New PCCC Design

Coding techniques have always been a major area of scientific interest. Due to this interest, many coding schemes were invented. Eventually, their implementation in various systems contributed in the evolvement of Wireless Communications. A breakthrough was definitely Turbo coding. Particularly, the concept of joining two or more convolutional encoders in parallel (PCCC) or in serial (SCCC), along with the iterative decoding technique, literally raised the expectations of the anticipated BER performance. In fact, Concatenated Convolutional Codes clearly outperform convolutional codes. Moreover, various systems, either under development or either for future use, will have high standards. The previous systems should present exceptional tolerance of noise effects and consequently a low overall number of received errors. For this purpose a new PCCC design was developed. The system’s performance analysis, using an AWGN channel, showed better results for various iterations compared to other schemes such as typical PCCC, SCCC and finally a Convolutional encoder with a Viterbi decoder.

A multiple copyright identification watermarking algorithm based on code division multiple access

A multiple watermarking algorithm is presented according to the multiple accessing technique of the code division multiple access （CDMA） system. Multiple watermarks are embedded into digital images in the wavelet transform domain. Each of the watermarks is embedded and extracted independently without impacts to each other. Multiple watermarks are convolution encoded and block interleaved, and the orthogonal Gold sequences are used to spread spectrum of the copyright messages. CDMA encoded water-mark messages are embedded into the wavelet sub-bands excluding the wavelet HH1 sub-bands. The embedment amplitude is decided by Watson＇ s perceptual model of wavelet transform domain, and the embedmeut position in the selected wavelet sub-bands is decided randomly by a pseudo-random noise （PN） sequence. As a blind watermm＇king algorithm, watermarks are extracted without original image. The watermarking capacity of proposed algorithm is also discussed. When two watermarks are embedded in an image at the same time, the capacity is larger than the capacity when a single watermark is embedded, and is smaller than the sum of the capacity of two separately embedded watermarks. Experimental results show that the proposed algorithm improves the detection bits error rate （BER） observably, and the multiple watermarks have a preferable robustness and invisibility.

Performance of Convolutionally Coded Multicarrier DS-CDMA System

A new convolutionally coded direct sequence (DS) CDMA system is proposed. The outputs of a convolutional encoder modulate multiple band-limited DS-CDMA waveforms. The receiver detects and combines signals for the desired user and feeds a soft-decision Viterbi decoder. The performance of this system is compared to that of a convolutionally coded single carrier DS CDMA system with a Rake receiver. At roughly equivalent receiver complexity, results will demonstrate superior performance of the coded multicarrier system.

NEW METHOD TO RECOVER INTERNET PACKET LOSSES USING(2,1,m)SYSTEMATIC CONVOLUTIONAL CODES

A new method to recover packet losses using (2,1,m) convolutional codes is proposed. The erasure correcting decoding algorithm and the decoding determinant theorem is presented. It is also proved that the codes with optimal distance profile have also optimal delay characteristic. Simulation results show that the proposed method can recover the packet losses more elliciently than RS codes over different decoding delay conditions and thus suits for different packet network delav conditions.

Lightweight Malicious Code Classification Method Based on Improved Squeeze Net

With the growth of the Internet,more and more business is being done online,for example,online offices,online education and so on.While this makes people’s lives more convenient,it also increases the risk of the network being attacked by malicious code.Therefore,it is important to identify malicious codes on computer systems efficiently.However,most of the existing malicious code detection methods have two problems:(1)The ability of the model to extract features is weak,resulting in poor model performance.(2)The large scale of model data leads to difficulties deploying on devices with limited resources.Therefore,this paper proposes a lightweight malicious code identification model Lightweight Malicious Code Classification Method Based on Improved SqueezeNet(LCMISNet).In this paper,the MFire lightweight feature extraction module is constructed by proposing a feature slicing module and a multi-size depthwise separable convolution module.The feature slicing module reduces the number of parameters by grouping features.The multi-size depthwise separable convolution module reduces the number of parameters and enhances the feature extraction capability by replacing the standard convolution with depthwise separable convolution with different convolution kernel sizes.In addition,this paper also proposes a feature splicing module to connect the MFire lightweight feature extraction module based on the feature reuse and constructs the lightweight model LCMISNet.The malicious code recognition accuracy of LCMISNet on the BIG 2015 dataset and the Malimg dataset reaches 98.90% and 99.58%,respectively.It proves that LCMISNet has a powerful malicious code recognition performance.In addition,compared with other network models,LCMISNet has better performance,and a lower number of parameters and computations.

A New Malicious Code Classification Method for the Security of Financial Software

The field of finance heavily relies on cybersecurity to safeguard its systems and clients from harmful software.The identification of malevolent code within financial software is vital for protecting both the financial system and individual clients.Nevertheless,present detection models encounter limitations in their ability to identify malevolent code and its variations,all while encompassing a multitude of parameters.To overcome these obsta-cles,we introduce a lean model for classifying families of malevolent code,formulated on Ghost-DenseNet-SE.This model integrates the Ghost module,DenseNet,and the squeeze-and-excitation(SE)channel domain attention mechanism.It substitutes the standard convolutional layer in DenseNet with the Ghost module,thereby diminishing the model’s size and augmenting recognition speed.Additionally,the channel domain attention mechanism assigns distinctive weights to feature channels,facilitating the extraction of pivotal characteristics of malevolent code and bolstering detection precision.Experimental outcomes on the Malimg dataset indicate that the model attained an accuracy of 99.14%in discerning families of malevolent code,surpassing AlexNet(97.8%)and The visual geometry group network(VGGNet)(96.16%).The proposed model exhibits reduced parameters,leading to decreased model complexity alongside enhanced classification accuracy,rendering it a valuable asset for categorizing malevolent code.

Theoretical Analysis of Bit-Error Probability for Convolutional Codes with Weight Enumerators

The paper introduces the state reduction algorithm and accelerated state reduction algorithm are used to compute the distance weight enumerator(transfer function)T[x,y] of convolutional codes.Next use computer simulation to compare upper bound on the bit error probability on an additive white Gaussian noise(AWGN) for maximum free distance(MFD) codes of previously found and optimum distance spectrum(ODS) codes with rate 1/4,overall constraint length are 5 and 7,respectively.Finally,a method of how to search for good convolutional codes is given.

Error Probability for Convolutional Codes with Weight Enumerating Function

The paper introduces the state reduction algorithm and accelerated state reduction algorithm are used to compute the distance weight enumerator(transfer function) T[x,y] of convolutional codes.Next use computer simulation to compare upper bound on the bit error probability on an additive white Gaussian noise (AWGN) for maximum free distance (MFD) codes of previously found and optimum distance spectrum (ODS) codes with rate 1/4,overall constraint length are 5 and 7,respectively. Finally,a method of how to search for good convolutional codes is given.

子宫动脉超声血流参数、血清LncRNA FAM99A、CCL21水平与妊娠期高血压疾病患者病情程度及预后的相关性分析

目的:探讨妊娠期高血压疾病(hypertensive disorders of pregnancy,HDP)患者子宫动脉超声血流参数、血清长链非编码RNA家族成员A序列相似性99(LncRNA FAM99A)、CC趋化因子配体21(CCL21)水平变化,并分析其与病情程度相关性及其对预后的评估价值。方法:回顾性选取2021年1月至2023年12月于开封市第三人民医院诊治的126例HDP患者作为研究组,另选取健康孕产妇42例作为对照组。比较两组不同病情程度者子宫动脉超声血流参数[子宫动脉收缩期/舒张期血流最大速度(systolic/diastolic,S/D)、子宫动脉阻力指数(resistance index,RI)、子宫动脉搏动指数(pulsation index,PI)]、血清LncRNA FAM99A、CCL21水平。分析子宫动脉超声血流参数、血清LncRNA FAM99A、CCL21与病情程度相关性。依据预后情况分为预后不良亚组27例、预后良好亚组99例,比较其子宫动脉超声血流参数、血清LncRNA FAM99A、CCL21水平。分析预后不良的影响因素。评价子宫动脉超声血流参数、血清LncRNA FAM99A、CCL21对预后不良的评估价值。结果:研究组S/D、RI、PI及血清CCL21水平高于对照组,血清LncRNA FAM99A水平低于对照组(P<0.05);S/D、RI、PI、CCL21与病情程度呈正相关(r=0.526、0.418、0.509、0.624,P<0.05),LncRNA FAM99A与病情程度呈负相关(r=-0.637,P<0.05);预后不良亚组S/D、RI、PI及血清CCL21水平高于预后良好亚组,血清LncRNA FAM99A水平低于预后良好亚组(P<0.05);S/D、RI、PI、CCL21为预后不良的危险因素,LncRNA FAM99A为预后不良的保护因素(P<0.05);S/D、RI、PI、LncRNA FAM99A、CCL21联合评估预后的AUC大于单项指标评估(P<0.05)。结论:HDP患者S/D、RI、PI及血清CCL21水平升高,血清LncRNA FAM99A水平降低,且与病情程度及预后密切相关,联合检测其水平对预后具有一定评估价值。

基于CCS软件的FIR滤波器实验设计

本文借助CCS软件完成FIR滤波器实验设计,采用窗函数法实现FIR滤波器设计,窗函数选择矩形窗。FIR滤波器实验设计主要包括输入序列x(n)、单位脉冲响应h(n)和线性卷积子程序。首先通过x(n)和h(n)设计,求出x(n)和h(n);其次调用线性卷积子程序计算h(n)与x(n)的线性卷积,求得FIR滤波器的输出序列y(n)。借助time/frequency菜单观察y(n)的时域波形和频谱。

基于CCS的“信号分析与处理”实验教学

针对本院目前"信号分析与处理课程"的实验教学现状,结合学生的实际情况,打破了以往习惯用Matlab进行仿真的定式。以实验室配备的NvDK-6000SA嵌入式网络图像与视频实验开发系统为平台,进行了基于CCS的"信号分析与处理"的实验教学研究。在对该系统平台熟悉和掌握的基础上,开出指导性、综合性实验、使学生能深入理解相关的教学内容,提高教学质量。也为部分学生自主独立进行开发研究打下基础,同时也为本科生的毕业设计及研究生创新实验搭建了一个良好的软硬件平台。

RS-CC的级联编码设计及其嵌入式FPGA实现

分析了802.16e无线通信系统,针对设计过程中经常出现的数据信息不同步问题,提出了一种基于RS(64,48,8)+CC(2,1,7)+交织的级联编码设计方案。该方案利用功能模块化的设计理念,达到了在不增加译码复杂度的情况下实现有效而可靠的通信。通过将各级编解码模块化,利用FPGA技术实现了整个级联纠错编译码系统。实验结果表明,模块化的FPGA嵌入式设计不仅提高了系统的稳定性,还大大缩短了开发周期。