Improved reduced-complexity bit and power allocation algorithms for multicarrier systems

Based on the iterative bit-filling procedure, a computationally efficient bit and power allocation algorithm is presented. The algorithm improves the conventional bit-filling algorithms by maintaining only a subset of subcarriers for computation in each iteration, which reduces the complexity without any performance degradation. Moreover, a modified algorithm with even lower complexity is developed, and equal power allocation is introduced as an initial allocation to accelerate its convergence. Simulation results show that the modified algorithm achieves a considerable complexity reduction while causing only a minor drop in performance.

Soft-output stack algorithm with lattice-reduction for MIMO detection

A computationally efficient soft-output detector with lattice-reduction （LR） for the multiple-input multiple-output （MIMO） systems is proposed. In the proposed scheme, the sorted QR de- composition is applied on the lattice-reduced equivalent channel to obtain the tree structure. With the aid of the boundary control, the stack algorithm searches a small part of the whole search tree to generate a handful of candidate lists in the reduced lattice. The proposed soft-output algorithm achieves near-optimal perfor- mance in a coded MIMO system and the associated computational complexity is substantially lower than that of previously proposed methods.

An Electronic Image Stabilization Algorithm Based on Efficient Block Matching on the Bitplane

This paper proposes an electronic image stabilization algorithm based on efficient block matching on the plane. This algorithm uses a hexagonal search algorithm, and uses the bit-planes to estimate and compensate for the translational motion between video sequences at the same time;As for the rotary motion vector generated in the video sequences, in order to highlight the intensity change of the image sequence, the algorithm firstly conducts Laplace transform for the reference frame, then select a number of characteristics at the image edge to make block matching with the current frame, calculate and compensate for the rotational movement that may exist finally. Through theoretical analysis and simula-tion, we prove that, as for a mixed translational and rotational motion video sequences, the proposed algorithm can reduce required time for block matching computation ,while improving the accuracy of the electronic image stabilization.

Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems

In multiuser massive Multiple Input Multiple Output(MIMO)systems,a large amount of antennas are deployed at the Base Station(BS).In this case,the Minimum Mean Square Error(MMSE)detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion operation.The optimization algorithms such as Gradient Descent(GD)method have received a lot of attention to realize the MMSE detection efficiently without a large scale matrix inversion operation.However,they converge slowly when the condition number of the MMSE filtering matrix(the coefficient matrix)increases,which can compromise the efficiency of their implementation.Moreover,their soft information computation also involves a large-scale matrix-matrix multiplication operation.In this paper,a low-complexity soft-output signal detector based on Adaptive Pre-conditioned Gradient Descent(APGD-SOD)method is proposed to realize the MMSE detection with soft-output for uplink multiuser massive MIMO systems.In the proposed detector,an Adaptive Pre-conditioner(AP)matrix obtained through the Quasi-Newton Symmetric Rank One(QN-SR1)update in each iteration is used to accelerate the convergence of the GD method.The QN-SR1 update supports the intuitive notion that for the quadractic problem one should strive to make the pre-conditioner matrix close to the inverse of the coefficient matrix,since then the condition number would be close to unity and the convergence would be rapid.By expanding the signal model of the massive MIMO system and exploiting the channel hardening property of massive MIMO systems,the computational complexity of the soft information is simplified.The proposed AP matrix is applied to the GD method as a showcase.However,it also can be used by Conjugate Gradient(CG)method due to its generality.It is demonstrated that the proposed detector is robust and its convergence rate is superlinear.Simulation results show that the proposed detector converges at most four iterations.Simulation results also show that the proposed approach achieves a better trade-off between the complexity and the performance than several existing detectors and achieves a near-optimal performance of the MMSE detector with soft-output at four iterations without a complicated large scale matrix inversion operation,which entails a big challenge for the efficient implementation.

A Bit-level Text Compression Scheme Based on the ACW Algorithm

This paper presents a description and performance evaluation of a new bit-level, lossless, adaptive, and asymmetric data compression scheme that is based on the adaptive character wordlength （ACW（n）） algorithm. The proposed scheme enhances the compression ratio of the ACW（n） algorithm by dividing the binary sequence into a number of subsequences （s）, each of them satisfying the condition that the number of decimal values （d） of the n-bit length characters is equal to or less than 256. Therefore, the new scheme is referred to as ACW（n, s）, where n is the adaptive character wordlength and s is the number of subsequences. The new scheme was used to compress a number of text files from standard corpora. The obtained results demonstrate that the ACW（n, s） scheme achieves higher compression ratio than many widely used compression algorithms and it achieves a competitive performance compared to state-of-the-art compression tools.

Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.

面向SIMD指令集的SM4算法比特切片优化

SM4算法是中国自主设计的商用分组密码算法,其加解密计算性能成为影响信息系统数据机密性保障的重要因素之一.现有SM4算法优化主要面向硬件设计和软件查表等方向展开研究,分别存在依赖特定硬件环境、效率低下且易遭受侧信道攻击等问题.比特切片技术通过对输入数据重组实现了并行化高效分组密码处理,可以抵御针对缓存的侧信道攻击.然而现有切片分组密码研究对硬件平台相关性强、处理器架构支持单一,并且并行化处理流水启动较慢,面向小规模数据的加解密操作难以充分发挥单指令多数据(single instruction multiple data,SIMD)等先进指令集的优势.针对上述问题,首先提出了一种跨平台的通用切片分组密码算法模型,支持面向不同的处理器指令字长提供一致化的通用数据切片方法.在此基础上,提出了一种面向SIMD指令集的细粒度切片并行处理SM4优化算法,通过细粒度明文切片重组与线性处理优化有效缩短算法启动时间.实验结果表明,相比通用SM4算法,优化的SM4比特切片算法加密速率最高可达438.0 MBps,加密每字节所需的时钟周期最快高达7.0 CPB(cycle/B),加密性能平均提升80.4%~430.3%.

数字图像混沌序列抽样加权强置乱算法仿真

数字图像置乱关系到个人隐私和信息安全问题,为了保护图像中的敏感信息,提出一种基于位交换和混沌优化的数字图像置乱算法。使用散列函数获得数字图像明文的密钥流,采用与明文相关的子密钥推导像素初始值,通过非线性交叉生成位交换算子,增强明文信息敏感性;运用Logistic方程得到混沌序列,引入抽样加权方法提高图像置乱强度,采用混合蛙跳方法按照族群划分实施信息传输,将混沌序列内的实数从小到大排列,初始混沌抽样后构成的混沌序列用于图像中,显著提升图像置乱强度,完成数字图像置乱。仿真结果表明,上述方法拥有极高的安全性和优秀的加密性能,可以为数字图像在各领域的安全使用提供可靠借鉴。

应答器上行链路信号自适应解调方法的FPGA实现

为降低电磁干扰对信号传输的影响,分析了应答器上行链路信号传输过程及其易遭受干扰信号的特点,设计了基于符号最小均方误差(least mean square,LMS)算法的自适应解调方法。为在硬件平台中实现该解调方法,通过仿真计算,确定LMS算法的自适应算法中间变量变化范围,使用截位操作完成权值系数的更新,设置均衡器长度、步长因子、中值滤波系数分别为1、1/64、16,可在不占用过多硬件资源情况下获得良好的解调性能。解调算法在现场可编程门阵列(field programmable gata array,FPGA)上予以验证,实验表明,当信噪比为6 dB时,FPGA中自适应解调误码率为0.000001,在信噪比大于等于6 dB时,实测误码率与仿真分析误码率基本一致;FPGA自适应解调方法在列车不同速度等级下误码率均小于10^(-6)。

装备系统BIT权衡分析与选择技术研究

根据不同的系统结构特性及测试任务需求,机内测试(BIT)存在多种分类方法。首先通过BIT工作流程分析明确了不同类型BIT之间的关系。然后,针对两种典型的系统级BIT分类方法——BIT运行模式与实现方式的权衡与选择方法进行了系统地研究。对于前者,在系统故障特性分析与BIT测试性指标要求基础上,结合问题特点设计合理的染色体编码方式和适应度函数,并利用遗传算法对BIT运行模式进行权衡与选择;对于后者,通过分析BIT对系统综合保障水平的影响,分别给出相应的定量计算方法,建立基于改进超效率DEA的BIT实现方式权衡与选择计算模型。最后通过案例验证本文所提出方法的可行性和有效性。通过上述分析过程,可以为装备系统BIT详细设计与应用奠定基础。

基于Bit承诺的合同网模型

在分析现有合同网模型的基础上,针对合同网协商过程中的安全问题,利用Bit承诺协议和RSA算法,给出一个具有安全性的合同网协商模型,保证Agent之间信息的安全传输,防止协作Agent反悔、拒绝履行承诺。该协商模型满足不可否认性和强可验证性,投标价格保密安全,技术简单,通信量小。

基于相关性模型的BIT诊断策略技术研究

提出了一种基于相关性模型的机内测试诊断策略。首先根据机内测试系统的诊断策略模型,以相关性矩阵为基础,综合考虑可靠性和测试代价等因素,利用AO*算法构建了决策树;然后根据信息量的大小依次选择测试点,并确定出优化的诊断策略序列。实例分析结果表明,该方法计算复杂度小,所得的诊断策略在较低的测试代价下可以有效提高机内测试的故障检测和隔离率。

大规模定制家具加工中心多钻头并行作业的优化

大规模定制家具超高的生产效率与高度个性化的产品两大特征决定了其需要依赖高水平的信息化与自动化组织生产,是家具制造业智能制造的前沿领域。运用数控加工中心开展钻孔作业是调和个性化产品制造过程中的矛盾、实现柔性生产的重要手段。当前,数控钻孔工序因其作业时间长且板件之间差异较大而往往成为制造过程中的瓶颈。为切实提高生产效率,本研究立足生产实际,从钻头与孔的位置关系中寻求突破口,提出了数控加工中心多钻头并行作业优化问题。以优化钻头排列为主要途径,减少下钻次数为核心目标,基于制造大数据与工艺规则挖掘信息并简化建模,采用差分进化算法求解钻头排列方案,进一步通过聚类算法探索出面向未来高自动化水平下的差异化钻头排列,形成了一套具有实际意义与普适性的优化方法。通过理论与实践验证了该方法的有效性,达到了缩减作业时间、提升加工效率的目的。对打通大规模定制家具制造瓶颈、推动定制家具智能制造具有一定的指导意义。

雷达BIT中信号检测与虚警抑制技术研究

在雷达信号优化检测的研究中,噪声干扰是雷达BIT系统产生虚警的重要因素,为降低噪声及在复杂的噪声中提取有用的信号,解决噪声干扰引起的虚警问题,在分析几种现代降噪方法的基础上,根据最大似然估计和广义似然比检测理论,提出了一种高斯包络线性调频自适应信号分解(AGCD)的广义似然比检测算法,在零均值、复高斯白噪声条件下具有恒虚警特性,解决了AGCD快速算法在噪声条件下的分解终止条件的阈值选择问题,可以用来对信号进行检测和降噪,有效抑制雷达BIT系统的虚警。仿真分析及结果说明了上述方法在信号检测虚警抑制方面的有效性。

机电BIT故障特征选择方法研究

提出一种故障特征选择方法 ,该方法采用类内类间距离作为特征评价准则 ,并利用遗传算法良好的寻优能力 ,解决特征的优选问题。轴承诊断实例证明 ,该方法有较好的寻优特征子集的能力 ,能够提高BIT系统的诊断精度 ,降低系统的虚警率 ,因而在机电BIT故障特征选择中有较好的应用前景。

一种基于位平面和位运动估计的视频数据压缩算法

高分辨率和高刷新率使得带宽传输的压力加倍。通常,视频压缩被用来解决这个问题。但传统的视频压缩方法不根据位平面进行编码和解码,会增加存储区域的成本。因此本文首先介绍了一种位恰可察觉失真(BJND)模型,计算了结合视点因子的位平面恰可察觉失真模型的阈值,并将亮度因子和纹理因子的恰可察觉失真(JND)阈值映射到位平面上,综合得出了位恰可察觉失真模型。然后提出了一种基于位运动估计算法的视频压缩方案。该方案使改进后的搜索范围不仅包括时间维度,还包括灰度维度。根据人眼的视觉特点和概率统计分析,方案新增了补充匹配块来替代残差数据,从而保证了压缩比的恒定性。实验结果表明,所提方案对低5位平面压缩具有最佳的综合效果,压缩比为1.38,数据传输量恒定,且恢复后的图像与原始图像并无明显差异,更加符合人眼的直观感受。

面向异构信号处理平台的量子调度算法

针对异构信号处理平台中已有调度算法的调度长度较大导致信号处理应用实时性下降问题,文中提出一种面向异构信号处理平台的量子调度算法。该算法采用任务优先级分流排序策略,得到更加准确的任务调度顺序。使用量子比特对任务分配方案进行编码,增加任务分配方案的多样性,且编码规则有助于跳出局部最优找到全局最优解。按照最小计算开销原则和任务复制思想进行处理器分配,减少任务间通信开销,并通过量子旋转门对量子编码方案进行更新,不断逼近最优解。仿真结果表明,所提算法能够减少调度长度,提升信号处理应用的实时性,进而提高平台的工作效率。

基于小波变换的图像bit纠错数据并行实现研究

针对图像传输的比特纠错去噪处理高效实现,本文提出了一种基于小波分解的比特纠错SIMDPE阵列数据并行计算方法。该方法,针对不同的小波子带,采用不同的并行计算策略;针对数据并行计算中的PE选择问题,采用PE标识预置的解决方法,即确保去噪效果,又使得数据并行操作更为规则、简洁,大幅度地提高了处理的速度。

求解Max-Re-SAT的离散混沌量子蝙蝠算法

针对最大正则可满足性问题求解算法的研究空缺,以及提升求解最大可满足性问题的智能优化算法的精度,基于蝙蝠算法(bat algorithm,BA),提出了一种基于离散混沌量子的蝙蝠算法。在该算法中,将连续数值转化为离散的二进制编码,对算法进行了离散化处理。该研究运用量子理论、引入量子比特编码和启发式量子变异,通过量子旋转门改变非最优个体的概率振幅来实现变异,解决了早熟和收敛速度慢的问题。在位置更新中,使用混沌映射替代固定参数,增强了灵活性和多样性,提高了全局寻优能力和求解效率。实验结果表明:在随机正则可满足性问题实例产生模型产生的不同规模算例上,所提算法的求解精度远远高于传统启发式算法;同时,与获奖的求解器相比,也具有一定的竞争力,验证了该算法的有效性。

基于随机共振的井下无线电磁2FSK信号解调

井场及周边电器所带来的噪声干扰会对接收到的无线电磁2FSK调制信号产生影响,在信噪比低时提取信号特征困难。为此,提出了一种基于浣熊算法的自适应双稳态随机共振系统,以降低2FSK信号的误码率。该方法充分利用浣熊算法的全局探索和局部优化平衡能力,通过并行选择和优化随机共振系统的多个参数,以获得系统输出的最大信噪比增益。利用卷积神经网络对随机共振系统输出的信号进行解调,并评估其误码率。仿真和试验结果表明,在低信噪比条件下,基于浣熊算法的随机共振系统输出信号的特征频率相对于蚁群优化算法更加显著,并且具有更低的误码率。研究结果可为井下信号实时传输提供技术支撑。