基于ADS的脉冲压缩雷达系统仿真与性能分析

在高级设计系统ADS仿真平台上,开发了一套具有良好通用性和灵活性的脉冲压缩雷达系统模型库,为今后开发大规模雷达系统模型库和进行复杂雷达系统仿真打下了基础;在此工作基础上,以巴克码脉冲压缩信号为例,建立了相应的雷达系统模型,并结合系统性能分析的需求,在该系统仿真设计过程中引入蒙特.卡罗模型,较全面地得到了系统基本性能指标值;由这些指标值可以看出,该巴克码脉冲压缩雷达系统性能良好;仿真结果与理论值和真实系统值相符,从而正确地完成了系统的方案论证和性能评估。     

Measuring code maintainability with deep neural networks

The maintainability of source code is a key quality characteristic for software quality. Many approaches have been proposed to quantitatively measure code maintainability. Such approaches rely heavily on code metrics, e.g., the number of Lines of Code and McCabe’s Cyclomatic Complexity. The employed code metrics are essentially statistics regarding code elements, e.g., the numbers of tokens, lines, references, and branch statements. However, natural language in source code, especially identifiers, is rarely exploited by such approaches. As a result, replacing meaningful identifiers with nonsense tokens would not significantly influence their outputs, although the replacement should have significantly reduced code maintainability. To this end, in this paper, we propose a novel approach (called DeepM) to measure code maintainability by exploiting the lexical semantics of text in source code. DeepM leverages deep learning techniques (e.g., LSTM and attention mechanism) to exploit these lexical semantics in measuring code maintainability. Another key rationale of DeepM is that measuring code maintainability is complex and often far beyond the capabilities of statistics or simple heuristics. Consequently, DeepM leverages deep learning techniques to automatically select useful features from complex and lengthy inputs and to construct a complex mapping (rather than simple heuristics) from the input to the output (code maintainability index). DeepM is evaluated on a manually-assessed dataset. The evaluation results suggest that DeepM is accurate, and it generates the same rankings of code maintainability as those of experienced programmers on 87.5% of manually ranked pairs of Java classes.     

Research on encoding and decoding of non-binary polar codes over GF(2m)

Binary Polar Codes (BPCs) have advantages of high-efficiency and capacity-achieving but suffer from large latency due to the Successive-Cancellation List (SCL) decoding. Non-Binary Polar Codes (NBPCs) have been investigated to obtain the performance gains and reduce latency under the implementation of parallel architectures for multi-bit decoding. However, most of the existing works only focus on the Reed-Solomon matrix-based NBPCs and the probability domain-based non-binary polar decoding, which lack flexible structure and have a large computation amount in the decoding process, while little attention has been paid to general non-binary kernel-based NBPCs and Log-Likelihood Ratio (LLR) based decoding methods. In this paper, we consider a scheme of NBPCs with a general structure over GF(2^m). Specifically, we pursue a detailed Monte-Carlo simulation implementation to determine the construction for proposed NBPCs. For non-binary polar decoding, an SCL decoding based on LLRs is proposed for NBPCs, which can be implemented with non-binary kernels of arbitrary size. Moreover, we propose a Perfect Polarization-Based SCL (PPB-SCL) algorithm based on LLRs to reduce decoding complexity by deriving a new update function of path metric for NBPCs and eliminating the path splitting process at perfect polarized (i.e., highly reliable) positions. Simulation results show that the bit error rate of the proposed NBPCs significantly outperforms that of BPCs. In addition, the proposed PPB-SCL decoding obtains about a 40% complexity reduction of SCL decoding for NBPCs.     

2010年我国恶意代码新特点的研究

文章以公安部2010年计算机病毒疫情调查活动的数据作为基础,以国家计算机病毒应急处理中心国家级恶意代码样本库作为支撑,结合信息网络安全通报机制成员单位汇总的情况和各反病毒研究机构发布的安全报告,阐述了2010年我国恶意代码发展的总体情况,分析了2010年我国恶意代码出现的新特点,探索了今后恶意代码的发展趋势并提出对策建议。     

强制访问控制是防范恶意代码的有效手段

该文从访问控制的机制上论述了强制访问控制对防范恶意代码的有效性,并介绍了在目前流行的主流操作系统实现强制访问控制的方法,并提出了对等级保护基本要求中对强制访问控制的修改建议。     

2011年6月网络安全监测数据分析

2011年6月,中国境内僵尸网络受控主机数量和木马受控主机数量较5月稍有下降,网页篡改数量下降。恶意代码活动情况方面,中国大陆地区约有38万个IP地址对应的主机被木马或僵尸程序控制,较5月下降6%;网站安全方面,6月中国大陆地区被篡改网站数量为3164个,较5月下降6%,其中被篡改政府网站数量为333个,较5月增长57%。     

伪码调相雷达高度表中多径信号抑制算法研究

多径误差是伪码调相雷达高度表中重要的误差源之一,为了提高系统的测距精度,建立了采用strobe相关器的伪码跟踪环数学模型,给出了其实现的结构框图;在考虑单路多径信号的基础上分析了多径信号的参数对伪码跟踪环的跟踪误差的影响,并在Matlab环境下对伪码跟踪环的跟踪性能进行了仿真;仿真结果表明在相同的多径参数条件下,strobe相关器可以有效地减小多径引起的跟踪误差,性能优于窄相关器,运算量较多径估计延迟锁定环要小,易于在硬件上实时实现,具有实际应用价值。     

基于源码分析的多核操作系统研究与人才培养实践

多核已成为处理器的主流,在传统的操作系统教学中引入操作系统如何支持多核的内容十分必要。本文从多核操作系统启动流程分析和多核操作系统调度两方面介绍基于源码分析的研究方法,借助现有的文献资料,学生不仅加深了对操作系统抽象概念的理解,也可以优化和修改内核。该方法目标准确,避免了盲目性。实践经验表明,经过几个月的高强度训练,学生的能力提升明显。     

二进制翻译中代码生成的子图覆盖算法

二进制翻译技术能够有效解决二进制兼容问题,促进新型体系结构的发展,也是虚拟机技术的重要组成部分,具有重要的研究和应用价值,但是其效率仍然有待提升,特别是目标代码生成的效率。设计了一种高效的目标代码生成算法——代码生成的子图覆盖算法(subgraph covering for code generation,SCCG),能够以尽可能少的代价生成精简的目标代码。该算法应用数据流图对二进制代码中的基本块进行建模,获取指令间的数据相关,并采用基于子图覆盖的贪心算法得到目标代码。在TransARM原型系统中进行了实现和测试,结果表明该算法获得了更优质的目标代码,并且成本得到了有效控制。     

Embedding HMMs-based models in a Euclidean space: the topological hidden Markov models

Current extensions of hidden Markov models such as structural, hierarchical, coupled, and others have the power to classify complex and highly organized patterns. However, one of their major limitations is the inability to cope with topology: When applied to a visible observation (VO) sequence, the traditional HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the VO sequence. To fulfill this need, we propose a novel paradigm named “topological hidden Markov models” (THMMs) that classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean space. This is achieved by modeling the noise embedded in the shape generated by the VO sequence. We cover the first and second level topological HMMs. We describe five basic problems that are assigned to a second level topological hidden Markov model: (1) sequence probability evaluation, (2) statistical decoding, (3) structural decoding, (4) topological decoding, and (5) learning. To show the significance of this research, we have applied the concept of THMMs to: (i) predict the ASCII class assigned to a handwritten numeral, and (ii) map protein primary structures to their 3D folds. The results show that the second level THMMs outperform the SHMMs and the multi-class SVM classifiers significantly.