New approaches to digital evidence

Digital evidence, such as digital signatures, is of crucial importance in the emerging digitally operating economy because it is easy to transmit, archive, search, and verify. Nevertheless, the initial promise of the usefulness of digital signatures was too optimistic. This calls for a systematic treatment of digital evidence. The paper provides a foundation for reasoning about digital evidence systems and legislation, thereby identifying the roles and limitations of digital evidence, in the apparently simple scenario where it should prove that an entity, A, agreed to a digital contract, d. Our approach is in sharp contrast to the current general views documented in the technical literature and in digital signature legislation. We propose an entirely new view of the concepts of certification, time stamping, revocation, and other trusted services, potentially leading to new, sounder business models for trusted services. Some of the, perhaps provocative, implications of our view are that certificates are generally irrelevant as evidence in a dispute, that it is generally irrelevant when a signature was generated, that a commitment to be liable for digital evidence cannot meaningfully be revoked, and that there is no need for mutually trusted authorities like certification authorities. We also propose a new type of digital evidence called digital declarations, based on a digital recording of a willful act indicating agreement to a document or contract.     

Achieving 1/2 log (1+SNR) on the AWGN channel with lattice encoding and decoding

We address an open question, regarding whether a lattice code with lattice decoding (as opposed to maximum-likelihood (ML) decoding) can achieve the additive white Gaussian noise (AWGN) channel capacity. We first demonstrate how minimum mean-square error (MMSE) scaling along with dithering (lattice randomization) techniques can transform the power-constrained AWGN channel into a modulo-lattice additive noise channel, whose effective noise is reduced by a factor of /spl radic/(1+SNR/SNR). For the resulting channel, a uniform input maximizes mutual information, which in the limit of large lattice dimension becomes 1/2 log (1+SNR), i.e., the full capacity of the original power constrained AWGN channel. We then show that capacity may also be achieved using nested lattice codes, the coarse lattice serving for shaping via the modulo-lattice transformation, the fine lattice for channel coding. We show that such pairs exist for any desired nesting ratio, i.e., for any signal-to-noise ratio (SNR). Furthermore, for the modulo-lattice additive noise channel lattice decoding is optimal. Finally, we show that the error exponent of the proposed scheme is lower bounded by the Poltyrev exponent.     

Artificial Intelligence-Assisted Label-Free Spectroscopic Quantification of Global DNA Cytosine Methylation in a Miniature Plasmonic Pickering Emulsion

Epigenetic DNA methylations are early and frequently observed events in a diversity of diseases such as cancer. Despite the considerable clinical values for cancer liquid biopsy, quantitative analysis of DNA methylations remains a major challenge due to the lack of rapid, sensitive detection techniques. Here, an artificial intelligence-assisted label-free surface-enhanced Raman spectroscopy (SERS) (iMeSERS) biosensor is reported for simultaneous quantification of C⁵-methylcytosine (^5mC) level and methylation ratio in DNA samples. This method utilizes the plasmonic Pickering emulsions as the biosensing platform for label-free SERS detection, formed upon the addition of a sub-microliter DNA sample to the hydrophobic Au nanostar-containing n-decane. Distinct spectral signatures of monophosphates of canonical deoxyribonucleotides (dNMPs) and the common methylation modification 5-methyl-2′-deoxycytidine (d^5mCMP) are identified and distinguished by the iMeSERS biosensor. The deep learning algorithms trained with SERS signatures of dNMPs and d^5mCMP are then applied to the quantitative analysis of global DNA methylation. The exceptional capability of the deep learning-driven approach is demonstrated for simultaneous quantification of the methylation ratio and level using a sub-microliter volume of DNA samples. This work shows the power of label-free SERS techniques combined with deep learning algorithms for quantitative analysis of epigenetic DNA modifications with great promises for clinical diagnosis.     

NGN承载网优化方案设计

下一代网络（NGN）的目标是建设一个能同时提供多种业务的综合网络,如语音、数据、图像、多媒体等,目前已开始规模商用。本文从NGN网络的特征开始分析,结合承栽网的需求与本公司网络结构的不足,提出相关的优化设计方案。     

非圆信号二维DOA 和初始相位联合估计方法

针对传统联合估计方法计算量大、需要多维谱峰搜索的问题,该文提出了一种基于垂直阵列结构的任意初始相位非圆信号2 维DOA (Direction Of Arrival)和初相联合估计方法,利用垂直阵列特点,将3维参数估计问题转化为可并行处理的3个2维参数估计,在每一个子阵上,同时使用噪声子空间正交性和信号子空间旋转不变性,将2维参数估计进一步转化为1维估计问题,最终只需要对扩展协方差矩阵进行一次特征分解即可实现2维DOA和初相的联合估计及自动配对。该方法适用于空间信源处于过载的情形和低信噪比、短快拍环境,可估计信源数为2(M1)。数值仿真验证了该算法的有效性。      

功率MOSFET零温度系数点的研究

为研究功率MOSFET零温度系数点（Zero Temperature Coefficient,ZTC）,不同栅极电压对温度系数的影响,本文基于阈值电压和迁移率的温度关系并结合功率MOSFET输出特性模型,得到了随栅极电压变化而出现的三种不同温度系数。利用实际测试验证了温度系数的变化规律,结果表明：在小栅极电压条件下,线性区和饱和区均为正温度系数;随栅极电压增大,线性区先进入负温度系数,饱和区仍然为正温度系数,并因此产生了零温度系数点;随栅极电压进一步增大,线性区和饱和区都进入负温度系数。同时根据测试数据,分析了ZTC在不同栅极电压条件下随温度的变化原因,并基于测试数据讨论了迁移率随栅极电压和温度的变化关系。     

连续波雷达直波泄露抑制技术研究

连续波雷达与脉冲雷达相比较有诸多的技术和战术优点,但由于连续波雷达存在严重的发射直波泄露信号,给连续波雷达的广泛应用设置了技术瓶颈。本文全面介绍了连续波雷达发射直波泄露信号抑制技术,并对连续波雷达检测能力进行了分析,给出了连续波雷达不仅要求对发射直波泄露信号具有良好的抑制,同时还要求发射信号必须具备低(超低)相位噪声指标要求及其运算公式。     

再入弹道估计中动力学模型和“当前”统计模型的性能比较

飞行器在再入段的弹道可用较为精确的模型描述,利用UKF,分别结合动力学模型和＂当前＂统计模型,对再入弹道（弹道式和机动式）进行了估计,并就其滤波性能进行了对比分析.仿真结果表明,在对弹道式再入飞行器弹道的实时滤波中,BRV-Exp模型要比CS模型更为合适,弹道估计精度得到明显提高;在对机动式再入飞行器弹道的实时滤波中,MRV-Wiener模型并不优于CS模型.     

基于ArcGIS 图元的军事标图系统

为提高军事标图系统的辅助决策能力,提出基于ArcGIS图元方法。通过多个控制点生成非规则军标,采用ArcGIS绘图函数替代图元动态绘制非规则军标,利用图元的属性标识符将军标与属性数据集成,设计和实现一个军事标图系统,并在实际工程中得到成功应用。结果表明：该系统不但标图便捷,而且具有辅助决策能力,为指挥人员作出科学决策提供强有力的手段。