提出了一种用于低温扫描隧道显微镜(CryoSTM)的低噪声高增益大带宽跨阻放大器(TIA)。在CryoSTM中与尖端样品组件连接的TIA称为CryoSTM-TIA。CryoSTM-TIA的跨阻增益为10Gohm，带宽超过100kHz，在100kHz时等效输入噪声电流功率谱密度小于4(fA)²/Hz。CryoSTM-TIA的低固有噪声是由于其特殊的设计: (1)其前置放大器(Pre-Amp)由低噪声低温高电子迁移率晶体管制成; (2)前置放大器采用卡片式配置，避免米勒效应，降低输入电容C_A; (3)将前置放大器输入端连接到尖端的电缆的电容，即C_I最小化; (4)热噪声源，如反馈电阻，放置在低温区。由于在反馈回路中应用了频率补偿，减小了C_A，减小了C_I，实现了高增益和大带宽。该仪器可用于低电导率材料扫描隧道光谱的快速高能量分辨率测量，特别是用于测量其扫描隧道发射噪声谱。

本文提出了一种用于低温扫描隧道显微镜(CryoSTM)的跨阻放大器(TIA)设计方案。在CryoSTM中带有尖端样品成分的TIA称为CryoSTM-TIA。该CryoSTM-TIA的跨阻增益在1Gohm，而其带宽大于300kHz。所提出的CryoSTM-TIA的独特特点是其前置放大器由单个低温高电子迁移率晶体管(HEMT)制成，因此在100kHz时仪器等效输入噪声电流功率谱密度低于4(fA)²/Hz。此外，应用“偏置冷却法”可用于原位控制HEMT掺杂区冻结DX^-中心的密度，改变其结构以降低器件噪声。利用该仪器，可以进行高能量分辨率的快速扫描隧道光谱测量。并且，它能够测量各种量子系统在原子尺度上的扫描隧道散粒噪声谱(STSNS)，即使散粒噪声非常低。它为通过测量STSNS来研究新的量子态提供了一个强大的工具，例如检测拓扑量子系统中马约拉纳束缚态的存在。

在无线传感器网络（WSNs）中，数据分发是一种重要的传输方式，需要满足以下要求：可靠性，节能和可扩展性。然而，现有的研究工作很少关注数据分发中所存在的攻击，导致数据分发的可靠性大打折扣。为了检测出WSNs数据分发中的节点克隆攻击，保证数据分发的高可靠性，提出了基于单轮零知识证明的节点克隆攻击检测方案。本方案通过构建析取-叠加码生成专属于各个节点的数字指纹，在单轮零知识证明方案中对节点的数字指纹进行验证，可以检测出没有正确数字指纹的克隆节点。仿真表明使用提出的检测方案，可以保证WSNs在数据分发过程中的高可靠性.

In wireless sensor networks (WSNs), data dissemination is an essential transmission mode, which needs to meet three requirements: reliability, energy saving and scalability. Existing research pays little attention to the attacks in data dissemination, resulting in a significant loss of the reliability of data dissemination. In order to detect node clone attacks in WSNs data dissemination and ensure high reliability of data dissemination, a node clone attack detection scheme based on single round zero knowledge proof is proposed. In this scheme, the digital fingerprint of each node is generated by constructing superimposed disjunct code, and the cloned node without correct digital fingerprint can be detected by verifying the digital fingerprint of the node in the single round zero-knowledge proof scheme. Simulation results show that the proposed scheme can ensure the high reliability of WSNs in the data dissemination process.

‘The spectral leakage (SL) from windowing and the picket fence effect (PEF) from discretization’ have been among the standard contents in textbooks for many decades. The SL and PEF would cause the distortions in amplitude, frequency, and phase of signals, which have always been of concern, and attempts have been made to solve them. This paper proposes two novel decomposition theorems that can totally eliminate the SL and PEF, they could broaden the knowledge of signal processing. First, two generalized eigenvalue equations are constructed for multifrequency discrete real signals and complex signals. The two decomposition theorems are then proved. On these bases, exact decomposition methods for real and complex signals are proposed. For a noise-free multifrequency real signal with m sinusoidal components, the frequency, amplitude, and phase of each component can be exactly calculated by using just 4m–1 discrete values and its second-order derivatives. For a multifrequency complex signal, only 2m–1 discrete values and its first-order derivatives are needed. The numerical experiments show that the proposed methods have very high resolution, and the sampling rate does not necessarily obey the Nyquist sampling theorem. With noisy signals, the proposed methods have extraordinary accuracy.