基于神经网络的三维宽场显微图像复原研究

提出一种利用BP神经网络进行三维宽场显微图像复原的非线性映射方法,将三维图像转化为二维图像进行处理,利用神经网络的学习能力,通过训练,建立含有散焦信息的二维模糊图像与二维清晰图像之间的映射关系,然后对切片堆叠进行逐幅复原,从而实现显微图像的三维复原.得到的复原图像在视觉上和定量分析上都获得了很好的效果.由于采用小规模神经网络,训练时间短,计算量小,使实时复原成为可能.     

Determination of Trace Mercury by Solid Substrate-Room Temperature Phosphorimetry Quenching Method Based on Catalytic Effect of Hg2+on Formation of the Ion Association Complex [Sn(XO)6]4+·[(Fin)4]

A new method for the determination of trace mercury by solid substrate-room temperature phosphorimetry (SS-RTP) quenching method has been established. In glycine-HCl buffer solution, xylenol orange (XO) can react with Sn⁴⁺ to form the complex [Sn(XO)₆]⁴⁺. [Sn(XO)₆]⁴⁺ can interact with Fin⁻ (fluorescein anion) to form the ion associate [Sn(XO)₆]⁴⁺·[(Fin)₄]⁻, which can emit strong and stable room temperature phosphorescence (RTP) on polyamide membrane (PAM). Hg²⁺ can catalyze H₂O₂ oxidizing the ion association complex [Sn(XO)₆]⁴⁺·[(Fin)₄]⁻, which causes the RTP to quench. The ΔIp value is directly proportional to the concentration of Hg²⁺ in the range of 0.016–1.6 fg spot⁻¹ (corresponding concentration: 0.040–4.0 pg ml⁻¹, 0.40 μl spot⁻¹), and the regression equation of working cure is ΔIp=10.03+83.15 m Hg²⁺ (fg spot⁻¹), (r=0.9987, n=6) and the detection limit (LD) is 3.6 ag spot⁻¹(corresponding concentration: 9.0×10^–15 g ml⁻¹, the sample volume: 0.4 μl). This simple, rapid, accurate method is of high selectivity and good repeatability, and it has been successfully applied to the determination of trace mercury in real samples. The reaction mechanism for catalyzing H₂O₂ oxidizing the ion association complex ([Sn(XO)₆]⁴⁺·[(Fin)₄]⁻) SS-RTP quenching method to determine trace mercury is also discussed.     

Unified Model for Generation Complex Networks with Utility Preferential Attachment

In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics ofthis new network are given.     

Event-based state estimation for a class of complex networks with time-varying delays: A comparison principle approach

The paper is concerned with the state estimation problem for a class of time-delayed complex networks with event-triggering communication protocol. A novel event generator function, which is dependent not only on the measurement output but also on a predefined positive constant, is proposed with hope to reduce the communication burden. A new concept of exponentially ultimate boundedness is provided to quantify the estimation performance. By means of the comparison principle, some sufficient conditions are obtained to guarantee that the estimation error is exponentially ultimately bounded, and then the estimator gains are obtained in terms of the solution of certain matrix inequalities. Furthermore, a rigorous proof is proposed to show that the designed triggering condition is free of the Zeno behavior. Finally, a numerical example is given to illustrate the effectiveness of the proposed event-based estimator.     

基于休眠机理的三维小基站蜂窝网络能效优化

小基站通常部署在写字楼、商贸区等城市密集区域以弥补传统宏基站在覆盖和传输方面的不足.小基站的分布一般是根据高峰时的网络负荷设计的,这必然导致网络负荷较低时的资源浪费.讨论了在平均接入率和信道容量双重约束下基于休眠机理的三维小基站蜂窝网络的能效优化问题.借助泊松点过程理论推导了三维小基站网络下行信道容量和平均接入率的数学表达式.通过分析下行信道容量和平均接入率的单调性得出同时满足传输信道容量和接入率要求的最佳休眠概率.分析了小基站最大用户连接数的最佳值,通过对该参数的合理配置,可以在满足通信指标的前提下最大程度地降低网络能耗.仿真结果表明,设计的基站休眠机理可以使小基站网络的能耗下降约21%.     

Community detection in signed networks based on discrete-time model

Community detection in signed networks has been studied widely in recent years. In this paper, a discrete difference equation is proposed to imitate the consistently changing phases of the nodes. During the interaction, each node will update its phase based on the difference equation. Each node has many different nodes connected with it, and these neighbors have different influences on it. The similarity between two nodes is applied to describe the influences between them. Nodes with high positive similarities will get together and nodes with negative similarities will be far away from each other.Communities are detected ultimately when the phases of the nodes are stable. Experiments on real world and synthetic signed networks show the efficiency of detection performance. Moreover, the presented method gains better detection performance than two existing good algorithms.     

Exploring the role of inhibitory coupling in duplex networks

The electrical coupling of myocytes and fibroblasts can play a role in inhibiting electrical impluse propagation in cardiac muscle. To understand the function of fibroblast–myocyte coupling in the aging heart, the spiral-wave dynamics in the duplex networks with inhibitory coupling is numerically investigated by the Br–Eiswirth model. The numerical results show that the inhibitory coupling can change the wave amplitude, excited phase duration and excitability of the system. When the related parameters are properly chosen, the inhibitory coupling can induce local abnormal oscillation in the system and the Eckhaus instability of the spiral wave. For the dense inhibitory network, the maximal decrement(maximal increment) in the excited phase duration can reach 24.3%(13.4%), whereas the maximal decrement in wave amplitude approaches 28.1%. Upon increasing the inhibitory coupling strength, the system excitability is reduced and even completely suppressed when the interval between grid points in the inhibitory network is small enough. Moreover, the inhibitory coupling can lead to richer phase transition scenarios of the system, such as the transition from a stable spiral wave to turbulence and the transition from a meandering spiral wave to a planar wave. In addition, the self-sustaining planar wave, the unique meandering of spiral wave and inward spiral wave are observed. The physical mechanisms behind the phenomena are analyzed.     

Phenomenology of nonlocal cellular automata

Dynamical systems with nonlocal connections have potential applications to economic and biological systems. This paper studies the dynamics of nonlocal cellular automata. In particular, all two-state, three-input nonlocal cellular automata are classified according to the dynamical behavior starting from random initial configurations and random wirings, although it is observed that sometimes a rule can have different dynamical behaviors with different wirings. The nonlocal cellular automata rule space is studied using a mean-field parametrization which is ideal for the situation of random wiring. Nonlocal cellular automata can be considered as computers carrying out computation at the level of each component. Their computational abilities are studied from the point of view of whether they contain many basic logical gates. In particular, I ask the question of whether a three-input cellular automaton rule contains the three fundamental logical gates: two-input rules AND and OR, and one-input rule NOT. A particularly interesting edge-of-chaos nonlocal cellular automaton, the rule 184, is studied in detail. It is a system of coupled selectors or multiplexers. It is also part of the Fredkin's gate—a proposed fundamental gate for conservative computations. This rule exhibits irregular fluctuations of density, large coherent structures, and long transient times.     

A multineuron interaction model for neural networks

A multineuron interaction model (RS model) with an energy function given by the product of the squared distances in phase space between the state of the net and the stored patterns is studied in detail within a mean-field approach. Two limits are considered: when the patterns and antipatterns are stored (as in the Hopfield model), PAS case, and when only the patterns are taken into account, OPS case. TheT=0 solutions for the proper memories are exactly obtained for all finite values of, as a consequence of the energy function: whenever one of the overlaps is exactly one the corresponding equations decouple and no configuration average is required. Special interest is focused on the OPS situation, which presents a peculiar phase space topology. On the other hand, the PAS configuration recovers the Hopfield model in the appropriate limit, while keeping associative memory abilities far beyond the critical values of other models when the full Hamiltonian is considered.     

Rank-based deactivation model for networks with age

We study the impact of age on network evolution which couples addition of new nodes and deactivation of old ones. During evolution, each node experiences two stages: active and inactive. The transition from the active state to the inactive one is based on the rank of the node. In this paper, we adopt age as a criterion of ranking, and propose two deactivation models that generalize previous research. In model A, the older active node possesses the higher rank, whereas in model B, the younger active node takes the higher rank. We make a comparative study between the two models through the node-degree distribution.