Neural networks with quantum gated nodes

With a view to investigating similarities in aspects of biological neural networks with quantum ones, so that quantum machines can be developed in future with some of the advantages of biological systems of information processing where a certain amount of indeterminism and the multiple connectivities between nodes offer advantages not seemingly obtainable from usual electronic devices working with classical gates, we present here some results for a quantum neural network with quantum gates. After reviewing the general principles of a biological network and a quantum one, we study a specific model network with qubits, i.e. quantum bits, replacing classical neurons having deterministic states, and also with quantum operators in place of the classical action potentials observed in biological contexts. With our choice of gates interconnecting the neural lattice, the state of the system behaves in ways reflecting both the strength of coupling between neurons as well as the initial conditions, as in biological systems. We find that, depending on whether there is a threshold for emission from excited to ground state, the system shows either chaotic oscillations or coherent ones with periodicity that depends on the strength of coupling. The initial input also affects the subsequent dynamic behavior of the system, which indicates that it can serve as a dynamic memory system analogous to biological ones. Our results seem to suggest that such quantum networks may contain some advantageous features of biological systems more efficiently than classical electronic devices.     

基于信道预测技术的闭环发射分集研究

利用瞬时信道信息去实现MIMO系统的自适应传输,需要发射机和接收机能准确地获得当前信道信息.但随着移动通信系统载波频率的提高和移动台运动速度的增加,信道时变性增加,对于快衰落信道,通过信道估计准确获得当前信道信息是不可能的.解决这个问题的一个有效办法就是信道预测.提出将SISO系统中的信道预测技术引入到MIMO系统,结合闭环发射分集,给出预测算法,通过仿真给出性能比较,表明在快衰落信道中,它能更好地将链路自适应地快速调整到信道的真实条件,提高整个系统的性能.     

多级量子无线网络传输

由于大气中充满噪声与干扰,在自由空间中进行远距离的量子通信成为一个难题。基于量子态纠缠交换的原理,通过两个中继点间接地在通信两端建立起量子信道,使得远距离量子信息的传输成为可能;通过研究源端及各中继点在不同情况下的量子态及接收端相应的量子态,进而对接收端所拥有的量子态施加不同逻辑门进行相应的逻辑变换,最终成功得到了源端所发出的信息;对具有N个中继点的系统进行了研究,解决了远距离的量子信息传输问题。     

Quantum Computing Using Quadrupolar Spins in Solid State NMR

Nuclear magnetic resonance (NMR) is a successful method for experimental implementation of quantum information processing. Most of the successful NMR quantum processors are small molecules in liquid state. In this case each spin half particle represents a qubit. Another approach is the usage of higher spin particles as multi qubit systems. We present the first solid state virtual 2-Qubit system, represented by the spin-3/2 nucleus ²³Na in a NaNO₃ single crystal. For this system we show how to create the pseudo pure states and we derive a set of propagators and logic gates corresponding to the selective excitation of single quantum transitions. With this set, the preparation of an entangled state is experimentally verified by state tomography, adjusted to the spin-3/2 system. PACS: 0.367Lx; 76.60-k     

A new real-time suboptimum filtering and prediction scheme for general nonlinear discrete dynamic systems with Gaussian or non-Gaussian noise

A new suboptimum state filtering and prediction scheme is proposed for nonlinear discrete dynamic systems with Gaussian or non-Gaussian disturbance and observation noises. This scheme is an online estimation scheme for real-time applications. Furthermore, this scheme is very suitable for state estimation under either constraints imposed on estimates or missing observations. State and observation models can be any nonlinear functions of the states, disturbance and observation noises as long as noise samples are independent, and the density functions of noise samples and conditional density functions of the observations given the states are available. State models are used to calculate transition probabilities from gates to gates. If these transition probabilities are known or can be estimated, state models are not needed for estimation. The proposed scheme (PR) is based upon state quantisation and multiple hypothesis testing. Monte Carlo simulations have shown that the performance of the PR, sampling importance resampling (SIR) particle filter and extended Kalman (EK) filter are all model-dependent, and that the performance of the PR is better than both the SIR particle filter and EK filter for some nonlinear models, simulation results of three of which are given in this article.     

Quantum demultiplexer of quantum parameter-estimation information in quantum networks

The quantum demultiplexer is constructed by a series of unitary operators and multipartite entangled states. It is used to realize information broadcasting from an input node to multiple output nodes in quantum networks. The scheme of quantum network communication with respect to phase estimation is put forward through the demultiplexer subjected to amplitude damping noises. The generalized partial measurements can be applied to protect the transferring efficiency from environmental noises in the protocol. It is found out that there are some optimal coherent states which can be prepared to enhance the transmission of phase estimation. The dynamics of state fidelity and quantum Fisher information are investigated to evaluate the feasibility of the network communication. While the state fidelity deteriorates rapidly, the quantum Fisher information can be enhanced to a maximum value and then decreases slowly. The memory effect of the environment induces the oscillations of fidelity and quantum Fisher information. The adjustment of the strength of partial measurements is helpful to increase quantum Fisher information.     

图像处理在基于Web服务的医疗影像管理系统中的实现

传统的医疗影像管理系统是基于专门的工作站及封闭的系统结构,由于近年来互联网技术的迅猛发展,已经用来进行有效的、低成本的生物影像信息传递。本文讨论了基于Web的医疗影像管理系统中,图像处理模块所涉及到的设计思想及主要功能的实现。     

Detection of hidden structures in nonstationary spike trains

We propose an algorithm for simultaneously estimating state transitions among neural states and nonstationary firing rates using a switching state-space model (SSSM). This algorithm enables us to detect state transitions on the basis of not only discontinuous changes in mean firing rates but also discontinuous changes in the temporal profiles of firing rates (e.g., temporal correlation). We construct estimation and learning algorithms for a nongaussian SSSM, whose nongaussian property is caused by binary spike events. Local variational methods can transform the binary observation process into a quadratic form. The transformed observation process enables us to construct a variational Bayes algorithm that can determine the number of neural states based on automatic relevance determination. Additionally, our algorithm can estimate model parameters from single-trial data using a priori knowledge about state transitions and firing rates. Synthetic data analysis reveals that our algorithm has higher performance for estimating nonstationary firing rates than previous methods. The analysis also confirms that our algorithm can detect state transitions on the basis of discontinuous changes in temporal correlation, which are transitions that previous hidden Markov models could not detect. We also analyze neural data recorded from the medial temporal area. The statistically detected neural states probably coincide with transient and sustained states that have been detected heuristically. Estimated parameters suggest that our algorithm detects the state transitions on the basis of discontinuous changes in the temporal correlation of firing rates. These results suggest that our algorithm is advantageous in real-data analysis.     

Acquisition of state transitions and concept formation in neural networks

Searching of state transitions is an important subject of problem solving in artificial intelligence, computer science, engineering and operations research. In artificial intelligence, a breadth-first search is optimal, with uniform cost, but it takes considerable time to obtain a solution. Neural networks process state transitions in parallel with learning ability. The authors have developed a search procedure for state transitions, that resembles a breadth-first search, using neural networks. First, the input pattern states are self-organized in the neural network, which consists of a Kohonen layer followed by a state-planning layer. The state-planning layer makes lateral connections between the cells of transitions. Then, the initial and the target states are given as a problem. The network shows an optimal transition pathway of states in the neuron firings. Next, the state-transition procedure is developed for the formation of a concept for action planning. Here, as the action planning, an integration between the symbols and the action pattern is carried out in the extended neural network.     

基于改进SSD算法的城市轨道交通多通道闸机控制研究

城市轨道交通多通道闸机独立工作,对多个闸机精准控制难度较大,所以研究基于改进SSD算法的城市轨道交通多通道闸机控制方法;将SSD网络与FPN网络相结合,结合先验框生成与损失函数优化实现SSD算法改进,利用改进SSD算法和数据融合结果识别通行目标;根据通行目标识别结果和传感器设备确定闸机通道内的通行情况,从而设计多通道闸机控制处理子程序,完成城市轨道交通多通道闸机控制;实验结果表明,在改进SSD算法作用下,闸机闭合状态始终与行人的当前行进方向保持一致,提高了控制精准度和实时通过率。     

Robust H/sub /spl infin// and mixed H/sub 2//H/sub /spl infin// filters for equalization designs of nonlinear communication systems: fuzzy interpolation approach

Generally, it is difficult to design equalizers for signal reconstruction of nonlinear communication channels with uncertain noises. In this paper, we propose the H/sub /spl infin// and mixed H/sub 2//H/sub /spl infin// filters for equalization/detection of nonlinear channels using fuzzy interpolation and linear matrix inequality (LMI) techniques. First, the signal transmission system is described as a state-space model and the input signal is embedded in the state vector such that the signal reconstruction (estimation) design becomes a nonlinear state estimation problem. Then, the Takagi-Sugeno fuzzy linear model is applied to interpolate the nonlinear channel at different operation points through membership functions. Since the statistics of noises are unknown, the fuzzy H/sub /spl infin// equalizer is proposed to treat the state estimation problem from the worst case (robust) point of view. When the statistics of noises are uncertain but with some nominal (or average) information available, the mixed H/sub 2//H/sub /spl infin// equalizer is employed to take the advantage of both H/sub 2/ optimal performance with nominal statistics of noises and the H/sub /spl infin// robustness performance against the statistical uncertainty of noises. Using the LMI approach, the fuzzy H/sub 2//H/sub /spl infin// equalizer/detector design problem is characterized as an eigenvalue problem (EVP). The EVP can be solved efficiently with convex optimization techniques.     

Feedback Stabilization of Singularly Perturbed Systems Under Information Constraints

The quantized feedback control for a class of singularly perturbed systems is addressed, in which the controlled system and the controller are connected via a limited capacity communication channel. First, a proper coder–decoder pair is presented such that the transmission error decays to zero exponentially under information constraints. Then, a control law in terms of linear matrix inequalities is constructed to render the resulting closed‐loop system input‐to‐state stable with regard to the transmission error. Thus the asymptotic stability of the closed‐loop system is guaranteed. It is shown that the proposed method is simple and easy to operate. Moreover, an upper bound of the small perturbation parameter for the stability of systems can be explicitly estimated with a workable computational way. Finally, two examples are presented to show the effectiveness of the proposed method.     

An observer-based adaptive neural network tracking control of robotic systems

Robotic systems have inherently nonlinear phenomena as joints undergo sliding and/or rotating. This in turn requires that the system running states be predicted correctly. This paper makes a full analysis of the robot states by applying observer-based adaptive wavelet neural network (OBAWNN) tracking control scheme to tackle these phenomena such as system uncertainties, multiple time-delayed state uncertainties, and external disturbances such that the closed loop system signals must obey uniform ultimate boundedness and achieve H^∞ tracking performance. The recurrent adaptive wavelet neural network model is used to approximate the dynamics of the robotic system, while an observer-based adaptive control scheme is to stabilize the system. The advantage of employing adaptive wavelet neural dynamics is that we can utilize the neuron information by activation functions to on-line tune the hidden-to-output weights, and the adaptation parameters to estimate the robot parameters and the bounds of the gains of delay states directly using linear analytical results. It is shown that the stability of the closed-loop system is guaranteed by some sufficient conditions derived from Lyapunov criterion and Riccati-inequality. Finally, a numerical example of a three-links rolling cart is given to illustrate the effectiveness of the proposed control scheme.     

Learning of Spatio–Temporal Codes in a Coupled Oscillator System

In this paper, we consider a learning strategy that allows one to transmit information between two coupled phase oscillator systems (called teaching and learning systems) via frequency adaptation. The dynamics of these systems can be modeled with reference to a number of partially synchronized cluster states and transitions between them. Forcing the teaching system by steady but spatially nonhomogeneous inputs produces cyclic sequences of transitions between the cluster states, that is, information about inputs is encoded via a “winnerless competition” process into spatio–temporal codes. The large variety of codes can be learned by the learning system that adapts its frequencies to those of the teaching system. We visualize the dynamics using “weighted order parameters (WOPs)” that are analogous to “local field potentials” in neural systems. Since spatio–temporal coding is a mechanism that appears in olfactory systems, the developed learning rules may help to extract information from these neural ensembles.      

Stabilization of switched affine systems with disturbed state‐dependent switching laws

In this paper, we investigate the stabilization problem for a class of switched affine systems with a state‐dependent switching law. Since the states measurements are in general subject to perturbations and noises, we propose a robust switching‐law design method. Qualitative conditions for the stability of the closed‐loop switched system are given. Stability conditions are also formulated as Linear Matrix Inequalities (LMIs) to allow numerical implementations. Results are illustrated by numerical examples to show the efficiency of the method and its limits.     

Real-time computing without stable states: a new framework for neural computation based on perturbations

A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.     

Distributed consensus of discrete‐time multi‐agent systems with multiplicative noises

In this paper, we consider the consensus problem of discrete‐time multi‐agent systems with multiplicative communication noises. Each agent can only receive information corrupted by noises from its neighbors and/or a reference node. The intensities of these noises are dependent on the relative states of agents. Under some mild assumptions of the noises and the structure of network, consensus is analyzed under a fixed topology, dynamically switching topologies and randomly switching topologies, respectively. By combining algebraic graph theory and martingale convergence theorem, sufficient conditions for mean square and almost sure consensus are given. Further, when the consensus is achieved without a reference, it is shown that the consensus point is a random variable with its expectation being the average of the initial states of the agents and its variance being bounded. If the multi‐agent system has access to the state of the reference, the state of each agent can asymptotically converge to the reference. Numerical examples are given to illustrate the effectiveness of our results. Copyright © 2014 John Wiley & Sons, Ltd.     

Disentanglement and quantum states transitions dynamics in spin-qutrit systems: dephasing random telegraph noise and the relevance of the initial state

Using negativity and realignment criterion as quantifiers of free and bound entanglements respectively, we present in details the analytical study of the entanglements and quantum states transitions dynamics in a two-qutrit system driven by dephasing random telegraph noise channel(s). Both collective and independent system–environment couplings as well as the Markovian and the non-Markovian regimes of the noise channel(s) are considered. Two non-equivalent initial states and their locally equivalent through a local unitary operation (LUO) are also considered. We demonstrate a stronger entanglement under independent Markovian environments than with a collective one; meanwhile, for the non-Markovian regime, entanglement is stronger under a collective environment than with independent ones. States transitions as well as the (re)activation of bound entanglement (for initially free entangled states) can be found for a specific class of initial states, but can, however, be avoided by means of a LUO on the initial state. While unavoidable disentanglement occurs for independents coupling, we demonstrate the possibility of indefinite free entanglement survival in the qutrit system under a common environment by converting the initial entangled state using the local unitary operation.     

采用压缩感知的协作多点信道反馈算法研究

协作多点技术（ Coordinated Multiple Points,CoMP）作为LTE-A的最重要候选技术之一,已经得到广泛的关注。在协作多点中,有很多种传输方案,其中一种基于联合传输下的全局预编码方案能够获得极佳的系统性能,而实现该方案的前提是基站可以得到较为完整的信道状态信息。因此在信道反馈时选择显式反馈,因为相比较其他反馈方式,它可以反馈更为完整的信道状态信息,而显式反馈的缺点为需要的反馈量过大。文中利用一种采用压缩感知的信道反馈新方法对联合传输下的全局预编码方案信道显式反馈信息进行压缩,仿真结果显示在不影响系统性能的前提下,能够有效地减少反馈量,使得系统发挥最佳性能。     

The Effect of System Stochastic Noise on SINS Initial Alignment

捷联惯导系统初始对准系统状态方程所描述的状态统计信息,受系统随机噪声驱动,系统随机噪声通过噪声驱动阵作用于状态。常用系统状态方程的系统噪声驱动阵为单位阵。经过分析发现系统噪声驱动阵为单位阵是有条件的;通过推导给出了系统状态方程可以简化此种形式的条件;指出了在水平陀螺、加速度计随机噪声水平不同时,若使用简化系统模型会引起状态估计误差;并通过单轴旋转多位置对准仿真实验进行了验证。仿真结果表明：若水平陀螺随机噪声水平不同,采用简化模型进行初始对准会引起状态估计误差。