Cellular radio channel assignment using a modified Hopfield network

The channel-assignment problem is important in mobile telephone communication. Since the usable range of the frequency spectrum is limited, the optimal channel-assignment problem has become increasingly important. A new channel-assignment algorithm using a modified Hopfield (1985, 1986) neural network is proposed. The channel-assignment problem is formulated as an energy-minimization problem that is implemented by a modified discrete Hopfield network. Also, a new technique to escape the local minima is introduced. In this algorithm, an energy function is derived, and the appropriate interconnection weights between the neurons are specified. The interconnection weights between the neurons are designed in such a way that each neuron receives inhibitory support if the constraint conditions are violated and receives excitatory support if the constraint conditions are satisfied. To escape the local minima, if the number of assigned channels are less than the required channel numbers (RCNs), one or more channels are assigned in addition to already assigned channels such that the total number of assigned channels is the same as the required number of channels in the cell even though the energy is increased. Various initialization techniques, which use the specific characteristics of frequency-assignment problems in cellular radio networks, such as cosite constraint (CSC), adjacent channel constraint (ACC), and cochannel constraint (CCC), and updating methods are investigated. In the previously proposed neural-network approach, some frequencies are fixed to accelerate the convergence time. In our algorithms, no frequency is fixed before the frequency-assignment procedure. This new algorithm, together with the proposed initialization and updating techniques and without fixing frequencies in any cells, has better performance results than the results reported previously utilizing fixed frequencies in certain cells     

Hopfield网络求解TSP的一种改进算法和理论证明

本文通过简化Ｈｏｐｆｉｌｅｄ神经网络求解问题的能量函数，提出了一种神经网络求解ＴＳＰ的改进算法，借助连接矩阵特征值的分析、从理论上证明了该算法保证获得ＴＳＰ有效解的原因。大量计算机模拟实验表明，该算法明显优于目前广泛应用的Ａｉｙｅｒ算法，具有收敛速度快、可避免无效解，易获得优化解等特点。     

Hopfield Neural Network Based on Estimation of Distribution for Two-Page Crossing Number Problem

This paper presents a Hopfield neural network (HNN) combined with estimation of distribution (EDA) for the two-page crossing number problem. In the proposed algorithm, once the network is trapped in local minima, the perturbation based on EDA can generate a new starting point for the HNN for further search, which is in a promising area characterized by a probability model and is not far away from the best solution found so far. The proposed algorithm can escape from local minima and further search better results. Simulation results show that the proposed algorithm is better than previous methods.      

Harmonic Minimization in Multilevel Inverters Using Modified Species-Based Particle Swarm Optimization

Harmonic minimization in multilevel inverters is a complex optimization problem that involves nonlinear transcendental equations having multiple local minima. In this paper, a solution to the harmonic minimization problem using a novel particle swarm optimization (PSO) approach based on species-based PSO (SPSO) is presented. The original SPSO is modified, which increased the robustness of algorithm to find global optimum of the search space. The proposed method is able to find the optimum switching angles when their number is increased, while it is not possible to determine them using either conventional iterative techniques or resultant theory method. Theoretical results are verified by experiments and simulations for an 11-level H-bridge inverter. Results show that the proposed method effectively minimizes a large number of specific harmonics, and the output voltage results in very low total harmonic distortion and switching frequency.      

Blind Source Separation: The Location of Local Minima in the Case of Finitely Many Samples

Cost functions used in blind source separation are often defined in terms of expectations, i.e., an infinite number of samples is assumed. An open question is whether the local minima of finite sample approximations to such cost functions are close to the minima in the infinite sample case. To answer this question, we develop a new methodology of analyzing the finite sample behavior of general blind source separation cost functions. In particular, we derive a new probabilistic analysis of the rate of convergence as a function of the number of samples and the conditioning of the mixing matrix. The method gives a connection between the number of available samples and the probability of obtaining a local minimum of the finite sample approximation within a given sphere around the local minimum of the infinite sample cost function. This shows the convergence in probability of the nearest local minima of the finite sample approximation to the local minima of the infinite sample cost function. We also answer a long-standing problem of the mean-squared error (MSE) behavior of the (finite sample) least squares constant modulus algorithm (LS-CMA), namely whether there exist LS-CMA receivers with good MSE performance. We demonstrate how the proposed techniques can be used to determine the required number of samples for LS-CMA to exceed a specified performance. The paper concludes with simulations that validate the results.      

Stochastic noise Process enhancement of Hopfield neural networks

Hopfield neural networks (HNN) are a class of densely connected single-layer nonlinear networks of perceptrons. The network's energy function is defined through a learning procedure so that its minima coincide with states from a predefined set. However, because of the network's nonlinearity, a number of undesirable local energy minima emerge from the learning procedure. This has shown to significantly effect the network's performance. In this brief, we present a stochastic process-enhanced binary HNN. Given a fixed network topology, the desired final distribution of states can be reached by modulating the network's stochastic process. We design this process, in a computationally efficient manner, by associating it with stability intervals of the nondesired stable states of the network. Our experimental simulations confirm the predicted improvement in performance.     

基于截断核范数正则化的协方差矩阵估计

非均匀杂波环境导致机载雷达用于协方差矩阵估计的样本不足。本文提出了一种基于截断核范数正则化（Truncated Nuclear Norm Regularization, TNNR）的协方差矩阵估计算法以满足小样本条件下机载雷达空时自适应处理（Space Time Adaptive Processing, STAP）应用。本文利用TNNR确保所估计杂波协方差矩阵（Clutter Covariance Matrix, CCM）的低秩特性,并将NP-hard问题转换为凸优化问题。不同于常规的秩最小化算法,如核范数松弛方法,本文所提出的TNNR算法仅最小化与矩阵的秩无关的较小奇异值的和,可以更加准确地约束矩阵的秩。在此基础上本文还利用CCM的块Toeplitz结构先验信息,可确保在连续域上进行信号建模,有效避免网格点失配问题。仿真结果表明本文所提出的算法在小样本条件下可更加准确地估计CCM且STAP性能更优。     

面向空天地异构网络的边缘计算部分任务卸载策略

空天地异构网络作为一种新型网络构架,是未来6G实现泛在连接的关键支撑。该文提出一种面向空天地异构网络(SAGIN)的移动边缘计算部分任务卸载方案。首先,分析了低轨(LEO)卫星的覆盖时间。其次,联合考虑用户与无人机(UAV)匹配关联因子、任务分配、带宽分配、无人机计算资源分配以及无人机轨迹,旨在建立一个能耗最小化问题。最后,采用交替迭代优化算法,将原非凸问题分解为3个子问题,并利用变量替换和连续凸逼近方法将问题转化为凸问题进行求解。仿真结果表明,所提算法具有良好的收敛性能,并有效地降低系统能耗。     

Effective Energy Adaptive and Consumption in Wireless Sensor Network Using Distributed Source Coding and Sampling Techniques

Multimedia is the process of handling multiple medium of messages over network with high rate data services in wireless cellular area networks. Communication is the process of exchanging information form one service to another. In wireless networks are significantly growth of affecting network performance and energy consumption. The major problem is end to end delay in each node and meets the quality of services. The followings are considered for implementing wireless sensor network such as reduces the network delay, propagation delay and energy consumption. The senor node can sense the encoding value and reduce the network traffic delay using mitigation method. This paper propose a unique approach to provide simple routing services with reduced traffic delay, end to end delay network performance and to achieve better performance using Distributed Source Coding and Effective Energy Consumption methods. In this paper we use optimal early detection algorithm for improving network performance and energy consumption problem. An iterative Shannon fano and Tuker method is used for finding optimal solution of each node values. Network Simulator-3 is used for simulating network environments and setup the experiments. Our proposed method shows high data rate, good performance and low energy consumptions. The results compare with existing methodologies and performance is good.

     

Markovian reconstruction using a GNC approach

This paper is concerned with the reconstruction of images (or signals) from incomplete, noisy data, obtained at the output of an observation system. The solution is defined in maximum a posteriori (MAP) sense and it appears as the global minimum of an energy function joining a convex data-fidelity term and a Markovian prior energy. The sought images are composed of nearly homogeneous zones separated by edges and the prior term accounts for this knowledge. This term combines general nonconvex potential functions (PFs) which are applied to the differences between neighboring pixels. The resultant MAP energy generally exhibits numerous local minima. Calculating its local minimum, placed in the vicinity of the maximum likelihood estimate, is inexpensive but the resultant estimate is usually disappointing. Optimization using simulated annealing is practical only in restricted situations. Several deterministic suboptimal techniques approach the global minimum of special MAP energies, employed in the field of image denoising, at a reasonable numerical cost. The latter techniques are not directly applicable to general observation systems, nor to general Markovian prior energies. This work is devoted to the generalization of one of them, the graduated nonconvexity (GNC) algorithm, in order to calculate nearly-optimal MAP solutions in a wide range of situations. In fact, GNC provides a solution by tracking a set of minima along a sequence of approximate energies, starting from a convex energy and progressing toward the original energy. In this paper, we develop a common method to derive efficient GNC-algorithms for the minimization of MAP energies which arise in the context of any observation system giving rise to a convex data-fidelity term and of Markov random field (MRF) energies involving any nonconvex and/or nonsmooth PFs. As a side-result, we propose how to construct pertinent initializations which allow us to obtain meaningful solutions using local minimization of these MAP energies. Two numerical experiments-an image deblurring and an emission tomography reconstruction-illustrate the performance of the proposed technique.     

Generalized Simulation-Based Posynomial Model Generation for Analog Integrated Circuits

This paper presents a new method to automatically generate posynomial symbolic expressions for the performance characteristics of analog integrated circuits. Both the coefficient set as well as the exponent set of the posynomial expression, for some performance as a function of the design variables, are determined based on performance data extracted from SPICE simulation results with device-level accuracy. Techniques from design of experiments (DOE) are used to generate an optimal set of sample points to fit the models. We will prove that the optimization problem formulated for this problem typically corresponds to a non-convex problem, but has no local minima. The presented method is capable of generating posynomial performance expressions for both linear and nonlinear circuits and circuit characteristics. This approach allows to automatically generate an accurate sizing model that can be used to compose a geometric program that fully describes the analog circuit sizing problem. The automatic generation avoids the time-consuming nature of hand-crafted analytic model generation. Experimental results illustrate the capabilities of the presented modeling technique.     

异步四元数神经网络模型的能量函数研究

本文证明了异步离散四元数神经网络模型在其动力学演化过程中，网络的能量函数单调递减，网络最终将稳定在一个平衡态上，存贮图像在能量函数空间中对应一能量极小点，为网络的稳定吸引子。     

随机petri网分析分组交换网中窗式流量控制机理

本文将随机Ｐｅｔｒｉ网与排队论相结合，对分组交换网中的窗式流量控制机理进行了描述与分析，建立了窗式流量控制机理的随机Ｐｅｔｒｉ网模型，并给出了随机Ｐｅｔｒｉ网模型的可达图及状态转移方程。为通信网的性能分析和评价提供了一种新的方法。     

基于互信息自编码和变分路由的胶囊网络结构优化

胶囊网络是一类有别于卷积神经网络的新型网络模型。该文尝试提高其泛化性和精准性:首先，利用变分路由来缓解经典路由对先验信息依赖性强、易导致模型过拟合的问题。通过使用高斯混合模型(GMM)来拟合低级矩阵胶囊，并利用变分法求取近似分布，避免了参数最大似然点估计的误差，用置信度评估来获得泛化性能的提高；其次，考虑到实际数据大多无标签或者标注困难，构建互信息评价标准的胶囊自编码器，实现特征参数的有效筛选。即通过引入局部编码器，只保留胶囊中对原始输入识别最有效的特征，在减轻网络负担的同时提高了其分类识别的精准性。该文的方法在MNIST, FashionMNIST, CIFAR-10和CIFAR-100等数据集上进行了对比测试，实验结果表明:该文方法对比经典胶囊网络，其性能得到显著改善。     

基于加权主动形状建模的人脸特征定位方法

主动形状建模是面部特征定位和人脸识别等模式识别领域中常用的一种方法。然而,由于受到初始情况、光照等诸多因素的影响,主动形状建模经常会陷入最优化过程中的局部最小问题,从而导致其性能下降。该文在传统主动形状模型基础上,提出了一种加权主动形状建模的方法,该方法可以有效地解决上述局部最小问题,并且更好地捕捉局部点的特征信息,从而更精确地进行面部特征定位。最后通过实验验证了上面的结论。     

Relocation routing for energy balancing in mobile sensor networks

Wireless sensor networks (WSNs) have been widely investigated in the past decades because of its applicability in various extreme environments. As sensors use battery, most works on WSNs focus on energy efficiency issues (e.g., local energy balancing problems) in statically deployed WSNs. Few works have paid attention to the global energy balancing problem for the scenario that mobile sensor nodes can move freely. In this paper, we propose a new routing protocol called global energy balancing routing protocol (GEBRP) based on an active network framework and node relocation in mobile sensor networks. This protocol achieves global energy efficiency by repairing coverage holes and replacing invalid nodes dynamically. Simulation and experiment results demonstrate that the proposed GEBRP achieves superior performance over the existing scheme. In addition, we analyze the delay performance of GEBRP and study how the delay performance is affected by various system parameters.Copyright © 2013 John Wiley & Sons, Ltd.     

多准则图象重建的神经网络模型及实现

本文提出一种采用Ｈｏｐｆｉｅｌｅ神经网络（Ｈｏｐｆｉｅｌｄ Ｎｅｉｒａｌ Ｎｅｔｗｏｒｋ简称ＨＮＮ）优化的图象重建算法。将图象重建问题转化为ＨＮＮ优化问题,取重建图象的峰值函数最小以及原始投影与再投影之间的误差平方和最小作为图象重建的优化目标,作为能量函数构造连续型ＨＮＮ模型,由ＨＮＮ能量函数极小化可得到重建问题的优化解。这种方法具有简单、计算量小、收敛快、便于并行计算等特点。对照ＡＲＴ算法,用计算机模拟产生的无噪声投影数据检验新算法,验证了新算法的优越性。     

Online Multicasting for Network Capacity Maximization in Energy-Constrained Ad Hoc Networks

In this paper, we present new algorithms for online multicast routing in ad hoc networks where nodes are energy-constrained. The objective is to maximize the total amount of multicast message data routed successfully over the network without any knowledge of future multicast request arrivals and generation rates. Specifically, we first propose an online algorithm for the problem based on an exponential function of energy utilization at each node. The competitive ratio of the proposed algorithm is analyzed if admission control of multicast requests is permitted. We then provide another online algorithm for the problem, which is based on minimizing transmission energy consumption for each multicast request and guaranteeing that the local network lifetime is no less than gamma times of the optimum, where gamma is constant with 0 < gammaleq 1. We finally conduct extensive experiments by simulations to analyze the performance of the proposed algorithms, in terms of network capacity, network lifetime, and transmission energy consumption for each multicast request. The experimental results clearly indicate that, for online multicast routing in ad hoc wireless networks, the network capacity is proportional to the network lifetime if the transmission energy consumption for each multicast request is at the same time minimized. This is in contrast to the implication by Kar et al. that the network lifetime is proportional to the network capacity when they considered the online unicast routing by devising an algorithm based on the exponential function of energy utilization at each node.     

基于ABSUM的MIMO雷达频谱兼容波形设计

该文讨论了多输入多输出(MIMO)雷达发射波形和接收滤波器的联合优化问题,以确保与叠加的授权通信网络频谱兼容。考虑信号相关杂波的干扰,在发射能量、相似性和频谱兼容约束下,所提出的信干噪比(SINR)最大化的优化问题是NP-hard问题。为此,首先引入辅助变量对原问题进行修正,然后提出了一种基于乘子块连续上界极小化的原对偶(ABSUM)算法求解该问题。此外,利用内点法求解在ABSUM算法每个更新过程中涉及的二次规划问题。最后,仿真结果表明,ABSUM算法在输出SINR、波束图、频谱特性等方面优于现有方法。