Adaptive opportunistic fair scheduling in power-controlled code division multiple access systems

The authors treat the multiuser scheduling problem for practical power-controlled code division multiple access (CDMA) systems under the opportunistic fair scheduling (OFS) framework. OFS is an important technique in wireless networks to achieve fair and efficient resource allocation. Power control is an effective resource management technique in CDMA systems. Given a certain user subset, the optimal power control scheme can be derived. Then the multiuser scheduling problem refers to the optimal user subset selection at each scheduling interval to maximise certain metric subject to some specific physical-layer constraints. The authors propose discrete stochastic approximation algorithms to adaptively select the user subset to maximise the instantaneous total throughput or a general utility. Both uplink and downlink scenarios are considered. They also consider the time-varying channels where the algorithm can track the time-varying optimal user subset. Simulation results to show the performance of the proposed algorithms in terms of the throughput/ utility maximisation, the fairness, the fast convergence and the tracking capability in time-varying environments are presented.     

Generality of matched filtering and minimum Euclidean distance projection for optical pattern recognition

Matched filtering followed by a minimum Euclidean distance projection onto realizable filter values was previously shown to optimize the signal-to-noise ratio for single training images in optical correlation pattern recognition. The algorithm is now shown to solve the combination of (1) standard statistical pattern-recognition metrics with multiple training images, (2) additive input noise of known power spectral density and also additive detection noise that is irreducible by the filter, (3) the building of the filter on arbitrary subsets of the complex unit disk, and (4) the use of observable correlator outputs only. The criteria include the Fisher ratio, the Bayes error and Bayes cost, the Chernoff and Bhattacharyya bounds, the population entropy and expected information, versions of signal-to-noise ratio that use other than second power in their norm, and the area under the receiver operating characteristic curve. Different criteria are optimized by different complex scalar weights.     

Resource Allocation for Throughput Maximization in Cognitive Radio Network with NOMA

Spectrum resources are the precious and limited natural resources. In order to improve the utilization of spectrum resources and maximize the network throughput, this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonal multiple access (CRN-NOMA). NOMA, as the key technology of the fifth-generation communication (5G), can effectively increase the capacity of 5G networks. The optimization problem proposed in this paper aims to maximize the number of secondary users (SUs) accessing the system and the total throughput in the CRN-NOMA. Under the constraints of total power, minimum rate, interference and SINR, CRN-NOMA throughput is maximized by allocating optimal transmission power. First, for the situation of multiple sub-users, an adaptive optimization method is proposed to reduce the complexity of the optimization solution. Secondly, for the optimization problem of nonlinear programming, a maximization throughput optimization algorithm based on Chebyshev and convex (MTCC) for CRN-NOMA is proposed, which converts multi-objective optimization problem into single-objective optimization problem to solve. At the same time, the convergence and time complexity of the algorithm are verified. Theoretical analysis and simulation results show that the algorithm can effectively improve the system throughput. In terms of interference and throughput, the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access (OFDMA). This paper provides important insights for the research and application of NOMA in future communications.     

An Optimized Algorithm for CR-MIMO Wireless Networks

With the rapid development of wireless communication technology, the spectrum resources are increasingly strained which needs optimal solutions. Cognitive radio (CR) is one of the key technologies to solve this problem. Spectrum sensing not only includes the precise detection of the communication signal of the primary user (PU), but also the precise identification of its modulation type, which can then determine the a priori information such as the PU’ service category, so as to use this information to make the cognitive user (CU) aware to discover and use the idle spectrum more effectively, and improve the spectrum utilization. Spectrum sensing is the primary feature and core part of CR. Classical sensing algorithms includes energy detection, cyclostationary feature detection, matched filter detection, and so on. The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal, but it is easily affected by noise and the threshold is not easy to determine. The combination of multiple-input multiple-output (MIMO) with CR improves the spectral efficiency and multi-path fading utilization. To best utilize the PU spectrum while minimizing the overall transmit power, an iterative technique based on semidefinite programming (SDP) and minimum mean squared error (MMSE) is proposed. Also, this article proposed a new method for max-min fairness beamforming. When compared to existing algorithms, the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-to-interference plus noise ratio (SINR). Furthermore, the proposed algorithm effectively improved the system performance in terms of number of iterations, interference temperature threshold and balance SINR level which makes it superior over the conventional schemes.     

Cost minimization of a multiple section power cable supplying several remote telecom equipment

An optimization problem is described, that arises in telecommunications and is associated with multiple cross-sections of a single power cable used to supply remote telecom equipments. The problem consists of minimizing the volume of copper material used in the cables and consequently the total cable cost. Two main formulations for the problem are introduced and some properties of the functions and constraints involved are presented. In particular it is shown that the optimization problems are convex and have a unique optimal solution. A Projected Gradient algorithm is proposed for finding the global minimum of the optimization problem, taking advantage of the particular structure of the second formulation. An analysis of the performance of the algorithm for given real-life problems is also presented. V. Anunciada passed away on September 29, 2007.     

Resistor-logic demultiplexers for nanoelectronics based on constant-weight codes

The voltage margin of a resistor-logic demultiplexer can be improved significantly by basing its connection pattern on a constant-weight code. Each distinct code determines a unique demultiplexer, and therefore a large family of circuits is defined. We consider using these demultiplexers for building nanoscale crossbar memories, and determine the voltage margin of the memory system based on a particular code. We determine a purely code-theoretic criterion for selecting codes that will yield memories with large voltage margins, which is to minimize the ratio of the maximum to the minimum Hamming distance between distinct codewords. For the specific example of a 64 × 64 crossbar, we discuss what codes provide optimal performance for a memory.     

An effective codebook search algorithm for vector quantization

Abstract

A new scheme that aims to cut down on the computational cost of the vector quantization (VQ) encoding procedure is proposed in this paper. In this scheme, the correlation between the codewords in the codebook is exploited and three test conditions are designed to filter out the impossible codewords in the codebook. The design of test conditions is based on the concept of integral projection.

From the experimental results, it is shown that the new scheme outperforms all the other schemes proposed so far in speeding up the VQ encoding procedure. When the codebook of 1024 codewords is used in the proposed scheme, the execution time it consumes is less than 2 per cent of that needed by the full search algorithm. The average time reduction rate is approximately 97.7 per cent compared to the execution time for the full search algorithm. In other words, the proposed scheme indeed provides an effective approach to speed up the VQ encoding procedure.     

Minimum Error Entropy Based EKF for GPS Code Tracking Loop

This paper investigates the minimum error entropy based extended Kalman filter (MEEKF) for multipath parameter estimation of the Global Positioning System (GPS). The extended Kalman filter (EKF) is designed to give a preliminary estimation of the state. The scheme is designed by introducing an additional term, which is tuned according to the higher order moment of the estimation error. The minimum error entropy criterion is introduced for updating the entropy of the innovation at each time step. According to the stochastic information gradient method, an optimal filer gain matrix is obtained. The mean square error criterion is limited to the assumption of linearity and Gaussianity. However, non-Gaussian noise is often encountered in many practical environments and their performances degrade dramatically in non-Gaussian cases. Most of the existing multipath estimation algorithms are usually designed for Gaussian noise. The I (in-phase) and Q (quadrature) accumulator outputs from the GPS correlators are used as the observational measurements of the EKF to estimate the multipath parameters such as amplitude, code delay, phase, and carrier Doppler. One reasonable way to obtain an optimal estimation is based on the minimum error entropy criterion. The MEEKF algorithm provides better estimation accuracy since the error entropy involved can characterize all the randomness of the residual. Performance assessment is presented to evaluate the effectivity of the system designs for GPS code tracking loop with multipath parameter estimation using the minimum error entropy based extended Kalman filter.     

基于检测系统非线性相关性的相空间重构时间延迟估计

提出一种确定时间延迟参数的新方法,非线性复自相关法。采用一个高次复自相关函数R(τ)检测系统的非线性相关性,通过寻找R(τ)的第一个局部极小值点来确定最优时间延迟。R(τ)时间复杂度低,对数据长度依赖性不强。选取四种典型混沌动力学系统,加入不同噪声水平的高斯白噪声,模拟含噪混沌时间序列,进行数值验证。结果表明,所得结果更合适,同时具有优秀的抗噪声能力。     

Real-time defect detection of steel wire rods using wavelet filters optimized by univariate dynamic encoding algorithm for searches

We propose a new defect detection algorithm for scale-covered steel wire rods. The algorithm incorporates an adaptive wavelet filter that is designed on the basis of lattice parameterization of orthogonal wavelet bases. This approach offers the opportunity to design orthogonal wavelet filters via optimization methods. To improve the performance and the flexibility of wavelet design, we propose the use of the undecimated discrete wavelet transform, and separate design of column and row wavelet filters but with a common cost function. The coefficients of the wavelet filters are optimized by the so-called univariate dynamic encoding algorithm for searches (uDEAS), which searches the minimum value of a cost function designed to maximize the energy difference between defects and background noise. Moreover, for improved detection accuracy, we propose an enhanced double-threshold method. Experimental results for steel wire rod surface images obtained from actual steel production lines show that the proposed algorithm is effective.     

Some results characterizing the finite time behaviour of the simulated annealing algorithm

TheSimulated Annealing algorithm is a probabilistic search technique for finding the minimum cost state in a set Ω. The algorithm has been successfully used to obtain near-optimal solutions for problems for which no other effective algorithms exist. For example, problems in integrated circuit layout and in finite impulse response (FIR) filter design have been solved using annealing. In most applications, Ω is finite set, and the annealing algorithm may be modelled as a time-inhomogeneous Markov chain on Ω with transition probabilities that are powers of a time varying parameter ε. It has been shown by several researchers that if ε is driven to 0 sufficiently slowly, then the algorithm will eventually find a minimum cost state in Ω with probability 1. In this paper, we will focus on the finite-time behaviour of the annealing algorithm. In particular, we will summarize some results relating the number of steps taken by the algorithm to the quality of the solutions obtained. These results provide qualitative as well as quantitative information about the status of the annealing algorithm after a finite number of steps. This will be illustrated using some examples.     

新颖的CB形态学串并复合散斑噪声滤波器

基于轮廓结构元素形态学(CB形态学)和广义多结构元素的数学形态学,构造了一种新颖的用于抑制图像中的散斑噪声的串并复合滤波器.首先采用全方位多结构元素的广义多刻度形态开最大滤波器与广义多刻度形态闭最小滤波器相并联的滤波器进行滤波,力求在滤波的同时图像几何细节损失尽可能少,然后通过级联的CB开和CB闭滤波器,再滤除剩余的斑点噪声,从而在有效抑制图像中的散斑噪声的同时更好地保持了图像的细节信息.分析和仿真表明了本文提出的滤波算法的有效性.     

Spectral histogram using the minimization algorithm-theory and applications to flaw detection

In ultrasonic flaw detection in large grained materials, backscattered grain noise often masks the flaw signal. To enhance the flaw visibility, a frequency diverse statistical filtering technique known as split-spectrum processing has been developed. This technique splits the received wideband signal into an ensemble of narrowband signals exhibiting different signal-to-noise ratios (SNR). Using a minimization algorithm, SNR enhancement can be obtained at the output. The nonlinear properties of the frequency diverse statistic filter are characterized based on the spectral histogram, which is the statistical distribution of the spectral windows selected by the minimization algorithm. The theoretical analysis indicates that the spectral histogram is similar in nature to the Wiener filter transfer function. Therefore, the optimal filter frequency region can be determined adaptively based on the spectral histogram without prior knowledge of the signal and noise spectra.     

Impulse noise reduction using hybrid neuro-fuzzy filter with improved firefly algorithm from X-ray bio-images

Noise filtering performance in medical images is improved using a neuro-fuzy network developed with the combination of a post processor and two neuro-fuzzy (NF) filters. By the fact, the Sugeno-type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro-fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels.     

Transient stability constrained optimal power flow using particle swarm optimisation

A novel approach based on the particle swarm optimisation (PSO) technique is proposed for the transient-stability constrained optimal power flow (TSCOPF) problem. Optimal power flow (OPF) with transient-stability constraints considered is formulated as an extended OPF with additional rotor angle inequality constraints. For this nonlinear optimisation problem, the objective function is defined as minimising the total fuel cost of the system. The proposed PSO-based approach is demonstrated and compared with conventional OPF as well as a genetic algorithm based counterpart on the IEEE 30-bus system. Furthermore, the effectiveness of the PSO-based TSCOPF in handling multiple contingencies is illustrated using the New England 39-bus system. Test results show that the proposed approach is capable of obtaining higher quality solutions efficiently in the TSCOPF problem     

Optimal Aeroacoustic Shape Design Using the Surrogate Management Framework

Shape optimization is applied to time-dependent trailing-edge flow in order to minimize aerodynamic noise. Optimization is performed using the surrogate management framework (SMF), a non-gradient based pattern search method chosen for its efficiency and rigorous convergence properties. Using SMF, design space exploration is performed not with the expensive actual function but with an inexpensive surrogate function. The use of a polling step in the SMF guarantees that the algorithm generates a convergent subsequence of mesh points in the parameter space. Each term of this subsequence is a weak local minimizer of the cost function on the mesh in a sense to be made precise later. We will discuss necessary optimality conditions for the design problem that are satisfied by the limit of this subsequence. Results are presented for an unsteady laminar flow past an acoustically compact airfoil. Constraints on lift and drag are handled within SMF by applying the filter pattern search method of Audet and Dennis, within which a penalty function is used to form and optimize a surrogate function. Optimal shapes that minimize noise have been identified for the trailing-edge problem in constrained and unconstrained cases. Results show a significant reduction (as much as 80%) in acoustic power with reasonable computational cost using several shape parameters. Physical mechanisms for noise reduction are discussed.     

Clock Error with a Wiener Predictor and by Numerical Calculation

This paper gives a closed-form solution for the minimum error one can expect from a linear prediction filter applied to a clock for which the fractional frequency power spectrum consists of white noise and the integral of white noise. Measurement error is also included. Expressing the problem as a Wiener filter rather than a Kalman filter simplifies the solution. The Kalman matrices in the Wiener representation are diagonal. This permits one to derive the optimum linear filter directly from the spectrum. Since the spectrum of flicker noise is not rational, no closed-form Wiener solution is possible. It is demonstrated, however, that all error terms including flicker can be taken into account by a technique of numerical integration in the frequency domain. The technique is valid for any filter for which the integrals, and consequently the error, do not diverge. It is shown that every first-order prediction filter with two poles must have the form of the Wiener filter, except for the position of the poles. A special case of this filter is shown to be the first-order exponential predictive filter. The error can be expressed in powers of the prediction time, with four coefficients, one for each spectral term. The values of these coefficients were calculated and plotted for the first-order exponential filter. These coefficients were used to calculate the error for three clocks. The results are plotted for prediction times of two hours and one day.     

基于方程误差法的IIR滤波有源噪声控制算法

在前馈有源噪声控制系统中会存在声反馈现象:次级声源产生的声信号反馈至参考传感器处.采用IIR滤波器能更好匹配有零极点的最优解,减弱声反馈影响.为克服FvLMS算法全局收敛性差的缺陷,提出采用比方程误差法(EEM)的计算量更小的EEM-FvLMS算法.在考虑声反馈的情况下,进行基于FvLMS算法和EEM-FvLMS算法的...     

自适应滤波算法在车辆宽高检测系统中的应用研究

车辆宽高检测系统采用激光测距的原理对车辆的宽高进行检测,其测量所得数据中参杂有高斯白噪声。为滤除测量数据中的高斯白噪声,将基于最小均方误差的自适应滤波算法应用于宽高仪的数据处理过程中,并通过Matlab中自带的自适应函数将其实现。通过仿真波形及实验测试数据表明:滤波效果明显,能将测量误差全部控制在5cm之内。因此,自适应滤波算法能够行之有效地应用于宽高检测系统中,达到提高其测量精度的目的。     

An efficient technique to solve combined economic and emission dispatch problem using modified Ant colony optimization

Economic load dispatch is one of the vital purposes in electrical power system operation, management and planning. Economic dispatch problem is one of the most important problems in electric power system operation. In large scale system, the problem is more complex and difficult to find out optimal solution because it is nonlinear function and it contains number of local optimal. Combined economic emission dispatch (CEED) problem is to schedule the committed generating units outputs to meet the required load demand at minimum operating cost with minimum emission simultaneously. The main aim of economic load dispatch is to reduce the total production cost of the generating system and at the same time the necessary equality and inequality constraints should also be fulfilled. This leads to the development of CEED techniques. There are various techniques proposed by several researchers to solve CEED problem based on optimization techniques. But still some problems such as slower convergence and higher computational complexity exist in using the optimization techniques such as GA for solving CEED problem. This paper proposes an efficient and reliable technique for combined fuel cost economic optimization and emission dispatch using the Modified Ant Colony Optimization algorithm (MACO) to produce better optimal solution. The simulation results reveal the significant performance of the proposed MACO approach.