A 3-D space vector modulation generalized algorithm for multilevel converters

A three-dimensional (3-D) space vector algorithm of multilevel converters for compensating harmonics and zero sequence in three-phase four-wire systems with neutral is presented. The low computational cost of the proposed method is always the same and it is independent of the number of levels of the converter. The conventional two-dimensional (2-D) space vector algorithms are particular cases of the proposed generalized modulation algorithm. In general, the presented algorithm is useful in systems with or without neutral, unbalanced load, triple harmonics and for generating 3-D control vectors.     

A physics-based algorithm for retrieving land-surface emissivityand temperature from EOS/MODIS data

The authors have developed a physics-based land-surface temperature (LST) algorithm for simultaneously retrieving surface band-averaged emissivities and temperatures from day/night pairs of MODIS (Moderate Resolution Imaging Spectroradiometer) data in seven thermal infrared bands. The set of 14 nonlinear equations in the algorithm is solved with the statistical recession method and the least-squares fit method. This new LST algorithm was tested with simulated MODIS data for 80 sets of hand-averaged emissivities calculated from published spectral data of terrestrial materials in wide ranges of atmospheric and surface temperature conditions. Comprehensive sensitivity and error analysis has been made to evaluate the performance of the new LST algorithm and its dependence on variations in surface emissivity and temperature, upon atmospheric conditions, as well as the noise-equivalent temperature difference (NEΔT) and calibration accuracy specifications of the MODIS Instrument. In cases with a systematic calibration error of 0.5%, the standard deviations of errors in retrieved surface daytime and nighttime temperatures fall between 0.4-0.5 K over a wide range of surface temperatures for mid-latitude summer conditions. The standard deviations of errors in retrieved emissivities in bands 31 and 32 (in the 10-12.5 μm IR spectral window region) are 0.009, and the maximum error in retrieved LST values falls between 2-3 K     

A generalized algorithm for evaluating distributed-programreliability

A one-step algorithm, GEAR (generalized evaluation algorithm for reliability), is introduced that computes the reliability of a distributed computing system (DCS), which usually consists of processing element, memory unit, input/output devices, data-files, and programs as its shared resources. The probability that a task or an application can be computed successfully by sharing the required resources on the DCS is termed the system reliability. Some of the important reliabilities defined using the above concept are discussed, including terminal-pair, computer-network, distributed-program, and distributed-system. GEAR is general enough to compute all four of these parameters, and does not require any prior knowledge about multiterminal connections for computing reliability expression. Many examples are included to illustrate the usefulness of GEAR for computing reliability measures of a DCS     

A generalized Euclidean algorithm for multisequence shift-registersynthesis

The problem of finding a linear-feedback shift register of shortest length capable of generating prescribed multiple sequences is considered. A generalized Euclidean algorithm, which is based on a generalized polynomial division algorithm, is presented. A necessary and sufficient condition for the uniqueness of the solution is given. When the solution is not unique, the set of all possible solutions is also derived. It is shown that the algorithm can be applied to the decoding of many cyclic codes for which multiple syndrome sequences are available. When it is applied to the case of a single sequence, the algorithm reduces to that introduced by Y. Sugiyama et al. (Inf. Control, vol.27, p.87-9, Feb. 1975) in the decoding of BCH codes     

用于反演云底高度的峰值面积积分算法

反演云底高度是激光云高仪最重要的应用之一,而找到一种能够有效反演不同类型云的云底高度算法是激光云高仪测云的关键。对于厚度比较大的云层测量可根据信号强度和脉冲宽度不同于噪声和气溶胶信号将其分选出来,而薄云的测量往往由于信号强度弱、脉冲宽度小而被忽略掉,为此提出了一种峰值面积积分算法反演云底高度。该算法通过对云回波信号进行面积积分,叠加放大信号中的云层信息,以峰值和面积积分值作为判断准则,通过选取合适的阈值,将云层与噪声、气溶胶区分开来,进而反演出云底高度。经实验验证表明,该算法能可靠地反演半导体激光云高仪探测范围内不同类型云的云底高度。     

A simple low-complexity algorithm for generalized spatial modulation

Generalized spatial modulation (GSM) is an extension of spatial modulation which is significant for the next generation communication systems. Optimal detection process for the GSM is the maximum-likelihood (ML) detection which jointly detects the antenna combinations and transmitted symbols. However, the receiver is much more complicated than SM due to inter-antenna interference and/or increased number of combinations. Therefore, the computational complexity of the ML detection grows with the number of transmit antennas and the signal constellation size. In this letter, we introduce a novel and simple detection algorithm which uses sub-optimal method based on the least squares solution to detect likely antenna combinations. Once the antenna indices are detected, ML detection is utilized to identify the transmitted symbols. For obtaining near-ML performance while keeping lower complexity than ML detection, sphere decoding is applied. Our proposed algorithm reduces the search complexity while achieving a near optimum solution. Computer simulation results show that the proposed algorithm performs close to the optimal (ML) detection resulting in a significant reduction of computational complexity.     

A quasi-Newton adaptive algorithm for generalized symmetriceigenvalue problem

We first recast the generalized symmetric eigenvalue problem, where the underlying matrix pencil consists of symmetric positive definite matrices, into an unconstrained minimization problem by constructing an appropriate cost function. We then extend it to the case of multiple eigen-vectors using an inflation technique. Based on this asymptotic formulation, we derive a quasi-Newton-based adaptive algorithm for estimating the required generalized eigen-vectors in the data case. The resulting algorithm is modular and parallel, and it is globally convergent with probability one. We also analyze the effect of inexact inflation on the convergence of this algorithm and that of inexact knowledge of one of the matrices (in the pencil) on the resulting eigenstructure. Simulation results demonstrate that the performance of this algorithm is almost identical to that of the rank-one updating algorithm of Karasalo (1986). Further, the performance of the proposed algorithm has been found to remain stable even over 1 million updates without suffering from any error accumulation problems     

A generalized Lloyd-type algorithm for adaptive transform coder design

In this paper, we establish a probabilistic framework for adaptive transform coding that leads to a generalized Lloyd type algorithm for transform coder design. Transform coders are often constructed by concatenating an ad hoc choice of transform with suboptimal bit allocation and quantizer design. Instead, we start from a probabilistic latent variable model in the form of a mixture of constrained Gaussian mixtures. From this model, we derive an transform coder design algorithm, which integrates optimization of all transform coder parameters. An essential part this algorithm is our introduction of a new transform basis-the coding optimal transform-which, unlike commonly used transforms, minimizes compression distortion. Adaptive transform coders can be effective for compressing databases of related imagery since the high overhead associated with these coders can be amortized over the entire database. For this work, we performed compression experiments on a database of synthetic aperture radar images. Our results show that adaptive coders improve compressed signal-to-noise ratio (SNR) by approximately 0.5 dB compared with global coders. Coders that incorporated the coding optimal transform had the best SNRs on the images used to develop the coder. However, coders that incorporated the discrete cosine transform generalized better to new images.     

A generalized recursive algorithm for wave-scattering solutions intwo dimensions

A generalized recursive algorithm valid for both the E _z and H_z wave scattering of densely packed scatterers in two dimensions is derived. This is unlike previously derived recursive algorithms which have been found to be valid only for E_z polarized waves. In this generalized recursive algorithm, a scatterer is first divided into N subscatterers. The n-subscatterer solution is then used to solve the (n+n')-subscatterer solution. The computational complexity of such an algorithm is found to be of O (N²) in two dimensions while providing a solution valid for all angles of incidence. This is better than the method of moments with Gaussian elimination, which has an O(N³) complexity     

A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors

A generalized expectation-maximization (GEM) algorithm is developed for Bayesian reconstruction, based on locally correlated Markov random-field priors in the form of Gibbs functions and on the Poisson data model. For the M-step of the algorithm, a form of coordinate gradient ascent is derived. The algorithm reduces to the EM maximum-likelihood algorithm as the Markov random-field prior tends towards a uniform distribution. Three different Gibbs function priors are examined. Reconstructions of 3-D images obtained from the Poisson model of single-photon-emission computed tomography are presented.     

广义空间调制系统的低复杂度检测算法

针对广义空间调制( GSM)系统接收端最大似然( ML)检测算法计算复杂度极高的缺点,提出了一种基于压缩感知( CS)信号重构理论的低复杂度信号检测算法。首先,在多输入多输出( MI-MO)信道模型下,通过改进正交匹配追踪( OMP)算法,得到一个激活天线索引备选集;然后,利用ML算法在该备选集中进行遍历搜索,检测出激活天线索引和星座调制符号。仿真结果表明所提算法的检测性能接近于ML算法,且复杂度约为ML算法的2%。因此,所提算法在保证检测性能的同时也大大降低了计算复杂度,实现了检测性能与复杂度之间的平衡。     

A 3-D generalized direct PWM algorithm for multilevel converters

A three-dimensional (3-D) generalized direct pulse width modulation (PWM) algorithm is proposed for multilevel converters in a three-phase, four-wire system. It is proved to be equivalent to the newly proposed generalized 3-D space vector modulation (SVM). However, the direct PWM greatly simplifies the calculation process and is much easier to implement in digital controllers. The direct PWM can be used in all applications needing a 3-D control vector, such as active filters, uninterruptible power supplies, etc. Simulation and experimental results are given to show the validity of the proposed control strategy.     

广义空间调制系统的正则化OMP检测算法

在广义空间调制（GSM）系统中,最大似然（ML）检测可以取得最优的检测性能,然而其计算复杂度随激活天线数的增加急剧增长。针对这一问题,提出了一种基于稀疏重构理论的低复杂度检测算法——正则化正交匹配追踪（ROMP）算法。该算法首先根据信道矩阵和当前残差的内积选取多个候选激活天线索引,接着对候选天线索引按正则化标准进行可靠性验证,剔除错误索引,缩小信号的搜索空间,最后通过求解最小二乘问题估计信号。仿真结果表明,与经典的正交匹配追踪（OMP）算法相比,所提算法以少许复杂度的增加为代价极大提升了检测性能,能够在检测性能与复杂度之间取得更好的折中。     

A generalized algorithm for the capacitance extraction of 3D VLSIinterconnects

In this paper, a generalized algorithm based upon the nonoverlapping domain decomposition method (NDDM) is presented for the capacitance extraction of three-dimensional (3-D) VLSI interconnects. The subdomains with conductors are analyzed by the finite-difference method, while the subdomains with pure dielectric layers are analyzed with the eigenmode expansion technique. The central processing unit time and memory size used by the NDDM are unrelated to the thickness of pure dielectric layers. NDDM's computing time grows as O(n) if the number of domain iterations is bounded. Also, benchmarks show that it is approximately 15 times less than those used by Ansoft's Maxwell SpiceLink. In addition, only a two-dimensional mesh is needed to analyze 3-D structures. This greatly reduces the algorithm complexity and makes it easy and straightforward to interface with layout automation software     

A generalized algorithm for constrained power control withcapability of temporary removal

The distributed constrained power control algorithm (DCPC) (Grandhi et al. 1995) has become one of the most widely accepted quality-based power control algorithms by the academic community. The DCPC has a property that the power may reach the maximum level when a user is experiencing degradation of channel quality. Unfortunately, using maximum transmitter power may not lead to sufficient improvement of channel quality and will thereto generate severe interference, affecting other users. This undesirable phenomenon happens more often when the system is congested. In this paper, we revisit and generalize the DCPC algorithm in order not to necessarily utilize the maximum power when the channel quality is poor. Under poor quality conditions, rather than combating the interference by maximizing power, we propose the concept of reducing the powers and temporarily removing users from the channel. We show that the energy consumption can be reduced through our generalized algorithm and we prove its convergence properties. Because of the decreased interference power, the capacity of the system is expected to increase. To validate this, we made computational experiments suggesting that the proposed algorithm can support more users in a congested system as compared to the previously suggested distributed removal algorithm gradual removals restricted (GRR)-DCPC (Andersin et al. 1996)     

基于空间分布的红外图像直方图均衡算法

随着非制冷焦平面阵列(UFPA)红外探测器的日益成熟，红外热成像技术越来越广泛应用于公安、消防、军事、医学、监控等领域。红外图像直方图分布集中，对比度低并通常伴有大量散粒噪声，需要处理以改进视觉效果。文中提出一种基于空间分布的图像直方图均衡算法，在图像增强同时又适当抑制噪声提升，实验表明该算法优于HE、PE、HP等算法，并具有较快的计算速度。     

一种广义预测自适应控制算法

广义预测控制是一种基于被控对象参数模型的控制算法,适用于复杂的、数学模型难以精确建立的控制系统。现研究了该算法,并对一个被控对象模型进行了仿真。结果表明,广义预测控制算法响应速度快,无超调,控制效果理想。     

Space-alternating generalized expectation-maximization algorithm

The expectation-maximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation problems. In the classical EM paradigm, one iteratively maximizes the conditional log-likelihood of a single unobservable complete data space, rather than maximizing the intractable likelihood function for the measured or incomplete data. EM algorithms update all parameters simultaneously, which has two drawbacks: 1) slow convergence, and 2) difficult maximization steps due to coupling when smoothness penalties are used. The paper describes the space-alternating generalized EM (SAGE) method, which updates the parameters sequentially by alternating between several small hidden-data spaces defined by the algorithm designer. The authors prove that the sequence of estimates monotonically increases the penalized-likelihood objective, derive asymptotic convergence rates, and provide sufficient conditions for monotone convergence in norm. Two signal processing applications illustrate the method: estimation of superimposed signals in Gaussian noise, and image reconstruction from Poisson measurements. In both applications, the SAGE algorithms easily accommodate smoothness penalties and converge faster than the EM algorithms     

A fuzzy genetic multiobjective optimization algorithm for a multilevel generalized assignment problem

We consider a task-operator-machine assignment problem where we seek to minimize the total execution time, to come as close as possible to a perfect load balance among the operators and not to exceed neither predefined inter-operator communication costs nor a prefixed number of resources. Besides, in an industrial environment where work force frequently changes, manufacturing systems need to be flexible and critical decisions have to be quickly taken. In this context, a fuzzy genetic multiobjective optimization algorithm is developed to solve a multilevel generalized assignment problem usually encountered in the clothing industry.     

A new algorithm for main carrier acquisition in deep space communications

Compared with common near space satellite Telemetry, Telecommand, and Communication (TT&C), deep space TT&C presents a more challenging environment such as long distance, very low Signal to Noise Ratio (SNR). How to acquire main carrier exactly becomes a hot focus for deep space communications. Already there emerged some main carrier acquisition algorithms, but they all require high SNR and small modulation index. In this paper, we develop a new acquire algorithm. First we use the spectral energy center algorithm to shorten the original sequence, filter out some noise and make the spectral more symmetric. Then we adopt the spectral symmetry algorithm to make full use of the whole spectrum information, and utilize FFT to reduce computation complexity. Simulation results show that our algorithm can acquire main carrier successfully under large modulation index and get good performance with low Carrier to Noise Ratio (CNR).