Tomographic-image reconstruction using a hybrid genetic algorithm

An improved tomographic-image reconstruction method is proposed that uses a hybrid genetic algorithm (GA) that hybridizes a conventional GA and a concurrent simplex method. For the purposes of discussion, an axisymmetric phantom density field is used with an interferometric optical projection. Tomographic-image reconstruction using the hybrid GA not only improves the convergence over the pure GA but also significantly reduces the computation time.     

Performance evaluation of a WDM/OCDM based hybrid optical switch utilizing efficient resource allocation

A hybrid optical switch （HOS） with physical layer of wavelength division multiplexing and optical code division multiplexing （WDM/OCDM） scheme is proposed. An additional feature to the HOS than optical cross connect （OXC） is that the controller can process requests for both circuit establishment and burst scheduling. In our study, the measurement criteria of HOS are the blocking probability, probability of error, and probability of outage. To simplify the analysis, no distinction is made between a circuit in progress and a burst in progress. Moreover, a minimum fit （MinF） resource allocation strategy is applied in order to increase the bandwidth efficiency and control the multiplexing interference of the OCDM. A 2D Markov model for the HOS is presented using the MinF strategy. Numerical results reveal that the code parameters and the resource allocation strategy greatly affect the performance. Certain periority can be achieved by assigning shorter codes to high periority users and longer codes to low periority users. Also, the probability of error and outage are reduced bv aonling the MinF strategy.     

基于改进遗传与模拟退火融合的RISP软硬件划分

软硬件划分是可重构指令集处理器在软硬件协同设计中的关键问题,通过对比遗传算法和经典模拟退火算法的优缺点,提出改进遗传算法的适应度函数,同时将Tsallis接受准则引入到经典模拟退火当中。其思路是用遗传算法的结果来制约模拟退火算法产生的随机状态,然后由模拟退火的接受准则以及产生的随机状态函数对遗传算法的种群进行更新,从而找到全局近似最优解。实验结果证明,改进算法与单一遗传算法以及经典模拟退火算法相比,其收敛速度和适应度更好,找到全局近似最优解的概率更大。     

A new flexible and enhancing bandwidth utilization burst dropping technique for an OBS network

Optical burst switching (OBS) has been proposed as a competitive hybrid switching technology to support the next generation optical Internet. However, due to their one-way resource reservation mechanism, OBS networks experience high bursts (thus packets) loss rate. In OBS networks, the contention is resolved either by dropping one of the contending bursts or more efficiently by dropping from one of the contending bursts only the parts that overlap with the other bursts. In both situations, only one data source will suffer the data loss in favor to the other. In this paper, a new burst flexible and enhancing bandwidth utilization burst dropping technique has been proposed for contention resolution in optical burst switched networks. When contention occurs, any part of a contending burst could be dropped, instead of only the head or tail of bursts. The proposed dropping scheme makes bandwidth utilization more efficient and flexible. Simulation results show that the proposed dropping scheme performs better than existing burst dropping schemes.     

BiPLS结合GA优选可见/近红外光谱MLR变量

利用反向区间偏最小二乘法(BiPLS)定位光谱糖度若干信息区间,运用遗传算法(GA)从中选择波长点,建立了多元线性回归(MLR)模型。光谱进行卷积平滑和二阶导数处理后,将光谱(225个数据点)分割成25个子区间时,BiPLS优化结果最优。在所定位的信息区间进行GA二次选择特征变量,运行100次依次选择入选频率较高的12个波长点。为简化MLR模型,对于入选的相邻波长选择频率较高者,最后选择 638,734,752,868,910,916和938 nm作为回归变量,建立的MLR预测模型相关系数(R2)、校正均方根误差(RMSEC)和预测均方根误差(RMSEP)分别为0.984,0.364和0.471,优于常用的逐步多元线性回归的建模结果。表明BiPLS结合GA可以有效地对李子糖度可见/近红外光谱MLR回归变量进行筛选,提高了模型的精度。     

Phase-shifting digital holography with burst imaging method

The application of a burst imaging method to digital holography (DH) with multiple image capturing is firstly demonstrated. Nobody has discussed this approach explicitly, although the burst imaging method is not so uncommon. The burst imaging method intermittently captures several images with a high repetition rate followed by a relatively long rest time. If the light wave from an object has a much smaller complex amplitude change in the frame interval of an image sensor than the precision of the DH system, the wave can be regarded as being in a static state. As a result, this solves a fatal problem in DH for an object that moves or deforms while capturing multiple interference images. In this research, the burst imaging method was applied to phase-shifting DH, and it is simply implemented by a high-speed image sensor and a continuous phase shifting which is good for high-speed phase shifting. The measurement error was analytically solved for the movement speed of an object. Finally, the proposed method was experimentally used to observe an evaporating droplet of ethanol on a glass substrate.

     

Network traffic prediction by a wavelet-based combined model

Network traffic prediction models can be grouped into two types, single models and combined ones. Combined models integrate several single models and thus can improve prediction accuracy. Based on wavelet transform, grey theory, and chaos theory, this paper proposes a novel combined model, wavelet--grey--chaos (WGC), for network traffic prediction. In the WGC model, we develop a time series decomposition method without the boundary problem by modifying the standard \grave\rm a trous algorithm, decompose the network traffic into two parts, the residual part and the burst part to alleviate the accumulated error problem, and employ the grey model GM(1,1) and chaos model to predict the residual part and the burst part respectively. Simulation results on real network traffic show that the WGC model does improve prediction accuracy.     

Maximum likelihood subband polynomial regression for robust speech recognition

In this paper, we propose a model adaptation algorithm based on maximum likelihood subband polynomial regression (MLSPR) for robust speech recognition. In this algorithm, the cepstral mean vectors of prior trained hidden Markov models (HMMs) are converted to the log-spectral domain by the inverse discrete cosine transform (DCT) and each log-spectral mean vector is divided into several subband vectors. The relationship between the training and testing subband vectors is approximated by a polynomial function. The polynomial coefficients are estimated from adaptation data using the expectation–maximization (EM) algorithm under the maximum likelihood (ML) criterion. The experimental results show that the proposed MLSPR algorithm is superior to both the maximum likelihood linear regression (MLLR) adaptation and maximum likelihood subband weighting (MLSW) approach. In the MLSPR adaptation, only a very small amount of adaptation data is required and therefore it is more useful for fast model adaptation.     

Hybrid OCS/OBS interconnect in intra-data-center network

We report on a data center network(DCN) architecture based on hybrid optical circuit switching(OCS) and optical burst switching(OBS) interconnect for dynamic DCN connectivity provisioning. With the combination of the centralized and distributed control of the software-defined optical networks, the proposed interconnect can achieve unprecedented flexibility in dealing with both mice and elephant flow in the DCN. Numerical simulation is employed to investigate the performance of the proposed architecture. The results show that the OBS module has preferable performance in dealing with a larger burst packet, and the throughput is constrained by the capacity of the server random access memory module.     

猝发混合扩频水声隐蔽通信技术

针对常规的连续载波调制扩频通信方式容易通过能量检测的方法被截获的问题,提出了一种猝发的混合扩频水声隐蔽通信方式,该方法通过在时间上随机的发送脉冲混合调制扩频信号来降低截获概率,而且可以比较灵活的通过调整平均占空比来满足不同隐蔽性的要求,但是该隐蔽性是以牺牲通信速率为代价的。猝发信号中混合了二进制相位键控(BPSK)、循环移位键控(CSK)和多进制频移键控(MFSK)扩频调制方式,采用自同步和时频二维搜索算法实现码元和载波同步。通过仿真分析了猝发通信系统的隐蔽性能和多途信道下的误码性能,并且通过了南海海试验证了算法的性能,在5 km的通信距离上,4 kHz的带宽内,单个脉冲信号的通信速率可以达到317 bps,误码率达到10-3以下。      

基于自适应本征正交分解混合模型的跨音速流场分析

采用非线性模型替代线性回归模型响应本征正交分解(POD)基函数的系数,并采用自适应抽样方法确定快照集合,实现了基于自适应POD混合模型的跨音速叶片复杂流动分析及流场拟合.首先通过比较基于线性回归模型和非线性回归模型的基函数系数响应精度,验证非线性回归模型的收敛性和精确性;之后通过与静态抽样方法进行对比,研究分析自适应抽样技术的优越性;最后开展基于自适应POD混合模型的全三维跨音速流场分析及流动拟合.结果表明,采用自适应POD混合模型,不仅能够清晰地识别三维跨音速流场中的敏感流动特征,还能精确地拟合设计空间内任意状态的流场及出口气动参数.     

Hybrid approach for loss recovery mechanism in OBS networks

This letter reports a study of a hybrid burst assembly and a hybrid burst loss recovery scheme （delay-based burst assembly and hybrid loss recovery （DBAHLR）） which selectively employs proactive or reactive loss recovery techniques depending on the classification of traffic into short term and long term, respectively. Traffic prediction and segregation of optical burst switching network flows into the long term and short term are conducted based on predicted link holding times using the hidden Markov model （HMM）. The hybrid burst assembly implemented in DBAHLR uses a consecutive average-based burst assembly to handle jitter reduction necessary in real-time applications, with variations in burst sizes due to the non-monotonic nature of the average delay handled by additional burst length thresholding. This dynamic hybrid approach based on HMM prediction provides overall a lower blocking probability and delay and more throughput when compared with forward segment redundancy mechanism or purely HMM prediction-based adaptive burst sizing and wavelength allocation （HMM-TP）.     

Chaos-based image encryption using a hybrid genetic algorithm and a DNA sequence

The paper studies a recently developed evolutionary-based image encryption algorithm. A novel image encryption algorithm based on a hybrid model of deoxyribonucleic acid (DNA) masking, a genetic algorithm (GA) and a logistic map is proposed. This study uses DNA and logistic map functions to create the number of initial DNA masks and applies GA to determine the best mask for encryption. The significant advantage of this approach is improving the quality of DNA masks to obtain the best mask that is compatible with plain images. The experimental results and computer simulations both confirm that the proposed scheme not only demonstrates excellent encryption but also resists various typical attacks.     

New multivariate noise model and data detection using the expectation maximization algorithm

A signal sequence detector in a high areal density recording channel is required to provide robust compensation against unexpected error events. Primarily, a number of error events are caused by media noise and nonlinear distortion. The same problem of signal sequence detection remains to be solved in a future magnetic recording system that comes in predisposed to trend for recording by large-sector size instead of existing single-sector one that consists of 512 information 8-bits bytes. For the above problem, this paper shows the signal estimation method based on statistical inference for such a finite mixture model with known number of degraded noise components. Our signal detection scheme with multivariate autoregressive models for total noise and the expectation maximization algorithm is applied to maximum a posteriori estimation for multivariate mixtures of noise. Furthermore, a non-binary low-density parity-check (LDPC) code is used for an error-correcting code that satisfies the specific run-length limited condition in the proposed system. It shows that the proposed error-correcting and signal detection methods are effective in estimating signal sequences degraded by media noise and in improving the error rate performances with respect to the conventional system using the binary LDPC code and univariate autoregressive model.     

基于高光谱的GA和SPA算法对赣南脐橙叶绿素定量分析

用遗传算法(GA)和连续投影算法(SPA)分别提取了赣南脐橙叶片高光谱图像的有效信息,对叶绿素的含量用偏最小二乘法(PLS)进行建模定量分析。高光谱图像标定后,提取感兴趣区域(ROI)的平均光谱,用GA和SPA算法分别选出了27和8条特征波长,然后用PLS对叶绿素含量建模。GA-PLS与SPA-PLS模型得到的预测集相关系数分别为0.80和0.83,均方根误差分别为2.45和2.30。结果表明：SPA-PLS模型具有较高的优势,可以结合高光谱技术对赣南脐橙叶绿素含量快速、无损的定量分析。     

Optimization method based on Generalized Pattern Search Algorithm to identify bridge parameters indirectly by a passing vehicle

Generalized Pattern Search Algorithm (GPSA) has rarely been investigated for structural health monitoring, but may have potential application in civil engineering, because it does not require any gradient information of the objective function. Meanwhile, indirect identification is an attractive concept that recognizes the bridge parameters by the vehicle responses. This paper proposes a theoretical indirect identification method based on optimization method, and the implementation is performed by the GPSA. Firstly, the GPSA theory is investigated, and a simple example is employed to describe the process of the algorithm. Secondly, a theoretical indirect identification method is proposed, based on the optimization method rather than the conventional transforms from time domain to frequency domain. The proposed method can identify the parameters of the vehicle–bridge system, including the bridge stiffness and the 1st frequency. Based on the optimization method, the feasibility and accuracy of GPSA are demonstrated with 0.06% of errors. The GPSA shows good robustness in the identifications with various noise levels, and the maximum error is about 3.30% and can be accepted for the engineering application even with a SNR 5 noise level. The computation time relies only on the function evaluation times, and is not positively related to the noise level. Thirdly, the performance of GPSA is compared with that of Genetic Algorithm (GA). The accuracy of GPSA and GA are approximately equivalent with various noise levels. Compared with GA, GPSA needs fewer iterations and much fewer evaluations, therefore is more efficient in the identification with an almost consistent accuracy with various noise levels.     

Optimized feed-forward neural-network algorithm trained for cyclotron-cavity modeling

The cyclotron cavity presented in this paper is modeled by a feed-forward neural network trained by the authors' optimized back-propagation(BP) algorithm. The training samples were obtained from simulation results that are for a number of defined situations and parameters and were achieved parametrically using MWS CST software; furthermore, the conventional BP algorithm with different hidden-neuron numbers, structures, and other optimal parameters such as learning rate that are applied for our purpose was also used here. The present study shows that an optimized FFN can be used to estimate the cyclotron-model parameters with an acceptable error function. A neural network trained by an optimized algorithm therefore shows a proper approximation and an acceptable ability regarding the modeling of the proposed structure. The cyclotron-cavity parameter-modeling results demonstrate that an FNN that is trained by the optimized algorithm could be a suitable method for the estimation of the design parameters in this case.     

Designing a genetic watermarking approach for 3D scenes

A new watermarking algorithm based on genetic algorithm (GA) in the transform domain is proposed. Unlike the existing computer-generated integral imaging based watermarking methods, the proposed method utilizes GA searching to the optimized transform domain to serve as a trade-off for watermark embedding. In this paper, 3D scene to be captured by using a virtual pinhole array and be computationally recorded as an elemental image array (EIA), watermarking with GA optimization and computer-generated holography is implemented. In the proposed GA optimization process, we utilize the fitness function to improve the visual quality of watermarked images and the robustness. Simulation results show that the proposed algorithm yields a holographic watermark that is imperceptibility to human eyes and robust to standard watermarking attacks. A comparison of the proposed watermarking method to the existing similar watermarking methods demonstrated that the proposed method generally outperforms completing methods in terms of imperceptibility and robustness.     

A Hybrid Rao-NM Algorithm for Image Template Matching

This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.     

Ultrahigh-frame-rate nitric oxide planar laser-induced fluorescence imaging

We demonstrate the ability to generate ultrahigh frequency burst sequences of deep UV at 226 nm by mixing the optical parametric oscillator signal output at 622 nm with third harmonic at 355 nm from a pulse burst laser system. We obtained 226 nm burst sequences with uniform burst envelopes, and the average pulse energy is approximately 0.5 mJ. Nitric oxide planar laser-induced fluorescence image sequences at ultrahigh (100 kHz) frame rates have been obtained.