Structure of complex elliptic Gm-C filters suitable for fully differential implementation

The complexification method of Gm-C filters is presented. Unlike the previously reported method, the proposed method is suitable for the realisation of the fully differential complex Gm-C filters with finite zeros. Based upon the proposed method, complexification of the third-order elliptic Gm-C filter is demonstrated and the practical transistor-level simulation results of the resulting complex filter are also given.     

Dual-mode design of fully differential circuits using fully balanced operational amplifiers

Fully differential (F-D) analogue circuits are usually designed focusing only on their differential-mode (DM) behaviour, without considering common-mode (CM) responses. A technique is presented for the design of both DM and CM circuit responses, using fully balanced operational amplifiers (FBOAs) as analogue building blocks. FBOAs work with CM and DM voltages as a whole, having an ideally infinite gain for both modes. This allows independent design of CM and DM dynamics. Inverting and non-inverting F-D topologies can be implemented in a simple way, similar to the implementation of their single-ended counterparts. Some typical application circuits are analysed and discussed and, as a design example, a 'double-mode oscillator' (a circuit that has independent CM and DM oscillations) was built and experimentally evaluated.     

自适应线性反馈与自适应微分反馈控制混沌

构造自适应线性反馈控制混沌Chen系统到达平衡点.应用Lyapunov第二方法证明受控系统可渐近稳定收敛到三个平衡点.将控制参数p看作第4个变量构成与受控系统等价的自治系统,数值仿真结果验证了受控系统(2)对指定平衡点的稳定收敛.构造自适应微分反馈控制混沌Chen系统,应用Ruoth Hurwitz定理证明受控系统(4)可稳定收敛到平衡点S+,S-,而不能收敛到平衡点S0.自适应方法的优点是控制信号微小简单,可在任意时刻加入,可以迅速将混沌系统控制到系统的平衡点,且在系统收敛到平衡点时,控制扰动u(t)也迅速趋于0.     

Selection of optimal filters for multispectral imaging

Preece and Claridge [IEEE Trans. Pattern Anal. Mach. Intell. 26, 913 (2004)] have proposed a technique for selecting filters for the maximally accurate recovery of object parameters such as chromophore concentrations from a multispectral image of an object. Their selection criteria are derived from an analysis of a model of light propagation in the object and take into account both errors in the modeling process and errors in the image acquisition process, as well as the inherent behavior and structure of the model. We investigate their method on simulated image data and show that filters selected according to their criteria are demonstrably superior to other choices.     

Design of predictive IIR filters via feedback extension of FIRforward predictors

Finite impulse response (FIR) predictors for polynomial signals and sinusoids are easy to design because of the available closed-form design formulae. On the other hand, those FIR predictors have two major drawbacks: the passband gain peak is usually greater than +3 dB, and a long FIR structure is needed to attain high attenuation in the stopband. Both of these characteristics cause severe problems, particularly in control instrumentation when the predictor operates inside a closed control loop. In this paper, we present a novel feedback extension scheme for FIR forward predictors. This extension makes it possible to easily design infinite impulse response (IIR) predictors with low passband ripple and high stopband attenuation. The new approach is illustrated with design examples     

Design and implementation of efficient IIR notch filters withquantization error feedback

Straightforward methods for the design of digital notch filters are presented. The design method is based on setting a zero of the filter at a notch frequency and placing a pole in its neighborhood such that the notch width is narrow enough while keeping the group delay of the filter sufficiently flat. A technique for efficient and well-behaved implementation with fixed-point signal processors is advanced, based on the use of quantization error feedback for roundoff noise reduction. The design approach is illustrated with numerical examples, and an assembly-language program for the family of TMS320 signal processors is provided     

Selection and design of multisection smoothing filters for data converters

A multisection smoothing filter for a mean-value data converter is analyzed and a design procedure for a given response speed is obtained. Belarus. Translated from Izmeritel’naya Tekhnika, No. 12, pp. 19–23, December, 1999.     

基于U型全卷积神经网络的路面裂缝检测

路面裂缝检测是道路运营和维护的一项重要工作,由于裂缝没有固定形状而且纹理特征受光照影响大,基于图像的精确裂缝检测是一项巨大的挑战。本文针对裂缝图像的特点,提出了一种U型结构的卷积神经网络UCrackNet。首先在跳跃连接中加入Dropout层来提高网络的泛化能力;其次,针对上采样中容易产生边缘轮廓失真的问题,采用池化索引对图像边界特征进行高保真恢复;最后,为了更好地提取局部细节和全局上下文信息,采用不同扩张系数的空洞卷积密集连接来实现感受野的均衡,同时嵌入多层输出融合来进一步提升模型的检测精度。在公开的道路裂缝数据集CrackTree206和AIMCrack上测试表明,该算法能有效地检测出路面裂缝,并且具有一定的鲁棒性。     

Automatic defect inspection system of colour filters using Taguchi-based neural network

A coloured filter is a critical part of an LCD panel, especially to present a high quality colour display. At present, the defect detection of colour filters is conducted by manual inspection in the final product stage. However, poor detection efficiency and subjective judgment of manual inspection undermine accuracy. Therefore, this study applied image processing technology and the neural network to detect surface defects of colour filters in order to prevent losses arising from incorrect detection, lower production costs, and effectively improve yield. A back-propagation neural network (BPNN) classifier was selected to train the features. The results showed that the proposed method can be successfully applied in defect detection of colour filters to reduce artificial detection errors. In addition, the Taguchi method was used with BPNN to save time searching optimal learning parameters by the trial and error method, which achieves faster convergence, smaller convergent errors and better recognition rate. The results proved that the root-mean-square error (RMSE) of the Taguchi-based BPNN at final convergence is 0.000254, and recognition rate reaches 94%. Therefore, the proposed method has good effects in detecting the micro defects of a colour filter panel.     

拟牛顿优化BP网络盲判决反馈均衡器

针对水声通信严重多途效应导致的码间干扰,利用神经网络良好的非线性拟合能力,将盲判决反馈均衡器结构与神经网络相结合,同时通过拟牛顿算法提升神经网络的收敛速度,提出了一种拟牛顿优化神经网络的盲判决反馈均衡器.用两个单隐层误差反向传播(Back Propagation,BP)网络替换判决反馈均衡器前馈和反馈滤波器,利用拟牛顿...     

Analysis of nonlinear fully intrinsic equations of geometrically exact beams using generalized differential quadrature method

In this paper, the generalized differential quadrature (GDQ) method is presented for solving the nonlinear, fully intrinsic equations of geometrically exact rotating and nonrotating beams. The fully intrinsic equations of beams involve only moments, forces, velocity and angular velocity, and in these equations, the displacements and rotations will not appear explicitly. This paper presents the generalized differential quadrature method for solution of these equations. To show the accuracy, validity and applicability of the proposed generalized differential quadrature method for solving the fully intrinsic beam equations, different cases are considered. It is found that the GDQ method gives very accurate results with very few numbers of discrete points and also has very low computational cost as compared to some other conventional numerical methods and therefore this method is very efficient, accurate and fast for solving the fully intrinsic equations.     

Application of probabilistic neural network for differential relaying of power transformer

Investigations towards the applicability of probabilistic neural networks (PNNs) as core classifiers to discriminate between magnetising inrush and internal fault of power transformer are made. An algorithm has been developed around the theme of conventional differential protection of transformer. It makes use of the ratio of the voltage-to-frequency and the amplitude of differential current for the detection of the operating condition of the transformer. The PNN has a significant advantage in terms of a much faster learning capability because it is constructed with a single pass of exemplar pattern set and without any iteration for weight adaptation. For the evaluation of the developed algorithm, transformer modelling and simulation of fault are carried out in power system computer-aided designing PSCAD/EMTDC. The operating condition detection algorithm is implemented in MATLAB     

Application of decision feedback equalisers based on multilayer neural network structures to compensate

Decision feedback equalisers (DFE) based on multilayer neural networks (MNNs) structure are simulated in a 10 Gbit/s optical channel of standard single mode fibre of 300 km. Simulation results show that the DFE based on the MNN structure significantly outperforms the traditional DFE in the equalisation of chromatic dispersion in medium to long haul optical communications (>220 km).     

Radial basis probabilistic neural network for differential protection of power transformer

Protection of medium- and large-power transformers has always remained an area of interest of relaying engineers. Conventionally, the protection is done making use of magnitude of various frequency components in differential current. A novel technique to distinguish between magnetising inrush and internal fault condition of a power transformer based on the difference in the current wave shape is developed. The proposed differential algorithm makes use of radial basis probabilistic neural network (RBPNN) instead of the conventional harmonic restraint- based differential relaying technique. A comparison of performance between RBPNN and heteroscedastic-type probabilistic neural network (PNN) is made. The optimal smoothing factor of heteroscedastic-type PNN is obtained by particle swarm optimisation technique. The results demonstrate the capability of RBPNN in terms of accuracy with respect to classification of differential current of the power transformer. For the verification of the developed algorithm, relaying signals for various operating conditions of the transformer, including internal faults and external faults, were obtained through PSCAD/EMTDC. The proposed algorithm has been implemented in MATLAB.     

Optical-mode neural network by use of the nonlinear response of a laser diode to external optical feedback

We present an intelligent all-optical neural network using a single laser diode that is provided with controlled external feedback. The outputs of the laser neural network (LNN) are represented in the optical domain by the longitudinal cavity modes of the laser diode. The inputs to the LNN are applied by means of adjusting the external feedback of each longitudinal mode through an optical vector-matrix multiplier. Supervised training of some basic input-output mappings is demonstrated by means of a stochastic learning algorithm. The stability and reproducibility of the LNN setup is examined.     

Selection of circulatory loops for patrol vehicles operating in a network

Many applications exist in which a vehicle or set of vehicles have the responsibility of patrolling and providing coverage over a wide area or network for the purpose of responding quickly to service requests generated randomly at demand points in the network. One of the decisions in the operation of such patrol vehicles is that of determining the home location or circulatory loop for free or idle vehicles. A vehicle is said to be idle if it is not responding or attending to a service request. Such home location can be a point, a set of points in a network, a circulatory loop, or a set of circulatory loops in the network. The actual number of home locations is system, fleet size, and analyst dependent. Whether a point or loop strategy is implemented, the specification of the home locations or loops is usually done with the objective of improving the response time of idle vehicles to service requests originating from demand stations. In cases where circulatory loop strategy is adopted, the problem is one of determining the best overall circulatory loop or loops that would improve the overall system response time. In a typical conventional or generalized network, several loops may exist. Unless a systematic procedure is developed, non-optimal loops could be selected. In this paper, methodologies for determining the best circulatory loops for patrol vehicles for a given problem scenario in both unidirectional and bidirectional networks are presented. The problem is modelled as a mathematical programming problem. Example problems are given to illustrate the application of the approach. The criterion for optimization is the minimization of the maximum system response time.     

Neural network pattern recognition by means of differential absorption Mueller matrix spectroscopy

Artificial neural network systems were built for detecting amino acids, sugars, and other solid organic matter by pattern recognition of their polarized light scattering signatures in the form of a Mueller matrix. Backward-error propagation and adaptive gradient descent methods perform network training. The product of the training is a weight matrix that, when applied as a filter, discerns the presence of the analytes on the basis of their cued susceptive Mueller matrix difference elements. This filter function can be implemented as a software or a hardware module to a future differential absorption Mueller matrix spectrometer.