Oscillation and convergence behaviours exhibited in an ‘unstable’ second‐order digital filter with saturation‐type non‐linearity

This letter explains the oscillatory behaviours exhibited in a second‐order digital filter with saturation‐type non‐linearity via the Hopf bifurcation theorem. It is shown that depending on the bifurcation parameter, the state variables may converge to zero even when the eigenvalues of the system matrix are outside the unit circle. Copyright © 2004 John Wiley & Sons, Ltd. abstract     

Transport properties of undoped Cd0.9Zn0.1Te grown by high pressure bridgman technique

The electron drift mobility of undoped Cd_0.9Zn_0.1Te grown by high-pressure Bridgman method is measured by a time-of-flight technique. The sample shows a room temperature mobility and mobility lifetime product of 950 cm²/Vs and 1.6 × 10⁻⁴cm²/V, respectively. The mobility increases monotonically with decreasing temperature to 3000 cm²/Vs at 100 K. The dominant scattering mechanism for the electron transport is discussed by comparing with the theoretical mobility obtained by iterative solution of the Boltzmann equation.     

Analytical form of globally optimal solution of weighted sum of intraclass separation and interclass separation

The Fisher linear discriminant analysis (FLDA)-based method is a common method for jointly optimizing the intraclass separation and the interclass separation of the projected feature vectors by defining the objective function as the ratio of the intraclass separation over the interclass separation. To address the eigenproblem of the FLDA, a quadratic equality constraint is imposed on the square of the \(l_{2}\) norm of the decision vector. However, the constrained optimization problem is highly nonconvex. This paper proposes to reformulate the objective function as a weighted sum of the intraclass separation and the interclass separation subject to the same quadratic equality constraint on the square of the \(l_{2}\) norm of the decision vector. Although both the objective function and the feasible set of the optimization problem are still nonconvex, this paper shows that the global minimum of the objective functional value is equal to the minimum singular value of the Hessian matrix of the objective function. Also, the globally optimal solution of the optimization problem is in the null space of the Hessian matrix minus this singular value multiplied by the identity matrix. As it is only required to find the singular value of the Hessian matrix, no numerical optimization-based computer aided design tool is required to find the globally optimal solution. Therefore, the globally optimal solution can be found in real time. Experimental results demonstrate the effectiveness of our proposed method.     

A parallel thinning algorithm based on stroke continuity detection

Thinning algorithms often cause stroke distortions at the crosses or intersections of strokes, which lead to bad results in pattern recognition tasks. In order to overcome these drawbacks, this paper proposes a parallel thinning algorithm based on stroke continuity detection. In the algorithm, before it uses the conditions of parallel algorithms to delete a boundary point, it first detects whether the boundary point is a reserved point to keep stroke’s continuity or not. Consequently, it can produce a skeleton with good symmetry, control the large deformation at the cross or intersection of strokes, and make a better skeleton more quickly. Moreover, it is practically immune to noise.     

Data imputation via conditional generative adversarial network with fuzzy c mean membership based loss term

Applied Intelligence - There are some missing values in the data when the data is acquired from the sensors or other equipments. This makes it difficult for performing the analysis based on the...     

Design of Nonuniform Transmultiplexers with Block Samplers and Single-Input Single-Output Linear Time-Invariant Filters Based on Perfect Reconstruction Condition

This paper proposes a design of a nonuniform transmultiplexer with block samplers and single-input single-output linear time-invariant filters. First, the perfect reconstruction condition of the nonuniform transmultiplexer is derived. Then, the design problem is formulated as an optimization problem. In particular, the perfect reconstruction error is minimized in the \(L_{1}\) norm sense subject to the frequency selectivities of the filters. Computer numerical simulation results show that the designed nonuniform transmultiplexer with block samplers is robust to the channel noise and achieves a small reconstruction error.     

Global convergence and limit cycle behavior of weights of perceptron.

In this paper, it is found that the weights of a perceptron are bounded for all initial weights if there exists a nonempty set of initial weights that the weights of the perceptron are bounded. Hence, the boundedness condition of the weights of the perceptron is independent of the initial weights. Also, a necessary and sufficient condition for the weights of the perceptron exhibiting a limit cycle behavior is derived. The range of the number of updates for the weights of the perceptron required to reach the limit cycle is estimated. Finally, it is suggested that the perceptron exhibiting the limit cycle behavior can be employed for solving a recognition problem when downsampled sets of bounded training feature vectors are linearly separable. Numerical computer simulation results show that the perceptron exhibiting the limit cycle behavior can achieve a better recognition performance compared to a multilayer perceptron.     

Stability of sinusoidal responses of marginally stable bandpass sigma delta modulators

In this paper, we analyse the stability of the sinusoidal responses of second‐order interpolative marginally stable bandpass sigma delta modulators (SDMs) with the sum of the numerator and denominator polynomials equal to one and explore new results on the more general second‐order interpolative marginally stable bandpass SDMs. These results can be further extended to the high‐order interpolative marginally stable bandpass SDMs. Copyright © 2006 John Wiley & Sons, Ltd.     

Fault detection and signal reconstruction for increasing operational availability of industrial gas turbines

The paper presents a generalization of multi-dimensional linear regression to facilitate multi-sensor fault detection and signal reconstruction through the use of analytical optimization. The proposed methodology is founded upon the solution of an optimal signal reconstruction problem. The technique is applied to the real time monitoring of exhaust gas temperature sensors and burner-tip temperature sensors, of a 14 MW industrial gas turbine. Key benefits of the proposed technique are that it facilitates (i) real-time detection of sensor faults and the number of sensors that are at fault in a multi-sensor system; (ii) reconstruction of measurements that would normally be expected from the sensor at fault—thereby facilitating improved unit availability; (iii) determining the minimum number of non-faulty sensors that are required to be available to continue unit operation without unduly compromising performance. The use of an analytical formulation to determine (i–iii) means that the resulting technique incurs low computational overhead and is readily applied to real-time monitoring and subsequent remedial action. Experimental results demonstrate the efficacy of the developed procedures to facilitate continued unit operation in the event of sensor faults. Whilst the application to industrial gas turbine machinery is used to focus the study, it should be noted that the proposed techniques are much more widely applicable to numerous industrial and commercial systems.