Latent vs. Observed Variables: Analysis of Irrigation Water Efficiency Using SEM and SUR

In this paper we compare conceptualising single factor technical and allocative efficiency as indicators of a single latent variable, or as separate observed variables. In the former case, the impacts on both efficiency types are analysed by means of structural equation modeling (SEM), in the latter by seemingly unrelated regression (SUR). We compare estimation results of the two approaches based on a dataset on single factor irrigation water use efficiency obtained from a survey of 360 farmers in the Guanzhong Plain, China. The main methodological findings are that SEM allows identification of the most important dimension of irrigation water efficiency (technical efficiency) via comparison of their factor scores and reliability. Moreover, it reduces multicollinearity and attenuation bias. It thus is preferable to SUR. The SEM estimates show that perception of water scarcity is the most important positive determinant of both types of efficiency, followed by irrigation infrastructure, income and water price. Furthermore, there is a strong negative reverse effect from efficiency on perception.

Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

In this paper, a novel and effective lip-based biometric identification approach with the Discrete Hidden Markov Model Kernel (DHMMK) is developed. Lips are described by shape features (both geometrical and sequential) on two different grid layouts: rectangular and polar. These features are then specifically modeled by a DHMMK, and learnt by a support vector machine classifier. Our experiments are carried out in a ten-fold cross validation fashion on three different datasets, GPDS-ULPGC Face Dataset, PIE Face Dataset and RaFD Face Dataset. Results show that our approach has achieved an average classification accuracy of 99.8%, 97.13%, and 98.10%, using only two training images per class, on these three datasets, respectively. Our comparative studies further show that the DHMMK achieved a 53% improvement against the baseline HMM approach. The comparative ROC curves also confirm the efficacy of the proposed lip contour based biometrics learned by DHMMK. We also show that the performance of linear and RBF SVM is comparable under the frame work of DHMMK.

Passive Intermodulation in Distributed Circuits with Cascaded Discrete Nonlinearities

The principle aspects of passive intermodulation (PIM) characterisation in distributed printed circuits with cascaded lumped nonlinearities are presented. Mechanisms of PIM generations have been investigated experimentally and modelled using the formalism of X-parameters. The devised equivalent circuit models are applied to the analysis of microstrip lines with distributed and cascaded lumped sources of nonlinearity. The dynamic measurements have revealed that PIM generation rates in straight and meandered microstrip lines differ and significantly deviate from those expected for the respective discrete sources of nonlinearity. The obtained results indicate that multiple physical sources of nonlinearity contribute to PIM generation in printed circuits. Finally, it is demonstrated that the electrical discontinuities can have significant effect on the overall PIM response of the distributed passive circuits and cause PIM product leakage and parasitic coupling between isolated circuit elements.