Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit

The objectives of this study were to use image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated kiwifruit slices. Kiwifruits were dehydrated implementing four different sucrose concentrations, at three processing temperatures and during four osmotic time periods. A multilayer neural network was developed by using the operation conditions as inputs to estimate water loss, solid gain, and color changes. It was found that artificial neural network with 16 neurons in hidden layer gives the best fitting with the experimental data, which made it possible to predict solid gain, water loss, and color changes with acceptable mean-squared errors (1.005, 2.312, and 2.137, respectively). These results show that artificial neural network could potentially be used to estimate mass transfer kinetics and color changes of dehydrated kiwifruit.     

Variable selection in linear regression: Several approaches based on normalized maximum likelihood

The use of the normalized maximum likelihood (NML) for model selection in Gaussian linear regression poses troubles because the normalization coefficient is not finite. The most elegant solution has been proposed by Rissanen and consists in applying a particular constraint for the data space. In this paper, we demonstrate that the methodology can be generalized, and we discuss two particular cases, namely the rhomboidal and the ellipsoidal constraints. The new findings are used to derive four NML-based criteria. For three of them which have been already introduced in the previous literature, we provide a rigorous analysis. We also compare them against five state-of-the-art selection rules by conducting Monte Carlo simulations for families of models commonly used in signal processing. Additionally, for the eight criteria which are tested, we report results on their predictive capabilities for real life data sets.     

Determining optimum location and capacity for micro hydropower plants in Lorestan province in Iran

In Iran and other developing countries, transmitting electricity power to remote and inaccessible areas is not cost effective and involves a lot of problems due to high transmission costs and insufficient supply. Therefore local water potential would seem a better alternative to supply electricity. This paper is an investigation into all the issues concerning finding suitable sites for micro hydropower plants in remote areas in Lorestan province in Iran. Therefore, the basins, river network, and rural electricity condition in the province were studied. Then, we came up with a list of locations which would lend themselves best to the installation of micro hydropower plants in accordance to the existing parameters. Finally, an estimation of optimum nominal capacity for each micro hydropower was studied and related economic concerns were discussed.     

Application of optimally distinguishable distributions to the detection of subspace signals in Gaussian noise of unknown level

A detection method based on optimally distinguishable distributions (ODD) was introduced recently. However, ODD testing as it was originally formulated has an important limitation because it does not accommodate models with nuisance parameters. This paper demonstrates how the difficulty can be circumvented in the case of subspace signals in Gaussian noise of unknown level. The key point is to define a partition of the parameter space. To this end, we analyze two different methods, and we choose one of them as basis for the new ODD detector. The performance of the detector is compared with that of the GLRT (generalized likelihood ratio test). Additionally, we compute the confidence indexes which are part of the ODD methodology.     

An 8-bit 150-MHz CMOS A/D converter

This paper describes an 8-bit 5-stage pipelined and interleaved analog-to-digital converter that performs analog processing only by means of open-loop circuits such as differential pairs and source followers to achieve a high conversion rate. The concept of sliding interpolation is proposed to obviate the need for a large number of comparators or interstage digital-to-analog converters and residue amplifiers. The pipelining scheme incorporates distributed sampling between the stages so as to relax the linearity-speed tradeoffs in the sample-and-hold circuits, A clock edge reassignment technique is also introduced that suppresses timing mismatches in interleaved systems, and a punctured interpolation method is proposed that reduces the integral nonlinearity error with negligible speed or power penalty. Fabricated in a 0.6-μm CMOS technology, the converter achieves differential and integral nonlinearities of 0.62 and 1.24 LSB, respectively, and a signal-to-(noise+distortion) ratio of 43.7 dB at a sampling rate of 150 MHz. The circuit draws 395 mW from a 3.3-V supply and occupies an area of 1.2×1.5 mm²     

Emission and absorption of optical phonons in Multigate Silicon Nanowire MOSFETs

In this paper we study the influence of emission/absorption processes due to optical phonons on the electrical properties of multigate silicon nanowire transistors. We show that low-energy phonons reduce drain current through backscattering of carriers by emission/absorption processes while high-energy phonons redistribute the current energy spectrum along the nanowire channel through phonon emission without significantly reducing the drain current drive. The influence of emission/absorption is investigated in different multigate silicon FET structures with uniform channel, single impurity, random doping atom distribution and oxide tunnel barriers. A three-dimensional quantum mechanical device simulator based on the NEGF formalism in coupled mode-space approach is used to model electron transport in the presence of optical phonon scattering mechanism. Electron-phonon scattering is accounted for by adopting the self-consistent Born approximation and using the deformation potential theory.