A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.     

Simulation and analysis of extractive distillation process in a valve tray column using the rate based model

Valve trays are becoming popular in the chemical process industries owing to their flexibility to handle a wide range of vapor throughputs. Using the rigorous rate based model, the importance of the non-equilibrium approach is demonstrated for a typical extractive distillation process in a Glitsch V-1 valve tray column. Simulation results based on an in-house developed code indicated that the rate based model predictions for a valve tray column operation showed significant differences relative to the equilibrium model. Even small errors in product purities translated into nonoptimal feed stage locations and inaccurate number of stages required. The counter-intuitive effect of high reflux ratio on separation is explained.     

Effect of MgO in the microstructure formation of zirconia mullite composites from sillimanite and zircon

The effect of MgO additive on the structural, microstructural and hardness properties of zirconia mullite (MUZ) has been discussed. The MgO additive in MUZ not only stabilizes the cubic zirconia phase but also acts as a sintering aid for the formation of cross-linked mullite grains. The electron micrographs of plasma fused MgO–MUZ shows a uniform arrangement of platelet structure of mullite and dendrite structure of zirconia on mullite surface. The micrograph of plasma sintered composites shows a ladder like structure and a complete cross-linked mullite grains whereas the surface morphology of conventionally sintered composites clearly indicates the presence of small and big grains close packed to each other. Appreciable hardness and higher optical band gap have been observed for plasma fused MgO–MUZ composites. A complete dissociation of sillimanite and zircon has been occurred in plasma fused composites for the complete conversion of MUZ whereas the complete dissociation of sillimanite and zircon has not observed in plasma sintered and conventionally sintered composites. These observations have been realized from the X-ray diffraction and Fourier transform infrared studies.     

An improved decision feedback equalization using a priori information

This paper addresses the exhaustive computational complexity of the maximum‐a‐posteriori equalizer and the inefficiency of the conventional decision feedback equalizer (DFE) algorithm in iterative equalization, especially when the higher‐level modulation is used with severely distorted Inter Symbol Interference channels. The new method proposed here improves the bit error rate (BER) performance by computing the extra metric r_n+1 using the feedback symbols from previous iteration and combining it with a priori information of the symbols. After each iteration, the hard‐detected symbols are saved in the memory as a priori data for next iteration. We verified the proposed algorithm for Binary Phase Shift Keying and 8‐phase shift keying modulation. The promising simulation results show that the BER performance given by the proposed low complexity DFE algorithm improved dramatically throughout the iterations when the conventional DFE has only insignificant improvement in the process of iterative equalization. Copyright © 2009 John Wiley & Sons, Ltd.     

Microstructural Analysis of Multi-phase Ultra-Thin Oxide Overgrowth on Al–Mg Alloy by High Resolution Transmission Electron Microscopy

High-resolution transmission electron microscopy analyses are carried out to understand the microstructure of the ultra-thin oxide-film grown on a (native) amorphous Al₂O₃-coated Al-0.8 at.% Mg alloy substrate at T = 600 K for t = 2 h and at pO₂ of 1 × 10^?2 Pa. This oxide-film is found to be non-uniformly thick with thicknesses varying from 1.50 to 4.60 nm. Occasionally, this oxide is found to diffuse into the Al–Mg alloy substrate, forming oxide thicknesses up to 10.5 nm. Overall, this oxide-film is found to consist of a mixed amorphous, (poly) crystalline and an intermediate amorphous-to-crystalline transition regions, with crystalline regions consisting mostly of MgO and the diffused oxide regions into the Al–Mg alloy substrate coated with γ-Al₂O₃. These observations are then compared with the experimental results obtained using angle-resolved X-ray Photoelectron Spectroscopy analysis and thermodynamic predictions for the growth of an ultra-thin oxide-film due to dry, thermal oxidation of Al–Mg alloy substrates.     

Opportunistic Mobile Data Offloading Using Machine Learning Approach

Wireless Personal Communications - Currently, cellular networks both 3G and 4G are heavily overloaded due to increasing usage of mobile applications. Offloading mobile data traffic through...     

Fog/low clouds detection over the Delhi Earth Station using the Ceilometer and the INSAT-3D/3DR satellite data

Fog is a meteorological phenomenon in which cloud has its base near to the surface which disrupts public health and transportation due to decrease in visibility. In the present study, a collective method has been implemented for detection of fog/low clouds over the Delhi Earth Station during fog season for the years 2016 and 2017. A detailed comparison has been performed with the use of ground-based cloud-base height measurements from the Ceilometer and the Indian National Satellites (INSAT-3D/3DR)-retrieved fog product. Fog/low clouds have been effectively detected by both the Ceilometer and the INSAT-3D/3DR during single-layer cloud in the range of 0–200 m in most of the cases. Since the INSAT-3D/3DR fog product is derived under clear sky condition, fog detection during multilayer clouds is a challenging task. Results indicate that the Ceilometer can capture fog/low clouds effectively even in presence of single- and multilayer clouds. Remote-sensing technology can provide better opportunity by providing complete temporal and spatial coverage of fog. The present technique which incorporates both ground-based Ceilometer and space-based INSAT-3D/3DR measurements is observed to be more promising than other conventional methods for an improved detection and monitoring of fog/low clouds.     

Reduced Complexity Dynamic Systems Using Approximate Control Moments

This paper deals with reduction of computational complexities in dynamic systems. This paper develops a novel method of reducing complexities with use of control moments of the system. Though the proposed method is validated through channel estimation in this paper, the same can be equally applied to any other dynamic systems. Encouraging results given in this paper prove that the computational complexities can be reduced up to 10⁴ with a marginal affordable loss of performance.