Trends and Profile in Enzyme Engineering Research during 1971–98

There is sufficient evidence to prove the potential of immobilized enzymes to be commercially successful in many industries, but a survey of products in biotechnology and some reports indicate its limited success. To visualize the factual status, the present study looks into trends and profiles of this field using scientometric methods. The salient results show a steady decline in outputs in the form of patents and publications since 1993 along with a decline in the number of groups from academics and industries. Among the countries involved, there is also a decline, though USA and Japan show some strength in basic and applied research, respectively.     

Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

Accurate node localization in wireless sensor networks (WSNs) is an essential for many networking protocols like clustering, routing, and network map building. The classical localization techniques such as multilateration and optimization‐based least square localization (OLSL) techniques estimate position of unknown node (UN) from the distance measured between all anchor nodes (ANs) and UNs. On the other hand, node localization using fixed terrestrial ANs suffers from poor localization accuracy because the ground to ground (GG) channel link is not reliable. By contrast, the mobile anchor deployed in unmanned aerial vehicle (UAV) provides high localization accuracy through reliable air to ground (AG) channel link. Still, the nonlinear distortion introduced in the wireless channel makes the distance measurement noisy. This noisy distance measurement also limits localization accuracy of classical localization techniques. Hence, the highly nonlinear artificial neural network (ANN) models such as multilayer perceptron (MLP) models can be applied effectively for node localization in UAV‐assisted WSNs. However, the MLP suffers from slow training speed, which limits its usage in real‐time applications. So, the extreme learning machine (ELM) is found to be a better alternative because it works on empirical error minimization theory, and its learning process requires only a single iteration. The detailed simulation analysis supports the proposed ELM localization scheme in terms of both localization accuracy and computational complexity.     

Implementation of an Efficient Artificial Bee Colony Algorithm for Node Localization in Unmanned Aerial Vehicle Assisted Wireless Sensor Networks

Node localization is a fundamental task in wireless sensor networks as it is useful for several localization based protocols and applications. Node localization using Global Poisoning System (GPS) employed fixed terrestrial anchor nodes suffers from high deployment cost and poor localization accuracy in GPS denied locations. These issues can be easily handled by deploying movable Unmanned Aerial Vehicles (UAVs). A movable UAV equipped with a single GPS module virtually increases number of anchor nodes and localizes a node at different locations. Hence, UAVs are cost effective and also provides high localization accuracy. As the flying altitude of UAV greatly influence localization accuracy, the present work firstly optimizes the flying height and then the node localization is defined as least square optimization problem using this optimal height. Since the classical received signal strength indicator based multilateration results high localization error, the least square localization using optimization techniques is found to be better alternative. The recently proposed Artificial Bee Colony (ABC) algorithm is a powerful optimization technique that can be applied for this optimization problem to achieve high accuracy. Thus, this paper aims at designing an ABC localization technique using UAV anchors to achieve minimum localization error. Further, we provide detailed simulation analysis to support the proposed ABC localization scheme.

     

Bridging semantic gap between high-level and low-level features in content-based video retrieval using multi-stage ESN–SVM classifier

Content-based video retrieval system aims at assisting a user to retrieve targeted video sequence in a large database. Most of the search engines use textual annotations to retrieve videos. These types of engines offer a low-level abstraction while the user seeks high-level semantics. Bridging this type of semantic gap in video retrieval remains an important challenge. In this paper, colour, texture and shapes are considered to be low-level features and motion is a high-level feature. Colour histograms convert the RGB colour space into YcbCr and extract hue and saturation values from frames. After colour extraction, filter mask is applied and gradient value is computed. Gradient and threshold values are compared to draw the edge map. Edges are smoothed for sharpening to remove the unnecessary connected components. These diverse shapes are then extracted and stored in shape feature vectors. Finally, an SVM classifier is used for classification of low-level features. For high-level features, depth images are extracted for motion feature identification and classification is done via echo state neural networks (ESN). ESN are a supervised learning technique and follow the principle of recurrent neural networks. ESN are well known for time series classification and also proved their effective performance in gesture detection. By combining the existing algorithms, a high-performance multimedia event detection system is constructed. The effectiveness and efficiency of proposed event detection mechanism is validated using MSR 3D action pair dataset. Experimental results show that the detection accuracy of proposed combination is better than those of other algorithms     

Performance of speaker localization using microphone array

Speaker localization is a technique to locate and track an active speaker from multiple acoustic sources using microphone array. Microphone array is used to improve the speech quality of recorded speech signal in meeting room and other places. In this work, the time delay estimation between source and each microphone is calculated using a localization method called time differences of arrival (TDOA). TDOA localization consists of two steps namely (a) a time delay estimator and (b) a localization estimator. For time delay estimation, the generalized cross-correlation using phase transform, the generalized cross correlation using maximum likelihood, linear prediction (LP) residual and the Hilbert envelope of the LP residual are chosen for estimating the location of a person. A new speaker localization algorithm known as group search optimization (GSO) algorithm is proposed. The performance of this algorithm is analyzed and compared with Gauss–Newton nonlinear least square method and genetic algorithm. Experimental results show that the proposed GSO method outperforms the other methods in terms of mean square error, root mean square error, mean absolute error, mean absolute percentage error, euclidean distance and mean absolute relative error.     

Characterization of zinc-nickel alloy electrodeposits obtained from sulphamate bath containing substituted aldehydes

Zinc alloy offers superior sacrificial protection to steel as the alloy dissolves more slowly than pure zinc. The degree of protection and the rate of dissolution depend on the alloying metal and its composition. Zinc-nickel alloy may also serve as at less toxic substitute for cadmium. In this paper the physico-chemical characterization of zinc-nickel electrodeposits obtained from sulphamate bath containing substituted aldehydes was carried out using hardness testing, X-ray diffraction, and corrosion resistance measurements. The corrosion behaviour of these samples in a 3.5% NaCl solution was examined. The decrease inI _corr and high charge transfer resistance indicated the improved corrosion resistance of these deposits.     

Radial basis function-based node localization for unmanned aerial vehicle-assisted 5G wireless sensor networks

Neural Computing and Applications - Localization or positioning of wireless sensor nodes is an essential task for a wide range of applications in wireless sensor networks-based fifth generation...     

Studies on optical and electrical properties of SILAR-deposited CuO thin films

CuO thin films prepared by SILAR technique using aqueous solutions of various pH values and their characterization are presented in this report. The pH dependence on structural, morphological, optical and electrical properties of the prepared films is studied. Thickness of films is found to vary in between 0.52 and 0.82 µm. Microstructural parameters such as crystallite size, strain and dislocation density of the film have been evaluated. The crystallographic behaviour of the film has shown that all the coated thin films are in monoclinic structure with (002) preferred orientation. The size of the crystallites is found to increase with the pH values. Surface morphological behaviour of the films prepared using different pH values are analysed. Optical properties of the films were analysed from absorption and transmittance studies of CuO thin films. Band gap energy values of CuO thin films have been found to decrease from 2.12 to 1.91 eV with increasing pH values of the solution. The thin film formed at a solution pH 11 has shown least resistivity and high carrier concentration. The I-V characteristics of n-Si/p-CuO heterojunction under 200-watts halogen lamp illumination show open-circuit voltage of ~ 0.37 V and short-circuit current of ~1.02 × 10^?6 A.     

Pyreno-chalcone dendrimers as an additive in the redox couple of dye-sensitized solar cells

Novel pyreno-chalcone dendrimers 1, 2, and 3 were synthesized and their ability to act as an additive in the redox couple (I⁻/I₃ ⁻) of dye-sensitized nanocrystalline TiO₂ solar cell has been tested. The redox couple doped with pyreno-chalcone dendrimer 3 gave a short circuit photocurrent density (J _sc) of 7.40 mA/cm², open circuit voltage (V _oc) of 820 mV, and a fill factor of 0.51, corresponding to an overall conversion efficiency (η) of 7.89% under 40 mW/cm² irradiation.     

Data clustering using <Emphasis Type="Italic">K</Emphasis>-Means based on Crow Search Algorithm

Cluster analysis is one of the popular data mining techniques and it is defined as the process of grouping similar data. K-Means is one of the clustering algorithms to cluster the numerical data. The features of K-Means clustering algorithm are easy to implement and it is efficient to handle large amounts of data. The major problem with K-Means is the selection of initial centroids. It selects the initial centroids randomly and it leads to a local optimum solution. Recently, nature-inspired optimization algorithms are combined with clustering algorithms to obtain the global optimum solution. Crow Search Algorithm (CSA) is a new population-based metaheuristic optimization algorithm. This algorithm is based on the intelligent behaviour of the crows. In this paper, CSA is combined with the K-Means clustering algorithm to obtain the global optimum solution. Experiments are conducted on benchmark datasets and the results are compared to those from various clustering algorithms and optimization-based clustering algorithms. Also the results are evaluated with internal, external and statistical experiments to prove the efficiency of the proposed algorithm.