Development of Solar Drying

Importance of Solar Drying is increasing worldwide, especially in areas where the use of the abundant, renewable and clean solar energy is essentially advantageous. In the developing countries and in rural areas the traditional open-air drying methods should be substituted by the more effective and more economic solar drying technologies.

R&D needs should be considered in the basic research and experimental fields; in performance measurement; in the modelling-simulation-design and testing. The international co-operation of experts should be improved and more efforts would be needed in the policy and in the public information.     

On the improvement of detection characteristics of trans-stilbene crystals by improving crystal perfection

Organic molecular scintillating crystals are noted for their good timing and particle discrimination process. Trans-stilbene is one such candidate noted for its good particle detection characteristics for the past five decades. Progressive strengthening of detection characteristics of trans-stilbene has been attempted by improving crystal perfection. A series of timing resolution studies have been carried out for the Bridgman grown trans-stilbene crystals under different experimental conditions. The results were compared with the previously reported values. Pulse shape discrimination process has been carried out for ²⁴¹Am and ²⁵²Cf sources and good discrimination has been obtained for gamma-alpha and gamma-neutron sources from the grown organic phosphor crystal. Electronic Publication     

Studies on nucleation kinetics of InxGa1−xN/GaN heterostructures

Nucleation kinetics during the growth of In_xGa_1−xN on a GaN substrate have been studied. The behavior of nonequilibrium between the In_xGa_1−xN and the GaN substrate has been analyzed, and hence, the expression derived for the stress-induced supercooling/superheating has been numerically evaluated. The maximum amount of stress-induced supercooling is found to be 1.017 K at x=0.12. These values are incorporated in the classical heterogeneous nucleation theory. Using the regular solution model, the interfacial tension between the nucleus and substrate and, hence, the interfacial tension between nucleus and mother phase and thermodynamical potential of the compounds have been calculated. The amount of driving force available for the nucleation has been determined for different compositions and degrees of supercooling. It has been shown that the value of the interaction parameter of InN-GaN plays a dominant role in nucleation and growth kinetics of In_xGa_1−xN on a GaN substrate. These values have been used to evaluate the nucleation parameters. It is shown that the nucleation barrier for the formation of a In_xGa_1−xN nucleus on a GaN substrate is minimum in the range of x=0.12 to x=0.17, and it has been qualitatively proved that good quality In_xGa_1−xN on GaN can be grown only in the range 0<x≤0.2.     

Modeling electric field transfer of excitation at cell junctions

The electric field (EF) model was first developed on a "breadboard" using physical electric components (e.g., resistors, capacitors, batteries) and was then modeled mathematically by a series of differential equations and matrix equations and simulated on a large computer (CDC-6400). The results obtained by the two methods agreed very closely. However, these two methods of analysis are quite cumbersome. Therefore, in order to simplify the EF simulation, we wanted to model it on the PSpice program. In this article we discuss how we succeeded in demonstrating transmission of excitation from cell to cell in cardiac muscle and smooth muscle based on EF transmission at the cell junctions.     

Optic flow field segmentation and motion estimation using a robustgenetic partitioning algorithm

Optic flow motion analysis represents an important family of visual information processing techniques in computer vision. Segmenting an optic flow field into coherent motion groups and estimating each underlying motion is a very challenging task when the optic flow field is projected from a scene of several independently moving objects. The problem is further complicated if the optic flow data are noisy and partially incorrect. In this paper, the authors present a novel framework for determining such optic flow fields by combining the conventional robust estimation with a modified genetic algorithm. The baseline model used in the development is a linear optic flow motion algorithm due to its computational simplicity. The statistical properties of the generalized linear regression (GLR) model are thoroughly explored and the sensitivity of the motion estimates toward data noise is quantitatively established. Conventional robust estimators are then incorporated into the linear regression model to suppress a small percentage of gross data errors or outliers. However, segmenting an optic flow field consisting of a large portion of incorrect data or multiple motion groups requires a very high robustness that is unattainable by the conventional robust estimators. To solve this problem, the authors propose a genetic partitioning algorithm that elegantly combines the robust estimation with the genetic algorithm by a bridging genetic operator called self-adaptation     

HamleDT: Harmonized multi-language dependency treebank

We present HamleDT—a HArmonized Multi-LanguagE Dependency Treebank. HamleDT is a compilation of existing dependency treebanks (or dependency conversions of other treebanks), transformed so that they all conform to the same annotation style. In the present article, we provide a thorough investigation and discussion of a number of phenomena that are comparable across languages, though their annotation in treebanks often differs. We claim that transformation procedures can be designed to automatically identify most such phenomena and convert them to a unified annotation style. This unification is beneficial both to comparative corpus linguistics and to machine learning of syntactic parsing.     

Using genetic algorithm to select the presentation order of training patterns that improves simplified fuzzy ARTMAP classification performance

The presentation order of training patterns to a simplified fuzzy ARTMAP (SFAM) neural network affects the classification performance. The common method to solve this problem is to use several simulations with training patterns presented in random order, where voting strategy is used to compute the final performance. Recently, an ordering method based on min–max clustering was introduced to select the presentation order of training patterns based on a single simulation. In this paper, another single simulation method based on genetic algorithm is proposed to obtain the presentation order of training patterns for improving the performance of SFAM. The proposed method is applied to a 40-class individual classification problem using visual evoked potential signals and three other datasets from UCI repository. The proposed method has the advantages of improved classification performance, smaller network size and lower training time compared to the random ordering and min–max methods. When compared to the random ordering method, the new ordering scheme has the additional advantage of requiring only a single simulation. As the proposed method is general, it can also be applied to a fuzzy ARTMAP neural network when it is used as a classifier.     

Optimized deep learning-based intrusion detection for wireless sensor networks

The technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature-inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.     

Error analysis of task performance with laptop in vibration environment

The present study investigates influences of vibration directions, vibration magnitudes, object sizes, object distances and angles of approach on producing errors while performing pointing and clicking activity on a laptop monitor similar to passengers working on moving trains. Attempts made outside the boundary of an object while performing the activity is registered as an error. The number of errors produced under different exposure conditions is computed. Higher vibration magnitudes, smaller size of the objects, and diagonal angular movements are found to create more errors and affected accuracy of the activity and hence showed difficulties to perform the activity.