An accurate and simplified small signal parameter extraction method for GaN HEMT

In this paper, development of a small signal model for 2 × 200 μm GaN HEMT based on the conventional 20-element model is presented. The proposed model presents a direct parameter extraction algorithm, instead of the hybrid optimization approach, that provides simplification, accuracy, and less computational complexity. The extrinsic elements are extracted using a modified cold pinch-off condition while discarding the unwanted forward biasing of the gate. The negative drain to source capacitance C_ds is also observed in the ohmic region (for smaller V_DS). An excellent agreement found between the measured and modeled data for a wide range of frequencies and bias values shows the effectiveness of the proposed approach. The proposed modeling technique is validated with a good agreement between the achieved bias dependency of intrinsic parameter values and the respective theoretical parameter values.     

Anti-Pathogenic and Technological Traits of Coagulase-Negative Staphylococci Isolated from Koozh,a Fermented Food Product of South India

The aim of this study was to assess the biopotency of fermented food product Koozh associated coagulase-negative staphylococci (CNS) against Mycobacterium tuberculosis H37Rv and non-mycobacterial pathogens, and to document some technologically relevant attributes. The anti-tubercular property of six CNS strains was exhibited using luciferase reporter phage assay. All strains revealed pronounced mycobactericidal property in terms of higher percentage of relative light unit reduction (> 90%). The antagonistic role of CNS strains against non-mycobacterial pathogens was evaluated using agar well diffusion assay. Staphylococcus hominis strain MANF2 was found to be the most effective against Staphylococcus aureus MTCC 96 and Yersinia enterocolitica MTCC 840 with arbitrary units of 1600.31 ± 22.3 and 1500.23 ± 22.3 AU/mL, respectively. Further, various technological attributes of staphylococci were determined using standard methodology. All strains exhibited promising carbohydrate fermentation ability at standard in vitro conditions. CNS strains were positive for exopolysaccharide production and expressed strain specific lipase activity. All the isolates lacked amylase activity and biofilm formation ability. All six CNS strains, particularly strain MANF2 exhibited significant rate of autolytic and catalase activity, and showed the potential to reduce nitrate to nitrite. Most importantly, these strains were categorized for safe utilization based on in vitro safety hazards test. Overall, the present investigation indicated the persuasive biopotency of CNS against M. tuberculosis H37Rv and non-mycobacterial pathogens, and revealed adaptable technological attributes that would make them relevant for formulation of staphylococcal starter cultures for the development of quality fermented food products.     

Small signal behavioral modeling technique of GaN high electron mobility transistor using artificial neural network: An accurate,fast, and reliable approach

This article reports a comparative study of two artificial neural network structures and associated variants used to describe and predict the behavior of 2 × 200 μm² GaN high electron mobility transistors (HEMTs), utilizing radiofrequency characterization. Two architectures namely multilayer perceptron and cascade feedforward, have been investigated in this work to develop the behavioral model. A study is conducted utilizing the two architectures, all trained using Levenberg‐Marquardt, in terms of accuracy, convergence rate, and generalization capability to develop the behavioral model of GaN HEMT. However, to ensure the robustness of the model, accuracy, convergence rate, time elapsed, and generalization capability of the proposed model is also tested under couple of training algorithms, activation functions, number of hidden layers and neuron embedded inside it, methods for initialization of weights and bias and certain other vital parameters playing vital role in influencing the model accuracy and effectiveness. An excellent agreement found between measured S‐parameters and the proposed model proves the effectiveness of the proposed approach and excellent prediction ability for a sweeping multibias set and broad frequency range of 1 to 18 GHz. Moreover, a very good generalization capability is also recorded under variation of crucial parameters of GaN HEMT‐based neural model.     

An insight into smartphone-based assistive solutions for visually impaired and blind people: issues,challenges and opportunities

Universal Access in the Information Society - Blind people are confronting a number of challenges in performing activities of daily life such as reading labels on a product, identification of...     

Improving social book search using structure semantics,bibliographic descriptions and social metadata

Social Book Search is an Information Retrieval (IR) approach that studies the impact of the Social Web on book retrieval. To understand this impact, it is necessary to develop a stronger classical baseline run by considering the contribution of query formulation, document representation, and retrieval model. Such a stronger baseline run can be re-ranked using metadata features from the Social Web to see if it improves the relevance of book search results over the classical IR approaches. However, existing studies neither considered collectively the contribution of the three mentioned factors in the baseline retrieval nor devised a re-ranking formula to exploit the collective impact of the metadata features in re-ranking. To fill these gaps in the literature, this research work first performs baseline retrieval by considering all three factors. For query formulation, it uses topic sets obtained from the discussion threads of LibraryThing. For book representation in indexing, it uses metadata from social websites including Amazon and LibraryThing. For the role of the retrieval model, it experiments with traditional, probabilistic, and fielded models. Second, it devises a re-ranking solution that exploits ratings, tags, reviews, and votes in reordering the baseline search results. Our best-performing retrieval methods outperform existing approaches on several topic sets and relevance judgments. The findings suggest that using all topic fields formulates the best search queries. The user-generated content gives better book representation if made part of the search index. Re-ranking the classical/baseline results improves relevance. The findings have implications for information science, IR, and Interactive IR.