Analog/RF and Power Performance Analysis of an Underlap DG AlGaN/GaN Based High-K Dielectric MOS-HEMT

Silicon - This paper exemplifies an exhaustive, figurative and subjective study on the RF performance and DC characteristics analysis of an Underlapped Double-Gate (U-DG) AlGaN/GaN...     

An Alternative Rotational Delivery Schedule for Improved Performance of Reservoir-based Canal Irrigation System

Alternative delivery scheduling approaches were tried to overcome the problem of low efficiency in eastern Indian irrigation projects. The features of the existing delivery schedules in the Left Bank Feeder Canal (LBFC) system of Kangsabati irrigation project, located in the state of West Bengal, India, have been studied. Four simulation scenarios were considered in the study. These are (1) actual canal scheduling with original transplanting date (S1), (2) Integrated Reservoir-based Canal Irrigation Model (IRCIM) scheduling for usual transplanting date (S2), (3) IRCIM scheduling for 5 days advanced transplanting date (S3), and (4) IRCIM scheduling for 5 days lagged transplanting date (S4). An attempt was made to develop a year-independent alternative delivery schedule based on the analysis of 11 years simulation results with all the above scenarios. For maximum number of simulation years, model performance was better for paddy transplanted on July 24, three irrigations from the reservoir between transplanting and harvesting, and restricting the release to minimum rule curve (MRC), i.e., maintaining the stage on any day above the minimum stage actually found in 16 years period. Hence, it was proposed as the year-independent alternative delivery schedule. This alternative delivery schedule saved 18.36% irrigation water over actual schedules, but increased the crop evapotranspiration by 0.61%. The proposed alternative delivery schedule also considered both the expected dry spells as well as critical growth periods of the crop during the simulation period. Thus, it was recommended to adopt the alternative delivery schedule instead of existing delivery schedule in the future years for the LBFC system of Kangsabati irrigation project.     

Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

Currently, breast cancer has been a major cause of deaths in women worldwide and the World Health Organization (WHO) has confirmed this. The severity of this disease can be minimized to the large extend, if it is diagnosed properly at an early stage of the disease. Therefore, the proper treatment of a patient having cancer can be processed in better way, if it can be diagnosed properly as early as possible using the better algorithms. Moreover, it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast tissues. To address the above said issues, this paper presents a hybrid model using the transfer learning to study the histopathological images, which help in detection and rectification of the disease at a low cost. Extensive dataset experiments were carried out to validate the suggested hybrid model in this paper. The experimental results show that the proposed model outperformed the baseline methods, with F-scores of 0.81 for DenseNet + Logistic Regression hybrid model, (F-score: 0.73) for Visual Geometry Group (VGG) + Logistic Regression hybrid model, (F-score: 0.74) for VGG + Random Forest, (F-score: 0.79) for DenseNet + Random Forest, and (F-score: 0.79) for VGG + Densenet + Logistic Regression hybrid model on the dataset of histopathological images.     

Shape-Tunable BaTiO3 Crystals Presenting Facet-Dependent Optical and Piezoelectric Properties

BaTiO₃ octahedra, edge-, and corner-truncated cubes, and cubes with four tunable sizes from 132 to 438 nm are synthesized by a solvothermal growth approach. Acetic acid treatment can cleanly remove BaCO₃ impurity. Rietveld refinement of X-ray diffraction patterns and Raman spectra help to confirm the particles have a tetragonal crystal structure. The crystals also exhibit size- and facet-dependent bandgap shifts. BaTiO₃ octahedra show larger piezoelectric, ferroelectric, and pyroelectric effects than truncated cubes and cubes. The measured dielectric constant differences should be associated with their various facet-dependent behaviors. Piezoelectric nanogenerators fabricated from BaTiO₃ octahedra consistently show the best performance than those containing truncated cubes and cubes. In particular, a nanogenerator with 30 wt.%-incorporated octahedra displays an open-circuit voltage of 23 V and short-circuit current of 324 nA. The device performance is also highly stable. The maximum output power reaches 3.9 µW at 60 MΩ. The fabricated nanogenerator can provide sufficient electricity to power light-emitting diodes. This work further demonstrates that various physical properties of semiconductor crystals show surface dependence.     

Stability analysis and controller design for Lur'e system with hysteresis nonlinearities: a negative-imaginary theory based approach

In this paper, absolute stability condition is investigated when a hysteresis nonlinearity is connected to a linear subsystem via positive feedback in Lur'e system framework. In particular, the nonlinearity is considered to belong to the time-invariant slope-restricted counter-clockwise hysteresis class. An absolute stability criterion is proposed in terms of the negative-imaginary system properties. In effect, the stability condition requires the linear subsystem to belong to the strongly strict negative-imaginary (SSNI) system class along with satisfying a matrix condition involving the DC-gain of the linear subsystem and the slope-upper-bounds of the nonlinearities. Compliance to the conditions guarantees asymptotic convergence of state-trajectories to an equilibrium set. Invoking the stability result and exploiting the relaxed minimality assumption of SSNI systems, a static state-feedback synthesis method is proposed to ensure absolute stability for such hysteretic systems. Tractable conditions in the form of linear matrix inequalities can be solved to obtain a stabilising state-feedback gain matrix. Finally, a numerical example of multi-input–multi-output system is presented to illustrate the usefulness of the proposed results.     

The human motion database: A cognitive and parametric sampling of human motion

Motion databases have a strong potential to guide progress in the field of machine recognition and motion-based animation. Existing databases either have a very loose structure that does not sample the domain according to any controlled methodology or too few action samples which limit their potential to quantitatively evaluate the performance of motion-based techniques. The controlled sampling of the motor domain in the database may lead investigators to identify the fundamental difficulties of motion cognition problems and allow the addressing of these issues in a more objective way. In this paper, we describe the construction of our Human Motion Database using controlled sampling methods (parametric and cognitive sampling) to obtain the structure necessary for the quantitative evaluation of several motion-based research problems. The Human Motion Database is organized into several components: the praxicon dataset, the cross-validation dataset, the generalization dataset, the compositionality dataset, and the interaction dataset. The main contributions of this paper include (1) a survey of human motion databases describing data sources related to motion synthesis and analysis problems, (2) a sampling methodology that takes advantage of a systematic controlled capture, denoted as cognitive sampling and parametric sampling, and (3) a novel structured motion database organized into several datasets addressing a number of aspects in the motion domain.     

A framework for ontology based decision support system for e-learning modules, business modeling and manufacturing systems

This paper identifies how ontology models can be vigorously used to define semantics and relationships in representing objects/modules for e-learning, business modeling support and manufacturing processing details. Further extraction of these relations by intelligent decision-support systems using data mining as a tool is discussed. The paper envisages the possibility of establishing a common solution platform for product development and customization leading to increased profitability and better resource utilization. It showcases ways to link these different ontological models leading to cross platform compatibility. It also tries to explore manufacturer-customer relationship and using them to provide quality analysis methods for further improvement in the product processing model.     

Selective Protection of Carbonyl Compounds over Nano-sized Nickel Catalysts

Abstract An efficient method for the preparation of 1,3-dithiolanes of aliphatic and both activated and deactivated aromatic carbonyl compounds with 1,2-ethanedithiol in the presence of a catalytic amount of inexpensive, easily recyclable, monodispersed, chemoselective Ni-nanoparticles having high TON and TOF is reported. An efficient method for the chemoselective thioacetalization of ketones in the presence of aldehydes using Ni-nanoparticles is also reported in this article. Our reaction is kinetically controlled and the method is relatively expedient, avoids the use of cost-prohibitive reagents, high temperatures and leads to excellent yield. Graphical Abstract Selective Protection of Carbonyl Compounds Over Nano-sized Nickel Catalysts Ajeet Kumar,^a Santosh Kumar,^a Amit Saxena,^a Arnab De,^b Subho Mozumdar^a* ^aDepartment of Chemistry, University of Delhi, Delhi, 110007, India; ^b Department of Chemistry, Indiana University, Bloomington, USA; subhoscom@yahoo.co.in An efficient method for the preparation of 1,3-dithiolanes of aliphatic and both activated and deactivated aromatic carbonyl compounds with 1,2-ethanedithiol in the presence of a catalytic amount of inexpensive, easily recyclable, monodispersed, chemoselective Ni-nanoparticles having high TON and TOF is reported. We report an efficient method for the chemoselective thioacetalization of ketones in the presence of aldehydes using Ni-nanoparticles. Our reaction is kinetically controlled and the method is relatively expedient, avoids the use of cost-prohibitive reagents, high temperatures and leads to excellent yield.