Sleep Pattern Study with Respect to Binaural Beats Using Sensors and Mobile Application

Wireless Personal Communications - Sleep disorders are common among people in the present lifestyle and this may occur due to irregular sleep patterns. The disordered sleep pattens arise due to...     

Improving photostriction of KNN-based ceramics by MnO2 additive and polarization

The effects of sintering additives on photostriction properties have been rarely reported and the polarization effect of KNN-based ceramics on the photostriction characteristics remains uncertain. In the present work, a highly feasible approach has been adopted to obtain improved photostrictive coefficient (α = 2.4 × 10^?9 m²/W) for KNN-based ceramics by adding appropriate amount (x = 0.5 wt%) of MnO₂ as sintering additives into 0.97(K_0.5Na_0.5)NbO₃-0.03(La_0.51Na_0.49)(Zr_0.54Ni_0.46)O₃ (97KNN-3LNNZ) ceramics. The appropriate amount of MnO₂ (x ≤ 0.5 wt%) in the ceramics does not alter the inherent multi-phase structure and results in uniform grain growth of the ceramics. The ferroelectricity and piezoelectricity can be optimized by the “softening” effect of Mn²⁺ and Mn³⁺ in KNN-based ceramics. The addition of MnO₂ increases energy band gap and also optimizes the optical absorption properties in the near-infrared band. 97KNN-3LNNZ+0.5 wt% MnO₂ ceramics show optimum overall performance. In addition to this, for x = 0.5 wt% sample, the power-dependent Raman spectra of ceramics measured before and after the electrical poling shows that the prior E-field poling can enhance the red shift corresponding to F_2g bending vibration mode. Subsequently, there are changes in bond angle of elements induced by light in oxygen octahedron. These phenomena play active roles in getting a deep understanding of the underlying mechanisms of photostriction for ferroelectric ceramics.     

A semi-supervised recurrent neural network for video salient object detection

Neural Computing and Applications - A semi-supervised, one-dimensional recurrent neural network (RNN) approach called RVS has been proposed in this paper for video salient object detection. The...     

Performance analysis of wideband microstrip patch antenna with amplified radiation

Microsystem Technologies - The problem of narrow bandwidth is a major concern in general for MPA (Microstrip Patch Antenna). In this study, we have performed certain methods to enhance the antenna...     

All-digital TX frequency synthesizer and discrete-time receiver for Bluetooth radio in 130-nm CMOS

We present a single-chip fully compliant Bluetooth radio fabricated in a digital 130-nm CMOS process. The transceiver is architectured from the ground up to be compatible with digital deep-submicron CMOS processes and be readily integrated with a digital baseband and application processor. The conventional RF frequency synthesizer architecture, based on the voltage-controlled oscillator and the phase/frequency detector and charge-pump combination, has been replaced with a digitally controlled oscillator and a time-to-digital converter, respectively. The transmitter architecture takes advantage of the wideband frequency modulation capability of the all-digital phase-locked loop with built-in automatic compensation to ensure modulation accuracy. The receiver employs a discrete-time architecture in which the RF signal is directly sampled and processed using analog and digital signal processing techniques. The complete chip also integrates power management functions and a digital baseband processor. Application of the presented ideas has resulted in significant area and power savings while producing structures that are amenable to migration to more advanced deep-submicron processes, as they become available. The entire IC occupies 10 mm/sup 2/ and consumes 28 mA during transmit and 41 mA during receive at 1.5-V supply.     

Ultra-shallow junction formation by spike annealing in a lamp-based or hot-walled rapid thermal annealing system: effect of ramp-up rate

Ultra-shallow p-type junction formation has been investigated using 1050°C spike anneals in lamp-based and hot-walled rapid thermal processing (RTP) systems. A spike anneal may be characterized by a fast ramp-up to temperature with only a fraction of a second soak-time at temperature. The effects of the ramp-up rate during a spike anneal on junction depth and sheet resistance were measured for rates of 40, 70 and 155°C/s in a lamp-based RTP, and for 50 and 85°C/s in a hot-walled RTP. B⁺ implants of 0.5, 2 and 5 keV at doses of 2×10¹⁴ and 2×10¹⁵ cm⁻² were annealed. A significant reduction in junction depth was observed at the highest ramp-up rate for the shallower 0.5-keV B implants, while only a marginal improvement was observed for 2- and 5-keV implants. It is concluded that high ramp-up rates can achieve the desired ultra-shallow junctions with low sheet resistance but only when used in combination with spike anneals and the lowest energy implants.     

Approximate dependencies in database systems

Functional dependencies are the most commonly used approach for capturing real-word integrity constraints which are to be reflected in a database. There are, however, many useful kinds of constraints, especially approximate ones, that cannot be represented correctly by functional dependencies and therefore are enforced via programs which update the database, if they are enforced at all. This tends to make such constraints invisible since they are not an explicit part of the database, increasing maintenance problems and the likelihood of inconsistencies. We propose a new approach, cluster dependencies, as a way to enforce approximate dependencies. By treating equality as a fuzzy concept and defining appropriate similarity measures, it is possible to represent a broad range of approximate constraints directly in the database by storing and accessing cluster definitions. We discuss different interpretations of cluster dependencies and describe the additional data structures needed to enforce them. We also contrast them with an existing approach, fuzzy functional dependencies, which are much more limited in the kind of approximate constraints they can represent.     

A new finite element model for welding heat sources

A mathematical model for weld heat sources based on a Gaussian distribution of power density in space is presented. In particular a double ellipsoidal geometry is proposed so that the size and shape of the heat source can be easily changed to model both the shallow penetration arc welding processes and the deeper penetration laser and electron beam processes. In addition, it has the versatility and flexibility to handle non-axisymmetric cases such as strip electrodes or dissimilar metal joining. Previous models assumed circular or spherical symmetry. The computations are performed with ASGARD, a nonlinear transient finite element (FEM) heat flow program developed for the thermal stress analysis of welds.* Computed temperature distributions for submerged arc welds in thick workpieces are compared to the measured values reported by Christensen¹ and the FEM calculated values (surface heat source model) of Krutz and Segerlind.² In addition the computed thermal history of deep penetration electron beam welds are compared to measured values reported by Chong.³ The agreement between the computed and measured values is shown to be excellent.     

Deep Convolutional Neural Network (Falcon) and transfer learning‐based approach to detect malarial parasite

Deep learning models have already benchmarked its demonstration in the applications of Medical Sciences. Present day medical industries suffer due to deadly disease such as malaria etc. As per the report from World Health Organization (WHO), it is noted that the amount of caution and care taken per patient by a human doctor to cure malaria is decreasing. To address this issue, this paper proposes an automated solution for the detection of malaria from the real-time image. The key idea of the proposed solution is to use a Deep Convolutional Neural Network (DCNN) called “Falcon” to detect the parasitic cells from blood smeared slide images of Malaria Screener. Furthermore, the class accuracy of the given dataset samples is maintained in order to model not only the normal case but to accurately predict the presence of malaria as well. Experimental results confirms that the model does not possess overfitting, class imbalance, and provides a reasonable classification report and trustworthy accuracy with 95.2?% when compared to the state-of-the-art Convolutional Neural Network (CNN) models.