A fuzzy logic rule-based automatic target recognizer

Methods for modeling and managing uncertainty in computer vision systems have received increased attention in recent years. Automatic target recognition is one application area of computer vision where the demands are particularly acute. In this article, fuzzy logic is proposed as a means of handling uncertainty in an expert system structure for automatic target recognition. A new technique for logical inference is described which is well-suited for this type of application. A prototype system has been developed and tested on multisensor and temporal images. the results are compared to a similar expert system which used a numeric uncertainty calculus.     

Variation in structural and dielectric properties of co-precipitated nanoparticles strontium ferrites due to value of pH

Nanoparticles of strontium ferrites with nominal composition SrFe₁₂O₁₉ were prepared by co-precipitation method, by decreasing pH from 13 to 8 with a regular step of 1. The secondary phase of α-Fe₂O₃ was increased with the decrease in pH. The crystallite size estimated from X-rays diffraction data was in the range 52-70 nm, which is much smaller than that already reported. Most of the particles formed had hexagonal structure, as observed by the scanning electron microscopy. Particle size and dielectric loss were increased where as dc electrical resistivity and dielectric constant were decreased with decrease in pH. The results show that the material synthesized with higher pH is phase pure and is potentially more suitable for high frequency applications.     

Tunable ferroelectric impedance matching networks and their impact on digital modulation system performance

In this paper the bit error rate performance and error vector magnitude of a tunable impedance matching network is analyzed assuming QPSK, 16-QAM and 64-QAM digital modulation schemes. The characterized tunable impedance matching network is based on barium–strontium–titanate ferroelectric thick-film varactors. Inherent dispersive behavior is subsumed into the forward transmission of the passive device. Due to this nonlinear phase response, in general to maximize the overall system performance, an agile tuning of the varactor values is demonstrated, taking into account the phase and group delay of s₂₁ parameter. Detailed signal simulation results based on measured data of a testbed are presented. The influence of varying matched impedances on the tuning behavior with different modulation bandwidths is discussed at a center frequency of 1.9 GHz.     

MRI-compatible intensity-modulated force sensor for cardiac catheterization procedures

This paper presents a novel, magnetic resonance imaging (MRI)-compatible, force sensor suitable for cardiac catheterization procedures. The miniature, fiber-optic sensor is integrated with the tip of a catheter to allow the detection of interaction forces with the cardiac walls. The optical fiber light intensity is modulated when a force acting at the catheter tip deforms an elastic element, which, in turn, varies the distance between a reflector and the optical fiber. The tip sensor has an external diameter of 9 Fr (3?mm) and can be used during cardiac catheterization procedures. The sensor is able to measure forces in the range of 0-0.85?N, with relatively small hysteresis. A nonlinear method for calibration is used and real-time MRI in vivo experiments are carried out, to prove the feasibility of this low-cost sensor, enabling the detection of catheter-tip contact forces under dynamic conditions.     

Modulation Based Combination of High Level Features Generated from SCCF & Contourlet Transforms for CBIR Applications

The need for suitable and cost-effective technologies rise with the growth of the internet of things (IoT) applications. These aim at handling voluminous data transmission in addition to minimum energy and latency cost constraints. LoRa networks are recommended for applications in confined spaces, long ranges, and less battery consumption requirements. However, the end devices in these networks communicate to all gateways in their ranges, thereby expediting energy unproductively in redundant transmissions. In our article, we explore the possibilities of whether LoRa networks could employ the advantages of clustering and propose two algorithms, path-based and data-centric, for such networks. We suggest that LoRaWAN technology with clustering can be apt for long-range, low power consumption IoT applications in the future. We study the impact of network density, node range, and cluster range on the energy consumption in data transmissions. The algorithms are compared with the inherent star-based communication of LoRa networks based on energy consumed, and our results show that, for dense deployments, clustering becomes advantageous.

     

On the practicability of a new bio sorbent: Lasani sawdust and coconut coir for cleanup of oil spilled on water

In this article, two bio-sorbents have been selected: lasani sawdust (LS) (a new bio-based material) and coconut coir (CC) for the removal of used motor oil from the aqueous phase. The physical nature of the materials was characterized using Fourier Transform Infrared Spectroscopy and Constitutional Analysis of lignin and cellulose. The adsorption process was evaluated using various kinetic and adsorption models. The evaluated sorption capacities for coconut coir and lasani sawdust were 12.82?g g^?1 and 0.36?g g^?1, respectively. Maximum sorption of oil from the aqueous solution conveniently took place in 20?minutes. To ascribe statistically which model describes the adsorption phenomenon best, Root Mean Square Error (RMSE) and Average Relative Error (ARE) were used. The kinetics of the adsorption was best described by Pseudo-second order. Similarly, Langmuir isotherm model had the least value for the two error functions and a higher q_max value for coir than for lasani. It was concluded that the increased absorptive ability of coir over lasani was due to the difference in the composition of lignin and cellulose of the two materials.     

Effect of polishing parameters on abrasive free chemical mechanical planarization of semi-polar(1122) aluminum nitride surface

An abrasive free chemical mechanical planarization(AFCMP) of semi-polar(1122) AlN surface has been demonstrated. The effect of slurry pH, polishing pressure, and platen velocity on the material removal rate(MRR) and surface quality(RMS roughness) have been studied. The effect of polishing pressure on the AFCMP of the(1122) AlN surface has been compared with that of the(1122) AlGaN surface. The maximum MRR has been found to be ~562 nm/h for the semi-polar(1122) AlN surface, under the experimental conditions of 38 kPa pressure, 90 rpm platen velocity, 30 rpm carrier velocity, slurry pH 3 and 0.4 M oxidizer concentration. The best root mean square(RMS) surface roughness of ~1.2 nm and ~0.7 nm, over a large scanning area of 0.70×0.96 mm², has been achieved on AFCMP processed semi-polar(1122) AlN and(AlGaN) surfaces using optimized slurry chemistry and processing parameters.     

Genomics and Epigenomics of Gestational Diabetes Mellitus: Understanding the Molecular Pathways of the Disease Pathogenesis

One of the most common complications during pregnancy is gestational diabetes mellitus (GDM), hyperglycemia that occurs for the first time during pregnancy. The condition is multifactorial, caused by an interaction between genetic, epigenetic, and environmental factors. However, the underlying mechanisms responsible for its pathogenesis remain elusive. Moreover, in contrast to several common metabolic disorders, molecular research in GDM is lagging. It is important to recognize that GDM is still commonly diagnosed during the second trimester of pregnancy using the oral glucose tolerance test (OGGT), at a time when both a fetal and maternal pathophysiology is already present, demonstrating the increased blood glucose levels associated with exacerbated insulin resistance. Therefore, early detection of metabolic changes and associated epigenetic and genetic factors that can lead to an improved prediction of adverse pregnancy outcomes and future cardio-metabolic pathologies in GDM women and their children is imperative. Several genomic and epigenetic approaches have been used to identify the genes, genetic variants, metabolic pathways, and epigenetic modifications involved in GDM to determine its etiology. In this article, we explore these factors as well as how their functional effects may contribute to immediate and future pathologies in women with GDM and their offspring from birth to adulthood. We also discuss how these approaches contribute to the changes in different molecular pathways that contribute to the GDM pathogenesis, with a special focus on the development of insulin resistance.     

The effect of pH,solid content,water chemistry and ore mineralogy on the galvanic interactions between chalcopyrite and pyrite and steel balls

The role of pH, solid content, water chemistry and ore mineralogy on the galvanic interactions between chalcopyrite and pyrite and low alloy steel balls were investigated in the grinding of Sarcheshmeh porphyry copper sulfide ore. All these factors strongly affect the galvanic current between the minerals and the steel during the grinding process. The galvanic current density decreased as the solution pH and percent solids increased. In addition, changing the water in the ball mill from tap to distilled water reduced the galvanic current between the minerals and the balls. Potentiodynamic polarization curves showed that pyrite and chalcopyrite demonstrated typical active-passive-transpassive anodic behavior in the grinding of copper ore. However, the nature of their transitions from the active to the passive state differed. This behavior was not seen in the grinding of pure minerals. In addition, an EDTA extraction technique was employed to quantify the amount of oxidized iron in the mill discharge. The amount of extractable iron was influenced by the same experimental factors and in the same way as the galvanic current.