Impact of adhesive ingredients on adhesion between rubber and brass-plated steel wire in tire

Proficiency on underlying mechanism of rubber-metal adhesion has been increased significantly in the last few decades. Researchers have investigated the effect of various ingredients, such as hexamethoxymethyl melamine, resorcinol, cobalt stearate, and silica, on rubber-metal interface. The role of each ingredient on rubber-metal interfacial adhesion is still a subject of scrutiny. In this article, a typical belt skim compound of truck radial tire is selected and the effect of each adhesive ingredient on adhesion strength is explored. Out of these ingredients, the effect of cobalt stearate is found noteworthy. It has improved adhesion strength by 12% (without aging) and by 11% (humid-aged), respectively, over control compound. For detailed understanding of the effect of cobalt stearate on adhesion, scanning electron microscopy and energy dispersive spectroscopy are utilized to ascertain the rubber coverage and distribution of elements. X-ray photoelectron spectroscopy results helped us to understand the impact of Cu_XS layer depth on rubber-metal adhesion. The depth profile of the Cu_XS layer was found to be one of the dominant factors of rubber-metal adhesion retention. Thus, this study has made an attempt to find the impact of different adhesive ingredients on the formation of Cu_XS layer depth at rubber-metal interface and establish a correlation with adhesion strength simultaneously.     

LGVTON: a landmark guided approach for model to person virtual try-on

Multimedia Tools and Applications - In this paper, we propose a Landmark Guided Virtual Try-On (LGVTON) method for clothes, which aims to solve the problem of clothing trials on e-commerce...     

Analysis of Sub-Threshold Adiabatic Logic Model Using Junctionless MOSFET for Low Power Application

Silicon - In this paper Junctionless Double Gate MOSFET based Efficient Charge Recovery Logic (JL-ECRL) circuits have been driven in sub-threshold regime for the first time in literature to...     

Robust vectorization method for electrical circuit drawings using component morphology

Pattern Analysis and Applications - Decomposition and representation of electrical circuit drawings to a suitable vector form has widespread applications related to data compression, storage,...     

Local jet pattern: a robust descriptor for texture classification

Multimedia Tools and Applications - Methods based on locally encoded image features have recently become popular for texture classification tasks, particularly in the existence of large intra-class...     

A spiral shaped regenerative microfluidic fuel cell with Ni‐C based porous electrodes

Microchannel geometry, electrode surface area, and better fuel utilization are important aspects of the performance of a microfluidic fuel cell (MFC). In this communication, a membraneless spiral‐shaped MFC fabricated with Ni as anode and C as a cathode supported over a porous filter paper substrate is presented. Vanadium oxychloride and dilute sulfuric acid solutions are used as fuel and electrolyte, respectively, in this fuel cell system. The device generates a maximum open‐circuit voltage of ~1.2 V, while the maximum energy density and current density generated from the fuel cell are ~10 mW cm^?2 and ~51 mA cm^?2, respectively. The cumulative energy density generated from the device after five cycles are measured as ~200 mW after regeneration of the fuel by applying external voltage. The spiral design of the fuel cell enables improved fuel utilization, rapid diffusive transport of ions, and in‐situ regeneration of the fuel. The present self‐standing spiral‐shaped MFC will eliminate the challenges associated with two inlet membrane‐less fuel cells and has the potential to scale up for commercial application in portable energy generation.     

FPGA Architecture for 2D Discrete Fourier Transform Based on 2D Decomposition for Large-sized Data

Applications based on Discrete Fourier Transforms (DFT) are extensively used in several areas of signal and digital image processing. Of particular interest is the two-dimensional (2D) DFT which is more computation- and bandwidth-intensive than the one-dimensional (1D) DFT. Traditionally, a 2D DFT is computed using Row-Column (RC) decomposition, where 1D DFTs are computed along the rows followed by 1D DFTs along the columns. Both application specific and reconfigurable hardware have utilized this scheme for high-performance implementations of 2D DFT. However, architectures based on RC decomposition are not efficient for large input size data due to memory bandwidth constraints. In this paper, we propose an efficient architecture to implement 2D DFT for large-sized input data based on a novel 2D decomposition algorithm. This architecture achieves very high throughput by exploiting the inherent parallelism due to the algorithm decomposition and by utilizing the row-wise burst access pattern of the external memory. A high throughput memory interface has been designed to enable maximum utilization of the memory bandwidth. In addition, an automatic system generator is provided for mapping this architecture onto a reconfigurable platform of Xilinx Virtex-5 devices. For a 2K ×2K input size, the proposed architecture is 1.96 times faster than RC decomposition based implementation under the same memory constraints, and also outperforms other existing implementations.     

Algorithm and Parallel Implementation of Particle Filtering and its Use in Waveform-Agile Sensing

Sequential Monte Carlo particle filters (PFs) are useful for estimating nonlinear non-Gaussian dynamic system parameters. As these algorithms are recursive, their real-time implementation can be computationally complex. In this paper, we analyze the bottlenecks in existing parallel PF algorithms, and propose a new approach that integrates parallel PFs with independent Metropolis–Hastings (PPF-IMH) resampling algorithms to improve root mean-squared estimation error (RMSE) performance. We implement the new PPF-IMH algorithm on a Xilinx Virtex-5 field programmable gate array (FPGA) platform. For a one-dimensional problem with 1,000 particles, the PPF-IMH architecture with four processing elements uses less than 5% of a Virtex-5 FPGA’s resource and takes 5.85 μs for one iteration. We also incorporate waveform-agile tracking techniques into the PPF-IMH algorithm. We demonstrate a significant performance improvement when the waveform is adaptively designed at each time step with 6.84 μs FPGA processing time per iteration.