Neuro-fuzzy based time-delay estimation using DCT coefficients

In this research, a neuro-fuzzy system (NFS) is introduced into the problem of time-delay estimation. Time-delay estimation deals with the problem of estimating a constant time delay embedded within a received noisy and delayed replica of a known reference signal. The received signal is filtered and discrete cosine transformed into DCT coefficients. The time delay is now encoded into the DCT coefficients. Only those few DCT coefficients which possess high sensitivity to time-delay variations are used as input to the NFS. The NFS is used for time-delay estimation because of its ability of learning highly nonlinear relationships and encoding them into its internal structure. This capability is used in learning the nonlinear relationship between the DCT coefficients of a delayed reference signal and the time delay embedded into this signal. The NFS is trained with several hundred training sets in which the highly sensitive DCT coefficients were applied as input, and the corresponding time delay was the output. In the testing phase, DCT coefficients of delayed signals were applied as input to the NFS and the system produced accurate time-delay estimates, as compared to those obtained by the classical cross-correlation technique.     

Application of polymeric flocculant for enhancing settling of the pond ash particles and water drainage from hydraulically stowed pond ash

Delayed settling of the ash particles and poor drainage of water from the pond ash are the major problems faced during the hydraulic stowing of pond ash. In this study the effect of polymeric flocculant on settling of the ash particles and drainage of water during pond ash stowing are investigated. In addition, the parameters, viz. drainage and absorption of water during pond ash stowing are quantified by stowing a mine goaf model with pond ash slurries of five different concentrations added with and without flocculant. The study revealed that addition of only 5 × 10^?6 of Sodium Carboxymethyl Cellulose (Na-CMC) flocculant with the pond ash slurries during stowing offers best result in terms of quicker settling of the ash particles and enhanced water drainage from the hydraulically stowed pond ash. Besides, it resulted in drainage of more than 85% of the total water used in the initial 45 min of stowing. The improvement in drainage is caused due to coagulation and flocculation of the pond ash particles because of charge neutralization and particle–particle bridging. This study may provide a basis for estimating the drainage and absorption of water during the real pond ash stowing operation in underground mines.     

An improved microfluidics approach for monitoring real-time interaction profiles of ultrafast molecular recognition

Our study illustrates the development of a microfluidics (MF) platform combining fluorescence microscopy and femtosecond/picosecond-resolved spectroscopy to investigate ultrafast chemical processes in liquid-phase diffusion-controlled reactions. By controlling the flow rates of two reactants in a specially designed MF chip, sub-100 ns time resolution for the exploration of chemical intermediates of the reaction in the MF channel has been achieved. Our system clearly rules out the possibility of formation of any intermediate reaction product in a so-called fast ionic reaction between sodium hydroxide and phenolphthalein, and reveals a microsecond time scale associated with the formation of the reaction product. We have also used the developed system for the investigation of intermediate states in the molecular recognition of various macromolecular self-assemblies (micelles) and genomic DNA by small organic ligands (Hoechst 33258 and ethidium bromide). We propose our MF-based system to be an alternative to the existing millisecond-resolved "stopped-flow" technique for a broad range of time-resolved (sub-100 ns to minutes) experiments on complex chemical∕biological systems.     

Measurement technique for the thermal properties of thin-film diaphragms embedded in calorimetric flow sensors

In diaphragm-based micromachined calorimetric flow sensors, convective heat transfer through the test fluid competes with the spurious heat shunt induced by the thin-film diaphragm where heating and temperature sensing elements are embedded. Consequently, accurate knowledge of thermal conductivity, thermal diffusivity, and emissivity of the diaphragm is mandatory for design, simulation, optimization, and characterization of such devices. However, these parameters can differ considerably from those stated for bulk material and they typically depend on the production process. We developed a novel technique to extract the thermal thin-film properties directly from measurements carried out on calorimetric flow sensors. Here, the heat transfer frequency response from the heater to the spatially separated temperature sensors is measured and compared to a theoretically obtained relationship arising from an extensive two-dimensional analytical model. The model covers the heat generation by the resistive heater, the heat conduction within the diaphragm, the radiation loss at the diaphragm’s surface, and the heat sink caused by the supporting silicon frame. This contribution summarizes the analytical heat transfer analysis in the microstructure and its verification by a computer numerical model, the measurement setup, and the associated thermal parameter extraction procedure. Furthermore, we report on measurement results for the thermal conductivity, thermal diffusivity, and effective emissivity obtained from calorimetric flow sensor specimens featuring dielectric thin-film diaphragms made of plasma enhanced chemical vapor deposition silicon nitride.     

Dynamic array PIN:A novel approach to secure NFC electronic payment between ATM and smartphone

ABSTRACT

Near Field Communication (NFC) technology has been used recently for electronic payment between an Automated Teller Machine (ATM) and a Smartphone. It is threatened by several attacks that can steal the user personal data like the password or the Personal Identification Number (PIN). In this paper, we present Dynamic Array PIN (DAP), a novel approach for user authentication on a Smartphone that uses NFC electronic payment with an ATM. Our analysis and experimentation prove that this technique protects against thirteen different attacks and is cost-effective in terms of required hardware, authentication time, computing power and storage space.     

Combined effect of enterocin AS-48 and high hydrostatic pressure to control food-borne pathogens inoculated in low acid fermented sausages

The single and combined effects of enterocin AS-48 and high hydrostatic pressure (HHP) on Listeria monocytogenes, Salmonellaenterica, and Staphylococcus aureus was investigated in fuet (a low acid fermented sausage) during ripening and storage at 7 °C or at room temperature. AS-48 (148 AU g⁻¹) caused a drastic 5.5 log cfu g⁻¹ decrease in L. monocytogenes (P < 0.001) and a significant (P < 0.01) inhibition (1.79 logs) for Salmonella at the end of ripening (10 d). After pressurization (400 MPa) and storage Listeria counts remained below 5 cfu g⁻¹ in all fuets containing AS-48 (pressurized or not). HHP alone had no anti-Listeria effect. HHP treatment significantly reduced Salmonella counts, with lowest levels in pressurized fuets with AS-48. S. aureus showed similar growth for all treatments and storage conditions. These results indicate that AS-48 can be applied alone to control L. monocytogenes and combined with HHP treatment to control Salmonella in fuets.     

Local commute-time guided MDS for 3D non-rigid object retrieval

In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs. We first segment the non-rigid shape into local partitions based on its salient features. Then, we calculate a local MDS problem for each partition, where the local commute time distance is used as weighting function in order to preserve local shape details. Finally, we aggregate the set of local MDS problems as a global constrained problem. The constraint is formulated using the biharmonic function between local salient features. In contrast to MDS method, the proposed local MDS is computationally efficient, parameters free and gives isometry-invariant forms with minimum features distortion. Due to these advantageous properties, the proposed method achieved good retrieval accuracy on non-rigid shape benchmark datasets.     

A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data.