The multiple measurement vector (MMV) problem addresses the identification of unknown input vectors that share common sparse support. Even though MMV problems have been traditionally addressed within the context of sensor array signal processing, the recent trend is to apply compressive sensing (CS) due to its capability to estimate sparse support even with an insufficient number of snapshots, in which case classical array signal processing fails. However, CS guarantees the accurate recovery in a probabilistic manner, which often shows inferior performance in the regime where the traditional array signal processing approaches succeed. The apparent dichotomy between the probabilistic CS and deterministic sensor array signal processing has not been fully understood. The main contribution of the present article is a unified approach that revisits the link between CS and array signal processing first unveiled in the mid 1990s by Feng and Bresler. The new algorithm, which we call compressive MUSIC, identifies the parts of support using CS, after which the remaining supports are estimated using a novel generalized MUSIC criterion. Using a large system MMV model, we show that our compressive MUSIC requires a smaller number of sensor elements for accurate support recovery than the existing CS methods and that it can approach the optimal -bound with finite number of snapshots even in cases where the signals are linearly dependent.
Dynamic tracking of sparse targets has been one of the important topics in array signal processing. Recently, compressed sensing (CS) approaches have been extensively investigated as a new tool for this problem using partial support information obtained by exploiting temporal redundancy. However, most of these approaches are formulated under single measurement vector compressed sensing (SMV-CS) framework, where the performance guarantees are only in a probabilistic manner. The main contribution of this paper is to allow deterministic tracking of time varying supports with multiple measurement vectors (MMV) by exploiting multi-sensor diversity. In particular, we show that a novel compressive MUSIC (CS-MUSIC) algorithm with optimized partial support selection not only allows removal of inaccurate portion of previous support estimation but also enables addition of newly emerged part of unknown support. Numerical results confirm the theory.
Resistive random access memory (ReRAM) is a promising candidate for future nonvolatile memories. Resistive switching in a metal-insulator-metal structure is generally assumed to be caused by the formation/rupture of nanoscale conductive filaments (CFs) under an applied electric field. The critical issue of ReRAM for practical memory applications, however, is insufficient repeatability of the operating voltage and resistance ratio. Here, we present an innovative approach to reliably and reproducibly control the CF growth in unipolar NiO resistive memory by exploiting uniform formation of insulating SiOx nanostructures from the self-assembly of a Si-containing block copolymer. In this way, the standard deviation (SD) of set and reset voltages was markedly reduced by 76.9% and 59.4%, respectively. The SD of high resistance state also decreased significantly, from 6.3 × 10(7) Ω to 5.4 × 10(4) Ω. Moreover, we report direct observations of localized metallic Ni CF formation and their controllable growth using electron microscopy and discuss electrothermal simulation results based on the finite element method supporting our analysis results.
Fuel cells, converting chemical energy from fuels into electricity directly without the need for combustion, are promising energy conversion devices for their potential applications as environmentally friendly, energy efficient power sources. However, to take fuel cell technology forward towards commercialization, we need to achieve further improvements in electrocatalyst technology, which can play an extremely important role in essentially determining cost‐effectiveness, performance, and durability. In particular, platinum‐ (Pt‐) based electrocatalyst approaches have been extensively investigated and actively pursued to meet those demands as an ideal fuel cell catalyst due to their most outstanding activity for both cathode oxygen reduction reactions and anode fuel oxidation reactions. In this review, we will address important issues and recent progress in the development of Pt‐based catalysts, their synthesis, and characterization. We will also review snapshots of research that are focused on essential dynamics aspects of electrocatalytic reactions, such as the shape effects on the catalytic activity of Pt‐based nanostructures, the relationships between structural morphology of Pt‐based nanostructures and electrochemical reactions on both cathode and anode electrodes, and the effects of composition and electronic structure of Pt‐based catalysts on electrochemical reaction properties of fuel cells.
Abstract Using a Clark-type oxygen electrode as sensor, a highly sensitive enzymatic assay method for hypoxanthine was developed, based on xanthine oxidase. The sensitivity of the assay is comparable to that of standard chemiluminescent techniques. The incorporation of sulfite, 0 to 150 mM, allowed hypoxanthine determination over the range of 5 nM to 100 μM. The response time was 4 min or less at 30°C.
In this study, round robin problems for the failure probabilities of a reactor pressure vessel are solved using the probabilistic fracture mechanics code. The flaw distribution and flaw density were modified to incorporate the effects of inspection quality. Then, the impact of the inspection quality and other key parameters on the failure probability was quantitatively evaluated. The results showed that the effect of inspection quality on the failure probability has the same characteristics irrespective of the two quite different transients and the wide range of fluence level. Overall, the various inspection qualities considered in this study resulted in about an order of magnitude difference in failure probability. Additionally, it was found that the effect of warm prestressing on the failure probability depends on the characteristics of the transients.
A new direction for developing electrocatalysts for hydrogen fuel cell systems has emerged, based on the fabrication of 3D architectures. These new architectures include extended Pt surface building blocks, the strategic use of void spaces, and deliberate network connectivity along with tortuosity, as design components. Various strategies for synthesis now enable the functional and structural engineering of these electrocatalysts with appropriate electronic, ionic, and electrochemical features. The new architectures provide efficient mass transport and large electrochemically active areas. To date, although there are few examples of fully functioning hydrogen fuel cell devices, these 3D electrocatalysts have the potential to achieve optimal cell performance and durability, exceeding conventional Pt powder (i.e., Pt/C) electrocatalysts. This progress report highlights the various 3D architectures proposed for Pt electrocatalysts, advances made in the fabrication of these structures, and the remaining technical challenges. Attempts to develop design rules for 3D architectures and modeling, provide insights into their achievable and potential performance. Perspectives on future developments of new multiscale designs are also discussed along with future study directions.
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