Electrodeposited reduced-graphene oxide/cobalt oxide electrodes for charge storage applications

In the present work, electrochemically reduced-graphene oxide/cobalt oxide composites for charge storage electrodes were prepared by a one-step pulsed electrodeposition route on stainless steel current collectors and after that submitted to a thermal treatment at 200 degrees C. A detailed physico-chemical characterization was performed by X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM) and Raman spectroscopy. The electrochemical response of the composite electrodes was studied by cyclic voltammetry and charge-discharge curves and related to the morphological and phase composition changes induced by the thermal treatment. The results revealed that the composites were promising materials for charge storage electrodes for application in redox supercapacitors, attaining specific capacitances around 430 F g(-1) at 1 A g(-1) and presenting long-term cycling stability. (C) 2016 Elsevier B.V. All rights reserved.

Signal subspace identification in hyperspectral imagery

Terrestrial remote sensing imagery involves the acquisition of information from the Earth's surface without physical contact with the area under study. Among the remote sensing modalities, hyperspectral imaging has recently emerged as a powerful passive technology. This technology has been widely used in the fields of urban and regional planning, water resource management, environmental monitoring, food safety, counterfeit drugs detection, oil spill and other types of chemical contamination detection, biological hazards prevention, and target detection for military and security purposes [2-9]. Hyperspectral sensors sample the reflected solar radiation from the Earth surface in the portion of the spectrum extending from the visible region through the near-infrared and mid-infrared (wavelengths between 0.3 and 2.5 µm) in hundreds of narrow (of the order of 10 nm) contiguous bands [10]. This high spectral resolution can be used for object detection and for discriminating between different objects based on their spectral xharacteristics [6]. However, this huge spectral resolution yields large amounts of data to be processed. For example, the Airbone Visible/Infrared Imaging Spectrometer (AVIRIS) [11] collects a 512 (along track) X 614 (across track) X 224 (bands) X 12 (bits) data cube in 5 s, corresponding to about 140 MBs. Similar data collection ratios are achieved by other spectrometers [12]. Such huge data volumes put stringent requirements on communications, storage, and processing. The problem of signal sbspace identification of hyperspectral data represents a crucial first step in many hypersctral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction (DR) yelding gains in data storage and retrieval and in computational time and complexity. Additionally, DR may also improve algorithms performance since it reduce data dimensionality without losses in the useful signal components. The computation of statistical estimates is a relevant example of the advantages of DR, since the number of samples required to obtain accurate estimates increases drastically with the dimmensionality of the data (Hughes phnomenon) [13].