A two stage vector quantization approach via self-organizing map

In this paper, a two-stage algorithm for vector quantization is proposed based on a self-organizing map (SOM) neural network. First, a conventional self-organizing map is modified to deal with dead codebooks in the learning process and is then used to obtain the codebook distribution structure for a given set of input data. Next, subblocks are classified based on the previous structure distribution with a prior criteria. Then, the conventional LBG algorithm is applied to these sub-blocks for data classification with initial values obtained via the SOM. Finally, extensive simulations illustrate that the proposed two-stage algorithm is very effective.

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.

Robustness enhancement of quantization based audio watermarking method using adaptive safe-band

This paper presents a novel adaptive safe-band for quantization based audio watermarking methods, aiming to improve robustness. Considerable number of audio watermarking methods have been developed using quantization based techniques. These techniques are generally vulnerable to signal processing attacks. For these conventional quantization based techniques, robustness can be marginally improved by choosing larger step sizes at the cost of significant perceptual quality degradation. We first introduce fixed size safe-band between two quantization steps to improve robustness. This safe-band will act as a buffer to withstand certain types of attacks. Then we further improve the robustness by adaptively changing the size of the safe-band based on the audio signal feature used for watermarking. Compared with conventional quantization based method and the fixed size safe-band based method, the proposed adaptive safe-band based quantization method is more robust to attacks. The effectiveness of the proposed technique is demonstrated by simulation results. © 2014 IEEE.

New opportunities for quantitative and time efficient 3D MRI of liquid and solid electrochemical cell components: Sectoral Fast Spin Echo and SPRITE.

The ability to image electrochemical processes in situ using nuclear magnetic resonance imaging (MRI) offers exciting possibilities for understanding and optimizing materials in batteries, fuel cells and supercapacitors. In these applications, however, the quality of the MRI measurement is inherently limited by the presence of conductive elements in the cell or device. To overcome related difficulties, optimal methodologies have to be employed. We show that time-efficient three dimensional (3D) imaging of liquid and solid lithium battery components can be performed by Sectoral Fast Spin Echo and Single Point Imaging with T1 Enhancement (SPRITE), respectively. The former method is based on the generalized phase encoding concept employed in clinical MRI, which we have adapted and optimized for materials science and electrochemistry applications. Hard radio frequency pulses, short echo spacing and centrically ordered sectoral phase encoding ensure accurate and time-efficient full volume imaging. Mapping of density, diffusivity and relaxation time constants in metal-containing liquid electrolytes is demonstrated. 1, 2 and 3D SPRITE approaches show strong potential for rapid high resolution (7)Li MRI of lithium electrode components.

Spin-crossover, mesomorphic and thermoelectrical properties of cobalt(II) complexes with alkylated N3-Schiff bases

Three new cobalt(ii) complexes, [Co(L12)2](BF4)2 (1), [Co(L14)2](BF4)2·H2O (2) and [Co(L16)2](BF4)2·H2O (3), where L12-16 are N3-Schiff bases appended with linear C12-16 carbon chains at the nitrogen atoms, were obtained in good yields by facile one-pot reactions. The single crystal X-ray structure of complex 1 shows a tetragonally compressed CoN6 coordination geometry. The melting temperatures of 1-3 were lower than 373 K, while their decomposition temperatures were above 473 K. All complexes have high-spin Co(ii) centres at 300 K and exhibit a columnar mesophase above 383 K. Complexes 1 and 3 showed normal thermal spin-crossover behaviour with weak hysteresis loops at about 320 K. Hence, these complexes showed uncoupled phase transitions (class iiia). The values for the Seebeck coefficient (Se) of the cobalt redox couples formed from 1 and 2 were 1.89 ± 0.02 mV K-1 and 1.92 ± 0.08 mV K-1, respectively, identifying them as potential thermoelectrochemical materials.