Lithium/V6O13 cells using silica nanoparticle-based composite electrolyte

The electrochemical behavior of Li/V₆O₁₃ cells is investigated at room temperature (22 °C) both in liquid electrolyte consisting of oligomeric poly(ethyleneglycol)dimethylether+lithium bis(trifluoromethylsulfonylimide) and composite electrolytes formed by blending the liquid electrolyte with silica nanoparticles (fumed silica). The addition of fumed silica yields a gel-like electrolyte that demonstrates the desirable property of suppressing lithium dendrite growth due to the rigidity and immobility of the electrolyte structure. The lithium/electrolyte interfacial resistance for composite gel electrolytes is less than that for the corresponding base-liquid electrolyte, and the charge-discharge cycle performance and electrochemical efficiency for the Li/V₆O₁₃ cell is significantly improved. The effect of fumed silica surface group on the electrochemical performance is discussed; the native hydrophilic silanol surface group appears better than fumed silica that is modified with a hydrophobic octyl surface moiety.     

Modification of oil palm empty fruit bunches with maleic anhydride: The effect on the tensile and dimensional stability properties of empty fruit bunch/polypropylene composites

Oil palm empty fruit bunch (EFB)‐filled polypropylene (PP) composites were produced. The EFB filler was chemically modified with maleic anhydride (MAH). The effects of the filler size and chemical modification of EFBs on the tensile and dimensional stability properties of EFB–PP composites were studied. The composites with MAH‐treated EFBs showed higher tensile strengths than those with untreated EFBs. This was attributed to the enhanced compatibility between the MAH‐treated EFBs and PP matrix, as shown in a scanning electron microscopy study. Fourier transform infrared analysis showed evidence of C?C and C?O bonds from MAH at 1630 and 1730 cm^?1, respectively. The MAH‐treated PP composites showed lower water absorption and thickness swelling than those with untreated EFBs. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 87: 827–835, 2003     

Exploiting Ontology for Software License Agreements

Software end-users need to sign licenses to seal an agreement with the product providers. Habitually, users agree with the license (i.e. terms and conditions) without fully understanding the agreement. To address this issue, an ontological model is developed to formulate the user requirements and license agreements formally. This paper, introduces ontological model that includes the abstract license ontology of common features found in di?erent license agreements. The abstract license ontology is then extended to a few real world license agreements. The resulting model can be used for di?erent purposes such as querying the appropriate licenses for a speciˉc requirement or checking the license terms and conditions with user requirements.     

移动自组织网中基于最优距离的概率广播方案

广播是移动Adhoc网络提供路由发现、地址解析等网络功能的一项重要操作．为了减少了泛洪广播算法中的端到端延迟,有效利用有限的带宽资源,解决广播风暴问题,文中提出了一种距离感知的广播技术,即基于最优距离的概率广播方案．本方案通过结合概率算法和距离感知来解决广播风暴问题,以高可达率和低延迟在网络中传播广播数据包．仿真结果显示与主流的泛洪算法和概率广播算法相比,本方案在可到达率、端到端延迟及重播概率等方面的性能有所提高．     

Electrical conductivity of concrete containing silica fume

The influence of silica fume on concrete properties represents an important technical research. In general, silica fume tends to improve both mechanical characteristics and durability of concrete. Thus the electrical properties of concrete containing silica fume can be studied to clarify its physical performance during hydration. The electrical conductivity of neat cement, mortar and concrete pastes was measured during setting and hardening. The ordinary Portland cement was partially replaced by different amounts of silica fume by weight. The changes in the electrical conductivity were reported during setting and hardening after gauging with water. The results of this study showed that the electrical conductivity can be used as an indication for the setting characteristics as well as the structural changes of the hardened pastes made with and without silica fume.     

Enhanced halftone-based secure and improved visual cryptography scheme for colour/binary Images

Visual Cryptography (VC) is gaining attraction during the past few years to secure the visual information in the transmission network. It enables the visual data i.e. handwritten notes, photos, printed text, etc. to encrypt in such a way that their decryption can be done through the human visual framework. Hence, no computational assistance is required for the decryption of the secret images they can be seen through naked eye. In this paper, a novel enhanced halftoning-based VC scheme is proposed that works for both binary and color images. Fake share is generated by the combination of random black and white pixels. The proposed algorithm consists of 3 stages i.e., detection, encryption, and decryption. Halftoning, Encryption, (2, 2) visual cryptography and the novel idea of fake share, make it even more secure and improved. As a result, it facilitates the original restored image to the authentic user, however, the one who enters the wrong password gets the combination of fake share with any real share. Both colored and black images can be processed with minimal capacity using the proposed scheme.

     

KP-Rank: a semantic-based unsupervised approach for keyphrase extraction from text data

Automatic key concept identification from text is the main challenging task in information extraction, information retrieval, digital libraries, ontology learning, and text analysis. The main difficulty lies in the issues with the text data itself, such as noise in text, diversity, scale of data, context dependency and word sense ambiguity. To cope with this challenge, numerous supervised and unsupervised approaches have been devised. The existing topical clustering-based approaches for keyphrase extraction are domain dependent and overlooks semantic similarity between candidate features while extracting the topical phrases. In this paper, a semantic based unsupervised approach (KP-Rank) is proposed for keyphrase extraction. In the proposed approach, we exploited Latent Semantic Analysis (LSA) and clustering techniques and a novel frequency-based algorithm for candidate ranking is introduced which considers locality-based sentence, paragraph and section frequencies. To evaluate the performance of the proposed method, three benchmark datasets (i.e. Inspec, 500N-KPCrowed and SemEval-2010) from different domains are used. The experimental results show that overall, the KP-Rank achieved significant improvements over the existing approaches on the selected performance measures.

     

Quality assessment of 3D synthesized images based on structural and textural distortion

Depth image based rendering (DIBR) is a popular technique for rendering virtual 3D views in stereoscopic and autostereoscopic displays. The quality of DIBR-synthesized images may decrease due to various factors, e.g., imprecise depth maps, poor rendering techniques, inaccurate camera parameters. The quality of synthesized images is important as it directly affects the overall user experience. Therefore, the need arises for designing algorithms to estimate the quality of the DIBR-synthesized images. The existing 2D image quality assessment metrics are found to be insufficient for 3D view quality estimation because the 3D views not only contain color information but also make use of disparity to achieve the real depth sensation. In this paper, we present a new algorithm for evaluating the quality of DIBR generated images in the absence of the original references. The human visual system is sensitive to structural information; any deg radation in structure or edges affects the visual quality of the image and is easily noticeable for humans. In the proposed metric, we estimate the quality of the synthesized view by capturing the structural and textural distortion in the warped view. The structural and textural information from the input and the synthesized images is estimated and used to calculate the image quality. The performance of the proposed quality metric is evaluated on the IRCCyN IVC DIBR images dataset. Experimental evaluations show that the proposed metric outperforms the existing 2D and 3D image quality metrics by achieving a high correlation with the subjective ratings.

     

Deep learning for graphics recognition: document understanding and beyond

International Journal on Document Analysis and Recognition (IJDAR) -