Mechanical,Thermal and Electrical Resisitivity Properties of Thermoplastic Composites Filled with Carbon Fibers and Carbon Particles

Composites consisting of carbon fibers (CF) and carbon particles (CP) in polypropylene (PP) matrix were melt-compounded. Composites were analyzed for their mechanical, electrical and thermal properties. Results indicate that the addition of these fillers improved the mechanical properties of the composites. Thermal conductivity was enhanced as the concentration of fillers was increased. Carbon fibers render the composites electrically conductive so we observed a percolation threshold near 10 wt.% of CF for PP/CF (PP and CF composite) and near 25 wt.% of CP for PP/CP (PP and carbon particle composite). All the results indicated that carbon fibers are more effective in improving the properties as compare to the carbon particles.     

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．     

A new technique for the construction of confusion component based on inverse LA-semigroups and its application in steganography

The present article introduced a novel idea for information hiding namely steganography. We have used new notions for the construction of the nonlinear component for block cipher based on inverse LA-semigroups. This nonlinear component fundamentally provides confidentiality in the proposed steganographic algorithm. The construction of the algorithm is fundamentally twofold. Firstly, we have constructed a novel scheme to design confusion component namely substitution box (S-box). Secondly, we have utilized the anticipated nonlinear component in digital steganography. The suggested algorithm is tested for different standard digital images. The authentication of the proposed algorithm is confirmed through statistical analysis.

     

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) -     

Modeling and Analysis of Temperature Compensation for Multi-temperature Zone Sintering Furnace Temperature Sensing

International Journal of Control, Automation and Systems - The surface temperature of workpieces in a multi-temperature zone sintering furnace is an important parameter to characterize the...