Current Issue : April - June Volume : 2012 Issue Number : 2 Articles : 7 Articles
In this paper the terahertz technology is briefly reviewed and terahertz sources, detectors, characteristics, application are discussed. There is increasing demand of unoccupied bandwidth for wireless communication and other new emerging applications. We know higher carrier frequency can allow fast transmission and reception of large amount of data. In recent years, Terahertz (THz) science and technology has shown great potential application and this field has entered a new phase of expansion in current technology and development field and has seen rapid progress. This article gives a brief overview of THz technology and summarizes the applications of THz technology....
Intuitionistic fuzzy set (IFS) was proposed in early 80�s. It is a well-known theory and has gained much attention from past and later researchers for application in various fields. As a development in mathematical study specifically in fuzzy mathematics, interval-valued intuitionistic fuzzy sets (IVIFS) were developed afterwards by Gargo and Atanssov as an extension of intuitionistic fuzzy sets which is also important and had been used in many related fields such as optimization and algebra. Hamming distance is one of the methods to calculate the distance between fuzzy numbers. However, Hamming distance in intuitionistic fuzzy sets and interval-valued intuitionistic fuzzy sets have not been fully explored. This paper aims to make a comparison between Hamming distance in intuitionistic fuzzy sets and interval-valued intuitionistic fuzzy sets. The stepwise conversion of intuitionistic fuzzy sets to interval-valued intuitionistic fuzzy sets is also proposed. The comparative analysis shows that the distance between these two sets is slightly differ due to boundaries in interval-valued intuitionistic fuzzy sets....
Background: Our goals are to develop a computational histopathology pipeline for characterizing tumor types\r\nthat are being generated by The Cancer Genome Atlas (TCGA) for genomic association. TCGA is a national\r\ncollaborative program where different tumor types are being collected, and each tumor is being characterized\r\nusing a variety of genome-wide platforms. Here, we have developed a tumor-centric analytical pipeline to process\r\ntissue sections stained with hematoxylin and eosin (H&E) for visualization and cell-by-cell quantitative analysis. Thus\r\nfar, analysis is limited to Glioblastoma Multiforme (GBM) and kidney renal clear cell carcinoma tissue sections. The\r\nfinal results are being distributed for subtyping and linking the histology sections to the genomic data.\r\nResults: A computational pipeline has been designed to continuously update a local image database, with limited\r\nclinical information, from an NIH repository. Each image is partitioned into blocks, where each cell in the block is\r\ncharacterized through a multidimensional representation (e.g., nuclear size, cellularity). A subset of morphometric\r\nindices, representing potential underlying biological processes, can then be selected for subtyping and genomic\r\nassociation. Simultaneously, these subtypes can also be predictive of the outcome as a result of clinical treatments.\r\nUsing the cellularity index and nuclear size, the computational pipeline has revealed five subtypes, and one\r\nsubtype, corresponding to the extreme high cellularity, has shown to be a predictor of survival as a result of a\r\nmore aggressive therapeutic regime. Further association of this subtype with the corresponding gene expression\r\ndata has identified enrichment of (i) the immune response and AP-1 signaling pathways, and (ii) IFNG, TGFB1, PKC,\r\nCytokine, and MAPK14 hubs.\r\nConclusion: While subtyping is often performed with genome-wide molecular data, we have shown that it can\r\nalso be applied to categorizing histology sections. Accordingly, we have identified a subtype that is a predictor of\r\nthe outcome as a result of a therapeutic regime. Computed representation has become publicly available through\r\nour Web site....
This work studies the task of automatic emotion detection in music. Music may evoke more than one different\r\nemotion at the same time. Single-label classification and regression cannot model this multiplicity. Therefore, this\r\nwork focuses on multi-label classification approaches, where a piece of music may simultaneously belong to more\r\nthan one class. Seven algorithms are experimentally compared for this task. Furthermore, the predictive power of\r\nseveral audio features is evaluated using a new multi-label feature selection method. Experiments are conducted\r\non a set of 593 songs with six clusters of emotions based on the Tellegen-Watson-Clark model of affect. Results\r\nshow that multi-label modeling is successful and provide interesting insights into the predictive quality of the\r\nalgorithms and features....
This paper deals with fault detection in dynamical systems where the state variables evolutions are constrained by inequality\r\nconstraints. The latter corresponds either to physical limitations or to safety specification. Two classical residual generation\r\napproaches are studied, namely, parity space and unknown input observer approaches, and are extended to monitor the inequality\r\nconstraints. A practical implementation on a real process is performed and permits to validate the relevance of the proposed\r\nmethods....
In this paper, controlled energization transformers is done using Artificial Intelligence (AI) technique. Radial Basis\r\nFunction Neural Network (RBFNN) is selected as AI tool. The most effective method for the limitation of the switching overvoltages\r\nis controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switch.\r\nWe introduce a harmonic index that it�s minimum value is corresponding to the best case switching time. ANN training is performed\r\nbased on equivalent circuit parameters of the network. Thus, trained ANN is applicable to every studied system. To verify the\r\neffectiveness of the proposed index and accuracy of the ANN-based approach, two case studies are presented and demonstrated....
Packet classification plays a crucial role for a number of network services such as policy-based routing, firewalls, and traffic billing,\nto name a few. However, classification can be a bottleneck in the above-mentioned applications if not implemented properly and\nefficiently. In this paper, we propose PCIU, a novel classification algorithm, which improves upon previously published work. PCIU\nprovides lower preprocessing time, lower memory consumption, ease of incremental rule update, and reasonable classification time\ncompared to state-of-the-art algorithms. The proposed algorithm was evaluated and compared to RFC and HiCut using several\nbenchmarks. Results obtained indicate that PCIU outperforms these algorithms in terms of speed, memory usage, incremental\nupdate capability, and preprocessing time. The algorithm, furthermore, was improved and made more accessible for a variety\nof applications through implementation in hardware. Two such implementations are detailed and discussed in this paper. The\nresults indicate that a hardware/software codesign approach results in a slower, but easier to optimize and improve within time\nconstraints, PCIU solution. A hardware accelerator based on an ESL approach using Handel-C, on the other hand, resulted in a\n31x speed-up over a pure software implementation running on a state of the art Xeon processor....
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