The Visual Representation of a Systematic Metaphor in a UK Media Text

Much has been written on the organizational power of metaphor in discourse, eg on metaphor ‘chains’ and ‘clusters’ of linguistic metaphor in discourse (Koller 2003, Cameron & Stelma 2004, Semino 2008) and the role of extended and systematic metaphor in organizing long stretches of language, even whole texts (Cameron et al 2009, Cameron & Maslen 2010, Deignan et al 2013, Semino et al 2013). However, at times, this work belies the intricacies of how a single metaphoric idea can impact on a text. The focus of this paper is a UK media article derived from a HM Treasury press release on alleviating poverty. The language of the article draws heavily on orientational (spatial) metaphors, particularly metaphors of movement around GOOD IS UP. Although GOOD IS UP can be considered a single metaphoric idea, the picture the reader builds up as they move line by line through this text is complex and multifaceted. I take the idea of “building up a picture” literally in order to investigate the schema of motion relating to GOOD IS UP. To do this, fifteen informants (Masters students at a London university), tutored in Cognitive Metaphor Theory, were asked to read the article and underline words and expressions they felt related to GOOD IS UP. The text was then read back to the informant with emphasis given to the words they had underlined, while they drew a pictorial representation of the article based on the meanings of these words, integrating their drawings into a single picture as they went along. I present examples of the drawings the informants produced. I propose that using Metaphor-led Discourse Analysis to produce visual material in this way offers useful insights into how metaphor contributes to meaning making at text level. It shows how a metaphoric idea, such as GOOD IS UP, provides the text producer with a rich and versatile meaning-making resource for constructing text; and gives a ‘mind-map’ of how certain aspects of a media text are decoded by the text receiver. It also offers a partial representation of the elusive, intermediate ‘deverbalized’ stage of translation (Lederer 1987), where the sense of the source text is held in the mind before it is transferred to the target language. References Cameron, L., R. Maslen, Z. Todd, J. Maule, P. Stratton & N. Stanley. 2009. ‘The discourse dynamic approach to metaphor and metaphor-led analysis’. Metaphor and Symbol, 24(2), 63-89. Cameron, L. & R. Maslen (eds). 2010. Metaphor Analysis: Research Practice in Applied Linguistics, Social Sciences and Humanities. London: Equinox. Cameron, L. & J. Stelma. 2004. ‘Metaphor Clusters in Discourse’. Journal of Applied Linguistics, 1(2), 107-136. Deignan, A., J. Littlemore & E. Semino. 2013. Figurative Language, Genre and Register. Cambridge: Cambridge University Press. Koller, V. 2003. ‘Metaphor Clusters, Metaphor Chains: Analyzing the Multifunctionality of Metaphor in Text’. metaphorik.de, 5, 115-134. Lederer, M. 1987. ‘La théorie interprétative de la traduction’ in Retour à La Traduction. Le Francais dans Le Monde. Semino, E. 2008. Metaphor in Discourse. Cambridge: Cambridge University Press. Semino, E., A. Deignan & J. Littlemore. 2013. ‘Metaphor, Genre, and Recontextualization’. Metaphor and Symbol. 28(1), 41-59.

A comparative study on the modified Max-Log-MAP turbodecoding by extrinsic information scaling

A simple but effective technique to improve the performance of the Max-Log-MAP algorithm is to scale the extrinsic information exchanged between two MAP decoders. A comprehensive analysis of the selection of the scaling factors according to channel conditions and decoding iterations is presented in this paper. Choosing a constant scaling factor for all SNRs and iterations is compared with the best scaling factor selection for changing channel conditions and decoding iterations. It is observed that a constant scaling factor for all channel conditions and decoding iterations is the best solution and provides a 0.2-0.4 dB gain over the standard Max- Log-MAP algorithm. Therefore, a constant scaling factor should be chosen for the best compromise.

The modified Max-Log-MAP turbo decoding algorithm by extrinsic information scaling for wireless applications

The iterative nature of turbo-decoding algorithms increases their complexity compare to conventional FEC decoding algorithms. Two iterative decoding algorithms, Soft-Output-Viterbi Algorithm (SOVA) and Maximum A posteriori Probability (MAP) Algorithm require complex decoding operations over several iteration cycles. So, for real-time implementation of turbo codes, reducing the decoder complexity while preserving bit-error-rate (BER) performance is an important design consideration. In this chapter, a modification to the Max-Log-MAP algorithm is presented. This modification is to scale the extrinsic information exchange between the constituent decoders. The remainder of this chapter is organized as follows: An overview of the turbo encoding and decoding processes, the MAP algorithm and its simplified versions the Log-MAP and Max-Log-MAP algorithms are presented in section 1. The extrinsic information scaling is introduced, simulation results are presented, and the performance of different methods to choose the best scaling factor is discussed in Section 2. Section 3 discusses trends and applications of turbo coding from the perspective of wireless applications.