England, Neo-Latin, and the Continental Journey: Linguistic and Textual Itineraries

This chapter examines the ramifications of continental travel and associated epistolary communication for English poets of the period. It argues that recourse to neo-Latin, the universal language of diplomacy, served not only to establish a sense of shared space—linguistic, cultural, generic—between England and the continent, but also to signal self-conscious differences (climatic, geographical, historical, political) between England and her continental peers. Through an investigation of a range of ‘performances’ on stages that were ‘academic’, poetic, autobiographical, and epistolographic, it assesses the central role of neo-Latin as a language that underwent a series of textual itineraries. These ‘itineraries’ manifest themselves in a number of ways. Neo-Latin as a shared linguistic medium can facilitate, and quite uniquely so, intertextual engagement with the classics, but now ancient Rome, its language, its mythology, its hierarchy of genres, are viewed through a seventeenth-century lens and appropriated by poets in both England and Italy to describe contemporary events, whether personal, or political. Close examination of the neo-Latin poetry of Milton and Marvell reveals, it is argued, a self-fashioning coloured by such textual itineraries and interchanges. The absorption and replication of continental literary and linguistic methodologies (the academic debate; the etymological play of Marinism; the hybridity of neo-Latin and Italian voices) reveal in short a linguistic and textual reciprocity that gave birth to something very new.

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.

DEX: self-healing expanders

Statistical data correction for unreliable memories

In this paper, we introduce a statistical data-correction framework that aims at improving the DSP system performance in presence of unreliable memories. The proposed signal processing framework implements best-effort error mitigation for signals that are corrupted by defects in unreliable storage arrays using a statistical correction function extracted from the signal statistics, a data-corruption model, and an application-specific cost function. An application example to communication systems demonstrates the efficacy of the proposed approach.

On the performance of LDPC and turbo decoder architectures with unreliable memories

In this paper, we investigate the impact of faulty memory bit-cells on the performance of LDPC and Turbo channel decoders based on realistic memory failure models. Our study investigates the inherent error resilience of such codes to potential memory faults affecting the decoding process. We develop two mitigation mechanisms that reduce the impact of memory faults rather than correcting every single error. We show how protection of only few bit-cells is sufficient to deal with high defect rates. In addition, we show how the use of repair-iterations specifically helps mitigating the impact of faults that occur inside the decoder itself.

Foreword

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Secure key generation from OFDM subcarriers' channel responses

The ability to exchange keys between users is vital in any wireless based security system. A key generation technique which exploits the randomness of the wireless channel is a promising alternative to existing key distribution techniques, e.g., public key cryptography. In this paper, a secure key generation scheme based on the subcarriers' channel responses in orthogonal frequency-division multiplexing (OFDM) systems is proposed. We first implement a time-variant multipath channel with its channel impulse response modelled as a wide sense stationary (WSS) uncorrelated scattering random process and demonstrate that each subcarrier's channel response is also a WSS random process. We then define the X% coherence time as the time required to produce an X% correlation coefficient in the autocorrelation function (ACF) of each channel tap, and find that when all the channel taps have the same Doppler power spectrum, all subcarriers' channel responses has the same ACF as the channel taps. The subcarrier's channel response is then sampled every X% coherence time and quantized into key bits. All the key sequences' randomness is tested using National Institute of Standards and Technology (NIST) statistical test suite and the results indicate that the commonly used sampling interval as 50% coherence time cannot guarantee the randomness of the key sequence.