Narrow-band variable center frequency single-loop and multistage sigma-delta modulators for bandpass signals

Oversampled narrow-band single-loop and multistage resonator-based bandpass sigma-delta (Σ-Δ) modulators that can accommodate different passband center to sampling frequency ratios are reported. These tunable bandpass configurations are designed by analytically determining and subsequently verifying through detailed empirical simulations the required compensation hardware to deliver enhanced noise-shaping. It is demonstrated that comparatively superior in-band signal-to-noise ratios and dynamic ranges are attributed to the inclusion of appropriate digital feedforward and feedback compensators within these structures.

Variable centre frequency resonator based bandpass Σ-Δ modulator

A novel resonator-based bandpass Σ-Δ modulator with an in-built variable centre frequency feature is presented. This structure is designed by analytically determining and subsequently verifying through behavioural level simulations the necessary compensation hardware to be placed in the feedback to ensure stability and good dynamic range performance.

Academics across borders: narratives of linguistic capital, language competence, and communication strategies

This article reports on a study that examined the personal employment paths of six international academics at a British university. To complement previous accounts of difficult migration, it focuses on the successful experiences of such academics, in particular how proficiency in English facilitated their move into employment in higher education (HE), and the linguistic competences and communication strategies they deploy in their daily activities. The article identifies key factors that have facilitated to their academic achievements and contributes to the understanding of the benefits and consequences of skilled migration. In conclusion, it suggests workplace pedagogy and policy responses that could facilitate other international academics' successful experiences in the UK HE sector.

A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiology

A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage