Transmission line matching effects on the performance of shunt-C/series-tuned and series-L/parallel-tuned Class-E amplifiers

The impact that the transmission-line load-network has on the performance of the recently introduced series-L/parallel-tuned Class-E amplifier and the classic shunt-C/series-tuned configuration when compared to optimally derived lumped load networks is discussed. In addition an improved load topology which facilitates harmonic suppression of up to 5 order as required for maximum Class-E efficiency as well as load resistance transformation and a design procedure involving the use of Kuroda's identity and Richard's transformation enable a distributed synthesis process which dispenses with the need for iterative tuning as previously required in order to achieve optimum Class-E operation. © 2005 IEEE.

Directional performance in motion transparency

Motion transparency provides a challenging test case for our understanding of how visual motion, and other attributes, are computed and represented in the brain. However, previous studies of visual transparency have used subjective criteria which do not confirm the existence of independent representations of the superimposed motions. We have developed measures of performance in motion transparency that require observers to extract information about two motions jointly, and therefore test the information that is simultaneously represented for each motion. Observers judged whether two motions were at 90 to one another; the base direction was randomized so that neither motion taken alone was informative. The precision of performance was determined by the standard deviations (S.D.s) of probit functions fitted to the data. Observers also made judgments of orthogonal directions between a single motion stream and a line, for one of two transparent motions against a line and for two spatially segregated motions. The data show that direction judgments with transparency can be made with comparable accuracy to segregated (non-transparent) conditions, supporting the idea that transparency involves the equivalent representation of two global motions in the same region. The precision of this joint direction judgment is, however, 2–3 times poorer than that for a single motion stream. The precision in directional judgment for a single stream is reduced only by a factor of about 1.5 by superimposing a second stream. The major effect in performance, therefore, appears to be associated with the need to compute and compare two global representations of motion, rather than with interference between the dot streams per se. Experiment 2tested the transparency of motions separated by a range of angles from 5 to 180 by requiring subjects to set a line matching the perceived direction of each motion. The S.D.s of these settings demonstrated that directions of transparent motions were represented independently for separations over 20. Increasing dot speeds from 1 to 10 deg/s improved directional performance but had no effect on transparency perception. Transparency was also unaffected by variations of density between 0.1 and 19 dots/deg2

Fast model based feature matching technique applied to airport lighting

The use of image processing techniques to assess the performance of airport landing lighting using images of it collected from an aircraft-mounted camera is documented. In order to assess the performance of the lighting, it is necessary to uniquely identify each luminaire within an image and then track the luminaires through the entire sequence and store the relevant information for each luminaire, that is, the total number of pixels that each luminaire covers and the total grey level of these pixels. This pixel grey level can then be used for performance assessment. The authors propose a robust model-based (MB) featurematching technique by which the performance is assessed. The development of this matching technique is the key to the automated performance assessment of airport lighting. The MB matching technique utilises projective geometry in addition to accurate template of the 3D model of a landing-lighting system. The template is projected onto the image data and an optimum match found, using nonlinear least-squares optimisation. The MB matching software is compared with standard feature extraction and tracking techniques known within the community, these being the Kanade–Lucus–Tomasi (KLT) and scaleinvariant feature transform (SIFT) techniques. The new MB matching technique compares favourably with the SIFT and KLT feature-tracking alternatives. As such, it provides a solid foundation to achieve the central aim of this research which is to automatically assess the performance of airport lighting.

An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

This paper presents a new approach to speech enhancement from single-channel measurements involving both noise and channel distortion (i.e., convolutional noise), and demonstrates its applications for robust speech recognition and for improving noisy speech quality. The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise for speech estimation. Third, we present an iterative algorithm which updates the noise and channel estimates of the corpus data model. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement.

Wide Matching - An Approach to Improving Noise Robustness for Speech Enhancement

It is shown that under certain conditions it is possible to obtain a good speech estimate from noise without requiring noise estimation. We study an implementation of the theory, namely wide matching, for speech enhancement. The new approach performs sentence-wide joint speech segment estimation subject to maximum recognizability to gain noise robustness. Experiments have been conducted to evaluate the new approach with variable noises and SNRs from -5 dB to noise free. It is shown that the new approach, without any estimation of the noise, significantly outperformed conventional methods in the low SNR conditions while retaining comparable performance in the high SNR conditions. It is further suggested that the wide matching and deep learning approaches can be combined towards a highly robust and accurate speech estimator.