This study shows significant regional differences in strain during ramped isometric contractions for the patellar tendon. Lower proximal strains in the posterior tendon compared to the anterior region may be associated with the suggestion of "stress shielding" as an etiological factor in insertional tendinopathy.

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The work describes an automated method of tracking dynamic ultrasound images using a normalized cross-correlation algorithm, applied to the patellar and gastrocnemius tendon. Displacement was examined during active and passive tendon excursions using B-mode ultrasonography. In the passive test where two regions of interest (2-ROI) were tracked, the automated tracking algorithm showed insignificant deviations from relative zero displacement for the knee (0.01 ± 0.04 mm) and ankle (–0.02 ± 0.04 mm) (P> .05). Similarly, when tracking 1-ROI the passive tests showed no significant differences (P> .05) between automatic and manual methods, 7.50 ± 0.60 vs 7.66 ± 0.63 mm for the patellar and 11.28 ± 1.36 vs 11.17 ± 1.35 mm for the gastrocnemius tests. The active tests gave no significant differences (P> .05) between automatic and manual methods with differences of 0.29 ± 0.04 mm for the patellar and 0.26 ± 0.01 mm for the gastrocnemius. This study showed that automatic tracking of in vivo displacement of tendon during dynamic excursion under load is possible and valid when compared with the standardized method. This approach will save time during analysis and enable discrete areas of the tendon to be examined.

Abstract-Speaker recognition has been developed and evolved over the past few decades into a supposedly mature technique. Existing methods typically utilize robust features extracted from clean speech. In real-world applications, especially security and forensics related ones, reliability of recognition becomes crucial, meanwhile limited speech samples and adverse acoustic conditions, most notably noise and reverberation, impose further complications. This paper is presented from a study into the behavior of typical speaker recognition systems in adverse retrieval phases. Following a brief review, a speaker recognition system was implemented using the MSR Identity Toolbox by Microsoft. Validation tests were carried out with clean speech and the speech contaminated by noise and/or reverberation of varying degrees. The image source method was adopted to take into account real acoustic conditions in the spaces. Statistical relationships between recognition accuracy and signal to noise ratios or reverberation times have therefore been established. Results show noise and reverberation can, to different extents, degrade the performance of recognition. Both reverberation time and direct to reverberation ratio can affect recognition accuracy. The findings may be used to estimate the accuracy of speaker recognition and further determine the likelihood a particular speaker.

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