High Intensity Focused Ultrasound (HIFU) is a noninvasive surgical method mainly targeting deeply located cancer tissue. Ultrasound is generated from an extemally located transducer and the beam is focused at the target volume, so that selective damage can be achieved without harm to overlying or surrounding tissues. The mechanism for cell killing can be combination of thermal and cavitational damage. Although cavitation can be an effective means of tissue destruction, the possibility of massive hemorrhage and the unpredictable nature of cavitational events prevent clinical application of cavitation. Hence, thermal damage has been a main focus related to HIFU research. 2D phased array transducer systems allow electronic scanning of focus, multi-foci, and anti-focus with multi-foci, so that HIFU becomes more applicable in clinical use. Currently, lack of noninvasive monitoring means of HIFU is the main factor to limit clinical applications, but development in MRI and Ultrasound Imaging techniques may be able to provide solutions to overcome this problem. With the development of advanced focusing algorithm and monitoring means, complete noninvasive surgery is expected to be implemented in the near future.

Osteoporosis is a skeletal disease characterized by two factors: reduced bone mass and microstructure disruption of bone tissue. These symptoms increase bone fragility and can contribute to eventual fracture. In recent years, quantitative ultrasound (QUS) technologies have played a growing role in the diagnosis of osteoporosis. Most of the commercial bone sonometers measure speed of sound and/or broadband ultrasound attenuation at peripheral skeletal sites. However, QUS parameters are purely empirical measures that have not yet been firmly linked to physical parameters, such as bone strength or porosity, and the underlying physics for their variations in cancellous bone is not well understood yet. This paper reviews the QUS technologies for the diagnosis of osteoporosis and also addresses several theoretical models, such as the Biot model, the scattering model, the stratified model, and the modified Biot-Attenborough model, for ultrasonic wave propagation in bone.

In the diagnostic ultrasound (US) transducer technology, the high frequency US(HFUS) transducer over 20 MHz is one of the current issues to be pursued for better resolution with the expense of penetration. HFUS single element transducers and the mechanical scanning systems for imaging are reviewed, and HFUS array transducers are also briefly summarized. HFUS applications such as the human applications in ophthalmology and dermatology and small animal applications for research purposes are reviewed with vascular and blood imaging in this paper.

This paper analyzes the performance of various single channel speech enhancement algorithms when they are applied to automatic speech recognition (ASR) systems as a preprocessor. The functional modules of speech enhancement systems are first divided into four major modules such as a gain estimator, a noise power spectrum estimator, a priori signal to noise ratio (SNR) estimator, and a speech absence probability (SAP) estimator. We investigate the relationship between speech recognition accuracy and the roles of each module. Simulation results show that the Wiener filter outperforms other gain functions such as minimum mean square error-short time spectral amplitude (MMSE-STSA) and minimum mean square error-log spectral amplitude (MMSE-LSA) estimators when a perfect noise estimator is applied. When the performance of the noise estimator degrades, however, MMSE methods including the decision directed module to estimate a priori SNR and the SAP estimation module helps to improve the performance of the enhancement algorithm for speech recognition systems.

Multiple-model speech recognizer has been shown to be quite successful in noisy speech recognition. However, its performance has usually been tested using the general speech front-ends which do not incorporate any noise adaptive algorithms. For the accurate evaluation of the effectiveness of the multiple-model frame in noisy speech recognition, we used the state-of-the-art front-ends and compared its performance with the well-known multi-style training method. In addition, we improved the multiple-model speech recognizer by employing N-best reference HMMs for interpolation and using multiple SNR levels for training each of the reference HMM.