Robust Source Localization in Reverberant Environments Based on Weighted Fuzzy Clustering

Successful localization of sound sources in reverberant enclosures is an important prerequisite for many spatial signal processing algorithms. We investigate the use of a weighted fuzzy-means cluster algorithm for robust source localization using location cues extracted from a microphone array. In orderto increase the algorithm's robustness against sound reflections, we incorporate observation weights to emphasize reliable cues over unreliable ones. The weights are computed from local feature statistics around sound onsets because it is known that these regions are least affected by reverberation. Experimental results illustrate the superiority of the method when compared with standard fuzzy clustering. The proposed algorithm successfully located two speech sources for a range of angular separations in room environments with reverberation times of up to 600 ms

Frame-Wise dynamic threshold based polyphonic acoustic event detection

Acoustic event detection, the determination of the acoustic event type and the localisation of the event, has been widely applied in many real-world applications. Many works adopt multi-label classification techniques to perform the polyphonic acoustic event detection with a global threshold to detect the active acoustic events. However, the global threshold has to be set manually and is highly dependent on the database being tested. To deal with this, we replaced the fixed threshold method with a frame-wise dynamic threshold approach in this paper. Two novel approaches, namely contour and regressor based dynamic threshold approaches are proposed in this work. Experimental results on the popular TUT Acoustic Scenes 2016 database of polyphonic events demonstrated the superior performance of the proposed approaches

Speech Enhancement Strategy for Speech Recognition Microcontroller under Noisy Environments

Industrial automation with speech control functions is generally installed with a speech recognition sensor which is used as an interface for users to articulate speech commands. However, recognition errors are likely to be produced when background noise surrounds the command spoken into the speech recognition microcontrollers. In this paper, a speech enhancement strategy is proposed to develop noise suppression filters in order to improve the accuracy of speech recognition microcontrollers. It uses a universal estimator, namely a neural network, to enhance the recognition accuracy of microcontrollers by integrating better signals processed by various noise suppression filters, where a global optimization algorithm, namely an intelligent particle swarm optimization, is used to optimize the inbuilt parameters of the neural network in order to maximize accuracy of speech recognition microcontrollers working within noisy environments. The proposed approach overcomes the limitations of the existing noise suppression filters intended to improve recognition accuracy. The performance of the proposed approach was evaluated by a speech recognition microcontroller, which is used in electronic products with speech control functions. Results show that the accuracy of the speech recognition microcontroller can be improved using the proposed approach, when working under low signal to noise ratio conditions in the industrial environments of automobile engines and factory machines

Real Time Surveillance for Low Resolution and Limited-Data Scenarios: An Image Set Classification Approach

This paper proposes a novel image set classification technique based on the concept of linear regression. Unlike most other approaches, the proposed technique does not involve any training or feature extraction. The gallery image sets are represented as subspaces in a high dimensional space. Class specific gallery subspaces are used to estimate regression models for each image of the test image set. Images of the test set are then projected on the gallery subspaces. Residuals, calculated using the Euclidean distance between the original and the projected test images, are used as the distance metric. Three different strategies are devised to decide on the final class of the test image set. We performed extensive evaluations of the proposed technique under the challenges of low resolution, noise and less gallery data for the tasks of surveillance, video-based face recognition and object recognition. Experiments show that the proposed technique achieves a better classification accuracy and a faster execution time compared to existing techniques especially under the challenging conditions of low resolution and small gallery and test data

Prevalence of vancomycin-resistant Enterococcus fecal colonization among kidney transplant patients

BACKGROUND: End stage renal disease patients are at risk of Vancomycin-Resistant Enterococcus (VRE) infections. The first reports of VRE isolation were from hemodialysis patients. However, to date, VRE fecal colonization rates as well as associated risk factors in kidney transplant patients have not yet been established in prospective studies. METHODS: We collected one or two stool samples from 280 kidney transplant patients and analysed the prevalence of VRE and its associated risk factors. Patients were evaluated according to the post-transplant period: group 1, less than 30 days after transplantation (102 patients), group 2, one to 6 months after transplantation (73 patients) and group 3, more than 6 months after transplantation (105 patients). RESULTS: The overall prevalence rate of fecal VRE colonization was 13.6% (38/280), respectively 13.7% for Group 1, 15.1% for group 2 and 12.4% for group 3. E. faecium and E. faecalis comprised 50% of all VRE isolates. No immunologic variables were clearly correlated with VRE colonization and no infections related to VRE colonization were reported. CONCLUSION: Fecal VRE colonization rates in kidney transplant patients were as high as those reported for other high-risk groups, such as critical care and hemodialysis patients. This high rate of VRE colonization observed in kidney transplant recipients may have clinical relevance in infectious complications

Процесс анализа угроз, влияющих на экономическую устойчивость предприятия

На основании проведенного исследования были выявлены факторы возникновения угроз, их группировка по степени воздействию на экономическую устойчивость предприятий и рассмотрена формализация процесса анализа угроз экономической устойчивости предприятий. В условиях рыночной экономики невозможно управлять предприятием без учета влияния угроз, а для эффективного управления важно не только знать об их присутствии, а и правильно идентифицировать конкретную угрозу.На підставі проведеного дослідження були виявлені чинники виникнення загроз, їх угруповання по степені впливу на економічну стійкість підприємств і розглянута формалізація процесу аналізу загроз економічної стійкості підприємств. В умовах ринкової економіки неможливо керувати підприємством без вивчення впливу загроз, а для ефективного керування важливо не тільки знати про їх присутність, а і правильно ідентифікувати конкретну загрозу.On the basis of the conducted research the factors of origin of threats were exposed, their gourmet on a degree to influence on economic stability of enterprises and formalization of process of analysis of threats of economic stability of enterprises is considered. In the conditions of market economy it is impossible to manage an enterprise without taking into account influencing of threats, and for the effective management it is important not only to know about their presence, and to identify the concrete threat correctly

A hybrid prognostic methodology for tidal turbine gearboxes

Tidal energy is one of promising solutions for reducing greenhouse gas emissions and it is estimated that 100 TWh of electricity could be produced every year from suitable sites around the world. Although premature gearbox failures have plagued the wind turbine industry, and considerable research efforts continue to address this challenge, tidal turbine gearboxes are expected to experience higher mechanical failure rates given they will experience higher torque and thrust forces. In order to minimize the maintenance cost and prevent unexpected failures there exists a fundamental need for prognostic tools that can reliably estimate the current health and predict the future condition of the gearbox.This paper presents a life assessment methodology for tidal turbine gearboxes which was developed with synthetic data generated using a blade element momentum theory (BEMT) model. The latter has been used extensively for performance and load modelling of tidal turbines. The prognostic model developed was validated using experimental data