A two-microphone noise reduction system for cochlear implant users with nearby microphones. Part II: Performance Evaluation

Users of cochlear implants (auditory aids, which stimulate the auditory nerve electrically at the inner ear) often suffer from poor speech understanding in noise. We evaluate a small (intermicrophone distance 7 mm) and computationally inexpensive adaptive noise reduction system suitable for behind-the-ear cochlear implant speech processors. The system is evaluated in simulated and real, anechoic and reverberant environments. Results from simulations show improvements of 3.4 to 9.3 dB in signal to noise ratio for rooms with realistic reverberation and more than 18 dB under anechoic conditions. Speech understanding in noise is measured in 6 adult cochlear implant users in a reverberant room, showing average improvements of 7.9–9.6 dB, when compared to a single omnidirectional microphone or 1.3–5.6 dB, when compared to a simple directional two-microphone device. Subjective evaluation in a cafeteria at lunchtime shows a preference of the cochlear implant users for the evaluated device in terms of speech understanding and sound quality.

Computational fluids domain reduction to a simplified fluid network

The primary goal of this project is to demonstrate the practical use of data mining algorithms to cluster a solved steady-state computational fluids simulation (CFD) flow domain into a simplified lumped-parameter network. A commercial-quality code, “cfdMine” was created using a volume-weighted k-means clustering that that can accomplish the clustering of a 20 million cell CFD domain on a single CPU in several hours or less. Additionally agglomeration and k-means Mahalanobis were added as optional post-processing steps to further enhance the separation of the clusters. The resultant nodal network is considered a reduced-order model and can be solved transiently at a very minimal computational cost. The reduced order network is then instantiated in the commercial thermal solver MuSES to perform transient conjugate heat transfer using convection predicted using a lumped network (based on steady-state CFD). When inserting the lumped nodal network into a MuSES model, the potential for developing a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track temperatures near specific objects (such as equipment in vehicles).

Sediment yield reduction in an agricultural watershed

With proper application of Best Management Practices (BMPs), the impact from the sediment to the water bodies could be minimized. However, finding the optimal allocation of BMP can be difficult, since there are numerous possible options. Also, economics plays an important role in BMP affordability and, therefore, the number of BMPs able to be placed in a given budget year. In this study, two methodologies are presented to determine the optimal cost-effective BMP allocation, by coupling a watershed-level model, Soil and Water Assessment Tool (SWAT), with two different methods, targeting and a multi-objective genetic algorithm (Non-dominated Sorting Genetic Algorithm II, NSGA-II). For demonstration, these two methodologies were applied to an agriculture-dominant watershed located in Lower Michigan to find the optimal allocation of filter strips and grassed waterways. For targeting, three different criteria were investigated for sediment yield minimization, during the process of which it was found that the grassed waterways near the watershed outlet reduced the watershed outlet sediment yield the most under this study condition, and cost minimization was also included as a second objective during the cost-effective BMP allocation selection. NSGA-II was used to find the optimal BMP allocation for both sediment yield reduction and cost minimization. By comparing the results and computational time of both methodologies, targeting was determined to be a better method for finding optimal cost-effective BMP allocation under this study condition, since it provided more than 13 times the amount of solutions with better fitness for the objective functions while using less than one eighth of the SWAT computational time than the NSGA-II with 150 generations did.

Body contouring surgery following bariatric surgery and dietetically induced massive weight reduction: a risk analysis

BACKGROUND: This study analyzed the impact of weight reduction method, preoperative, and intraoperative variables on the outcome of reconstructive body contouring surgery following massive weight reduction. METHODS: All patients presenting with a maximal BMI >/=35 kg/m(2) before weight reduction who underwent body contouring surgery of the trunk following massive weight loss (excess body mass index loss (EBMIL) >/= 30%) between January 2002 and June 2007 were retrospectively analyzed. Incomplete records or follow-up led to exclusion. Statistical analysis focused on weight reduction method and pre-, intra-, and postoperative risk factors. The outcome was compared to current literature results. RESULTS: A total of 104 patients were included (87 female and 17 male; mean age 47.9 years). Massive weight reduction was achieved through bariatric surgery in 62 patients (59.6%) and dietetically in 42 patients (40.4%). Dietetically achieved excess body mass index loss (EBMIL) was 94.20% and in this cohort higher than surgically induced reduction EBMIL 80.80% (p < 0.01). Bariatric surgery did not present increased risks for complications for the secondary body contouring procedures. The observed complications (26.9%) were analyzed for risk factors. Total tissue resection weight was a significant risk factor (p < 0.05). Preoperative BMI had an impact on infections (p < 0.05). No impact on the postoperative outcome was detected in EBMIL, maximal BMI, smoking, hemoglobin, blood loss, body contouring technique or operation time. Corrective procedures were performed in 11 patients (10.6%). The results were compared to recent data. CONCLUSION: Bariatric surgery does not increase risks for complications in subsequent body contouring procedures when compared to massive dietetic weight reduction.

Reduction in rat phosphatidylethanolamine binding protein-1 (PEBP1) after chronic corticosterone treatment may be paralleled by cognitive impairment: a first study

Chronic stress is associated with hippocampal atrophy and cognitive dysfunction. This study investigates how long-lasting administration of corticosterone as a mimic of experimentally induced stress affects psychometric performance and the expression of the phosphatidylethanolamine binding protein (PEBP1) in the adult hippocampus of one-year-old male rats. Psychometric investigations were conducted in rats before and after corticosterone treatment using a holeboard test system. Rats were randomly attributed to 2 groups (n = 7) for daily subcutaneous injection of either 26.8 mg/kg body weight corticosterone or sesame oil (vehicle control). Treatment was continued for 60 days, followed by cognitive retesting in the holeboard system. For protein analysis, the hippocampal proteome was separated by 2D electrophoresis (2DE) followed by image processing, statistical analysis, protein identification via peptide mass fingerprinting and gel matching and subsequent functional network mapping and molecular pathway analysis. Differential expression of PEBP1 was additionally quantified by Western blot analysis. Results show that chronic corticosterone significantly decreased rat hippocampal PEBP1 expression and induced a working and reference memory dysfunction. From this, we derive the preliminary hypothesis that PEBP1 may be a novel molecular mediator influencing cognitive integrity during chronic corticosterone exposure in rat hippocampus.

DIMENSION REDUCTION FOR POWER SYSTEM MODELING USING PCA METHODS CONSIDERING INCOMPLETE DATA READINGS

Principal Component Analysis (PCA) is a popular method for dimension reduction that can be used in many fields including data compression, image processing, exploratory data analysis, etc. However, traditional PCA method has several drawbacks, since the traditional PCA method is not efficient for dealing with high dimensional data and cannot be effectively applied to compute accurate enough principal components when handling relatively large portion of missing data. In this report, we propose to use EM-PCA method for dimension reduction of power system measurement with missing data, and provide a comparative study of traditional PCA and EM-PCA methods. Our extensive experimental results show that EM-PCA method is more effective and more accurate for dimension reduction of power system measurement data than traditional PCA method when dealing with large portion of missing data set.