Increasing Accuracy Performance through Optimal Feature Extraction Algorithms

This research developed models and techniques to improve the three key modules of popular recognition systems: preprocessing, feature extraction, and classification. Improvements were made in four key areas: processing speed, algorithm complexity, storage space, and accuracy. The focus was on the application areas of the face, traffic sign, and speaker recognition. In the preprocessing module of facial and traffic sign recognition, improvements were made through the utilization of grayscaling and anisotropic diffusion. In the feature extraction module, improvements were made in two different ways; first, through the use of mixed transforms and second through a convolutional neural network (CNN) that best fits specific datasets. The mixed transform system consists of various combinations of the Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT), which have a reliable track record for image feature extraction. In terms of the proposed CNN, a neuroevolution system was used to determine the characteristics and layout of a CNN to best extract image features for particular datasets. In the speaker recognition system, the improvement to the feature extraction module comprised of a quantized spectral covariance matrix and a two-dimensional Principal Component Analysis (2DPCA) function. In the classification module, enhancements were made in visual recognition through the use of two neural networks: the multilayer sigmoid and convolutional neural network. Results show that the proposed improvements in the three modules led to an increase in accuracy as well as reduced algorithmic complexity, with corresponding reductions in storage space and processing time

An Overview Of Recent Window Based Feature Extraction Algorithms For Speaker Recognition

An important first step in speaker recognition is the extraction of unique and reliable features that can identify speakers from speech signals. Feature extraction methods have evolved in the last 20 years, with window frame algorithms in particular showing promise. This paper compares and contrasts recent window frames algorithms using the Center for Spoken Language Understanding (CLSU) database through experiments. The different coefficients used and compared are: Real Cepstral Coefficients (RCC), Mel Cepstral Coefficients (MFCC), Linear Predictive Cepstral Coefficients (LPCC), and Perceptual Linear Predictive Cepstral Coefficients (PLPCC). The feature extraction methods will be used in conjunction with a Vector Quantization (VQ) method and a Euclidean distance classifier to find the best recognition rate among the feature extraction features. A survey of published state-of-the-art, window-based, feature extraction methods are evaluated against published results. © 2012 IEEE

Robust Speaker Recognition System Employing Covariance Matrix And Eigenvoice

This paper presents an original speaker recognition system that utilizes a quantized spectral covariance matrix on the input to a two-dimensional Principal Component Analysis (2DPCA) function. Eigenvoice algorithm is used as a classifying tool and is generated by the features of a group of speakers. The proposed system is selective in acquiring acoustic parameters and leads to a significant decrease in storage requirements. The system is robust in a noisy environment with recognition rates as high as 92% at 0dB SNR. Concatenated vowels that make up the speech signal are extracted from the TIMIT database and the noise environment is acquired from the NOIZEOUS database. © 2013 IEEE

Fractional deep dermal ablation induces tissue tightening

Background and objectiveDue to the significant risk profile associated with traditional ablative resurfacing, a safer and less invasive treatment approach known as fractional deep dermal ablation (FDDA) was recently developed. We report the results of the first clinical investigation of this modality for treatment of photodamaged skin.Study design/materials and methodsTwenty-four subjects received treatments on the inner forearm with a prototype fractional CO(2) laser device (Reliant Technologies Inc., Mountain View, CA) at settings of 5-40 mJ/MTZ and 400 MTZ/cm(2). Clinical and histological effects were assessed by study investigators 1 week, 1 month, and 3 months following treatment. Thirty subjects were then enrolled in a multi-center study for treatment of photodamage using the same device. Subjects received 1-2 treatments on the face and neck, with energies ranging from 10 to 40 mJ/MTZ and densities ranging from 400 to 1,200 MTZ/cm(2). Study investigators assessed severity of post-treatment responses during follow-up visits 48 hours, 1 week, 1 month, and 3 months following treatment. Using a standard quartile improvement scale (0-4), subjects and investigators assessed improvement in rhytides, pigmentation, texture, laxity and overall appearance 1 and 3 months post-treatment.ResultsClinical and histologic results demonstrated that fractional delivery of a 10,600 nm CO(2) laser source offers an improved safety profile with respect to traditional ablative resurfacing, while still effectively resurfacing epidermal and dermal tissue. Forearm and facial treatments were well-tolerated with no serious adverse events observed. Eighty-three percent of subjects exhibited moderate or better overall improvement (50-100%), according to study investigator quartile scoring.ConclusionsFDDA treatment is a safe and promising new approach for resurfacing of epidermal and deep dermal tissue targets

Plasma Concentrations of Soluble Suppression of Tumorigenicity-2 and Interleukin-6 Are Predictive of Successful Liberation From Mechanical Ventilation in Patients With the Acute Respiratory Distress Syndrome*

ObjectivesSoluble suppression of tumorigenicity-2 and interleukin-6 concentrations have been associated with the inflammatory cascade of acute respiratory distress syndrome. We determined whether soluble suppression of tumorigenicity-2 and interleukin-6 levels can be used as prognostic biomarkers to guide weaning from mechanical ventilation and predict the need for reintubation.Design, setting, and patientsWe assayed plasma soluble suppression of tumorigenicity-2 (n = 826) concentrations and interleukin-6 (n = 755) concentrations in the Fluid and Catheter Treatment Trial, a multicenter randomized controlled trial of conservative fluid management in acute respiratory distress syndrome. We tested whether soluble suppression of tumorigenicity-2 and interleukin-6 levels were associated with duration of mechanical ventilation, the probability of passing a weaning assessment, and the need for reintubation.Measurements and main resultsIn models adjusted for Acute Physiology and Chronic Health Evaluation score and other relevant variables, patients with higher day 0 and day 3 median soluble suppression of tumorigenicity-2 and interleukin-6 concentrations had decreased probability of extubation over time (day 0 soluble suppression of tumorigenicity-2: hazard ratio, 0.85; 95% CI, 0.72-1.00; p = 0.05; day 0 interleukin-6: hazard ratio, 0.64; 95% CI, 0.54-0.75; p < 0.0001; day 3 soluble suppression of tumorigenicity-2: hazard ratio, 0.64; 95% CI, 0.54-0.75; p < 0.0001; and day 3 interleukin-6: hazard ratio, 0.73; 95% CI, 0.62-0.85; p = 0.0001). Higher biomarker concentrations were also predictive of decreased odds of passing day 3 weaning assessments (soluble suppression of tumorigenicity-2: odds ratio, 0.62: 95% CI, 0.44-0.87; p = 0.006 and interleukin-6: odds ratio, 0.61; 95% CI, 0.43-0.85; p = 0.004) and decreased odds of passing a spontaneous breathing trial (soluble suppression of tumorigenicity-2: odds ratio, 0.45; 95% CI, 0.28-0.71; p = 0.0007 and interleukin-6 univariate analysis only: odds ratio, 0.55; 95% CI, 0.36-0.83; p = 0.005). Finally, higher biomarker levels were significant predictors of the need for reintubation for soluble suppression of tumorigenicity-2 (odds ratio, 3.23; 95% CI, 1.04-10.07; p = 0.04) and for interleukin-6 (odds ratio, 2.58; 95% CI, 1.14-5.84; p = 0.02).ConclusionsHigher soluble suppression of tumorigenicity-2 and interleukin-6 concentrations are each associated with worse outcomes during weaning of mechanical ventilation and increased need for reintubation in patients with acute respiratory distress syndrome. Biomarker-directed ventilator management may lead to improved outcomes in weaning of mechanical ventilation in patients with acute respiratory distress syndrome