Speaker recognition utilizing distributed DCT-II based Mel frequency cepstral coefficients and fuzzy vector quantization

In this paper, a new and novel Automatic Speaker Recognition (ASR) system is presented. The new ASR system includes novel feature extraction and vector classification steps utilizing distributed Discrete Cosine Transform (DCT-II) based Mel Frequency Cepstral Coefficients (MFCC) and Fuzzy Vector Quantization (FVQ). The ASR algorithm utilizes an approach based on MFCC to identify dynamic features that are used for Speaker Recognition (SR). A series of experiments were performed utilizing three different feature extraction methods: (1) conventional MFCC; (2) Delta-Delta MFCC (DDMFCC); and (3) DCT-II based DDMFCC. The experiments were then expanded to include four classifiers: (1) FVQ; (2) K-means Vector Quantization (VQ); (3) Linde, Buzo and Gray VQ; and (4) Gaussian Mixed Model (GMM). The combination of DCT-II based MFCC, DMFCC and DDMFCC with FVQ was found to have the lowest Equal Error Rate for the VQ based classifiers. The results found were an improvement over previously reported non-GMM methods and approached the results achieved for the computationally expensive GMM based method. Speaker verification tests carried out highlighted the overall performance improvement for the new ASR system. The National Institute of Standards and Technology Speaker Recognition Evaluation corpora was used to provide speaker source data for the experiments.     

An ontology-driven framework towards building enterprise semantic information layer

Enterprises Information Systems (EIS) have been applied for decades in Computer-Aided Engineering (CAE) and Computer-Aided Design (CAD), where huge amount of increasing data is stored in the heterogeneous and distributed systems. As systems evaluating, system redesign and reengineering are demanded. A facing challenge is how to interoperate among different systems by overcoming the gap of conceptual heterogeneity. In this article, an enlarged data representation called semantic information layer (SIL) is described for facilitating heterogeneous systems interoperable. SIL plays a role as mediation media and knowledge representation among heterogeneous systems. The SIL building process is based on ontology engineering, including ontology extraction from relational database (RDB), ontology enrichment and ontology alignment. Mapping path will maintain the links between SIL and data source, and query implementation and user interface are for retrieving data and interacting with end users. We described fully a practical ontology-driven framework for building SIL and introduced extensively relevant standards and techniques for implementing the framework. In the core part of ontology development, a dynamic multi-strategies ontology alignment with automatic matcher selection and dynamic similarity aggregation is proposed. A demonstration case study in the scenario of mobile phone industry is used to illustrate the proposed framework.     

Effect of enhanced coagulation by KMnO(4) on the fouling of ultrafiltration membranes

Pre-coagulation enhanced by KMnO(4) before ultrafiltration (KCUF) was compared with normal pre-coagulation by alum (CUF) in the ultrafiltration of water from the Songhua River, China. The trans-membrane pressure (TMP) with KCUF was much lower than that when alum alone was used. With KCUF a slower increment of TMP occurred, even under conditions of high river water turbidity. The results also showed that the removal of COD, UV(254) and TOC was appreciably higher after adding 0.5mg/L KMnO(4) compared with CUF. Although assimilable organic carbon (AOC) was increased by permanganate treatment, the AOC of the permeate from KCUF was nearly the same as that from CUF, showing that the cake layer on the surface of KCUF membrane could adsorb small molecules more effectively than that of CUF. This result was confirmed by the apparent molecular weight (MW) distribution measured by size exclusion chromatography (SEC). It was shown that flocs formed by KMnO(4) and alum were larger than those formed only by alum, causing higher removal of flocs and higher permeation flux. Lower NOM was found in the permeate from the KCUF systems because oxidation and adsorption of organic matter on the flocs occurred. The membrane was partly clogged by organic matter or other materials including some small flocs.     

Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR

Remote sensing of invasive species is a critical component of conservation and management efforts, but reliable methods for the detection of invaders have not been widely established. In Hawaiian forests, we recently found that invasive trees often have hyperspectral signatures unique from that of native trees, but mapping based on spectral reflectance properties alone is confounded by issues of canopy senescence and mortality, intra- and inter-canopy gaps and shadowing, and terrain variability. We deployed a new hybrid airborne system combining the Carnegie Airborne Observatory (CAO) small-footprint light detection and ranging (LiDAR) system with the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) to map the three-dimensional spectral and structural properties of Hawaiian forests. The CAO-AVIRIS systems and data were fully integrated using in-flight and post-flight fusion techniques, facilitating an analysis of forest canopy properties to determine the presence and abundance of three highly invasive tree species in Hawaiian rainforests.

The LiDAR sub-system was used to model forest canopy height and top-of-canopy surfaces; these structural data allowed for automated masking of forest gaps, intra- and inter-canopy shadows, and minimum vegetation height in the AVIRIS images. The remaining sunlit canopy spectra were analyzed using spatially-constrained spectral mixture analysis. The results of the combined LiDAR-spectroscopic analysis highlighted the location and fractional abundance of each invasive tree species throughout the rainforest sites. Field validation studies demonstrated < 6.8% and < 18.6% error rates in the detection of invasive tree species at  7 m² and  2 m² minimum canopy cover thresholds. Our results show that full integration of imaging spectroscopy and LiDAR measurements provides enormous flexibility and analytical potential for studies of terrestrial ecosystems and the species contained within them.     

Wetland change mapping for the U.S. mid-Atlantic region using an outlier detection technique

Although the impacts of wetland loss are often felt at regional scales, effective planning and management require a comparative assessment of local needs, costs, and benefits. Satellite remote sensing can provide spatially explicit, synoptic land cover change information to support such an assessment. However, a common challenge in conventional remote sensing change detection is the difficulty of obtaining phenologically and radiometrically comparable data from the start and end of the time period of interest. An alternative approach is to use a prior land cover classification as a surrogate for historic satellite data and to examine the self-consistency of class spectral reflectances in recent imagery. We produced a 30-meter resolution wetland change probability map for the U.S. mid-Atlantic region by applying an outlier detection technique to a base classification provided by the National Wetlands Inventory (NWI). Outlier-resistant measures – the median and median absolute deviation – were used to represent spectral reflectance characteristics of wetland class populations, and formed the basis for the calculation of a pixel change likelihood index. The individual scene index values were merged into a consistent region-wide map and converted to pixel change probability using a logistic regression calibrated through interpretation of historic and recent aerial photography. The accuracy of a regional change/no-change map produced from the change probabilities was estimated at 89.6%, with a Kappa of 0.779. The change probabilities identify areas for closer inspection of change cause, impact, and mitigation potential. With additional work to resolve confusion resulting from natural spatial heterogeneity and variations in land use, automated updating of NWI maps and estimates of areal rates of wetland change may be possible. We also discuss extensions of the technique to address specific applications such as monitoring marsh degradation due to sea level rise and mapping of invasive species.     

Maximum-likelihood registration of range images with missing data

Missing data are common in range images, due to geometric occlusions, limitations in the sensor field of view, poor reflectivity, depth discontinuities, and cast shadows. Using registration to align these data often fails, because points without valid correspondences can be incorrectly matched. This paper presents a maximum likelihood method for registration of scenes with unmatched or missing data. Using ray casting, correspondences are formed between valid and missing points in each view. These correspondences are used to classify points by their visibility properties, including occlusions, field of view, and shadow regions. The likelihood of each point match is then determined using statistical properties of the sensor, such as noise and outlier distributions. Experiments demonstrate a high rates of convergence on complex scenes with varying degrees of overlap.     

Fixed-Parameter Complexity of Minimum Profile Problems

The profile of a graph is an integer-valued parameter defined via vertex orderings; it is known that the profile of a graph equals the smallest number of edges of an interval supergraph. Since computing the profile of a graph is an NP-hard problem, we consider parameterized versions of the problem. Namely, we study the problem of deciding whether the profile of a connected graph of order n is at most n−1+k, considering k as the parameter; this is a parameterization above guaranteed value, since n−1 is a tight lower bound for the profile. We present two fixed-parameter algorithms for this problem. The first algorithm is based on a forbidden subgraph characterization of interval graphs. The second algorithm is based on two simple kernelization rules which allow us to produce a kernel with linear number of vertices and edges. For showing the correctness of the second algorithm we need to establish structural properties of graphs with small profile which are of independent interest. A preliminary version of the paper is published in Proc. IWPEC 2006, LNCS vol. 4169, 60–71.     

Thermodynamic modeling of the Na–Al–Ti–H system and Ti dissolution in sodium alanates

Thermodynamic assessments were made to optimize thermodynamic models and parameter fits to selected experimental and first principles hypothetical predicted phase data within the Na–Al–Ti–H system. This enabled thermodynamic modeling of Ti solubility within the sodium alanates: NaAlH₄ and Na₃AlH₆, and the relative stability of Ti-bearing phases. The modeling provides insights into the role of Ti originating from Ti-based activating agents commonly referred to as ‘catalysts’ in promoting reversibility of the Na–Al–H dehydrogenation and rehydrogenation reactions under moderate temperature and pressure conditions relevant to H storage applications. Preliminary assessments were made to evaluate H solubility in bcc-Ti and hcp-Ti, and stability of the hydride δ-TiH₂. To model possible Ti dissolution in NaAlH₄ and α-Na₃AlH₆, sub-lattice models were applied. A repulsive interaction is predicted by first principles calculations when Ti is dissolved in NaAlH₄ or α-Na₃AlH₆, which becomes stronger with increasing temperature. Although Ti is virtually insoluble in NaAlH₄ or α-Na₃AlH₆, a small addition of TiCl₃ will induce a thermodynamic driving force for formation of TiH₂ and/or TiAl₃. The addition of pure Ti shows a weaker effect than TiCl₃ and leads to formation of TiH₂ only. Based on a combined interpretation of present thermodynamic modeling and prior experimental observations, the TiAl₃ and TiH₂ phases are ascribed to have a catalytic effect, not a thermodynamic destabilization effect, on the reversibility of the dehydrogenation/rehydrogenation reactions in the Na–Al–H system.     

Inter-Image Statistics for 3D Environment Modeling

In this article we present a method for automatically recovering complete and dense depth maps of an indoor environment by fusing incomplete data for the 3D environment modeling problem. The geometry of indoor environments is usually extracted by acquiring a huge amount of range data and registering it. By acquiring a small set of intensity images and a very limited amount of range data, the acquisition process is considerably simplified, saving time and energy consumption. In our method, the intensity and partial range data are registered first by using an image-based registration algorithm. Then, the missing geometric structures are inferred using a statistical learning method that integrates and analyzes the statistical relationships between the visual data and the available depth on terms of small patches. Experiments on real-world data on a variety of sampling strategies demonstrate the feasibility of our method. Electronic Supplementary Material  The online version of this article () contains supplementary material, which is available to authorized users.     

Cyclic voltammetry on electrode surfaces covered with porous layers: An analysis of electron transfer kinetics at single-walled carbon nanotube modified electrodes

Cyclic voltammetry is recorded of the oxidation of ferrocyanide on a glassy carbon electrode modified with multiple layers of single-walled carbon nanotubes. The current response is interpreted in terms of semi-infinite planar diffusion towards the macro-electrode surface and in terms of oxidation of the electroactive species trapped in pockets in between the nanotubes. A thin layer model is used to illustrate the effects of diffusion within a porous layer. It is found that a semi-infinite planar diffusion model alone is not appropriate for interpreting the kinetics of the electron transfer at this electrode surface. In particular, caution should be exercised in respect of comparing voltammetric peak-to-peak potential separations between naked electrodes and nanotube-modified electrodes for the inference of electrocatalysis via electron transfer via the nanotubes.