Discriminative Tandem Features for HMM-based EEG Classification

Abstract—We investigate the use of discriminative feature extractors in tandem configuration with generative EEG classification system. Existing studies on dynamic EEG classification typically use hidden Markov models (HMMs) which lack discriminative capability. In this paper, a linear and a non-linear classifier are discriminatively trained to produce complementary input features to the conventional HMM system. Two sets of tandem features are derived from linear discriminant analysis (LDA) projection output and multilayer perceptron (MLP) class-posterior probability, before appended to the standard autoregressive (AR) features. Evaluation on a two-class motor-imagery classification task shows that both the proposed tandem features yield consistent gains over the AR baseline, resulting in significant relative improvement of 6.2% and 11.2 % for the LDA and MLP features respectively. We also explore portability of these features across different subjects. Index Terms- Artificial neural network-hidden Markov models, EEG classification, brain-computer-interface (BCI)

Coherent Patterning of Matter Waves with Subwavelength Localization

We propose the Subwavelength Localization via Adiabatic Passage (SLAP) technique to coherently achieve state-selective patterning of matter waves well beyond the diffraction limit. The SLAP technique consists in coupling two partially overlapping and spatially structured laser fields to three internal levels of the matter wave yielding state-selective localization at those positions where the adiabatic passage process does not occur. We show that by means of this technique matter wave localization down to the single nanometer scale can be achieved. We analyze in detail the potential implementation of the SLAP technique for nano-lithography with an atomic beam of metastable Ne* and for coherent patterning of a two-component 87Rb Bose-Einstein condensate.Comment: 6 pages, 5 figure

Isolation of a kojic acid-producing fungus capable of using starch as a carbon source

A fungal strain (S33-2), able to grow on cooked starch and produce a substantially high level of kojic acid, was isolated from morning glory flower (Bixa orellana). The fungus was characterized and identified as Aspergillus flavus. The effect of different types of starch (sago, potato and corn starch) on growth of strain S33-2 and kojic acid production was examined using shake flasks. It was found that strain S33-2 grew well on all types of starch investigated. However, kojic acid production was highest when corn starch was used, with the maximum kojic acid obtained being comparable to fermentation using glucose. The highest kojic acid production (19.2 g l-1) was obtained when 75 g l-1 corn starch was used. This gave a yield, based on starch consumed, and an overall productivity of 0.256 g g-1 and 0.04 g l-1 h-1, respectively

Gray-level co-occurrence matrix bone fracture detection

Problem statement: Currently doctors in orthopedic wards inspect the bone x-ray images according to their experience and knowledge in bone fracture analysis. Manual examination of x-rays has multitude drawbacks. The process is time-consuming and subjective. Approach: Since detection of fractures is an important orthopedics and radiologic problem and therefore a Computer Aided Detection(CAD) system should be developed to improve the scenario. In this study, a fracture detection CAD based on GLCM recognition could improve the current manual inspection of x-ray images system. The GLCM for fracture and non-fracture bone is computed and analysis is made. Features of Homogeneity, contrast, energy, correlation are calculated to classify the fractured bone. Results: 30 images of femur fractures have been tested, the result shows that the CAD system can differentiate the x-ray bone into fractured and nonfractured femur. The accuracy obtained from the system is 86.67. Conclusion: The CAD system is proved to be effective in classifying the digital radiograph of bone fracture. However the accuracy rate is not perfect, the performance of this system can be further improved using multiple features of GLCM and future works can be done on classifying the bone into different degree of fracture specifically

Cross match-CHMM fusion for speaker adaptation of voice biometric

The most significant factor affecting automatic voice biometric performance is the variation in the signal characteristics, due to speaker-based variability, conversation-based variability and technology variability. These variations give great challenge in accurately modeling and verifying a speaker. To solve this variability effects, the cross match (CM) technique is proposed to provide a speaker model that can adapt to variability over periods of time. Using limited amount of enrollment utterances, a client barcode is generated and can be updated by cross matching the client barcode with new data. Furthermore, CM adds the dimension of multimodality at the fusion-level when the similarity score from CM can be fused with the score from the default speaker modeling. The scores need to be normalized before the fusion takes place. By fusing the CM with continuous Hidden Markov Model (CHMM), the new adapted model gave significant improvement in identification and verification task, where the equal error rate (EER) decreased from 6.51% to 1.23% in speaker identification and from 5.87% to 1.04% in speaker verification. EER also decreased over time (across five sessions) when the CM is applied. The best combination of normalization and fusion technique methods is piecewise-linear method and weighted sum

Estimating dynamic model parameters for adaptive protection and control in power system

This paper presents a new approach in estimating important parameters of power system transient stability model such as inertia constant H and direct axis transient reactance xd' in real time. It uses a variation of unscented Kalman filter (UKF) on the phasor measurement unit (PMU) data. The accurate estimation of these parameters is very important for assessing the stability and tuning the adaptive protection system on power swing relays. The effectiveness of the method is demonstrated in a simulated data from 16-machine 68-bus system model. The paper also presents the performance comparison between the UKF and EKF method in estimating the parameters. The robustness of method is further validated in the presence of noise that is likely to be in the PMU data in reality

A panel of cultivate specific marker based on polymorphisms at microsatellite markers for Iranian cultivated Almonds (Prunus dulcis).

Molecular markers developed for Prunus also offer a powerful tool to study the evolution of the genome, and for understanding of genome structure and determinants of genetic diversity. Two hundred eighty almond genotypes/cultivars from different origins distributed throughout Iran besides some foreign cultivars and their hybrids with Iranian ones were collected. Microsatellite analysis was carried out using 9 pair flanking SSR sequences previously cloned and sequenced specifically in almond. The total number of detected alleles was 152 (9 to 20 alleles per locus with an average of 16.87). The mean PIC value of the polymorphic loci wasrelatively high (0.81) and the mean value for He was 0.83, so that we were able to distinguish 98% of the genotypes using 5 loci. Incluster analysis, the genotypes were divided into 2 major groups, foreign cultivars and Iranian almond genotypes. Principal coordinate analysis based on Shared Allele method indicated proper distribution of the studied markers through the genome. Some specific markers were recorded among the germ plasm which can be used efficiently in rapid and precise identification of the related genotypes and also in breeding programs through MAS. Genotypes were coded using our suggested coding method for genotype molecular identification