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

Gambaran budaya masyarakat Brunei dalam kumpulan puisi Diam-Diam karya K. Manis

Kajian ini merupakan sebuah kajian kepustakaan yang bertujuan mengenal pasti dan membincangkan gambaran budaya masyarakat Brunei dalam kumpulan puisi Diam-Diam (2010) karya K. Manis. Kumpulan ini mengandungi 73 buah sajak. Data kepustakaan dianalisis dengan memanfaatkan kaedah analisis kandungan bersandarkan konsep budaya yang dikemukakan Edward B. Tylor (1974) dan juga yang diajukan oleh A. Aziz Deraman (2005). Hasil analisis mendapati enam gambaran budaya masyarakat Brunei yang diangkat penyair. Enam gambaran budaya masyarakat itu adalah yang bergantung hidup pada sumber pertanian, masyarakat yang berhadapan dengan musibah, gambaran akan adat orang Besar-Besar, gambaran budaya berbudi pada tanah, gambaran budaya warga bernegara dan gambaran perubahan budaya. Enam gambaran itu dapat dikelompokkan kepada tiga aspek kehidupan masyarakat iaitu pertanian, alam sekitar dan hidup bernegara. Dapatan kajian juga merumuskan tiada gambaran budaya yang berkait dengan aspek perdagangan antarabangsa, petroleum, politik dan pendidikan, walhal kegiatan-kegiatan ini merupakan teras kemajuan dan pembinaan masyarakat bernegara di Brunei. Berdasarkan dapatan ini, beberapa cadangan wajar diambil perhatian khususnya kepada pihak Dewan Bahasa dan Pustaka Negara Brunei Darussalam. Antaranya usul supaya dilaksanakan kajian lanjut tentang budaya masyarakat Brunei yang juga perlu diperluaskan kepada kumpulan-kumpulan puisi penyair dan genre-genre lain yang melibatkan prosa tradisional dan moden. Ia pasti sahaja memberikan sumbangan besar kepada khazanah kesusasteraan Brunei mahupun kesusasteraan Melayu Nusantara

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

A Comparison of Supervised Learning Techniques for Predicting the Mortality of Patients with Altered State of Consciousness

The study attempts to identify a potentially reliable supervised learning technique for predicting the outcomes of mortality in an altered state of consciousness (ASC) patients. ASC is a state distinguished from ordinary waking consciousness, which is a common phenomenon in the Emergency Department (ED). Thirty (30) distinctive attributes or features are commonly used to recognize ASC. The study accordingly applied these features to model the prediction of mortality in ASC patients. Supervised learning techniques are found to be suitable for such classification problems. Consequently, the study compared five supervised learning techniques that are commonly applied to evaluate the risk of mortality using health-related datasets, namely Decision Tree, Neural Network, Random Forest, Naïve Bayes, and Logistic Regression. The labeled dataset comprised patient records captured by the Universiti Sains Malaysia hospital’s Emergency Medicine department from June to November 2008. The cleaned dataset was divided into two parts. The larger part was used for training and the smaller part, for evaluation. Since the ratio between training and testing samples varies between individual supervised learning techniques, we studied the performance of the modeled techniques by also varying the proportion of the training data to the dataset. We applied four percentage splits; 66%, 75%, 80%, and 90% to allow for 3-, 4-, 5- and 10-fold cross-validation experiments to evaluate the accuracy of the analyzed techniques. The variation helped to lessen the chance of over fitting, and averaged the effects of various conditions on accuracy. The experiments were conducted in the WEKA environment. The results indicated that Random Forest is the most reliable technique to model for predicting the mortality in ASC patients with acceptable accuracy, sensitivity, and specificity of 70.9%, 76.3%, and 65.5%, respectively. The results are further confirmed by SROC analysis. The findings of the study serve as a fundamental step towards a comprehensive study in the future

Compressive Behaviour Of Syntactic Foam Filled With Epoxy Hollow Spheres Having Different Wall Thickness.

An innovative approach of producing epoxy syntactic foams was developed by incorporating single-coated and double-coated of epoxy hollow spheres within epoxy resin matrix

Effect Of Compound Formulation On The Production And Properties Of Epoxidised Natural Rubber (Enr-25) Foams.

In this study, Epoxidized Natural Rubber (ENR-25) formulations are compounded and tested to obtain a stable expandable rubber foam as well as to determine the foam cell physical morphology and its mechanical properties. The experiment was carried out by employing different ratio of rubber blend between ENR-25 and natural rubber (SMR-L), different amount of blowing agent which is Sodium Bicarbonate and different ratio of accelerator between Tetramethylthiuram-disulfenamide (TMTD) and N-cyclohexyl-2-benzotiazolsulfenamide (CBS)

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