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In this article, a new alignment-layer approach based on block copolymers (BCPs) is proposed for mobility anisotropy and enhancement of organic thin-film transistors (OTFTs). In particular, the poly(3-hexylthiophene) (P3HT) TFT with polystyrene-b/ock-poly(methyl methacrylate) alignment layer was studied. With max dichroic ratio 1.23 in polarized absorption spectra of P3HT and corresponding device mobility anisotropy 4.63, taking place at the highest annealing temperature of 230°C, the BCP alignment approach proved to be an effective tool for orientation control of polymers such as the P3HT. However, because of negative effect of thermal annealing that causes twisting in P3HT backbones, the achieved mobility enhancement was only a meager 41%. Nevertheless, with rich varieties in the composition and complexity of BCPs, the proposed BCP alignment design represents an interesting alternative to existing alignment approaches to orientation control of P3HT and mobility anisotropy and enhancement of the resultant OTFTs. Besides, the phase-alternate surface morphology of the BCP alignment layer was confirmed through a series of atomic force microscopy, X-ray photoelectron microscopy, and field-emission scanning electron microscopy. POLYM. ENG. SCI., 52:2581-2587, 2012. [C] 2012 Society of Plastics Engineers, INTRODUCTION During fabrication of organic thin-film transistors (OTFTs), it is generally believed that the carrier transport efficiency can be improved with higher degree of micro-crystallization and better order and proper [...]

We present weakly interacting massive particles (WIMPs) search results performed using two approaches of effective field theory from the China Dark Matter Experiment (CDEX), based on the data from both CDEX-1B and CDEX-10 stages. In the nonrelativistic effective field theory approach, both time-integrated and annual modulation analyses were used to set new limits for the coupling of WIMP-nucleon effective operators at 90% confidence level (C.L.) and improve over the current bounds in the low mχregion. In the chiral effective field theory approach, data from CDEX-10 were used to set an upper limit on WIMP-pion coupling at 90% C.L. We for the first time extended the limit to the mχ < 6 GeV/c2 region. [ABSTRACT FROM AUTHOR]

The studies on seizure prediction problem have shown great improvement these years. Machine learning based seizure prediction method shows great performance by doing pattern recognition on high-dimensional bivariate synchronization features. However, the computation loading of the machine learning based method may be too high to meet wearable or implantable devices with the power and area constraints. In this work, channel selection is proposed to reduce the channel number from 22 to less than 6 channels and therefore more than 93.73% of the computation loading is saved through the method. The best result shows successful rate of 60.6% in 3-channel cases of ECoG database and successful rate of 70% in 3-channel cases of EEG database.

Epilepsy is one of the most common brain disorders in the world. The spontaneous seizure onset influences the daily life of epilepsy patients. The studies on feature extraction and feature classification from Electroencephalography(EEG) signal in seizure prediction methods have shown great improvement these years. However, the variation issue of EEG signal (being awake, being asleep, severity of epilepsy, etc.) poses a fundamental difficulty in seizure prediction problem. The traditional off-line training method trains the model using a fixed training set, and expects the performance of the model to remain stable even after a long period of time, and thus suffers from variation issue. In this paper, we propose an on-line retraining method to leverage the recent input data by gradually enlarging the training set and retraining the model. Also, a simple post-processing scheme is incorporated to reduce false alarms. We develop our method based on the state of the art machine learning based classification of bivariate patterns method. The performance of the method is evaluated on Electrocorticogram(ECoG) recording from Freiburg database as well as long-term scalp EEG recording from CHB-MIT EEG Database and National Taiwan University Hospital. The proposed method achieves 74.2% sensitivity on ECoG database and 52.2% sensitivity on scalp EEG database, while improving the sensitivity of off-line training method by 29.0% and 17.4% in ECoG database and EEG database respectively. The experimental result suggests that on-line retraining can greatly improve the reliability and is promising for future seizure prediction method development.

On-chip implementation of Hilbert-Huang transform (HHT) has great impact to analyze the non-linear and non-stationary biomedical signals on wearable or implantable sensors for the real-time applications. Cubic spline interpolation (CSI) consumes the most computation in HHT, and is the key component for the HHT processor. In tradition, CSI in HHT is usually performed after the collection of a large window of signals, and the long latency violates the realtime requirement of the applications. In this work, we propose to keep processing the incoming signals on-line with small and overlapped data windows without sacrificing the interpolation accuracy. 58% multiplication and 73% division of CSI are saved after the data reuse between the data windows.