Dissociative adsorption of pyrrole on Si(111)-(7×7)

Pyrrole adsorption on Si(111)-(7×7) has been investigated using high-resolution electron energy loss spectroscopy (HREELS), thermal desorption spectroscopy, scanning tunneling microscopy (STM), and theoretical calculations. Compared to physisorbed pyrrole, chemisorption leads to the appearance of N–Si and Si–H vibrational features, together with the absence of N–H stretching mode. This clearly demonstrates the dissociative nature of pyrrole chemically binding on Si(111)-(7×7) through the breakage of N–H bond. Based on STM results, the resulting fragments of pyrrolyl and H atom are proposed to bind with an adatom and an adjacent rest atom, respectively. The STM images further reveal that the adsorption is site selective. The faulted center adatoms are most favored, followed by unfaulted center adatoms, faulted corner adatoms, and unfaulted corner adatoms. In addition, the chainlike pattern of reacted adatoms was observed, implying the possible existence of attractive interaction between adsorbed pyrrolyl and the precursor state. Theoretical calculation confirms that the dissociative adsorption with pyrrolyl bonded to an adatom and H atom to an adjacent rest atom is energetically favored compared to the associative cycloaddition involving the two alpha-carbon atoms of pyrrole and an adatom–rest atom pair. ©2003 American Institute of Physics

STM STUDIES OF THIOPHENE, FURAN, AND PYRROLE ADSORPTION ON SI(111)-7X7

Master'sMASTER OF SCIENC

Chemisorptions of some acene molecules on Si(111)-7x7

Ph.DDOCTOR OF PHILOSOPH

Wide Residual Network for Vision-based Static Hand Gesture Recognition

Hand gesture is a communication tool that allows messages to be conveyed, actions to be performed through hand gestures. Hence, it has the ability to simplify communication and enhance human computer interaction. This paper proposed Wide Residual Network for static hand gesture recognition. WRN improves feature propagation and gradient flows by utilizing shortcut connection in residual block. Wide residual block further improves upon residual block by increasing the width of the network and improving feature reuse, and thereby allowing the depth of the network to be trimmed and fewer trainable parameters to be learned. The network is experimented on three public datasets and compared with existing convolutional neural network (CNN) variants proposed for static hand gesture recognition. Experimental results show Wide Residual Network outperforms the existing CNN variants proposed for hand gesture recognition

Hand gesture recognition via enhanced densely connected convolutional neural network

Hand gesture recognition (HGR) serves as a fundamental way of communication and interaction for human being. While HGR can be applied in human computer interaction (HCI) to facilitate user interaction, it can also be utilized for bridging the language barrier. For instance, HGR can be utilized to recognize sign language, which is a visual language represented by hand gestures and used by the deaf and mute all over the world as a primary way of communication. Hand-crafted approach for vision-based HGR typically involves multiple stages of specialized processing, such as hand-crafted feature extraction methods, which are usually designed to deal with particular challenges specifically. Hence, the effectiveness of the system and its ability to deal with varied challenges across multiple datasets are heavily reliant on the methods being utilized. In contrast, deep learning approach such as convolutional neural network (CNN), adapts to varied challenges via supervised learning. However, attaining satisfactory generalization on unseen data is not only dependent on the architecture of the CNN, but also dependent on the quantity and variety of the training data. Therefore, a customized network architecture dubbed as enhanced densely connected convolutional neural network (EDenseNet) is proposed for vision-based hand gesture recognition. The modified transition layer in EDenseNet further strengthens feature propagation, by utilizing bottleneck layer to propagate the features being reused to all the feature maps in a bottleneck manner, and the following Conv layer smooths out the unwanted features. Differences between EDenseNet and DenseNet are discerned, and its performance gains are scrutinized in the ablation study. Furthermore, numerous data augmentation techniques are utilized to attenuate the effect of data scarcity, by increasing the quantity of training data, and enriching its variety to further improve generalization. Experiments have been carried out on multiple datasets, namely one NUS hand gesture dataset and two American Sign Language (ASL) datasets. The proposed EDenseNet obtains 98.50% average accuracy without augmented data, and 99.64% average accuracy with augmented data, outperforming other deep learning driven instances in both settings, with and without augmented data

Convolutional neural network with spatial pyramid pooling for hand gesture recognition

Hand gesture provides a means for human to interact through a series of gestures. While hand gesture plays a significant role in human–computer interaction, it also breaks down the communication barrier and simplifies communication process between the general public and the hearing-impaired community. This paper outlines a convolutional neural network (CNN) integrated with spatial pyramid pooling (SPP), dubbed CNN–SPP, for vision-based hand gesture recognition. SPP is discerned mitigating the problem found in conventional pooling by having multi-level pooling stacked together to extend the features being fed into a fully connected layer. Provided with inputs of varying sizes, SPP also yields a fixed-length feature representation. Extensive experiments have been conducted to scrutinize the CNN–SPP performance on two well-known American sign language (ASL) datasets and one NUS hand gesture dataset. Our empirical results disclose that CNN–SPP prevails over other deep learning-driven instances

Fingers Bending Motion Controlled Electrical Wheelchair by Using Flexible Bending Sensors with Kalman filter Algorithm

Abstract Severely disabled people have difficulties to use joystick in controlling electrical power wheelchair because controlling the joystick requires a large force which is more than the threshold for severely disabled people. It is difficult for them to use joystick to provide precise commands to the electrical system of the wheelchair because they cannot control over the deck tilt angles of joystick precisely. Thus, the idea of using fingers bending motion to control electrical wheelchair provides a solution for this problem. However, trembling fingers motions from disabled people generate signal noise that cause the motion control of the wheelchair not running smoothly. The objective of this paper is to tackle signal noises that are caused by trembling fingers motion. Three filtering methods were conducted which are Moving Average, Low-Pass, and Kalman Filters. The results indicate that Kalman Filter has significantly improved the smoothness of fingers bending command signal to the electrical wheelchair as compared to Moving Average and Low-Pass Filter. 638 Kok Seng Eu et al