Understanding social networking sites adoption in China: A comparison of pre-adoption and post-adoption

Prior experience is an important determinant factor of individual behavior. This paper developed a theoretical model to predict the adoption intention of pre-adopters and post-adopters on social networking sites based on the theory of planned behavior. Using data from online surveys of netizens in China, the proposed model was tested in the context of pre-adoption and post-adoption by using the Partial Least Squares (PLS) technique. Then, multi-groups analysis was explored to compare the difference between the two groups. The results show that attitude, subjective norm and perceived behavior control have significant effect on the adoption intention of pre-adopters and post-adopters, and there is no significant difference between the two groups. In addition, information, meeting new people, and conformity motivations have the same significant effect on both groups. However, entertainment motivation has a significant effect on pre-adopters but connecting with old friends has none; in contrast, connecting with old friends has significant effect on post-adopters while entertainment motivation has no significant effect.     

Microscale surface thermometry using SU8/Rhodamine-B thin layer

In the present paper, we propose a microscale surface thermometry using SU8/Rhodamine-B thin layer (SRTL). The ultra-thin SRTL is coated on the microheater which serves as a heat source for microscale heating. Surface-temperature distribution of the SRTL is measured with high spatial and temperature resolutions in a temperature range between 30 and 80 °C. Propagation of isotherms on the SRTL is visualized with a temperature resolution on the order of 0.1 °C. Spatial error in the measurement was analyzed, which depends on the size of interrogation window. The present concept of SU8 thin film in combination with Rhodamine-B signifies a considerable improvement in the indirect measurement method of surface temperature inside polymer-based microfluidic channels.     

Clipping algorithms for solving the nearest point problem over reduced convex hulls

The nearest point problem (NPP), i.e., finding the closest points between two disjoint convex hulls, has two classical solutions, the Gilbert-Schlesinger-Kozinec (GSK) and Mitchell-Dem’yanov-Malozemov (MDM) algorithms. When the convex hulls do intersect, NPP has to be stated in terms of reduced convex hulls (RCHs), made up of convex pattern combinations whose coefficients are bound by a μ<1 value and that are disjoint for suitable μ. The GSK and MDM methods have recently been extended to solve NPP for RCHs using the particular structure of the extreme points of a RCH. While effective, their reliance on extreme points may make them computationally costly, particularly when applied in a kernel setting. In this work we propose an alternative clipped extension of classical MDM that results in a simpler algorithm with the same classification accuracy than that of the extensions already mentioned, but also with a much faster numerical convergence.     

Learning low-rank Mercer kernels with fast-decaying spectrum

Low-rank representations have received a lot of interest in the application of kernel-based methods. However, these methods made an assumption that the spectrum of the Gaussian or polynomial kernels decays rapidly. This is not always true and its violation may result in performance degradation. In this paper, we propose an effective technique for learning low-rank Mercer kernels (LMK) with fast-decaying spectrum. What distinguishes our kernels from other classical kernels (Gaussian and polynomial kernels) is that the proposed always yields low-rank Gram matrices whose spectrum decays rapidly, no matter what distribution the data are. Furthermore, the LMK can control the decay rate. Thus, our kernels can prevent performance degradation while using the low-rank approximations. Our algorithm has favorable in scalability—it is linear in the number of data points and quadratic in the rank of the Gram matrix. Empirical results demonstrate that the proposed method learns fast-decaying spectrum and significantly improves the performance.     

On chip novel video streaming system for bi-network multicasting protocols

In the past three decades, tremendous Ethernet-related research has been done, which has led to today's ubiquitous Ethernet technology. On the other hand, with the emergence of new network needs, a new protocol, the IEEE 1394 standard serial bus (or Firewire) was introduced. Firewire is suitable for high-quality audio/video applications which do not perform well in the best-effort-based Ethernet technology. However, since Firewire is a serial bus, it has harsh cable length limitations as compared to Ethernet capabilities.In this paper, we present a novel on-chip system that receives Firewire video and transmits it in multicast mode using Ethernet protocol. A major advantage of this novel system is to utilize the existing Ethernet infrastructure to extend the range of Firewire video streaming to reach remote nodes and make it even accessible to nodes with a single Ethernet interface. This will have tremendous impact on Firewire applications such as deploying Firewire cameras in big-scale security-sensitive buildings or industrial facilities with image-based remote quality control.This novel chip utilizes the concept of Ethernet multicasting transmission mode for video streaming. The proposed chip design converts the IEEE 1394 isochronous traffic to the Ethernet multicast frame format via two off-chip asynchronous write and read buffers.The goal of this research is to design an On Chip Novel Video Streaming System that avoids performance bottlenecks in the software protocol conversion of these two important network protocols. The author decided to study these two networks because of their broad use and cable power provisioning capabilities. The novel system design is implemented using a customized field programmable gate array (FPGA), which enables the integration of various system components on one chip. The designed prototype is studied using both network monitoring tools and analytical techniques, to verify its function and compare it with the existing approaches.Performance measures show that the On Chip Novel Video Streaming System consumes less than 21 mW of power for 100 Mbps and 82 mW of power for 1 Gbps, and utilizes 57% of a Xilinx Spartan 2-100E-6FT256 FPGA resources. Hence, it is possible to incorporate further extensions. Experimental results show that 88% of the network utilization can be achieved, due to the use of the customized, FPGA-based design of bi-network traffic conversion.     

Speed and rotor flux estimation of induction machines using a two-stage extended Kalman filter

This paper presents an effective implementation of an extended Kalman filter used for the estimation of both rotor flux and rotor velocity of an induction motor. An algorithm proposed by Hsieh and Chen in [Hsieh, C.S., & Chen, F.C. (1999). Optimal solution of the two-stage Kalman estimator. IEEE Transactions on automatic control, 44(1), 194-199] for linear parameter estimation is extended to non-linear estimation, where parameters such as the velocity of an induction machine are present in the transition matrix and in the augmented state space. Compared to a straightforward implementation of an extended Kalman filter, our modified optimal two-stage Kalman estimator reduces the number of arithmetic operations by 25%, allowing higher sampling rate or the use of a cheaper microcontroller.     

Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment

Colour, marbling and surface texture properties of beef longissimus dorsi muscle are used in some countries to grade carcasses according to their expected eating quality. Handheld VIA systems are being used to augment the grader assessments, however attempts have been made to develop higher resolution image systems to give consistent and objective predictions of quality based on these properties. Previous efforts have been unable to model sufficiently the variation in eating quality. A new approach has been applied whereby beef carcasses were subjected to homogenous post-slaughter treatment to minimize variation in eating quality related to other factors such as chilling temperature and hanging method. Furthermore a wider range of features were used to better characterize colour and marbling and the wavelet transform was used to characterize texture. Objective and sensory panel tests were performed to evaluate the beef eating qualities. Classical statistical methods of multilinear regression (MLR) and partial least squares regression (PLSR) were used to develop predictive models. It was possible to explain a greater portion of variation in eating quality than before (up to r²=0.83). Carcasses were classified as high or low quality with a high rate of correct classifications (90%). Genetic algorithms were used to select the model subsets.     

nAIT: A source analysis and instrumentation framework for nesC

Automated software engineering methods support the construction, maintenance, and analysis of both new and legacy systems. Their application is commonplace in desktop- and enterprise-class systems due to the productivity and reliability benefits they afford. The contribution of this article is to present an applied foundation for extending the use of such methods to the flourishing domain of wireless sensor networks. The objective is to enable developers to construct tools that aid in understanding both the static and dynamic properties of reactive, event-based systems. We present a static analysis and instrumentation toolkit for the nesC language, the defacto standard for sensor network development. We highlight the novel aspects of the toolkit, analyze its performance, and provide representative case-studies that illustrate its use.