High-Efficient Parallel CAVLC Encoders on Heterogeneous Multicore Architectures

This article presents two high-efficient parallel realizations of the context-based adaptive variable length coding (CAVLC) based on heterogeneous multicore processors. By optimizing the architecture of the CAVLC encoder, three kinds of dependences are eliminated or weaken, including the context-based data dependence, the memory accessing dependence and the control dependence. The CAVLC pipeline is divided into three stages: two scans, coding, and lag packing, and be implemented on two typical heterogeneous multicore architectures. One is a block-based SIMD parallel CAVLC encoder on multicore stream processor STORM. The other is a component-oriented SIMT parallel encoder on massively parallel architecture GPU. Both of them exploited rich data-level parallelism. Experiments results show that compared with the CPU version, more than 70 times of speedup can be obtained for STORM and over 50 times for GPU. The implementation of encoder on STORM can make a real-time processing for 1080p @30fps and GPU-based version can satisfy the requirements for 720p real-time encoding. The throughput of the presented CAVLC encoders is more than 10 times higher than that of published software encoders on DSP and multicore platforms

Statistical Analysis Methods in Engineering Education Research: A state-of-the-art Review

In the past, many studies were applied various statistical analysis methods to evaluate students' learning achievement and satisfaction for improving the effectiveness of online teaching. However, most of these decided to rely on relatively fixed fundamental quantitative methodologies to determine essential results. Few studies have adequately classified statistical methods in engineering education to critically consider correlational trends or causal mechanisms in the field and make research results more explanatory and inclusive. Therefore, our main challenge is appropriately selecting quantitative or qualitative statistical methods used in online engineering education to make the research results more convincing. To fill this 'gap,' this article re-examines previous papers to summarize a statistical method in the online engineering discipline from diverse perspectives and construct a new mechanism of evaluating statistical methods for effective research in this field. Our goal is to provide an unexplored review of statistical methods of the online teaching and learning process considering the engineering educational perspective

Singaporean and Taiwanese pre-service teachers' beliefs and their attitude towards ICT: A comparative study

Teachers' epistemological and pedagogical beliefs and their attitude about ICT are identified as the second-order barrier for the integration of ICT into classrooms. In this paper, we report the findings obtained from our recent survey and conducted among Singaporean and Taiwanese pre-service teachers (N=108). The results indicate that the teachers' epistemological beliefs were generally relativistic. They were also inclined to believe rather strongly the constructivist notion of teaching. The profile we obtained in this study seems to suggest that pre-service teachers from Singapore and Taiwan are holding beliefs that are congruent to the education reform efforts. However, the teachers' attitude about ICT use does not seem to relate to their epistemological and pedagogical beliefs. The findings suggest that further effort needs to be taken in order to foster more productive use of ICT to support constructivism-oriented teaching. These results need to be verified with further study

Delayed implantation of a peripheral nerve graft reduces motoneuron survival but does not affect regeneration following spinal root avulsion in adult rats

Adult spinal motoneurons can regenerate their axons into a peripheral nerve (PN) graft following root avulsion injury if the graft is implanted immediately after the lesion is induced. The present study was designed to determine how avulsed motoneurons respond to a PN graft if implantation takes place a few days to a few weeks later. Survival, regeneration, and gene expression changes of injured motoneurons after delayed PN graft implantation were studied. The survival rates of spinal motoneurons were 78%, 65%, 57%, or 53% if a PN graft was implanted immediately, 1, 2, or 3 weeks after root avulsion, respectively. Interestingly, most of the surviving motoneurons were able to regenerate their axons into the graft regardless of the delay. All regenerating motoneurons expressed p75, but not nNOS, while all motoneurons that failed to regenerate expressed nNOS, but not p75. p75 and nNOS may, therefore, be used as markers for success or failure to regenerate axons. In the group with immediate graft implantation, 85% of the surviving motoneurons extended axons into the PN graft, while in the groups in which implantation was delayed 1, 2, or 3 weeks, 84%, 82%, and 83% of the surviving motoneurons, respectively, were found to have regenerated into the grafts. These findings indicate that avulsed spinal motoneurons retain the ability to regenerate for at least 3 weeks, and perhaps for as long as they survive. Therefore, the delayed implantation of a PN graft after root avulsion may provide a continued conducive environment to support regeneration.published_or_final_versio

The effects of escitalopram on myocardial apoptosis and the expression of Bax and Bcl-2 during myocardial ischemia/reperfusion in a model of rats with depression

BackgroundMajor depressive disorder (MDD) is an independent risk factor for coronary heart disease (CHD), and influences the occurrence and prognosis of cardiovascular events. Although there is evidence that antidepressants may be cardioprotective after acute myocardial infarction (AMI) comorbid with MDD, the operative pathophysiological mechanisms remain unclear. Our aim was therefore to explore the molecular mechanisms of escitalopram on myocardial apoptosis and the expression of Bax and Bcl-2 in a rat model of depression during myocardial ischemia/reperfusion (I/R).MethodsRats were divided randomly into 3 groups (n = 8): D group (depression), DI/R group (depression with myocardial I/R) and escitalopram + DI/R group. The rats in all three groups underwent the same chronic mild stress and separation for 21 days, at the same time, in the escitalopram + DI/R group, rats were administered escitalopram by gavage (10 mg/kg/day). Ligation of the rat¿s left anterior descending branch was done in the myocardial I/R model. Following which behavioral tests were done. The size of the myocardial infarction was detected using 1.5% TTC dye. The Tunel method was used to detect apoptotic myocardial cells, and both the Rt-PCR method and immunohistochemical techniques were used to detect the expression of Bcl¿2 and Bax.ResultsCompared with the D and DI/R groups, rats in Escitalopram + DI/R group showed significantly increased movements and sucrose consumption (P < .01). Compared with the DI/R group, the myocardial infarct size in the escitalopram + DI/R group was significantly decreased (P < .01). Compared with the D group, there were significantly increased apoptotic myocardial cells in the DI/R and escitalopram + DI/R groups (P < .01); however compared with the DI/R group, apoptotic myocardial cell numbers in the escitalopram + DI/R group were significantly decreased (P < .01). Compared with the DI/R group, there was a down-regulated Bax:Bcl-2 ratio in the escitalopram + DI/R group (P < .01).ConclusionsThese results suggest that in patients with AMI comorbid with MDD, there is an increase in pro-apoptotic pathways that is reversed by escitalopram. This suggests that clinically escitalopram may have a direct cardioprotective after acute myocardial infarction

EEG-based driver fatigue detection using hybrid deep generic model

© 2016 IEEE. Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG

A Hybrid Fuzzy Cognitive Map/Support Vector Machine Approach for EEG-Based Emotion Classification Using Compressed Sensing

© 2018, Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature. Due to the high dimensional, non-stationary and non-linear properties of electroencephalogram (EEG), a significant portion of research on EEG analysis remains unknown. In this paper, a novel approach to EEG-based human emotion study is presented using Big Data methods with a hybrid classifier. An EEG dataset is firstly compressed using compressed sensing, then, wavelet transform features are extracted, and a hybrid Support Vector Machine (SVM) and Fuzzy Cognitive Map classifier is designed. The compressed data is only one-fourth of the original size, and the hybrid classifier has the average accuracy by 73.32%. Comparing to a single SVM classifier, the average accuracy is improved by 3.23%. These outcomes show that psychological signal can be compressed without the sparsity identity. The stable and high accuracy classification system demonstrates that EEG signal can detect human emotion, and the findings further prove the existence of the inter-relationship between various regions of the brain

Low-magnetic-field control of dielectric constant at room temperature realized in Ba0.5Sr1.5Zn2Fe12O22

We show that room temperature resistivity of Ba0.5Sr1.5Zn2Fe12O22 single crystals increases by more than three orders of magnitude upon being subjected to optimized heat treatments. The increase in the resistivity allows the determination of magnetic field (H)-induced ferroelectric phase boundaries up to 310 K through the measurements of dielectric constant at a frequency of 10 MHz. Between 280 and 310 K, the dielectric constant curve shows a peak centered at zero magnetic field and thereafter decreases monotonically up to 0.1 T, exhibiting a magnetodielectric effect of 1.1%. This effect is ascribed to the realization of magnetic field-induced ferroelectricity at an H value of less than 0.1 T near room temperature. Comparison between electric and magnetic phase diagrams in wide temperature- and field-windows suggests that the magnetic field for inducing ferroelectricity has decreased near its helical spin ordering temperature around 315 K due to the reduction of spin anisotropy in Ba0.5Sr1.5Zn2Fe12O22