148 research outputs found

    VeNet: Hybrid Stacked Autoencoder Learning for Cooperative Edge Intelligence in IoV

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    Emerging applications of the Internet of Vehicles (IoV) require the wireless transmission of growing amounts of data, e.g., vehicle location and sensor data, over unreliable and increasingly congested wireless links between the mobile vehicles and the Road Side Units (RSUs); also, urban areas are becoming increasingly congested with vehicle road traffic. Road traffic management and data network traffic management to address these challenges require accurate representations of the road and network traffic, which are difficult due to the wide temporal and spatial correlations in the road and network traffic. We address this representation problem by designing, implementing, and evaluating the VeNet deep learning system to exploit the wirelessly transmitted data to predict future vehicle locations and network traffic. We develop the novel VeNet hybrid learning system that employs a stacked autoencoder (AE) consisting of a central AE and multiple local AEs that jointly feed into a Long-Short Term Memory (LSTM). We propose a new training algorithm for the hybrid VeNet learning system. The novel VeNet hybrid learning system conducts spatial learning that accounts for the spatial and temporal correlations in the dataset gathered from the mobile vehicles. Evaluations that involve measurements with custom-made Raspberry Pi vehicles indicate that the VeNet learning model significantly reduces the required signalling network traffic and prediction errors (down to approx. three quarters) compared to existing prediction models. At the same time, VeNet reduces the energy consumption on the vehicles as well as the learning delay

    Investigation of the DBA Algorithm Design Space for EPONs

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    Latinx and Caucasian Elementary School Children’s Knowledge of and Interest in Engineering Activities

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    Ethnic minorities, such as Latinx people of Hispanic or Latino origin, and women earn fewer engineering degrees than Caucasians and men. With shifting population dynamics and high demands for a technically qualified workforce, it is important to achieve broad participation in the engineering workforce by all ethnicities and both genders. Previous research has examined the knowledge of and interest in engineering among students in grades five and higher. In contrast, the present study examined elementary school students in grades K–5. The study found that older students in grades 4 and 5 had both greater knowledge of engineering occupational activities and greater interest in engineering than younger students in grades K–3. Moreover, Caucasian students had greater knowledge and interest levels than Latinx students. There were no significant differences between boys and girls, nor any significant interactions among gender, grade level, and ethnicity. A significant positive correlation between knowledge of engineering occupational activities and interest in engineering was also found. The findings suggest that early engineering outreach interventions are important. Such early interventions could potentially contribute to preserving the equivalent interest levels of males and females for engineering as students grow older. Also, ethnic disparities in engineering knowledge and interest could potentially be mitigated through early interventions