Mobile Edge Computing Task Offloading Strategy Based on Parking Cooperation in the Internet of Vehicles.

Due to the limited computing capacity of onboard devices, they can no longer meet a large number of computing requirements. Therefore, mobile edge computing (MEC) provides more computing and storage capabilities for vehicles. Inspired by a large number of roadside parking vehicles, this paper takes the roadside parking vehicles with idle computing resources as the task offloading platform and proposes a mobile edge computing task offloading strategy based on roadside parking cooperation. The resource sharing and mutual utilization among roadside vehicles, roadside units (RSU), and cloud servers (cloud servers) were established, and the collaborative offloading problem of computing tasks was transformed into a constraint problem. The hybrid genetic algorithm (HHGA) with a mountain-climbing operator was used to solve the multi-constraint problem, to reduce the delay and energy consumption of computing tasks. The simulation results show that when the number of tasks is 25, the delay and energy consumption of the HHGA algorithm is improved by 24.1% and 11.9%, respectively, compared with Tradition. When the task size is 1.0 MB, the HHGA algorithm reduces the system overhead by 7.9% compared with Tradition. Therefore, the proposed scheme can effectively reduce the total system cost during task offloading.

Research on Emotion Recognition Method Based on Adaptive Window and Fine-Grained Features in MOOC Learning.

In MOOC learning, learners' emotions have an important impact on the learning effect. In order to solve the problem that learners' emotions are not obvious in the learning process, we propose a method to identify learner emotion by combining eye movement features and scene features. This method uses an adaptive window to partition samples and enhances sample features through fine-grained feature extraction. Using an adaptive window to partition samples can make the eye movement information in the sample more abundant, and fine-grained feature extraction from an adaptive window can increase discrimination between samples. After adopting the method proposed in this paper, the four-category emotion recognition accuracy of the single modality of eye movement reached 65.1% in MOOC learning scenarios. Both the adaptive window partition method and the fine-grained feature extraction method based on eye movement signals proposed in this paper can be applied to other modalities.

[Design and experiment of a needle-to-cylinder electrode structure realizing the negative DC glow discharge in ambient air].

A new needle-to-cylinder electrode structure was designed to realize the stable glow discharge in ambient air. The stainless steel needle tip with diameter 56.4 microm and the copper cylinder with diameter 4mm were chosen as the cathode and the anode respectively, which were kept parallel by accurate mechanical structure. In the condition that the distance between the needle and the cylinder is 2 mm, the ballasting resistor is 10 M(omega), the discharge resistor is 10 M(omega), the testing resistor is 1 k(omega), and the discharge voltage is -2 740 V, without air flow in ambient air and at room temperature, the stable glow discharge between the needle and the cylinder was realized. Three different discharge modes can be observed: corona discharge, glow discharge and spark, which were verified by the discharge waveform stored in the oscilloscope, and the discharge pictures were recorded by digital camera. The needle-to-cylinder electrode structure is easy to fabricate by the MEMS technology, which can be used as the ion source of the portable analyzing instruments.