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In this work, the impacts of various pulsing schemes on endurance are comprehensively investigated. Trapezoidal and triangular waveforms are considered in endurance cycling tests. For endurance cycling with the trapezoidal waveforms, different rising time (Tr)/falling time (Tf), e.g., 0.05– $5 \mu \text{s}$ , with a fixed pulse width (Twidth) and different pulse width (Twidth), i.e., 0- $10 \mu \text{s}$ , with fixed rising time (Tr)/falling time (Tf) are used. As for the endurance cycling with the triangular waveforms, the frequencies are ranged from 1 kHz to 1 MHz. The results indicate that a shorter rising time (Tr)/falling time (Tf) results in a completely different endurance characteristic, and a longer Twidth leads to an earlier breakdown. Furthermore, the higher frequency of the triangular waveform results in a larger remnant polarization (2Pr) after endurance cycling. Overall, the endurance is highly dependent on the pulsing schemes, suggesting that a standardized methodology for the endurance evaluation is necessary for fair benchmarks and qualification of the ferroelectric-based technologies.

Proton exchange membrane fuel cell has become the most widely used fuel cell type in fuel cell vehicles. Estimation of fuel cell operating conditions can ensure the safety of the system, determine whether the system is faulty, and provide help for controller design. Aiming at the air supply system of PEMFC, considering the non-linearity and coupling of the system, a condition estimation algorithm based on the TrAdaboost algorithm is proposed to estimate whether the membrane thickness of the proton exchange membrane inside the stack is normal or not. Through the analysis of the model, the key variables affecting the membrane thickness of the stack are written into the system measurement values, and the working conditions of the stack are estimated by using the proposed algorithm. Finally, the proposed hybrid algorithm is verified on the Matlab simulation platform, and the results show that the proposed method can accurately track the real-time working conditions of the fuel cell stack.

This paper introduces an innovative model which incorporates vehicle On-Board Unit (OBU) data and roadside video information to provide instant alert messages to drivers. We apply computer vision techniques to perform real-time danger event detection and to identify specific surrounding vehicles that should be alerted. Different from traditional broadcast-based alerting, we propose to send these instant alert messages to the target vehicles by unicast and geocast. To do so, an accurate method is required to analyze the spatial relation of vehicles. Also, to confine our alert messages to only those target vehicles, we rely on roadside cameras and apply a sensor fusion technique that can link a video object with its communication MAC address. Through this innovative idea, we integrate computer vision with 5G networks and enable transmitting instant alerts to precise vehicles without interfering irrelevant vehicles. How to incorporate our system with 3GPP V2X by setting proper transmission parameters is also addressed. To validate our idea, we present four common road danger events and show how our model works. To the best of our knowledge, this is the first work bringing computer vision to instant messaging.

In this paper, we proposed very large-scale optical frame switching networks based on Torus Topology Network and Hexagonal Topology Network with WSS (wavelength selective switch) for future data centers. All nodes of very large-scale optical frame switching networks use WSS switch modules and the communications between nodes may use multiple links or multiple λs(wavelengths) where a frame is defined including many packets or flows with same destination. Finally, we evaluate performance of proposed very large-scale optical frame switching networks with different number of links and λswhere their advantages and disadvantages are also analyzed. We present performance evaluation and comparison for a 4-level Hexagonal Topology Network (96 nodes) and a 10×10 Torus Topology Network (100 nodes) with multiple links and λs,as they have a similar number of nodes in their network topologies. The traffic pattern for simulation is captured from the real core network.

An oxide aperture strained quantum well VCSEL model was built based on measured results. The indium composition of MQW was changed to maximize the frequency response. The simulation result shows that the bandwidth can be improved and reach 30.88GHz.

Two different package structures of hybrid perovskite quantum dots light emitting diodes were demonstrated with stable and continuous light emission. With boron nitride nanoparticles, the lifetime of device is expected to be thousands of hours.

In this work, we demonstrate the Luminescent Down Shifting (LDS) effect of the single-junction GaAs solar cell with 514 nm Perovskite quantum dots with and without bake treatment. One of results shows that J sc from 22.22 mA/cm2 to 23.38 mA/cm2 and the efficiency from 17.70% to 18.96% as high as 5.22% and 7.12% enhancement. The enhancement includes antireflection and LDS effect, via our calculated the increase J sc from LDS effect was 31.65%. Further storage lifetime test shows great reliability from these devices can be expected.

In this work, we discuss the feasibility of two color photons for boosting up the short-circuit current and power conversion efficiency in tandem solar cell. One of them is by luminescent down-shifting (LDS) effect, and the other one is the infrared light emitting diode at 820nm. The CdZnSeS/ZnS quantum dots are used in this study. It can convert ultra-violet photons to visible ones which could be efficiently transformed into electron-hole pairs. The infrared-radiation LED can increase photo-generated carriers of the bottom GaAs solar cell. Finally, short-circuit current would significantly increase from 10.46 mA/cm2 to 13.81 mA/cm2, and power conversion efficiency increase from 19.89 to 25.22%. This result shows the possible double-LDS effect for solar cell enhancement in the future.

The waste management is one of the challenges in the smart cities. The waste containers are typically placed in the public areas. Without well management, the waste containers may be overflowed or give off unpleasant smell, which affect the public health. This paper proposes a smart waste management system, by using the IoT (Internet of Things) technology.

At present, there are two kinds of data collection methods of most environmental sensor networks, one is static data collection, the sensing points are placed in fixed positions, the sense data are fed back to the database via wireless or wired network and stored. As for the other one, in order to collect data where the network is underdeveloped, the on-board system moves to the region and collect the environmental data. However, the two data collection methods have space constraint. Taking observation on air pollutant as an example, more and more countries reduce the external cost resulted from air pollution actively in recent years. In order to attain this goal, the generation of pollutants must be prevented. Therefore, the pollutant data of 3D space are required for tracing the source of pollutants, only reducing the discharge of toxic substances from the source can improve the air quality. The aforesaid two methods have constraints. In order to attain this goal, this paper uses a quadcopter as aerial vehicle to collect the environmental data at different heights, including temperature, humidity and PM2.5 concentration, which are sent to the database via wireless transmission module, and the data are displayed on visualized webpage. Finally, the relevance analysis is implemented for the data at different heights.

Accurate description of the relationship between sensor loading deformation and elastomeric structure is very important to improving design efficiency. In this paper, we focus on the spoke torque sensor, according to the deformation characteristics of the sensor under loading, and analyzes the relationship among stress, strain and disturbance of the torque sensor in combination with the deformation compatibility conditions, and establishes the analytical model of the torsion stiffness of the sensor. Finally, by comparing the finite element simulation results, it is verified that by considering the deformation coordination conditions, the precision of the analytical modeling of the torque sensor can be improved.