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Accurate detection of independent moving objects is the key to ensure the safety of driverless vehicles during travelling. In this study, a moving object detection method based on a vehicle-mounted binocular camera is proposed. Firstly, a feature matching points selection strategy is designed for high accuracy camera ego-motion estimation. Then, the residual optical flow filed generated only by moving objects is estimated by taking into account the camera ego-motion and global mixed optical flow. A dynamic threshold segmentation method based on disparity is proposed to separate the moving regions from the residual optical flow field. Finally, 3D and 2D information are combined for extracting each single moving object from moving regions. The innovation of the proposed method is that it can detect any type and any size of moving object theoretically by using the dense motion information of the images. By taking the data in KITTI database as the test samples, the detection precision of the moving objects using the proposed algorithm can reach up to 91% without considering tracking strategy.

The recent applications of Unmanned Aerial Vehicles (UAVs) and Remotely Piloted Air Systems (RPAS) often require reliable and fast two-way communications between UAVs, base stations and consumers using terrestrial cellular networks. Two crucial questions are whether existing ground networks can effectively interact with UAVs in three-dimensional space during intensive traffic and under what data transmission modes it is possible to provide the necessary Quality of Service (QoS). To answer this question, the UAV/RPAS communication channel model “BS-ATM-HUB-RPAS” with a ground network was designed and investigated. The dependencies of dropped packets, message Travel Time (TT) and HUB Average Utilization on the Transaction Size (TS), the link bandwidth, the Bit Error Rate (BER) and the Packet Fail Chance for different distribution laws of Time Between Transactions (TBT) were analyzed. A significant benefit has been observed in using the LogNormal TBT distribution law rather than the Const and Exponential TBT distributions for the dependencies of dropped packets versus TS, HUB Average Utilization versus TS, and HUB Average Utilization versus link bandwidth. However, for the dependency of message Travel Time on the Transaction Size, the type of TBT distribution did not play a significant role; for all distributions, with increasing transaction size, the time of their transmission via the channel was increased. The importance and usefulness of such a numerical analysis lies in the ability to set traffic parameters and observe the resulting throughput, packet loss, and number of bit errors and QoS in a channel under certain transmission modes.

Around 1985, there were 20 serious flooding accidents in Chinese coal mines that caused heavy casualties and property losses. As a result, geophysical mine-prospecting technologies for predicting geological hazards have attracted increasing attention.

In this paper, transient electromagnetic response excited by ramp-shaped current using a conical source is studied for the sensing of underground environments. The transient time responses from the conical source with either multi-turn coils or a single loop coil are investigated and compared. Although with the same magnetic moment, the turn-off time for conical source is longer than that of the single loop-this is due to the fact that the time of entering different testing stages using a multi-turn coil system is often earlier than that for a single loop system. To correct turn-off error, the conical source type transmitter is used and the one-dimensional modeling of a layered media, in which the calculation of the turn-off response is carried over the entire time range by solving directly an upper limit-variable integral. The turn-off correction method matched the resistivity and the layer thickness, which is critical for the detection of shallow depth problems. Finally, a field example is presented to illustrate the effect of turn-off time on the interpretation of transient sounding data for a conical source system.

In the field of human health monitoring, intelligent diagnostic methods have drawn much attention recently to tackle the health problems and challenges faced by patients. In this paper, an efficient and flexible diagnostic method is proposed, which enables the simultaneous use of a machine learning method and sparsity-based representation technique. Specifically, the proposed method is based on a convolutional neural network (CNN) and generalized minimax-concave (GMC) method. First, measured potential signals, for instance, electroencephalogram (EEG) and electrocardiogram (ECG) signals are directly inputted into the designed network based on CNN for health conditions classification. The designed network adopts small convolution kernels to enhance the performance of feature extraction. In the training process, small batch samples are applied to improve the generalization of the model. Meanwhile, the “Dropout” strategy is applied to overcome the overfitting problem in fully connected layers. Then, for a record of the interested EEG or ECG signal, the sparse representation of useful time-frequency features can be estimated via the GMC method. Case studies of seizure detection and arrhythmia signal analysis are adopted to verify the performance of the proposed method. The experimental results demonstrate that the proposed method can effectively identify different health conditions and maximally enhance the sparsity of time-frequency features.

Lasers are frequently used to locally open dielectric layers for contacting the front or rear side of advanced silicon wafer solar cell architectures. For reasons of cost, laser processes are usually carried out in ambient environment, which inevitably leads to the oxidation of silicon. In order to achieve maximum solar cell efficiency, the laser processing has to be optimized thoroughly. The current research only focuses on minimizing laser-induced defects in silicon. However, our investigation shows another side effect of laser ablation, i.e., laser-induced oxidation, which is often neglected and undesirable for the subsequent metallization process. In this paper, we investigated the impact of laser-induced oxidation, which is caused by laser ablation in air, on the performance of silicon wafer solar cells. We show that by applying a postlaser etching step, laser-induced defects and oxide film can be removed effectively, providing improved conditions for the subsequent metallization process.

A novel thermionic cusp electron gun operating in the temperature-limited regime that produces a large-orbit electron beam through a nonadiabatic magnetic-field reversal was designed, analyzed, and optimized to give an electron-beam ideal for driving gyro-devices, particularly in the millimeter-to-submillimeter-wavelength range due to its small cross-sectional size. The annular-shaped axis-encircling electron beam had a beam current of 1.5 A at an acceleration potential of 40 kV, a tunable velocity ratio alpha (= vperp/vz) between one and three, an optimized axial velocity spread A.vx/vz of ~8%, and a relative alpha spread Deltakappa/alpha of ~10% at an alpha value of 1.65.