The image copy–move forgery can achieve the realistic effects without leaving any obvious manipulation traces because the copied source and pasted target belong to the homogenous sources. Image copy–move forgery detection (CMFD) is still a serious challenge issue for researchers, e.g. computational efficiency, occlusive (smoothing) object detection, and partial object detection, under various post-processing and geometrical transformations. This paper proposes a novel CMFD scheme using the characteristics of local descriptors. First, the proposed method deeply analyzes the structure and mines the inherent characteristics of the local descriptor (SURF) for feature extraction in the coarse and smooth regions. Subsequently, the proposed method takes the kernel features as coarse feature matching to decrease matching costs. Then, the found candidate keypoints with a relatively small quantity are used with complete features to perform fine keypoint matching for getting suspicious candidate keypoint pairs. Furthermore, based on the deep mining of the inherent local descriptor characteristics, the proposed method can indicate the forgery region localizations through keypoint pairs, even the forgeries suffering from various geometrical transformations. Then, the proposed method proposes adaptive dual-filtering algorithms of [Formula: see text]-means relying on keypoint characteristics, and spatial distance for hierarchical progress of feature filtering. Finally, Delaunay Triangulation relies on the true-positive keypoint pairs to perform the forgery region matting effectively and efficiently. The experimental results demonstrate that the proposed method can achieve the best F1 scores compared to the state-of-the-art methods in most cases, especially in anti-scaling transformations.

We present a deep learning-based thumbnail generation method called CropNet in this paper. Unlike previous deep learning-based methods, such as Fast-AT, which can utilize detectors introduced in object detection frameworks and generate thousands of proposals, our detector is straightforward and concise, thereby ensuring that the final cropping window is computed by its center and width, with the input aspect ratio. To achieve this goal, CropNet learns specific filters to estimate the center position and utilizes a cascade structure of filters and single neuron for width inference. In addition, CropNet optimizes the center and width jointly for optimal results. We collect a data set of more than 29,000 thumbnail annotations to train CropNet and perform cross-validation between existing data sets. Experiments show that CropNet outperforms existing techniques. Our result is achieved at a test-time speed of 17 ms per image, which is six times faster than the fastest method at present.

Off-highway dump truck contains amounts of potential energy and kinetic energy. During the braking process, these two parts are consumed in the form of heat on the braking resistance, and the energy consumption will be about 24 percent of total energy consumption of the Truck. Hence, it is necessary to recover the braking energy. This paper, aiming at the characteristics of electric drive system in off-highway dump truck, proposes a new type of DC/DC main circuit which is a series connection topology that includes super-capacitor and battery hybrid energy storage, the DC bus's rate voltage is 1800v. The bottom controller is designed based on DSP + FPGA, which realizes the efficient control and protection of the system, and completes the recovery and utilization function of the braking energy in off-highway dump trucks. In the host computer, the monitoring system designed by utilizing of C#.net language, and the relay protection 103 communication protocol has been used to improve system stability. In this paper, the dynamic test platform of braking energy recovery system has been completed, after the dynamic test, the system has been install in Komatsu 730E dump truck of Pingshuo Anjialing surface coal mine to complete the field operation. The experimental result shows the effectiveness and reliability of the system.

Tire Pressure Monitoring System (TPMS) modules that are mounted in tires are usually powered by batteries that cannot be replaced or recharged. Since most of the power consumed by these modules is due to the wireless communication, antenna efficiency is a primary concern. TPMS modules typically employ antennas that are much smaller than a wavelength at the frequency of operation. Peak efficiencies are obtained by antennas with a high quality factor, but high-Q antennas are susceptible to detuning caused by environmental factors beyond the control of the TPMS designer. This paper investigates the performance of loop- and whip-antenna designs commonly used in TPMS applications, and explores the environmental factors that affect the radiation efficiency and quality factor of these antennas.