Most existing technology mapping algorithms use graph covering approaches and suffer from the netlist structural bias problem. Chen and Cong proposed a simultaneous simple gate decomposition with technology mapping algorithm that encodes many gate decomposition choices into the netlist. However, their algorithm suffers from the long runtime problem due to a large set of choices. Later on, A. Mishchenko et al. proposed a mapping algorithm based on choice generation with the so-called lossless synthesis. Nevertheless, their algorithm cannot guarantee to find and keep all good choices a priori before mapping. In this paper, we propose a simultaneous mapping with gate decomposition algorithm named AGDMap. Our algorithm uses cut-enumeration based engine. Bin packing algorithm is used for simple gate decomposition during cut enumeration. Input sharing based cut cost computation is used during iterative cut selection for logic duplication reduction. Based on a set of EPFL benchmark suite and HLS generated designs, our algorithm produces results with significant area improvement. Compared with the lossless synthesis algorithm, for area optimization, our average improvement is 12.4%. For delay optimization, we get results with similar delay and 9.2% area reduction.

Recently, the siamese convolutional neural network plays an important role in the field of visual tracking, which can obtain high tracking accuracy and good real-time performance. However, the requirement of offline training a specific neural network results in the hardware source and time consumption. In order to improve the tracking efficiency and save computation resources, we adopt pre-trained densely connected neural network to extract robust target features. Since the pre-trained model is mainly used for classification task, it is not appropriate to directly adopt these deep features for visual tracking. We design a regression network to measure the importance of each channel to the target, and then propose a weighting fusion strategy to select the suitable features for visual tracking. Besides, we provide deep analysis about the proposed channel weighting method to demonstrate the superiority of this method through visualization of feature heatmaps. Extensive experiments on four classical benckmarks show that compared with state-of-the-art methods, our algorithm achieves the best results on several standard indicators and comparable results on other indicators.

We propose a novel subsurface pipeline mapping and 3D reconstruction method by fusing ground-penetrating radar (GPR) scans and camera images. To facilitate the simultaneous detection of multiple pipelines, we model the GPR sensing process and prove hyperbola response for general scanning with nonperpendicular angles. Furthermore, we fuse visual simultaneous localization and mapping outputs, encoder readings with GPR scans to classify hyperbolas into different pipeline groups. We extensively apply the J-linkage method and maximum likelihood estimation with error analysis to improve algorithm robustness and accuracy. As a result, we optimally estimate the radii and locations of all pipelines. We have implemented our method and tested it in physical experiments with representative pipeline configurations. Two different kinds of 3-m-long pipes are used, with radii being 4.62 and 3.02 cm, respectively. The results show that our method successfully reconstructs all subsurface pipes. Moreover, the average estimation errors for two orientation angles of pipelines are 1.73° and 0.73°, respectively. The average localization error is 4.47 cm. Note to Practitioners-Automatic and accurate underground pipeline mapping technology is very important in civil construction projects. Lack of 3D utility pipeline maps may lead to accidental damage in civil construction and maintenance. Although ground-penetrating radar (GPR-based pipeline mapping methods have been studied for several years, these methods require the perpendicular scanning with respect to the pipe, which is impossible to guarantee in practice since the orientations of pipelines are unknown. Furthermore, these traditional methods can only estimate one pipeline at a time in a survey area and require prior knowledge of pipe diameter. We propose a robotic subsurface pipeline mapping method with a GPR and a camera to handle difficult factors such as multiple pipes, unknown pipeline orientation, and unknown pipeline diameters. Hence, we can perform GPR scanning along any generic linear trajectories. Our method has been tested in physical experiments with representative pipeline configurations. The results are sufficiently accurate, and it proves that our method can be an effective technology to reconstruct the underground pipelines.

We propose a novel subsurface pipeline mapping method by fusing Ground Penetrating Radar (GPR) scans and camera images. To facilitate the simultaneous detection of multiple pipelines, we model the GPR sensing process and prove hyperbola response for general scanning with non-perpendicular angles. Furthermore, we fuse visual simultaneous localization and mapping outputs, encoder readings with GPR scans to classify hyperbolas into different pipeline groups. We extensively apply the J-Linkage method and maximum likelihood estimation to improve algorithm robustness and accuracy. As the result, we optimally estimate the radii and locations of all pipelines. We have implemented our method and tested it in physical experiments with representative pipeline configurations. The results show that our method successfully reconstructs all subsurface pipes. Moreover, the average localization error is 4.69cm.

Using large number of antennas at base station (BS) to formulate a multi-user multiple-input multiple-output (MU-MIMO) system, i.e., the massive MIMO, has attracted more and more attention recently, due to the high spectrum and energy efficiency it has. When there is correlation and mutual coupling, Rusek et al. showed that the channel may lose asymptotic orthogonality as the number of antennas increases. In this paper, we investigate the dual-polarized (DP) antenna array with exponential correlation and coupling model and prove that under the assumption that the correlation and coupling decreases exponentially with the distance between the antennas, the channels are still asymptotically orthogonal and we still have the same massive MIMO effect with the single-polarized (SP) antenna array. Through simulations, the asymptotic expressions are verified and the benefits brought by DP antenna array in massive MIMO system are quantitatively shown for both uniform linear array (ULA) and uniform planar array (UPA) configurations.