Landscape succession : oil field transformed through landscape process

The Inglewood Oil Field is located in Los Angeles, California. It is a fully functioning and one of the largest urban oil field in the United States. Facing the increasingly tense relationship between neighboring communities and the expansion of oil fields, the site has attracted a lot of public attention over the years.How to integrate the landscape process as part of the evolution of the industry is critical to this project. This project examines landscape architecture as a remediating agent within a fully functioning oil field. It proposes a process to heal the site through a simultaneous evolution. By using phyto-treatments and seeding and planting strategies, the proposal demonstrates a new approach to how the two processes could work together, as industry continues unabated and landscape heals over time. In the meanwhile, the proposal also preserves the dramatic visual shifts existing on the site and offers an opportunity for adjacent neighborhoods to witness the site transformation

Comparison and Fusion Research of Graduate Training between China and France in the Double First-Rate Background

Research on graduate training between China and France are made in the paper. Aiming at the problems in training goal, speciality characteristics and teaching method, measurements which can be taken are presented associating with Chinese situation and French education. It is verified that the methods are practical and feasible

Teaching Practice about Flipped Classroom on Circuit Course

In view of the problems in circuit course, flipped classroom is introduced and new teaching mode is explored. Teaching design, teaching reform suggestion, teaching effects and results are presented. It is verified that this kind of teaching mode can enhance the enthusiasm, initiative and participation of the students, teaching efficiency is also improved. It is also a good way for comprehensive practice

Comparison on Training Mode of Graduate Education between France and China

France and China both are of typical power centralization with similar political management systems, and there are same and different aspects existing in higher education. so comparison between the two countries is meaningful. Curriculum provision, innovation ability cultivating and training mode are compared between France and China. Development trend of graduate education is also presented. Some enlightenment may be obtained to improve Chinese higher education level further

Angular-Distance Based Channel Estimation for Holographic MIMO

This paper investigates the channel estimation for holographic MIMO systems by unmasking their distinctions from the conventional one. Specifically, we elucidate that the channel estimation, subject to holographic MIMO's electromagnetically large antenna arrays, has to discriminate not only the angles of a user/scatterer but also its distance information, namely the three-dimensional (3D) azimuth and elevation angles plus the distance (AED) parameters. As the angular-domain representation fails to characterize the sparsity inherent in holographic MIMO channels, the tightly coupled 3D AED parameters are firstly decomposed for independently constructing their own covariance matrices. Then, the recovery of each individual parameter can be structured as a compressive sensing (CS) problem by harnessing the covariance matrix constructed. This pair of techniques contribute to a parametric decomposition and compressed deconstruction (DeRe) framework, along with a formulation of the maximum likelihood estimation for each parameter. Then, an efficient algorithm, namely DeRe-based variational Bayesian inference and message passing (DeRe-VM), is proposed for the sharp detection of the 3D AED parameters and the robust recovery of sparse channels. Finally, the proposed channel estimation regime is confirmed to be of great robustness in accommodating different channel conditions, regardless of the near-field and far-field contexts of a holographic MIMO system, as well as an improved performance in comparison to the state-of-the-art benchmarks.Comment: This paper has been accepted for publication in IEEE JSA

High-Throughput Contention-Free Concurrent Interleaver Architecture for Multi-Standard Turbo Decoder

To meet the higher data rate requirement of emerging wireless communication technology, numerous parallel turbo decoder architectures have been developed. However, the interleaver has become a major bottleneck that limits the achievable throughput in the parallel decoders due to the massive memory conflicts. In this paper, we propose a flexible Double-Buffer based Contention-Free (DBCF) interleaver architecture that can efficiently solve the memory conflict problem for parallel turbo decoders with very high parallelism. The proposed DBCF architecture enables high throughput concurrent interleaving for multi-standard turbo decoders that support UMTS/HSPA+, LTE and WiMAX, with small datapath delays and low hardware cost. We implemented the DBCF interleaver with a 65nm CMOS technology. The implementation of this highly efficient DBCF interleaver architecture shows significant improvement in terms of the maximum throughput and occupied chip area compared to the previous work.HuaweiNational Science Foundatio

A Linear Method to Derive 3D Projective Invariants from 4 Uncalibrated Images

A well-known method proposed by Quan to compute projective invariants of 3D points uses six points in three 2D images. The method is nonlinear and complicated. It usually produces three possible solutions. It is noted previously that the problem can be solved directly and linearly using six points in five images. This paper presents a method to compute projective invariants of 3D points from four uncalibrated images directly. For a set of six 3D points in general position, we choose four of them as the reference basis and represent the other two points under this basis. It is known that the cross ratios of the coefficients of these representations are projective invariant. After a series of linear transformations, a system of four bilinear equations in the three unknown projective invariants is derived. Systems of nonlinear multivariable equations are usually hard to solve. We show that this form of equations can be solved linearly and uniquely. This finding is remarkable. It means that the natural configuration of the projective reconstruction problem might be six points and four images. The solutions are given in explicit formulas

Properties of localization in silicon-based lattice periodicity breaking photonic crystal waveguides

The light localization effects in silicon photonic crystal cavities at different disorder degrees have been studied using the finite difference time domain (FDTD) method in this paper. Numerical results showed that localization occurs and enhancement can be gained in the region of the cavity under certain conditions. The stabilities of the localization effects due to the structural perturbations have been investigated too. Detailed studies showed that when the degree of structural disorder is small(about 10%), the localization effects are stable, the maximum enhancement factor can reach 16.5 for incident wavelength of 785 nm and 23 for 850 nm in the cavity, with the degree of disorder about 8%. The equivalent diameter of the localized spot is almost constant at different disorder degrees, approximating to {\lambda \mathord{/ {\vphantom {\lambda 7}} \kern-\nulldelimiterspace} 7}λ/7, which turned out to be independent on the structural perturbation

Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation

Traditional 3D segmentation methods can only recognize a fixed range of classes that appear in the training set, which limits their application in real-world scenarios due to the lack of generalization ability. Large-scale visual-language pre-trained models, such as CLIP, have shown their generalization ability in the zero-shot 2D vision tasks, but are still unable to be applied to 3D semantic segmentation directly. In this work, we focus on zero-shot point cloud semantic segmentation and propose a simple yet effective baseline to transfer the visual-linguistic knowledge implied in CLIP to point cloud encoder at both feature and output levels. Both feature-level and output-level alignments are conducted between 2D and 3D encoders for effective knowledge transfer. Concretely, a Multi-granularity Cross-modal Feature Alignment (MCFA) module is proposed to align 2D and 3D features from global semantic and local position perspectives for feature-level alignment. For the output level, per-pixel pseudo labels of unseen classes are extracted using the pre-trained CLIP model as supervision for the 3D segmentation model to mimic the behavior of the CLIP image encoder. Extensive experiments are conducted on two popular benchmarks of point cloud segmentation. Our method outperforms significantly previous state-of-the-art methods under zero-shot setting (+29.2% mIoU on SemanticKITTI and 31.8% mIoU on nuScenes), and further achieves promising results in the annotation-free point cloud semantic segmentation setting, showing its great potential for label-efficient learning