Available parking slot detection is the first step for autonomous parking systems. In this paper, we propose a novel parking slot detection method that uses the center information regression and the occupancy classification of the parking slot. We design a self-calibrated convolutions network (SCCN) to obtain the position, length, occupancy and direction, which can also infer the parking slot type according to the prediction results. The method divides an around view monitor (AVM) image into the 16 × 16 grid cells and performs a SCCN detector for feature extraction. Subsequently, the whole parking slot can be easily inferred via prior geometric information and detection results. We adopt the heatmap, MultiBins, and midline to detect the center keypoint, direction and occupancy, respectively. And we quantitatively evaluate the performance of the proposed method on the public PS2.0 datasets. The experimental results show the outperformance by a precision rate of 99.35%, a recall rate of 99.17%, an occupancy classification accuracy of 99.12% and all correctly inferred types of parking slots on the datasets.

High-performance distributed transactions require efficient remote operations on database memory and protocol metadata. The high communication cost of this workload calls for hardware acceleration. Recent research has applied RDMA to this end, leveraging the network controller to manipulate host memory without consuming CPU cycles on the target server. However, the basic read/write RDMA primitives demand trade-offs in data structure and protocol design, limiting their benefits. SmartNICs are a flexible alternative for fast distributed transactions, adding programmable compute cores and on-board memory to the network interface. Applying measured performance characteristics, we design Xenic, a SmartNIC-optimized transaction processing system. Xenic applies an asynchronous, aggregated execution model to maximize network and core efficiency. Xenic's co-designed data store achieves low-overhead remote object accesses. Additionally, Xenic uses flexible, point-to-point communication patterns between SmartNICs to minimize transaction commit latency. We compare Xenic against prior RDMA- and RPC-based transaction systems with the TPC-C, Retwis, and Smallbank benchmarks. Our results for the three benchmarks show 2.42x, 2.07x, and 2.21x throughput improvement, 59%, 42%, and 22% latency reduction, while saving 2.3, 8.1, and 10.1 threads per server.

The study of strongly frustrated magnetic systems has drawn great attentions from both theoretical and experimental physics. Efficient simulations of these models are essential for understanding their exotic properties. Here we present PEPS++, a novel computational paradigm for simulating frustrated magnetic systems and other strongly correlated quantum many-body systems. PEPS++ can accurately solve these models at the extreme scale with low cost and high scalability on modern heterogeneous supercomputers. We implement PEPS++ on Sunway TaihuLight based on a carefully designed tensor computation library for manipulating high-rank tensors and optimize it by invoking various high-performance matrix and tensor operations. By solving a 2D strongly frustrated$J_1$-$J_2$model with over ten million cores, PEPS++ demonstrates the capability of simulating strongly correlated quantum many-body problems at unprecedented scales with accuracy and time-to-solution far beyond the previous state of the art.

Aiming at the problems in gait planning of the biped robots, including the complex model, low stability, etc., a novel fuzzy omni-directional gait planning algorithm (FOGPA) is proposed. At first, this method puts forward a new separated omni-directional gait planning model, which combines the straight walking planning algorithm based on the improved Hermite interpolation and the rotation motion together. And then, a fuzzy gait parameter adjustment algorithm is put forward to control the gait parameters including the step size and rotation speed dynamically. At last, the fuzzy control results are used to get the gait data of robot real-timely. The experiment results show that the FOGPA improves the stability and robustness of gait in a certain degree and also improves the adaptability to the complex environment of the robot.