Spacecraft pose estimation from monocular images presents significant challenges due to complex environmental conditions such as occlusions, illumination variations, and background interference, as well as estimation inaccuracies caused by multi-scale variations in object distance and viewpoint. To address these issues, this paper proposes a novel monocular pose estimation algorithm. In concrete terms, a high-resolution feature extraction framework is constructed using Higher-HRNet, a variant of the High-Resolution Network (HRNet), to generate multi-scale feature maps and enhance spatial feature representation. To balance estimation accuracy with real-time performance under limited computational resources, lightweight Ghost-BasicBlock and Ghost-Bottleneck modules are designed to reduce model complexity. Moreover, to mitigate the loss of feature representation capacity induced by model compression, a Biformer-Receptive Attention (BRA) mechanism is incorporated in the decoding stage to strengthen spatial-context modeling and improve keypoint localization accuracy. As the culminating process, the six-degree-of-freedom pose of the spacecraft is estimated by integrating the Efficient Perspective-n-Point (EPnP) algorithm with Random Sample Consensus (RANSAC). Experimental results on the SPEED dataset demonstrate that the proposed method achieves a mean translation error (meanET) of 0.0077m and a mean rotation error (meanER) of 0.0232°, with respective medians (medianET and medianER) corresponding to 0.0038m and 0.0112°, respectively—outperforming current state-of-the-art methods.

With the continuous shrinking of feature size, detection of lithography hotspots has been raised as one of the major concerns in Design-for-Manufacturability (DFM) of semiconductor processing. Hotspot detection, along with other DFM measures, trades off turnaround time for the yield of IC manufacturing, and thus a simplified but wide-ranging pattern definition is a key to the problem. Layout pattern clustering methods, which group geometrically similar layout clips into clusters, have been vastly proposed to identify layout patterns efficiently. To minimize the clustering number for subsequent DFM processing, in this article, we propose a geometric-matching-based clip relocation technique to increase the opportunity of pattern clustering. Particularly, we formulate the lower bound of the clustering number as a maximum-clique problem, and we have also proved that the clustering problem can be solved by the result of the maximum-clique very efficiently. Compared with the experimental results of the state-of-the-art approaches on ICCAD 2016 Contest benchmarks, the proposed method can achieve the optimal solutions for all benchmarks with very competitive runtime. To evaluate the scalability, the ICCAD 2016 Contest benchmarks are extended and evaluated. Moreover, experimental results on the extended benchmarks demonstrate that our method can reduce the cluster number by 16.59% on average, while the runtime is 74.11% faster on large-scale benchmarks compared with previous works.

Timing closure is a critical but effort-taking task in VLSI designs. In placement stage, a fast and accurate net delay estimator is highly desirable to guide the timing optimization prior to routing, and thus reduce the timing pessimism and shorten the design turn-around time. To handle the timing uncertainty at the placement stage, we propose a fast net delay timing predictor based on machine learning, which extract the fully timing features using a look-ahead RC network. Experimental results show that the proposed timing predictor has achieved average correlation over 0.99 with the post-routing sign-off timing results obtained in Synopsys PrimeTime.

As the transistor feature size continuously shrinks, design for manufacturability (DFM) has become a crucial concern. Layout pattern classification, which groups geometrically similar layout clips into clusters, has been widely utilized in a variety of DFM applications, such as hotspot library generation, hierarchical data storage, and systematic yield optimization. In this paper, we have proposed a maximum clique based method to obtain the lower bound of the clustering count and have proven that the lower bound is the theoretical optimal solution. To solve the clustering problem, we formulate it as a Set-Covering Problem (SCP) and utilize the result of the maximum clique to help the SCP quickly converge. Compared with the experimental results of the state-of-the-art approaches on ICCAD 2016 Contest benchmarks, our proposed method can achieve optimal solutions for all benchmarks with an approximate minimum run-time.

The paper presents a visualization prototype that tackles the issue of public emotion. It emphasizes the exploration process in the aim of creating reflections for the viewer to observe the whole social context as well as individual perspectives. Urban traffic conversation on SNS (Social Networking Site) is our current interest. The prototype we present is made on real post streams captured from Chinese most popular microblog Sina Weibo. After experimenting on two online prototypes we determine a visualizing flow to lead the viewer going through the insights from macro to micro view in three interaction frameworks: City Sentiment, Related Topics, and Post Content. Through showing the visualizing flow with interaction mode, data analysis, and prototype construction, the paper ends with discussing about design considerations in creating reflections on public traffic emotion in visualization prototype.

The UbiComp research community has a long history of interdisciplinary research on technology, engineering, and human behavior. But, one disciplinary voice that often seems to be missing is design. Due to the value of design to integrate knowledge and insights from across disciplines and focus on the future world, it seems that design might play a critical role in helping UbiComp research through its holistic approach. China is the world leader in manufacturing, and it is almost never the place where the innovative, high-tech products and services are designed. With UbiComp in China, now seems the perfect time to discuss bridging east and west and bringing design to UbiComp. This workshop aims to provide a disciplinary forum for researchers and experts from design computer science, psychology and anthropology to exchange ideas on the issue of collaboration. It will build understanding across disciplines and explore the potential opportunity for design in UbiComp research and practice.