The integration of generative AI into immersive prototyping tools offers unprecedented potential to accelerate VR content creation. However, a critical gap exists in understanding the most effective interaction paradigms. We present DreamCraft, a novel VR prototyping system designed to integrate two fundamental workflows: scene-level creation, where users generate entire, navigable environments, and object-level creation, where scenes are iteratively populated with AI-generated assets. A formal user study with 12 VR designers was conducted to evaluate these workflows, evaluating both workflows through qualitative feedback and quantitative metrics, including task completion time, creative exploration, and perceived cognitive load. Our primary contribution is a set of empirically-grounded design guidelines derived from this analysis. These guidelines provide a crucial framework for the development of next-generation, user-centric generative tools for immersive virtual environments, directly informing future research and system design in the field.

The high speed dual phase operation domino circuit, which includes high-performance and reliable characteristics is proposed, and the circuit design technique with practical implementation is presented. The cell-based automatic synthesis flow supports the quick design of high performance chips. The test chip of a dual-phase 64 bit high-speed multiplier with a built-in performance adjustment mechanism is successfully validated using TSMC 0.18 technology. The test chip shows ×2.7 performance improvement compared to the conventional static CMOS logic design.

Exploiting a charge sharing method enables a performance power management design for domino circuits. The domino circuits have both high performance and low power consumption. A test chip has been successfully validated using TSMC 0.13um CMOS technology. Reductions in dynamic power consumption of 68% and static power consumption of 15% are achieved.

We propose a viewpoint-independent object-detection algorithm that detects objects in videos based on their 2-D and 3-D information. Object-specific quasi-3-D templates are proposed and applied to match objects' 2-D contours and to calculate their 3-D sizes. A quasi-3-D template is the contour and the 3-D bounding cube of an object viewed from a certain panning and tilting angle. Pedestrian templates amounting to 2660 and 1995 vehicle templates encompassing 19 tilting and 35 panning angles are used in this study. To detect objects, we first match the 2-D contours of object candidates with known objects' contours, and some object templates with large 2-D contour-matching scores are identified. In this step, we exploit some prior knowledge on the viewpoint on which the object is viewed to speed up the template matching, and the viewpoint likelihood for each contour-matched template is also assigned. Then, we calculate the 3-D widths, heights, and lengths of the contour-matched candidates, as well as the corresponding 3-D-size-matching scores. The overall matching score is obtained by combining the aforementioned likelihood and scores. The major contributions of this paper are to explore the joint use of 2-D and 3-D features in object detection. It shows that, by considering 2-D contours and 3-D sizes, one can achieve promising object detection rates. The proposed algorithms were evaluated on both pedestrian and vehicle sequences. It yielded significantly better detection results than the best results reported in PETS 2009, showing that our algorithm outperformed the state-of-the-art pedestrian-detection algorithms.

In this paper, we introduce an image watermarking scheme using singular value decomposition (SVD) and improved micro-genetic algorithm (micro-GA). In an SVD-based watermarking scheme, the singular values of a cover image are modified by multiple scaling factors to embed the watermark image. Determining proper values of scaling factors to reduce visual artifacts is viewed as an optimization problem and we use the improved micro-GA to search the feasible solution. Experimental results are provided to illustrate the proposed approach is robust to common signal processing attacks.