In this brief, a high-speed [multiway parallel (MWP)] hardware-implemented deflate data compressor (DDC) is proposed for reducing the storage of solid-state drives (SSDs). To minimize the area of the DDC, registers instead of static random access memories (SRAMs) are utilized for building hash tables because multiway data within the DDC are able to access a register-based hash table simultaneously. To further reduce the area of the DDC, the output data of indefinite length are concatenated with a tree-type hardware architecture for reducing the overall concatenation complexity. Moreover, a solid mathematical foundation is established for optimizing the latency values of Lempel–Ziv (LZ)77 circuit, the Huffman encoding circuit, and the output data concatenation circuit within the MWP DDC. The results show that the proposed MWP DDC is capable of achieving a 12.1-Gb/s throughput and a 1.76 compression ratio (CR) with a 1.17-mm2area and 0.103-$\mu $s latency, under the synthesis of SMIC 55-nm process design kits (PDKs). Hence, the proposed DDC satisfies the SSD compression requirement for a universal serial bus (USB) 3.2 connector.

The limitations of current AR assembly training systems’ guided information have prompted exploration into enhancing efficiency and user experience. By integrating information decay strategies with individual learning status, this paper focuses on customizing decay to improve AR training. Against the backdrop of AR manual assembly training, we designed a user-centered adaptive gradual decay training program (“Adapt Grad Dec”) and developed a corresponding AR system using Unity 3D. To evaluate the effectiveness of “Adapt Grad Dec” in AR manual assembly training, a user study was conducted that compared it against both pre-programmed decay conditions and a non-decay condition, drawing on insights from previous research. The pre-programmed decay conditions included three variations: (1) only gradual decay of training information (“Only Grad Dec”), (2) fast decay of training information with manual adjustment options (“Fst Dec and Manl Adj”), and (3) gradual decay with manual adjustment options (“Grad Dec and Manl Adj”). In contrast, the un-decay condition involved no reduction of training information throughout the process (“UnDec”). Results demonstrated that participants performed better in various aspects under the “Adapt Grad Dec” condition compared to “Only Grad Dec.” Based on findings, we propose that combining individual learning conditions with gradual decay strategies is a potentially effective approach to enhancing AR training outcomes. Additionally, the study also discusses limitations and future research directions.

The projection-augmented reality assembly assistance system, which supports natural multi-modal human–computer interaction, has the potential to improve interaction efficiency. However, the effectiveness of the interaction method is compromised by the interference caused by the dynamically changing industrial environment. To address this issue, this study proposed a multi-model interaction model based on fuzzy theory and developed an environment perception decision-making interaction system. This system continuously monitors real-time environment data, evaluates interaction performance, and visually recommends the optimal method to assist users in completing their tasks. A comparative experiment was conducted between the proposed system and a non-environment perception interaction system. The collected data, including interaction success rate, task time, system usability, and user experience, were analyzed. The results demonstrated that the proposed system effectively enhances the adaptability and comfort of computer systems in complex environments and improves communication efficiency between humans and machines.

Dynamic textures are temporally continuous and infinitely varying sequences of images with certain spatial and temporal properties and have many potential applications. Such applications usually require dynamic texture segmentation that is not an easy task especially when the background is cluttered and textured. In this paper, we analyze the spatio-temporal slices of dynamic texture using Fourier analysis and then we propose a simple and efficient approach to dynamic texture segmentation using 3-D Fourier transform. Experiments on several dynamic texture sequences show the effectiveness of the proposed approach.