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Shinji Sakai

dblp:160/2437 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2019
0000-0002-1041-4798ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%
Human-computer interaction and pervasive computing
1 paper
Haptics and multimodal interaction · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Virtual and augmented reality › locomotion
redirected walking
0.412019
Virtual Rotation with Visuo-Haptics · VR 2019
Virtual and augmented reality › locomotion › redirected walking
rotation gain
0.412019
Virtual Rotation with Visuo-Haptics · VR 2019

Methods — techniques the papers use, named apart from their topics

psychophysical experiment · 0.8
YearPublicationVenuePosition
2019 Virtual Rotation with Visuo-Haptics
abstract
Redirected walking has been proposed as a means of making a narrow space feel wide in a virtual space. We examined the effect of visuo-haptics on the detection threshold of rotation gain when participants walked around a wall. We found that the threshold was affected by visuo-haptics only when they walked around the outside of the wall but was not affected when they walked around the inside of the wall.
Akihiro Nakamura, Shinji Sakai, Kazunori Shidoji
VR2
2018 Rearranging Random Issue Queue with High IPC and Short Delay
abstract
Single-thread performance has remained mostly static for more than a decade. Among structures in a processor, the issue queue (IQ) is a structure that significantly affects the performance. To achieve high performance, high IPC and a short delay are required for the IQ, which have failed to be achieved in conventional IQs. We propose a novel IQ organization that we call the rearranging random issue queue (RRQ). The RRQ realizes an age-aware instruction selection in the IQ where instructions are ordered randomly. The RRQ divides the IQ into small (OQ: old queue) and large portions (MQ: main queue), where instructions in the OQ are prioritized using a simple select logic. To achieve age-aware selection, a small number of the oldest instructions in the MQ are moved to the OQ every cycle. Our implementation of the RRQ does not complicate the IQ circuit, and hardly increases the delay of the IQ. Evaluation results obtained in architectural simulation show that the RRQ achieves IPC as high as the shifting queue with compaction that realizes the perfect age-aware selection. Our evaluation results also show that the performance of the RRQ significantly outweighs that of processors with an IQ that has the age matrix, which suffers a long delay of the IQ.
Shinji Sakai, Taishi Suenaga, Ryota Shioya, Hideki Ando
ICCD1