Eye-shaped keyboard for dual-hand text entry in virtual reality

We propose an eye-shaped keyboard for high-speed text entry in virtual reality (VR), having the shape of dual eyes with characters arranged along the curved eyelids, which ensures low density and short spacing of the keys. The eye-shaped keyboard references the QWERTY key sequence, allowing the users to benefit from their experience using the QWERTY keyboard. The user interacts with an eye-shaped keyboard using rays controlled with both the hands. A character can be entered in one step by moving the rays from the inner eye regions to regions of the characters. A high-speed auto-complete system was designed for the eye-shaped keyboard. We conducted a pilot study to determine the optimal parameters, and a user study to compare our eye-shaped keyboard with the QWERTY and circular keyboards. For beginners, the eye-shaped keyboard performed significantly more efficiently and accurately with less task load and hand movement than the circular keyboard. Compared with the QWERTY keyboard, the eye-shaped keyboard is more accurate and significantly reduces hand translation while maintaining similar efficiency. Finally, to evaluate the potential of eye-shaped keyboards, we conducted another user study. In this study, the participants were asked to type continuously for three days using the proposed eye-shaped keyboard, with two sessions per day. In each session, participants were asked to type for 20min, and then their typing performance was tested. The eye-shaped keyboard was proven to be efficient and promising, with an average speed of 19.89 words per minute (WPM) and mean uncorrected error rate of 1.939%. The maximum speed reached 24.97 WPM after six sessions and continued to increase.

A Genetic Algorithm on Multiple Sequences Alignment Problems in Biology

The study and comparison of sequences of characters from a finite alphabet is relevant to various areas of science, notably molecular biology. The measurement of sequence similarity involves the consideration of the possible sequence alignments in order to find an optimal one for which the “distance” between sequences is minimum. In biology informatics area, it is a more important and difficult problem due to the long length (100 at least) of sequence, this cause the compute complexity and large memory require. By associating a path in a lattice to each alignment, a geometric insight can be brought into the problem of finding an optimal alignment, this give an obvious encoding of each path. This problem can be solved by applying genetic algorithm, which is more efficient than dynamic programming and hidden Markov model using commomly now.

Design of a New Chaos Circuit and Its Encryption to Digital Information

A new hyperchaos circuit system with simple structure is designed in this paper. It can be implemented with changeable dimensions 3, 5, 7, 9, etc, therefore it can afford different security grades for applications. The dynamic characters of the 7th order circuit have been analyzed as an example. Using the hyperchaos time sequence produced by this circuit as key sequence, the encryption and decryption to digital signals of text and image files have been realized, and the real-time ability has been increased with encrypting digital signals per byte.

Action recognition from arbitrary views using 3D-key-pose set

Recovering three-dimensional （3D） human pose sequence from arbitrary view is very difficult, due to loss of depth information and self-occlusion. In this paper, view-independent 3D-key-pose set is selected from 3D action samples, for the purpose of representing and recognizing those same actions from a single or few cameras without any restriction of the relative orientations between cameras and subjects. First, 3D-key-pose set is selected from the 3D human joint sequences of 3D training action samples that are built from multiple viewpoints. Second, 3D key pose sequence, which matches best with the observation sequence, is selected from the 3D-key- pose set to represent the observation sequence of arbitrary view. 3D key pose sequence contains many discriminative view-independent key poses but cannot accurately describe pose of every frame in the observation sequence. Considering the above reasons, pose and dynamic of action are modeled respectively in this paper. Exemplar-based embedding and probability of unique key pose are applied to model pose property. Complementary dynamic feature is extracted to model these actions that share the same poses but have different dynamic features. Finally, these action models are fused to recognize observation sequence from a single or few cameras. Effectiveness of the proposed approach is demonstrated with experiments on IXMAS dataset.