arXiv subjects
Publications and source records attributed to Jiajia Chen.
Kernel adaptive filtering (KAF) is proposed for nonlinearity-tolerant optical direct detection. For 7x128Gbit/s PAM4 transmission over 33.6km 7-core-fiber, KAF only needs 10 equalizer taps to reach KP4-FEC limit (BER@2.2e-4), whereas decision-feedback-equalizer needs 43 equalizer taps to reach HD-FEC limit (BER@3.8e-3).
This research addressed whether interactions with social robots could facilitate joint attention of the autism spectrum disorder (ASD). Two conditions of initiators, namely 'Human' vs. 'Robot' were measured with 15 children with ASD and 15 age-matched typically developing (TD) children. Apart from fixation and gaze transition, a new longest common subsequence (LCS) approach was proposed to analyze eye-movement traces. Results revealed that children with ASD showed deficits of joint attention. Compared to the human agent, robot facilitate less fixations towards the targets, but it attracted more attention and allowed the children to show gaze transition and to follow joint attention logic. This results highlight both potential application of LCS analysis on eye-tracking studies and of social robot to intervention.
In this paper, we proposed a theoretical model in the far-infrared and terahertz (THz) bands, which is a dumbbell-shaped graphene metamaterial arrays with a combination of graphene nanorod and two semisphere-suspended heads. We report a detailed theoretical investigation on how to enhance localized electric field and the absorption in the dumbbell-shaped graphene metamaterial arrays. The simulation results show that by changing the geometrical parameters of the structure and the Fermi level of graphene, we can change the absorption characteristics. Furthermore, we have discovered that the resonant wavelength is insensitive to TM polarization. In addition, we also find that the double-layer graphene arrays have better absorption characteristics than single-layer graphene arrays. This work allows us to achieve tunable terahertz absorber, and may also provide potential applications in optical filter and biochemical sensing.
It is known that adverse environments such as high reverberation and low signal-to-noise ratio (SNR) pose a great challenge to indoor sound source localization. To address this challenge, in this paper, we propose a sound source localization algorithm based on probabilistic neural network, namely Generalized cross correlation Classification Algorithm (GCA). Experimental results for adverse environments with high reverberation time T60 up to 600ms and low SNR such as -10dB show that, the average azimuth angle error and elevation angle error by GCA are only 4.6 degrees and 3.1 degrees respectively. Compared with three recently published algorithms, GCA has increased the success rate on direction of arrival estimation significantly with good robustness to environmental changes. These results show that the proposed GCA can localize accurately and robustly for diverse indoor applications where the site acoustic features can be studied prior to the localization stage.
A novel code construction algorithm is presented to find all the possible code families for code reconfiguration in an OCDMA system. The algorithm is developed through searching all the complete subgraphs of a constructed graph. The proposed algorithm is flexible and practical for constructing optical orthogonal codes (OOCs) of arbitrary requirement. Simulation results show that one should choose an appropriate code length in order to obtain sufficient number of code families for code reconfiguration with reasonable cost.