The original two-way continuous-variable quantum-key-distribution (CV-QKD) protocols [S. Pirandola, S. Mancini, S. Lloyd and S. L. Braunstein, Nat. Phys. 4 (2008) 726] give the security against the collective attack on the condition of the tomography of the quantum channels. We propose a family of new two-way CV-QKD protocols and prove their security against collective entangling cloner attacks without the tomography of the quantum channels. The simulation result indicates that the new protocols maintain the same advantage as the original two-way protocols whose tolerable excess noise surpasses that of the one-way CV-QKD protocol. We also show that all sub-protocols within the family have higher secret key rate and much longer transmission distance than the one-way CV-QKD protocol for the noisy channel.

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Source noise affects the security of continuous-variable quantum key distribution (CV QKD), and is difficult to analyze. We propose a model to characterize Gaussian source noise through introducing a neutral party (Fred) who induces the noise with a general unitary transformation. Without knowing Fred's exact state, we derive the security bounds for both reverse and direct reconciliations and show that the bound for reverse reconciliation is tight.

In this paper, a new method of human detection based on depth map from 3D sensor Kinect is proposed. First, the pixel filtering and context filtering are employed to roughly repair defects on the depth map due to information inaccuracy captured by Kinect. Second, a dataset consisting of depth maps with various indoor human poses is constructed as benchmark. Finally, by introducing Kirsch mask and three-value codes to Local Binary Pattern, a novel Local Ternary Direction Pattern (LTDP) feature descriptor is extracted and is used for human detection with SVM as classifier.The performance for the proposed approach is evaluated and compared with other five existing feature descriptors using the same SVM classifier. Experiment results manifest the effectiveness of the proposed approach.

Depth map contains the space information of objects and is almost free from the influence of light, and it attracts many research interests in the field of machine vision used for human detection. Therefore, hunting a suitable image feature for human detection on depth map is rather attractive. In this paper, we evaluate the performance of the typical features on depth map. A depth map dataset containing various indoor scenes with human is constructed by using Microsoft's Kinect camera as a quantitative benchmark for the study of methods of human detection on depth map. The depth map is smoothed with pixel filtering and context filtering so as to reduce particulate noise. Then, the performance of five image features and a new feature is studied and compared for human detection on the dataset through theoretic analysis and simulation experiments. Results show that the new feature outperforms other descriptors. Int. J. Model. Simul. Sci. Comput. 2014.05. Downloaded from www.worldscientific.com by NANYANG TECHNOLOGICAL UNIVERSITY on 08/25/15. For personal use only.