Securing Internet of Things with Lightweight IPsec

Real-world deployments of wireless sensor networks (WSNs) require secure communication. It is important that a receiver is able to verify that sensor data was generated by trusted nodes. In some cases it may also be necessary to encrypt sensor data in transit. Recently, WSNs and traditional IP networks are more tightly integrated using IPv6 and 6LoWPAN. Available IPv6 protocol stacks can use IPsec to secure data exchange. Thus, it is desirable to extend 6LoWPAN such that IPsec communication with IPv6 nodes is possible. It is beneficial to use IPsec because the existing end-points on the Internet do not need to be modified to communicate securely with the WSN. Moreover, using IPsec, true end-to-end security is implemented and the need for a trustworthy gateway is removed. In this paper we provide End-to-End (E2E) secure communication between an IP enabled sensor nodes and a device on traditional Internet. This is the first compressed lightweight design, implementation, and evaluation of 6LoWPAN extension for IPsec on Contiki. Our extension supports both IPsec's Authentication Header (AH) and Encapsulation Security Payload (ESP). Thus, communication endpoints are able to authenticate, encrypt and check the integrity of messages using standardized and established IPv6 mechanisms

3D Randomized Connection Network with Graph-based Label Inference

In this paper, a novel 3D deep learning network is proposed for brain MR image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity. The convolutional LSTM and 3D convolution are employed as network units to capture the long-term and short-term 3D properties respectively. To assemble these two kinds of spatial-temporal information and refine the deep learning outcomes, we further introduce an efficient graph-based node selection and label inference method. Experiments have been carried out on two publicly available databases and results demonstrate that the proposed method can obtain competitive performances as compared with other state-of-the-art methods

From curing patients to healing society : the honourable Dr. Edward Che-hung Leong

Dr. Edward Che-hung Leong, GBM, GBS, OBE, JP, a private medical practitioner specialised in urology, was born into a medical family. Leong is well-known to most Hong Kong people for his surgery skills. He has been praised as the Golden Surgeon Leong (金刀梁). He is also named the Master of Public Office (公職王). Since 1988, he has been a Legislative Councilor representing the Medical Functional Constituency, as well as many other public service roles of the Government and quangos, including Chairmanship of the Elderly Commission, in which his works were highly appraised. For years Doctor Leung has enthusiastically engaged in serving the society. Recognising his contributions to the society, the government has awarded him the honours of Justice of Peace, Order of the British Empire, Gold Bauhinia Star, and Grand Bauhinia Medal. Dr. Leong is now serving as the Chairman of the University of Hong Kong Council, Chairman of the Committee on Elder Academy Development Foundation, Elderly Commission and other public service roles. There is an old saying that doctors can be classified into three classes, the best one cures the society; the middle the person; the lowest the sickness. How did Dr. Leong go through the process from curing patients to healing society

From Lyapunov modes to the exponents for hard disk systems

We demonstrate the preservation of the Lyapunov modes by the underlying tangent space dynamics of hard disks. This result is exact for the zero modes and correct to order $\epsilon$ for the transverse and LP modes where $\epsilon$ is linear in the mode number. For sufficiently large mode numbers the dynamics no longer preserves the mode structure. We propose a Gram-Schmidt procedure based on orthogonality with respect to the centre space that determines the values of the Lyapunov exponents for the modes. This assumes a detailed knowledge of the modes, but from that predicts the values of the exponents from the modes. Thus the modes and the exponents contain the same information

Silver-Russell syndrome in Hong Kong

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Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring

DOI 10.1109/TCSVT.2008.2005612In this work, we study how continuous video monitoring and intelligent video processing can be used in eldercare to assist the independent living of elders and to improve the efficiency of eldercare practice. More specifically, we develop an automated activity analysis and summarization for eldercare video monitoring. At the object level, we construct an advanced silhouette extraction, human detection and tracking algorithm for indoor environments. At the feature level, we develop an adaptive learning method to estimate the physical location and moving speed of a person from a single camera view without calibration. At the action level, we explore hierarchical decision tree and dimension reduction methods for human action recognition. We extract important ADL (activities of daily living) statistics for automated functional assessment. To test and evaluate the proposed algorithms and methods, we deploy the camera system in a real living environment for about a month and have collected more than 200 hours (in excess of 600 G bytes) of activity monitoring videos. Our extensive tests over these massive video datasets demonstrate that the proposed automated activity analysis system is very efficient.This work was supported in part by National Institute of Health under Grant 5R21AG026412