We present the philosophy, design, and initial evaluation of the Trio Testbed, a new outdoor sensor network deployment that consists of 557 solar-powered motes, seven gateway nodes, and a root server. The testbed covers an area of approximately 50,000 square meters and was in continuous operation during the last four months of 2005. This new testbed in one of the largest solar-powered outdoor sensor networks ever constructed and it offers a unique platform on which both systems and application software can be tested safely at scale. The testbed is based on Trio, a new mote platform that provides sustainable operation, enables efficient in situ interaction, and supports fail-safe programming. The motivation behind this testbed was to evaluate robust multi-target tracking algorithms at scale. However, using the testbed has stressed the system software, networking protocols, and management tools in ways that have exposed subtle but serious weaknesses that were never discovered using indoor testbeds or smaller deployments. We have been iteratively improving our support software, with the eventual aim of creating a stable hardware-software platform for sustainable, scalable, and flexible testbed deployments.

Abstract-A new definition of mutual impedance for two dipole antennas is introduced to characterize the mutual coupling effect between two dipole antennas in a more accurate manner. The calculation method and the measurement procedure for the new mutual impedance are given. Measurement and theoretical results on two monopole antennas were obtained as an example. The successful applications of the new mutual impedance in the compensation of mutual coupling effect in direction finding and adaptive interference suppression with significantly improved results showed the importance of the new mutual impedance.

Quantum optical coherence tomography (QOCT) makes use of an entangled twin-photon light source to carry out axial optical sectioning. We have probed the longitudinal structure of a sample comprising multiple surfaces in a dispersion-cancelled manner while simultaneously measuring the group-velocity dispersion of the interstitial media between the reflecting surfaces. The results of the QOCT experiments are compared with those obtained from conventional optical coherence tomography (OCT). 4145-4154 (1995). 13. L. Mandel and E. Wolf, Optical Coherence and Quantum Optics (Cambridge, New York, 1995), ch. 22. 14. C. K. Hong, Z. Y. Ou, and L. Mandel, "Measurement of subpicosecond time intervals between two photons by interference," Phys. Rev. Lett. 59, 2044Lett. 59, -2046Lett. 59, (1987

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A novel organic-inorganic composite, sorbic acid intercalated zinc aluminum layered double hydroxides (SA-ZnAl-LDHs) has been successfully assembled by a simple direct coprecipitation method. A holistic approach including normal XRD, FT-IR, and UV-Vis measurements and simultaneous TG/DTA/MS and in situ HT-XRD techniques was employed to explore the supramolecular intercalation structure and the thermal decomposition properties of as-synthesized SA-ZnAl-LDHs material.

Cyber attacks pose crucial threats to computer system security, and put digital treasuries at excessive risks. This leads to an urgent call for an effective intrusion detection system that can identify the intrusion attacks with high accuracy. It is challenging to classify the intrusion events due to the wide variety of attacks. Furthermore, in a normal network environment, a majority of the connections are initiated by benign behaviors. The class imbalance issue in intrusion detection forces the classifier to be biased toward the majority/benign class, thus leave many attack incidents undetected. Spurred by the success of deep neural networks in computer vision and natural language processing, in this paper, we design a new system named DeepIDEA that takes full advantage of deep learning to enable intrusion detection and classification. To achieve high detection accuracy on imbalanced data, we design a novel attack-sharing loss function that can effectively move the decision boundary towards the attack classes and eliminates the bias towards the majority/benign class. By using this loss function, DeepIDEA respects the fact that the intrusion mis-classification should receive higher penalty than the attack mis-classification. Extensive experimental results on three benchmark datasets demonstrate the high detection accuracy of DeepIDEA. In particular, compared with eight state-of-the-art approaches, DeepIDEA always provides the best class-balanced accuracy.

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