ALBERT

Description: A sensor network, unlike a traditional communication network, is deeply embedded in physical environments and its operation is mainly driven by the event activities in the environment. In long-term operations, the event activities usually show certain patterns that can be learned and exploited to optimize the network design. However, this has been underexplored in the literature. One work related to this is using Activity Transition Probability Graph (ATPG) for radio duty cycling [Tang et al. (2011) ActSee: Activity-Aware Radio Duty-Cycling for Sensor Networks in Smart Environments. Proc. IEEE INSS 2011 , Penghu, Taiwan, June 12–15. IEEE Press]. In this paper, we present a novel Energy and Activity-aware Routing (EAR) protocol for sensor networks. As a case study, we have evaluated EAR with the data trace of real Smart Environments. In EAR an ATPG is learned and built from the event activity patterns. EAR is an online routing protocol, that chooses the next-hop relay node by utilizing: activity pattern information in the ATPG graph and a novel index of energy balance in the network. EAR extends the network lifetime by maintaining an energy balance across the nodes in the network, while meeting the application performance with desired throughput and low data delivery latency. We theoretically prove that: (i) the network throughput with EAR achieves a competitive ratio (i.e. the ratio of the performance of any offline algorithm that has knowledge of all past and future packet arrivals to the performance of our online algorithm) that is asymptotically optimal, and (ii) EAR achieves a lower bound in the network lifetime. Extensive experimental results from: (i) a 82 node Motelab sensor network testbed [Werner-Allen et al. (2005) MoteLab: A Wireless Sensor Network Testbed. Proc. ACM IPSN 2005 , Los Angeles, CA, USA, April 25–27, pp. 483–488. IEEE Press, NJ, USA] and (ii) a varying size network (20–100) in sensor network simulator TOSSIM, validate that EAR outperforms the existing methods both in terms of network performance (network lifetime, network energy consumption) and application performance (low latency, desired throughput) for an energy-constrained sensor network.

Description: Java embedded systems often include Java middleware classes installed on the client device. For higher performance, they can be compiled into machine code before runtime using an ahead-of-time compiler (AOTC). There are many approaches to AOTC, yet a bytecode-to-C (b-to-C) AOTC which translates the bytecode into the C code and then compiles it using an existing optimizing compiler such as gcc would be the most straightforward one. This paper explores a few important design and optimization issues of a b-to-C AOTC, including the compilation form for the translated C code, the call interfaces among translated and interpreted Java methods, and Java-specific optimizations by the AOTC that can complement the gcc optimizations. We evaluate these issues with our b-to-C AOTC implemented on the MIPS platform for the Sun's CDC VM to understand their performance impact.

Description: Our aim is to realize semantic face image retrieval (FIR)\ by bridging the semantic-gap between low-level visual features of images and the high-level labels that describe facial features. This allows a user to retrieve face images based on a description of face features rather than an example image. We approach the semantic-gap problem by developing a fuzzy-based method of finding a mapping between the low-level features and the vocabulary that describes the semantics of the face features. The contribution of this paper is a new method of using fuzzy clustering and fuzzy inference methods to derive the degree of membership for each semantic label to a new image. Our experiments show that our approach has good results for annotating images, and provides a sound foundation for local face feature-based FIR systems. We have made available a demonstration system online. Further, our system is not domain specific and can be generalized and applied to other problems in the field of image retrieval.

Description: In this paper, we propose an approach termed segment-based features (SBFs) to classify time series. The approach is inspired by the success of the component- or part-based methods of object recognition in computer vision, in which a visual object is described as a number of characteristic parts and the relations among the parts. Utilizing this idea in the problem of time series classification, a time series is represented as a set of segments and the corresponding temporal relations. First, a number of interest segments are extracted by interest point detection with automatic scale selection. Then, a number of feature prototypes are collected by random sampling from the segment set, where each feature prototype may include single segment or multiple ordered segments. Subsequently, each time series is transformed to a standard feature vector, i.e. SBF, where each entry in the SBF is calculated as the maximum response (maximum similarity) of the corresponding feature prototype to the segment set of the time series. Based on the original SBF, an incremental feature selection algorithm is conducted to form a compact and discriminative feature representation. Finally, a multi-class support vector machine is trained to classify the test time series. Extensive experiments on different time series datasets, including one synthetic control dataset, two sign language datasets and one gait dynamics dataset, have been performed to evaluate the proposed SBF method. Compared with other state-of-the-art methods, our approach achieves superior classification performance, which clearly validates the advantages of the proposed method.

Description: The use of the Internet for business-critical and real-time services is growing day after day. Random node (link) failures and targeted attacks against the network affect all types of traffic, but mainly critical services. For these services, most of the time it is not possible to wait for the complete network recovery; the best approach is to act in a proactive way by improving redundancy and network robustness. In this paper, we study network resilience and propose a resilience factor to measure the network level of robustness and protection against targeted attacks. We also propose strategies to improve resilience by simple alterations in the network topology. Our proposal is compared with previous approaches, and experimental results on selected network topologies confirmed the effectiveness of the approach.

Description: For hiding messages into multiple least significant bit (MLSB) planes, a new weighted stego-image (WS)\ steganalysis method is proposed to estimate the ratio of messages hidden into each bit plane. First, a new WS with multiple weights is constructed, and it is proved that when the squared Euclidean distance between the WS and the cover image is minimal, the weight parameters are equal to the embedding ratios in MLSB planes. Afterward, based on this result and an estimation of cover image, a simple estimation equation is derived to estimate the embedding ratio in each bit plane. Experimental results show that the new steganalysis method performs more stably with the change of embedding ratios than typical structural steganalysis, and outperforms the typical structural steganalysis method on the estimation accuracy when the embedding ratio in any bit plane is larger than 0.4.