Visual Synset: Towards a higher-level visual representation
We present a higher-level visual representation, visual synset, for object categorization. The visual synset improves the traditional bag of words representation with better discrimination and invariance power. First, the approach strengthens the inter-class discrimination power by constructing an intermediate visual descriptor, delta visual phrase, from frequently co-occurring visual word-set with similar spatial context. Second, the approach achieves better intra-class invariance power, by clustering delta visual phrases into visual synset, based their probabilistic dasiasemanticspsila, i.e. class probability distribution. Hence, the resulting visual synset can partially bridge the visual differences of images of same class. The tests on Caltech-101 and Pascal-VOC 05 dataset demonstrated that the proposed image representation can achieve good accuracies.

title: Face Alignment with Unified Subspace Optimization of Active Statistical Models
abstract: Active statistical models including active shape models and active appearance models are very powerful for face alignment. They are composed of two parts: the subspace model(s) and the search process. While these two parts are closely correlated, existing efforts treated them separately and had not considered how to optimize them overall. Another problem with the subspace model(s) is that the two kinds of parameters of subspaces (the number of components and the constraints on the components) are also treated separately. So they are not jointly optimized. To tackle these two problems, an unified subspace optimization method is proposed. This method is composed of two unification aspects: (I) unification of the statistical model and the search process: the subspace models are optimized according to the search procedure; (2) unification of the number of components and the constraints: the two kinds of parameters are modelled in an unified way, such that they can be optimized jointly. Experimental results demonstrate that our method can effectively find the optimal subspace model and significantly improve the performance

title: SNCC: A Selective Network-Coded Cooperation Scheme in Wireless Networks
abstract: Network-coded cooperation is a new communication paradigm that exploits spatial diversity by pooling distributed "communication" (relay) and also the distributed "computation" resources (coding) of different nodes in a network so as to increase wireless networks system performance. In this paper, we develop a selective network-coded cooperation (SNCC) scheme suitable for multiple unicast transmission pairs in a single-cell wireless network. The relay node performs network coding on received information from multiple sources before forwarding the network-coded data towards the corresponding destinations, which can decode their desired data from the network-coded data and the data they overhear from the other sources. The performance analysis and evaluation in terms of the single-pair outage probability and diversity-multiplexing tradeoff show that under SNCC any transmission pair achieves a full diversity order of M+1 (M is the number of available relays) while holding a better diversity-multiplexing tradeoff performance than existing cooperative cooperation schemes. Moreover, due to the mechanism that the relay adapts the data on which to perform network coding according to the observed source-to-relay channel quality, SNCC smartly avoids error propagation due to incorporating erroneous data into network-coded data, which is verified by numerical results of the single-pair outage probability.

title: Opportunistic channel selection approach under collision probability constraint in cognitive radio systems
abstract: In this work, we consider a cognitive radio system with multiple primary channels and one secondary user, and then we introduce a channel-usage pattern model and some basic concepts in this system. Based on this system model and the basic concepts, we propose two opportunistic channel selection algorithms to optimize the throughput of the secondary user: minimum collision rate channel selection algorithm and minimum handoff rate channel selection algorithm. According to the two algorithms, we, respectively, present the channel selection scheme based on minimum collision rate algorithm (CSS-MCRA) and the channel selection scheme based on minimum handoff rate algorithm (CSS-MHRA) under the constraint that the collision probability is bounded below collision tolerable level. Theoretical analysis and simulation results both show that, on one hand, both CSS-MCRA scheme and CSS-MHRA can follow the constraint of collision tolerable level; on the other hand, the performance of CSS-MCRA scheme is better than that of CSS-MHRA scheme if handoff latency is zero or very low, while the performance of CSS-MHRA scheme is better than that of CSS-MCRA scheme if handoff latency is long enough.

title: Cooperative Autonomic Management in Dynamic Distributed Systems
abstract: The centralized management of large distributed systems is often impractical, particularly when the both the topology and status of the system change dynamically. This paper proposes an approach to application-centric self-management in large distributed systems consisting of a collection of autonomic components that join and leave the system dynamically. Cooperative autonomic components self-organize into a dynamically created overlay network. Through local information sharing with neighbors, each component gains access to global information as needed for optimizing performance of applications. The approach has been validated and evaluated by developing a decentralized autonomic system consisting of multiple autonomic application managers previously developed for the In-VIGO grid-computing system. Using analytical results from complex random network and measurements done in a prototype system, we demonstrate the robustness, self-organization and adaptability of our approach, both theoretically and experimentally.

title: Power Allocation in OFDM-Based Cognitive Radio Systems
abstract: In this paper, we investigate the optimal power allocation strategy that aims at maximizing the capacity in OFDM- based cognitive radio systems. We show that the traditional water-filling algorithm applied in general OFDM systems needs to be modified due to the per subchannel power constraints in such systems. An iterative partitioned water-filling algorithm is proposed and proved to be optimal based on the convex optimization theory.

title: Uplink-Downlink Duality of ICI Cancellation in OFDMA Systems with Carrier-Frequency Offset
abstract: In orthogonal frequency-division multiplex access (OFDMA) systems, including uplink and downlink, the carrier- frequency offset (CFO) between the multiple transceivers introduces inter-carrier interference (ICI). In this paper, we first develop the uplink-downlink duality of ICI cancellation in OFDMA systems with CFO, then propose a uniform iterative ICI cancellation scheme that can be applied to both uplink and downlink. In uplink, this scheme acts as the detection at the receiver, while in downlink, it acts as the preceding at the transmitter. Moreover, by the studies of the convergence behavior of the iterations, a sufficient condition for the convergence is given, and it is proved that this iterative scheme is equivalent to the zero-forcing (ZF) scheme, but with much lower complexity. The simulation results indicate that due to the uplink-downlink duality, this iterative scheme can effectively suppress the ICI due to CFO in both uplink and downlink.