Towards Real-Time Distributed Signal Modeling for Brain-Machine Interfaces
New architectures for Brain-Machine Interface communication and control use mixture models for expanding rehabilitation capabilities of disabled patients. Here we present and test a dynamic data-driven (BMI) Brain-Machine Interface architecture that relies on multiple pairs of forward-inverse models to predict, control, and learn the trajectories of a robotic arm in a real-time closed-loop system. A method of window-RLS was used to compute the forward-inverse model pairs in real-time and a model switching mechanism based on reinforcement learning was used to test the ability to map neural activity to elementary behaviors. The architectures were tested with in vivodata and implemented using remote computing resources.

title: On the Use of Virtualization and Service Technologies to Enable Grid-Computing
abstract: The In-VIGO approach to Grid-computing relies on the dynamic establishment of virtual grids on which application services are instantiated. In-VIGO was conceived to enable computational science to take place In Virtual Information Grid Organizations. Having its first version deployed on July of 2003, In-VIGO middleware is currently used by scientists from various disciplines, a noteworthy example being the computational nanoelectronics research community (http://www.nanohub.org). All components of an In-VIGO-generated virtual grid – machines, networks, applications and data – are themselves virtual and services are provided for their dynamic creation. This article reviews the In-VIGO approach to Grid-computing and overviews the associated middleware techniques and architectures for virtualizing Grid components, using services for creation of virtual grids and automatically Grid-enabling unmodified applications. The In-VIGO approach to the implementation of virtual networks and virtual application services are discussed as examples of Grid-motivated approaches to resource virtualization and Web-service creation.

title: A user-level secure grid file system
abstract: A grid-wide distributed file system provides convenient data access interfaces that facilitate fine-grained cross-domain data sharing and collaboration. However, existing widely-adopted distributed file systems do not meet the security requirements for grid systems. This paper presents a Secure Grid File System (SGFS) which supports GSI-based authentication and access control, end-to-end message privacy, and integrity. It employs user-level virtualization of NFS to provide transparent grid data access leveraging existing, unmodified clients and servers. It supports user and application-tailored security customization per SGFS session, and leverages secure management services to control and configure the sessions. The system conforms to the GSI grid security infrastructure and allows for seamless integration with other grid middleware. A SGFS prototype is evaluated with both file system benchmarks and typical applications, which demonstrates that it can achieve strong security with an acceptable overhead, and substantially outperform native NFS in wide-area environments by using disk caching.

title: Autonomic resource management in virtualized data centers using fuzzy logic-based approaches
abstract: Data centers, as resource providers, are expected to deliver on performance guarantees while optimizing resource utilization to reduce cost. Virtualization techniques provide the opportunity of consolidating multiple separately managed containers of virtual resources on underutilized physical servers. A key challenge that comes with virtualization is the simultaneous on-demand provisioning of shared physical resources to virtual containers and the management of their capacities to meet service-quality targets at the least cost. This paper proposes a two-level resource management system to dynamically allocate resources to individual virtual containers. It uses local controllers at the virtual-container level and a global controller at the resource-pool level. An important advantage of this two-level control architecture is that it allows independent controller designs for separately optimizing the performance of applications and the use of resources. Autonomic resource allocation is realized through the interaction of the local and global controllers. A novelty of the local controller designs is their use of fuzzy logic-based approaches to efficiently and robustly deal with the complexity and uncertainties of dynamically changing workloads and resource usage. The global controller determines the resource allocation based on a proposed profit model, with the goal of maximizing the total profit of the data center. Experimental results obtained through a prototype implementation demonstrate that, for the scenarios under consideration, the proposed resource management system can significantly reduce resource consumption while still achieving application performance targets.

title: Supporting application-tailored grid file system sessions with WSRF-based services
abstract: This paper presents novel service-based grid data management middleware that leverages standards defined by WSRF specifications to create and manage dynamic grid file system sessions. A unique aspect of the service is that the sessions it creates can be customized to address application data transfer needs. Application-tailored configurations enable selection of both performance-related features (block-based partial file transfers and/or whole-file transfers, cache parameters and consistency models) and reliability features (file system copy-on-write checkpointing to aid recovery of client-side failures; replication, autonomous failure detection and data access redirection for server-side failures). These enhancements, in addition to cross-domain user identity mapping and encrypted communication, are implemented via user level proxies managed by the service, requiring no changes to existing kernels. Sessions established using the service is mounted as distributed file systems and can be used transparently by unmodified binary applications. The paper analyzes the use of the service to support virtual machine based grid systems and workflow execution, and also reports on the performance and reliability of service managed wide-area file system sessions with experiments based on scientific applications (NanoMOS/Matlab, CHID, GAUSS and SPECseis).

title: Application-Tailored Cache Consistency for Wide-Area File Systems
abstract: The inability to perform optimizations based on application-specific information presents a hurdle to the deployment of pervasive LAN file systems across WAN environments. This paper proposes a novel approach addressing this problem through application-tailored caching and consistency in widearea file systems. It leverages widely available Network File System (NFS) deployments without any modifications to kernels nor applications, and employs middleware to dynamically establish Grid-wide Virtual File System (GVFS) sessions with application-tailored cache consistency. Two consistency models are discussed in this paper: a relaxed model based on invalidation polling, and a stronger model based on delegation and callback. Experimental evaluation based on microbenchmarks and scientific applications show that with application-tailored cache consistency, GVFS is able to both improve application runtimes and reduce server load significantly, compared to kernel-level NFS in WAN.

title: Iterative Precoding Scheme for Downlink OFDMA System with Carrier-Frequency Offset
abstract: In orthogonal frequency-division multiplex access (OFDMA) downlink systems, the carrier-frequency offset (CFO) between the multiple transceivers introduces inter-carrier interference (ICI). In this letter, we propose an iterative precoding scheme to suppress the ICI due to CFO. This scheme is applied at the transmitter, and can jointly cancel the ICI for all the receivers. Moreover, by the studies of the convergence behavior of the iterations, a sufficient condition for the convergence is presented. The theoretical analysis and simulation results both show that this iterative scheme is equivalent to the zero-forcing (ZF) scheme in function, but with much lower complexity.

title: A CoS-enabled Request Scheduling Policy
abstract: As a lightweight SLA between service providers and consumers, class of service (CoS) can avoid the runtime overhead brought by multiple negotiation of service agreements. This is especially important in grid environment where lots of transient services exist. We bring CoS into grid environment and propose a QoS-enabled framework named CQEF for grid service. In this paper, we present a CoSenabled request scheduling policy for CoS deployment in CQEF. According to this policy, incoming requests can be identified and classified based on the CoS information attached to them and subsequently be assigned to different waiting queues with different priorities. Besides, queuing scheduling algorithm is used to achieve the CoS enabling purpose. A prototype has been developed over the Globus platform. The test results show that, by using this policy, service providers are able to offer differentiated services for different CoS and QoS of premium CoSs will be guaranteed preferentially.

title: Robust Transmission for Multiuser MIMO Downlink Systems with Imperfect CSIT
abstract: The performance of multiuser MIMO (MU-MIMO) downlink systems with preceding and decoding method by minimizing the total mean square error (TMSE) of all data streams of all users (T-MMSE) depends on the accuracy of the channel state information (CSI) available at the transmitter (CSIT). In this paper, a robust transmission scheme is proposed to suppress the interferences among data streams and users caused by imperfect CSIT. At the transmitter, the precoders are obtained through an iterative procedure by minimizing the expectation of the TMSE of all data streams of all users conditioned on the estimated CSI under a total transmit power constraint. At each user, a linear decoder is calculated based on the minimum mean square error (MMSE) criteria according to the realtime CSI. To fulfill it, a new pilot-sending architecture is introduced. Numerical results demonstrate that the proposed scheme is robust not only to the channel uncertainty at the transmitter but also to the channel uncertainty at each user, and can effectively improve the system performance.

title: Efficient Transmission Strategies Against Channel Time Variations in Multiuser MIMO Downlink Systems
abstract: Based on the analysis of the effect of the outdated channel state information (CSI) available at the transmitter (CSIT) on multiuser MIMO (MU-MIMO) downlink systems with block zero-forcing precoding (BZFP) method, two linear processing methods, BZFP at the transmitter with total MMSE decoding at the receiver (TBZF-RTMMSE) and BZFP at the transmitter with partial MMSE decoding at the receiver (TBZF- RPMMSE) are proposed. To fulfill them, a new pilot-sending and channel estimation architecture is introduced. Analysis and simulation results show that the two proposed methods can effectively reduce the impairment of the outdated CSIT and improve the system sum capacitv in the suitable time delav region of the CSIT.

title: Exploring knowledge of sub-domain in a multi-resolution bootstrapping framework for concept detection in news video
abstract: In this paper, we present a model based on a multi-resolution, multi-source and multi-modal (M3) bootstrapping framework that exploits knowledge of sub-domains for concept detection in news video. Because the characteristics and distributions of data in different sub-domains are different, we model and analyze the video in each sub-domain separately using a transductive framework. Along with this framework, we propose a "pseudo-Vapnik combined error bound" to tackle the problem of imbalanced distribution of training data in certain segments of sub-domains. For effective fusion of multi-modal features, we utilize multi-resolution inference and constraints to permit evidences from different modal features to support each other. Finally, we employ a bootstrapping technique to leverage unlabeled data to boost the overall system performance. We test our framework by detecting semantic concepts in the TRECVID 2004 dataset. Experimental results demonstrate that our approach is effective.