7656393
CAMRIT: control-based adaptive middleware for real-time image transmission
Real-time image transmission is crucial to an emerging class of distributed embedded systems operating in open network environments. Examples include avionics mission re-planning over Link-16, security systems based on wireless camera networks, and online collaboration using camera phones. Meeting image transmission deadlines is a key challenge in such systems due to unpredictable network conditions. In this paper, we present CAMRIT, a control-based adaptive middleware framework for real-time image transmission in distributed real-time embedded systems. CAMRIT features a distributed feedback control loop that meets image transmission deadlines by dynamically adjusting the quality of image tiles. We derive an analytic model that captures the dynamics of a distributed middleware architecture. A control theoretic methodology is applied to systematically design a control algorithm with analytic assurance of system stability and performance, despite uncertainties in network bandwidth. Experimental results demonstrate that CAMRIT can provide robust real-time guarantees for a representative application scenario.
269391
Integrated coverage and connectivity configuration in wireless sensor networks
An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes, while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully, the active nodes must maintain both sensing coverage and network connectivity. Furthermore, the network must be able to configure itself to any feasible degrees of coverage and connectivity in order to support different applications and environments with diverse requirements. This paper presents the design and analysis of novel protocols that can dynamically configure a network to achieve guaranteed degrees of coverage and connectivity. This work differs from existing connectivity or coverage maintenance protocols in several key ways: 1) We present a Coverage Configuration Protocol (CCP) that can provide different degrees of coverage requested by applications. This flexibility allows the network to self-configure for a wide range of applications and (possibly dynamic) environments. 2) We provide a geometric analysis of the relationship between coverage and connectivity. This analysis yields key insights for treating coverage and connectivity in a unified framework: this is in sharp contrast to several existing approaches that address the two problems in isolation. 3) Finally, we integrate CCP with SPAN to provide both coverage and connectivity guarantees. We demonstrate the capability of our protocols to provide guaranteed coverage and connectivity configurations, through both geometric analysis and extensive simulations.
15273314
Feedback control real-time scheduling in ORB middleware
Existing real-time ORB middleware standards such as RT-CORBA do not adequately address the challenges of 1) providing robust performance guarantees portably across different platforms, and 2) managing unpredictable workload. To overcome this limitation, we have developed software called FCS/nORB that integrates a Feedback Control real-lime Scheduling (FCS) service with the nORB small-footprint real-time ORB designed for networked embedded systems. FCS/nORB features feedback control loops that provide real-time performance guarantees by automatically adjusting the rate of remote method invocations transparently to an application. FCS/nORB thus enables real-time applications to be truly portable in terms of real-time performance as well as functionality, without the need for hand tuning. This paper presents the design, implementation, and evaluation of FCS/nORB. Our extensive experiments on a Linux testbed demonstrate that FCS can provide deadline miss ratio and utilization guarantees in face of changes in the platform and task execution times, while introducing a small amount of overhead.
5719252
End-to-end utilization control in distributed real-time systems
An increasing number of distributed real-time systems face the critical challenge of providing end-to-end quality of service (QoS) guarantees in open and unpredictable environments. In particular, such systems often need to guarantee the CPU utilization on multiple processors in order to achieve overload protection and meet end-to-end deadlines while task execution times are unpredictable. While the recently developed feedback control real-time scheduling algorithms have shown promise, they cannot handle the common end-to-end task model in distributed systems where each task is comprised of a chain of subtasks distributed on multiple processors. We present the end-to-end utilization control (EUCON) algorithm that features a distributed feedback loop that dynamically enforces desired CPU utilization bounds on multiple processors based on online performance measurements EUCON is based on a model predictive control approach that models the utilization control problem on a distributed platform as a multivariable constrained optimization problem. A multiinput-multioutput model predictive controller is designed based on a difference equation model that describes the dynamic behavior of distributed real-time systems. Both control theoretic analysis and simulations demonstrate that EUCON can provide robust utilization guarantees even when task execution times deviate from the estimation or vary significantly at run-time.
1347401
Decentralized utilization control in distributed real-time systems
Many real-time systems must control their CPU utilizations in order to meet end-to-end deadlines and prevent overload. Utilization control is particularly challenging in distributed real-time systems with highly unpredictable workloads and a large number of end-to-end tasks and processors. This paper presents the decentralized end-to-end utilization control (DEUCON) algorithm that can dynamically enforce desired utilizations on multiple processors in such systems. In contrast to centralized control schemes adopted in earlier work, DEUCON features a novel decentralized control structure that only requires localized coordination among neighbor processors. DEUCON is systematically designed based on recent advances in distributed model predictive control theory. Both control-theoretic analysis and simulations show that DEUCON can provide robust utilization guarantees and maintain global system stability despite severe variations in task execution times. Furthermore, DEUCON can effectively distribute the computation and communication cost to different processors and tolerate considerable communication delay between local controllers. Our results indicate that DEUCON can provide scalable and robust utilization control for large-scale distributed real-time systems executing in unpredictable environments
5867346
Feedback utilization control in distributed real-time systems with end-to-end tasks
An increasing number of distributed real-time systems face the critical challenge of providing quality of service guarantees in open and unpredictable environments. In particular, such systems often need to enforce utilization bounds on multiple processors in order to avoid overload and meet end-to-end deadlines even when task execution times are unpredictable. While recent feedback control real-time scheduling algorithms have shown promise, they cannot handle the common end-to-end task model where each task is comprised of a chain of subtasks distributed on multiple processors. This paper presents the end-to-end utilization control (EUCON) algorithm that adaptively maintains desired CPU utilization through performance feedbacks loops. EUCON is based on a model predictive control approach that models utilization control on a distributed platform as a multivariable constrained optimization problem. A multi-input-multi-output model predictive controller is designed based on a difference equation model that describes the dynamic behavior of distributed real-time systems. Both control theoretic analysis and simulations demonstrate that EUCON can provide robust utilization guarantees when task execution times deviate from estimation or vary significantly at runtime.
26503645
Bluetooth: Opening a blue sky for healthcare
Over the last few years, there has been a blossoming of developing mobile healthcare programs. Bluetooth technology, which has the advantages of being low-power and inexpensive, whilst being able to transfer moderate amounts of data over a versatile, robust and secure radio link, has been widely applied in mobile healthcare as a replacement for cables. This paper discussed the applications of Bluetooth technology in healthcare. It started with the brief description of the history of Bluetooth technology, its technical characteristics, and the latest developments. Then the applications of Bluetooth technology in healthcare sector were reviewed. The applications are based on two basic types of links of Bluetooth technology: point-to-point link and point-to-multipoint link. The special requirements from healthcare and the challenges of successful application of Bluetooth in healthcare will be discussed. At last the future development of Bluetooth technology and its impacts on healthcare were envisioned.
14053941
AnnoSearch: Image Auto-Annotation by Search
Although it has been studied for several years by computer vision and machine learning communities, image annotation is still far from practical. In this paper, we present AnnoSearch, a novel way to annotate images using search and data mining technologies. Leveraging the Web-scale images, we solve this problem in two-steps: 1) searching for semantically and visually similar images on the Web, 2) and mining annotations from them. Firstly, at least one accurate keyword is required to enable text-based search for a set of semantically similar images. Then content-based search is performed on this set to retrieve visually similar images. At last, annotations are mined from the descriptions (titles, URLs and surrounding texts) of these images. It worth highlighting that to ensure the efficiency, high dimensional visual features are mapped to hash codes which significantly speed up the content-based search process. Our proposed approach enables annotating with unlimited vocabulary, which is impossible for all existing approaches. Experimental results on real web images show the effectiveness and efficiency of the proposed algorithm.
1326168
Detect and Track Latent Factors with Online Nonnegative Matrix Factorization
Detecting and tracking latent factors from temporal data is an important task. Most existing algorithms for latent topic detection such as Nonnegative Matrix Factorization (NMF) have been designed for static data. These algorithms are unable to capture the dynamic nature of temporally changing data streams. In this paper, we put forward an online NMF (ONMF) algorithm to detect latent factors and track their evolution while the data evolve. By leveraging the already detected latent factors and the newly arriving data, the latent factors are automatically and incrementally updated to reflect the change of factors. Furthermore, by imposing orthogonality on the detected latent factors, we can not only guarantee the unique solution of NMF but also alleviate the partial-data problem, which may cause NMF to fail when the data are scarce or the distribution is incomplete. Experiments on both synthesized data and real data validate the efficiency and effectiveness of our ONMF algorithm.
13915062
Intrapersonal subspace analysis with application to adaptive Bayesian face recognition
We propose a subspace distance measure to analyze the similarity between intrapersonal face subspaces, which characterize the variations between face images of the same individual. We call the conventional intrapersonal subspace the average intrapersonal subspace (AIS) because the image differences often come from a large number of persons. We call an intrapersonal subspace specific intrapersonal subspace (SIS) if the image differences are from just one person. We demonstrate that SIS varies from person to person and most SISs are not similar to AIS. Based on these observations, we introduce the maximum a posteriori (MAP) adaptation to the problem of SIS estimation, and apply it to the Bayesian face recognition algorithm. Experimental results show that the adaptive Bayesian algorithm outperforms the non-adaptive Bayesian algorithm as well as Eigenface and Fisherface methods when a small number of adaptation images are available.
15578823
Exploring statistical correlations for image retrieval
Bridging the cognitive gap in image retrieval has been an active research direction in recent years, of which a key challenge is to get enough training data to learn the mapping functions from low-level feature spaces to high-level semantics. In this paper, image regions are classified into two types: key regions representing the main semantic contents and environmental regions representing the contexts. We attempt to leverage the correlations between types of regions to improve the performance of image retrieval. A Context Expansion approach is explored to take advantages of such correlations by expanding the key regions of the queries using highly correlated environmental regions according to an image thesaurus. The thesaurus serves as both a mapping function between image low-level features and concepts and a store of the statistical correlations between different concepts. It is constructed through a data-driven approach which uses Web data (images, their surrounding textual annotations) as training data source to learn the region concepts and to explore the statistical correlations. Experimental results on a database of 10,000 general-purpose images show the effectiveness of our proposed approach in both improving search precision (i.e. filter irrelevant images) and recall (i.e. retrieval relevant images whose context may be varied). Several major factors which have impact on the performance of our approach are also studied.