14476885
Energy-efficient caching and prefetching with data consistency in mobile distributed systems
Summary form only given. In mobile distributed systems, vital resources like battery power and wireless channel bandwidth impose significant challenges in ubiquitous information access. We propose a novel energy and bandwidth efficient data caching mechanism, called greedydual least utility (GD-LU), that enhances dynamic data availability while maintaining consistency. The proposed utility-based caching mechanism considers several characteristics of mobile distributed systems, such as connection-disconnection, mobility handoff, data update and user request patterns to achieve significant energy savings in mobile devices. Based on the utility function derived from an analytical model, we propose a cache replacement algorithm and a passive prefetching algorithm to cache and prefetch data objects. Our comprehensive simulation experiments demonstrate that the proposed mechanism achieves more than 10% energy saving and near-optimal performance tradeoff between access latency and energy consumption.
1571784
QoS middleware support for pervasive computing applications
Today, pervasive computing technologies are being developed to provide automated, real-time, continual, and unobtrusive user services in dynamic heterogeneous environments such as telemedicine, manufacturing, space endeavors, crisis management, and military. However, the full potential of pervasive computing cannot be realized without enabling middleware technologies - henceforth referred to as "middleware services"; i.e., they provide services for high level applications. In this paper, we introduce middleware services that are required to address the challenges related to adapting to dynamically changing situations, meeting communication QoS requirements, and achieving scalability in large-scale pervasive computing applications over heterogeneous network infrastructures. Our approach in developing middleware support for pervasive computing is based on the community computing concept, which provides us with a unified method to dynamically integrate the middleware services with each other and with high level applications on a just-in-time basis. The proposed middleware services for pervasive computing are highly modular, lightweight, and easily deployable to meet the needs of pervasive computing applications in networked environments. In this paper, we describe our proposed QoS algorithms and techniques for synergistic integration of middleware services into pervasive computing applications. In addition, we propose new performance metrics and a benchmark suite approach to evaluate middleware services in pervasive computing. Finally, we present some early prototype results from our proof of concept implementation.
10852915
Cooperative Caching with Optimal Radius in Hybrid Wireless Networks
In this paper, we propose a novel caching scheme, called energy efficient cooperative caching with optimal radius (ECOR), to overcome the limitations of hybrid wireless networks such as high energy consumption, long access latency and poor load balance. In ECOR, each mobile user (MU) forms a cooperation zone with MUs in proximity to share cached data items. An analytical model of ECOR is developed to determine the optimal radius of the cooperation zone based on MU’s location, data popularity and node density. Experimental results show that ECOR achieves significant performance improvement in power saving, network throughput and load balance in comparison with other existing approaches.
5533650
The hierarchical cliques interconnection network
The fully connected network possesses extremely good topological, fault-tolerant, and embedding properties. However, due to its high degree, the fully connected network has not been an attractive candidate for building parallel computers. On the other hand, tree-based networks are popular as parallel computer networks, even though they suffer from poor fault-tolerance and embedding properties. The hierarchical cliques interconnection network described in this paper incorporates positive features of the fully connected network and the tree network. In other words, the hierarchical cliques possess such desirable properties as low diameter, low degree, self routing, versatile embedding, good fault-tolerance and strong resilience. Hierarchical cliques can efficiently embed most important networks and possess a scalable, modular structure. Further, by combining hierarchical cliques with fat trees congestion in the upper levels can be alleviated.
7854172
OBJ CUT
In this paper, we present a principled Bayesian method for detecting and segmenting instances of a particular object category within an image, providing a coherent methodology for combining top down and bottom up cues. The work draws together two powerful formulations: pictorial structures (PS) and Markov random fields (MRFs) both of which have efficient algorithms for their solution. The resulting combination, which we call the object category specific MRF, suggests a solution to the problem that has long dogged MRFs namely that they provide a poor prior for specific shapes. In contrast, our model provides a prior that is global across the image plane using the PS. We develop an efficient method, OBJ CUT, to obtain segmentations using this model. Novel aspects of this method include an efficient algorithm for sampling the PS model, and the observation that the expected log likelihood of the model can be increased by a single graph cut. Results are presented on two object categories, cows and horses. We compare our methods to the state of the art in object category specific image segmentation and demonstrate significant improvements.
14151624
Solving Markov Random Fields using Second Order Cone Programming Relaxations
This paper presents a generic method for solvingMarkov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP). Though in general solving MRFs is NP-hard, we propose a second order cone programming relaxation scheme which solves a closely related (convex) approximation. In terms of computational efficiency, our method significantly outperforms the semidefinite relaxations previously used whilst providing equally (or even more) accurate results. Unlike popular inference schemes such as Belief Propagation and Graph Cuts, convergence is guaranteed within a small number of iterations. Furthermore, we also present a method for greatly reducing the runtime and increasing the accuracy of our approach for a large and useful class of MRFs. We compare our approach with the state-of-the-art methods for subgraph matching and object recognition and demonstrate significant improvements.
7287405
Learning Layered Pictorial Structures from Video
We propose a new unsupervised learning method to obtain a layered pictorial structure (LPS) representation of an articulated object from video sequences. It will be seen that this is related in turn to methods for learning sprite based representations of an image. The method we describe involves a new generative model for performing segmentation on a set of images. Included in this model are the effects of motion blur and occlusion. An initial estimate of the parameters of the model is obtained by dividing the scene into rigidly moving components. The estimate of the matte of each part is refined using a variation of the α-expansion graph cut algorithm. This method has the advantage of achieving a strong local minimum over labels. Results are demonstrated on animals for which an articulated LPS representation is naturally suited.
14783291
A large-vocabulary continuous speech recognition system for Hindi
In this paper we present two new techniques that have been used to build a large-vocabulary continuous Hindi speech recognition system. We present a technique for fast bootstrapping of initial phone models of a new language. The training data for the new language is aligned using an existing speech recognition engine for another language. This aligned data is used to obtain the initial acoustic models for the phones of the new language. Following this approach requires less training data. We also present a technique for generating baseforms (phonetic spellings) for phonetic languages such as Hindi. As is inherent in phonetic languages, rules generally capture the mapping of spelling to phonemes very well. However, deep linguistic knowledge is required to write all possible rules, and there are some ambiguities in the language that are difficult to capture with rules. On the other hand, pure statistical techniques for base and generation require large amounts of training data that are not readily available. We propose a hybrid approach that combines rule-based and statistical approaches in a two-step fashion. We evaluate the performance of the proposed approaches through various phonetic classification and recognition experiments.
10875247
Measuring Parallelism in Computation-Intensive Scientific/Engineering Applications
Describes COMET, (concurrency measurement tool), a software tool for measuring parallelism in large scientific/engineering applications. The proposed tool measures the total parallelism present in programs, filtering out the effects of communication/synchronization delays, finite storage, limited number of processors, the policies for management of processors and storage, etc. Although an ideal machine that can exploit the total parallelism is not realizable, such measures would aid the calibration and design of various architectures/compilers. The proposed software tool accepts ordinary Fortran programs as input. Therefore, parallelism can be measured easily on many fairly big programs. Some measurements for parallelism obtained with the help of this tool are also reported. It is observed that the average parallelism in the chosen programs is in the range of 500-3500 Fortran statements executing concurrently in each clock cycle in an idealized environment. >