12219640
Maximum-diversity transmissions over doubly selective wireless channels
High data rates and multipath propagation give rise to frequency-selectivity of wireless channels, while carrier frequency offsets and mobility-induced Doppler shifts introduce time-selectivity in wireless links. The resulting time- and frequency-selective (or doubly selective) channels offer joint multipath-Doppler diversity gains. Relying on a basis expansion model of the doubly selective channel, we prove that the maximum achievable multipath-Doppler diversity order is determined by the rank of the correlation matrix of the channel's expansion coefficients, and is multiplicative in the effective degrees of freedom that the channel exhibits in the time and frequency dimensions. Interestingly, it turns out that time-frequency reception alone does not guarantee maximum diversity, unless the transmission is also designed judiciously. We design such block precoded transmissions. The corresponding designs for frequency-selective or time-selective channels follow as special cases, and thorough simulations are provided to corroborate our theoretical findings.
19696528
Block differential encoding for rapidly fading channels
Rapidly fading channels provide Doppler-induced diversity, but are also challenging to estimate. To bypass channel estimation, we derive two novel block differential (BD) codecs. Relying on a basis expansion model for time-varying channels, our differential designs are easy to implement, and can achieve the maximum possible Doppler diversity. The first design (BD-I) relies on a time-frequency duality, based on which we convert a time-varying channel into multiple frequency-selective channels, and subsequently into multiple flat-fading channels using orthogonal frequency-division multiplexing. Combined with a group partitioning scheme, BD-I offers flexibility to trade off decoding complexity with performance. Our second block differential design (BD-II) improves the bandwidth efficiency of BD-I at the price of increased complexity at the receiver, which relies on decision-feedback decoding. Simulation results corroborate our theoretical analysis, and compare with competing alternatives.
2822268
Online Mining in Sensor Networks
Online mining in large sensor networks just starts to attract interest. Finding patterns in such an environment is both compelling and challenging. The goal of this position paper is to understand the challenges and to identify the research problems in online mining for sensor networks. As an initial step, we identify the following three problems to work on: (1) sensor data irregularities detection; (2) sensor data clustering; and (3) sensory attribute correlations discovery. We also outline our preliminary proposal of solutions to these problems.
5702274
Positioning Dynamic Storage Caches for Transient Data
Simulations, experiments and observatories are generating a deluge of scientific data. Even more staggering is the ever growing application demand to process and assimilate these datasets. Application users perform a range of data operations, collaborate and share data in many novel ways. The current storage landscape is struggling to keep up with these trends in scientific data processing. Application users pay the price due to over-crowded shared filesystems, or expensive storage area networks, or not enough local storage, or high-latency archival or wide-area transfers. In order to sustain and maximize I/O bandwidth relative to increasing CPU speeds, applications must take advantage of large amounts of intermediate commodity storage, However, intermediate storage presents new challenges above and beyond the traditional distributed file system paradigm: persistent scheduling, storage/CPU coallo-cation, namespace management, lifetime management, and novel application interfaces. In this paper, we describe applications that require intermediate storage management, suggest several open research problems, and illustrate two systems - Freeloader and Tactical Storage - that attack different aspects of these problems
7609568
Mining Maximal Correlated Member Clusters in High Dimensional Database
Mining high dimensional data is an urgent problem of great practical importance. Although some data mining models such as frequent patterns and clusters have been proven to be very successful for analyzing very large data sets, they have some limitations. Frequent patterns are inadequate to describe the quantitative correlations among nominal members. Traditional cluster models ignore distances of some pairs of members, so a pair of members in one big cluster may be far away. As a combination and complementary of both techniques, we propose the Maximal-Correlated-Member-Cluster (MCMC) model in this paper. The MCMC model is based on a statistical measure reflecting the relationship of nominal variables, and every pair of members in one cluster satisfy unified constraints. Moreover, in order to improve algorithm's efficiency, we introduce pruning techniques to reduce the search space. In the first phase, a Tri-correlation inequation is used to eliminate unrelated member pairs, and in the second phase, an Inverse-Order-Enumeration-Tree (IOET) method is designed to share common computations. Experiments over both synthetic datasets and real life datasets are performed to examine our algorithm's performance. The results show that our algorithm has much higher efficiency than the naive algorithm, and this model can discover meaningful correlated patterns in high dimensional database.
369805
Flexible and efficient parallel I/O for large-scale multi-component simulations
In this paper, we discuss our experience of providing high performance parallel I/O for a large-scale, on-going, multi-disciplinary simulation project for solid propellant rockets. We describe the performance and data management issues observed in this project and present our solutions, including (1) support for relatively fine-grained distribution of irregular datasets in parallel I/O, (2) a flexible data management facility for inter-module communication, and (3) two schemes to overlap computation with I/O. Performance results obtained from both development and production platforms show that our I/O optimizations can dramatically reduce the simulation's visible I/O cost, as well as the number of disk files, and significantly improve the overall performance. Meanwhile, our data management facility helps to provide simulation developers with simple user interfaces for parallel I/O.
2357877
Edge-based rich representation for vehicle classification
In this paper, we propose an approach to vehicle classification under a mid-field surveillance framework. We develop a repeatable and discriminative feature based on edge points and modified SIFT descriptors, and introduce a rich representation for object classes. Experimental results show the proposed approach is promising for vehicle classification in surveillance videos despite great challenges such as limited image size and quality and large intra-class variations. Comparisons demonstrate the proposed approach outperforms other methods