A stationary visual census technique for quantitatively assessing community structure of coral reef fishes

A new method is described and evaluated for visually sampling reef fish community structure in environments with highly diverse and abundant reef fish populations. The method is based on censuses of reef fishes taken within a cylinder of 7.5 m radius by a diver at randomly selected, stationary points. The method provides quantitative data on frequency of occnrrence, fish length, abundance, and community composition, and is simple, fast, objective, and repeatable. Species are accumulated rapidly for listing purposes, and large numbers of samples are easily obtained for statistical treatment. The method provides an alternative to traditional visual sampling methods. Observations showed that there were no significant differences in total numbers of species or individuals censused when visibility ranged between 8 and 30 m. The reefs and habitats sampled were significant sources of variation in number of species and individuals censused, but the diver was not a significant influence. Community similarity indices were influenced significantly by the specific sampling site and the reef sampled, but were not significantly affected by the habitat or diver (PDF file contains 21 pages.

Dispersion of Observed Position Angles of Submillimeter Polarization in Molecular Clouds

One can estimate the characteristic magnetic field strength in GMCs by comparing submillimeter polarimetric observations of these sources with simulated polarization maps developed using a range of different values for the assumed field strength. The point of comparison is the degree of order in the distribution of polarization position angles. In a recent paper by H. Li and collaborators, such a comparison was carried out using SPARO observations of two GMCs, and employing simulations by E. Ostriker and collaborators. Here we reexamine this same question, using the same data set and the same simulations, but using an approach that differs in several respects. The most important difference is that we incorporate new, higher angular resolution observations for one of the clouds, obtained using the Hertz polarimeter. We conclude that the agreement between observations and simulations is best when the total magnetic energy (including both uniform and fluctuating field components) is at least as large as the turbulent kinetic energy.Comment: revised, accepted version; to appear in The Astrophysical Journal; 20 pages, 2 figures, 2 table

Exploiting random walks for robust, scalable, structure-free data aggregation and routing in mobile ad-hoc networks (MANETs)

The focus of this thesis is on the design of scalable data aggregation protocols for Mobile Ad-hoc Networks (MANETs). Data aggregation Protocols that rely on network structures such as trees or backbones are not well suited for MANETs because the underlying topology of MANETs is constantly changing. On the other hand, unstructured techniques such as flooding and gossiping have a high messaging overhead and take a long time to finish. Therefore, in this thesis, we explore the use of random walks as a structure-free alternative for data aggregation in MANETs.;The basic idea is to introduce one or more tokens that successively visit each node in a MANET by executing a random walk and compute the aggregate state. While random walks are simple, robust and overhead-free, plain random walks tend to be slow in visiting all nodes because the token can get stuck in regions of already visited nodes. Therefore, we first introduce self-repelling random walks (SRRW) in which at each step, the token chooses a neighbor that has been visited the least number of times. While SRRW significantly speeds up random walks in the initial stages, towards the end a slowdown is observed when a significant fraction of nodes are already visited. To address this shortcoming, we then develop two complementary strategies that speed up data aggregation.;First, we introduce gradient biased random walks (a pull-based strategy) where short temporary multi-hop gradients are used to pull the tokens toward unvisited node. We prove that gradient biased random walks achieve a cover time of O(N) and message overhead of O(NlogN) where N is the number of nodes in the network. Next, we introduce a push-based strategy in which self-repelling random walks are complemented by a single step push phase before the random walk phase, in which each node broadcasts its information to its neighbors. We show that this small push goes a long way in speeding up data aggregation. Push based random walks finish data aggregation in O(N) message and time. Finally, we describe hierarchical extension of the push-based protocol which can produce multi-resolution aggregates at each node using only O(NlogN) messages.;All our results are validated using simulations in ns-3 in networks ranging from 100 to 4000 nodes under different network densities, node speed and mobility models

Explora : interactive querying of multidimensional data in the context of smart cities

Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-latency requests, typical of visual exploratory applications on spatiotemporal data, which leverages the stream processing for deriving-on ingestion time-synopsis data structures that concisely capture the spatial and temporal trends and dynamics of the sensed variables and serve as compacted data sets to provide fast (approximate) answers to visual queries on smart city data. The experimental evaluation conducted on proof-of-concept implementations of Explora, based on traditional database and distributed data processing setups, accounts for a decrease of up to 2 orders of magnitude in query latency compared to queries running on the base raw data at the expense of less than 10% query accuracy and 30% data footprint. The implementation of the framework on real smart city data along with the obtained experimental results prove the feasibility of the proposed approach

Query Morphing: A Proximity-Based Approach for Data Exploration

We are living in age where large information in the form of structured and unstructured data is generated through social media, blogs, lab simulations, sensors etc. on daily basis. Due to this occurrences, acquisition of relevant information becomes a challenging task for humans. Fundamental understanding of complex schema and content is necessary for formulating data retrieval request. Therefore, instead of search, we need exploration in which a naïve user walks through the database and stops when satisfactory information is met. During this, a user iteratively transforms his search request in order to gain relevant information; morphing is an approach for generation of various transformation of input. We proposed ‘Query morphing’, an approach for query reformulation based on data exploration. Identified design concerns and implementation constraints are also discussed for the proposed approach

A study of data coding technology developments in the 1980-1985 time frame, volume 2

The source parameters of digitized analog data are discussed. Different data compression schemes are outlined and analysis of their implementation are presented. Finally, bandwidth compression techniques are given for video signals