On the Implementation of a Streaming Video over Peer to Peer network using Middleware Components

Video streaming applications have hitherto been supported by single server systems. A major drawback of such a solution is that it increases the server load. The server restricts the number of clients that can be simultaneously supported due to limitation in bandwidth. The constraints of a single server system can be overcome in video streaming if we exploit the endless resources available in a distributed and networked system. We explore a P2P system for streaming video applications. In this paper we build a P2P streaming video (SVP2P) service in which multiple peers co-operate to serve video segments for new requests, thereby reducing server load and bandwidth used. Our simulation shows the playback latency using SVP2P is roughly 1/4th of the latency incurred when the server directly streams the video. Bandwidth consumed for control messages (overhead) is as low as 1.5% of the total data transfered. The most important observation is that the capacity of the SVP2P grows dynamically.

Discovering injective episodes with general partial orders

Frequent episode discovery is a popular framework for temporal pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Currently algorithms exist for episode discovery only when the associated partial order is total order (serial episode) or trivial (parallel episode). In this paper, we propose efficient algorithms for discovering frequent episodes with unrestricted partial orders when the associated event-types are unique. These algorithms can be easily specialized to discover only serial or parallel episodes. Also, the algorithms are flexible enough to be specialized for mining in the space of certain interesting subclasses of partial orders. We point out that frequency alone is not a sufficient measure of interestingness in the context of partial order mining. We propose a new interestingness measure for episodes with unrestricted partial orders which, when used along with frequency, results in an efficient scheme of data mining. Simulations are presented to demonstrate the effectiveness of our algorithms.

A survey of temporal data mining

Data mining is concerned with analysing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships which in turn lead to better understanding of the underlying processes. The field of temporal data mining is concerned with such analysis in the case of ordered data streams with temporal interdependencies. Over the last decade many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as statistics, machine learning and databases, the literature is scattered among many different sources. In this article, we present an overview of techniques of temporal data mining.We mainly concentrate on algorithms for pattern discovery in sequential data streams.We also describe some recent results regarding statistical analysis of pattern discovery methods.

A general geomorphological recession flow model for river basins

Recession flows in a basin are controlled by the temporal evolution of its active drainage network (ADN). The geomorphological recession flow model (GRFM) assumes that both the rate of flow generation per unit ADN length (q) and the speed at which ADN heads move downstream (c) remain constant during a recession event. Thereby, it connects the power law exponent of -dQ/dt versus Q (discharge at the outlet at time t) curve, , with the structure of the drainage network, a fixed entity. In this study, we first reformulate the GRFM for Horton-Strahler networks and show that the geomorphic ((g)) is equal to D/(D-1), where D is the fractal dimension of the drainage network. We then propose a more general recession flow model by expressing both q and c as functions of Horton-Strahler stream order. We show that it is possible to have = (g) for a recession event even when q and c do not remain constant. The modified GRFM suggests that is controlled by the spatial distribution of subsurface storage within the basin. By analyzing streamflow data from 39 U.S. Geological Survey basins, we show that is having a power law relationship with recession curve peak, which indicates that the spatial distribution of subsurface storage varies across recession events. Key Points The GRFM is reformulated for Horton-Strahler networks. The GRFM is modified by allowing its parameters to vary along streams. Sub-surface storage distribution controls recession flow characteristics.

Effect of side confining walls on the growth rate of compressible mixing layers

Model free simulations are performed to study the effect of the presence of side wall in compressible mixing of two parallel dissimilar gaseous streams with significant temperature difference. The turbulence statistics shows the three dimensional nature of the flow with and without the presence of side walls. The presence of side wall neither makes the flow field two dimensional, nor suppresses three dimensional disturbances. However, the comparison of shear layer growth rate and wall pressures reveal a better match with the two dimensional simulation results. This better match is explained on the basis of formation of oblique structures due to the presence of side walls which also suppress the distribution of momentum in third direction making the pressures to be higher as compared with the case without side walls. (C) 2013 Elsevier Ltd. All rights reserved.

Pattern-growth based frequent serial episode discovery

Frequent episode discovery is a popular framework for pattern discovery from sequential data. It has found many applications in domains like alarm management in telecommunication networks, fault analysis in the manufacturing plants, predicting user behavior in web click streams and so on. In this paper, we address the discovery of serial episodes. In the episodes context, there have been multiple ways to quantify the frequency of an episode. Most of the current algorithms for episode discovery under various frequencies are apriori-based level-wise methods. These methods essentially perform a breadth-first search of the pattern space. However currently there are no depth-first based methods of pattern discovery in the frequent episode framework under many of the frequency definitions. In this paper, we try to bridge this gap. We provide new depth-first based algorithms for serial episode discovery under non-overlapped and total frequencies. Under non-overlapped frequency, we present algorithms that can take care of span constraint and gap constraint on episode occurrences. Under total frequency we present an algorithm that can handle span constraint. We provide proofs of correctness for the proposed algorithms. We demonstrate the effectiveness of the proposed algorithms by extensive simulations. We also give detailed run-time comparisons with the existing apriori-based methods and illustrate scenarios under which the proposed pattern-growth algorithms perform better than their apriori counterparts. (C) 2013 Elsevier B.V. All rights reserved.

A prototype of a first-responder indoor localization system

In this paper we present an approach to build a prototype. model of a first-responder localization system intended for disaster relief operations. This system is useful to monitor and track the positions of the first-responders in an indoor environment, where GPS is not available. Each member of the first responder team is equipped with two zero-velocity-update-aided inertial navigation systems, one on each foot, a camera mounted on a helmet, and a processing platform strapped around the waist of the first responder, which fuses the data from the different sensors. The fusion algorithm runs real-time on the processing platform. The video is also processed using the DSP core of the computing machine. The processed data consisting of position, velocity, heading information along with video streams is transmitted to the command and control system via a local infrastructure WiFi network. A centralized cooperative localization algorithm, utilizing the information from Ultra Wideband based inter-agent ranging devices combined with the position estimates and uncertainties of each first responder, has also been implemented.

Getting a kick out of it: multimodal signalling during male–male encounters in the foot-flagging frog Micrixalus aff. saxicola from the Western Ghats of India

Several anuran species use multimodal signals to communicate in diverse social contexts. Our study describes acoustic and visual behaviours of the Small Torrent Frog (Micrixalus aff. saxicola), a diurnal frog endemic to the Western Ghats of India. During agonistic interactions males display advertisement calls, foot-flagging and tapping (foot lifting) behaviours to signal the readiness to defend perching sites in perennial streams. Results from a quantitative video analysis of male–male interactions indicate that footflagging displays were used as directional signals toward the opponent male, but were less abundant than calls. The acoustic and visual signals were not functionally linked. The call of Micrixalus aff. saxicola thereby did not act as an alert signal. Analysis of behavioural transitions revealed that kicking behaviours (physical attacks) significantly elicited kicks from interacting males. We suggest that foot-flagging displays ritualized from this frequently observed fighting technique to reduce physical attacks.

Providing QoS in a cognitive radio network

We consider the problem of wireless channel allocation (whenever the channels are free) to multiple cognitive radio users in a Cognitive Radio Network (CRN) so as to satisfy their Quality of Service (QoS) requirements efficiently. The CRN base station may not know the channel states of all the users. The multiple channels are available at random times. In this setup Opportunistic Splitting can be an attractive solution. A disadvantage of this algorithm is that it requires the metrics of all users to be an independent, identically distributed sequence. However we use a recently generalized version of this algorithm in which the optimal parameters are learnt on-line through stochastic approximation and metrics can be Markov. We provide scheduling algorithms which maximize weighted-sum system throughput or are throughput or delay optimal. We also consider the scenario when some traffic streams are delay sensitive.

Getting a kick out of it: multimodal signalling during male-male encounters in the foot-flagging frog Micrixalus aff. saxicola from the Western Ghats of India

Several anuran species use multimodal signals to communicate in diverse social contexts. Our study describes acoustic and visual behaviours of the Small Torrent Frog (Micrixalus aff. saxicola), a diurnal frog endemic to the Western Ghats of India. During agonistic interactions males display advertisement calls, foot-flagging and tapping (foot lifting) behaviours to signal the readiness to defend perching sites in perennial streams. Results from a quantitative video analysis of male-male interactions indicate that foot-flagging displays were used as directional signals toward the opponent male, but were less abundant than calls. The acoustic and visual signals were not functionally linked. The call of Micrixalus aff. saxicola thereby did not act as an alert signal. Analysis of behavioural transitions revealed that kicking behaviours (physical attacks) significantly elicited kicks from interacting males. We suggest that foot-flagging displays ritualized from this frequently observed fighting technique to reduce physical attacks.

A framework for post-silicon realization of arbitrary instruction extensions on reconfigurable data-paths

In this paper we present a framework for realizing arbitrary instruction set extensions (IE) that are identified post-silicon. The proposed framework has two components viz., an IE synthesis methodology and the architecture of a reconfigurable data-path for realization of the such IEs. The IE synthesis methodology ensures maximal utilization of resources on the reconfigurable data-path. In this context we present the techniques used to realize IEs for applications that demand high throughput or those that must process data streams. The reconfigurable hardware called HyperCell comprises a reconfigurable execution fabric. The fabric is a collection of interconnected compute units. A typical use case of HyperCell is where it acts as a co-processor with a host and accelerates execution of IEs that are defined post-silicon. We demonstrate the effectiveness of our approach by evaluating the performance of some well-known integer kernels that are realized as IEs on HyperCell. Our methodology for realizing IEs through HyperCells permits overlapping of potentially all memory transactions with computations. We show significant improvement in performance for streaming applications over general purpose processor based solutions, by fully pipelining the data-path. (C) 2014 Elsevier B.V. All rights reserved.

Adsorption study of silica gel particle for improvement in design of adsorption beds used in solar driven cooling units

Simulations using Ansys Fluent 6.3.26 have been performed to look into the adsorption characteristics of a single silica gel particle exposed to saturated humid air streams at Re=108 & 216 and temperature of 300K. The adsorption of the particle has been modeled as a source term in the species and the energy equations using a Linear Driving Force (LDF) equation. The interdependence of the thermal and the water vapor concentration field has been analysed. This work is intended to aid in understanding the adsorption effects in silica gel beds and in their efficient design. (C) 2013 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Online Estimation of Evolving Human Visual Interest

Regions in video streams attracting human interest contribute significantly to human understanding of the video. Being able to predict salient and informative Regions of Interest (ROIs) through a sequence of eye movements is a challenging problem. Applications such as content-aware retargeting of videos to different aspect ratios while preserving informative regions and smart insertion of dialog (closed-caption text) into the video stream can significantly be improved using the predicted ROIs. We propose an interactive human-in-the-loop framework to model eye movements and predict visual saliency into yet-unseen frames. Eye tracking and video content are used to model visual attention in a manner that accounts for important eye-gaze characteristics such as temporal discontinuities due to sudden eye movements, noise, and behavioral artifacts. A novel statistical-and algorithm-based method gaze buffering is proposed for eye-gaze analysis and its fusion with content-based features. Our robust saliency prediction is instantiated for two challenging and exciting applications. The first application alters video aspect ratios on-the-fly using content-aware video retargeting, thus making them suitable for a variety of display sizes. The second application dynamically localizes active speakers and places dialog captions on-the-fly in the video stream. Our method ensures that dialogs are faithful to active speaker locations and do not interfere with salient content in the video stream. Our framework naturally accommodates personalisation of the application to suit biases and preferences of individual users.

Exploration of supersonic confined mixing layer: Effect of dissimilar gases at different temperatures

The growth rate of high-speed mixing layer between two dissimilar gases is explored through the model free simulation results. To analyse the cause for the higher mixing layer growth rate in comparison to the existing values reported in literature, the results were compared with the model free simulations of mixing of two high-speed streams of nitrogen (similar gas) at matched temperature and density. The analysis indicates that pressure and density fluctuations no longer remain correlated completely for the mixing layer formed between two dissimilar gases at different temperatures in contrast to the complete pressure density correlation for similar gases. It has been observed that the correlation between temperature and density fluctuations is near -1.0 for dissimilar gases in the mixing layer region and is much higher than for similar gases. It is concluded that mixing layer of similar gases shows a decrease in growth rate due to compressibility effect, while that of dissimilar gases shows a decrease due to dominant temperature effect on density.

Effect of functional groups on separating carbon dioxide from CO2/N-2 gas mixtures using edge functionalized graphene nanoribbons

Development of microporous adsorbents for separation and sequestration of carbon dioxide from flue gas streams is an area of active research. In this study, we assess the influence of specific functional groups on the adsorption selectivity of CO2/N-2 mixtures through Grand Canonical Monte Carlo (GCMC) simulations. Our model system consists of a bilayer graphene nanoribbon that has been edge functionalized with OH, NH2, NO2, CH3 and COOH. Ab initio Moller-Plesset (MP2) calculations with functionalized benzenes are used to obtain binding energies and optimized geometries for CO2 and N-2. This information is used to validate the choice classical forcefields in GCMC simulations. In addition to simulations of adsorption from binary mixtures of CO2 and N-2, the ideal adsorbed solution theory (IAST) is used to predict mixture isotherms. Our study reveals that functionalization always leads to an increase in the adsorption of both CO2 and N-2 with the highest for COOH. However, significant enhancement in the selectivity for CO2 is only seen with COOH functionalized nanoribbons. The COOH functionalization gives a 28% increase in selectivity compared to H terminated nanoribbons, whereas the improvement in the selectivity for other functional groups are much Enure modest. Our study suggests that specific functionalization with COOH groups can provide a material's design strategy to improve CO2 selectivity in microporous adsorbents. Synthesis of graphene nanoplatelets with edge functionalized COOH, which has the potential for large scale production, has recently been reported (Jeon el, al., 2012). (C) 2014 Elsevier Ltd. All rights reserved,