Dynamic Streaming Research Articles

Security and performance analysis of SCDSP

There are few approaches that attempt to add dynamicity to the structure of stream ciphers in order to improve their security level. SCDSP is a dynamic stream cipher that based on these approaches. It uses dynamic structure and parameters to increase the complexity of the cipher to improve its security level. The dynamic parameters are specified using bits from the secret key. In this paper, SCDSP is evaluated by conducting a performance and security analysis. Furthermore, a comparison between SCDSP and the seven winners of eSTREAM competition is performed. The results show that SCDSP is very promising for practical use.

Identification, characteristics and classification of cryogenic block streams

Cryogenic block streams consist of a stream of rocks superficially resembling a stream deposit but lacking a matrix, usually occurring on a valley or gully floor or on slopes that are less steep than the maximum angle of repose of coarse sediments. They are usually formed on perennially frozen ground, but can also occur as relict landforms. There are three main active kinds forming today, viz ., Siberian and Tibetan dynamic rock streams and lag block streams. During their formation, the blocks in the active Siberian and Tibetan dynamic block streams move downslope at up to 1 m/a. They are forming today on the Tibetan Plateau and in the more arid parts of south-central Siberia, although the processes involved in the movement are different. In the case of the Tibetan type, individual blocks slide downslope over the substrate in winter on an icy coating in areas of minimal winter precipitation. The Siberian type develops in areas of 15–80 cm of winter snow cover and an MAAT (mean annual air temperature) of ?4 ℃ to ?17 ℃. The movement is due to creep of snow and ice and collapse of the blocks downslope during thawing. Lag block streams are formed by meltwater flowing over the surface of sediment consisting primarily of larger blocks with a limited amount of interstitial sediment. The erosion of the matrix is primarily in the spring in areas of higher winter precipitation on 10°–30° slopes. The blocks remain stationary, but the interstitial sediment is washed out by strong seasonal flows of meltwater or rain to form an alluvial fan. The boulders undergo weathering and become more rounded in the process. Lag block streams can also develop without the presence of permafrost in areas with cold climates or glaciers. Block streams also occur as relict deposits in older deposits under various climatic regimes that are unsuitable for their formation today. An example of relict lag block streams with subangular to subrounded blocks occurs in gullies on the forested mountainsides at Felsen in Germany, and is the original 'felsenmeer'. Similar examples occur near Vitosha Mountain in Bulgaria. The 'stone runs' in the Falkland Islands are examples of the more angular relict lag block streams. In both Tasmania and the Falkland Islands, they mask a more complex history, the underlying soils indicating periods of tropical and temperate soil formation resulting from weathering during and since the Tertiary Period. Block streams have also been reported from beneath cold-based glaciers in Sweden, and below till in Canada, and when exhumed, can continue to develop.

Incremental SVM-based classification in dynamic streaming networks

Incremental SVM-based classification in dynamic streaming networks

End-to-End Latency Analysis of Dataflow Scenarios Mapped Onto Shared Heterogeneous Resources

The design of embedded wireless and multimedia applications requires temporal analysis to verify if real-time constraints such as throughput and latency are met. This paper presents a design-time analytical approach to derive a conservative upper bound to the maximum end-to-end latency of a streaming application. Existing analytical approaches often assume static application models, which cannot cope with the data-dependent execution of dynamic streaming applications. Consequently, they give overly pessimistic upper bounds. In this paper, we use an expressively richer dataflow model of computation as an application model. The model supports adaptive applications that change their graph structure, execution times, and data rates, depending on their mode of operation, or scenario. We first formalize the latency analysis problem in the presence of dynamically switching scenarios. We characterize each scenario with a compact matrix in (max, +) algebra using a symbolic execution of one graph iteration. The resulting matrices are then composed to derive a bound to the end-to-end latency under a periodic source. Aperiodic sources such as sporadic streams can be analyzed by reduction to a periodic reference. We demonstrate the applicability of the technique with dataflow models from the wireless application domain. Moreover, the method is illustrated with a tradeoff analysis in resource reservation under a throughput constraint. The evaluation shows that the approach has a low runtime, which enables it to be effectively integrated in multiprocessor design flows of streaming applications.

Consequence modeling of dynamic source terms

While several dispersion modeling tools provide modeling tools for static situations, it is challenging to model conditions when there are several variables continually changing. This article discusses several modeling approaches to solve the source term of a release event, including: tracking the concentration of vapor inside of a structure; accounting for heat of solution of a leak of strong acid while deluge is applied; and modeling the output from a scrubber given a dynamic input stream. Upon development of the source term, a method for applying the source term to common dispersion modeling packages is explained. © 2016 American Institute of Chemical Engineers Process Saf Prog 35: 392–398, 2016

Chunk Size Aware Buffer-Based Algorithm to Improve Viewing Experience in Dynamic HTTP Streaming

Chunk Size Aware Buffer-Based Algorithm to Improve Viewing Experience in Dynamic HTTP Streaming

Modelling rules for automating the Evented WEb by semantic technologies

The Live Web is characterised by a new way of interacting with the Web through dynamic streams of relevant real-time contextual information to users. These sources of massive data usually overwhelm them, because they are not able to consume that amount of data. Task Automation Services (TASs) are platforms or apps that allow their users to author automation rules to combine events from streams while reducing the effort for handling incoming information. While these platforms are a reality, they suffer from two major drawbacks: (i) the only incoming data streams available are those the TASs developers decided to include in the system, and (ii) they lack of a mechanism to reason over large scale data outside their platform. To face these challenges, this paper contributes in (i) reviewing the existing state of the art including research and commercial work given their relevance. Based on the lessons learnt from this review, (ii) we propose the Evented WEb ontology (EWE), that models the Evented WEb domain, and in particular those concepts around TASs. EWE enables scalability, interoperability and definition of rules with reasoning over Linked Open Data (LOD) cloud. To illustrate these contributions, (iii) a semantic TAS has been implemented that benefits from the advantages EWE offers, and solves a realistic problem using semantic technologies. Finally, (iv) to validate the ontology covers the domain it models, a thorough ontology evaluation is presented.

QoS guarantee protocol based on combination of opportunistic dynamic cloud service and cooperative multimedia stream for wireless sensor networks

Abstract In real-time multimedia applications, the most problems are how to store, process, and transport the multimedia data with cloud, how to optimize the cloud platform to satisfy the user demand, as well as how to provide the quality of service (QoS) guarantees by combining cloud platform and wireless sensor networks (WSNs). To overcome these issues, the multimedia cloud was designed according to the characteristics of group of pictures (GOP), determining degree, and video frame density. The opportunistic dynamic multimedia cloud platform was proposed on the basis of the variation of packet error rate, decodable frame ratio and peak signal-to-noise ratio (PSNR) with channel quality. Then, the optimal multi-relay hierarchical cooperative multimedia transmission scheme was designed and implemented by reconstructing the cooperative multimedia stream based on the framing of GOP. For enhancing the multimedia cloud computing efficiency and decreasing the complexity of encoding and decoding of multimedia stream, we combined the opportunistic dynamic multimedia cloud platform and cooperative multimedia stream, which can also enhance the resources utilization of WSNs. Finally, the QoS guarantee protocol with the combination of opportunistic dynamic multimedia cloud platform and cooperative multimedia stream for WSNs (ODM-CM-WSN) was presented. Especially, the layered architectural model for multimedia applications and the opportunistic cooperative multimedia cloud service model were developed. The mathematical and simulation results demonstrate that the proposed QoS guarantee protocol can satisfy the various requirements of multimedia applications and provide reliable and efficient cloud services with WSNs.

Hydrodynamic control of benthic mats of Didymosphenia geminata at the reach scale

Algal blooms caused by the stalk-forming diatom Didymosphenia geminata impact the habitat structure and functioning of stream ecosystems. We evaluated site-specific geomorphic controls on D. geminata in a cobble bed river in the Front Range of the Rocky Mountains. Entrainment potential, defined as the ratio of mean bed shear stress to the critical shear stress, was found to be the most important control on total abundance. A comparison with critical thresholds for sediment transport showed that the requirements for flushing flows to control D. geminata blooms are similar to that required for channel maintenance. Environmental parameters influencing the morphology of the algal mats and the potential for D. geminata to dominate the benthic algal community were also investigated. The results not only contribute to understanding of the controls on benthic algal growth in dynamic streams but also for management of water releases as mitigation for the control of future blooms of D. geminata.

Dynamic Scheduling of Irregular Stream Programs toward Many-Core Scalability

The stream programming model has received much interest because it naturally exposes task, data, and pipeline parallelism. However, most priorwork has focused on the static scheduling of regular stream programs. Therefore, irregular applications cannot be handled in static scheduling, and the load imbalance caused by static scheduling faces scalability limitations in many-core systems. In this paper, we introduce the DANBI programming model, which supports irregular stream programs, and propose dynamic scheduling techniques. Scheduling irregular stream programs is very challenging, and the load imbalance becomes a major hurdle to achieving scalability. Our dynamic load-balancing scheduler exploits producer-consumer relationships already expressed in the DANBI program to achieve scalability. Moreover, it effectively avoids the thundering-herd problem and dynamically adapts to load imbalance in a probabilistic manner. It surpasses prior static stream scheduling approaches which are vulnerable to load imbalance and also surpasses prior dynamic stream scheduling approaches which result in many restrictions on supported program types, on the scope of dynamic scheduling, and on data ordering preservation. Our experimental results on a 40-core server show that DANBI achieves an almost linear scalability and outperforms state-of-the-art parallel runtimes by up to 2.8 times.

Keys for Redesign Industrial Facilities in a Current Productive Environment

Today companies don’t have time and resources availability to stop their production to evaluate new layout and strategies, unless the results are guaranteed. Developing or redesigning products, gathering them in a product family and creating product platforms can be expensive tasks that represent a meaningful outlay for companies if they don’t have the adequate tools in order to facilitate the work. Thus, it is important to define the most appropriate manufacturing system as well as the performance of the chain value and the equipment layout in order to achieve an optimal production with the best quality and the shortest times and production costs. Therefore, computational tools, validated by working strategies and philosophies as Lean Manufacturing (LM) and Product Lifecycle Management (PLM) become necessary. After the evaluation of products, the value chain and the layout, these tools allow the construction of models and simulations as dynamic Value Stream Map (dynamic VSM), to analyze the actual process functioning or future process plans and PLM software, to estimate production flows, equipment and human labor requirements without stopping the normal production activities and providing competitive advantages to the company.

Edge-based Mining of Frequent Subgraphs from Graph Streams

In the current era of Big data, high volumes of valuable data can be generated at a high velocity from high-varieties of data sources in various real-life applications ranging from sensor networks to social networks, from bio-informatics to chemical informatics. In addition, Big data are also available in business, education, engineering, finance, healthcare, scientific, telecommunication, and transportation domains. A collection of these data can be viewed as a big dynamic graph structure. Embedded in them are implicit, previously unknown, and potentially useful knowledge. Consequently, efficient knowledge discovery algorithms for mining frequent subgraphs from these dynamic streaming graph structured data are in demand. On the one hand, some existing algorithms discover collections of frequently co-occurring edges, which may be disjoint. On the other hand, some other existing algorithms discover frequent subgraphs by requiring very large memory space. With high volumes of Big data, available memory space may be limited. To discover collections of frequently co-occurring connected edges, we present in this paper two efficient algorithms that require small memory space. Evaluation results show the efficiency of our edge-based algorithms in mining frequent subgraphs from graph streams.

Reply to comment on “Suburban watershed nitrogen retention: Estimating the effectiveness of stormwater management structures” by Koch et al. (Elem Sci Anth3:000063, July 2015)

AbstractWe reply to a comment on our recent structured expert judgment analysis of stormwater nitrogen retention in suburban watersheds. Low relief, permeable soils, a dynamic stream channel, and subsurface flows characterize many lowland Coastal Plain watersheds. These features result in unique catchment hydrology, limit the precision of streamflow measurements, and challenge the assumptions for calculating runoff from rainfall and catchment area. We reiterate that the paucity of high-resolution nitrogen loading data for Chesapeake Bay watersheds warrants greater investment in long-term empirical studies of suburban watershed nutrient budgets for this region.

Multiobjective Design Optimization in the Lightweight Dataflow for DDDAS Environment (LiD4E) 1

In this paper, we introduce new methods for multiobjective, system-level optimization that have been incorporated into the Lightweight Dataflow for Dynamic Data Driven Application Systems (DDDAS) Environment (LiD4E). LiD4E is a design tool for optimized implementation of dynamic, data-driven stream mining systems using high-level dataflow models of computation. More specifically, we develop in this paper new methods for integrated modeling and optimization of real-time stream mining constraints, multidimensional stream mining performance (precision and recall), and energy efficiency. Using a design methodology centered on data-driven control of and coordination between alternative dataflow subsystems for stream mining (classification modes), we develop systematic methods for exploring complex, multidimensional design spaces associated with dynamic stream mining systems, and deriving sets of Pareto-optimal system configurations that can be switched among based on data characteristics and operating constraints.

Learning Dynamic Stream Weights For Coupled-HMM-based Audio-visual Speech Recognition

With the increasing use of multimedia data in communication technologies, the idea of employing visual information in automatic speech recognition (ASR) has recently gathered momentum. In conjunction with the acoustical information, the visual data enhances the recognition performance and improves the robustness of ASR systems in noisy and reverberant environments. In audio-visual systems, dynamic weighting of audio and video streams according to their instantaneous confidence is essential for reliably and systematically achieving high performance. In this paper, we present a complete framework that allows blind estimation of dynamic stream weights for audio-visual speech recognition based on coupled hidden Markov models (CHMMs). As a stream weight estimator, we consider using multilayer perceptrons and logistic functions to map multidimensional reliability measure features to audiovisual stream weights. Training the parameters of the stream weight estimator requires numerous input-output tuples of reliability measure features and their corresponding stream weights. We estimate these stream weights based on oracle knowledge using an expectation maximization algorithm. We define 31-dimensional feature vectors that combine model-based and signal-based reliability measures as inputs to the stream weight estimator. During decoding, the trained stream weight estimator is used to blindly estimate stream weights. The entire framework is evaluated using the Grid audio-visual corpus and compared to state-of-the-art stream weight estimation strategies. The proposed framework significantly enhances the performance of the audio-visual ASR system in all examined test conditions.

Prospects for the role of magnesium in solar-hydrogen energy-system

It is most common to talk about energy systems that would utilize solar energy to split water to produce hydrogen. This way, solar energy has been stored in the form of hydrogen, being an ideal energy-carrier. However we are still facing the problem of how to store and transport hydrogen to end users? The overall scope of the proposed energy scheme involves using solar to vaporize a dynamic stream of pre-concentrated sea water (brine) flowing along an inclined Preferential Salt Separator (P.S.S.). This evaporation process takes place by direct solar radiation or by using a heating system powered by a photovoltaic source. Magnesium chloride salts soluble in sea water, will separate as end product. Fresh water will be produced as a byproduct as well. Anhydrous magnesium chloride is collected and then electrolyzed next to produce magnesium metal using energy generated by solar power. Once produced, magnesium represents a reliable source of stored energy that could be exported by air, sea or other means of transportation to remote locations for power-generation. Magnesium is used on site, to construct a galvanic cell that consists of magnesium/iron electrodes generating electricity. Water introduced to the cell is electrolyzed to produce hydrogen. Another option is to use magnesium as a storage-medium to store hydrogen in the form of magnesium hydride.

Engaging High-Sensation Seekers: The Dynamic Interplay of Sensation Seeking, Message Visual-Auditory Complexity and Arousing Content

This study extends the research on message-sensation value (MSV) by treating it as a dynamic stream of complex visual-auditory information and arousing content (MSV-d). Real-time attentional and emotional responses to this dynamic stream during the PSA viewing process are indicated by psychophysiological measures. Dynamic models are used to systematically examine endogenous and exogenous influences on message processing to more accurately understand the effects of MSV-d variables and individuals' sensation seeking tendencies during the processing of the PSAs. An important finding is that generally, increasing visual-auditory complexity activates an approach tendency in those with high sensation-seeking tendencies but activates an avoidance tendency in those with low sensation-seeking tendencies, and this response pattern is moderated by arousing content.

Caching Eliminates the Wireless Bottleneck in Video Aware Wireless Networks

Wireless video is the main driver for rapid growth in cellular data traffic. Traditional methods for network capacity increase are very costly and do not exploit the unique features of video, especially asynchronous content reuse. In this paper we give an overview of our work that proposed and detailed a new transmission paradigm exploiting content reuse and the widespread availability of low-cost storage. Our network structure uses caching in helper stations (femtocaching) and/or devices, combined with highly spectrally efficient short-range communications to deliver video files. For femtocaching, we develop optimum storage schemes and dynamic streaming policies that optimize video quality. For caching on devices, combined with device-to-device (D2D) communications, we show that communications within clusters of mobile stations should be used; the cluster size can be adjusted to optimize the tradeoff between frequency reuse and the probability that a device finds a desired file cached by another device in the same cluster. In many situations the network throughput increases linearly with the number of users, and the tradeoff between throughput and outage is better than in traditional base-station centric systems. Simulation results with realistic numbers of users and channel conditions show that network throughput can be increased by two orders of magnitude compared to conventional schemes.

Multifunctional nutrient-binding proteins adapt human symbiotic bacteria for glycan competition in the gut by separately promoting enhanced sensing and catalysis.

ABSTRACTTo compete for the dynamic stream of nutrients flowing into their ecosystem, colonic bacteria must respond rapidly to new resources and then catabolize them efficiently once they are detected. The Bacteroides thetaiotaomicron starch utilization system (Sus) is a model for nutrient acquisition by symbiotic gut bacteria, which harbor thousands of related Sus-like systems. Structural investigation of the four Sus outer membrane proteins (SusD, -E, -F, and -G) revealed that they contain a total of eight starch-binding sites that we demonstrated, using genetic and biochemical approaches, to play distinct roles in starch metabolism in vitro and in vivo in gnotobiotic mice. SusD, whose homologs are abundant in the human microbiome, is critical for the initial sensing of available starch, allowing sus transcriptional activation at much lower concentrations than without this function. In contrast, seven additional binding sites across SusE, -F, and -G are dispensable for sus activation. However, they optimize the rate of growth on starch in a manner dependent on the expression of the bacterial polysaccharide capsule, suggesting that they have evolved to offset the diffusion barrier created by this structure. These findings demonstrate how proteins with similar biochemical behavior can serve orthogonal functions during different stages of cellular adaptation to nutrients. Finally, we demonstrated in gnotobiotic mice fed a starch-rich diet that the Sus binding sites confer a competitive advantage to B. thetaiotaomicron in vivo in a manner that is dependent on other colonizing microbes. This study reveals how numerically dominant families of carbohydrate-binding proteins in the human microbiome fulfill separate and sometimes cooperative roles to optimize gut commensal bacteria for nutrient acquisition.

RIVER

For high-performance embedded hard-real-time systems, ASICs and FPGAs hold advantages over general-purpose processors and graphics accelerators (GPUs). However, developing signal processing architectures from scratch requires significant resources. Our design methodology is based on sets of configurable building blocks that provide storage, dataflow, computation, and control. Based on our building blocks, we generate hundreds of thousands of our dynamic streaming engine processors that we call DSEs. We store our DSEs in a repository that can be queried for (online) design space exploration. From this repository, DSEs can be downloaded and instantiated within milliseconds on FPGAs. If a loss of flexibility can be tolerated then ASIC implementations are feasible as well. In this article we focus on FPGA implementations. Our DSEs vary in cores, computational lanes, bitwidths, power consumption, and frequency. To the best of our knowledge we are the first to propose online design space exploration based on repositories of precompiled cores that are assembled of common building blocks. For demonstration purposes we map algorithms for image processing and financial mathematics to DSEs and compare the performance to existing highly optimized signal and graphics accelerators.