Multiple Input Streams Research Articles

Overall particles flow pattern in a two-zone gas-solid fluidized bed with a secondary-gas stream

Fluidized bed reactors with multiple input streams have been developed over the last decades to satisfy industries in the scope of multiphase catalytic reactions, and two-zone fluidized beds have been developed to perform reaction and catalyst regeneration simultaneously. However, the presence of the secondary gas nozzle in the bed significantly impacts the hydrodynamics and efficiency. Particle image velocimetry (PIV) and digital image analysis (DIA) techniques have been used to study different particle flow patterns in a two-zone pseudo-2D fluidized bed with lateral gas injection. The secondary flow nozzles' location has been changed, and different particle flow patterns have been observed in the fluidized bed. It was found that at low excess gas flow rates, when the lateral nozzle is located at the middle part of the bed, particles move downwardly at the bed center and upwardly near the walls; while when the lateral nozzle is located at the top part of the bed, particles have a reverse movement trend. At higher gas velocities for both mentioned nozzle locations, particles move upwardly at the bed center and downwardly near the walls. The formation of large bubbles was less probable when the nozzle was located at the middle of the bed.

Cloud computing simulation model with a sporadic mechanism of parallel task solving control

A simulation model of a computer system built in the Simulink (SimEvent) environment is considered. According to the queuing theory, the system is classified as G/G/n/∞. This means that there are multiple input streams in the system, their queue is infinite, and two feedbacks are applied. These feedbacks reflect the situation of the repeated processing in case of failure or lack of a solution at the first processing attempt. The system architecture under consideration is focused on parallel processing of a certain class of tasks, while the tasks themselves are data-independent. The model is investigated for uniformly distributed and exponential input streams. The situation of continuous streams for several types of tasks is considered, for which priorities and the numbers of partitioning fragments vary. The number of fragments determines the degree of parallelism in the execution of the task. The paper shows a method for automatically determining the optimal number of task fragments to guarantee its completion within the target period. The use of sporadic control mechanisms for a number of the task fragments received in a continuous stream and the priorities managing of each of the task fragments are proposed. The proposed mechanism of the sporadic management made it possible to significantly speed up the tasks completion within the target deadline. As a result, the load on the computing system has been reduced and the efficiency of its operation has been increased. The use of the proposed algorithms significantly simplifies the scheduling mechanisms in the computer system, which allows you to exclude the scheduler.

A Human-in-the-Loop Probabilistic CNN-Fuzzy Logic Framework for Accident Prediction in Vehicular Networks

The vehicle accident prediction methods are designed to improve the vehicular safety and reduce the rescue response time in the case of an accident. The existing accident prediction methods, however, do not involve Human-in-the-Loop, i.e., do not consider the emotional state of a driver to predict the likelihood of an accident. We propose a Probabilistic Convolutional Neural Network (CNN)-Fuzzy Logic framework that involves Human-in-the-Loop and takes into account the multiple input streams of sensor generated data, i.e., human emotions and traffic data. The features extracted from the CNN model are fed to our designed probabilistic graph-based inference model to determine the accident probability. The probability is then mapped with accident severity through fuzzy membership functions for accident prediction. The experiment results show the promising performance of our proposed framework, i.e., 93.1% accuracy of face expressions, 76.2% accuracy of heartbeat, and 76.9% accuracy of traffic inputs and predicts the accident likelihood with 90% accuracy. The comparison, with related works, shows that the proposed framework can predict accidents with higher probabilities.

REACT: Scalable and High-Performance Regular Expression Pattern Matching Accelerator for In-Storage Processing

This article proposes REACT, a regular expression matching accelerator, which can be embedded in a modern Solid-State Drive (SSD) and a novel data access scheduling algorithm for high matching throughput. Specifically, REACT, including our data access scheduling algorithm, increases the utilization of SSD and the degree of internal memory parallelism for pattern matching processes. While the low-level flash exhibits long latency, modern SSDs in practice achieve high I/O performance by utilizing the massive internal parallelism at the system-level. However, exploiting the parallelism is limited for pattern matching since the sub-blocks, which constitute an input data and can be placed in multiple flash pages, should be tested in a sequence to process the input correctly. This limitation can induce low utilization of the accelerator. To address this challenge, the proposed REACT simultaneously processes multiple input streams with a parallel processing architecture to maximize matching throughput by hiding the long and irregular latency. The scheduling algorithm finds a data stream which requires a sub-block in closest time and prioritizes the access request to reduce the data stall of REACT. REACT achieves maximum 22.6 percent of matching throughput improvement on a 16-channel high-performance SSD compared to the accelerator without the proposed scheduling algorithm.

Sparse bursts optimize information transmission in a multiplexed neural code

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets.

Flexible online monitoring for high-energy physics with Pyrame

The present work reports on the new online monitoring capabilities of the software suite Pyrame, an open-source framework designed for high energy physics (HEP) applications. Pyrame provides an easy-to-deploy solution for command, control and data-acquisition of particle detectors and related test-benches. Pyrame’s new online monitoring architecture is based on the distribution of the data treatment operations among multiple modules in the system, with multiple input and output streams. Uncontrolled data loss is prevented by providing data at the speed of consumers. In addition to the distributed data treatment capabilities, Pyrame includes a performance-oriented module dedicated to real-time data acquisition, capable of handling and storing data at 4 Gbit/s for further treatment.

Efficient Data Streaming Multiway Aggregation through Concurrent Algorithmic Designs and New Abstract Data Types

Data streaming relies on continuous queries to process unbounded streams of data in a real-time fashion. It is commonly demanding in computation capacity, given that the relevant applications involve very large volumes of data. Data structures act as articulation points and maintain the state of data streaming operators, potentially supporting high parallelism and balancing the work among them. Prompted by this fact, in this work we study and analyze parallelization needs of these articulation points, focusing on the problem of streaming multiway aggregation, where large data volumes are received from multiple input streams. The analysis of the parallelization needs, as well as of the use and limitations of existing aggregate designs and their data structures, leads us to identify needs for appropriate shared objects that can achieve low-latency and high-throughput multiway aggregation. We present the requirements of such objects as abstract data types and we provide efficient lock-free linearizable algorithmic implementations of them, along with new multiway aggregate algorithmic designs that leverage them, supporting both deterministic order-sensitive and order-insensitive aggregate functions. Furthermore, we point out future directions that open through these contributions. The article includes an extensive experimental study, based on a variety of continuous aggregation queries on two large datasets extracted from SoundCloud, a music social network, and from a Smart Grid network. In all the experiments, the proposed data structures and the enhanced aggregate operators improved the processing performance significantly, up to one order of magnitude, in terms of both throughput and latency, over the commonly used techniques based on queues.

The Stixel World: A medium-level representation of traffic scenes

Recent progress in advanced driver assistance systems and the race towards autonomous vehicles is mainly driven by two factors: (1) increasingly sophisticated algorithms that interpret the environment around the vehicle and react accordingly, and (2) the continuous improvements of sensor technology itself. In terms of cameras, these improvements typically include higher spatial resolution, which as a consequence requires more data to be processed. The trend to add multiple cameras to cover the entire surrounding of the vehicle is not conducive in that matter. At the same time, an increasing number of special purpose algorithms need access to the sensor input data to correctly interpret the various complex situations that can occur, particularly in urban traffic.By observing those trends, it becomes clear that a key challenge for vision architectures in intelligent vehicles is to share computational resources. We believe this challenge should be faced by introducing a representation of the sensory data that provides compressed and structured access to all relevant visual content of the scene. The Stixel World discussed in this paper is such a representation. It is a medium-level model of the environment that is specifically designed to compress information about obstacles by leveraging the typical layout of outdoor traffic scenes. It has proven useful for a multitude of automotive vision applications, including object detection, tracking, segmentation, and mapping.In this paper, we summarize the ideas behind the model and generalize it to take into account multiple dense input streams: the image itself, stereo depth maps, and semantic class probability maps that can be generated, e.g., by deep convolutional neural networks. Our generalization is embedded into a novel mathematical formulation for the Stixel model. We further sketch how the free parameters of the model can be learned using structured SVMs.

Asymmetry of Neuronal Combinatorial Codes Arises from Minimizing Synaptic Weight Change

Synaptic change is a costly resource, particularly for brain structures that have a high demand of synaptic plasticity. For example, building memories of object positions requires efficient use of plasticity resources since objects can easily change their location in space and yet we can memorize object locations. But how should a neural circuit ideally be set up to integrate two input streams (object location and identity) in case the overall synaptic changes should be minimized during ongoing learning? This letter provides a theoretical framework on how the two input pathways should ideally be specified. Generally the model predicts that the information-rich pathway should be plastic and encoded sparsely, whereas the pathway conveying less information should be encoded densely and undergo learning only if a neuronal representation of a novel object has to be established. As an example, we consider hippocampal area CA1, which combines place and object information. The model thereby provides a normative account of hippocampal rate remapping, that is, modulations of place field activity by changes of local cues. It may as well be applicable to other brain areas (such as neocortical layer V) that learn combinatorial codes from multiple input streams.

Performance Evaluation of the Centralized Spectrum Access Strategy with Multiple Input Streams in Cognitive Radio Networks

Performance Evaluation of the Centralized Spectrum Access Strategy with Multiple Input Streams in Cognitive Radio Networks

Global workspace dynamics: cortical "binding and propagation" enables conscious contents.

A global workspace (GW) is a functional hub of binding and propagation in a population of loosely coupled signaling elements. In computational applications, GW architectures recruit many distributed, specialized agents to cooperate in resolving focal ambiguities. In the brain, conscious experiences may reflect a GW function. For animals, the natural world is full of unpredictable dangers and opportunities, suggesting a general adaptive pressure for brains to resolve focal ambiguities quickly and accurately. GW theory aims to understand the differences between conscious and unconscious brain events. In humans and related species the cortico-thalamic (C-T) core is believed to underlie conscious aspects of perception, thinking, learning, feelings of knowing (FOK), felt emotions, visual imagery, working memory, and executive control. Alternative theoretical perspectives are also discussed. The C-T core has many anatomical hubs, but conscious percepts are unitary and internally consistent at any given moment. Over time, conscious contents constitute a very large, open set. This suggests that a brain-based GW capacity cannot be localized in a single anatomical hub. Rather, it should be sought in a functional hub – a dynamic capacity for binding and propagation of neural signals over multiple task-related networks, a kind of neuronal cloud computing. In this view, conscious contents can arise in any region of the C-T core when multiple input streams settle on a winner-take-all equilibrium. The resulting conscious gestalt may ignite an any-to-many broadcast, lasting ∼100–200 ms, and trigger widespread adaptation in previously established networks. To account for the great range of conscious contents over time, the theory suggests an open repertoire of binding1 coalitions that can broadcast via theta/gamma or alpha/gamma phase coupling, like radio channels competing for a narrow frequency band. Conscious moments are thought to hold only 1–4 unrelated items; this small focal capacity may be the biological price to pay for global access. Visuotopic maps in cortex specialize in features like color, retinal size, motion, object identity, and egocentric/allocentric framing, so that a binding coalition for the sight of a rolling billiard ball in nearby space may resonate among activity maps of LGN, V1-V4, MT, IT, as well as the dorsal stream. Spatiotopic activity maps can bind into coherent gestalts using adaptive resonance (reentry). Single neurons can join a dominant coalition by phase tuning to regional oscillations in the 4–12 Hz range. Sensory percepts may bind and broadcast from posterior cortex, while non-sensory FOKs may involve prefrontal and frontotemporal areas. The anatomy and physiology of the hippocampal complex suggest a GW architecture as well. In the intact brain the hippocampal complex may support conscious event organization as well as episodic memory storage.

Holistically Stream-based Processing Xtwig Queries

Unlike a twig query, an Xtwig query contains some selection predicates with reverse axes which are either ancestor or parent. To evaluate such queries in the stream-based context, some rewriting rules have been proposed to transform the paths with reverse axes into equivalent reverse-axis-free ones. However, the transformation method is expensive due to multiple scanning input streams and the generation of unnecessary intermediate results. To solve these problems, a holistic stream-based algorithm XtwigStack is proposed for Xtwig queries. Experiments show that XtwigStack is much more efficient than the transformation method.

A FRAMEWORK FOR SOLVING SEQUENCE PROBLEM OF MULTIPLE INPUT STREAMS

In this research, a framework is proposed for answering the question: whether a dominant sequence exists for the multiple self-contained input streams destined for a computation provider running the requested applications concurrently. The framework is extended to find an optimal semidominant sequence for multiple input streams. This paper also shows that an existent universal dominant sequence can be formed by permuting a set of dominant input streams. In this paper, the neighbor-connected graph is first proposed to model the sequence problem of multiple input streams. Then, an algorithm with time complexity O(nLn) is proposed to determine the existence of a dominant data-receiving sequence, where L is the maximal length of the input streams plus one and n is the number of input streams. Examples of applying the framework to solve the sequence problems for several computation-intensive applications are also given. Furthermore, the framework is extended to find an optimal semidominant sequence using an algorithm with time complexity O(nLnlogδ*), where δ* is the maximal degree that the sequence diverges from all the optimal subsequences. Finally, the property of a universal dominant sequence is studied and the performance of the sequence is compared with those of other sequences.

Reconfigurable hardware solution to parallel prefix computation

This paper presents the design and implementation of an efficient reconfigurable parallel prefix computation hardware on field-programmable gate arrays (FPGAs). The design is based on a pipelined dataflow algorithm, and control logic is added to reconfigure the system for arbitrary parallelism degree. The system receives multiple input streams of elements in parallel and produces output streams in parallel. It has an advantage of controlling the degree of parallelism explicitly at run time. The time complexity of the design is O(d+(N?d)/d), where d and N are parallelism degree and stream size, respectively. When the stream size is sufficiently larger than the initial trigger time of the pipeline (d), the time complexity becomes O(N/d). Unlike the prefix computation circuits found in the literature, the design is scalable for different problem sizes including unknown sized data. The design is modular based on a finite state machine, and implemented and tested for target FPGA devices Xilinx Spartan2S XC2S300EFT256-6Q and XC2S600EFG676-6.

The Dissociative Processing Style: A Cognitive Organization Activated by Perceived or Actual Threat in Clinical Dissociators

SUMMARY This paper proposes a cognitive organization that operates during times of perceived or actual threat in individuals with dissociative psychopathology. This organization, referred to as the dissociative processing style (DPS), serves as a threat monitoring system. It is characterized by (1) a shift from selective attention processing to multiple streams of information processing, (2) weakened cognitive inhibitory functioning which allows these streams to be operational and (3) the directing of awareness towards some and away from other information streams. Whilst DPS activation has the potential for adaptive and protective functions, it also heightens the likelihood of dissociative symptom experience and dissociation itself. Dissociation is understood as a failure to integrate encoded information from multiple input streams. The DPS is argued to be activated by top-down processes which signal danger, such as the appraisal of contextual cues. A clinical example is used to highlight the characteristics of the DPS.

Statistical Learning Within and Between Modalities

When learners encode sequential patterns and generalize their knowledge to novel instances, are they relying on abstract or stimulus-specific representations? Research on artificial grammar learning (AGL) has shown transfer of learning from one stimulus set to another, and such findings have encouraged the view that statistical learning is mediated by abstract representations that are independent of the sense modality or perceptual features of the stimuli. Using a novel modification of the standard AGL paradigm, we obtained data to the contrary. These experiments pitted abstract processing against stimulus-specific learning. The findings show that statistical learning results in knowledge that is stimulus-specific rather than abstract. They show furthermore that learning can proceed in parallel for multiple input streams along separate perceptual dimensions or sense modalities. We conclude that learning sequential structure and generalizing to novel stimuli inherently involve learning mechanisms that are closely tied to the perceptual characteristics of the input.

Decomposition property in a discrete-time queue with multiple input streams and service interruptions

This paper studies a discrete-time single-server queue with two independent inputs and service interruptions. One of the inputs to the queue is an independent and identically distributed process. The other is a much more general process and it is not required to be Markov nor is it required to be stationary. The service interruption process is also general and it is not required to be Markov or to be stationary. This paper shows that a stochastic decomposition property for the virtual waiting-time process holds in the discrete-time single-server queue with service interruptions. To the best of the author's knowledge, no stochastic decomposition results for virtual waiting-time processes in non-work-conserving queues, such as queues with service interruptions, have been obtained before and only work-conserving queues have been studied in the literature.

Decomposition property in a discrete-time queue with multiple input streams and service interruptions

This paper studies a discrete-time single-server queue with two independent inputs and service interruptions. One of the inputs to the queue is an independent and identically distributed process. The other is a much more general process and it is not required to be Markov nor is it required to be stationary. The service interruption process is also general and it is not required to be Markov or to be stationary. This paper shows that a stochastic decomposition property for the virtual waiting-time process holds in the discrete-time single-server queue with service interruptions. To the best of the author's knowledge, no stochastic decomposition results for virtual waiting-time processes in non-work-conserving queues, such as queues with service interruptions, have been obtained before and only work-conserving queues have been studied in the literature.

Worst average queueing delay of multiple leaky-bucket-regulated streams and jumping-window regulated stream

This paper presents deterministic, worst-case analysis of a queueing system whose multiple homogeneous input streams are regulated by the associated leaky buckets and the queueing system that has a single stream regulated by the jumping-window. Queueing delay averaged over all items is used for performance measure, and the worst-case input traffic and the worst-case performance are identified for both queueing systems. For the former queueing system, the analysis explores different phase relations among leaky-bucket token generations. This paper observes how the phase differences among the leaky buckets affect the worst-case queueing performance. Then, this paper relates the worst-case performance of the former queueing system with that of the latter (the single stream case, as in the aggregate streams from many users, whose item arrivals are regulated by one jumping-window). It is shown that the worst-case performance of the latter is identical to that of the former in which all leaky buckets have the same phase and have particular leaky bucket parameters.

A min, + system theory for constrained traffic regulation and dynamic service guarantees

By extending the system theory under the (min, +) algebra to the time-varying setting, we solve the problem of constrained traffic regulation and develop a calculus for dynamic service guarantees. For a constrained traffic-regulation problem with maximum tolerable delay d and maximum buffer size q, the optimal regulator that generates the output traffic conforming to a subadditive envelope f and minimizes the number of discarded packets is a concatenation of the g-clipper with g(t) = min[f(t+ d), f (t)+q] and the maximal f-regulator. The g-clipper is a bufferless device, which optimally drops packets as necessary in order that its output be conformant to an envelope g. The maximal f-regulator is a buffered device that delays packets as necessary in order that its output be conformant to an envelope f. The maximal f-regulator is a linear time-invariant with impulse response f, under the (min, +) algebra. To provide dynamic service guarantees in a network, we develop the concept of a dynamic server as a basic network element. Dynamic servers can be joined by concatenation, filter bank summation, and feedback to form a composite dynamic server. We also show that dynamic service guarantees for multiple input streams sharing a work-conserving link can be achieved by a dynamic service curve earliest deadline scheduling algorithm, if an appropriate admission control is enforced.