Discrete Stream Research Articles

The nonconforming virtual element method for Oseen’s equation using a stream-function formulation

We approximate the solution of the stream function formulation of the Oseen equations on general domains by designing a nonconforming Morley-type virtual element method. Under a suitable assumption on the continuous problem’s coefficients, the discrete scheme is well-posed. By introducing an enriching operator, we derive an a priori estimate of the error in a discrete H2 norm. By post-processing the discrete stream function, we compute the discrete velocity and vorticity fields. Furthermore, we recover an approximate pressure field by solving a Stokes-like problem in a nonconforming Crouzeix–Raviart-type virtual element space that is in a Stokes-complex relation with the Morley-type space of the virtual element approximation. Finally, we confirm our theoretical estimates by solving benchmark problems that include a convex and a nonconvex domain.

Learning Temporal-Ordered Representation for Spike Streams Based on Discrete Wavelet Transforms

Spike camera, a new type of neuromorphic visual sensor that imitates the sampling mechanism of the primate fovea, can capture photons and output 40000 Hz binary spike streams. Benefiting from the asynchronous sampling mechanism, the spike camera can record fast-moving objects and clear images can be recovered from the spike stream at any specified timestamps without motion blurring. Despite these, due to the dense time sequence information of the discrete spike stream, it is not easy to directly apply the existing algorithms of traditional cameras to the spike camera. Therefore, it is necessary and interesting to explore a universally effective representation of dense spike streams to better fit various network architectures. In this paper, we propose to mine temporal-robust features of spikes in time-frequency space with wavelet transforms. We present a novel Wavelet-Guided Spike Enhancing (WGSE) paradigm consisting of three consecutive steps: multi-level wavelet transform, CNN-based learnable module, and inverse wavelet transform. With the assistance of WGSE, the new streaming representation of spikes can be learned. We demonstrate the effectiveness of WGSE on two downstream tasks, achieving state-of-the-art performance on the image reconstruction task and getting considerable performance on semantic segmentation. Furthermore, We build a new spike-based synthesized dataset for semantic segmentation. Code and Datasets are available at https://github.com/Leozhangjiyuan/WGSE-SpikeCamera.

Event-Based Optical Flow Estimation with Spatio-Temporal Backpropagation Trained Spiking Neural Network.

The advantages of an event camera, such as low power consumption, large dynamic range, and low data redundancy, enable it to shine in extreme environments where traditional image sensors are not competent, especially in high-speed moving target capture and extreme lighting conditions. Optical flow reflects the target's movement information, and the target's detailed movement can be obtained using the event camera's optical flow information. However, the existing neural network methods for optical flow prediction of event cameras has the problems of extensive computation and high energy consumption in hardware implementation. The spike neural network has spatiotemporal coding characteristics, so it can be compatible with the spatiotemporal data of an event camera. Moreover, the sparse coding characteristic of the spike neural network makes it run with ultra-low power consumption on neuromorphic hardware. However, because of the algorithmic and training complexity, the spike neural network has not been applied in the prediction of the optical flow for the event camera. For this case, this paper proposes an end-to-end spike neural network to predict the optical flow of the discrete spatiotemporal data stream for the event camera. The network is trained with the spatio-temporal backpropagation method in a self-supervised way, which fully combines the spatiotemporal characteristics of the event camera while improving the network performance. Compared with the existing methods on the public dataset, the experimental results show that the method proposed in this paper is equivalent to the best existing methods in terms of optical flow prediction accuracy, and it can save 99% more power consumption than the existing algorithm, which is greatly beneficial to the hardware implementation of the event camera optical flow prediction., laying the groundwork for future low-power hardware implementation of optical flow prediction for event cameras.

Stability of feeding cylinder shell under torsion in pneumo-mechanical spinning machines

The essence of the pneumo-mechanical spinning method lies in the transformation of the tape into a discrete stream of fibers, in the formation of a wedge-shaped ribbon from this stream in the form of an open end in the groove of the spinning chamber and in the formation of yarn from the ribbon by torsion, which is removed from the chamber and wound into a bobbin. Yarn obtained on a rotor spinning machine differs in structure from yarn from ring spinning machines, it has a lower breaking load and is produced with a higher filling twist.

A systematic approach to brain dynamics: cognitive evolution theory of consciousness.

The brain integrates volition, cognition, and consciousness seamlessly over three hierarchical (scale-dependent) levels of neural activity for their emergence: a causal or 'hard' level, a computational (unconscious) or 'soft' level, and a phenomenal (conscious) or 'psyche' levelrespectively. The cognitive evolution theory (CET) is based on three general prerequisites: physicalism, dynamism, and emergentism, which entail five consequences about the nature of consciousness: discreteness, passivity, uniqueness, integrity, and graduation. CET starts from the assumption that brains should have primarily evolved as volitional subsystems of organisms, not as prediction machines. This emphasizes the dynamical nature of consciousness in terms of critical dynamics to account for metastability, avalanches, and self-organized criticality of brain processes, then coupling it with volition and cognition in a framework unified over the levels. Consciousness emerges near critical points, and unfolds as a discrete stream of momentary states, each volitionally driven from oldest subcortical arousal systems. The stream is the brain's way of making a difference via predictive (Bayesian) processing. Its objective observables could be complexity measures reflecting levels of consciousness and its dynamical coherency to reveal how much knowledge (information gain) the brain acquires over the stream. CET also proposes a quantitative classification of both disorders of consciousness and mental disorders within that unified framework.

Adaptive Slicing Method of the Spatiotemporal Event Stream Obtained from a Dynamic Vision Sensor.

The dynamic vision sensor (DVS) measures asynchronously change of brightness per pixel, then outputs an asynchronous and discrete stream of spatiotemporal event information that encodes the time, location, and sign of brightness changes. The dynamic vision sensor has outstanding properties compared to sensors of traditional cameras, with very high dynamic range, high temporal resolution, low power consumption, and does not suffer from motion blur. Hence, dynamic vision sensors have considerable potential for computer vision in scenarios that are challenging for traditional cameras. However, the spatiotemporal event stream has low visualization and is incompatible with existing image processing algorithms. In order to solve this problem, this paper proposes a new adaptive slicing method for the spatiotemporal event stream. The resulting slices of the spatiotemporal event stream contain complete object information, with no motion blur. The slices can be processed either with event-based algorithms or by constructing slices into virtual frames and processing them with traditional image processing algorithms. We tested our slicing method using public as well as our own data sets. The difference between the object information entropy of the slice and the ideal object information entropy is less than 1%.

NOHAR - NOvelty discrete data stream for Human Activity Recognition based on smartphones with inertial sensors

Smartphone sensing capabilities have enabled human activity recognition (HAR) solutions to better understand human behavior through computational techniques. However, such solutions have suffered from scalability problems due to the high consumption of computational resources (e.g. memory and processing) and the difficulty of acting in real time due to not observing data evolution over time. These problems occur because the HAR solutions for smartphones have been solved through offline learning with models limited by a data history. The disadvantage of this approach is that human activities constantly change over time and are strongly influenced by the physical environment and user profile. To overcome such problem, this paper proposes a novel low-cost learning algorithm called NOHAR (NOvelty discrete data stream for Human Activity Recognition), focused on continuous flow of data analysis. NOHAR is an online classification algorithm based on symbolic data generated by a discretization process using algorithms as SAX and SFA. The advantages of these algorithms are their abilities to compress and reduce the dimensionality of data. In addition, this paper proposes a new framework called DISTAR (DIscrete STream learning for Activity Recognition) focused on the data streaming analysis. Its goals include standardizing the development of online algorithms for symbolic data. Experimental results using three databases show that NOHAR is on average 33 times faster than the state of the art and can reduce memory consumption by an average of 99.97%.

Magnetic-field modeling with surface currents. Part I. Physical and computational principles of bfieldtools

Surface currents provide a general way to model magnetic fields in source-free volumes. To facilitate the use of surface currents in magneto-quasistatic problems, we have implemented a set of computational tools in a Python package named bfieldtools. In this work, we describe the physical and computational principles of this toolset. To be able to work with surface currents of the arbitrary shape, we discretize the currents on triangle meshes using piecewise-linear stream functions. We apply analytical discretizations of integral equations to obtain the magnetic field and potentials associated with the discrete stream function. In addition, we describe the computation of the spherical multipole expansion and a novel surface-harmonic expansion for surface currents, both of which are useful for representing the magnetic field in source-free volumes with a small number of parameters. Lastly, we share examples related to magnetic shielding and the surface-coil design using the presented tools.

Economic and mathematical analysis of efficiency leasing in the electronic industry

The article, using the example of electronic industry enterprises, examines the operation of financial leasing of high-tech equipment to upgrade fixed assets in order to produce competitive products. An analysis is made of the effectiveness of the use of equipment necessary for the production of leased (rented) equipment as a special investment project implemented by the lessee, in which rental payments are an investment part of the project, and the profit from the sale of manufactured products makes up its profitable part. Analytical indicators of reduced net income and profitability index are supplemented by a payback period indicator, for the calculation of which a laborious recursive-logical procedure is used in practice. To obtain an analytical expression of the payback period, an original technique was used based on replacing a discrete payment stream with a financially equivalent continuous stream of payments. The obtained analytical formula made it possible to determine the fundamental economic and mathematical conditions for the payback of this operation for the lessee.

Base Level and Lithologic Control of Drainage Reorganization in the Sierra de las Planchadas, NW Argentina

AbstractPlanform drainage reorganization in catchments is a common response to changes in boundary conditions. Rivers reorganize through two predominant mechanisms: discrete stream capture events and progressive divide migration. Using general geomorphic observations, flow azimuth analysis, χ anomalies, local headwater relief, and the normalized steepness index (ksn), we examine drainage reorganization, its potential controls, and the response timescales for drainages in the Sierra de las Planchadas, NW Argentina. We additionally expand on the comparison of trunk and tributary ksn values as a potential tool for recognizing drainage reorganization. We identify three significant patterns of reorganization: (1) the migration of the main drainage divide (MDD) toward the hinterland, (2) capture of longitudinal drainages by transverse reaches, and (3) shrinking foreland catchments that will result in changes to basin outlet spacing. We determine that the migration of the MDD is due to local base level differences between the major range‐bounding basins, while more local patterns, such as the capture of longitudinal drainages, are the result of the distribution of erosionally resistant lithologies. We additionally assess the response timescales required for topologic changes to the drainage network, such as divide migration; we conclude that, as suggested by modeling studies, they are longer than the timescale required for channel profile adjustment.

Description of the information process as a discrete stream.

The work considers the forecasting of the process of functioning of a dynamic complex system, which is a big problem because of the considerable volume of information circulating in it and many random factors affecting its operation. In this paper, the structure of the information process described in the form of a discrete information flow is considered. To determine portions of the information flow information for the formation of a control action, these flows are identified and structured based on determining the dimension of the attractors of dynamical systems in finite-dimensional spaces. To manage a discrete information process, the number of control checks that make it possible to determine the duration of this process predicts it. In this case, the pieces of information and time intervals for each portion of the discrete information stream are considered the same. As a result of the conducted researches the program complex on formation of predicted parameters of training with definition of portions of training information is developed. The paper gives comparative results of the duration of training - experimental and predicted.

Expansion of the known distribution of the coastal tailed frog, Ascaphus truei, in British Columbia, Canada, using robust eDNA detection methods.

The coastal tailed frog (Ascaphus truei) is endemic to the Pacific Northwest of North America and is listed as a species of Special Concern under the Canadian Species at Risk Act. Its range is limited to British Columbia where it occurs widely west of the Coast Mountain Ranges extending north almost to the Alaskan Panhandle. The present study focused on surveying within the Cayoosh, Bridge (Shulaps), Seton, Anderson, Carpenter, and Downton Lake drainages. Four years of previous inventory efforts using conventional time-constrained search (TCS) methods detected tailed frog at 23/292 discrete sites (7.9% detection rate) in seven watersheds. Non-invasive environmental DNA (eDNA) methods hold promise for cryptic and low-abundance species detection. We rigorously validated a quantitative real-time polymerase chain reaction (qPCR)-based tool for detecting coastal tailed frog eDNA in water samples. This eASTR4 test is highly specific and sensitive. We applied a two-step targeted eDNA analysis approach on duplicate filtered water samples from a total of 72 sites collected over five days. The first IntegritE-DNA step mitigates false negative results and tests all DNA samples for the ability to support amplification from endogenous plant chloroplast DNA as a measure of sample viability. Three DNA samples failed this step even after inhibitor clean up suggesting that these samples were poor quality and not reliable for targeted species’ DNA analyses. All other DNA samples were deemed viable and were then tested for species-specific DNA. Coastal tailed frog eDNA was detected in 55/72 (76%) discrete stream reaches; nine sites with historical known occurrence were all eDNA positive. The false negative rate for TCS compared to eDNA methods was 58%. The results expand known coastal tailed frog distribution to 24 watersheds effectively more than tripling extant occurrences and confirm a previously suspected, apparently isolated coastal tailed frog metapopulation in the Shulaps drainage.

A two-stage heuristic approach for a multi-item inventory system with limited budgetary resource and all-units discount

Abstract This paper deals with a multi-item inventory system with a discrete stochastic demand stream under continuous review in which a limited resource and all-units quantity discount in purchasing are available. The resource is sharable and price-dependent which can be investments or budgetary. Most existing models focus on the resource restriction as a hard constraint, which is not always the common case since in some inventory systems resource shortage could be provided by renting extra resource and incurs the resource shortage cost to the system. In this case, as one of the recent extensions of (r, Q) inventory models, the resource constraint is treated as a soft constraint. By considering the discount opportunity besides the soft price-dependent resource constraint, the resource shortage cost will be dependent directly on quantity discount parameters. So, in the multi-item case the discounted price of each item is involved in determining other optimal policies. The problem is formulated as an integer nonlinear programming model. A heuristic method is presented to solve the model. According to this method, during the stages of the algorithm, the problem is separated into sub-problems which are solved by an exact method. A local search is used to improve the solutions during the algorithm procedure. In order to evaluate the efficiency of the solution approach, a lower bound on the minimum cost of the problem is proposed. Numerical evaluations based on the lower bound show that the proposed method with an average relative error equal to 3.91% performs well.

Formation of the predicted training parameters in the form of a discrete information stream

In work process of training in the form of a discrete information stream is considered. On each of stages of the considered process portions of the training information and quality of their assimilation are analysed. Individual characteristics and reaction trained for every portion of information on appropriate sections are defined. The control algorithm of training with the predicted number of control checks of the trainee who allows to define what operating influence is considered it is necessary to create for the trainee. On the basis of this algorithm the vector of probabilities of ignorance of elements of the training information is received. As a result of the conducted researches the algorithm on formation of the predicted training parameters is developed. In work the task of comparison of duration of training received experimentally with predicted on the basis of it is solved the conclusion is drawn on efficiency of formation of the predicted training parameters. The program complex on the basis of the values of individual parameters received as a result of experiments on each trainee who allows to calculate individual characteristics is developed, to form rating and to monitor process of change of parameters of training.

Effects of polymer properties on jetting performance of electrohydrodynamic printing

ABSTRACTWe perform systematic study on the jetting performance of electrohydrodynamic (EHD) with an insulating polymers such as polystyrene (PS) and poly(methyl methacrylate) (PMMA). EHD printing applies electrostatic field to ink droplet hang on nozzle tip, which causes the deformation of the meniscus to generate discrete droplets or continuous jet stream. Although EHD jetting mechanism has been frequently investigated with conducting or semiconducting materials, there still needs to elucidate EHD jetting of insulating polymer materials for producing controllable droplets. In the present study, we focused on how the physical/chemical properties (conductivity, dielectric constant, and molecular weight) of an insulating polymer affect jetting behavior of EHD printing (especially, the deformation of the meniscus and the corresponding morphology of the printed one). The relationship between the printing parameters and applied voltage is also investigated, thereby allowing the optimization of EHD printings for PS and PMMA. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45044.

An approach to developing the SIEM system for the Internet of Things

This article suggests an approach to building systems for analyzing security incidents in the Internet of Things as a self-similar system within the ambit of the normal operation of its constituent objects. The graph model of the Internet of Things as a cyberphysical system has been developed and used as basis to prove that the analysis of paired relations selected from a discrete message stream from devices will be enough to detect security incidents.

Isolation of ventral stream EEG sources using Steady State EEG and Independent Components Analysis

Abstract It has long been known that the ventral stream of the visual system contains discrete areas sensitive to object categories, including the Fusiform Face Area (FFA), the Parahippocampal Place Area (PPA) and the Lateral Occipital Complex (LOC). These areas have been well studied using fMRI, but the limited temporal resolution of fMRI has prevented investigation of the temporal dynamics of interactions between them. While event related potentials (ERPs) have high temporal resolution, their limited spatial resolution has hampered the isolation of signals from discrete ventral stream sources, especially non-face areas. Here we explore a possible solution to this problem using a combination of steady state EEG (SSEEG) and Independent Components Analysis (ICA). Our goal is to use ICA to generate a reliable set of spatial filters for ventral stream sources that can be used in ERP paradigms similarly to the way localizer scans are used in fMRI. SSEEG was recorded from 10 subjects while they viewed pictures of objects, faces, scenes, and scrambled objects flashing at a rate of 3.5 hz in a block design. The data for each subject were decomposed using ICA and the scalp distribution and spectral decomposition of each Independent Component (IC) was examined for each stimulus type. After removal of artifactual components, ICs were labeled as face-sensitive, object-sensitive, or scene-sensitive if 1) they showed an increase in power at 3.5 hz and 2) 3.5 hz power was selectively enhanced for a stimulus type. The vast majority of the category-sensitive components had postero-lateral scalp distributions as one would expect from ventral stream sources. All 10 subjects had face-sensitive ICs; object- and scene-sensitive ICs were each seen in 6 subjects. These data suggest that it is possible to isolate ventral stream sources of EEG. Future work will test the reliability and functional validity of category-sensitive ICs. Meeting abstract presented at VSS 2015.

Regularities of steel wear under the impact of discrete water-droplet stream. Part II: Stage of the developed droplet-impingement erosion

Formation of the erosive wear of austenitic steel under multicycle droplet-impingement impact has been studied. It has been shown that, with an existing relief on the steel surface, the bubble cavitation becomes the predominant wear factor under droplet-impingement erosion. The conditions for development of the bubble cavitation as well as its mechanisms and the regularities of its influence on the erosive destruction of the surface layer have been investigated.

Regularities of steel wear under the impact of discrete water-droplet stream, Part I: Initial stage of droplet-impingement erosion

Key features of plastic deformation and erosive wear craters that develop in the surface layer of austenitic and sorbite structure steels during the incubation period of the droplet-impingement erosion have been studied. Using electron microscopy, nanoindentation, and simulation, it has been shown that the degradation of the surface at the initial erosion stages is caused by fatigue processes that develop under hydrodynamic cavitation. Based on Goodman’s fatigue model, the conditions for the safe operation of the material under the impact of droplet impingements are defined.

An Improved Direct-Forcing Immersed Boundary Method for Fluid-Structure Interaction Simulations1

In the present work, we present an improved version of the direct-forcing immersed boundary (IB) method proposed in Wang and Zhang (2011, “An Immersed Boundary Method Based on Discrete Stream Function Formulation for Two- and Three-Dimensional Incompressible Flows,” J. Comput. Phys., 230(9), pp. 3479–3499). In order to obtain an accurate prediction of local surface force, measures have been taken to suppress the unphysical spatial oscillations in the Lagrangian forcing. A fluid-structure interaction (FSI) solver has been developed by using the improved IB method for the fluid and the finite difference method for the structure. Several flow problems are simulated to validate our method. The testing cases include flows over a stationary cylinder and a stationary flat plate, two-dimensional flow past an inextensible flexible filament, and three-dimensional flow past a flapping flag. The results obtained in the present work agree well with those from the literature.