scholarly journals Parallel Computing of Particle Trajectory Sonification to Enable Real-time Interactivity

2017 ◽  
Author(s):  
Jiajun Yang ◽  
Thomas Hermann
Keyword(s):  
Parallel Computing ◽  
Real Time ◽  
Vector Quantization ◽  
Particle Trajectory ◽  
Model Space ◽  
High Dimensional ◽  
Complex Data ◽  
Virtual Particles ◽  
First Time ◽  
Number Of Particles

In this paper, we revisit, explore and extend the Particle Trajectory Sonification (PTS) model, which supports cluster analysis of high-dimensional data by probing a model space with virtual particles which are ‘gravitationally’ attracted to a mode of the dataset’s potential function. The particles’ kinetic energy progression of as function of time adds directly to a signal which constitutes the sonification. The exponential increase in computation power since its conception in 1999 enables now for the first time to investigate real-time interactivity in such complex interweaved dynamic sonification models. We speeded up the computation of the PTS model with (i) data optimization via vector quantization, and (ii) parallel computing via OpenCL. We investigated the performance of sonifying high-dimensional complex data under different approaches. The results show a substantial increase in speed when applying vector quantization and parallelism with CPU. GPU parallelism provided a substantial speedup for very large number of particles comparing to using CPU but did not show enough benefit for a low number of particles due to copying overhead. A hybrid OpenCL implementation is presented to maximize the benefits of both worlds.

scholarly journals Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth

Metabolites ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 5
Author(s):  
Soeren Wenck ◽  
Marina Creydt ◽  
Jule Hansen ◽  
Florian Gärber ◽  
Markus Fischer ◽  
...  
Keyword(s):  
Random Forest ◽  
Black Box ◽  
High Dimensional ◽  
Data Sets ◽  
Complex Data ◽  
Learning Approaches ◽  
Metabolomics Data ◽  
Class Differences ◽  
Minimal Depth ◽  
First Time

For the untargeted analysis of the metabolome of biological samples with liquid chromatography–mass spectrometry (LC-MS), high-dimensional data sets containing many different metabolites are obtained. Since the utilization of these complex data is challenging, different machine learning approaches have been developed. Those methods are usually applied as black box classification tools, and detailed information about class differences that result from the complex interplay of the metabolites are not obtained. Here, we demonstrate that this information is accessible by the application of random forest (RF) approaches and especially by surrogate minimal depth (SMD) that is applied to metabolomics data for the first time. We show this by the selection of important features and the evaluation of their mutual impact on the multi-level classification of white asparagus regarding provenance and biological identity. SMD enables the identification of multiple features from the same metabolites and reveals meaningful biological relations, proving its high potential for the comprehensive utilization of high-dimensional metabolomics data.

Probing the Dynamic Self-Assembly Behaviour of Photoswitchable Wormlike Micelles in Real Time

2018 ◽  
Author(s):  
Elaine A. Kelly ◽  
Judith E. Houston ◽  
Rachel Evans
Keyword(s):  
Real Time ◽  
Absorption Spectroscopy ◽  
Self Assembly ◽  
Uv Light ◽  
Wormlike Micelles ◽  
Tetraethylene Glycol ◽  
Light Illumination ◽  
First Time ◽  
Dependent Processes

Understanding the dynamic self-assembly behaviour of azobenzene photosurfactants (AzoPS) is crucial to advance their use in controlled release applications such as<i></i>drug delivery and micellar catalysis. Currently, their behaviour in the equilibrium <i>cis-</i>and <i>trans</i>-photostationary states is more widely understood than during the photoisomerisation process itself. Here, we investigate the time-dependent self-assembly of the different photoisomers of a model neutral AzoPS, <a>tetraethylene glycol mono(4′,4-octyloxy,octyl-azobenzene) </a>(C<sub>8</sub>AzoOC<sub>8</sub>E<sub>4</sub>) using small-angle neutron scattering (SANS). We show that the incorporation of <i>in-situ</i>UV-Vis absorption spectroscopy with SANS allows the scattering profile, and hence micelle shape, to be correlated with the extent of photoisomerisation in real-time. It was observed that C<sub>8</sub>AzoOC<sub>8</sub>E<sub>4</sub>could switch between wormlike micelles (<i>trans</i>native state) and fractal aggregates (under UV light), with changes in the self-assembled structure arising concurrently with changes in the absorption spectrum. Wormlike micelles could be recovered within 60 seconds of blue light illumination. To the best of our knowledge, this is the first time the degree of AzoPS photoisomerisation has been tracked <i>in</i><i>-situ</i>through combined UV-Vis absorption spectroscopy-SANS measurements. This technique could be widely used to gain mechanistic and kinetic insights into light-dependent processes that are reliant on self-assembly.

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

scholarly journals Molecular Survey of Vector-Borne Pathogens of Dogs and Cats in Two Regions of Saudi Arabia

Pathogens ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 25
Author(s):  
Abdullah D. Alanazi ◽  
Abdulaziz S. Alouffi ◽  
Mohamed S. Alyousif ◽  
Mohammad Y. Alshahrani ◽  
Hend H. A. M. Abdullah ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Real Time ◽  
Real Time Pcr ◽  
Bartonella Henselae ◽  
Public Awareness ◽  
Effective Control ◽  
Vector Borne Diseases ◽  
Babesia Vogeli ◽  
Vector Borne ◽  
First Time

Dogs and cats play an important role as reservoirs of vector-borne pathogens, yet reports of canine and feline vector-borne diseases in Saudi Arabia are scarce. Blood samples were collected from 188 free-roaming dogs and cats in Asir (70 dogs and 44 cats) and Riyadh (74 dogs), Saudi Arabia. The presence of Anaplasma spp., Bartonella spp., hemotropic Mycoplasma spp., Babesia spp., and Hepatozoon spp. was detected using a multiplex tandem real-time PCR. PCR-positive samples were further examined with specific conventional and real-time PCR followed by sequencing. Dogs from Riyadh tested negative for all pathogens, while 46 out of 70 dogs (65.7%) and 17 out of 44 cats (38.6%) from Asir were positive for at least one pathogen. Positive dogs were infected with Anaplasma platys (57.1%), Babesia vogeli (30%), Mycoplasma haemocanis (15.7%), and Bartonella henselae (1.4%), and cats were infected with Mycoplasma haemofelis (13.6%), Candidatus Mycoplasma haemominutum (13.6%), B. henselae (9.2%), and A. platys (2.27%), all of which are reported for the first time in Saudi Arabia. Co-infection with A. platys and B. vogeli was detected in 17 dogs (24.28%), while coinfections were not detected in cats. These results suggest that effective control and public awareness strategies for minimizing infection in animals are necessary.

scholarly journals Period-Life of a Branching Process with Migration and Continuous Time

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 868
Author(s):  
Khrystyna Prysyazhnyk ◽  
Iryna Bazylevych ◽  
Ludmila Mitkova ◽  
Iryna Ivanochko
Keyword(s):  
Random Process ◽  
Generating Function ◽  
Continuous Time ◽  
Branching Process ◽  
Probability Generating Function ◽  
Time Interval ◽  
The Moment ◽  
First Time ◽  
Critical Branching Process ◽  
Number Of Particles

The homogeneous branching process with migration and continuous time is considered. We investigated the distribution of the period-life τ, i.e., the length of the time interval between the moment when the process is initiated by a positive number of particles and the moment when there are no individuals in the population for the first time. The probability generating function of the random process, which describes the behavior of the process within the period-life, was obtained. The boundary theorem for the period-life of the subcritical or critical branching process with migration was found.

Intensifiers on Tyneside

2010 ◽  
Vol 31 (3) ◽  
pp. 252-287 ◽  
Author(s):  
Katie Barnfield ◽  
Isabelle Buchstaller
Keyword(s):  
Real Time ◽  
Longitudinal Analysis ◽  
Developmental Trajectories ◽  
Time Data ◽  
Longitudinal Changes ◽  
Real Time Data ◽  
Quantitative Development ◽  
First Time

We report on longitudinal changes in the system of intensification in an innovative corpus that spans five decades of dialectal speech. Our analyses allow us — for the first time in a British context — to trace the quantitative development in the variable across four generations. Longitudinal analysis across real and apparent time determines the effect of extralinguistic and intralinguistic variables on intensification in Tyneside and tests to what extent real time data corroborates trends reported from previous apparent time analyses. Long-term competition within the variable manifests itself in distinctive developmental trajectories: expansion — both proportionally within the variable as well as across adjectival categories — tends to follow one of three types of patterns, exemplified, respectively, by really, so and dead. Variant retraction, however, follows only one schema. Importantly, numerical decline in the system does not necessarily go hand in hand with a reduction in breadth of application.

Improved interactive color visualization approach for hyperspectral images

2021 ◽  
pp. 147387162110481
Author(s):  
Haijun Yu ◽  
Shengyang Li
Keyword(s):  
Real Time ◽  
Hyperspectral Images ◽  
High Dimensional ◽  
Interactive Control ◽  
Output Image ◽  
Dr Method ◽  
The Rich ◽  
Low Dimensional ◽  
Color Visualization ◽  
Fusion Coefficient

Hyperspectral images (HSIs) have become increasingly prominent as they can maintain the subtle spectral differences of the imaged objects. Designing approaches and tools for analyzing HSIs presents a unique set of challenges due to their high-dimensional characteristics. An improved color visualization approach is proposed in this article to achieve communication between users and HSIs in the field of remote sensing. Under the real-time interactive control and color visualization, this approach can help users intuitively obtain the rich information hidden in original HSIs. Using the dimensionality reduction (DR) method based on band selection, high-dimensional HSIs are reduced to low-dimensional images. Through drop-down boxes, users can freely specify images that participate in the combination of RGB channels of the output image. Users can then interactively and independently set the fusion coefficient of each image within an interface based on concentric circles. At the same time, the output image will be calculated and visualized in real time, and the information it reflects will also be different. In this approach, channel combination and fusion coefficient setting are two independent processes, which allows users to interact more flexibly according to their needs. Furthermore, this approach is also applicable for interactive visualization of other types of multi-layer data.

scholarly journals Online Near real-time Mine Disaster Monitoring System Based on Geosensor Networks

2016 ◽  
Vol 12 (03) ◽  
pp. 64
Author(s):  
Haifeng Hu
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Satellite System ◽  
Open Pit ◽  
Observation Data ◽  
Complex Data ◽  
Disaster Monitoring ◽  
Process Observation ◽  
Global Navigation Satellite ◽  
Safety And Stability

Abstract—An online automatic disaster monitoring system can reduce or prevent geological mine disasters to protect life and property. Global Navigation Satellite System receivers and the GeoRobot are two kinds of in-situ geosensors widely used for monitoring ground movements near mines. A combined monitoring solution is presented that integrates the advantages of both. In addition, a geosensor network system to be used for geological mine disaster monitoring is described. A complete online automatic mine disaster monitoring system including data transmission, data management, and complex data analysis is outlined. This paper proposes a novel overall architecture for mine disaster monitoring. This architecture can seamlessly integrate sensors for long-term, remote, and near real-time monitoring. In the architecture, three layers are used to collect, manage and process observation data. To demonstrate the applicability of the method, a system encompassing this architecture has been deployed to monitor the safety and stability of a slope at an open-pit mine in Inner Mongolia.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

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