scholarly journals Adaptive Method for Modeling of Temporal Dependencies between Fields of Vision in Multi-Camera Surveillance Systems

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1303
Author(s):  
Karol Lisowski ◽  
Andrzej Czyżewski
Keyword(s):  
Statistical Data ◽  
Input Parameter ◽  
Gaussian Mixture ◽  
Adaptive Method ◽  
Transition Time ◽  
Surveillance Systems ◽  
Modified Particle Swarm Optimization ◽  
Novel Approach ◽  
Transition Times ◽  
Temporal Dependencies

A method of modeling the time of object transition between given pairs of cameras based on the Gaussian Mixture Model (GMM) is proposed in this article. Temporal dependencies modeling is a part of object re-identification based on the multi-camera experimental framework. The previously utilized Expectation-Maximization (EM) approach, requiring setting the number of mixtures arbitrarily as an input parameter, was extended with the algorithm that automatically adapts the model to statistical data. The probabilistic model was obtained by matching to the histogram of transition times between a particular pair of cameras. The proposed matching procedure uses a modified particle swarm optimization (mPSO). A way of using models of transition time in object re-identification is also presented. Experiments with the proposed method of modeling the transition time were carried out, and a comparison between previous and novel approach results are also presented, revealing that added swarms approximate normalized histograms very effectively. Moreover, the proposed swarm-based algorithm allows for modelling the same statistical data with a lower number of summands in GMM.

scholarly journals A Unified Formulation of k-Means, Fuzzy c-Means and Gaussian Mixture Model by the Kolmogorov–Nagumo Average

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 518
Author(s):  
Osamu Komori ◽  
Shinto Eguchi
Keyword(s):  
Pareto Distribution ◽  
Statistical Data ◽  
Learning Algorithm ◽  
Survival Function ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Simulation Studies ◽  
Probabilistic Framework ◽  
Underlying Distribution ◽  
Fuzzy C Means

Clustering is a major unsupervised learning algorithm and is widely applied in data mining and statistical data analyses. Typical examples include k-means, fuzzy c-means, and Gaussian mixture models, which are categorized into hard, soft, and model-based clusterings, respectively. We propose a new clustering, called Pareto clustering, based on the Kolmogorov–Nagumo average, which is defined by a survival function of the Pareto distribution. The proposed algorithm incorporates all the aforementioned clusterings plus maximum-entropy clustering. We introduce a probabilistic framework for the proposed method, in which the underlying distribution to give consistency is discussed. We build the minorize-maximization algorithm to estimate the parameters in Pareto clustering. We compare the performance with existing methods in simulation studies and in benchmark dataset analyses to demonstrate its highly practical utilities.

Characterization of ultra-intense laser in radiation damping regime using ponderomotive scattering

2022 ◽  
Author(s):  
Amol Holkundkar ◽  
Felix Mackenroth
Keyword(s):  
Laser Pulse ◽  
Electron Bunch ◽  
Input Parameter ◽  
Radiation Reaction ◽  
Quantitative Agreement ◽  
Intense Laser ◽  
Intense Laser Pulse ◽  
Scattered Electron ◽  
Novel Approach ◽  
Particle Simulations

Abstract We present a novel approach to analyzing phase-space distributions of electrons ponderomotively scattered off an ultra-intense laser pulse and comment on implications for thus conceivable in-situ laser-characterization schemes. To this end, we present fully relativistic test particle simulations of electrons scattered from an ultra-intense, counter-propagating laser pulse. The simulations unveil non-trivial scalings of the scattered electron distribution with the laser intensity, pulse duration, beam waist, and energy of the electron bunch. We quantify the found scalings by means of an analytical expression for the scattering angle of an electron bunch ponderomotively scattered from a counter-propagating, ultra-intense laser pulse, also accounting for radiation reaction (RR) through the Landau-Lifshitz (LL) model. For various laser and bunch parameters, the derived formula is in excellent quantitative agreement with the simulations. We also demonstrate how in the radiation-dominated regime a simple re-scaling of our model's input parameter yields quantitative agreement with numerical simulations based on the LL model.

scholarly journals Analysis of dengue model with fractal-fractional Caputo–Fabrizio operator

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Fatmawati ◽  
Muhammad Altaf Khan ◽  
Cicik Alfiniyah ◽  
Ebraheem Alzahrani
Keyword(s):  
Statistical Data ◽  
Dengue Infection ◽  
Fractional Operator ◽  
Novel Approach ◽  
Reduction Number ◽  
Best Fitting ◽  
The Stability ◽  
Parameter Values ◽  
Graphical Illustration ◽  
Stability Results

AbstractIn this work, we study the dengue dynamics with fractal-factional Caputo–Fabrizio operator. We employ real statistical data of dengue infection cases of East Java, Indonesia, from 2018 and parameterize the dengue model. The estimated basic reduction number for this dataset is $\mathcal{R}_{0}\approx2.2020$ R 0 ≈ 2.2020 . We briefly show the stability results of the model for the case when the basic reproduction number is $\mathcal{R}_{0} <1$ R 0 < 1 . We apply the fractal-fractional operator in the framework of Caputo–Fabrizio to the model and present its numerical solution by using a novel approach. The parameter values estimated for the model are used to compare with fractal-fractional operator, and we suggest that the fractal-fractional operator provides the best fitting for real cases of dengue infection when varying the values of both operators’ orders. We suggest some more graphical illustration for the model variables with various orders of fractal and fractional.

Self-Management of Schedule following in Three Teenagers with Autism

1995 ◽  
Vol 20 (3) ◽  
pp. 190-196 ◽  
Author(s):  
Bobby Newman ◽  
Dawn M. Buffington ◽  
Mairead A. O'grady ◽  
Mary E. Mcdonald ◽  
Claire L. Poulson ◽  
...  
Keyword(s):  
Treatment Phase ◽  
Noncontingent Reinforcement ◽  
Transition Time ◽  
Multiple Baseline ◽  
Self Management ◽  
Accurate Identification ◽  
Baseline Conditions ◽  
Transition Times

A multiple baseline across students design was used to investigate the effects of a self-management package on schedule following by three teenagers with autism. During baseline conditions, noncontingent reinforcement was provided. In the treatment phase, students contingently self-reinforced the verbal identification of transition times. Systematic increases in accurate identification of transitions were observed across all students. Accurate identification of transition time and self-reinforcement were maintained in a one-month follow-up.

scholarly journals Transition from Suppressed to Active Convection Modulated by a Weak Temperature Gradient–Derived Large-Scale Circulation

2015 ◽  
Vol 72 (2) ◽  
pp. 834-853 ◽  
Author(s):  
C. L. Daleu ◽  
S. J. Woolnough ◽  
R. S. Plant
Keyword(s):  
Temperature Gradient ◽  
Large Scale ◽  
Deep Convection ◽  
Potential Temperature ◽  
Mean Value ◽  
Transition Time ◽  
Large Scale Circulation ◽  
Transition Times ◽  
Active Convection ◽  
Fully Coupled

Abstract Numerical simulations are performed to assess the influence of the large-scale circulation on the transition from suppressed to active convection. As a model tool, the authors used a coupled-column model. It consists of two cloud-resolving models that are fully coupled via a large-scale circulation that is derived from the requirement that the instantaneous domain-mean potential temperature profiles of the two columns remain close to each other. This is known as the weak temperature gradient approach. The simulations of the transition are initialized from coupled-column simulations over nonuniform surface forcing, and the transition is forced in the dry column by changing the local and/or remote surface forcings to uniform surface forcing across the columns. As the strength of the circulation is reduced to zero, moisture is recharged into the dry column and a transition to active convection occurs once the column is sufficiently moistened to sustain deep convection. Direct effects of changing surface forcing occur over the first few days only. Afterward, it is the evolution of the large-scale circulation that systematically modulates the transition. Its contributions are approximately equally divided between the heating and moistening effects. A transition time is defined to summarize the evolution from suppressed to active convection. It is the time when the rain rate in the dry column is halfway to the mean value obtained at equilibrium over uniform surface forcing. The transition time is around twice as long for a transition that is forced remotely compared to a transition that is forced locally. Simulations in which both local and remote surface forcings are changed produce intermediate transition times.

An Intelligent Video Surveillance System for Android Smart Phone

2013 ◽  
Vol 850-851 ◽  
pp. 884-888 ◽  
Author(s):  
Gang Yang ◽  
Xin Tan ◽  
Yong Rui Zhang
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Surveillance System ◽  
Smart Phone ◽  
Image Data ◽  
Gaussian Mixture ◽  
Moving Object ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Surveillance Technology

Video surveillance technology is playing an important role, and it is widely used in some fields. With the popularity of Android OS, it draws researchers attention to increase the development of video surveillance systems on the platform. This paper presents a smart real-time video surveillance system based on Android smart phone. This system detects moving object by using improved GMM (Gaussian Mixture Mode) algorithm, recognizes invading human with cascade classifier, processes image data with coder & decoder, transmits data over RTP (Real-time Transport Protocol). It also applies some methods to improve the accuracy of moving object detection and recognition, speed up recognition process. The experimental evidences show that it can realize real-time video surveillance and smart alarm.

scholarly journals Analysis and characterization of transition states in metabolic systems. Transition times and the passivity of the output flux

1991 ◽  
Vol 276 (1) ◽  
pp. 231-236 ◽  
Author(s):  
N V Torres ◽  
J Sicilia ◽  
E Meléndez-Hevia
Keyword(s):  
Steady State ◽  
Biological Significance ◽  
Transient State ◽  
Steady States ◽  
Transition Time ◽  
Enzyme Assays ◽  
Progress Curve ◽  
Metabolic Systems ◽  
Transition Times

In this paper we study the transitions between steady states in metabolic systems. In order to deal with this task we divided the total metabolite concentration at steady state, sigma, into two new fractions, delta (the Output Transition Time) and tau beta (Input Transition Time), which are related with the course of output and input mass to the system respectively. We show the equivalence time between these terms and the Total Transition Time, tau T, previously defined [Easterby (1986) Biochem. J. 233, 871-875]. Next, we define a new magnitude, the Output Passivity of a transition, rho, which quantifies a new aspect of the transition phase that we call the passivity of the output progress curve. With these magnitudes, all of them being experimentally accessible, several features of the transient state can be measured. We apply the present analysis to (a) the case of coupled enzyme assays, which allows us to reach conclusions about the progress curves in these particular transitions and the equivalence between tau sigma and tau delta, and (b) some experimental results that allow us to discuss the biological significance of the Output Passivity in the transition between steady states in metabolic systems.

scholarly journals MicrobeTrace: Retooling Molecular Epidemiology for Rapid Public Health Response

2020 ◽  
Author(s):  
Ellsworth M. Campbell ◽  
Anthony Boyles ◽  
Anupama Shankar ◽  
Jay Kim ◽  
Sergey Knyazev ◽  
...  
Keyword(s):  
Public Health ◽  
Molecular Epidemiology ◽  
Spanning Trees ◽  
Healthcare Providers ◽  
Contact Tracing ◽  
Surveillance Systems ◽  
Multiple Sources ◽  
Public Health Response ◽  
Link Type ◽  
Novel Approach

AbstractMotivationOutbreak investigations use data from interviews, healthcare providers, laboratories and surveillance systems. However, integrated use of data from multiple sources requires a patchwork of software that present challenges in usability, interoperability, confidentiality, and cost. Rapid integration, visualization and analysis of data from multiple sources can guide effective public health interventions.ResultsWe developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. Using publicly available HIV sequences and other data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020.Availability and ImplementationMicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully-operational without an internet connection. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. The source code is available at https://github.com/cdcgov/[email protected]

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