Application of Monte Carlo Simulation-Based Photon Migration for Enhanced Understanding of Near-Infrared (NIR) Diffuse Reflectance. Part II: Photon Radial Diffusion in NIR Chemical Images

Controlling texture and microstructure evolution during annealing processes is very important for optimizing properties of steels. Theories used to explain annealing processes are complicated and always case dependent. An recently developed Monte Carlo simulation based model offers an effective tool for studying annealing process and can be used to verify the arbitrarily defined theories that govern such processes. The computer model takes Orientation Image Microscope (OIM) measurements as an input. The abundant information contained in OIM measurement allows the computer model to incorporate many structural characteristics of polycrystalline materials such as, texture, grain boundary character, grain shape and size, phase composition, chemical composition, stored elastic energy, and the residual stress. The outputs include various texture functions, grain boundary and grain size statistics that can be verified by experimental results. Graphical representation allows us to perform virtual experiments to monitor each step of the structural transformation. An example of applying this simulation to Si steel is given.

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Spectroscopy in heterogenous media and applications for bioprocess and environmental monitoring

International Journal of Photoenergy ◽

10.1155/s1110662x02000041 ◽

2002 ◽

Vol 4 (1) ◽

pp. 21-26 ◽

Cited By ~ 3

Author(s):

Frank Schael ◽

Oliver Reich ◽

Sonja Engelhard

Keyword(s):

Diffuse Reflectance ◽

Near Infrared ◽

Transport Theory ◽

Radiation Transport ◽

Diffusion Theory ◽

Nile Blue ◽

Milk Powder ◽

Photon Migration ◽

Heterogenous Media ◽

Reflectance Measurements

Diffuse reflectance measurements and photon migration studies with near infrared (NIR) diode lasers were employed to elucidate experimental methods for determining absorption and scattering coefficients and species concentrations in heterogenous media. Measurements were performed at a number of wavelengths utilizing several laser sources some of which were widely tunable. In order to establish the applicability of simple photon migration models derived from radiation transport theory and to check the experimental boundary conditions of our measurements, simple light scattering solutions (such as suspensions of titanium dioxide, latex particles, and solutions of milk powder) containing dyes (such as nile blue, isosulfan blue) were investigated. The results obtained from diffuse-reflectance studies at different sourcedetector distances were in accordance with predictions from simple photon diffusion theory. Applications of reflectance measurements for monitoring of cell growth during fermentation processes and forin-situinvestigations of soils are presented.

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Research on the Near-Infrared (NIR) Photon Migration in the Knee

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.760-762.388 ◽

2013 ◽

Vol 760-762 ◽

pp. 388-391

Author(s):

Yan Ping Chen ◽

Xiong Ma ◽

Chun Bin Li ◽

Song Qing Chen

Keyword(s):

Monte Carlo ◽

Muscle Tissue ◽

Layered Structure ◽

Near Infrared ◽

Propagation Model ◽

Osteoarthritis Of The Knee ◽

Photon Migration ◽

Photon Propagation ◽

Non Invasive ◽

Distribution Rule

To study the phenomena of photon migration in the knee joint is very important in the field of non-invasive near-infrared optical early diagnosis of osteoarthritis of the knee (KOA). In this paper, a photon propagation model of the knee layered structure based on Monte Carlo method is proposed. The migration trace and distribution rule of the photons in knee layered structure are simulated by the Monte Carlo modeling. The proportion of photons which collide with bone tissue then migrate out of the muscle tissue and photons directly migrate out of muscle tissue is calculated and analyzed. The conclusion is that the MC method provided in this study is useful to analyze the photon migration in knee layered structure and to place the detector in a suitable position.

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A Novel Monte-Carlo Simulation-Based Model for Malware Detection (eRBCM)

Electronics ◽

10.3390/electronics10222881 ◽

2021 ◽

Vol 10 (22) ◽

pp. 2881

Author(s):

Muath Alrammal ◽

Munir Naveed ◽

Georgios Tsaramirsis

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Information Systems ◽

Malware Detection ◽

New Model ◽

Malicious Activity ◽

Model Based ◽

Simulation Based ◽

Computer Based ◽

Reinforcement Model

The use of innovative and sophisticated malware definitions poses a serious threat to computer-based information systems. Such malware is adaptive to the existing security solutions and often works without detection. Once malware completes its malicious activity, it self-destructs and leaves no obvious signature for detection and forensic purposes. The detection of such sophisticated malware is very challenging and a non-trivial task because of the malware’s new patterns of exploiting vulnerabilities. Any security solutions require an equal level of sophistication to counter such attacks. In this paper, a novel reinforcement model based on Monte-Carlo simulation called eRBCM is explored to develop a security solution that can detect new and sophisticated network malware definitions. The new model is trained on several kinds of malware and can generalize the malware detection functionality. The model is evaluated using a benchmark set of malware. The results prove that eRBCM can identify a variety of malware with immense accuracy.

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