Sub-pattern texture recognition using intelligent focal-plane imaging sensor of small window-size

1999 ◽  
Vol 20 (11-13) ◽  
pp. 1133-1139 ◽  
Author(s):  
Tamás Szirányi ◽  
Attila Hanis
Keyword(s):  
Focal Plane ◽  
Window Size ◽  
Texture Recognition ◽  
Small Window ◽  
Imaging Sensor

Influence of Pore Size on Enzymes Adsorption in Mesostructure Cellular Foams (MCFs)

2009 ◽  
Vol 610-613 ◽  
pp. 109-113
Author(s):  
Wei Na ◽  
Qi Wei ◽  
Ze Chang Zou ◽  
Zhi Hong Wang ◽  
Qun Yan Li ◽  
...  
Keyword(s):  
Pore Size ◽  
High Surface ◽  
High Surface Area ◽  
Window Size ◽  
Molecular Size ◽  
Suitable Host ◽  
Small Window ◽  
Cellular Foams ◽  
Silica Materials ◽  
Large Pore Volume

Mesoporous silica materials are attractive candidates for enzymes immobilization due to their high surface area, tunable pore size, large pore volume and biocompatibility. In this work, two different enzymes, papain, a small globular enzyme with molecular diameter of 3.6nm, and catalase, a relatively larger enzyme with molecular diameter of 10.4nm were introduced into the pores of siliceous mesostructed cellular foams (MCFs) that had a large cellular pores of 29nm and a small window size of 12nm, respectively. The amount of adsorbed enzymes was found to be dependent on the molecular size of enzymes. The amount of adsorbed catalase was more than two times that of adsorbed papain, suggesting that MCFs with large pores is a suitable host for large enzymes adsorption. The blocking of pores which resulted from aggregation of enzymes in the windows of MCFs and the higher leaching of enzymes form MCFs may be the reason of lower papain adsorption capacity in MCFs.

A division-of-focal-plane spectral-polarization imaging sensor

2012 ◽  
Author(s):  
Meenal Kulkarni ◽  
Viktor Gruev
Keyword(s):  
Focal Plane ◽  
Polarization Imaging ◽  
Imaging Sensor

scholarly journals Performance estimation of Adaptive threshold median filter design in image Denoising

2018 ◽  
Vol 7 (2.16) ◽  
pp. 33
Author(s):  
Shruti Bhargava Choubey ◽  
Abhishek Choubey
Keyword(s):  
Filter Design ◽  
Median Filter ◽  
Window Size ◽  
Performance Estimation ◽  
Intermediate Data ◽  
Small Window ◽  
Field Programmable ◽  
Image Details ◽  
Better Than

Picture denoising is utilized as a part of numerous fields like PC vision, remote detecting, medicinal imaging, apply autonomy and so forth. In a significant number of these applications the presence of rash clamour in the procured pictures is a standout amongst the most widely recognized issues.  The concept of this method is to provide simple but efficient method of image de-noising using filter to improve the performance and reduce the complexity of implementation. This method use the combination of average filtering and median filtering to remove the noise and produce better results with small window size 3x3. So the image details preservation is also better with small window. Mathematical results show that quality of image is better than the other filtering methods. Hardware implementation of this method is also very easy; because less number of calculations required removing the noise. Reconfigurable hardware filters may be embedded with photo acquirements provision to gain that goal. Field programmable doorway order (FPGA) is appropriate because pipelining or parallelism facts processing. What’s more, though the filtering algorithm techniques huge amount over data, however such does no longer require to shops a cluster regarding intermediate data and has the consequent properties: easy of computing or reproducible, for this reason it is suitable to be applied the usage of FPGA.  

scholarly journals Complementary fluorescence-polarization microscopy using division-of-focal-plane polarization imaging sensor

2012 ◽  
Vol 17 (11) ◽  
pp. 116001 ◽  
Author(s):  
Yang Liu ◽  
Timothy York ◽  
Walter Akers ◽  
Gail Sudlow ◽  
Viktor Gruev ◽  
...  
Keyword(s):  
Fluorescence Polarization ◽  
Focal Plane ◽  
Polarization Microscopy ◽  
Polarization Imaging ◽  
Fluorescence Polarization Microscopy ◽  
Imaging Sensor

scholarly journals LFO2: An Enhanced Version of Learning-From-OPT Caching Algorithm

2021 ◽  
Author(s):  
Yipkei Kwok ◽  
David L. Sullivan
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
State Of The Art ◽  
Window Size ◽  
Learning Model ◽  
The State ◽  
Small Window ◽  
Machine Learning Model ◽  
New Machine

Recent machine learning-based caching algorithm have shown promise. Among them, Learning-FromOPT (LFO) is the state-of-the-art supervised learning caching algorithm. LFO has a parameter named Window Size, which defines how often the algorithm generates a new machine-learning model. While using a small window size allows the algorithm to be more adaptive to changes in request behaviors, experimenting with LFO revealed that the performance of LFO suffers dramatically with small window sizes. This paper proposes LFO2, an improved LFO algorithm, which achieves high object hit ratios (OHR) with small window sizes. This results show a 9% OHR increase with LFO2. As the next step, the machine-learning parameters will be investigated for tuning opportunities to further enhance performance.

scholarly journals On restarting automata with auxiliary symbols and small window size

2021 ◽  
Vol 55 ◽  
pp. 9
Author(s):  
František Mráz ◽  
Friedrich Otto
Keyword(s):  
Window Size ◽  
Expressive Power ◽  
Deterministic Case ◽  
Pushdown Automaton ◽  
Context Sensitive ◽  
Small Window ◽  
Context Free Language ◽  
Context Free ◽  
The Church ◽  
Free Language

Here we show that for monotone RWW- (and RRWW-) automata, window size two is sufficient, both in the nondeterministic as well as in the deterministic case. For the former case, this is done by proving that each context-free language is already accepted by a monotone RWW-automaton of window size two. In the deterministic case, we first prove that each deterministic pushdown automaton can be simulated by a deterministic monotone RWW-automaton of window size three, and then we present a construction that transforms a deterministic monotone RWW-automaton of window size three into an equivalent automaton of the same type that has window size two. Furthermore, we study the expressive power of shrinking RWW- and RRWW-automata the window size of which is just one or two. We show that for shrinking RRWW-automata that are nondeterministic, window size one suffices, while for nondeterministic shrinking RWW-automata, we already need window size two to accept all growing context-sensitive languages. In the deterministic case, shrinking RWW- and RRWW-automata of window size one accept only regular languages, while those of window size two characterize the Church-Rosser languages.

Optimum Window Size for Quantitating Trace Elements Using Linear Least Squares Fit With Eels

1999 ◽  
Vol 5 (S2) ◽  
pp. 938-939
Author(s):  
Ruoya Ho ◽  
Jiang Lin Feng ◽  
Zhifeng Shao ◽  
Andrew P. Somlyo
Keyword(s):  
Least Squares ◽  
Window Size ◽  
Dry Weight ◽  
Elemental Distribution ◽  
Small Signal ◽  
Linear Least Squares ◽  
Experimental Conditions ◽  
Large Window ◽  
Small Window ◽  
Least Squares Fit

Trace element quantitation with EELS is a powerful tool for providing information about elemental distribution in biological and materials sciences. Linear least squares fit (LSF) to standards of the signal and the background is a widely used method for retrieving small signals superimposed on a large, slowly varying background. Theoretically, for most accurate background fitting, the fitting window should be as large as possible. However, when using a large window, there may be a mismatch between the fine structure of the standard and the measured background, due to instability of the instrument and the experimental conditions and radiation damage. This mismatch, distributed over the entire window, can severely affect the accuracy of the small signal extracted with LSF. To overcome this problem, a small window, slightly larger than the signal is often used. Fig. 1 shows the results of extracting a small Ca signal by using respectively, a large and a small window for fitting an EELS spectrum collected from a specimen containing 26.3 mmol/kg dry weight Ca.

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