scholarly journals A Fast Inverse Algorithm Based on the Multigrid Technique for Cloud Tomography

2014 ◽  
Vol 31 (7) ◽  
pp. 1653-1662 ◽  
Author(s):  
Jun Zhou ◽  
Hengchi Lei ◽  
Lei Ji ◽  
Tuanjie Hou
Keyword(s):  
Target Area ◽  
Inverse Algorithm ◽  
Convergence Problem ◽  
Slow Convergence ◽  
Retrieval Accuracy ◽  
Smoothing Property ◽  
Previous Algorithm ◽  
Flight Scheme ◽  
Grid Level ◽  
Real Time Application

Abstract A fast inverse algorithm based on the half-V cycle scheme (HV) of the multigrid technique is developed for cloud tomography. Fourier analysis shows that the slow convergence problem caused by the smoothing property of the iterative algorithm can be effectively alleviated in HV by performing iterations from the coarsest to the finest grid. In this way, the resolvable scales of information contained in observations can be retrieved efficiently on the coarser grid level and the unresolvable scales are left as errors on the finer grid level. Numerical simulations indicate that, compared with the previous algorithm without HV (NHV), HV can significantly reduce the runtime by 89%–96.9% while retaining a similar level of retrieval accuracy. For the currently feasible two-level flight scheme for a 20-km-wide target area, convergence can be accelerated from 407 s in NHV to 13 s in HV. This reduction in time would be multiplied several times if the target area were much wider; but then segmental retrieval would be required to avoid exceeding the time limit of cloud tomography. This improvement represents an important saving in terms of computing resources and ensures the real-time application of cloud tomography in a much wider range of fields.

Is Partial-Dimension Convergence a Problem for Inferences from MCMC Algorithms?

2007 ◽  
Vol 16 (2) ◽  
pp. 153-178 ◽  
Author(s):  
Jeff Gill
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Convergence Problem ◽  
Slow Convergence ◽  
Mcmc Algorithms ◽  
Multiple Dimensions ◽  
Analytical Work ◽  
Complex Models ◽  
Slow Mixing

Increasingly, political science researchers are turning to Markov chain Monte Carlo methods to solve inferential problems with complex models and problematic data. This is an enormously powerful set of tools based on replacing difficult or impossible analytical work with simulated empirical draws from the distributions of interest. Although practitioners are generally aware of the importance of convergence of the Markov chain, many are not fully aware of the difficulties in fully assessing convergence across multiple dimensions. In most applied circumstances, every parameter dimension must be converged for the others to converge. The usual culprit is slow mixing of the Markov chain and therefore slow convergence towards the target distribution. This work demonstrates the partial convergence problem for the two dominant algorithms and illustrates these issues with empirical examples.

Research on GMM Background Modeling and its Covariance Estimation

2011 ◽  
Vol 383-390 ◽  
pp. 2327-2333
Author(s):  
Yun Chu Zhang ◽  
Ru Min Zhang ◽  
Shi Jun Song
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Background Modeling ◽  
Covariance Estimation ◽  
Parameters Estimation ◽  
Convergence Problem ◽  
Slow Convergence ◽  
Segmentation Accuracy ◽  
The Stability

This paper analyzes the background modeling mechanism using Gaussian mixture model and the stability /plasticity dilemma in parameters estimation of GMM background model. To solve the slow convergence problem of Gaussian mean and covariance update formula given by Stauffer, a new updating strategy is proposed, which weighs the model adaptability and motion segmentation accuracy. Experiments show that the proposed algorithm improves the accuracy of modal learning and speed of covariance convergence.

FIRST RECORD OF MANTIDS (MANTODEA: INSECTA) FROM INDIA

2018 ◽  
Vol 24 (1) ◽  
Author(s):  
SHVETA PATEL ◽  
RAJENDRA SINGH
Keyword(s):  
Uttar Pradesh ◽  
First Record ◽  
Target Area ◽  
Extensive Survey ◽  
First Time

Extensive survey of mantids in the northeastern Uttar Pradesh was conducted. Two mantid species were recorded for the first time from the target area, viz.: Pyrgomantis pallida, 1917 and Bactromantis mexicana.

PERAN AIR REBUSAN DAUN SALAM (SYZGIUM POLYANTHUM) DALAM MENURUNKAN KADAR ASAM URAT

2016 ◽  
Vol 5 (2) ◽  
pp. 83-91
Author(s):  
Miftafu Darussalam ◽  
Dwi Kartika Rukmi
Keyword(s):  
Uric Acid ◽  
Vascular Inflammation ◽  
Vascular Lesion ◽  
Wilcoxon Test ◽  
Test Time ◽  
Control Group ◽  
Target Area ◽  
Bioactive Substances ◽  
Control Group Design ◽  
Boiled Water

Background: Uric acid is a final product or a waste that is resulted from the metabolism of purines. A high level of uric acid (hyperuricemia) will cause several health problems, such as vascular inflammation, smooth muscle proliferation, and vascular lesion in kidneys. The syzygium polyanthum leaves contain bioactive substances that may affect the level of uric acid in blood. Objective: This study aimed to determine the influence of boiled water of syzygium polyanthum leaves to the changes of uric acid levels in the target area of Puskesmas Pandak 1 Bantul. Methods: This study employed pre- and post-test without control group design. The population consisted of all patients with hyperuricemia in the target area of Puskesmas Pandak 1 Bantul. Sample was selected with a concecutive sampling, gaining a total number of 24 respondents. Data were analyzed with the Wilcoxon test. The dose of boiled water of syzygium polyanthum leaves intake was 0.36g/ KgBW, once a day for 14 days. Result: This research showed that the boiled water of syzygium polyanthum leaves decreased hyperuricemia (uric acid levels), along with the significancy value of 0.009 (p <0.05). At the pre-test time, the average level of uric acid reached 7.279 mg/dl, and after the treatment, it decreased to 6.76 mg/dl. Conclusion: This study has established evidence that the boiled water of syzygium polyanthum leaves is able to decrease hyperuricemia (uric acid level in blood). Keywords: syzygium polyanthum, boiled water of syzygium polyanthum leaves, hyperuricemia

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

scholarly journals Evaluation of Optimal Vibrotactile Feedback for Force-Controlled Upper Limb Myoelectric Prostheses

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5209 ◽  
Author(s):  
Andrea Gonzalez-Rodriguez ◽  
Jose L. Ramon ◽  
Vicente Morell ◽  
Gabriel J. Garcia ◽  
Jorge Pomares ◽  
...  
Keyword(s):  
Real Time ◽  
Upper Limb ◽  
Closed Loop ◽  
Fine Tuning ◽  
Vibrotactile Feedback ◽  
Loop Control ◽  
Myoelectric Prostheses ◽  
Amplitude Level ◽  
Real Time Application ◽  
Selection Of

The main goal of this study is to evaluate how to optimally select the best vibrotactile pattern to be used in a closed loop control of upper limb myoelectric prostheses as a feedback of the exerted force. To that end, we assessed both the selection of actuation patterns and the effects of the selection of frequency and amplitude parameters to discriminate between different feedback levels. A single vibrotactile actuator has been used to deliver the vibrations to subjects participating in the experiments. The results show no difference between pattern shapes in terms of feedback perception. Similarly, changes in amplitude level do not reflect significant improvement compared to changes in frequency. However, decreasing the number of feedback levels increases the accuracy of feedback perception and subject-specific variations are high for particular participants, showing that a fine-tuning of the parameters is necessary in a real-time application to upper limb prosthetics. In future works, the effects of training, location, and number of actuators will be assessed. This optimized selection will be tested in a real-time proportional myocontrol of a prosthetic hand.

scholarly journals Development of a Topical Insulin Polymeric Nanoformulation for Skin Burn Regeneration: An Experimental Approach

2021 ◽  
Vol 22 (8) ◽  
pp. 4087
Author(s):  
Maria Quitério ◽  
Sandra Simões ◽  
Andreia Ascenso ◽  
Manuela Carvalheiro ◽  
Ana Paula Leandro ◽  
...  
Keyword(s):  
Wound Healing ◽  
Healing Process ◽  
In Vitro Release ◽  
Peptide Hormone ◽  
Plga Nanoparticles ◽  
Wound Healing Process ◽  
Target Area ◽  
Encapsulation Process

Insulin is a peptide hormone with many physiological functions, besides its use in diabetes treatment. An important role of insulin is related to the wound healing process—however, insulin itself is too sensitive to the external environment requiring the protective of a nanocarrier. Polymer-based nanoparticles can protect, deliver, and retain the protein in the target area. This study aims to produce and characterize a topical treatment for wound healing consisting of insulin-loaded poly-DL-lactide/glycolide (PLGA) nanoparticles. Insulin-loaded nanoparticles present a mean size of approximately 500 nm and neutral surface charge. Spherical shaped nanoparticles are observed by scanning electron microscopy and confirmed by atomic force microscopy. SDS-PAGE and circular dichroism analysis demonstrated that insulin preserved its integrity and secondary structure after the encapsulation process. In vitro release studies suggested a controlled release profile. Safety of the formulation was confirmed using cell lines, and cell viability was concentration and time-dependent. Preliminary safety in vivo assays also revealed promising results.

Sign in / Sign up

Export Citation Format

Share Document