Different Number Of Iterations Research Articles

Non-local mean denoising using multiple PET reconstructions

ObjectivesNon-local mean (NLM) filtering has been broadly used for denoising of natural and medical images. The NLM filter relies on the redundant information, in the form of repeated patterns/textures, in the target image to discriminate the underlying structures/signals from noise. In PET (or SPECT) imaging, the raw data could be reconstructed using different parameters and settings, leading to different representations of the target image, which contain highly similar structures/signals to the target image contaminated with different noise levels (or properties). In this light, multiple-reconstruction NLM filtering (MR-NLM) is proposed, which relies on the redundant information provided by the different reconstructions of the same PET data (referred to as auxiliary images) to conduct the denoising process.MethodsImplementation of the MR-NLM approach involved the use of twelve auxiliary PET images (in addition to the target image) reconstructed using the same iterative reconstruction algorithm with different numbers of iterations and subsets. For each target voxel, the patches of voxels at the same location are extracted from the auxiliary PET images based on which the NLM denoising process is conducted. Through this, the exhaustive search scheme performed in the conventional NLM method to find similar patches of voxels is bypassed. The performance evaluation of the MR-NLM filter was carried out against the conventional NLM, Gaussian and bilateral post-reconstruction approaches using the experimental Jaszczak phantom and 25 whole-body PET/CT clinical studies.ResultsThe signal-to-noise ratio (SNR) in the experimental Jaszczak phantom study improved from 25.1 when using Gaussian filtering to 27.9 and 28.8 when the conventional NLM and MR-NLM methods were applied (p value < 0.05), respectively. Conversely, the Gaussian filter led to quantification bias of 35.4%, while NLM and MR-NLM approaches resulted in a bias of 32.0% and 31.1% (p value < 0.05), respectively. The clinical studies further confirm the superior performance of the MR-NLM method, wherein the quantitative bias measured in malignant lesions (hot spots) decreased from − 12.3 ± 2.3% when using the Gaussian filter to − 3.5 ± 1.3% and − 2.2 ± 1.2% when using the NLM and MR-NLM approaches (p value < 0.05), respectively.ConclusionThe MR-NLM approach exhibited promising performance in terms of noise suppression and signal preservation for PET images, thus translating into higher SNR compared to the conventional NLM approach. Despite the promising performance of the MR-NLM approach, the additional computational burden owing to the requirement of multiple PET reconstruction still needs to be addressed.

Comparison of D-Wave Quantum Annealing and Classical Simulated Annealing for Local Minima Determination

Restricted Boltzmann Machines trained with different numbers of iterations were used to provide a diverse set of energy functions each containing many local valleys (LVs) with different energies, widths, escape barrier heights, etc. They were used to verify the previously reported possibility of using the D-Wave quantum annealer (QA) to find potentially important LVs in the energy functions of Ising spin glasses that may be missed by classical searches. For classical search, extensive simulated annealing (SA) was conducted to find as many LVs as possible regardless of the computational cost. SA was conducted long enough to ensure that the number of SA-found LVs approaches that and eventually significantly exceeds the number of the LVs found by a single call submitted to the D-Wave. Even after a prohibitively long SA search, as many as 30-50% of the D-Wave-found LVs remained not found by the SA. In order to establish if LVs found only by the D-Wave represent potentially important regions of the configuration space, they were compared to those that were found by both techniques. While the LVs found by the D-Wave but missed by SA predominantly had higher energies and lower escape barriers, there was a significant fraction having intermediate values of the energy and barrier height. With respect to most other important LV parameters, the LVs found only by the D-Wave were distributed in a wide range of the parameters' values. It was established that for large or small, shallow or deep, wide or narrow LVs, the LVs found only by the D-Wave are distinguished by a few-times smaller size of the LV basin of attraction (BoA). Apparently, the size of the BoA is not or at least is less important for QA search compared to the classical search, allowing QA to easily find many potentially important (e.g., wide and deep) LVs missed by even prohibitively lengthy classical searches.

Elastic Wave Propagation in Lattice Metamaterials with Koch Fractal

In this study, the wave propagation properties of lattice metamaterials with Koch fractal structures are investigated in terms of band structures and directional wave propagation. The analytical models of lattice metamaterials are established using the finite element method, and the dispersion relation is solved using the Bloch’s theorem. The band structures of the lattice metamaterials with different numbers of iterations are studied, and the group velocities at a selected frequency are calculated to analyze the directional wave propagation characteristics. Furthermore, dynamic responses of the finite structures are calculated using commercial finite element software to verify the band gaps and directional wave propagation behaviors in the lattice metamaterials. The results show that multiple and low band gaps are present in the lattice materials with various geometric parameters of the Koch fractal, and the position of the lowest band gap decreases as the number of iterations increases. The results indicate the potential applications of lattice metamaterials with Koch fractals for vibration isolation and multi-functional design.

ШИРОКОСМУГОВІ АНТЕНИ НА ОСНОВІ КІЛЬЦЕВОЇ ГЕОМЕТРІЇ

В роботі розглянуто варіанти моделей квазіфрактальних кільцевих структур. Для їх синтезу запропонований векторний опис фрактальної трансформації окремих сегментів початкової геометричної форми. Поєднання декомпозиції геометричної форми та багатовимірного фрактального підходу дозволяє спростити синтез фрактальних 3D-структур та/або використовувати кілька видів фракталів, у тому числі, з різною кількістю ітерацій. Такий підхід дозволяє досягти широкосмуговості та багатодіапазонності антенних систем. При цьому досліджено вплив рівня сегментації та варіантів схеми живлення на просторово-частотні характеристики антени. Через складність опису взаємодії антен неевклідової геометрії з радіохвилями для їх синтезу та аналізу обрано методи чисельного моделювання. Для аналізу просторово-частотних характеристик проектованих антенних рішень використані такі показники, як зворотні втрати, діаграма спрямованості та коефіцієнт стоячої хвилі. Отримані оцінки відносної смуги пропускання δf = 0,9 свідчать про перспективність запропонованого підходу для реалізації антенних систем з високим рівнем широкосмуговості

Extractive Arabic Text Summarization Using Modified PageRank Algorithm

This paper proposed an approach for Arabic text summarization. Text summarization is one of the natural language processing's applications which is used for reducing the original text amount and retrieving only the important information from the original text. The Arabic language has a complex morphological structure which makes it very difficult to extract nouns to be used as a feature for summarization process. Therefore, Al-Khalil morphological analyzer is used to solve the problem of nouns extraction. The proposed approach is a graph-based system, which represents the document as a graph where the vertices of the graph are the sentences. A Modified PageRank algorithm is applied with an initial score for each node that is the number of nouns in this sentence. More nouns in the sentence mean more information, so nouns count used here as initial rank for the sentence. Edges between sentences are the cosine similarity between the sentences, to get a final summary that contains sentences with more information and well connected with each other. The process of text summarization consists of three major stages: pre-processing stage, features extraction and graph construction stage, and finally applying the Modified PageRank algorithm and summary extraction. The Modified PageRank algorithm used a different number of iterations to find the number returns the best summary results, and the extracted summary depends on compression ratio, taking into account removing redundancy depending on the overlapping between the sentences. To evaluate the performance of this approach EASC Corpus is used as a standard. LexRank and TextRank algorithms were used under the same circumstances, the proposed approach provides better results when compared with other Arabic text summarization techniques. The proposed approach performs efficiently with the number of iteration 10,000.

Modelling of saturation current of an organic field-effect transistor with accounting for contact resistances

The saturation current of an organic field-effect transistor was calculated numerically by bisection method taking into account the source and drain resistances. Dependences of the saturation current as a function of the gate voltage, centred on the threshold voltage, and as a function of the “extrinsic” (taking into account the source and drain resistances) saturation voltage are presented. The calculations were carried out using an iterative scheme assuming the saturation current to be zero for iteration zero and using different numbers of iterations. In addition, we carried out a compact modelling of the saturation current in the framework of the approach we proposed earlier. It is shown that when in the equation for the compact modelling initial value of saturation current of zero value (corresponding to zero iteration) is used, a good agreement with the bisection method of over a wide range of gate voltages is obtained.

High-Speed FPGA-based of the Particle Swarm Optimization using HLS Tool

The Particle Swarm Optimization (PSO) is a heuristic search method inspired by different biological populations on their swarming or collaborative behavior. This novel work has implemented PSO for the Travelling Salesman Problem (TSP) in high-level synthesis to reduce the computational time latency. The high-level synthesis design generates an estimation of the hardware resources needed to implement the PSO algorithm for TSP on FPGA. The targeted FPGA of this algorithm is the Xilinx Zynq family. The algorithm has been implemented for getting the best route between 5 given cities with given distances. The research has used 7 number of particles for a different number of iterations for generating the best route between those 5 cities. The overall latency has been reduced due to the applied optimization techniques. This paper also implemented and parallelized the same algorithm on CPU Intel I7 Processor; the result shows the FPGA implementation gives better results than CPU on the comparison of performance.

Impact of the Number of Iterations in Compressed Sensing Reconstruction on Ultrafast Dynamic Contrast-enhanced Breast MR Imaging.

Purpose:To assess the impact of the number of iterations of compressed sensing (CS) reconstruction on the kinetic parameters and image quality in dynamic contrast-enhanced (DCE)-MRI of the breast, with prospectively undersampled CS-accelerated scans.Materials and Methods:Breast examinations including ultrafast DCE-MRI using CS were conducted for 21 patients. Images were reconstructed with different numbers of iterations. The peak enhancement ratio of the aorta and wash-in slope, initial area under the curve, and Ktrans of the breast lesions were measured. The root mean square error and structural similarity between the images using 50 iterations and images with a lower number of iterations were evaluated as criterion for quantitative image evaluation.Results:Using an insufficient number of iterations, the contrast-enhanced effect was highly underestimated. In all semi-quantitative parameters, the number of iterations that stabilized the parameters in malignant lesions was higher than that in benign lesions. At least 15 iterations were needed for semi-quantitative parameters. For Ktrans, there were no significant differences between 10 and 50 iterations in both malignant and benign lesions.Conclusion:The kinetic parameters using ultrafast DCE-MRI with CS are affected by the number of iterations, especially in malignant lesions. However, if the images are reconstructed with an adequate number of iterations, ultrafast DCE-MRI with CS can be a powerful technique having high temporal and spatial resolution.

Analysis variation value momentum algorithm backpropagation method in the recognizing process of temperature pattern in Medan

In this paper, From the results of the study it can be concluded. A different target error will produce a different number of iterations. The smaller the target error, the greater the number of iterations. The smaller the target error, the better the accuracy value (the greater). The number of iterations in the training process with variations in momentum values is smaller than without variations in momentum values. The highest level of accuracy is achieved at the target error of 0.001 with a momentum value of 0.4 which is 96.62%.

Impact of Metaheuristic Iteration on Artificial Neural Network Structure in Medical Data

Medical data classification is an important factor in improving diagnosis and treatment and can assist physicians in making decisions about serious diseases by collecting symptoms and medical analyses. In this work, hybrid classification optimization methods such as Genetic Algorithm (GA), Particle Swam Optimization (PSO), and Fireworks Algorithm (FWA), are proposed for enhancing the classification accuracy of the Artificial Neural Network (ANN). The enhancement process is tested through two experiments. First, the proposed algorithms are applied on five benchmark medical data sets from the repository of the University of California in Irvine (UCI). The model with the best results is then used in the second experiment, which focuses on tuning the parameters of the selected algorithm by choosing a different number of iterations in ANNs with different numbers of hidden layers. Enhanced ANN with the three optimization algorithms are tested on biological gene sequence big dataset obtained from The Cancer Genome Atlas (TCGA) repository. GA and FWA are statistically significant but PSO was statistically not, and GA overcame PSO and FWA in performance. The methodology is successful and registers improvements in every step, as significant results are obtained.

Comparison of Three Un-mixing Based Hyperspectral Image Fusion Techniques

ABSTRACT With the availability of multi sensor data in many fields, such as remote sensing, medical imaging or machine vision, sensor fusion has emerged as a new and promising research area. It is possible to have several images of the same scene providing different information although the scene is the same. This is because each image has been captured with a different sensor. A non-negative matrix factorization (NNMF) un mixing based fusion technique with vertex component analysis (VCA) based end member initialization and simple multiplicative update to improve the spatial resolution and to preserve the spectral resolution of the hyper spectral image is proposed. Its performance is analyzed with different number of iterations and end member initializations. A Constrained Non Negative Matrix Factorization unmixing based fusion technique is developed by adding a regularization term to the objective function to preserve the spectral resolution of the hyper spectral image, and its performance is analyzed with different number of iterations and end members. A rank two NNMF and hierarchical clustering based end member initialization and block principal pivoting algorithm based abundance estimation technique, for fusing hyper spectral image and simulated multispectral image is proposed and its performance is analyzed for different overlapping and non overlapping group of multispectral and hyper spectral bands. The performance of the above three methods are compared and analyzed. The obtained results show that the performance of rank two NNMF hierarchical clustering based fusion technique is better than the other two constrained and unconstrained NNMF un mixing based techniques. Also, the performance of these three proposed multi sensor image fusion techniques are compared with an existing image fusion technique.

Bayesian calibration of simple forest models with multiplicative mathematical structure: A case study with two Light Use Efficiency models in an alpine forest

Forest models are increasingly being used to study ecosystem functioning, through simulation of carbon fluxes and productivity in different biomes and plant functional types all over the world. Several forest models based on the concept of Light Use Efficiency (LUE) rely mostly on a simplified mathematical structure and empirical parameters, require little amount of data to be run, and their computations are usually fast. However, possible calibration issues must be investigated in order to ensure reliable results.Here we addressed the important issue of delayed convergence when calibrating LUE models, characterized by a multiplicative structure, with a Bayesian approach. We tested two models (Prelued and the Horn and Schulz (2011a) model), applying three Markov Chain Monte Carlo-based algorithms with different number of iterations, and different sets of prior parameter distributions with increasing information content. The results showed that recently proposed algorithms for adaptive calibration did not confer a clear advantage over the Metropolis–Hastings Random Walk algorithm for the forest models used here, and that a high number of iterations is required to stabilize in the convergence region. This can be partly explained by the multiplicative mathematical structure of the models, with high correlations between parameters, and by the use of empirical parameters with neither ecological nor physiological meaning. The information content of the prior distributions of the parameters did not play a major role in reaching convergence with a lower number of iterations.We conclude that there is a need for a more careful approach to calibration to solve potential problems when applying models characterized by a multiplicative mathematical structure. Moreover, the calibration proved time consuming and mathematically difficult, so advantages of using a computationally fast and user-friendly model were lost due to the calibration process needed to obtain reliable results.

Perception of Iterated Rippled Noise Periodicity in Cochlear Implant Users

Pitch perception is more challenging for individuals with cochlear implants (CIs) than normal-hearing subjects because the signal processing by CIs is restricted. Processing and perceiving the periodicity of signals may contribute to pitch perception. Whether individuals with CIs can discern pitch within an iterated rippled noise (IRN) signal is still unclear. In a prospective controlled psychoacoustic study with 34 CI users and 15 normal-hearing control subjects, the difference limen between IRN signals with different numbers of iterations was measured. In 7 CI users and 15 normal-hearing control listeners with single-sided deafness, pitch matching between IRN and harmonic complex tones was measured. The pitch onset response (POR) following signal changes from white noise to IRN was measured electrophysiologically. The CI users could discriminate different numbers of iteration in IRN signals, but worse than normal-hearing listeners. A POR was measured for both normal-hearing subjects and CI users increasing with the pitch salience of the IRN. This indicates that the POR could serve as an objective measure to monitor progress during audioverbal therapy after CI surgery.

Combining Different Approaches to Improve Arabic Text Documents Classification

The objective of this research is to improve Arabic text documents classification by combining different classification algorithms. To achieve this objective we build four models using different combination methods. The first combined model is built using fixed combination rules, where five rules are used; and for each rule we used different number of classifiers. The best classification accuracy, 95.3%, is achieved using majority voting rule with seven classifiers, and the time required to build the model is 836 seconds. The second combination approach is stacking, which consists of two stages of classification. The first stage is performed by base classifiers, and the second by a meta classifier. In our experiments, we used different numbers of base classifiers and two different meta classifiers: Naive Bayes and linear regression. Stacking achieved a very high classification accuracy, 99.2% and 99.4%, using Naive Bayes and linear regression as meta classifiers, respectively. Stacking needed a long time to build the models, which is 1963 seconds using naive Bayes and 3718 seconds using linear regression, since it consists of two stages of learning. The third model uses AdaBoost to boost a C4.5 classifier with different number of iterations. Boosting improves the classification accuracy of the C4.5 classifier; 95.3%, using 5 iterations, and needs 1175 seconds to build the model, while the accuracy is 99.5% using 10 iterations and requires 1966 seconds to build the model. The fourth model uses bagging with decision tree. The accuracy is 93.7% achieved in 296 seconds when using 5 iterations, and 99.4% when using 10 iteration requiring 471 seconds. We used three datasets to test the combined models: BBC Arabic, CNN Arabic, and OSAC datasets. The experiments are performed using Weka and RapidMiner data mining tools. We used a platform of Intel Core i3 of 2.2 GHz CPU with 4GB RAM. The results of all models showed that combining classifiers can effectively improve the accuracy of Arabic text documents classification.

A Green Ant-Based method for Path Planning of Unmanned Ground Vehicles

Planning of optimal/shortest path is required for proper operation of unmanned ground vehicles (UGVs). Although most of the existing approaches provide proper path planning strategy, they cannot guarantee reduction of consumed energy by UGVs, which is provided via onboard battery with constraint power. Hence, in this paper, a new ant-based path planning approach that considers UGV energy consumption in its planning strategy is proposed. This method is called Green Ant (G-Ant) and integrates an ant-based algorithm with a power/energy consumption prediction model to reach its main goal, which is providing a collision-free shortest path with low power consumption. G-Ant is evaluated and validated via simulation tools. Its performance is compared with ant colony optimization, genetic algorithm, and particle swarm optimization approaches. Various scenarios were simulated to evaluate G-Ant performance in terms of UGV travel time, travel length, computational time by taking into account different numbers of iterations, different numbers of obstacle, and different population sizes. The obtained results show that the G-Ant outperforms the existing methods in terms of travel length and number of iteration.

Development and Evaluation of Image Reconstruction Algorithms for a Novel Desktop SPECT System.

Objective (s):Various iterative reconstruction algorithms in nuclear medicine have been introduced in the last three decades. For each new imaging system, it is wise to select appropriate image reconstruction algorithms and evaluate their performance. In this study, three approaches of image reconstruction were developed for a novel desktop open-gantry SPECT system, PERSPECT, to assess their performance in terms of the quality of the resultant reconstructed images.Methods:In the present work, a proposed image reconstruction algorithm for the PERSPECT, referred to as quasi-simultaneous multiplicative algebraic reconstruction technique (qSMART), together with two popular image reconstruction methods, maximum-likelihood expectation-maximization (MLEM) and ordered-subsets EM (OSEM), were implemented and compared. Analytic and Monte Carlo simulations were applied for data acquisition of various phantoms including a micro-Derenzo phantom. All acquired data were reconstructed by the three algorithms using different number of iterations (1-40 ). A thorough set of figures-of-merit was utilized to quantitatively compare the generated images.Results:OSEM depicted reconstructed images of higher (or matching) quality in comparison to qSMART. MLEM also reached nearly similar quality as OSEM but at higher number of iterations. The graph of data discrepancy revealed that the ranking of the three approaches in terms of convergence speed is as qSMART, OSEM, and MLEM. Furthermore, bias-versus-noise curves indicated that optimal bias-noise results were achieved using OSEM.Conclusion:The results showed that although qSMART can be applied for image reconstruction if being halted in the early iterations (up to 5), the best achievable quality of images is obtained using the OSEM.

Assessing convergence diagnostic tests for Bayesian Cox regression

ABSTRACTThe Markov chain Monte Carlo (MCMC) method generates samples from the posterior distribution and uses these samples to approximate expectations of quantities of interest. For the process, researchers have to decide whether the Markov chain has reached the desired posterior distribution. Using convergence diagnostic tests are very important to decide whether the Markov chain has reached the target distribution. Our interest in this study was to compare the performances of convergence diagnostic tests for all parameters of Bayesian Cox regression model with different number of iterations by using a simulation and a real lung cancer dataset.

Collective Prediction of Disease-Associated miRNAs Based on Transduction Learning.

The discovery of human disease-related miRNA is a challenging problem for complex disease biology research. For existing computational methods, it is difficult to achieve excellent performance with sparse known miRNA-disease association verified by biological experiment. Here, we develop CPTL, a Collective Prediction based on Transduction Learning, to systematically prioritize miRNAs related to disease. By combining disease similarity, miRNA similarity with known miRNA-disease association, we construct a miRNA-disease network for predicting miRNA-disease association. Then, CPTL calculates relevance score and updates the network structure iteratively, until a convergence criterion is reached. The relevance score of node including miRNA and disease is calculated by the use of transduction learning based on its neighbors. The network structure is updated using relevance score, which increases the weight of important links. To show the effectiveness of our method, we compared CPTL with existing methods based on HMDD datasets. Experimental results indicate that CPTL outperforms existing approaches in terms of AUC, precision, recall, and F1-score. Moreover, experiments performed with different number of iterations verify that CPTL has good convergence. Besides, it is analyzed that the varying of weighted parameters affect predicted results. Case study on breast cancer has further confirmed the identification ability of CPTL.

A three phase optimization method for precopy based VM live migration.

Virtual machine live migration is a method of moving virtual machine across hosts within a virtualized datacenter. It provides significant benefits for administrator to manage datacenter efficiently. It reduces service interruption by transferring the virtual machine without stopping at source. Transfer of large number of virtual machine memory pages results in long migration time as well as downtime, which also affects the overall system performance. This situation becomes unbearable when migration takes place over slower network or a long distance migration within a cloud. In this paper, precopy based virtual machine live migration method is thoroughly analyzed to trace out the issues responsible for its performance drops. In order to address these issues, this paper proposes three phase optimization (TPO) method. It works in three phases as follows: (i) reduce the transfer of memory pages in first phase, (ii) reduce the transfer of duplicate pages by classifying frequently and non-frequently updated pages, and (iii) reduce the data sent in last iteration of migration by applying the simple RLE compression technique. As a result, each phase significantly reduces total pages transferred, total migration time and downtime respectively. The proposed TPO method is evaluated using different representative workloads on a Xen virtualized environment. Experimental results show that TPO method reduces total pages transferred by 71 %, total migration time by 70 %, downtime by 3 % for higher workload, and it does not impose significant overhead as compared to traditional precopy method. Comparison of TPO method with other methods is also done for supporting and showing its effectiveness. TPO method and precopy methods are also tested at different number of iterations. The TPO method gives better performance even with less number of iterations.

Comparison of uncertainty in fatigue tests obtained by the Monte Carlo method in two softwares

The Supplement 1 to the “Guide to the expression of uncertainty in measurement” indicates the Monte Carlo method for calculating the expanded measurement uncertainty. The objective of this work is to compare the measurement uncertainty values obtained via Monte Carlo method through two commercial softwares (Matlab® and Crystal Ball®) for the parameter ‘adjusted strain’, obtained from fatigue tests. Simulations were carried out using different number of iterations and different levels of confidence. The results showed that there are short differences between the measurement uncertainty values generated by different software.