Different Number Of Iterations Research Articles

Optimization of the FPM iterative process based on bright-field spectral overlap rate analysis.

Fourier ptychographic microscopy (FPM) reconstructs high-resolution images through multiple iterations on a large number of sub-images at different angles, a process that is time-consuming. For a long time, various methods for optimizing the efficiency of FPM based on the acquisition process and algorithms have been proposed. However, there has been no specific analysis of the impact that the sub-images involved in the reconstruction have on the final result. In this Letter, we conduct the first, to our knowledge, analysis of the impact of a single sub-image on the reconstruction result of a high-resolution image in different numbers of iterations and obtain a curve depicting the change in image quality after the sub-images are involved in the reconstruction in different cycles. By analyzing this curve, the sub-images that exert a negative impact on the resulting image are exported along with their corresponding LED positions. On this basis, we propose the concept of bright-field spectral overlap ratio to distinguish whether the sub-images have a positive impact on the reconstruction results under different acquisition conditions and remove the sub-images that have a negative impact on the results during the iterative process. Both simulation and real experimental results demonstrate that our algorithm can reduce the reconstruction time while maintaining image quality. Moreover, it can be combined with other methods to improve reconstruction efficiency.

Universally Quantitative Band-Selective Pure Shift NMR Spectroscopy.

NMR spectroscopy is often described as a quantitative analytical technique. Strictly, only the simple pulse-acquire experiment is universally quantitative, but the poor signal resolution of the 1H NMR pulse-acquie experiment frequently complicates quantitative analysis. Pure shift NMR techniques provide higher resolution, by reducing signal overlap, but they are susceptible to a variety of sources of site-dependent signal loss. Here, we introduce a new method that corrects for signal loss from such sources in band-selective pure shift NMR experiments, by performing different numbers of iterations of the same pulse sequence elements before acquisition to allow extrapolation back to the loss-free signal. We apply this method to both interferogram and semi-realtime acquisition modes, obtaining integrals within 1% of those acquired from a pulse-acquire experiment for a three-component mixture.

Comparative Analysis of Improved Dirichlet Process Mixture Model

Due to the development of information technology, large amounts of data are generated every day in various industries such as engineering, healthcare, finance, anomaly detection, image recognition, and artificial intelligence. This massive data poses the challenge of analyzing accurately and appropriate classifications. The traditional clustering methods require specifying the number of clusters and are mostly based on distance, which cannot effectively consider the correlations between different indicators of high-dimensional and multi-source data. Moreover, the number of clusters cannot automatically adjust when new data is generated. In order to improve the clustering analysis of high-dimensional and multi-source data in a big data environment, this study utilizes non-parametric mixture models based on distribution clustering, which does not require specifying the number of clusters and can auto update with the data. By combining Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and the non-parametric Bayesian method called Dirichlet Process Mixture Model (DPMM), the Bayesian non-parametric PCA model (PCA-DPMM) and Bayesian non-parametric t-SNE model (TSNE-DPMM) are proposed. The Chinese restaurant process of DPMM is used for sampling by introducing a finite normal mixture distribution. The clustering results on the iris dataset are compared and analyzed. The accuracy of DPMM and TSNE-DPMM reaches 0.97, while PCA-DPMM achieves a maximum accuracy of only 0.94. When different numbers of iterations are set, TSNE-DPMM maintains an accuracy ranging from 0.92 to 0.97, DPMM ranges from 0.66 to 0.97, and PCA-DPMM ranges from 0.73 to 0.94. Therefore, the proposed TSNE-DPMM ensures accuracy and exhibits better model stability in clustering results. Future research can explore the improvement of the model by incorporating deep learning algorithms, among others, to further enhance its performance. Additionally, applying the TSNE-DPMM model to data analysis in other fields is also a future research direction. Through these efforts, we can better tackle the challenges of analyzing high-dimensional and multi-source data in a big data environment and extract valuable information from it.

Multi-Carrier NOMA-Empowered Wireless Federated Learning With Optimal Power and Bandwidth Allocation

Wireless federated learning (WFL) undergoes a communication bottleneck in uplink, limiting the number of users that can upload their local models in each global aggregation round. This paper presents a new multi-carrier non-orthogonal multiple-access (MC-NOMA)-empowered WFL system under an adaptive learning setting of Flexible Aggregation. Since a WFL round accommodates both local model training and uploading for each user, the use of Flexible Aggregation allows the users to train different numbers of iterations per round, adapting to their channel conditions and computing resources. The key idea is to use MC-NOMA to concurrently upload the local models of the users, thereby extending the local model training times of the users and increasing participating users. A new metric, namely, Weighted Global Proportion of Trained Mini-batches (WGPTM), is analytically established to measure the convergence of the new system. Another important aspect is that we maximize the WGPTM to harness the convergence of the new system by jointly optimizing the transmit powers and subchannel bandwidths. This nonconvex problem is converted equivalently to a tractable convex problem and solved efficiently using variable substitution and Cauchy’s inequality. As corroborated experimentally using a convolutional neural network and an 18-layer residential network, the proposed MC-NOMA WFL can efficiently reduce communication delay, increase local model training times, and accelerate the convergence by over 40%, compared to its existing alternative.

Pairing Hamiltonians of Nearest-Neighbor Interacting Superconducting Qubits on an IBM Quantum Computer

A quantum simulation experiment pairing Hamiltonians of nearest-neighbor interacting superconducting qubits was performed with a complete set of algorithms on an IBM Quantum Computer-IBMq Lima. The experiment revealed that the fidelity is a function of iteration using Suzuki–Trotter decomposition for four different types of nearest-neighbor Heisenberg, XY, transverse, and longitudinal Ising superconducting qubit couplings of Hamiltonians. The experiment displayed the models of how the experimental density matrices shift from the theoretical density matrices and how their behavior changes with different numbers of iterations. It also demonstrated the reconstruction of quantum states and how the states change as a function of iteration with the IBM Quantum Computer-IBMq Lima. The time evolutions of the states for different models were also shown to predict the dominance of each state.

On the Improvement of the Heuristic Algorithm for Packing Circles into a Circle of Minimum Radius

The article is devoted to investigation of a heuristic algorithm for solving the competitive problem "Circles Dense packing into a circle of minimum radius" and development of its improved version using Shor's r-algorithm with step dichotomy. The heuristic algorithm was developed by Bohdan Zadorozhnyi, a third-year student of Uzhhorod National University. The program implemented on its basis took second place in the competition and used less time than the maximum time, which, according to the competition, was allocated for a one-time launch of the program for 50 competitive tasks. The article describes the research results of how the Shor r-algorithm can be used to improve efficiency of the heuristic algorithm. The algorithm for improving the heuristic solution is implemented according to the following scheme. We start from the best arrangement of circles found by the heuristic algorithm. Then, we perform the following four stages of the algorithm: Stage 1. We set a rather large value of the initial step (we determine it depending on the input data or simply choose it as a large step); Stage 2. We run the r-algorithm from the starting point and find the local minimum of the corresponding nonlinear programming problem; Stage 3. If the local minimum found guarantees a smaller radius of the outer circle, then the local minimum point becomes the starting point; the local minimum found does not guarantee a smaller radius of the outer circle, then the size of the step h is reduced by half. Stage 4. If the step value is greater than the specified value, then we proceed to Stage 2. Otherwise, we finish work of the algorithm with solution improvement. Python 3.9.5 programming language and NumPy 1.24.2 library were used for software implementation of both algorithms. For 50 competitive tests and different number of iterations of the heuristic algorithm (10, 20, 30, 40 and 50 iterations), the analysis of the obtained results was conducted according to three parameters: the number of points obtained, program execution time and the number of exchanges of circles in one iteration of the heuristic algorithm. For most of the competitive tests, the refined algorithm allows you to get better results in points, in particular, in most cases the improvement is up to 2 points, and in some cases – from 3 to 6 points. The biggest improvements (6 points) are seen in tests, where the circles have the same or close radii. Here, the heuristic algorithm works not so effectively, therefore, for such tests, due to the algorithm with refinement, it is possible to significantly improve the results of competitive tasks in terms of points. Keywords: circle packing, heuristics, r-algorithm, Python 3.9.5, NumPy 1.24.2.

Robustness of quantum coherence and quantum criticality in spin-1 many-body system

The tripartite quantum coherence for spin-12 and bipartite quantum coherence for spin-1 many-body systems are studied with the quantum renormalization group method to investigate the quantum criticality behaviour of both systems. We find both spin-12 and spin-1 systems exhibit quantum criticality behaviour after different number of iterations. However, the bipartite quantum coherence for spin-1 system reaches the quantum criticality behaviour in lesser number of iterations than the tripartite quantum coherence for spin-12 system. The quantum criticality behaviour of both systems is also investigated through non-analytic and the scaling behaviour of quantum coherence. Additionally, quantum correlations and quantum phase transition are observed to be more vigorous with higher spin comparatively to the number of parties (i.e., bipartite, tripartite, multipartite) involved in a quantum correlations.

Modelling and Simulation of various detection algorithms in uplink Massive MIMO systems: A Comparative Analysis

Signal detection is one of the major issues in uplink massive MIMO (multiple input multiple output) systems due to the deployment of large number of antennas. In this research article, the modelling and comparative investigation of various detection algorithms in massive MIMO systems is done to achieve the optimal error rate performance with low complexity. The simulation-based comparison is conducted between equalization based linear detectors, approximate matrix inversion based linear detectors and non-linear BOX equalization detectors. The error rate performance is evaluated with different MIMO scenarios in terms of different ratio between number of users and base station (BS) antennas (β) and different number of iterations (n). The complexity analysis is also done for all the detectors. Simulation results manifest that the BOX equalization detector ADMIN provides the best performance with the lowest complexity. It is also observed that the approximate matrix inversion based linear detectors and the OCD detector perform better with the increased value of (β).

Factorization‐based active contour segmentation and pelican optimization‐based modified bidirectional long short‐term memory for ovarian tumor detection

AbstractThis paper proposes a novel three‐dimensional convolution neural network‐based modified bidirectional long short‐term memory with pelican optimization (3D CNN based MBiLSTM with PO) algorithm for multiclass ovarian tumor detection. Initially, the International Collaboration on Cancer Reporting endometrial cancer dataset images are provided in pre‐processing phase, which uses a pre‐emphasis filter to process the input image. In the segmentation phase, pre‐processed data is then partitioned into diverse subgroups (i.e., pixels), which minimizes the complexity of images. In this paper, a factorization‐based active contour technique is employed in the effective segmentation of images. The segmented features are then extracted and classified using the 3D CNN‐MBiLSTM with PO algorithm. Finally, the experimental results are conducted and compared with various other approaches for various performance metrics. Each metric is evaluated with respect to the different number of iterations. The accuracy, sensitivity, and specificity have reached a higher value of 98.5%, 96%, and 98.25%, respectively.

Comparative Study of Analytic and Numerical Solutions of Steady-state Temperature Distribution on Semi-circular Plate Using Laplace Equation

In this paper numerical methods have been used to solve two dimensional steady state heat flow problem in polar coordinates with Dirichlet boundary conditions inside a semi-circular plate and the work focuses on the numerical methods for solving Laplace equation; finite difference schemes and Gauss elimination method. The numerical solution is compared with exact solution of the same problem. Finally, we analyze the absolute error in different number of iterations to check the accuracy of schemes.

A faster dynamic convergency approach for self-organizing maps

This paper proposes a novel variable learning rate to address two main challenges of the conventional Self-Organizing Maps (SOM) termed VLRSOM: high accuracy with fast convergence and low topological error. We empirically showed that the proposed method exhibits faster convergence behavior. It is also more robust in topology preservation as it maintains an optimal topology until the end of the maximum iterations. Since the learning rate adaption and the misadjustment parameter depends on the calculated error, the VLRSOM will avoid the undesired results by exploiting the error response during the weight updation. Then the learning rate is updated adaptively after the random initialization at the beginning of the training process. Experimental results show that it eliminates the tradeoff between the rate of convergence and accuracy and maintains the data's topological relationship. Extensive experiments were conducted on different types of datasets to evaluate the performance of the proposed method. First, we experimented with synthetic data and handwritten digits. For each data set, two experiments with a different number of iterations (200 and 500) were performed to test the stability of the network. The proposed method was further evaluated using four benchmark data sets. These datasets include Balance, Wisconsin Breast, Dermatology, and Ionosphere. In addition, a comprehensive comparative analysis was performed between the proposed method and three other SOM techniques: conventional SOM, parameter-less self-organizing map (PLSOM2), and RA-SOM in terms of accuracy, quantization error (QE), and topology error (TE). The results indicated the proposed approach produced superior results to the other three methods.

Reconstructed SPECT images of 177Lu homogeneous cylindrical phantom used for calibration and texture analysis

In a clinical contest, it is common to use dedicated phantoms to perform quality assurance test to check the performance of a SPECT system. Some of these phantoms are also used to calibrate the system for dosimetric evaluation of patients undergoing radiometabolic cancer therapy. In this work, a 3D-OSEM reconstructed 177Lu SPECT dataset of a homogeneous cylindrical phantom is described. This dataset was acquired to investigate the variation of the SPECT calibration factor, counts convergence, noise and uniformity by varying the number of subsets and iterations. In particular, the dataset is composed of images reconstructed using five different numbers of subsets and sixteen different numbers of iterations, for a total of 80 different configurations. The dataset is suitable for comparison with other reconstruction algorithms (e.g. FBP, MLEM, etc.) and radionuclides (e.g. technetium, yttrium). In regards to the uniformity issue, the same dataset allows the user to perform radiomic investigations on the influence of the border effect on the reconstructed images.

THE PERFORMANCE EVALUATION OF MULTI USER OFDM ORTHOGONAL CHAOTIC VECTOR SHIFT KEYING SUPPORTED BY LDPC

Recently, LDPC code have become very important research area in wireless communication due to its ability to increase the capacity in a wireless fading environment, with low implementation complexity. In this paper, LDPC are combined with Multi User OFDM Orthogonal Chaotic Vector Shift Keying (MU-OFDM-OCVSK) communication system to improve the BER performance over multi-path Rayleigh fading channels. Two types of LDPC decoder are introduced that are Log-Domain and Min-Sum decoder. The system is simulated using MATLAB program version 2019a for different scenarios which include different number of iterations, different block lengths, different number of users and different number of spreading factor. The results show that a coding gain in a range of (4.5 – 7) dB is achieved between the coded and uncoded MU-OFDM-OCVSK system. The results also show that the Min-Sum decoder outperform the Log-Domain decoder in all scenarios.

Voronoi-Based Discrete Element Analyses to Assess the Influence of the Grain Size and Its Uniformity on the Apparent Fracture Toughness of Notched Rock Specimens

The fracture toughness reflects the rock resistance to crack propagation, and therefore represents an important parameter for rock fracture assessments. From a strict point of view, the real fracture toughness (it {K}_{mathrm{it IC}}) corresponds to a cracked situation in which the notch radius is theoretically equal to zero. However, most of the defects in rocks have a finite radius and, therefore, should be studied as notch-type defects. Here, the notch effect is numerically studied together with the influence of the grain size and the sorting coefficient (grain size uniformity) on the apparent fracture toughness (it {K}_{mathrm{it IN}}). To this end, several four-point bending tests with different U-shaped notch radii, mean grain sizes and degrees of uniformity in grain size and shape have been simulated using the Discrete Element Method. In order to represent the grains of the rocks, the Voronoi tessellation is used to create randomly sized and distributed polygonal blocks. These Voronoi polygons have been defined, on the one hand, by an average edge length of 1, 2 and 3 mm, and, on the other hand, by a different number of iterations (n) in the relaxation process during the generation of the polygons, which defines the grain size uniformity. The numerical analyses performed and the interpretation of the results show a clear notch effect in all the studied cases, as the apparent fracture toughness (it {K}_{mathrm{it IN}}) increases with notch radius. Finally, the obtained stress fields at the notch tip have been compared to those obtained from the traditional finite element method.

A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique.

Recently, Internet of Things (IoT) technology has emerged in many aspects of life, such as transportation, healthcare, and even education. IoT technology incorporates several tasks to achieve the goals for which it was developed through smart services. These services are intelligent activities that allow devices to interact with the physical world to provide suitable services to users anytime and anywhere. However, the remarkable advancement of this technology has increased the number and the mechanisms of attacks. Attackers often take advantage of the IoTs’ heterogeneity to cause trust problems and manipulate the behavior to delude devices’ reliability and the service provided through it. Consequently, trust is one of the security challenges that threatens IoT smart services. Trust management techniques have been widely used to identify untrusted behavior and isolate untrusted objects over the past few years. However, these techniques still have many limitations like ineffectiveness when dealing with a large amount of data and continuously changing behaviors. Therefore, this paper proposes a model for trust management in IoT devices and services based on the simple multi-attribute rating technique (SMART) and long short-term memory (LSTM) algorithm. The SMART is used for calculating the trust value, while LSTM is used for identifying changes in the behavior based on the trust threshold. The effectiveness of the proposed model is evaluated using accuracy, loss rate, precision, recall, and F-measure on different data samples with different sizes. Comparisons with existing deep learning and machine learning models show superior performance with a different number of iterations. With 100 iterations, the proposed model achieved 99.87% and 99.76% of accuracy and F-measure, respectively.

Модель Ізінга для опису магнітних властивостей елементів системи керування авіаційним двигуном

The creation of competitive engines is impossible without the development and implementation of new materials and design and technological solutions. At present, in the engines, nickel alloys obtained by directional crystallization are widely used, including monocrystalline, granular alloys. The use of various composite materials, permanent joints from dissimilar materials is constantly expanding, extensive research is being carried out to create a structure from monolithic ceramics and intermetallic compounds. The successful introduction of unconventional materials is impossible without a thorough study of their structural strength, magnetic properties, features of deformation, and destruction, taking into account the specifics of these materials when developing the design and manufacturing technology of a part or engine unit. The article discusses a software package for the optimal design of control system elements for aircraft gas turbine engines using a description of their magnetic properties. Due to the modular structure of the optimization program, various optimization procedures and calculation programs can be used. It was determined that, for the Ising model, the formation of domains with a given spread of spins at a temperature T and a vector of magnetic induction B is calculated array element using a random number generator. The number of tests L depends on the size of the sample. In the Ising model, all parts of the N*N system are contenders for testing. So, Step corresponds to the number of Monte Carlo steps. Since the parts are selected once on average, it is possible to select one particle several times or not to select it at all. Therefore, the Step value should be significantly greater than one. For a convenient implementation of a certain model, its application with a graphical magnetization interface with a different number of iterations has been considered. When executing the command file, the following should be performed: building a geometric model, building a finite element mesh, applying loads, calculating the stress-strain state (SSS), displaying the results.

Research on Human Resource Recommendation Algorithm Based on Machine Learning

The economic environment has changed dramatically around the world in recent years, generating favorable conditions for the growth of small- and medium-sized firms. The socioeconomic development and international integration of China are greatly influenced by the growth in both quality and quantity, the scale of operations, and the internal force of small- and medium-sized businesses. Moreover, in comparison with other developed countries around the world, Chinese small- and medium-sized enterprises continue to face many limitations in terms of size and contribution levels and have not yet fully realized their potentials due to difficulties and poor quality; human resources in this field are still lacking. This study defines the current state of human resources in small and medium firms, the factors that impede development, and the steps that can be taken to overcome these obstacles in order to assist human resource development in this sector during the current period. This study uses machine learning (ML) techniques to manage and analyze human resource data in modern enterprises. The ML techniques realize the functions of the human resource system and reduce the business volume in human resource in order to improve the efficiency and management of the human resource work. In this paper, we designed and implemented the wage forecasting model in human resources that uses a gradient descent algorithm, its types, and backpropagation (BP) neural network to improve the accuracy of the forecasting model. We performed multiple experiments by using a various number of neurons in the hidden layers, different number of iterations, and several types of gradient descent algorithms. The BP neural network model was performed brilliantly by attaining the training accuracy of 89.98% and validation accuracy of 84.05%. The experimental results show the significance and importance of the proposed work.

A Comparative Study between Discrete Stochastic Arithmetic and Floating-Point Arithmetic to Validate the Results of Fractional Order Model of Malaria Infection

The researchers aimed to study the nonlinear fractional order model of malaria infection based on the Caputo-Fabrizio fractional derivative. The homotopy analysis transform method (HATM) is applied based on the floating-point arithmetic (FPA) and the discrete stochastic arithmetic (DSA). In the FPA, to show the accuracy of the method we use the absolute error which depends on the exact solution and a positive value ε. Because in real life problems we do not have the exact solution and the optimal value of ε, we need to introduce a new condition and arithmetic to show the efficiency of the method. Thus the CESTAC (Controle et Estimation Stochastique des Arrondis de Calculs) method and the CADNA (Control of Accuracy and Debugging for Numerical Applications) library are applied. The CESTAC method is based on the DSA. Also, a new termination criterion is used which is based on two successive approximations. Using the CESTAC method we can find the optimal approximation, the optimal error and the optimal iteration of the method. The main theorem of the CESTAC method is proved to show that the number of common significant digits (NCSDs) between two successive approximations are almost equal to the NCSDs of the exact and approximate solutions. Plotting several graphs, the regions of convergence are demonstrated for different number of iterations k = 5, 10. The numerical results based on the simulated data show the advantages of the DSA in comparison with the FPA.

Forecasting in Small Business Management

This work aims to verify an authorial forecasting method from a system of interdependent equations, which is based on empirical equations of the structural form and is mainly intended for econometric micromodels. The prediction procedure will be analogous to the so-called chain prediction that is used for recursive models. The difference—compared with the prediction from a recursive model—entails the necessity of using one of the reduced-form empirical equations to begin the procedure of constructing a sequence of forecasts from successive structural-form empirical equations. The research results presented above indicate that the above-proposed iterative forecasting method from structural-form equations of a system of interdependent equations guarantees synchronization of forecasts as part of a closed cycle of relations. A different number of iterations is required to obtain convergent forecasts. It can be noticed that the further ahead the forecasted period is, the more iterations should be carried out to obtain convergent forecasts. Small business management with the use of forecasting can be done remotely. Rapid updates of statistical information will require cloud-based communication. Completion of data in a cloud will allow, on one hand, accurate assessment of expired forecasts and, on the other, to update the predictor equations. This can be carried out at any place with Internet access.

Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging

Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated.For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.