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1
Hu, Xiao-Xiao, and Kit Ian Kou. "Phase-based edge detection algorithms." Mathematical Methods in the Applied Sciences 41, no. 11 (2017): 4148–69. http://dx.doi.org/10.1002/mma.4567.
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Li, Fanxing, Wei Yan, Fupin Peng, Simo Wang, and Jialin Du. "Enhanced Phase Retrieval Method Based on Random Phase Modulation." Applied Sciences 10, no. 3 (2020): 1184. http://dx.doi.org/10.3390/app10031184.
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The phase retrieval method based on random phase modulation can wipe out any ambiguity and stagnation problem in reconstruction. However, the two existing reconstruction algorithms for the random phase modulation method are suffering from problems. The serial algorithm from the spread-spectrum phase retrieval method can realize rapid convergence but has poor noise immunity. Although there is a parallel framework that can suppress noise, the convergence speed is slow. Here, we propose a random phase modulation phase retrieval method based on a serial–parallel cascaded reconstruction framework to simultaneously achieve quality imaging and rapid convergence. The proposed serial–parallel cascaded method uses the phased result from the serial algorithm to serve as the initialization of the subsequent parallel process. Simulations and experiments demonstrate that the superiorities of both serial and parallel algorithms are fetched by the proposed serial–parallel cascaded method. In the end, we analyze the effect of iteration numbers from the serial process on the reconstruction performance to find the optimal allocation scope of iteration numbers.
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Yugov, Vsevolod, and Itsuo Kumazawa. "Online Boosting Algorithm Based on Two-Phase SVM Training." ISRN Signal Processing 2012 (August 14, 2012): 1–8. http://dx.doi.org/10.5402/2012/740761.
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We describe and analyze a simple and effective two-step online boosting algorithm that allows us to utilize highly effective gradient descent-based methods developed for online SVM training without the need to fine-tune the kernel parameters, and we show its efficiency by several experiments. Our method is similar to AdaBoost in that it trains additional classifiers according to the weights provided by previously trained classifiers, but unlike AdaBoost, we utilize hinge-loss rather than exponential loss and modify algorithm for the online setting, allowing for varying number of classifiers. We show that our theoretical convergence bounds are similar to those of earlier algorithms, while allowing for greater flexibility. Our approach may also easily incorporate additional nonlinearity in form of Mercer kernels, although our experiments show that this is not necessary for most situations. The pre-training of the additional classifiers in our algorithms allows for greater accuracy while reducing the times associated with usual kernel-based approaches. We compare our algorithm to other online training algorithms, and we show, that for most cases with unknown kernel parameters, our algorithm outperforms other algorithms both in runtime and convergence speed.
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Ahmadi, Hamed, and Chen-Fu Chiang. "Quantum phase estimation with arbitrary constant-precision phase shift operators." Quantum Information and Computation 12, no. 9&10 (2012): 864–75. http://dx.doi.org/10.26421/qic12.9-10-9.
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While Quantum phase estimation (QPE) is at the core of many quantum algorithms known to date, its physical implementation (algorithms based on quantum Fourier transform (QFT) ) is highly constrained by the requirement of high-precision controlled phase shift operators, which remain difficult to realize. In this paper, we introduce an alternative approach to approximately implement QPE with arbitrary constant-precision controlled phase shift operators. The new quantum algorithm bridges the gap between QPE algorithms based on QFT and Kitaev's original approach. For approximating the eigenphase precise to the nth bit, Kitaev's original approach does not require any controlled phase shift operator. In contrast, QPE algorithms based on QFT or approximate QFT require controlled phase shift operators with precision of at least Pi/2n. The new approach fills the gap and requires only arbitrary constant-precision controlled phase shift operators. From a physical implementation viewpoint, the new algorithm outperforms Kitaev's approach.
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Naimipour, Naveed, Shahin Khobahi, Mojtaba Soltanalian, Haleh Safavi, and Harry C. Shaw. "Unfolded Algorithms for Deep Phase Retrieval." Algorithms 17, no. 12 (2024): 587. https://doi.org/10.3390/a17120587.
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Exploring the idea of phase retrieval has been intriguing researchers for decades due to its appearance in a wide range of applications. The task of a phase retrieval algorithm is typically to recover a signal from linear phase-less measurements. In this paper, we approach the problem by proposing a hybrid model-based, data-driven deep architecture referred to as Unfolded Phase Retrieval (UPR), which exhibits significant potential in improving the performance of state-of-the-art data-driven and model-based phase retrieval algorithms. The proposed method benefits from the versatility and interpretability of well-established model-based algorithms while simultaneously benefiting from the expressive power of deep neural networks. In particular, our proposed model-based deep architecture is applied to the conventional phase retrieval problem (via the incremental reshaped Wirtinger flow algorithm) and the sparse phase retrieval problem (via the sparse truncated amplitude flow algorithm), showing immense promise in both cases. Furthermore, we consider a joint design of the sensing matrix and the signal processing algorithm and utilize the deep unfolding technique in the process. Our numerical results illustrate the effectiveness of such hybrid model-based and data-driven frameworks and showcase the untapped potential of data-aided methodologies to enhance existing phase retrieval algorithms.
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Choi, Seong Woo. "Scheduling Algorithm for Reentrant Flexible Flow Shop with the Objective Function of Minimizing Orders’ Total Flow Time." Korean Production and Operations Management Society 34, no. 1 (2023): 129–43. http://dx.doi.org/10.32956/kopoms.2023.34.1.129.
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This study focuses on a scheduling problem in a flexible flow shop, where there are serial stages, each with identical parallel machines. We suggest heuristic algorithms for the problem to minimize the total flow time of a given set of orders. Each order is composed of multiple lots and is processed on any parallel machines at each stage. This shop has reentrant flows since products for specific orders should visit the processing stages twice. We suggest algorithms of two types. The algorithms of the first type are three-phase algorithms, and the second type's algorithm is one-phase. In the first phase of three-phase algorithms, an initial sequence is obtained using priority dispatching rules, and then a construction algorithm is used to obtain sequences for each stage in the second phase. Finally, in the third phase, lots that visit the stages for the second time are scheduled using priority dispatching rules. Computational experiments are performed on randomly generated test problems to evaluate the performance of the suggested algorithms. Results show that the order-based algorithms perform better than the lot-based algorithms and an algorithm used in practice.
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Wu, Ting, Yuling Yang, Hao Wang, et al. "Investigation of an Improved Angular Spectrum Method Based on Holography." Photonics 11, no. 1 (2023): 16. http://dx.doi.org/10.3390/photonics11010016.
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Digital holography (DH) is a novel, real-time, non-destructive, and quantitative phase-contrast imaging method that is particularly suitable for label-free live biological cell imaging and real-time dynamic monitoring. It is currently a research hotspot in the interdisciplinary field of optics and biomedical sciences, both domestically and internationally. This article proposes an improved angle spectrum algorithm based on holographic technology, which reconstructs a cellular hologram based on phase information. Optical images and chromosome cell images, reconstructed using holographic technology at different diffraction distances under the improved angle spectrum algorithm, were analyzed and compared. The optimal diffraction distance for reconstructing chromosome cell images was selected, and chromosome cell images reproduced using traditional angle spectrum algorithms, angle spectrum algorithms combined with GS, and improved angle spectrum algorithms were compared. Comparative experiments with the different models show that the proposed algorithm is superior to traditional angle spectrum algorithms in reconstructing cell images based on phase information. Furthermore, experiments have shown that images reconstructed using the improved algorithm can resolve high signal-to-noise ratio information. This algorithmic improvement provides new applications for cellular detection in clinical diagnostics and is more suitable for cell phase reconstruction in practical applications.
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Zou, Feng, Lei Wang, Xinhong Hei, Debao Chen, Qiaoyong Jiang, and Hongye Li. "Bare-Bones Teaching-Learning-Based Optimization." Scientific World Journal 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/136920.
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Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.
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Pan, Jeng-Shyang, Li-Fa Liu, Shu-Chuan Chu, Pei-Cheng Song, and Geng-Geng Liu. "A New Gaining-Sharing Knowledge Based Algorithm with Parallel Opposition-Based Learning for Internet of Vehicles." Mathematics 11, no. 13 (2023): 2953. http://dx.doi.org/10.3390/math11132953.
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Heuristic optimization algorithms have been proved to be powerful in solving nonlinear and complex optimization problems; therefore, many effective optimization algorithms have been applied to solve optimization problems in real-world scenarios. This paper presents a modification of the recently proposed Gaining–Sharing Knowledge (GSK)-based algorithm and applies it to optimize resource scheduling in the Internet of Vehicles (IoV). The GSK algorithm simulates different phases of human life in gaining and sharing knowledge, which is mainly divided into the senior phase and the junior phase. The individual is initially in the junior phase in all dimensions and gradually moves into the senior phase as the individual interacts with the surrounding environment. The main idea used to improve the GSK algorithm is to divide the initial population into different groups, each searching independently and communicating according to two main strategies. Opposite-based learning is introduced to correct the direction of convergence and improve the speed of convergence. This paper proposes an improved algorithm, named parallel opposition-based Gaining–Sharing Knowledge-based algorithm (POGSK). The improved algorithm is tested with the original algorithm and several classical algorithms under the CEC2017 test suite. The results show that the improved algorithm significantly improves the performance of the original algorithm. When POGSK was applied to optimize resource scheduling in IoV, the results also showed that POGSK is more competitive.
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Doria-Cerezo, Arnau, Victor Repecho, and Domingo Biel. "Three-Phase Phase-Locked Loop Algorithms Based on Sliding Modes." IEEE Transactions on Power Electronics 36, no. 9 (2021): 10842–51. http://dx.doi.org/10.1109/tpel.2021.3064674.
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Lu, Chang, Chuanwei Wang, and Riyi Lin. "The Development and Application of EOS-based VT Phase Behavior Calculation Algorithms in Petroleum Industry." Science Insights 41, no. 6 (2022): 697–712. http://dx.doi.org/10.15354/si.22.or028.
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The use of equation of state (EOS)-based phase behavior calculations is widespread in the petroleum industry, including the calculation of oil and gas reserves, production forecasting, and optimization of enhanced oil recovery (EOR) plans, surface separator design, and pipe flow calculation. The most commonly used method for providing phase behavior information is PT phase-equilibrium-calculation algorithms, which have been extensively studied for decades. However, simulation and engineering design of these processes using VT phase-equilibrium- calculation algorithms is sometimes more convenient than using conventional PT algorithms and has distinct advantages. The VT algorithm has been continuously improved over the last decade to ensure calculation accuracy, robustness, and efficiency, and it has been gradually applied in the petroleum industry. This article provides an overview of research findings in the field of EOS-based VT phase behavior calculation algorithms and their applications in oil and gas engineering. The Helmholtz-free-energy minimization approach, the Gibbs-free-energy minimization approach, and the nested approach based on the PT algorithm are three typical VT algorithm approaches discussed. The petroleum industry’s main applications of phase equilibrium calculation using the VT algorithm are described. Furthermore, some existing problems are identified, and several prospects for the application of the VT algorithm in the petroleum engineering field are presented. A critical review of the current state of the VT algorithm process, we believe, will fill the gap by shedding light on the process’s flaws and limitations, future development areas, and new research topics.
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Xin, Jinlong, Guisheng Liao, Zhiwei Yang, and Haoming Shen. "Ambiguity Resolution for Passive 2-D Source Localization with a Uniform Circular Array." Sensors 18, no. 8 (2018): 2650. http://dx.doi.org/10.3390/s18082650.
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This paper proposes two novel phase-based algorithms for the passive localization of a single source with a uniform circular array (UCA) under the case of measuring phase ambiguity based on two phase difference observation models, which are defined as the unambiguous-relative phase observation model (UARPOM) and the ambiguous-relative phase observation model (ARPOM). First, by analyzing the varying regularity of the phase differences between the adjacent array elements of a UCA, the corresponding relationship between the phase differences and the azimuth and elevation angle of the signal is derived. Based on the two phase observation models, two corresponding novel algorithms, namely, the phase integral accumulation and the randomized Hough transform (RHT), are addressed to resolve the phase ambiguity. Then, by using the unambiguous phase differences, the closed-form estimates of the azimuth and elevation angles are determined via a least squares (LS) algorithm. Compared with the existing phase-based methods, the proposed algorithms improve the estimation accuracy. Furthermore, our proposed algorithms are more flexible for the selection of an array radius. Such an advantage could be applied more broadly in practice than the previous methods of ambiguity resolution. Simulation results are presented to verify the effectiveness of the proposed algorithm.
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Pu, Yurui, and Chaoliang Chen. "A comparison of cross-correlation-based and phase-correlation-based image registration algorithms for optical coherence tomographic angiography." Chinese Optics Letters 22, no. 7 (2024): 071101. http://dx.doi.org/10.3788/col202422.071101.
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Qi, Zhaoshuai, Xiaojun Liu, Jingqi Pang, Yifeng Hao, Rui Hu, and Yanning Zhang. "PSNet: A Deep Learning Model-Based Single-Shot Digital Phase-Shifting Algorithm." Sensors 23, no. 19 (2023): 8305. http://dx.doi.org/10.3390/s23198305.
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In contrast to traditional phase-shifting (PS) algorithms, which rely on capturing multiple fringe patterns with different phase shifts, digital PS algorithms provide a competitive alternative to relative phase retrieval, which achieves improved efficiency since only one pattern is required for multiple PS pattern generation. Recent deep learning-based algorithms further enhance the retrieved phase quality of complex surfaces with discontinuity, achieving state-of-the-art performance. However, since much attention has been paid to understanding image intensity mapping, such as supervision via fringe intensity loss, global temporal dependency between patterns is often ignored, which leaves room for further improvement. In this paper, we propose a deep learning model-based digital PS algorithm, termed PSNet. A loss combining both local and global temporal information among the generated fringe patterns has been constructed, which forces the model to learn inter-frame dependency between adjacent patterns, and hence leads to the improved accuracy of PS pattern generation and the associated phase retrieval. Both simulation and real-world experimental results have demonstrated the efficacy and improvement of the proposed algorithm against the state of the art.
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Kim, Chyon Hae, Hiroshi Tsujino, and Shigeki Sugano. "Rapid Short-Time Path Planning for Phase Space." Journal of Robotics and Mechatronics 23, no. 2 (2011): 271–80. http://dx.doi.org/10.20965/jrm.2011.p0271.
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This paper addresses optimal motion for general machines. Approximation for optimal motion requires a global path planning algorithm that precisely calculates the whole dynamics of a machine in a brief calculation. We propose a path planning algorithm that consists of path searching and pruning algorithms. The pruning algorithmis based on our analysis of state resemblance in general phase space. To confirm precision, calculation cost, optimality and applicability of the proposed algorithm, we conducted several shortest time path planning experiments for the dynamic models of double inverted pendulums. Precision to reach the goal states of the pendulums was better than other algorithms. Calculation cost was 58 times faster at least. We could tune optimality of proposed algorithm via resolution parameters. A positive correlation between optimality and resolutions was confirmed. Applicability was confirmed in a torque based position and velocity feedback control simulation. As a result of this simulation, the double inverted pendulums tracked planned motion under noise while keeping within torque limitations.
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Shen, Qian, and Wenbo Liu. "A Novel Digital Image Encryption Algorithm Based on Orbit Variation of Phase Diagram." International Journal of Bifurcation and Chaos 27, no. 13 (2017): 1750204. http://dx.doi.org/10.1142/s0218127417502042.
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Chaotic systems have been widely used in digital image encryption algorithms because of their characteristics of deterministic randomness, extreme sensitivity to initial values, etc. Although these chaos-based algorithms are good at performance in general, most of them are ineffective when confronting attacks such as the chosen plain image attack. So, this paper proposes a new digital image encryption algorithm based on orbit variation of phase diagram (AOVPD), which modifies the iterative sequence of chaotic system with the pixel values of plain image. Theoretical analysis proves that the proposed AOVPD has the ability to resist chosen plain image attack within two rounds of operation, while the existing algorithms could be cracked in the same situation. To be specific, AOVPD is effective when confronting various attacks including chosen plain image attack. Also, simulation results show that the proposed algorithm has an outstanding safety performance.
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Xu, Jingjiang, Shaozhen Song, Yuandong Li, and Ruikang K. Wang. "Complex-based OCT angiography algorithm recovers microvascular information better than amplitude- or phase-based algorithms in phase-stable systems." Physics in Medicine & Biology 63, no. 1 (2017): 015023. http://dx.doi.org/10.1088/1361-6560/aa94bc.
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Tu, Yaqing, Ting’ao Shen, Haitao Zhang, and Ming Li. "Two New Sliding DTFT Algorithms for Phase Difference Measurement Based on a New Kind of Windows." Measurement Science Review 14, no. 6 (2014): 350–56. http://dx.doi.org/10.2478/msr-2014-0048.
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Abstract For the ultra-low frequency signals or adjacent Nyquist frequency signals, which exist in the vibration engineering domain, the traditional DTFT-based algorithm shows serious bias for phase difference measurement. It is indicated that the spectrum leakage and negative frequency contribution are the essential causes of the bias. In order to improve the phase difference measurement accuracy of the DTFT-based algorithm, two new sliding DTFT algorithms for phase difference measurement based on a new kind of windows are proposed, respectively. Firstly, the new kind of windows developed by convolving conventional rectangular windows is introduced, which obtains a stronger inhibition of spectrum leakage. Then, with negative frequency contribution considered, two new formulas for phase difference calculation under the new kind of windows are derived in detail. Finally, the idea of sliding recursive is proposed to decrease the computational load. The proposed algorithms are easy to be realized and have a higher accuracy than the traditional DTFT-based algorithm. Simulations and engineering applications verified the feasibility and effectiveness of the proposed algorithms.
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Al-Momani, Mohammad, Seba Al-Gharaibeh, Ali Al-Dmour, and Allaham Ahmed. "Islanding Detection Method Based Artificial Neural Network." Jordan Journal of Energy 1, no. 1 (2022): 19–36. http://dx.doi.org/10.35682/jje.v1i1.3.
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This paper presents a new islanding detection technique based on an artificial neural network (ANN) for a doubly fed induction wind turbine (DFIG). This technique takes advantage of ANN as pattern classifiers. Five different ANN systems are presented in this paper based on various inputs: three phase power, phase voltage, phase current, neutral voltage, and neutral current. An ANN structure is trained for each input, and the comparison between the different structures is presented. Feedforward ANN structures are used for the five systems. Three different learning algorithms are used: backpropagation and two artificial optimization techniques: Genetic Algorithm (GA) and Cuckoo optimization algorithm. For each method in each training technique, the results and the cost function are presented. The comparison of different inputs different algorithms is conducted. MATLAB 2020a is used to simulate the ANN structure and code the training algorithms. A detailed discussion of the input sample rate has also been manipulated to make the computational burden a factor in assessing the performance.
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Zhang Zhihui, 张志会, 王华英 Wang Huaying, 刘佐强 Liu Zuoqiang, et al. "Phase Unwrapping Algorithms Based on Fast Fourier Transform." Laser & Optoelectronics Progress 49, no. 12 (2012): 120902. http://dx.doi.org/10.3788/lop49.120902.
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Cozzi, A., B. Crespi, F. Valentinotti, and F. Wörgötter. "Performance of phase-based algorithms for disparity estimation." Machine Vision and Applications 9, no. 5-6 (1997): 334–40. http://dx.doi.org/10.1007/s001380050052.
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Ikotun, Abiodun M., and Absalom E. Ezugwu. "Enhanced Firefly-K-Means Clustering with Adaptive Mutation and Central Limit Theorem for Automatic Clustering of High-Dimensional Datasets." Applied Sciences 12, no. 23 (2022): 12275. http://dx.doi.org/10.3390/app122312275.
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Metaheuristic algorithms have been hybridized with the standard K-means to address the latter’s challenges in finding a solution to automatic clustering problems. However, the distance calculations required in the standard K-means phase of the hybrid clustering algorithms increase as the number of clusters increases, and the associated computational cost rises in proportion to the dataset dimensionality. The use of the standard K-means algorithm in the metaheuristic-based K-means hybrid algorithm for the automatic clustering of high-dimensional real-world datasets poses a great challenge to the clustering performance of the resultant hybrid algorithms in terms of computational cost. Reducing the computation time required in the K-means phase of the hybrid algorithm for the automatic clustering of high-dimensional datasets will inevitably reduce the algorithm’s complexity. In this paper, a preprocessing phase is introduced into the K-means phase of an improved firefly-based K-means hybrid algorithm using the concept of the central limit theorem to partition the high-dimensional dataset into subgroups of randomly formed subsets on which the K-means algorithm is applied to obtain representative cluster centers for the final clustering procedure. The enhanced firefly algorithm (FA) is hybridized with the CLT-based K-means algorithm to automatically determine the optimum number of cluster centroids and generate corresponding optimum initial cluster centroids for the K-means algorithm to achieve optimal global convergence. Twenty high-dimensional datasets from the UCI machine learning repository are used to investigate the performance of the proposed algorithm. The empirical results indicate that the hybrid FA-K-means clustering method demonstrates statistically significant superiority in the employed performance measures and reducing computation time cost for clustering high-dimensional dataset problems, compared to other advanced hybrid search variants.
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Guo, Long Yuan, Chang Yin Sun, Guo Yun Zhang, and Jian Hui Wu. "Variable Window Stereo Matching Based on Phase Congruency." Applied Mechanics and Materials 380-384 (August 2013): 3998–4001. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3998.
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The major challenge in area stereo matching algorithms is to find appropriate window size and shape. Phase congruency is robust to noise and can reflects the gray changes. With these benefits, the paper first detects image and determines pixels characteristics according to phase congruency. Then, different window will be used to matching according to different feature pixels. After using cost function which combined with non-parametric measure and gray value, the final disparity map will be obtained. The result of experiment indicates that the algorithm can generates more accurate disparity map.
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Chen, Ju, Yuan Gao, Mohd Shareduwan Mohd Kasihmuddin, et al. "MTS-PRO2SAT: Hybrid Mutation Tabu Search Algorithm in Optimizing Probabilistic 2 Satisfiability in Discrete Hopfield Neural Network." Mathematics 12, no. 5 (2024): 721. http://dx.doi.org/10.3390/math12050721.
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The primary objective of introducing metaheuristic algorithms into traditional systematic logic is to minimize the cost function. However, there is a lack of research on the impact of introducing metaheuristic algorithms on the cost function under different proportions of positive literals. In order to fill in this gap and improve the efficiency of the metaheuristic algorithm in systematic logic, we proposed a metaheuristic algorithm based on mutation tabu search and embedded it in probabilistic satisfiability logic in discrete Hopfield neural networks. Based on the traditional tabu search algorithm, the mutation operators of the genetic algorithm were combined to improve its global search ability during the learning phase and ensure that the cost function of the systematic logic converged to zero at different proportions of positive literals. Additionally, further optimization was carried out in the retrieval phase to enhance the diversity of solutions. Compared with nine other metaheuristic algorithms and exhaustive search algorithms, the proposed algorithm was superior to other algorithms in terms of time complexity and global convergence, and showed higher efficiency in the search solutions at the binary search space, consolidated the efficiency of systematic logic in the learning phase, and significantly improved the diversity of the global solution in the retrieval phase of systematic logic.
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Muñoz, Mario A., and Kate A. Smith-Miles. "Performance Analysis of Continuous Black-Box Optimization Algorithms via Footprints in Instance Space." Evolutionary Computation 25, no. 4 (2017): 529–54. http://dx.doi.org/10.1162/evco_a_00194.
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This article presents a method for the objective assessment of an algorithm’s strengths and weaknesses. Instead of examining the performance of only one or more algorithms on a benchmark set, or generating custom problems that maximize the performance difference between two algorithms, our method quantifies both the nature of the test instances and the algorithm performance. Our aim is to gather information about possible phase transitions in performance, that is, the points in which a small change in problem structure produces algorithm failure. The method is based on the accurate estimation and characterization of the algorithm footprints, that is, the regions of instance space in which good or exceptional performance is expected from an algorithm. A footprint can be estimated for each algorithm and for the overall portfolio. Therefore, we select a set of features to generate a common instance space, which we validate by constructing a sufficiently accurate prediction model. We characterize the footprints by their area and density. Our method identifies complementary performance between algorithms, quantifies the common features of hard problems, and locates regions where a phase transition may lie.
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Zhao, Tieyu, and Yingying Chi. "Modified Gerchberg–Saxton (G-S) Algorithm and Its Application." Entropy 22, no. 12 (2020): 1354. http://dx.doi.org/10.3390/e22121354.
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The Gerchberg–Saxton (G-S) algorithm is a phase retrieval algorithm that is widely used in beam shaping and optical information processing. However, the G-S algorithm has difficulty obtaining the exact solution after iterating, and an approximate solution is often obtained. In this paper, we propose a series of modified G-S algorithms based on the Fresnel transform domain, including the single-phase retrieval (SPR) algorithm, the double-phase retrieval (DPR) algorithm, and the multiple-phase retrieval (MPR) algorithm. The analysis results show that the convergence of the SPR algorithm is better than that of the G-S algorithm, but the exact solution is not obtained. The DPR and MPR algorithms have good convergence and can obtain exact solutions; that is, the information is recovered losslessly. We discuss the security advantages and verification reliability of the proposed algorithms in image encryption. A multiple-image encryption scheme is proposed, in which n plaintexts can be recovered from n ciphertexts, which greatly improves the efficiency of the system. Finally, the proposed algorithms are compared with the current phase retrieval algorithms, and future applications are discussed. We hope that our research can provide new ideas for the application of the G-S algorithm.
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Liu, Zhen, Xin Chen, Zhenhua Wei, Tianpeng Liu, Linlin Li, and Bo Peng. "Ambiguity Analysis and Resolution for Phase-Based 3D Source Localization under Given UCA." International Journal of Antennas and Propagation 2019 (April 18, 2019): 1–12. http://dx.doi.org/10.1155/2019/4743829.
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Under uniform circular array, by employing some algebraic schemes to exploit the phase information of receiving data and further estimate the source’s three-dimensional (3D) parameters (azimuth angle, elevation angle, and range), a series of novel phase-based algorithms with low computational complexity have been proposed recently. However, when the array diameter is larger than source’s half-wavelength, these algorithms would suffer from phase ambiguity problem. Even so, there always exist certain positions, where the source’s parameters can still be determined with nonambiguity. Therefore, this paper first investigates the zone of ambiguity-free source 3D localization using phase-based algorithms. For the ambiguous zone, a novel ambiguity resolution algorithm named ambiguity traversing and cosine matching (ATCM) is presented. In ATCM, the phase differences of centrosymmetric sensors under different ambiguities are utilized to match a cosine function with sensor number-varying, and the source’s unambiguous rough angles can be derived from amplitude and initial phase of the cosine function. Then, the unambiguous angles are employed to resolve the phase ambiguity of the phase-based 3D parameter estimation algorithm, and the source’s range as well as more precise angles can be achieved. Theoretical analyses and numerical examples show that, apart from array diameter and source’s frequency, the sensor number and spacing of employed sensors are two key factors determining the unambiguous zone. Moreover, simulation results demonstrate the effectiveness and satisfactory performance of our proposed ambiguity resolution algorithm.
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Zhang, Songsong, and Haisong Huang. "Step Surface Profile Measurement Based on Fringe Projection Phase-Shifting Using Selective Sampling." Photonics 8, no. 12 (2021): 592. http://dx.doi.org/10.3390/photonics8120592.
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Fringe projection is a non-contact optical method that is widely used in the optical precision measurement of complex stepped surfaces. However, the accuracy of the fringe phase extraction employed has a direct impact on the measurement precision of the surface shape. Where phase-shifting measurement is used, the classical equal step phase extraction algorithm can only be used to measure simple and smooth surfaces, and leads to measurement errors on complex stepped surfaces, which affects the accuracy of the phase extraction. In addition, the iterative process lasts for a long time, resulting in a low efficiency. This paper proposes a step-by-step phase-shifting extraction algorithm based on selective sampling to measure the contour of the stepped surface. Firstly, the fringe pattern is sampled at equal intervals to reduce the iterative calculation time. Finally, the accurate measurement phase is calculated by the alternating iteration method. The phase extraction accuracy and iteration times are compared in experimental measurements between classical iterative algorithms such as four-step phase-shifting algorithms and the variable phase shift phase interpolation algorithm based on selective sampling. It is shown that the variable frequency phase-shifting extraction algorithm based on selective sampling has a shorter operation time, smaller error, and higher accuracy than the traditional iterative algorithm in fringe projection measuring complex stepped surfaces.
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Zhang, Yinan, Guangxue Wang, Shirui Peng, Yi Leng, Guowen Yu, and Bingqie Wang. "Near-Field Beamforming Algorithms for UAVs." Sensors 23, no. 13 (2023): 6172. http://dx.doi.org/10.3390/s23136172.
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This study presents three distributed beamforming algorithms to address the challenges of positioning and signal phase errors in unmanned aerial vehicle (UAV) arrays that hinder effective beamforming. Firstly, the array’s received signal phase error model was analyzed under near-field conditions. In the absence of navigation data, a beamforming algorithm based on the Extended Kalman Filter (EKF) was proposed. In cases where navigation data were available, Taylor expansion was utilized to simplify the model, the non-Gaussian noise of the compensated received signal phase was approximated to Gaussian noise, and the noise covariance matrix in the Kalman Filter (KF) was estimated. Then, a beamforming algorithm based on KF was developed. To further estimate the Gaussian noise distribution of the received signal phase, the noise covariance matrix was iteratively estimated using unscented transformation (UT), and here, a beamforming algorithm based on the Unscented Kalman Filter (UKF) was proposed. The proposed algorithms were validated through simulations, illustrating their ability to suppress the malign effects of errors on near-field UAV array beamforming. This study provides a reference for the implementation of UAV array beamforming under varying conditions.
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Pereira, Leonardo T., and Claudio F. M. Toledo. "Speeding up Search-Based Algorithms for Level Generation in Physics-Based Puzzle Games." International Journal on Artificial Intelligence Tools 26, no. 05 (2017): 1760019. http://dx.doi.org/10.1142/s0218213017600193.
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This research uses Machine Learning (ML) techniques in order to aid search-based (SB) algorithms to improve level generation for physics-based puzzle games. These algorithms’ performance are improved by reducing simulation time when the ML techniques are applied. Classification algorithms prevent levels with undesired traits to be evaluated during the simulation phase of the SB procedures. An Angry Birds clone is used for conducting the experiments and results report improvement using the combined approach against every SB algorithm by itself.
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Zhen, Tao, Lei Yan, and Peng Yuan. "Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm." Algorithms 12, no. 12 (2019): 253. http://dx.doi.org/10.3390/a12120253.
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Gait phase detection is a new biometric method which is of great significance in gait correction, disease diagnosis, and exoskeleton assisted robots. Especially for the development of bone assisted robots, gait phase recognition is an indispensable key technology. In this study, the main characteristics of the gait phases were determined to identify each gait phase. A long short-term memory-deep neural network (LSTM-DNN) algorithm is proposed for gate detection. Compared with the traditional threshold algorithm and the LSTM, the proposed algorithm has higher detection accuracy for different walking speeds and different test subjects. During the identification process, the acceleration signals obtained from the acceleration sensors were normalized to ensure that the different features had the same scale. Principal components analysis (PCA) was used to reduce the data dimensionality and the processed data were used to create the input feature vector of the LSTM-DNN algorithm. Finally, the data set was classified using the Softmax classifier in the full connection layer. Different algorithms were applied to the gait phase detection of multiple male and female subjects. The experimental results showed that the gait-phase recognition accuracy and F-score of the LSTM-DNN algorithm are over 91.8% and 92%, respectively, which is better than the other three algorithms and also verifies the effectiveness of the LSTM-DNN algorithm in practice.
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Guha, Siddharth, Abdalla Ibrahim, Qian Wu, et al. "Machine learning-based identification of contrast-enhancement phase of computed tomography scans." PLOS ONE 19, no. 2 (2024): e0294581. http://dx.doi.org/10.1371/journal.pone.0294581.
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Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew’s correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.
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Hang Ho, Yuen, Humaira Nisar, and Muhammad Burhan Khan. "Segmentation of Activated Sludge Filaments using Phase Contrast Images." Oriental journal of computer science and technology 11, no. 3 (2018): 145–53. http://dx.doi.org/10.13005/ojcst11.03.03.
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Segmentation algorithms play an important role in image processing and analysis. The identification of objects and process monitoring strongly depends on the accuracy of the segmentation algorithms. Waste water treatment plants are used to treat wastewater from municipal and industrial plants. Activated sludge process is used in wastewater treatment plants to biodegrade the organic constituents present in waste water. This biodegradation is done with the help of microorganisms and bacteria. There are two important types of microscopic organisms present in the activated sludge plants, named as flocs as filaments, which are visible under microscope. In this paper we study the microscopic images of wastewater using phase contrast microscopy. The images are acquired from wastewater sample using a microscope. The samples of wastewater are collected from domestic wastewater treatment plant aeration tank. Our main aim is to segment threadlike organisms knows as filaments. Several segmentation algorithms (such as edge based algorithm, k-means algorithm, texture based algorithm, and watershed algorithm) will be explored and their performance will be compared using gold approximations of the images. The performance of the algorithms are evaluated using different performance metrics, such as Rand Index, specificity, variation of information, and accuracy. We have found that edge based segmentation works well for phase contrast microscopic images of activated sludge wastewater.
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Lee, Haemin, Chang-Sik Jung, and Ki-Wan Kim. "Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images." Sensors 21, no. 7 (2021): 2370. http://dx.doi.org/10.3390/s21072370.
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Autofocus is an essential technique for airborne synthetic aperture radar (SAR) imaging to correct phase errors mainly due to unexpected motion error. There are several well-known conventional autofocus methods such as phase gradient autofocus (PGA) and minimum entropy (ME). Although these methods are still widely used for various SAR applications, each method has drawbacks such as limited bandwidth of estimation, low convergence rate, huge computation burden, etc. In this paper, feature preserving autofocus (FPA) algorithm is newly proposed. The algorithm is based on the minimization of the cost function containing a regularization term. The algorithm is designed for postprocessing purpose, which is different from the existing regularization-based algorithms such as sparsity-driven autofocus (SDA). This difference makes the proposed method far more straightforward and efficient than those existing algorithms. The experimental results show that the proposed algorithm achieves better performance, convergence, and robustness than the existing postprocessing autofocus algorithms.
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Hou, Shuai, Yujiao Li, Meijuan Bai, et al. "Phase Prediction of High-Entropy Alloys by Integrating Criterion and Machine Learning Recommendation Method." Materials 15, no. 9 (2022): 3321. http://dx.doi.org/10.3390/ma15093321.
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The comprehensive properties of high-entropy alloys (HEAs) are highly-dependent on their phases. Although a large number of machine learning (ML) algorithms has been successfully applied to the phase prediction of HEAs, the accuracies among different ML algorithms based on the same dataset vary significantly. Therefore, selection of an efficient ML algorithm would significantly reduce the number and cost of the experiments. In this work, phase prediction of HEAs (PPH) is proposed by integrating criterion and machine learning recommendation method (MLRM). First, a meta-knowledge table based on characteristics of HEAs and performance of candidate algorithms is established, and meta-learning based on the meta-knowledge table is adopted to recommend an algorithm with desirable accuracy. Secondly, an MLRM based on improved meta-learning is engineered to recommend a more desirable algorithm for phase prediction. Finally, considering poor interpretability and generalization of single ML algorithms, a PPH combining the advantages of MLRM and criterion is proposed to improve the accuracy of phase prediction. The PPH is validated by 902 samples from 12 datasets, including 405 quinary HEAs, 359 senary HEAs, and 138 septenary HEAs. The experimental results shows that the PPH achieves performance than the traditional meta-learning method. The average prediction accuracy of PPH in all, quinary, senary, and septenary HEAs is 91.6%, 94.3%, 93.1%, and 95.8%, respectively.
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Li, Qing Qian, and Yong Hui Zhang. "An Optimization Algorithm of Phase Angle Calculation Based on the FPGA Implementation." Applied Mechanics and Materials 513-517 (February 2014): 439–43. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.439.
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An optimization algorithm of phase angle calculation based on FPGA implementation is mainly described in this paper .From two arctangent function calculation algorithms analysis, we combine the strengths of them, and propose a optimized algorithm of phase angle calculation. The theoretical analysis and hardware validation shows that the algorithm has good performances such as high accuracy, good real-time performance, less resource consumption. At the same time, this algorithm can be comprehensive, and can be considered as an IP core to realize the high-speed, parallel calculation of transcendental function.
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Pedroza, Angel David, José I. De la Rosa, Rogelio Rosas, et al. "Acoustic Individual Identification in Birds Based on the Band-Limited Phase-Only Correlation Function." Applied Sciences 10, no. 7 (2020): 2382. http://dx.doi.org/10.3390/app10072382.
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A new technique based on the Band-Limited Phase-Only Correlation (BLPOC) function to deal with acoustic individual identification is proposed in this paper. This is a biometric technique suitable for limited data individual bird identification. The main advantage of this new technique, in contrast to traditional algorithms where the use of large-scale datasets is assumed, is its ability to identify individuals by the use of only two samples from the bird species. The proposed technique has two variants (depending on the method used to analyze and extract the bird vocalization from records): automatic individual verification algorithm and semi-automatic individual verification algorithm. The evaluation of the automatic algorithm shows an average precision that is over 80% for the identification comparatives. It is shown that the efficiencies of the algorithms depend on the complexity of the vocalizations.
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Yao, Liguo, Guanghui Li, Panliang Yuan, Jun Yang, Dongbin Tian, and Taihua Zhang. "Reptile Search Algorithm Considering Different Flight Heights to Solve Engineering Optimization Design Problems." Biomimetics 8, no. 3 (2023): 305. http://dx.doi.org/10.3390/biomimetics8030305.
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The reptile search algorithm is an effective optimization method based on the natural laws of the biological world. By restoring and simulating the hunting process of reptiles, good optimization results can be achieved. However, due to the limitations of natural laws, it is easy to fall into local optima during the exploration phase. Inspired by the different search fields of biological organisms with varying flight heights, this paper proposes a reptile search algorithm considering different flight heights. In the exploration phase, introducing the different flight altitude abilities of two animals, the northern goshawk and the African vulture, enables reptiles to have better search horizons, improve their global search ability, and reduce the probability of falling into local optima during the exploration phase. A novel dynamic factor (DF) is proposed in the exploitation phase to improve the algorithm’s convergence speed and optimization accuracy. To verify the effectiveness of the proposed algorithm, the test results were compared with ten state-of-the-art (SOTA) algorithms on thirty-three famous test functions. The experimental results show that the proposed algorithm has good performance. In addition, the proposed algorithm and ten SOTA algorithms were applied to three micromachine practical engineering problems, and the experimental results show that the proposed algorithm has good problem-solving ability.
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Gelchinsky, B., E. Landa, and V. Shtivelman. "Algorithms of phase and group correlation." GEOPHYSICS 50, no. 4 (1985): 596–608. http://dx.doi.org/10.1190/1.1441935.
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In this paper we consider algorithms of phase and group correlation which are based on different assumptions regarding the character of wave field. In order to construct the correlation algorithm, the wave field is presented as a product of an envelope and a normalized seismogram. Phase correlation is performed on the normalized seismogram, while group correlation is performed on the perigram, a low cut version of the envelope function. The central point of the correlation algorithm is the construction of a functional which characterizes the main correlation properties of the wave field. This functional is computed for different values of parameters which appear in the expressions approximating phase and group traveltime curves. Several types of correlation functionals are considered. The next step of the correlation algorithm is analysis of the previously obtained functionals; this is performed using a system of inequalities based on a number of assumptions regarding the properties of wave fields. The results of this analysis permit us to make a decision regarding the presence of a signal and to estimate the parameters of detected waves. Examples illustrating application of the proposed algorithm to synthetic and field data are presented.
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Li, Jiaoyang, Kexuan Sun, Hang Ma, Ariel Felner, T. K. Kumar, and Sven Koenig. "Moving Agents in Formation in Congested Environments." Proceedings of the International Symposium on Combinatorial Search 11, no. 1 (2021): 131–32. http://dx.doi.org/10.1609/socs.v11i1.18525.
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In this paper, we formalize and study the Moving Agents in Formation (MAiF) problem, that combines the tasks of finding short collision-free paths for multiple agents and keeping them in close adherence to a desired formation. Previous work includes controller-based algorithms, swarm-based algorithms, and potential-field-based algorithms. They usually focus on only one or the other of these tasks, solve the problem greedily without systematic search, and thus generate costly solutions or even fail to find solutions in congested environment. In this paper, we develop a two-phase search algorithm, called SWARM-MAPF, whose first phase is inspired by swarm-based algorithms (in open regions) and whose second phase is inspired by multi-agent path-finding (MAPF) algorithms (in congested regions). In the first phase, SWARM-MAPF selects a leader among the agents and finds a path for it that is sufficiently far away from the obstacles so that the other agents can preserve the desired formation around it. It also identifies the critical segments of the leader's path where the other agents cannot preserve the desired formation and the refinement of which has thus to be delegated to the second phase. In the second phase, SWARM-MAPF refines these segments. Theoretically, we prove that SWARM-MAPF is complete. Empirically, we show that SWARM-MAPF scales well and is able to find close-to-optimal solutions.
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Zhang, Xiaoyu, Genxiang Chen, and Qi Zhang. "LCOS-SLM Based Intelligent Hybrid Algorithm for Beam Splitting." Electronics 11, no. 3 (2022): 428. http://dx.doi.org/10.3390/electronics11030428.
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The iterative Fourier transform algorithm (IFTA) is widely used in various optical communication applications based on liquid crystal on silicon spatial light modulators. However, the traditional iterative method has many disadvantages, such as a poor effect, an inability to select an optimization direction, and the failure to consider zero padding or phase quantization. Moreover, after years of development, the emergence of various variant algorithms also makes it difficult for researchers to choose one. In this paper, a new intelligent hybrid algorithm that combines the IFTA and differential evolution algorithm is proposed in a novel way. The reliability of the proposed algorithm is verified by beam splitting, and the IFTA and symmetrical IFTA algorithms, for comparison, are introduced. The hybrid algorithm improves the defects above while considering the zero padding and phase quantization of a computer-generated hologram, which optimizes the directional optimization in the diffraction efficiency and the fidelity of the output beam and improves the results of these two algorithms. As a result, the engineers’ trouble in the selection of an algorithm has also been reduced.
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Ameer Noory, Zainab, and Raaed F. Hassan. "Performance Comparison of Five-Level Active Neutral Point Converter Based on Phase Disposition-PWM and Alternate Phase Opposition Disposition-PWM." ITM Web of Conferences 50 (2022): 03001. http://dx.doi.org/10.1051/itmconf/20225003001.
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The work in this paper presents the performance analysis of the reduced component count converter which is the 5-Level Active Neutral Point Converter (5LANPC). This 5-level converter has been configured by stacking the traditional 3-Level Neutral Point Converter with the Flying Capacitor converter. Two types of control algorithms were considered and compared to explore the performance of the 5LANPC. The first algorithm was based on the Phase-Disposition-Pulse Width Modulation (PD-PWM), while the second one was based on the Alternate Phase Opposition Disposition-Pulse Width Modulation (APOD-PWM). These algorithms are used to determine the required voltage level and according to the required level the state of the switches is selected through a simplified voltage balance algorithm. This voltage balance algorithm deals with the redundant switching states to maintain the voltages of the 5LANPC capacitors at a specified level. The comparison between these two modulation strategies was performed by simulation based on MATLAB/Simulink package. Simulation results showed compelling outcomes involving the two techniques concerning the voltage and current characteristics, as well as the equilibrium in the capacitor voltages. By comparing the simulation results, it was found that the performance of the system is relatively better using the PD-PWM strategy.
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Burkov, Artem, Andrey Turlikov, and Roman Rachugin. "Analyzing and stabilizing multichannel ALOHA with the use of the preamble-based exploration phase." Information and Control Systems, no. 5 (October 28, 2022): 49–59. http://dx.doi.org/10.31799/1684-8853-2022-5-49-59.
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Introduction: Internet of Things devices are actively used within the framework of Massive Machine-Type Communication scenarios. The interaction of devices is carried out by random multiple-access algorithms with limited throughput. To improve throughput one can use orthogonal preambles in the ALOHA-type class of algorithms. Purpose: To analyze ALOHA-based algorithms using the exploration phase and to calculate the characteristics for the algorithm with and without losses with a finite number of channels. Results: We have described a system model that employs random access for data transmission over a common communication channel with the use of orthogonal preambles and exploration phase. We have obtained a formula for numerical calculation of the throughput of an algorithm channel with losses with an infinite number of preambles and a given finite number of channels. The calculation results for several values of the number of independent channels are presented. A modification of the algorithm using the exploration phase and repeated transmissions is proposed and described. The system in question can work without losses. For this system, we have given the analysis of the maximum input throughput up to which the system operates stably. Also, the average delay values for the algorithm that were obtained by simulation modeling are shown. By reducing the number of available preambles, the results obtained can be used as an upper bound on the system throughput. Practical relevance: The results obtained allow to assess the potential for improving the throughput of random multiple-access systems in 6G networks through the application of the exploration phase.
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Wu, Zong-Sheng, Wei-Ping Fu, and Ru Xue. "Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem." Computational Intelligence and Neuroscience 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/292576.
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Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well.
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Wang Huaying, 王华英, 于梦杰 Yu Mengjie, 刘飞飞 Liu Feifei, and 刘佐强 Liu Zuoqiang. "Four phase unwrapping algorithms based on fast Fourier transform." High Power Laser and Particle Beams 25, no. 5 (2013): 1129–33. http://dx.doi.org/10.3788/hplpb20132505.1129.
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Liu, Qian, Wen Huang, and Xiaobin Yue. "Dual-mode phase-shifting interferometry based on iterative algorithms." Physica Scripta 94, no. 2 (2019): 025501. http://dx.doi.org/10.1088/1402-4896/aaf532.
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Pu-Cha, Zhong, and Bao Wan-Su. "Research on Quantum Searching Algorithms Based on Phase Shifts." Chinese Physics Letters 25, no. 8 (2008): 2774–77. http://dx.doi.org/10.1088/0256-307x/25/8/011.
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Fayyaz, Zahra, Nafiseh Mohammadian, M. Reza Rahimi Tabar, Rayyan Manwar, and Kamran Avanaki. "A comparative study of optimization algorithms for wavefront shaping." Journal of Innovative Optical Health Sciences 12, no. 04 (2019): 1942002. http://dx.doi.org/10.1142/s1793545819420021.
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By manipulating the phase map of a wavefront of light using a spatial light modulator, the scattered light can be sharply focused on a specific target. Several iterative optimization algorithms for obtaining the optimum phase map have been explored. However, there has not been a comparative study on the performance of these algorithms. In this paper, six optimization algorithms for wavefront shaping including continuous sequential, partitioning algorithm, transmission matrix estimation method, particle swarm optimization, genetic algorithm (GA), and simulated annealing (SA) are discussed and compared based on their efficiency when introduced with various measurement noise levels.
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Wang, Yong, Wen Wang, Mu Zhou, Aihu Ren, and Zengshan Tian. "Remote Monitoring of Human Vital Signs Based on 77-GHz mm-Wave FMCW Radar." Sensors 20, no. 10 (2020): 2999. http://dx.doi.org/10.3390/s20102999.
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In recent years, non-contact radar detection technology has been able to achieve long-term and long-range detection for the breathing and heartbeat signals. Compared with contact-based detection methods, it brings a more comfortable and a faster experience to the human body, and it has gradually received attention in the field of radar sensing. Therefore, this paper extends the application of millimeter-wave radar to the field of health care. The millimeter-wave radar first transmits the frequency-modulated continuous wave (FMCW) and collects the echo signals of the human body. Then, the phase information of the intermediate frequency (IF) signals including the breathing and heartbeat signals are extracted, and the Direct Current (DC) offset of the phase information is corrected using the circle center dynamic tracking algorithm. The extended differential and cross-multiply (DACM) is further applied for phase unwrapping. We propose two algorithms, namely the compressive sensing based on orthogonal matching pursuit (CS-OMP) algorithm and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) algorithm, to separate and reconstruct the breathing and heartbeat signals. Then, a frequency-domain fast Fourier transform and a time-domain autocorrelation estimation algorithm are proposed to calculate the respiratory and heartbeat rates. The proposed algorithms are compared with the contact-based detection ones. The results demonstrate that the proposed algorithms effectively suppress the noise and harmonic interference, and the accuracies of the proposed algorithms for both respiratory rate and heartbeat rate reach about 93%.
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Khabarlak, K. S. "FASTER OPTIMIZATION-BASED META-LEARNING ADAPTATION PHASE." Radio Electronics, Computer Science, Control, no. 1 (April 7, 2022): 82. http://dx.doi.org/10.15588/1607-3274-2022-1-10.
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Context. Neural networks require a large amount of annotated data to learn. Meta-learning algorithms propose a way to decrease number of training samples to only a few. One of the most prominent optimization-based meta-learning algorithms is MAML. However, its adaptation to new tasks is quite slow. The object of study is the process of meta-learning and adaptation phase as defined by the MAML algorithm.Objective. The goal of this work is creation of an approach, which should make it possible to: 1) increase the execution speed of MAML adaptation phase; 2) improve MAML accuracy in certain cases. The testing results will be shown on a publicly available few-shot learning dataset CIFAR-FS.Method. In this work an improvement to MAML meta-learning algorithm is proposed. Meta-learning procedure is defined in terms of tasks. In case of image classification problem, each task is to try to learn to classify images of new classes given only a few training examples. MAML defines 2 stages for the learning procedure: 1) adaptation to the new task; 2) meta-weights update. The whole training procedure requires Hessian computation, which makes the method computationally expensive. After being trained, the network will typically be used for adaptation to new tasks and the subsequent prediction on them. Thus, improving adaptation time is an important problem, which we focus on in this work. We introduce lambda pattern by which we restrict which weight we update in the network during the adaptation phase. This approach allows us to skip certain gradient computations. The pattern is selected given an allowed quality degradation threshold parameter. Among the pattern that fit the criteria, the fastest pattern is then selected. However, as it is discussed later, quality improvement is also possible is certain cases by a careful pattern selection.Results. The MAML algorithm with lambda pattern adaptation has been implemented, trained and tested on the open CIFAR-FS dataset. This makes our results easily reproducible.Conclusions. The experiments conducted have shown that via lambda adaptation pattern selection, it is possible to significantly improve the MAML method in the following areas: adaptation time has been decreased by a factor of 3 with minimal accuracy loss. Interestingly, accuracy for one-step adaptation has been substantially improved by using lambda patterns as well. Prospects for further research are to investigate a way of a more robust automatic pattern selection scheme.