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Design method of optical detection systems based on transimpedance amplifiers

REVIEW OF SCIENTIFIC INSTRUMENTS 2021年第11期92卷 114704-114704页

作者： Vazquez, Patricia M. E. Veiras, Francisco E. Ciocci Brazzano, Ligia Sorichetti, Patricio A. Univ Buenos Aires Fac Ingn Dept Fis GLOmAe Paseo Colon 850C1063ACV Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Argentina CONICE Godoy Cruz 2290C1425FQB Buenos Aires DF Argentina

Designing detection systems based on transimpedance amplifiers is a complex task because noise, frequency response, and stability are coupled constraints. This work presents a straightforward design method of detection systems based on transimpedance amplifiers. We take into account the objectives, scope of the design, and requirements and specifications, including the input signal levels. According to the small-signal model, the noise and stability are analyzed in detail. We present a systematic procedure to search for the acceptable values of the feedback network components based on these analyses. Then, we define a merit function to compare the performance of the acceptable combinations of feedback network components. For every acceptable combination, the function gives a quantitative measure of the degree of compliance for each design parameter: signal-to-noise ratio, highest operating frequency, and phase margin. As an example, we apply the method to optimize the design of an optical detection system using a PIN photodiode and a low-noise operational amplifier.

关键词： Topics Operational amplifier Electronic noise Optoelectronic devices optimization algorithms Detectors Photodiodes Photodetectors Analog circuits Electronic circuits Transimpedance amplifiers

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Data Delivery in a Disaster or Quarantined Area Divided into Triangles Using DTN-Based algorithms for Unmanned Aerial Vehicles

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SENSORS 2021年第11期21卷 3572-3572页

作者： Udroiu, Razvan Deaconu, Adrian Marius Nanau, Corina-Stefania Transilvania Univ Brasov Dept Mfg Engn 29 Eroilor Blvd Brasov 500036 Romania Transilvania Univ Brasov Dept Math & Comp Sci 29 Eroilor Blvd Brasov 500036 Romania

The communication in quarantined areas, e.g., due to the new COVID-19 pandemic, between isolated areas and in areas with technical damage has resulted in a great deal of interest concerning the safety of the population. A new method for ensuring communication between different areas, using unmanned aerial vehicle (UAV) networks with a well-established mobility schedule is proposed. UAVs fly based on a mission plan using regular polygons covering an area from a map. The area is considered to be equidistantly covered with points, grouped in triangles which are further grouped into hexagons. In this paper, UAVs, including battery charging or battery swapping stations and light weight Wi-Fi boards, are used for the data transfer among drones and stations using delivery protocols. UAV network analysis and evaluation (lengths of the arcs in seconds) based on experimental preliminary flight tests are proposed. Multiple simulations are performed based on six DTN algorithms, single-copy, and multiple-copies algorithms, and the efficiency of data transmission (delivery rate and latency) is analyzed. A very good delivery rate of 0.973 is obtained using the newly introduced TD-UAV Dijkstra algorithm.

关键词： unmanned aerial vehicle (UAV) network drone delay tolerant networks (DTN) optimization algorithms mobility schedule quarantined areas experimental flight

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Secure beamforming with nonorthogonal multiple access transmission in cooperative CR networks for Internet of Things

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COMPUTER NETWORKS 2021年 197卷 108334-108334页

作者： Li, Quanzhong Yang, Liang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China GuangDong Prov Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

In this paper, secure beamforming design in cooperative cognitive radio networks for Internet of Things (IoT) is investigated, where we maximize the secrecy sum rate for the IoT devices (IoDs) to protect their communication and employ the nonorthogonal multiple access scheme at the cognitive access point (AP) to serve the cognitive users. The formulated secure beamforming optimization problem is a non-convex problem and its global optimal solution is very hard to find. To overcome this difficulty, we propose an iterative algorithm based on semidefinite programming and monotonic optimization method to obtain the global optimal solution, which has high computational complexity and is suitable for the case that the cognitive AP has a powerful computing ability. For some application scenarios in which the cognitive AP has a lower computing ability, we propose suboptimal solutions based on zero-forcing scheme, which have much lower computational complexity. Furthermore, we derive the limit value of the secrecy sum rate for the IoDs when the transmit power of the cognitive AP goes to infinity, which can serve as an upper bound for our proposed solutions. Numerical results are also provided to verify the effectiveness of our proposed solutions.

关键词： Internet of Things Cognitive radio Secure beamforming Nonorthogonal multiple access optimization algorithms

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Parameter estimation of triple diode photovoltaic model using an artificial ecosystem-based optimizer

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第11期31卷

作者： El-Dabah, Mahmoud A. El-Sehiemy, Ragab A. Becherif, M. Ebrahim, M. A. Al Azhar Univ Fac Engn Elect Engn Dept Cairo Egypt Kafrelsheikh Univ Fac Engn Elect Engn Dept Kafr Al Sheikh Egypt Univ Bourgogne Franche Comte CNRS UTBM FEMTO ST Inst Belfort France Benha Univ Fac Engn Shoubra Dept Elect Engn Cairo Egypt

This manuscript proposes a modern optimization framework for parameter extraction of a triple-diode model of the unknown solar cell and Photovoltaic (PV) module parameters. The suggested optimization framework is based on applying a new metaheuristic optimization algorithm called Artificial Ecosystem-based Optimizer (AEO) to determine the nine unknown parameters of the triple-diode model of PV equivalent circuit model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. In this context, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. This objective function achieves the closeness degree between the estimated and experimental data. On the way to accomplish this study, the proposed AEO is carried out on three different commercial PV cells/modules. To assess the proposed algorithm, a comprehensive comparison study is used compared with several well-matured optimization algorithms reported in the literature. The attained numerical results prove the high precision and fast response of the proposed AEO algorithm for identifying multiple PV models.

关键词： artificial ecosystem-based optimization (AEO) optimization algorithms parameter extraction photovoltaic triple diode model

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Quantum-Behaved Particle Swarm optimization for Parameter optimization of Support Vector Machine

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JOURNAL OF CLASSIFICATION 2019年第3期36卷 576-598页

作者： Tharwat, Alaa Hassanien, Aboul Ella Frankfurt Univ Appl Sci Fac Comp Sci & Engn D-60318 Frankfurt Germany Cairo Univ Fac Comp & Informat Giza Egypt SRGE Giza Egypt

Support vector machine (SVM) parameters such as penalty parameter and kernel parameters have a great influence on the complexity and accuracy of SVM model. In this paper, quantum-behaved particle swarm optimization (QPSO) has been employed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (QPSO-SVM), the experiment adopted seven standard classification datasets which are obtained from UCI machine learning data repository. For verification, the results of the QPSO-SVM algorithm are compared with the standard PSO, and genetic algorithm (GA) which is one of the well-known optimization algorithms. Moreover, the results of QPSO are compared with the grid search, which is a conventional method of searching parameter values. The experimental results demonstrated that the proposed model is capable to find the optimal values of the SVM parameters. The results also showed lower classification error rates compared with standard PSO and GA algorithms.

关键词： Quantum particle swarm optimization (QPSO) optimization algorithms Support vector machine (SVM) Classification Parameter optimization

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A transgenic genetic algorithm design method that helps to increase the design freedom of metasurfaces

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JOURNAL OF PHYSICS D-APPLIED PHYSICS 2021年第13期54卷

作者： Yuan, Qi Ma, Hua Sui, Sai Wang, Jiafu Meng, Yueyu Pang, Yongqiang Qu, Shaobo Air Force Engn Univ Dept Basic Sci Xian 710051 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shanxi Peoples R China

While the optimization of design is an effective way to increase the performance of metasurfaces, there is still much to be done to improve optimization algorithms. In this work, a transgenic genetic algorithm (TGA) proposed for metasurface design offers higher performance and more design freedom. Based on GAs, transgenic technology is introduced into metasurface design to improve both performance and flexibility. We describe the generation of a transgenic factor (TF), how it operates on metasurface design, and explain the feasibility of enhancing design freedom using a TGA. To target the broadband regime and high efficiency, an electromagnetic polarization converter and an absorber are then designed by TGA, respectively. As a result, polarization conversion of more than -1 dB and absorption of over 90% were achieved from 8.09 GHz to 24.90 GHz and 7.71 GHz to 20.01 GHz, respectively. Both simulated and measured results are highly consistent, which validates the stable performance achieved by the proposed TGA design. What is more, a performance comparison between different TFs illustrates the subtle influence of TFs, revealing that the performance is improved to some extent by an increase of surface-design freedom. Significantly, this effort provides more freedom in the design method, leading to the realization of many expected improvements.

关键词： optimization algorithms metasurfaces transgenic technology flexibility freedom of surface design

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On the Powerball Method: Variants of Descent Methods for Accelerated optimization

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IEEE CONTROL SYSTEMS LETTERS 2019年第3期3卷 601-606页

作者： Yuan, Ye Li, Mu Liu, Jun Tomlin, Claire Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Amazon Web Serv Palo Alto CA 94303 USA Univ Waterloo Dept Appl Math Waterloo ON N2L 3G1 Canada Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

We propose a new method to accelerate the convergence of optimization algorithms. This method, termed Powerball method, simply adds a power coefficient.. [0,1) to the gradient used in various gradient-based optimization schemes. In its essence, the Powerball method can be regarded as the steepest gradient descent with respect to the p-norm, where p = 1 + (1/gamma). As a motivation, we first present the continuous-time models of the proposed optimization schemes and analyze their finite-time convergence properties. We then develop several variants of the Powerball method that empirically outperform the standard descent methods, especially during the initial iterations. On multiple real datasets, we demonstrate that the proposed methods can provide a tenfold speedup of the convergence of both (stochastic) gradient descent and limited-memory Broyden-Fletcher-GoldfarbShanno (L-BFGS) methods.

关键词： optimization algorithms optimization theory

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k-Means image segmentation using Mumford-Shah model

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JOURNAL OF ELECTRONIC IMAGING 2021年第6期30卷 063029-063029页

作者： Shah, Nilima Patel, Dhanesh Franti, Pasi Maharaja Sayajirao Univ Baroda Dept Appl Math Fac Technol & Engn Vadodara Gujarat India Univ Eastern Finland UEF Sch Comp Joensuu Finland

k-Means (KM) is well known for its ease of implementation as a clustering technique. It has been applied for color quantization in RGB, YUV, hyperspectral image, Lab, and other spaces, but this leads to fragmented segments as the pixels are clustered only in the color space without considering connectivity. The problem has been attacked by adding connectivity constraints, or using joint color and spatial features (r, g, b, x, y), which prevent fragmented and nonconvex segments. However, it does not take into account the complexity of the shape itself. The Mumford-Shah model has been earlier used to overcome this problem but with slow and complex mathematical optimization algorithms. We integrate Mumford-Shah model directly into KM context and construct a fast and simple implementation of the algorithm. The proposed approach uses standard KM algorithm with distance function derived from Mumford-Shah model so that it optimizes both the content and the shape of the segments jointly. We demonstrate by experiments that the proposed algorithm provides better results than comparative methods when compared using various error evaluation criteria. The algorithm is applied on 100 images in the Weizmann dataset and two remote sensing images. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License.

关键词： image segmentation clustering k-means Mumford-Shah segmentation optimization algorithms

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Active-Set based Inexact Interior Point QP Solver for Model Predictive Control 21st

Active-Set based Inexact Interior Point QP Solver for Model ...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Frey, Jonathan Di Cairano, Stefano Quirynen, Rien Mitsubishi Elect Res Labs Cambridge MA 02139 USA Univ Freiburg Dept IMTEK Freiburg Germany

Interior point methods are applicable to a large class of problems and can be very reliable for convex optimization, even without a good initial guess for the optimal solution. Active-set methods, on the other hand, are often restricted to linear or quadratic programming but they have a lower computational cost per iteration and superior warm starting properties. The present paper proposes an approach for improving the numerical conditioning and warm starting properties of interior point methods, based on an active-set identification strategy and inexact Newton-type optimization techniques. In addition, we show how this reduces the average computational cost of the linear algebra operations in each interior point iteration. We developed an efficient C code implementation of the active-set based interior point method (ASIPM) and show that it can be competitive with state of the art solvers for a standard case study of model predictive control stabilizing an inverted pendulum on a cart. Copyright (C) 2020 The Authors.

关键词： optimization algorithms Interior point methods Model predictive control

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Automatic variogram model fitting of a variogram map based on the Fourier integral method

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COMPUTERS & GEOSCIENCES 2021年 156卷 104891-104891页

作者： de Carvalho, Paulo Roberto Moura da Costa, Joao Felipe Coimbra Leite Fed Univ Rio Grande do Sul UFRGS Av Bento Goncalves 9500Bloco 4Predio 75 BR-91501970 Porto Alegre RS Brazil Petr Brasileiro SA Av Horacio de Macedo 9503 AndarEscritorio 1 BR-21941915 Rio De Janeiro RJ Brazil

One of the cornerstones of geostatistics is the variogram. Fitting a variogram model to observed data is certainly one of the routine tasks in daily geostatistics practice. The central role of the variogram and the difficulty of performing variogram modeling manually call for an automatic, or at least a computer-assisted, method. Fitting a parametric curve to observational points in an XY chart is far from new and the GSLib software package has included a module (called VARFIT) to perform automatic variogram fitting in 1D since its debut many years ago. Nevertheless, extending its objective function to fitting to variogram map (2D) resulted in a method with poor performance. Hence, this work proposes a computational method to fit variogram models to variogram maps with a different objective function based on the principles of the Fourier Integral Method (FIM). A map is synthesized from the theoretical variogram model using the principles of FIM. The metric of the objective function is the sum of squares of differences between each pixel value of the synthetic map and the input map. The genetic algorithm uses this metric to find the variogram parameters of a model with good fitness. Since the genetic algorithm has a stochastic component, the best fit is not certain. Hence, several runs with different random number generator seeds are performed. The parameters of all fitted models are collected and cluster analysis in parameter space is done with the DBSCAN algorithm. The centers of the clusters are the parameters of the sought variogram model. Open-source software was written and made available, in which the proposed objective function was tested alongside the 2D version of the one in VARFIT. Results showed better effectiveness of the proposed method in fitting variogram models for the variogram map case.

关键词： Geostatistics Automatic variogram modeling Fourier integral method optimization algorithms Clustering algorithms

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