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Journal articles on the topic 'DCA, DC programming'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Le, Hoai Minh, Hoai An Le Thi, Tao Pham Dinh, and Van Ngai Huynh. "Block Clustering Based on Difference of Convex Functions (DC) Programming and DC Algorithms." Neural Computation 25, no. 10 (October 2013): 2776–807. http://dx.doi.org/10.1162/neco_a_00490.

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We investigate difference of convex functions (DC) programming and the DC algorithm (DCA) to solve the block clustering problem in the continuous framework, which traditionally requires solving a hard combinatorial optimization problem. DC reformulation techniques and exact penalty in DC programming are developed to build an appropriate equivalent DC program of the block clustering problem. They lead to an elegant and explicit DCA scheme for the resulting DC program. Computational experiments show the robustness and efficiency of the proposed algorithm and its superiority over standard algorithms such as two-mode K-means, two-mode fuzzy clustering, and block classification EM.

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2

Wang, Meihua, Fengmin Xu, and Chengxian Xu. "A Branch-and-Bound Algorithm Embedded with DCA for DC Programming." Mathematical Problems in Engineering 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/364607.

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The special importance of Difference of Convex (DC) functions programming has been recognized in recent studies on nonconvex optimization problems. In this work, a class of DC programming derived from the portfolio selection problems is studied. The most popular method applied to solve the problem is the Branch-and-Bound (B&B) algorithm. However, “the curse of dimensionality” will affect the performance of the B&B algorithm. DC Algorithm (DCA) is an efficient method to get a local optimal solution. It has been applied to many practical problems, especially for large-scale problems. A B&B-DCA algorithm is proposed by embedding DCA into the B&B algorithms, the new algorithm improves the computational performance and obtains a global optimal solution. Computational results show that the proposed B&B-DCA algorithm has the superiority of the branch number and computational time than general B&B. The nice features of DCA (inexpensiveness, reliability, robustness, globality of computed solutions, etc.) provide crucial support to the combined B&B-DCA for accelerating the convergence of B&B.

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3

Astorino, Annabella, Massimo Di Francesco, Manlio Gaudioso, Enrico Gorgone, and Benedetto Manca. "Polyhedral separation via difference of convex (DC) programming." Soft Computing 25, no. 19 (April 7, 2021): 12605–13. http://dx.doi.org/10.1007/s00500-021-05758-6.

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AbstractWe consider polyhedral separation of sets as a possible tool in supervised classification. In particular, we focus on the optimization model introduced by Astorino and Gaudioso (J Optim Theory Appl 112(2):265–293, 2002) and adopt its reformulation in difference of convex (DC) form. We tackle the problem by adapting the algorithm for DC programming known as DCA. We present the results of the implementation of DCA on a number of benchmark classification datasets.

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4

Le Thi, Hoai An, Manh Cuong Nguyen, and Tao Pham Dinh. "A DC Programming Approach for Finding Communities in Networks." Neural Computation 26, no. 12 (December 2014): 2827–54. http://dx.doi.org/10.1162/neco_a_00673.

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Automatic discovery of community structures in complex networks is a fundamental task in many disciplines, including physics, biology, and the social sciences. The most used criterion for characterizing the existence of a community structure in a network is modularity, a quantitative measure proposed by Newman and Girvan ( 2004 ). The discovery community can be formulated as the so-called modularity maximization problem that consists of finding a partition of nodes of a network with the highest modularity. In this letter, we propose a fast and scalable algorithm called DCAM, based on DC (difference of convex function) programming and DCA (DC algorithms), an innovative approach in nonconvex programming framework for solving the modularity maximization problem. The special structure of the problem considered here has been well exploited to get an inexpensive DCA scheme that requires only a matrix-vector product at each iteration. Starting with a very large number of communities, DCAM furnishes, as output results, an optimal partition together with the optimal number of communities [Formula: see text]; that is, the number of communities is discovered automatically during DCAM’s iterations. Numerical experiments are performed on a variety of real-world network data sets with up to 4,194,304 nodes and 30,359,198 edges. The comparative results with height reference algorithms show that the proposed approach outperforms them not only on quality and rapidity but also on scalability. Moreover, it realizes a very good trade-off between the quality of solutions and the run time.

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Le Thi, Hoai An, and Tao Pham Dinh. "DC programming and DCA: thirty years of developments." Mathematical Programming 169, no. 1 (January 24, 2018): 5–68. http://dx.doi.org/10.1007/s10107-018-1235-y.

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6

Le Thi, Hoai An, and Vinh Thanh Ho. "Online Learning Based on Online DCA and Application to Online Classification." Neural Computation 32, no. 4 (April 2020): 759–93. http://dx.doi.org/10.1162/neco_a_01266.

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We investigate an approach based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) for online learning techniques. The prediction problem of an online learner can be formulated as a DC program for which online DCA is applied. We propose the two so-called complete/approximate versions of online DCA scheme and prove their logarithmic/sublinear regrets. Six online DCA-based algorithms are developed for online binary linear classification. Numerical experiments on a variety of benchmark classification data sets show the efficiency of our proposed algorithms in comparison with the state-of-the-art online classification algorithms.

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7

Kebaili, Zahira, and Mohamed Achache. "Solving nonmonotone affine variational inequalities problem by DC programming and DCA." Asian-European Journal of Mathematics 13, no. 03 (December 17, 2018): 2050067. http://dx.doi.org/10.1142/s1793557120500679.

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In this paper, we consider an optimization model for solving the nonmonotone affine variational inequalities problem (AVI). It is formulated as a DC (Difference of Convex functions) program for which DCA (DC Algorithms) are applied. The resulting DCA are simple: it consists of solving successive convex quadratic program. Numerical experiments on several test problems illustrate the efficiency of the proposed approach in terms of the quality of the obtained solutions and the speed of convergence.

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8

Phan, Duy Nhat, Hoai An Le Thi, and Tao Pham Dinh. "Sparse Covariance Matrix Estimation by DCA-Based Algorithms." Neural Computation 29, no. 11 (November 2017): 3040–77. http://dx.doi.org/10.1162/neco_a_01012.

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This letter proposes a novel approach using the [Formula: see text]-norm regularization for the sparse covariance matrix estimation (SCME) problem. The objective function of SCME problem is composed of a nonconvex part and the [Formula: see text] term, which is discontinuous and difficult to tackle. Appropriate DC (difference of convex functions) approximations of [Formula: see text]-norm are used that result in approximation SCME problems that are still nonconvex. DC programming and DCA (DC algorithm), powerful tools in nonconvex programming framework, are investigated. Two DC formulations are proposed and corresponding DCA schemes developed. Two applications of the SCME problem that are considered are classification via sparse quadratic discriminant analysis and portfolio optimization. A careful empirical experiment is performed through simulated and real data sets to study the performance of the proposed algorithms. Numerical results showed their efficiency and their superiority compared with seven state-of-the-art methods.

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9

Le Thi, Hoai An, Xuan Thanh Vo, and Tao Pham Dinh. "Efficient Nonnegative Matrix Factorization by DC Programming and DCA." Neural Computation 28, no. 6 (June 2016): 1163–216. http://dx.doi.org/10.1162/neco_a_00836.

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In this letter, we consider the nonnegative matrix factorization (NMF) problem and several NMF variants. Two approaches based on DC (difference of convex functions) programming and DCA (DC algorithm) are developed. The first approach follows the alternating framework that requires solving, at each iteration, two nonnegativity-constrained least squares subproblems for which DCA-based schemes are investigated. The convergence property of the proposed algorithm is carefully studied. We show that with suitable DC decompositions, our algorithm generates most of the standard methods for the NMF problem. The second approach directly applies DCA on the whole NMF problem. Two algorithms—one computing all variables and one deploying a variable selection strategy—are proposed. The proposed methods are then adapted to solve various NMF variants, including the nonnegative factorization, the smooth regularization NMF, the sparse regularization NMF, the multilayer NMF, the convex/convex-hull NMF, and the symmetric NMF. We also show that our algorithms include several existing methods for these NMF variants as special versions. The efficiency of the proposed approaches is empirically demonstrated on both real-world and synthetic data sets. It turns out that our algorithms compete favorably with five state-of-the-art alternating nonnegative least squares algorithms.

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10

Le Thi, Hoai An, and Duy Nhat Phan. "DC programming and DCA for sparse optimal scoring problem." Neurocomputing 186 (April 2016): 170–81. http://dx.doi.org/10.1016/j.neucom.2015.12.068.

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11

Hoai An, Le Thi, Le Hoai Minh, and Pham Dinh Tao. "Optimization based DC programming and DCA for hierarchical clustering." European Journal of Operational Research 183, no. 3 (December 2007): 1067–85. http://dx.doi.org/10.1016/j.ejor.2005.07.028.

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12

Le Thi, Hoai An, and Tao Pham Dinh. "On solving Linear Complementarity Problems by DC programming and DCA." Computational Optimization and Applications 50, no. 3 (February 23, 2011): 507–24. http://dx.doi.org/10.1007/s10589-011-9398-y.

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13

Le Thi, Hoai An, Hoai Minh Le, Tao Pham Dinh, and Ngai Van Huynh. "Binary classification via spherical separator by DC programming and DCA." Journal of Global Optimization 56, no. 4 (February 11, 2012): 1393–407. http://dx.doi.org/10.1007/s10898-012-9859-6.

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14

Le Thi, Hoai An, and Duy Nhat Phan. "DC programming and DCA for sparse Fisher linear discriminant analysis." Neural Computing and Applications 28, no. 9 (February 11, 2016): 2809–22. http://dx.doi.org/10.1007/s00521-016-2216-9.

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15

Le, Hoai Minh, Hoai An Le Thi, and Manh Cuong Nguyen. "Sparse semi-supervised support vector machines by DC programming and DCA." Neurocomputing 153 (April 2015): 62–76. http://dx.doi.org/10.1016/j.neucom.2014.11.051.

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16

Bazikar, Fatemeh, Saeed Ketabchi, and Hossein Moosaei. "DC programming and DCA for parametric-margin ν-support vector machine." Applied Intelligence 50, no. 6 (February 11, 2020): 1763–74. http://dx.doi.org/10.1007/s10489-019-01618-x.

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17

Pham Dinh, Tao, Nguyen Canh Nam, and Hoai An Le Thi. "DC programming and DCA for globally solving the value-at-risk." Computational Management Science 6, no. 4 (August 6, 2009): 477–501. http://dx.doi.org/10.1007/s10287-009-0099-2.

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18

Pham Dinh, Tao, Vinh Thanh Ho, and Hoai An Le Thi. "DC programming and DCA for solving Brugnano–Casulli piecewise linear systems." Computers & Operations Research 87 (November 2017): 196–204. http://dx.doi.org/10.1016/j.cor.2016.04.005.

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19

An, Le Thi Hoai, M. Tayeb Belghiti, and Pham Dinh Tao. "A new efficient algorithm based on DC programming and DCA for clustering." Journal of Global Optimization 37, no. 4 (August 9, 2006): 593–608. http://dx.doi.org/10.1007/s10898-006-9066-4.

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20

Tran, Thi Thuy, Hoai An Le Thi, and Tao Pham Dinh. "DC programming and DCA for enhancing physical layer security via cooperative jamming." Computers & Operations Research 87 (November 2017): 235–44. http://dx.doi.org/10.1016/j.cor.2016.11.003.

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21

Li, Jieya, and Liming Yang. "Robust sparse principal component analysis by DC programming algorithm." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 3183–93. http://dx.doi.org/10.3233/jifs-191617.

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The classical principal component analysis (PCA) is not sparse enough since it is based on the L2-norm that is also prone to be adversely affected by the presence of outliers and noises. In order to address the problem, a sparse robust PCA framework is proposed based on the min of zero-norm regularization and the max of Lp-norm (0 < p ≤ 2) PCA. Furthermore, we developed a continuous optimization method, DC (difference of convex functions) programming algorithm (DCA), to solve the proposed problem. The resulting algorithm (called DC-LpZSPCA) is convergent linearly. In addition, when choosing different p values, the model can keep robust and is applicable to different data types. Numerical simulations are simulated in artificial data sets and Yale face data sets. Experiment results show that the proposed method can maintain good sparsity and anti-outlier ability.

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22

Tran, Thi Thuy, Hoai An Pham Thi, Tao Pham Dinh, and Nhu Tuan Nguyen. "DC programming and DCA for enhancing physical layer security via relay beamforming strategies." Optimization Letters 15, no. 7 (January 8, 2021): 2377–405. http://dx.doi.org/10.1007/s11590-020-01696-8.

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23

Pham Dinh, T., H. A. Le Thi, and F. Akoa. "Combining DCA (DC Algorithms) and interior point techniques for large-scale nonconvex quadratic programming." Optimization Methods and Software 23, no. 4 (August 2008): 609–29. http://dx.doi.org/10.1080/10556780802263990.

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24

Geng, Juan, Laisheng Wang, and Yanfei Wang. "A non-convex algorithm framework based on DC programming and DCA for matrix completion." Numerical Algorithms 68, no. 4 (May 29, 2014): 903–21. http://dx.doi.org/10.1007/s11075-014-9876-2.

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25

Le Thi, Hoai An, and Tao Pham Dinh. "A continuous approach for the concave cost supply problem via DC programming and DCA." Discrete Applied Mathematics 156, no. 3 (February 2008): 325–38. http://dx.doi.org/10.1016/j.dam.2007.03.024.

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26

An, Le Thi Hoai, and Pham Dinh Tao. "The DC (Difference of Convex Functions) Programming and DCA Revisited with DC Models of Real World Nonconvex Optimization Problems." Annals of Operations Research 133, no. 1-4 (January 2005): 23–46. http://dx.doi.org/10.1007/s10479-004-5022-1.

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27

Le Thi, Hoai An, Mahdi Moeini, and Tao Pham Dinh. "Portfolio selection under downside risk measures and cardinality constraints based on DC programming and DCA." Computational Management Science 6, no. 4 (August 6, 2009): 459–75. http://dx.doi.org/10.1007/s10287-009-0098-3.

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28

Le Thi, Hoai An, Vinh Thanh Ho, and Tao Pham Dinh. "A unified DC programming framework and efficient DCA based approaches for large scale batch reinforcement learning." Journal of Global Optimization 73, no. 2 (August 21, 2018): 279–310. http://dx.doi.org/10.1007/s10898-018-0698-y.

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29

Ngo, Tung Son, Ngoc Anh Bui, Thi Thuy Tran, Phuong Chi Le, Dinh Chien Bui, The Duy Nguyen, Lac Duong Phan, Quoc Tuan Kieu, Ba Son Nguyen, and Son N. Tran. "Some Algorithms to Solve a Bi-Objectives Problem for Team Selection." Applied Sciences 10, no. 8 (April 14, 2020): 2700. http://dx.doi.org/10.3390/app10082700.

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In real life, many problems are instances of combinatorial optimization. Cross-functional team selection is one of the typical issues. The decision-maker has to select solutions among ( k h ) solutions in the decision space, where k is the number of all candidates, and h is the number of members in the selected team. This paper is our continuing work since 2018; here, we introduce the completed version of the Min Distance to the Boundary model (MDSB) that allows access to both the “deep” and “wide” aspects of the selected team. The compromise programming approach enables decision-makers to ignore the parameters in the decision-making process. Instead, they point to the one scenario they expect. The aim of model construction focuses on finding the solution that matched the most to the expectation. We develop two algorithms: one is the genetic algorithm and another based on the philosophy of DC programming (DC) and its algorithm (DCA) to find the optimal solution. We also compared the introduced algorithms with the MIQP-CPLEX search algorithm to show their effectiveness.

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30

Le Thi, Hoai An. "DC programming and DCA for supply chain and production management: state-of-the-art models and methods." International Journal of Production Research 58, no. 20 (August 29, 2019): 6078–114. http://dx.doi.org/10.1080/00207543.2019.1657245.

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31

Schleich, Julien, Hoai An Le Thi, and Pascal Bouvry. "Solving the minimum M-dominating set problem by a continuous optimization approach based on DC programming and DCA." Journal of Combinatorial Optimization 24, no. 4 (May 3, 2011): 397–412. http://dx.doi.org/10.1007/s10878-011-9396-0.

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32

Dong, Hongbo, and Min Tao. "On the Linear Convergence to Weak/Standard d-Stationary Points of DCA-Based Algorithms for Structured Nonsmooth DC Programming." Journal of Optimization Theory and Applications 189, no. 1 (March 10, 2021): 190–220. http://dx.doi.org/10.1007/s10957-021-01827-w.

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33

Pereira Júnior, Carlos Donizete, Camila Souza de Oliveira Guimarães, Aline Cristina Souza da Silva, Aldo Rogelis Aquiles Rodrigues, Maria Aparecida da Glória, Vicente de Paula Antunes Teixeira, Niels Olsen Saraiva Câmara, et al. "Influence of the Expression of Inflammatory Markers on Kidney after Fetal Programming in an Experimental Model of Renal Failure." Journal of Immunology Research 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/9151607.

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Objective. To evaluate the expression of inflammatory markers in experimental renal failure after fetal programming. Methods. The offspring aged two and five months were divided into four groups: CC (control dams, control offspring); DC (diabetic dams, control offspring); CFA (control dams, folic acid offspring, 250 mg/Kg); and DFA (diabetic dams, folic acid offspring). Gene expression of inflammatory markers MCP-1, IL-1, NOS3, TGF-β, TNF-α, and VEGF was evaluated by RT-PCR. Results. MCP-1 was increased in the CFA and DFA groups at two and five months of age, as well as in DC5 when compared to CC5. There was a higher expression of IL-1 in the CFA2, DFA2, and DC2 groups. There was a decrease in NOS3 and an increase in TNF-α in DFA5 in relation to CFA5. The gene expression of TGF-β increased in cases that had received folic acid at two and five months, and VEGF decreased in the CFA5 and DFA5 groups. DC5 showed increased VEGF expression in comparison with CC5. Conclusions. Gestational diabetes mellitus and folic acid both change the expression of inflammatory markers, thus demonstrating that the exposure to harmful agents in adulthood has a more severe impact in cases which underwent fetal reprogramming.

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34

Cohen, Peter A., Gary K. Koski, Brian J. Czerniecki, Kevin D. Bunting, Xin-Yuan Fu, Zhengqi Wang, Wen-Jun Zhang, et al. "STAT3- and STAT5-dependent pathways competitively regulate the pan-differentiation of CD34pos cells into tumor-competent dendritic cells." Blood 112, no. 5 (September 1, 2008): 1832–43. http://dx.doi.org/10.1182/blood-2007-12-130138.

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Abstract The clinical outcomes of dendritic cell (DC)–based immunotherapy remain disappointing, with DCs often displaying a tenuous capacity to complete maturation and DC1 polarization in the tumor host. Surprisingly, we observed that the capacity for successful DC1 polarization, including robust IL12p70 production, could be regulated by STAT-dependent events even prior to DC differentiation. Exposure of CD34pos cells to single-agent granulocyte-macrophage colony-stimulating factor (GMCSF) induced multilineage, STAT5-dependent differentiation, including DCs that failed to mature in the absence of further exogenous signals. In contrast, Flt3L induced nearly global differentiation of CD34pos cells into spontaneously maturing DCs. IL-6 synergized with Flt3L to produce explosive, STAT3-dependent proliferation of phenotypically undifferentiated cells that nevertheless functioned as committed DC1 precursors. Such precursors not only resisted many tumor-associated immunosuppressants, but also responded to tumor contact or TGFβ with facilitated DC maturation and IL12p70 production, and displayed a superior capacity to reverse tumor-induced T-cell tolerance. GMCSF preempted Flt3L or Flt3L plus IL-6 licensing by blocking STAT3 activation and promoting STAT5-dependent differentiation. Paradoxically, following overt DC differentiation, STAT5 enhanced whereas STAT3 inhibited DC1 polarization. Therefore, nonoverlapping, sequential activation of STAT3 and STAT5, achievable by sequenced exposure to Flt3L plus IL-6, then GMCSF, selects for multilog expansion, programming, and DC1 polarization of tumor-competent DCs from CD34pos cells.

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35

Rodríguez Bustinza, Ricardo, and Ewar Mamani Churayra. "CONTROL DE LA VELOCIDAD EN TIEMPO REAL DE UN MOTOR DC CONTROLADO POR LÓGICA DIFUSA TIPO PD+I USANDO LABVIEW." Revista Cientifica TECNIA 21, no. 1 (April 7, 2017): 5. http://dx.doi.org/10.21754/tecnia.v21i1.92.

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En este artículo, se presenta un método basado en inteligencia artificial para controlar una planta motor DC por un microordenador personal (PC), el que interactuando hardware y software logra el control de la velocidad del motor DC en tiempo real usando el algoritmo de control Difuso-PD+I. La adquisición de datos e identificación de los parámetros del motor DC han sido necesarias para el control de la velocidad del motor DC, por medio de la tarjeta de adquisición de datos PCI NIDAQ 6024E cuya interface corre en tiempo real que usa el Workshop Real-Time (RTW), el archivo de datos es procesado con la herramienta de identificación del programa Matlab llamada IDENT. El prototipo del sistema computadora-controlador se diseña empleando la programación grafica de LabVIEW, en este caso se hace uso de las herramientas Fuzzy Logic Control y Simulation Module. El control en tiempo real del sistema se lleva a cabo en el laboratorio usando el convertidor digital-a-analógico (DAC) y encoder formado por dos sensores de efecto hall de tipo incremental que por medio de un convertidor frecuencia voltaje se logra procesar las señales desde las entradas analógicas de la NIDAQ. Se verifican los resultados de simulación de computadora experimentalmente, los que demuestran que la señal de control diseñada puede hacer que la salida del sistema prototipo siga eficientemente las referencias impuestas con mínimo sobrepaso y error en estado estacionario nulo. Palabras clave.- Motor DC, Adquisición de datos, Identificación de parámetros, Diseño del controlador e implementación. ABSTRACTIn this article, a method is presented based on artificial intelligence to control a plant DC motor for a personal microcomputer (PC), that interacted hardware and software achieves the control of the speed of the DC motor in real time using the control algorithm Fuzzy-PD+I. The acquisition of data and identification of the parameters of the DC motor have been necessary for the control of the speed of the motor DC, by means of the card of acquisition of data PCI NIDAQ 6024E whose interface runs in the real time that the Workshop Real-Time uses (RTW), the file of data is processed with the tool of identification of the program called IDENT of Matlab. The prototype of the system computer-controller is designed using the graphic programming of LabVIEW, in this case use of the tools Fuzzy Logic Control and Simulation Module. The control in real time of the system is carried out in the laboratory using the digital-to-analogical converter (DAC) and incremental encoder formed by two sensors of effect hall that is possible to process the signs from the analogical input of the NIDAQ by means of a convertor frequency voltage. The results of computer simulation are verified experimentally, those that demonstrate that the designed control sign can make that the exit of the system prototype follows the references imposed with minimum overshoot and null steady-state error. Keywords.- DC Motor, Data acquisition, Parameters identification, Control design and implementation.

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36

Tran, Thi Thuy, Hoai An Pham Thi, Tao Pham Dinh, and Nhu Tuan Nguyen. "DC programming and DCA for enhancing physical layer security via relay beamforming strategies." Optimization Letters, January 8, 2021. http://dx.doi.org/10.1007/s11590-020-01696-8.

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37

Leslous, Fadila, Philippe Marthon, Oukacha Brahim, and Ouanes Mohand. "WITHDRAWN: Nonconvex optimization based on DC programming and DCA in the search of a global optimum of a nonconvex function." Journal of the Egyptian Mathematical Society, October 2015. http://dx.doi.org/10.1016/j.joems.2015.08.002.

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38

YADAV, ANUJA, and NANDKISHOR GHODKE. "PROGRAMMABLE, ANALOGUE, BIPOLAR, DC ± 4.095 VOLTAGE REFERENCE, INTERFACED THROUGH USB PORT AND COMPLIED IN LABVIEW." International Journal of Electronics and Electical Engineering, October 2014, 133–37. http://dx.doi.org/10.47893/ijeee.2014.1142.

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A designed DAC circuit to generate an analog bipolar DC voltage reference 4.095V for an in-house application interfaced through USB port for automation. A USB to Parallel FIFO development module UM245R, the latest device to interface from FTDI Chip is incorporated for the same. Compiled the supporting application software in a Graphical Programming Language “LabVIEW” by National Instruments with executable VI functions provided by FTDI.

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39

YADAV, ANUJA, and NANDKISHOR GHODKE. "PROGRAMMABLE, ANALOGUE, BIPOLAR, DC ± 4.095 VOLTAGE REFERENCE, INTERFACED THROUGH USB PORT AND COMPLIED IN LABVIEW." International Journal of Electronics and Electical Engineering, October 2014, 133–37. http://dx.doi.org/10.47893/ijeee.2014.1142.

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A designed DAC circuit to generate an analog bipolar DC voltage reference 4.095V for an in-house application interfaced through USB port for automation. A USB to Parallel FIFO development module UM245R, the latest device to interface from FTDI Chip is incorporated for the same. Compiled the supporting application software in a Graphical Programming Language “LabVIEW” by National Instruments with executable VI functions provided by FTDI.

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