Optimum nonuniform transmultiplexer design

This paper considers an optimum nonuniform FIR transmultiplexer design subject to specifications in the frequency domain. Our objective is to minimize the sum of the ripple energy for all the individual filters, subject to the specifications on amplitude and aliasing distortions, and to the passband and stopband specifications for the individual filters. This optimum nonuniform transmultiplexer design problem can be formulated as a quadratic semi-infinite programming problem. The dual parametrization algorithm is extended to the design of this nonuniform transmultiplexer problem. If the lengths of the filters are sufficiently long and the set of decimation integers is compatible, then our algorithm guarantees that the solution obtained will give rise to the global minimum, and the required specifications are satisfied

Optimal PWM control of switched-capacitor DC/DC power converters via model transformation and enhancing control techniques

Abstract—This paper presents an efficient and effective method for an optimal pulse width modulated (PWM) control of switched-capacitor DC/DC power converters. Optimal switching instants are determined based on minimizing the output ripple magnitude, the output leakage voltage and the sensitivity of the output load voltage with respect to both the input voltage and the load resistance. This optimal PWM control strategy has several advantages over conventional PWM control strategies: 1) It does not involve a linearization, so a large signal analysis is performed. 2) It guarantees the optimality. The problem is solved via both the model transformation and the optimal enhancing control techniques. A practical example of the PWM control of a switched-capacitor DC/DC power converter is presented

Enabling Quality Control for Entity Resolution: A Human and Machine Cooperation Framework

Even though many machine algorithms have been proposed for entity resolution, it remains very challenging to find a solution with quality guarantees. In this paper, we propose a novel HUman and Machine cOoperation (HUMO) framework for entity resolution (ER), which divides an ER workload between the machine and the human. HUMO enables a mechanism for quality control that can flexibly enforce both precision and recall levels. We introduce the optimization problem of HUMO, minimizing human cost given a quality requirement, and then present three optimization approaches: a conservative baseline one purely based on the monotonicity assumption of precision, a more aggressive one based on sampling and a hybrid one that can take advantage of the strengths of both previous approaches. Finally, we demonstrate by extensive experiments on real and synthetic datasets that HUMO can achieve high-quality results with reasonable return on investment (ROI) in terms of human cost, and it performs considerably better than the state-of-the-art alternatives in quality control.Comment: 12 pages, 11 figures. Camera-ready version of the paper submitted to ICDE 2018, In Proceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018

Efficient algorithm for solving semi-infinite programming problems and their applications to nonuniform filter bank designs

An efficient algorithm for solving semi-infinite programming problems is proposed in this paper. The index set is constructed by adding only one of the most violated points in a refined set of grid points. By applying this algorithm for solving the optimum nonuniform symmetric/antisymmetric linear phase finite-impulse-response (FIR) filter bank design problems, the time required to obtain a globally optimal solution is much reduced compared with that of the previous proposed algorith

Fuzzy switching systems: minimizing discontinuities and ripple magnitude and energy

This paper presents an efficient and effective method to determine optimal switching instants of fuzzy switching systems such that both the ripple magnitude and energy of the fuzzy switching systems are minimized. The method is based on optimal switching control techniques, where an optimal enhancing control method is used. This method has several advantages over the traditional methods. Firstly, it does not require the process of linearization. Secondly, it guarantees to achieve optimality. For illustration, a practical example of an optimal pulse width modulated fuzzy control of a switched-capacitor DC/DC power converter is presented

Design of nonuniform near allpass complementary FIR filters via a semi-infinite programming technique

In this paper, we consider the problem of designing a set of nonuniform near allpass complementary FIR filters. This problem can be formulated as a quadratic semi-infinite programming problem, where the objective is to minimize the sum of the ripple energy for the individual filters, subject to the passband and stopband specifications as well as to the allpass complementary specification. The dual parameterization method is used for solving the linear quadratic semi-infinite programming problem

Design of near allpass strictly stable minimal phase real valued rational IIR filters

In this brief, a near-allpass strictly stable minimal-phase real-valued rational infinite-impulse response filter is designed so that the maximum absolute phase error is minimized subject to a specification on the maximum absolute allpass error. This problem is actually a minimax nonsmooth optimization problem subject to both linear and quadratic functional inequality constraints. To solve this problem, the nonsmooth cost function is first approximated by a smooth function, and then our previous proposed method is employed for solving the problem. Computer numerical simulation result shows that the designed filter satisfies all functional inequality constraints and achieves a small maximum absolute phase error

Optimum design of discrete-time differentiators via semi-infinite programming approach

In this paper, a general optimum full band high order discrete-time differentiator design problem is formulated as a peak constrained least square optimization problem. That is, the objective of the optimization problem is to minimize the total weighted square error of the magnitude response subject to the peak constraint of the weighted error function. This problem formulation provides a great flexibility for the tradeoff between the ripple energy and the ripple magnitude of the discrete-time differentiator. The optimization problem is actually a semi-infinite programming problem. Our recently developed dual parametrization algorithm is applied for solving the problem. The main advantage of employing the dual parameterization algorithm for solving the problem is the guarantee of the convergence of the algorithm and the obtained solution being the global optimal solution that satisfies the corresponding continuous constraints. Moreover, the computational cost of the algorithm is lower than that of algorithms implementing the semi-definite programming approach