2,532 research outputs found
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 design of nonuniform FIR transmultiplexer using semi-infinite programming
This paper considers an optimum nonuniform FIR transmultiplexer design problem 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 this nonuniform transmultiplexer design problem. If the lengths of the filters are sufficiently long and the set of decimation integers is compatible, then a solution exists. Since the problem is formulated as a convex problem, if a solution exists, then the solution obtained is unique and the local solution is a global minimum
Hierarchical Pruning of Deep Ensembles with Focal Diversity
Deep neural network ensembles combine the wisdom of multiple deep neural
networks to improve the generalizability and robustness over individual
networks. It has gained increasing popularity to study deep ensemble techniques
in the deep learning community. Some mission-critical applications utilize a
large number of deep neural networks to form deep ensembles to achieve desired
accuracy and resilience, which introduces high time and space costs for
ensemble execution. However, it still remains a critical challenge whether a
small subset of the entire deep ensemble can achieve the same or better
generalizability and how to effectively identify these small deep ensembles for
improving the space and time efficiency of ensemble execution. This paper
presents a novel deep ensemble pruning approach, which can efficiently identify
smaller deep ensembles and provide higher ensemble accuracy than the entire
deep ensemble of a large number of member networks. Our hierarchical ensemble
pruning approach (HQ) leverages three novel ensemble pruning techniques. First,
we show that the focal diversity metrics can accurately capture the
complementary capacity of the member networks of an ensemble, which can guide
ensemble pruning. Second, we design a focal diversity based hierarchical
pruning approach, which will iteratively find high quality deep ensembles with
low cost and high accuracy. Third, we develop a focal diversity consensus
method to integrate multiple focal diversity metrics to refine ensemble pruning
results, where smaller deep ensembles can be effectively identified to offer
high accuracy, high robustness and high efficiency. Evaluated using popular
benchmark datasets, we demonstrate that the proposed hierarchical ensemble
pruning approach can effectively identify high quality deep ensembles with
better generalizability while being more time and space efficient in ensemble
decision making.Comment: To appear on ACM Transactions on Intelligent Systems and Technolog
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
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 interpolative sigma-delta modulators via semi-indefinite programming
This correspondence considers the optimized design of interpolative sigma delta modulators (SDMs). The first optimization problem is to determine the denominator coefficients. The objective of the optimization problem is to minimize the passband energy of the denominator of the loop filter transfer function (excluding the dc poles) subject to the continuous constraint of this function defined in the frequency domain. The second optimization problem is to determine the numerator coefficients in which the cost function is to minimize the stopband ripple energy of the loop filter subject to the stability condition of the noise transfer function (NTF) and signal transfer function (STF). These two optimization problems are actually quadratic semi-infinite programming (SIP) problems. By employing the dual-parameterization method, global optimal solutions that satisfy the corresponding continuous constraints are guaranteed if the filter length is long enough. The advantages of this formulation are the guarantee of the stability of the transfer functions, applicability to design of rational infinite-impulse-response (IIR) filters without imposing specific filter structures, and the avoidance of iterative design of numerator and denominator coefficients. Our simulation results show that this design yields a significant improvement in the signal-to-noise ratio (SNR) and have a larger stability range, compared with the existing designs
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
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
Optimal design of magnitude responses of rational infinite impulse response filters
This correspondence considers a design of magnitude responses of optimal rational infinite impulse response (IIR) filters. The design problem is formulated as an optimization problem in which a total weighted absolute error in the passband and stopband of the filters (the error function reflects a ripple square magnitude) is minimized subject to the specification on this weighted absolute error function defined in the corresponding passband and stopband, as well as the stability condition. Since the cost function is nonsmooth and nonconvex, while the constraints are continuous, this kind of optimization problem is a nonsmooth nonconvex continuous functional constrained problem. To address this issue, our previous proposed constraint transcription method is applied to transform the continuous functional constraints to equality constraints. Then the nonsmooth problem is approximated by a sequence of smooth problems and solved via a hybrid global optimization method. The solutions obtained from these smooth problems converge to the global optimal solution of the original optimization problem. Hence, small transition bandwidth filters can be obtained
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
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