New evaluation scheme for software function approximation with non-uniform segmentation

International audience

A new version of the nearest-neighbour approximating and eliminating search algorithm (AESA) with linear preprocessing time and memory requirements

For applying micro/nano technologies and Micro-Electro-Mechanical System (MEMS) technologies in the Radio Frequency (RF) field to manufacture miniature microstrip antennas. A novel MEMS dual-band patch antenna designed using slot-loaded and short-circuited size-reduction techniques is presented in this paper. By controlling the short-plane width, the two resonant frequencies, f₁₀ and f₃₀, can be significantly reduced and the frequency ratio (f₃₀/f₁₀) is tunable in the range 1.7~2.3. The Haar-Wavelet-Based multiresolution time domain (H-MRTD) with compactly supported scaling function for a full three-dimensional (3-D) wave to Yee's staggered cell is used for modeling and analyzing the antenna for the first time. Associated with practical model, an uniaxial perfectly matched layer (UPML) absorbing boundary conditions was developed, In addition , extending the mathematical formulae to an inhomogenous media. Numerical simulation results are compared with those using the conventional 3-D finite-difference time-domain (FDTD) method and measured. It has been demonstrated that, with this technique, space discretization with only a few cells per wavelength gives accurate results, leading to a reduction of both memory requirement and computation time.

In topology optimization of transient problems, memory requirements and computational costs often become prohibitively large due to the backward‐in‐time adjoint equations. Common approaches such as the Checkpointing (CP) and Local‐in‐Time (LT) algorithms reduce memory requirements by dividing the temporal domain into intervals and by computing sensitivities on one interval at a time. The CP algorithm reduces memory by recomputing state solutions instead of storing them. This leads to a significant increase in computational cost. The LT algorithm introduces approximations in the adjoint solution to reduce memory requirements and leads to a minimal increase in computational effort. However, we show that convergence can be hampered using the LT algorithm due to errors in approximate adjoints. To reduce memory and/or computational time, we present two novel algorithms. The hybrid Checkpointing/Local‐in‐Time (CP/LT) algorithm improves the convergence behavior of the LT algorithm at the cost of an increased computational time but remains more efficient than the CP algorithm. The Parallel‐Local‐in‐Time (PLT) algorithm reduces the computational time through a temporal parallelization in which state and adjoint equations are solved simultaneously on multiple intervals. State and adjoint fields converge concurrently with the design. The effectiveness of each approach is illustrated with two‐dimensional density‐based topology optimization problems involving transient thermal or flow physics. Compared to the other discussed algorithms, we found a significant decrease in computational time for the PLT algorithm. Moreover, we show that under certain conditions, due to the use of approximations in the LT and PLT algorithms, they exhibit a bias toward designs with short characteristic times. Finally, based on the required memory reduction, computational cost, and convergence behavior of optimization problems, guidelines are provided for selecting the appropriate algorithms.

Traditional extension has been a top down process: scientists developed products and methods which, following promulgation by extension agencies, farmers were expected to adopt. Extension agents considered farmers who failed to adopt new techniques to be recalcitrant and irrational. Farmers' attitudes and their lack of knowledge were considered to be the main barriers to adoption. Little consideration was given to farmers' points of view. The idea that resistance or reluctance to change might have some logical basis was never considered. Recent analysis reveals that most ‘barriers’ have a rational basis and can be categorised as: conflicting information; risk; implementation costs and capital outlay; intellectual outlay; loss of flexibility; complexity; and incompatibility with other aspects of farm management and farm and personal objectives. Social and perceptional issues also affect adoption. This paper examines the conceptual concerns about the efficacy of traditional extension practices and ...

Droop control is a common control strategy used in DC microgrids to manage power distribution among parallel Distributed Energy Resource (DER) converters. With droop control, the static dc bus voltage varies in a specified range according to load conditions. In order to maintain the dynamic dc bus voltage in the same range during load changes, droop-controlled DER converters usually have either large output capacitance or high voltage loop bandwidth. The latter enables the adoption of small output capacitance that improves the dc bus behaviour also during bus faults. However, with traditional control techniques, for example, the single-sampled PID controllers, the voltage loop bandwidth is limited by the control delay, including the computation time and the modulation delay. From this point of view, oversampled hysteresis control is appealing, because it reduces the modulation delay by removing the modulator. Also, the hysteretic nature allows nonlinear switching actions during transient, which further speeds up the dynamic response. Consequently, small output capacitance can be used without concerns of overvoltage and undervoltage. Moreover, hysteresis control is typically implemented on Field Programmable Gate Arrays (FPGAs). Differently, this paper focuses on the less-expensive Digital Signal Processors (DSPs) implementation. The influence of additional computation time introduced by DSP on the converter's stability is also analyzed. Simulations and experiments are carried out to verify the performance of the DSP-implemented hysteresis droop controller.

To mitigate outpatient care delivery inefficiencies induced by resource shortages and demand heterogeneity, this paper focuses on the problem of allocating and sequencing multiple medical resources so that patients scheduled for clinical care can experience efficient and coordinated care with minimum total waiting time. We leverage highly granular location data on people and medical resources collected via Real-Time Location System technologies to identify dominant patient care pathways. A novel two-stage Stochastic Mixed Integer Linear Programming model is proposed to determine the optimal patient sequence based on the available resources according to the care pathways that minimize patients’ expected total waiting time. The model incorporates the uncertainty in care activity duration via sample average approximation.We employ a Monte Carlo Optimization procedure to determine the appropriate sample size to obtain solutions that provide a good trade-off between approximation accuracy and computational time. Compared to the conventional deterministic model, our proposed model would significantly reduce waiting time for patients in the clinic by 60%, on average, with acceptable computational resource requirements and time complexity. In summary, this paper proposes a computationally efficient formulation for the multi-resource allocation and care sequence assignment optimization problem under uncertainty. It uses continuous assignment decision variables without timestamp and position indices, enabling the data-driven solution of problems with real-time allocation adjustment in a dynamic outpatient environment with complex clinical coordination constraints.

Reducing message-length variations in resource-constrained embedded systems implemented using the Controller Area Network (CAN) protocol

On a way to save memory when solving time domain boundary integral equations for acoustic and vibroacoustic applications

A new high-speed redundant CORDIC processor is designed and implemented based on the double rotation method, which turns out to be the two-dimensional (2D) Householder CORDIC, a special case of the generalized Householder CORDIC in the 2D Euclidean vector space. The new processor has the advantages of regular structure and high throughput rate. The pipelined structure with radix-2 signed-digit (SD) redundant arithmetic is adopted to reduce the carry-propagation delay of the adders while the digit-serial structure alleviates the burden of the hardware cost and I/O requirement. Compared to previously proposed designs, the new CORDIC processor preserves the constant scaling factor, an important merit of the original CORDIC, and thus does not require any complicated division or square-root operations for variable scaling factor calculation. Furthermore, the processor is well suited to VLSI implementation since it does not call for any irregularly inserted correcting iterations. Both angle calculation mode for computing trigonometric function and vector rotation mode for plane rotations are supported. Practical VLSI chip implementation of the fixed-point redundant CORDIC processor using 0.6 μm standard cell library is given including detailed numerical error analysis.

We recently proposed the Edgewise Greedy Algorithm (EGA) for learning a decomposable Markov network of treewidth k approximating a given joint probability distribution of n discrete random variables. The main ingredient of our algorithm is the stepwise forward selection algorithm (FSA) due to Deshpande, Garofalakis, and Jordan. EGA is an efficient alternative to the algorithm (HGA) by Malvestuto, which constructs a model of treewidth k by selecting hyperedges of order k+1. In this paper, we present results of empirical studies that compare HGA, EGA and FSA-K which is a straightforward application of FSA, in terms of approximation accuracy (measured by KL-divergence) and computational time. Our experiments show that (1) on the average, all three algorithms produce similar approximation accuracy; (2) EGA produces comparable or better approximation accuracy and is the most efficient among the three. (3) Malvestuto's algorithm is the least efficient one, although it tends to produce better accuracy when the treewidth is bigger than half of the number of random variabls; (4) EGA coupled with local search has the best approximation accuracy overall, at a cost of increased computation time by 50 percent.

Optimal analysis for optimal design of cyclic symmetric structures subject to frequency constraints

In this paper we present two numerical methods for the calculation of Gaussian field incident on a pinhole. The first method is based on Kirchhoff's Huygens aperture field method and the other on the spectrum of plane wave approach. Numerous calculations proved that Kirchhoff's Huygens method is faster and gives accurate results in the Fraunchofer region. The plane wave spectrum is the only one giving valid results ranging from the radiating aperture to, theoretically any distance, but the computational time is rapidly increasing for distances where the other method gives accurate and fast results.

To improve the performance of denoising algorithm for industrial radiographic testing (RT) images, an optimized wavelet denoising algorithm using hybrid noise model (WDHM) is proposed. Firstly, a hybrid noise model is constructed by analyzing the noise components to solve the problem of noise variance estimation when wavelet denoising is adopted for RT images. Then, a wavelet processing threshold is determined by the hybrid noise model, and noise in RT images is reduced by wavelet denoising. Meanwhile, a fixed-point median processing is used for eliminating image distortion caused by wavelet denoising. Comparing with conventional wavelet and Wiener filter denoising, the experimental results show that WDHM not only gets good denoising effectiveness, but also keeps a good balance between removing noise and preserving edge characteristics well.

Fast computation of RBF coefficients using FFT