scholarly journals A Hybrid Algorithm for Predicting Median-Plane Head-Related Transfer Functions from Anthropometric Measurements

2019 ◽  
Vol 9 (11) ◽  
pp. 2323
Author(s):  
Xuejie Liu ◽  
Hao Song ◽  
Xiaoli Zhong
Keyword(s):  
Hybrid Algorithm ◽  
Transfer Functions ◽  
Spectral Characteristics ◽  
Median Plane ◽  
Objective Evaluation ◽  
Spectral Distortion ◽  
Anthropometric Parameters ◽  
Independent Components ◽  
Weight Coefficients ◽  
Selection Of

Since head-related transfer functions (HRTFs) represent the interactions between sounds and physiological structures of listeners, anthropometric parameters represent a straightforward way to customize (or predict) individualized HRTFs. This paper proposes a hybrid algorithm for predicting median-plane individualized HRTFs using anthropometric parameters. The proposed hybrid algorithm consists of three parts: decomposition of HRTFs; selection of key anthropometric parameters; and establishing a prediction formula. Firstly, an independent component analysis (ICA) is applied to median-plane HRTFs from multiple subjects to obtain independent components and subject-dependent weight coefficients. Then, a factor analysis is used to select key anthropometric parameters relevant to HRTFs. Finally, a regression formula that connects ICA weight coefficients to key anthropometric parameters is established by a multiple linear regression. Further, the effectiveness of the proposed hybrid algorithm is verified by an objective evaluation via spectral distortion and a subjective localization experiment. The results show that, when compared with generic Knowles Electronics Manikin for Acoustic Research (KEMAR) HRTFs, the spectral characteristics of the predicted HRTFs are closer to those of the individualized HRTFs. Moreover, the predicted HRTFs can alleviate front–back and up–down confusion and improve the accuracy of localization for most subjects.

scholarly journals A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral Modulation

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 685
Author(s):  
Jiao Jiao ◽  
Lingda Wu ◽  
Kechang Qian
Keyword(s):  
Spectral Characteristics ◽  
Real Data ◽  
Objective Evaluation ◽  
Coefficient Matrix ◽  
Spectral Information ◽  
Data Sets ◽  
Spectral Distortion ◽  
Connected Component ◽  
Spectral Modulation ◽  
Injection Coefficient

In order to improve the spatial resolution of multispectral (MS) images and reduce spectral distortion, a segmentation-cooperated pansharpening method using local adaptive spectral modulation (LASM) is proposed in this paper. By using the k-means algorithm for the segmentation of MS images, different connected component groups can be obtained according to their spectral characteristics. For spectral information modulation of fusion images, the LASM coefficients are constructed based on details extracted from images and local spectral relationships among MS bands. Moreover, we introduce a cooperative theory for the pansharpening process. The local injection coefficient matrix and LASM coefficient matrix are estimated based on the connected component groups to optimize the fusion result, and the parameters of the segmentation algorithm are adjusted according to the feedback from the pansharpening result. In the experimental part, degraded and real data sets from GeoEye-1 and QuickBird satellites are used to assess the performance of our proposed method. Experimental results demonstrate the validity and effectiveness of our method. Generally, the method is superior to several classic and state-of-the-art pansharpening methods in both subjective visual effect and objective evaluation indices, achieving a balance between the injection of spatial details and maintenance of spectral information, while effectively reducing the spectral distortion of the fusion image.

A Spectral Optimization Algorithm for Multi-Objective Prototype Selection

1995 ◽  
Author(s):  
Andy Dong ◽  
Alice M. Agogino
Keyword(s):  
Optimization Algorithm ◽  
Parametric Design ◽  
Selection Process ◽  
Objective Evaluation ◽  
Design Synthesis ◽  
Multi Objective ◽  
Trade Offs ◽  
Spectral Optimization ◽  
The Right ◽  
Selection Of

Abstract In design synthesis, engineering prototypes make an ideal representation medium for preliminary designs. Unlike parametric design wherein a pre-specified design is parametrically varied, design synthesis demands artistic creativity and engineering experience to transform the previously known components, relationships and designs into a new form. The process compels the designer to ascertain which prototypes will, in some sense, best satisfy the design task. The challenge in this assignment lies in selecting the “right” design prototype. This selection process typically entails an objective evaluation of different designs that perform the same functions or have similar intended behavior and comparing trade-offs between alternate designs. This paper introduces a multi-objective spectral optimization algorithm for the selection of design prototypes based upon their functional representations. The optimization algorithm returns an index of rank, scoring the functional similarity of the proposed design to the goal design. Two illustrative examples apply the algorithm to the selection of a heat fin and beam.

scholarly journals Competitive Binding Investigations and Quantitation in Surface-Enhanced Raman Spectra of Binary Dye Mixtures

2017 ◽  
Vol 72 (1) ◽  
pp. 60-68 ◽  
Author(s):  
Mircea A. Comanescu ◽  
Cyril Muehlethaler ◽  
John R. Lombardi ◽  
Marco Leona ◽  
Thomas A. Kubic
Keyword(s):  
Spectral Characteristics ◽  
Synthesis Method ◽  
Nanoparticle Synthesis ◽  
Peak Height ◽  
Calibration Models ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Standard Calibration ◽  
One Year ◽  
Selection Of

This research presents a study in surface-enhanced Raman quantitation of dyes present in mixtures of alizarin and purpurin using standard calibration curves and Langmuir isotherm calibration models. Investigations of the nature of competitive adsorption onto silver nanoparticles by centrifugation indicates that both dyes in the mixture interact with the nanoparticles simultaneously, but only the stronger adsorbing one is seen to dominate the spectral characteristics. Calibration can be carried out by careful selection of peaks characteristic to each dye in the mixture. Comparisons of peak height and peak area calibrations reveal that peak heights, when selected by the maximum value and accounting for peak shifts, prove the better model for quantitation. It is also shown that the microwave nanoparticle synthesis method produces stable nanoparticles with a shelf-life of at least one year that give very little variation within and between uses.

scholarly journals A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 507 ◽  
Author(s):  
José García ◽  
Paola Moraga ◽  
Matias Valenzuela ◽  
Hernan Pinto
Keyword(s):  
Transfer Function ◽  
Unsupervised Learning ◽  
Knapsack Problem ◽  
Hybrid Algorithm ◽  
Transfer Functions ◽  
Random Operators ◽  
Multidimensional Knapsack Problem ◽  
Multidimensional Knapsack ◽  
Learning Technique ◽  
Binarization Method

This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators.

Study on Gas Turbine-Based CCHP System with Multi-Objective Evaluation Index

2013 ◽  
Vol 860-863 ◽  
pp. 1366-1369 ◽  
Author(s):  
Dan Dan Sun ◽  
Cheng Yang ◽  
Fei Zeng
Keyword(s):  
Gas Turbine ◽  
Steam Generator ◽  
Heat Recovery ◽  
Objective Evaluation ◽  
Evaluation Index ◽  
Heat Recovery Steam Generator ◽  
Evaluation Indexes ◽  
Multi Objective ◽  
Combined Cooling ◽  
Weight Coefficients

Currently, there are many evaluation indexes for gas turbine-based combined cooling, heating and power （CCHP）. In this paper, a multi-objective evaluation index (MEI) model was suggested and weight coefficients were considered in the model. The CCHP system evaluated in this study was composed of gas turbine + heat recovery steam generator (HRSG ) + LiBr absorption chiller. The gas turbine-based CCHP system was evaluated and the component capacity was optimized with the proposed MEI. The study provides a reference for the allocation and operation of gas turbine-based CCHP.

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