Accurate acoustic source localization at a low sampling rate (less than 10 kHz) is still a challenging problem for small portable systems, especially for a multitasking micro-embedded system. A modification of the generalized cross-correlation (GCC) method with the up-sampling (US) theory is proposed and defined as the US-GCC method, which can improve the accuracy of the time delay of arrival (TDOA) and source location at a low sampling rate. In this work, through the US operation, an input signal with a certain sampling rate can be converted into another signal with a higher frequency. Furthermore, the optimal interpolation factor for the US operation is derived according to localization computation time and the standard deviation (SD) of target location estimations. On the one hand, simulation results show that absolute errors of the source locations based on the US-GCC method with an interpolation factor of 15 are approximately from 1/15- to 1/12-times those based on the GCC method, when the initial same sampling rates of both methods are 8 kHz. On the other hand, a simple and small portable passive acoustic source localization platform composed of a five-element cross microphone array has been designed and set up in this paper. The experiments on the established platform, which accurately locates a three-dimensional (3D) near-field target at a low sampling rate demonstrate that the proposed method is workable.

Intelligent technology has had a profound impact on working patterns and the needs of office workers. The corresponding office environments have also changed to some extent. As more and more intelligent functions are integrated, a question as to whether the intelligent office environment really meets needs of office workers needs to be considered. In this study, a methodology that is applicable to improve biophilic and intelligent designs simultaneously in office environments was developed according to the needs of office workers. The observations of office environments, interviews, and questionnaires of an office group were performed. The physical and psychological health needs of office workers, as well as the correlations between six office environmental factors and six intelligent functions were discussed using the data of 211 office workers. Then, 12 patterns of biophilic design were addressed from existing biophilic theory, and the results of a user study, as well as the corresponding design framework of intelligent office products, which are the carriers of intelligent functions, were described. The feasibility and the positive influence in the office have been illustrated through a design case and its evaluation in real office environments. This study mainly explores the design of office environments from the perspective of humans with the hope to provide helpful guidance for intelligent offices and related workspace design in the future.

This paper develops a fast filtering algorithm based on vibration systems theory and neural information exchange approach. The characters, including the derivation process and parameter analysis, are discussed and the feasibility and the effectiveness are testified by the filtering performance compared with various filtering methods, such as the fast wavelet transform algorithm, the particle filtering method and our previously developed single degree of freedom vibration system filtering algorithm, according to simulation and practical approaches. Meanwhile, the comparisons indicate that a significant advantage of the proposed fast filtering algorithm is its extremely fast filtering speed with good filtering performance. Further, the developed fast filtering algorithm is applied to the navigation and positioning system of the micro motion robot, which is a high real-time requirement for the signals preprocessing. Then, the preprocessing data is used to estimate the heading angle error and the attitude angle error of the micro motion robot. The estimation experiments illustrate the high practicality of the proposed fast filtering algorithm.

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