室内化学污染与净化方法的探讨

分析了产生室内化学污染的原因。结合城市住宅和办公室的实际情况，指出了控制室内污染源是减轻室内化学污染的根本措施，进行室内自然通风和净化器净化的方法是减轻室内化学污染的有效措施。提出了活性炭吸附剂的改进建议。     

室内概率阈值反向最近邻查询

室内空间变得越发的庞大和复杂,随之产生了越来越多的室内空间查询需求.目前已有文献提出了针对室内空间环境的范围查询和最近邻查询,而作为常见的空间查询类型的反向最近邻查询,尚未有相关的研究.为此,提出了室内概率阈值反向最近邻查询和基于定位设备的设备可达图模型.在图模型基础上,提出了室内概率阈值反向最近邻查询处理算法,该算法由基于图模型的批量剪枝、基于室内距离的剪枝、基于概率的剪枝和概率计算4部分构成,通过剪枝策略修剪掉不可能出现在结果集中的对象,从而缩小了查询空间,提高了效率.     

基于网络教育的网上支付系统的设计和实现

现代远程教育——网络教育的影响力越来越大,接受这一新教育模式的学生规模也在日益增长,但教育的传统收费方式限制了学生的异地学习。探讨了引进网上支付这个已经被其他行业广泛接受的支付方式的可能性,设计了整个报名、支付一体化系统的流程架构以及讨论了根据目前情况具体实现的方法。     

基于麦克风阵列声音信号定位方法的研究

提出了一种在嘈杂环境下利用麦克风阵列对声音信号定位的方法。该方法对每个麦克风采集的声音信号进行经验模态分解,然后根据各个IMF信号的归一化能量挑选出主要的IMF分量进行信号重构,从而实现对信号进行降噪处理。将降噪后的信号使用互功率谱相位法进行相关运算,计算出不同麦克风声音信号出现的时间差异。根据信号时延和麦克风之间的几何位置关系计算出声音信号的位置。为了验证本文所提出的定位算法,进行了语音信号定位实验,通过实验实测的数据分析对比分析,验证了本文提出的方法比传统的定位算法要优越。     

智能停车场管理系统的探讨

针对传统停车场存在着收费管理混乱及整体运作效率低等问题,设计了一种基于RFID的智能停车场管理系统。介绍了系统的基本功能、总体结构,并对RFID技术、车位引导技术和车牌自动识别技术进行了详细的探讨。     

An indoor localization system in a multiblock workspace

As service robots and other ubiquitous technology have evolved, an increasing need for the autonomous navigation of mobile objects has arisen. In a large number of localization schemes, the absolute-position estimation method, which relies on navigation beacons or landmarks, has been widely used as it has the advantages of being economical and accurate. However, only a few of these schemes have expanded their application to complicated workspaces, or those that have many rooms or blocks. As the navigation of mobile objects in complicated workspaces is vital for ubiquitous technology, multiblock navigation is necessary. This article presents methodologies and techniques for the multiblock navigation of the indoor localization system with active beacon sensors. This new indoor localization system design includes ultrasonic attenuation compensation, dilution-of-precision analysis, and a fault detection and isolation algorithm using redundant measurements. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

室内定位技术分析

对位置信息的识别是普适计算中的一个重要研究领域,其中大多教的应用都是在室内环境下.介绍位置识别的一般技术与方法,展示目前在室内定位领域兵有代表性的研究性和商业性的定位系统,分析各种技术的长处及其局限性,提出今后的研究方向.     

Passive infrared sensor dataset and deep learning models for device-free indoor localization and tracking

Location estimation or localization is one of the key components in IoT applications such as remote health monitoring and smart homes. Amongst device-free localization technologies, passive infrared (PIR) sensors are one of the promising options due to their low cost, low energy consumption, and good accuracy. However, most of the existing systems are complexly designed and difficult to deploy in real life, in addition, there is no public dataset available for researchers to benchmark their proposed localization and tracking methods. In this paper, we propose a system and a dataset collected from our PIR system consisting of commercial-of-the-shelf (COTS) sensors without any modification. Our dataset includes profile data of 36 classes that have over 1,000 samples of different walking directions and test data consisting of multiple scenarios with a sequence length of over 2,000 timesteps. To evaluate our system and dataset, we implement various deep learning methods such as CNN, RNN, and CNN–RNN. Our results prove the applicability and feasibility of our system and illustrate the viability of deep learning methods for PIR-based localization and tracking. We also show that our dataset can be converted for coordinate estimation so that deep learning methods and particle filter approaches can be applied to estimate coordinates. As a result, the best performer achieves a distance error of 0.25 m.     

Automated construction of Wi-Fi-based indoor logical location predictor using crowd-sourced photos with Wi-Fi signals

Owing to the recent proliferation of smartphones and the SNS, a large number of images taken by smartphones at various places have been uploaded to SNSs. In addition, smartphones are equipped with various sensors such as Wi-Fi modules that enable us to generate an image associated with the sensory information that represents the context in which the image was captured. This study demonstrates the benefits of images associated with Wi-Fi signals in the automated construction of a Wi-Fi-based indoor logical location classifier that predicts a semantic location label of a user’s position for shopping complexes. In this study, a logical location class refers to the store class label in a shopping complex, such as Starbucks and H&M. Given a collection of images associated with Wi-Fi signals taken at a shopping complex and the complex’s floor plan, the proposed method first estimates the store label at which an image was taken by analyzing the image and crawled online images of branch stores. Then, the 2D coordinates of the images taken at branch stores on the floor coordinate system can be estimated using the floor plan. Subsequently, by using the Wi-Fi signals of the branch store images and their estimated 2D coordinates, we construct a transformation function that maps Wi-Fi signals onto the 2D coordinates, and we adopt this function to predict an indoor location class of an observed Wi-Fi scan from a smartphone possessed by an end user. The proposed transformation function comprises an ensemble of sub-functions designed based on CVAEs. Finally, we demonstrate the effectiveness of the proposed method for three actual shopping complexes.     

FM radio for indoor localization with spontaneous recalibration

The position of mobile users has become highly important information in pervasive computing environments. Indoor localization systems based on Wi–Fi signal strength fingerprinting techniques are widely used in office buildings with an existing Wi–Fi infrastructure. Our previous work has proposed a solution based on exploitation of a FM signal to deal with environments not covered with Wi–Fi signal or environments with only a single Wi–Fi access point. However, a general problem of indoor wireless positioning systems pertains to signal degradation due to the environmental factors affecting signal propagation. Therefore, in order to maintain a desirable level of localization accuracy, it becomes necessary to perform periodic calibrations of the system, which is either time consuming or requires dedicated equipment and expert knowledge. In this paper, we present a comparison of FM versus Wi–Fi positioning systems and a combination of both systems, exploiting their strengths for indoors positioning. We also address the problem of recalibration by introducing a novel concept of spontaneous recalibration and demonstrate it using the FM localization system. Finally, the results related to device orientation and localization accuracy are discussed.