scholarly journals Access-Point Centered Window-Based Radio-Map Generation Network

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6107
Author(s):  
Won-Yeol Kim ◽  
Soo-Ho Tae ◽  
Dong-Hoan Seo
Keyword(s):  
Moving Objects ◽  
Access Point ◽  
Floor Plan ◽  
Adversarial Learning ◽  
Training Time ◽  
Indoor Space ◽  
Indoor Radio ◽  
Map Generation ◽  
Generation Network ◽  
Radio Map

Fingerprinting is the term used to describe a common indoor radio-mapping positioning technology that tracks moving objects in real time. To use this, a substantial number of measurement processes and workflows are needed to generate a radio-map. Accordingly, to minimize costs and increase the usability of such radio-maps, this study proposes an access-point (AP)-centered window (APCW) radio-map generation network (RGN). The proposed technique extracts parts of a radio-map in the form of a window based on AP floor plan coordinates to shorten the training time while enhancing radio-map prediction accuracy. To provide robustness against changes in the location of the APs and to enhance the utilization of similar structures, the proposed RGN, which employs an adversarial learning method and uses the APCW as input, learns the indoor space in partitions and combines the radio-maps of each AP to generate a complete map. By comparing four learning models that use different data structures as input based on an actual building, the proposed radio-map learning model (i.e., APCW-based RGN) obtains the highest accuracy among all models tested, yielding a root-mean-square error value of 4.01 dBm.

Adversarial Learning-Enabled Automatic WiFi Indoor Radio Map Construction and Adaptation With Mobile Robot

2020 ◽  
Vol 7 (8) ◽  
pp. 6946-6954 ◽  
Author(s):  
Han Zou ◽  
Chun-Lin Chen ◽  
Maoxun Li ◽  
Jianfei Yang ◽  
Yuxun Zhou ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Adversarial Learning ◽  
Map Construction ◽  
Indoor Radio ◽  
Radio Map

scholarly journals Blind Stereo Image Quality Evaluation Based on Convolutional Network and Saliency Weighting

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Wujie Zhou
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Rapid Development ◽  
Stereo Image ◽  
Convolutional Network ◽  
Image Quality Evaluation ◽  
Map Generation ◽  
Versatile Tool ◽  
Generation Network ◽  
Quality Map

With the rapid development of stereo image applications, there is an increasing demand to develop a versatile tool to evaluate the perceived quality of stereo images. Therefore, in this study, a blind stereo image quality evaluation (SIQE) algorithm based on convolutional network and saliency weighting is proposed. The main network framework used by the algorithm is the quality map generation network, which is used to train the distortion image dataset and quality map label to obtain an optimal network framework. Finally, the left view, right view, and cyclopean view of the stereo image are used as inputs to the network frame, respectively, and then weighted fusion for the final stereo image quality score. The experimental results reveal that the proposed SIQE algorithm can improve the accuracy of the image quality prediction and prediction score to a certain extent and has good generalization ability.

scholarly journals Discovering Influential Positions in RFID-Based Indoor Tracking Data

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 330
Author(s):  
Ye Jin ◽  
Lizhen Cui
Keyword(s):  
Moving Objects ◽  
Indoor Localization ◽  
Rapid Development ◽  
Shopping Mall ◽  
Position Tracking ◽  
Tracking Data ◽  
H Index ◽  
Indoor Space ◽  
Indoor Tracking ◽  
Indoor Position Tracking

The rapid development of indoor localization techniques such as Wi-Fi and RFID makes it possible to obtain users’ position-tracking data in indoor space. Indoor position-tracking data, also known as indoor moving trajectories, offer many new opportunities to mine decision-making knowledge. In this paper, we study the detection of highly influential positions from indoor position-tracking data, e.g., to detect highly influential positions in a business center, or to detect the hottest shops in a shopping mall according to users’ indoor position-tracking data. We first describe three baseline solutions to this problem, which are count-based, density-based, and duration-based algorithms. Then, motivated by the H-index for evaluating the influence of an author or a journal in academia, we propose a new algorithm called H-Count, which evaluates the influence of an indoor position similarly to the H-index. We further present an improvement of the H-Count by taking a filtering step to remove unqualified position-tracking records. This is based on the observation that many visits to a position such as a gate are meaningless for the detection of influential indoor positions. Finally, we simulate 100 moving objects in a real building deployed with 94 RFID readers over 30 days to generate 223,564 indoor moving trajectories, and conduct experiments to compare our proposed H-Count and H-Count* with three baseline algorithms. The results show that H-Count outperforms all baselines and H-Count* can further improve the F-measure of the H-Count by 113% on average.

scholarly journals WI-FI RSS FINGERPRINTING FOR INDOOR LOCALIZATION USING AUGMENTED REALITY

2020 ◽  
Vol V-4-2020 ◽  
pp. 57-64
Author(s):  
A. Ahmad ◽  
P. Claudio ◽  
A. Alizadeh Naeini ◽  
G. Sohn
Keyword(s):  
Augmented Reality ◽  
Indoor Localization ◽  
Facility Management ◽  
Ease Of Use ◽  
Reference Points ◽  
Floor Plan ◽  
Indoor Space ◽  
Fingerprinting Method ◽  
Mapping Techniques ◽  
And Performance

Abstract. Indoor localization has attracted the attention of researchers for wide applications in areas like construction, facility management, industries, logistics, and health. The Received Signal Strength (RSS) based fingerprinting method is widely adopted because it has a lower cost over other methods. RSS is a measurement of the power present in the received radio signal. While this fingerprinting method is very popular, there is a significant amount of effort required for collecting fingerprints for indoor space. In this paper, we propose an RSS fingerprinting method using Augmented Reality (AR) that does not rely on an external sensor resulting in ease of use and maintenance. This method uses spatial mapping techniques to help align the floor plan of existing buildings; then, after the alignment, we map local device coordinates to global coordinates. After this process, we partition the space in equally distanced reference points for RSS fingerprint collection. We developed an application for Microsoft HoloLens to align the floor plan and collect fingerprints on reference points. Then we tested collected fingerprints with existing RSS based indoor localization methods for its accuracy and performance.

scholarly journals Learning Attention for Object Tracking with Adversarial Learning Network

2020 ◽  
Author(s):  
Xu Cheng ◽  
Chen Song ◽  
Yongxiang Gu ◽  
Beijing Chen ◽  
Lin Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Large Scale ◽  
Fine Tuning ◽  
Object Localization ◽  
Generative Adversarial Network ◽  
Adversarial Learning ◽  
Training Time ◽  
Learning Network ◽  
Object State

Abstract Artificial intelligence has been widely studied on solving intelligent surveillance analysis and security problems in recent years. Although many multimedia security approaches have been proposed by using deep learning network model, there are still some challenges on their performances which deserve in-depth research. On one hand, high computational complexity of current deep learning methods makes it hard to be applied to real-time scenario. On the other hand, it is difficult to obtain the specific features of a video by fine-tuning the network online with the object state of the first frame, which fails to capture rich appearance variations of the object. To solve above two issues, in this paper, an effective object tracking method with learning attention is proposed to achieve the object localization and reduce the training time in adversarial learning framework. First, a prediction network is designed to track the object in video sequences. The object positions of the first ten frames are employed to fine-tune prediction network, which can fully mine a specific features of an object. Second, the prediction network is integrated into the generative adversarial network framework, which randomly generates masks to capture object appearance variations via adaptively dropout input features. Third, we present a spatial attention mechanism to improve the tracking performance. The proposed network can identify the mask that maintains the most robust features of the objects over a long temporal span. Extensive experiments on two large-scale benchmarks demonstrate that the proposed algorithm performs favorably against state-of-the-art methods.

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