With the development of the wireless communication technology and intelligent mobile phone, the positioning services based on Wi-Fi and mobile phone are increasingly demanded. In this paper, a Wi-Fi fingerprint localization method is proposed on the basis of important access points (IAP). For the Wi-Fi fingerprint, Wi-Fi access point with the highest received signal strength (RSS) is denoted as the important access point. At the localization stage, the fingerprints are chosen with the same IAP as the estimated fingerprint from the database. Then, the distance and the AP repetition of the fingerprints are used to calculate the similarity degree. The location of the fingerprint which matches the estimated fingerprint well can be regarded as the estimated location. Experimental results show that the proposed algorithm can achieve high accuracy in indoor environment.

A remote mobile health monitoring system with mobile phone and web service capabilities is proposed in this paper. It provides an end‐to‐end solution; specifically, (1) physiologic parameters, including respiration rate and heart rate, are measured by wearable sensors and recorded by a mobile phone which presents the graphical interface for the user to observe his/her health status more easily; (2) it provides doctors and family members with necessary data through a web interface and enables authorized personnel to monitor the patient’s condition and to facilitate remote diagnosis; and (3) it supports real‐time alarming and positioning services during an urgent situation, such as a tumble or a heart attack, so that unexpected events can be handled in a timely manner. Experimental results show that the proposed system can reliably monitor the physiologic parameters and conveniently report the user’s position.

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this article, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multimap matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance.

Existing lightweight salient object detection (SOD) methods aim to solve the problem of high computational costs that is prevalent with heavyweight methods. However, compared with heavyweight methods, the detection accuracy of lightweight methods is greatly reduced while real-time performance is not significantly improved. Therefore, we aim to establish a trade off between computational cost and detection performance by improving the network efficiency. We propose a fast and extremely lightweight end-to-end wavelet neural network (ELWNet) for real-time salient object detection. ELWNet can achieve salient object detection and segmentation at approximately 70FPS (GPU), 19FPS (CPU) with 76K parameters and 0.38G FLOPs. We introduce wavelet transform theory into a neural network, proposing a wavelet transform module (WTM), a wavelet transform fusion module (WTFM), a novel feature residual mechanism, and construct an efficient architecture. The wavelet transform theory is integrated into the neural network to realize the interaction between the features in the frequency and the time domain. Meanwhile, ELWNet does not rely on a pre-trained model, which significantly reduces redundant features. We validate the performance of ELWNet using five well-known datasets, and demonstrate state-of-the-art performance compared with 24 other SOD models in terms of being lightweight, detection accuracy and real-time capabilities. Our method maintains high detection performance while reducing the number of model parameters by approximately 99% compared with heavyweight methods.

Cross-view geo-localization refers to recognizing images of the same geographic target obtained from different platforms (such as drone-view, satellite-view and ground-view). However, cross-view geo-localization is challenging as image capture using different platforms coupled with extreme viewpoint variations can cause significant changes to the visual image content. Existing methods mainly focus on mining the fine-grained features or the contextual information in neighboring areas, but ignore the complete information of the entire image and the association of contextual information of adjacent regions. Therefore, a multi-branch joint representation learning network model based on information fusion strategies is proposed to solve this cross-view geo-localization problem. Firstly, we obtain feature information from the image through global information fusion branch and local information fusion branch to help the network learn the discernable information in the different images. In addition, a local-guided-global information fusion branch is introduced to make local information assist global features to enhance the learning of potential information in the images. Secondly, we introduced different information fusion strategies in each branch to increase the extraction of contextual information through expanding the global receptive field, thus improving the performance of the model. Finally, a series of experiments is carried out on four prevailing benchmark datasets, namely University-1652, SUES-200, CVUAS and CVACT datasets. The quantitative comparisons from the experiments clearly indicate that the proposed network framework has great performance. For example, compared with some state-of-the-art methods, the quantitative improvements of the R@1 and AP on the University-1652 datasets are 1.91%, 2.18% and 1.55%, 2.99% in both tasks, respectively.

Cross-view geo-localization refers to the task of matching the same geographic target using images obtained from different platforms, such as drone-view and satellite-view. However, the view angle of images obtained through different platforms will vary greatly, which can bring great challenges to the cross-view geo-localization task. Therefore, we propose a multilevel feedback joint representation learning network based on adaptive area elimination to solve the cross-view geo-localization problem. In our network model, we first process the extracted global features to obtain part-level and patch-level features. We then utilize these features as feedback to the global features to extract the contextual information in the global features and improve the robustness of the extracted features. In addition, as images obtained from different platforms differ, there will always be some interference when matching images. Therefore, we introduce an adaptive area elimination strategy to erase the interference information in the global features and assist the model in obtaining crucial information. On this basis, the feature correlation loss function is designed to constrain learning when using global feature information, thereby eliminating possible interference, which can improve the network model performance. Finally, a series of experiments is carried out using two well-known benchmarks, namely, University-1652 and SUES-200, and the experimental results show that the proposed network model achieves competitive results, thereby demonstrating the effectiveness of the proposed model.