RGB-D object tracking has attracted considerable attention recently, achieving promising performance thanks to the symbiosis between visual and depth channels. However, given a limited amount of annotated RGB-D tracking data, most state-of-the-art RGB-D trackers are simple extensions of high-performance RGB-only trackers, without fully exploiting the underlying potential of the depth channel in the offline training stage. To address the dataset deficiency issue, a new RGB-D dataset named RGBD1K is released in this paper. The RGBD1K contains 1,050 sequences with about 2.5M frames in total. To demonstrate the benefits of training on a larger RGB-D data set in general, and RGBD1K in particular, we develop a transformer-based RGB-D tracker, named SPT, as a baseline for future visual object tracking studies using the new dataset. The results, of extensive experiments using the SPT tracker demonstrate the potential of the RGBD1K dataset to improve the performance of RGB-D tracking, inspiring future developments of effective tracker designs. The dataset and codes will be available on the project homepage: https://github.com/xuefeng-zhu5/RGBD1K.

LoRa, which is considered as an appealing wireless technique for Low-Power Wide-Area Networks (LPWANs), has found wide applications in fields such as smart cities, intelligent agriculture. Despite its popularity, there exists a growing concern about secure communications mainly due to the free frequency band and minimalist design specified in LoRa communications. For example, an attacker can forge messages to launch spoofing attack. To mitigate the threat, an authentication mechanism is needed. In this paper, we propose a lightweight node authentication scheme named SLoRa for LoRa networks by leveraging two physical layer features-Carrier Frequency Offset (CFO) and spatial-temporal link signature. In particular, we propose a novel CFO compensation algorithm, and identify slight CFO variations by adopting linear fitting for received upchirps to mitigate the noise's randomness on fine-grained CFO estimation. Besides, we can obtain fine-grained link signatures without the conventional de-convolution operation based on the theoretical analysis. Then, we show how these two physical-layer features complement each other to conquer the drift challenge brought by weather and environment variations. Combining these two features, SLoRa can distinguish whether the received signal is conveyed from a legitimate LoRa node or not. Experiments covering indoor and outdoor scenarios are conducted to demonstrate a high accuracy for node authentication in SLoRa, which is around 97% indoors and 90% outdoors.

Rail transit systems are important parts of modern cities such as the urban subways and intercity trains. During the travelling time, many passengers access the networks via cellular communications for working or entertainment. However, intermittent connection of LTE signals usually decreases the user experiences, especially due to the poor LTE link quality of underground subways. On the other hand, we observe that there are usually multiple cellular operators covering a city. For example, AT&T and T-Mobile in American cities, China Mobile and China Unicom in Chinese cities. Based on the different eNodeB distributions of different operators, we propose to study a new problem cross-operator switch. Unlike the handover between eNodeBs from one operator, cross-operator switch can switch to an eNodeB belonging to other operators. Although current mobile phones support dual SIM cards and Android supports the quick swap between two cards, it is still challenging to determine which eNodeB to switch because of no information exchange between different operators. To address this challenge, we propose a robust Crowdsensing based Switch (CrowdSwitch) solution dedicated for rail transit systems. CrowdSwitch utilizes mass mobile devices of passengers to sense and collect LTE signals along subway tracks, builds the signal heat map (S-Map) in the cloud servers, and finally recommends the optimal switch taking advantages of the known tracks. We implement CrowdSwitch in off-the-shelf mobile phones and conduct extensive experiments on Shanghai Metro. The results show that CrowdSwitch can depict the accurate LTE distribution and always recommend the optimal operator for users to connect.

Surfing the Internet with mobile phones is a popular fashion to kill time in subways. However, users usually meet the intermittent connectivity caused by the high- speed movement, leading to poor user experience. We observe that almost every city is covered by multiple cellular operators. In addition, more and more mobile phones support multiple SIM cards. These motivate us to leverage multiple cellular operators together for a reliable connectivity, which is also a trend for next generation cellular network. It is challenging to build collaborations between operators, because there is no interaction between different operators and the traditional handover methods would consume much time on cross-operator cellular detection. To address these challenges, we propose a Crowdsensing based Switch (CrowdSwitch) system between multiple cellular operators in subways. CrowdSwitch uses a large number of mobile phones to measure and collect wireless signals from different locations along subway tracks, uploads them to cloud servers for analysis, and finally recommends the optimal switch strategy to users. We implement CrowdSwitch in off-the-shelf mobile phones and conduct extensive experiments on Shanghai Metro. The results show that CrowdSwitch can depict the accurate LTE distribution and recommend the optimal operator for users to connect.