Tzung-Shi Chen

In this paper, a home care assistance system is proposed for elder human activity recognition that combines 2D LiDAR and deep learning technology. Firstly, the 2D LiDAR is used to scan the room interior data, and cluster algorithms are used to identify high-density areas that may be objects. The resulting data is then classified into human and non-human clusters. The coordinates of the human clusters are sequentially recorded to generate trajectory graphs. These trajectory graphs possess both spatial and temporal attributes, and are processed using spatial-temporal graph convolutional networks to achieve accurate classification of human activities. Furthermore, the system is designed to detect abnormal trajectories that may indicate a fall, and can send out a warning signal to notify the caregiver for emergency assistance. With these advanced features, the home care assistance system can improve the safety and well-being of elderly individuals living alone or with limited assistance.

In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs.

The isolation of a virus using cell culture to observe its cytopathic effects (CPEs) is the main method for identifying the viruses in clinical specimens. However, the observation of CPEs requires experienced inspectors and excessive time to inspect the cell morphology changes. In this study, we utilized artificial intelligence (AI) to improve the efficiency of virus identification. After some comparisons, we used ResNet-50 as a backbone with single and multi-task learning models to perform deep learning on the CPEs induced by influenza, enterovirus, and parainfluenza. The accuracies of the single and multi-task learning models were 97.78% and 98.25%, respectively. In addition, the multi-task learning model increased the accuracy of the single model from 95.79% to 97.13% when only a few data of the CPEs induced by parainfluenza were provided. We modified both models by inserting a multiplexer and de-multiplexer layer, respectively, to increase the correct rates for known cell lines. In conclusion, we provide a deep learning structure with ResNet-50 and the multi-task learning model and show an excellent performance in identifying virus-induced CPEs.

Coordinated Multi-Point (CoMP) is an important technique in B4G/5G networks. With CoMP, multiple base stations can be clustered to compose a cooperating set to improve system throughput, especially for the users in cell edges. Existed studies have discussed how to mitigate overloading scenarios and enhance system throughput with CoMP statically. However, static cooperation fixes the set size and neglects the fast-changing of B4G/5G networks. Thus, this paper provides a full study of off-peak hours and overloading scenarios. During off-peak hours, we propose to reduce BSs’ transmission power and use the free radio resource to save energy while guaranteeing users’ QoS. In addition, if large-scale activities happen with crowds gathering or in peak hours, we dynamically compose the cooperating set based on instant traffic requests to adjust base stations’ BSs’ transmission power; thus, the system will efficiently offload the traffic to the member cells which have available radio resources in the cooperating set. Experimental results show that the proposed schemes enhance system throughput, radio resource utilization, and energy efficiency, compared to other existing schemes.

In this paper, we propose an adaptive mechanism of trap coverage with a robust area coverage model, which employs mobile sensors for applications in mobile sensor networks (MSNs) and in the Internet of Things (IoT). Many promising applications including the target tracking and mobile sensing can be reasonably realized and improved after incorporating the characteristics of trap coverage mechanism on the basis of adaptively adjusting the trap size and sensor mobility. The trap evidently exists throughout the deployment of sensors in WSNs, in which the target has predictably vanished or application service remains undetected. The properties of the trap in the trap coverage mechanism are contrary to the purpose of target tracking and services detection. This creates a serious problem for target tracking and services detection in MSNs. This study proposes a robust mechanism of trap coverage involving the use of mobile sensors in target tracking and services detection for applications in MSNs. The experimental results revealed that the proposed method efficiently reduces the target-missing time and the total number of unavailable sensors, and also enhances the maintenance of trap coverage through the movement of mobile sensors in MSNs based on the simulation results and analysis. Performance comparison is conducted by adjusting the total amount of sensors in an adaptively trap coverage. The novel mechanism makes MSNs a much more flexible and provides a cost-effective solution in IoT than static sensor networks.