In this paper, we propose a 2.45 GHz RF energy harvesting system to power a battery free Internet of Things (IoT) sensor node. The system operates from an RF signal with a power as low as -20 dBm and includes an RF to DC converter, a storage super capacitor and a voltage DC/DC up-conversion. The system components are sized to provide the required electric energy to operate the connected object. The tested connected object includes an ARM microcontroller, an ultra low-power sensor (temperature, pressure and humidity) and a Bluetooth Low Energy interface for a wireless transmission toward a terminal such as a smartphone or tablet. It requires 200 mu J per transmission cycle, which must be provided by the proposed harvesting system. This system is tested in an anechoic chamber and using a constant power RF signal to characterize the energy recovery time needed for measuring and transmitting the sensor data. Promising results show that the system is capable of sending data after 5 hours of RF energy harvesting from a source distant with one meter.
Unmanned aerial vehicle (UAV) technologies have played an important role in the beyond-fifth-generation (B5G) networks. To meet the increasing demand for UAV swarm communications, in this paper we propose an intra-swarm UAV-to-UAV (IaS-U2U) communication mechanism for enabling data services in the so-called out-of-coverage (OOC) scenario, where the conventional terrestrial cellular network infrastructure is not available, for example in the cases of mountains, oceans, deserts and forests. Specifically, an optimization problem is formulated by jointly considering the single-pair transmission rate (STR) and the propulsion power (PP), where the UAVs' pairing and relative positioning are taken into account. To solve the complicated joint optimization problem, we decompose it into the UAV pairing process and the relative position optimization (RPO) process, where in the latter a successive convex approximation (SCA) based RPO algorithm is proposed. Simulation results show that the new scheme can provide notable gains in terms of energy efficiency (EE) over selected benchmark methods under different conditions.
People with dyslexia have difficulties in fluently reading and writing characters which highly affect their learning progress. It is very important to identify dyslexic students that need intervention and extra support during their childhood. However, the waiting time for dyslexia assessment services is often long. To address the above problem, we propose a cloud-based early identification system of dyslexia. We design and develop a mobile app with AWS cloud platform as server. We have identified 27 representative traditional Chinese characters for handwriting data collection. After the first round of the data collection, 66 children aged 5-7 were recruited in Hong Kong. We carry out K-means clustering algorithm to investigate the characteristics of data points on a feature map for each character. We find out that some Chinese words contain more distinguishable characteristics for identifying children with dyslexia. Since children aged 5-7 are still learning how to write traditional Chinese characters properly, children with no risk of dyslexia still have certain possibilities of writing characters with characteristics of handwriting by children with dyslexia. It increases the difficulties of the early identification on developmental dyslexia of Chinese language. Finally, we present our findings and future work.
