As unmanned aerial vehicle (UAV) emerged as a flexible acquisition system that is widely used in military and civilian fields, efficient target tracking algorithm is in urgent need for UAV‐based computer vision. Although research studies have been reported on typical interferences in the tracking process such as scale change, occlusion, distortion etc., some issues still exist for the target tracking algorithm based on UAV vision. This study exploited the features hidden in different colour spaces, and proposed a multi‐feature multi‐filter fusion tracking method that combines the HSV (hue, saturation and value) colour space with the histogram of oriented gradient (HOG) feature. The HSV colour space is proved to be able to discriminate objects under different conditions. The HOG of each HSV channel is utilised to train Kernelised correlation filters (KCF), respectively. The final tracking result is the candidate result with the biggest peak sidelobe ratio (PSR). Computer simulations proved that the fusion strategy proposed in this study can effectively improve the tracking performance of the tracker especially when the image sequences are interfered by deformation, occlusion, low resolution, etc. The performance of the tracker is also tested on UAV.

This paper proposes a low power application dedicated protocol stack which consists of a contention‐constrained p‐CSMA (CCP) protocol over the LoRa modulation technique and a consecutive successful transmission controlled transport (CSTCT) protocol. The aim of these innovative protocols is to boost the network performance in terms of throughput and energy consumption while the transmission latency is still acceptable. As a reference, firstly, p‐CSMA is evaluated with the consideration of under‐noise‐level communication. Then, the CCP protocol is proposed with the consideration of the speciality of the carrier sensing feature provided by the LoRa modem, which differs from the traditional energy level detection carrier sensing method. The CSTCT protocol resides at the CCP MAC layer which is light‐weight and devised to further improve the overall throughput. The performance of the protocol stack is evaluated in simulations using various settings with predefined parameters that are drawn from experiments. The results show that our CCP MAC protocol and CSTCT transport protocol provide 10% higher throughput compared to the classic p‐CSMA when the offered load is lower than 1.3 while the energy consumption is reduced by a factor of 2 and it remains latency limited.

Low‐cost off‐the‐shelf particulate matter (PM) sensors have the potentiality to be used for evaluating the air quality in outdoor settings. Monitoring of air quality in surface coal mines is an example of such applications. In coal mines, long‐term exposure to inhalation of coal dust is harmful and can lead to coal workers' pneumoconiosis, which is a potentially disabling lung disease. Therefore, continual monitoring of air quality in coal mines is a must and vital and can potentially assist in preventing such diseases. Although, using and deploying of the existing low‐cost and lightweight sensors can help to improve monitoring resolution in a much cost‐effective manner, there are some concerns regarding the reliability of the collected data from these sensors. Therefore, low‐cost PM sensors are required to initially be compared with the standard reference instruments and then be calibrated. In this study, three different types of low‐cost, light‐scattering‐based widely available PM sensors (Shinyei PPD42NS, Sharp GP2Y1010AU0F and Laser SEN0177) are evaluated, compared, and calibrated with the reference instruments in a controlled environment as well as in a field experiment (surface coal mine).