The capacity of general mobile ad hoc networks (MANETs) remains largely unknown up to now, which significantly hinders the development and commercialization of such networks. Available throughput capacity studies of MANETs mainly focus on either the order sense capacity scaling laws, the exact throughput capacity under a specific algorithm, or the exact throughput capacity without a careful consideration of critical wireless interference and transmission range issues. In this paper, we explore the exact throughput capacity for a class of MANETs, where we adopt group-based scheduling to schedule simultaneous link transmissions for interference avoidance and allow the transmission range of each node to be adjusted. We first determine a general throughput capacity upper bound for the concerned MANETs, which holds for any feasible packet delivery algorithm in such networks. We then prove that the upper bound we determined is just the exact throughput capacity for this class of MANETs by showing that for any traffic input rate within the throughput capacity upper bound, there exists a corresponding two-hop relay algorithm to stabilize such networks. A closed-form upper bound for packet delay is further derived under any traffic input rate within the throughput capacity. Finally, based on the network capacity result, we examine the impacts of transmission range and node density upon network capacity.

The original single-shot multibox detector (SSD) algorithm has good detection accuracy and speed for regular object recognition. However, the SSD is not suitable for detecting small objects for two reasons: 1) the relationships among different feature layers with various scales are not considered, 2) the predicted results are solely determined by several independent feature layers. To enhance its detection capability for small objects, this study proposes an improved SSD-based algorithm called proportional channels' fusion SSD (PCF-SSD). Three enhancements are provided by this novel PCF-SSD algorithm. First, a fusion feature pyramid model is proposed by concatenating channels of certain key feature layers in a given proportion for object detection. Second, the default box sizes are adjusted properly for small object detection. Third, an improved loss function is suggested to train the above-proposed fusion model, which can further improve object detection performance. A series of experiments are conducted on the public database Pascal VOC to validate the PCF-SSD. On comparing with the original SSD algorithm, our algorithm improves the mean average precision and detection accuracy for small objects by 3.3% and 3.9%, respectively, with a detection speed of 40FPS. Furthermore, the proposed PCF-SSD can achieve a better balance of detection accuracy and efficiency than the original SSD algorithm, as demonstrated by a series of experimental results.