Multi-object tracking (MOT) is essential to many tasks related to the smart transportation. Detecting and tracking humans on the road can give a vital feedback for either the moving vehicle or traffic control to ensure better driving safety and traffic flow. However, most trackers face a common problem of identity (ID) switch, resulting in an incomplete human trajectory prediction. In this paper, we propose a Transformer-based tracklet linking method called TransLink to mitigate the association failures. Specifically, the self-attention mechanism is well exploited to get the feature representation for tracklets, followed by a multilayer perceptron to predict the association likelihood, which can be further used in determining the tracklet association. Experiments on the MOT dataset demonstrate the effectiveness of the proposed module in lifting the tracking performances.

With the rapid development of autonomous driving, tracking on-road pedestrians raises more attention in the public. Currently, most researches focus on single camera based tracking or tracking across multiple static cameras. Tracking across multiple moving cameras has not been well studied yet. In this paper, we propose a workflow for tracking pedestrians across multiple moving cameras, leveraging the state-of-the-art single camera based tracking method of FairMOT. We consider different factors such as appearance features, motion information, and camera spatial distribution to improve the tracking performance. The experimental results carried on a multi-target multi-moving camera tracking dataset show the feasibility of the proposed scheme in solving the tracking issue in a complex environmental setting.

As an important part of the future multi-electric aircraft, the reliability of switched reluctance motor (SRM) starting/generating system is one of the most concerned performances. SRM control is inseparable from the position signal, but the presence of the axis position sensors brings reliability risks. Based on direct instantaneous torque control (DITC), this paper studies the position sensorless technology of SRM in the electric / generation state. Based on the current waveform of the motor in the electric/ generation state, the position partition methods suitable for two states individually are proposed to avoid low-quality position signals in the aligned and unaligned position areas. In each position area, the magnetic flux and current signals corresponding to the conducting observation phase are calculated in real time, and the position signal is obtained by looking up the table. As for the position delay caused by filtering, the speed feedback is to introduced to realize adaptive compensation. After simulation verification, compared with the single-cycle single-point position sensorless detection method, the partition position detection method based on magnetic flux/ current method is suitable for wider range of speed and has better dynamic response.

Data deduplication, though being efficient in removing duplicate chunks, introduces chunk fragmentation which decreases restore performance. Rewriting algorithms are proposed to reduce the chunk fragmentation. Delta compression is often used as a complement for data deduplication to further improve storage efficiency. We observe that delta compression introduces a new type of chunk fragmentation stemming from improper delta compression for chunks of which the base chunks are fragmented. The new type of chunk fragmentation severely decreases restore performance and cannot be addressed by existing rewriting algorithms. To address this problem, we propose SDC, a scheme performing post-deduplication delta compression only for the chunks of which the bases can be directly found in the restore cache to eliminate additional disk reads for base chunks, thus avoiding the new type of chunk fragmentation. In addition, self-referenced chunks can be fragmented, which decrease restore performance, and these fragmented chunks can serve as bases to decrease the restore performance repeatedly. We propose a hybrid rewriting scheme for SDC to rewrite such fragmented chunks. Experimental results show that SDC improves the restore performance of the approach that directly performs delta compression after data deduplication by 2.9-16.9x, and achieves more than 95 percent of its compression gains.