Dynamic vision sensors (DVSs), renowned for their low latency and sparse event-driven output, have garnered significant attention in machine vision applications, particularly in latency-sensitive applications like object tracking. However, current DVS-based object tracking systems have not fully utilized the advantages of DVS due to their frame-based processing or the high computing intensity of their algorithms. This brief presents EDMOT, a fully event-driven multiobject tracking system for DVS, achieving 25–65 ns latency at 200 MHz. EDMOT introduces three key innovations: 1) a novel event-driven update mechanism that processes only the latest event and the expired oldest event, minimizing computational overhead; 2) a dual-threshold tracking strategy that decouples object formation and motion phases, significantly improving tracking accuracy; and 3) a row–column feature memory with flag registers, enabling object separation within eight clock cycles. The proposed EDMOT is evaluated on public datasets, demonstrating superior tracking accuracy compared to prior methods. Finally, EDMOT was implemented at the HLMC 55 nm, supporting 20–100 Me/s throughput. To the best of our knowledge, this is a multiobject tracking system with minimal delay and the highest event processing throughput.

The Dynamic Vision Sensor (DVS) is a new type of bionic vision image sensor that offers the advantages of low latency, low power consumption, and high dynamics range compared to conventional sensors. However, background activity (BA) noise will degrade the quality of the DVS output data and lead to unnecessary bandwidth overhead. In dark environments, pixel arrays generate abundant noise, and conventional spatiotemporal filters can hardly achieve satisfactory results. To solve this problem, we exploit the difference in event density distribution between the actual event and noise and propose a denoising method that utilizes the event densities with two neighbors of different radii. Compared to spatiotemporal filters, our approach reduces the error rate on synthetic datasets by at least 35%. Meanwhile, our approach is subjectively more visually appealing. With our denoising method, the performance of DVS can be better in dark conditions.

Dynamic vision sensors (DVS) have significant potential in scenes involving high-speed motion and extreme light. However, DVS is sensitive to background active noise, which will degrade the quality of the output. The ordinary$O(N^{2})$-Space spatiotemporal filter's memory complexity is high. It needs$N\times N$memory cells ($N\times N$is the resolution on the sensor). Some works reduce memory complexity by sacrificing the performance of the filter. To ensure the filtering effect and reduce the filter's memory complexity, this paper proposes a novel filter: Queue-based spatiotemporal filter. Moreover, based on the Queue-based spatiotemporal filter, this paper proposes a clustering algorithm that can cluster while filtering. Experiments show that the proposed filter's performance is similar to the$O(N^{2})$-Space spatiotemporal filter while having a lower memory complexity. Besides, using the proposed clustering algorithm, the objects in motion can be clustered with low calculation complexity.

Vehicle energy consumption standards are important when conducting relevant tests, which provide uniform comparison means for different industries and institutions. For fuel cell electric vehicles (FCEVs), as one kind of current clean energy vehicles that people prefer, the energy consumption standards are still need to improve. This paper classifies Chinese existing vehicle energy consumption standards, concentrated on the consumption test and calculations methods of different fuel type vehicles, including internal combustion engine vehicles (ICE-V), hybrid electric vehicles (HEVs), pure electric vehicles (EVs) and FCEVs. There are many shortcomings in FCEV energy consumption standards compared to other type vehicles. And then, fuel cell electric vehicles' powertrain efficiency and driving range are compared under NEDC and a local sampling cycles. In order to accurately test their energy consumptions and promote the improvement of relevant standards and regulations, this paper proposes that the standards of fuel cell vehicles need to consider the influence of working conditions and battery consumption. The standard system can also be improved from the limitation of energy consumption and evaluation.

Fuel cell air supply system has a delay due to compressor during load-changing process, and cannot supply air to the stack quickly, which may lead to oxygen starvation. Oxygen starvation not only affects the output power of the fuel cell, but also leads to a decrease in catalyst performance and fuel cell durability. This paper proposes an adaptive PID controller strategy based on fuzzy logic control for fuel cell air supply system. The control objective is to adjust the oxygen excess ratio to a suitable value to prevent oxygen starvation. The simulation results show that the proposed controller can quickly and stably adjust the oxygen excess ratio to the suitable value.