This paper presents a bioinspired, multi-channel auditory signal encoder based on volatile memristors, designed to mimic the short-term adaptation behavior of the human auditory system. The system integrates a threshold generator with an asynchronous delta modulator (ADM) to dynamically adjust threshold voltages based on real-time memristor behavior. The encoder is implemented with standard 130nm CMOS technology, occupying a compact area of 0.44 mm × 0.185 mm per channel and consuming 299.85 μW per channel. The adaptive resolution of the encoder is validated in simulations using a memristor model derived from real device data, demonstrating adaptive output firing rates as a result of memristor resistance volatility. With a maximum delay of 45.25 ns for a 1 kHz sound input, the design is well-suited for spike-domain neuromorphic systems.

Forecasting pedestrians' future motions is essential for autonomous driving systems to safely navigate in urban areas. However, existing prediction algorithms often overly rely on past observed trajectories and tend to fail around abrupt dynamic changes, such as when pedestrians suddenly start or stop walking. We suggest that predicting these highly non-linear transitions should form a core component to improve the robustness of motion prediction algorithms. In this paper, we introduce the new task of pedestrian stop and go forecasting. Considering the lack of suitable existing datasets for it, we release TRANS, a benchmark for explicitly studying the stop and go behaviors of pedestrians in urban traffic. We build it from several existing datasets annotated with pedestrians' walking motions, in order to have various scenarios and behaviors. We also propose a novel hybrid model that leverages pedestrian-specific and scene features from several modalities, both video sequences and high-level attributes, and gradually fuses them to integrate multiple levels of context. We evaluate our model and several baselines on TRANS, and set a new benchmark for the community to work on pedestrian stop and go forecasting.

Direct current internal resistance (DCIR) is a key parameter to determine consistency of power characteristics of a battery pack. This consistency is influenced by batteries' internal temperature, which reflects consistency of the batteries' thermal characteristics inherently. In this paper, an evaluation method for thermal consistency of batteries' DCIR is proposed. Arrhenius coefficient of DCIR is selected as the index of thermal characteristics of each battery. Statistics analysis is conducted for quantitative consistency evaluation. Experimental results show that thermal consistency of batteries' DCIR can be accurately obtained with proposed method.