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Localization is a fundamental and crucial module for autonomous vehicles. Most of the existing localization methodologies, such as signal-dependent methods (RTK-GPS and Bluetooth), simultaneous localization and mapping (SLAM), and map-based methods, have been utilized in outdoor autonomous driving vehicles and indoor robot positioning. However, they suffer from severe limitations, such as signal-blocked scenes of GPS, computing resource occupation explosion in large-scale scenarios, intolerable time delay, and registration divergence of SLAM/map-based methods. In this article, a self-localization framework, without relying on GPS or any other wireless signals, is proposed. We demonstrate that the proposed homogeneous normal distribution transform algorithm and two-way information interaction mechanism could achieve centimeter-level localization accuracy, which reaches the requirement of autonomous vehicle localization for instantaneity and robustness. In addition, benefitting from hardware and software co-design, the proposed localization approach is extremely light-weighted enough to be operated on an embedded computing system, which is different from other LiDAR localization methods relying on high-performance CPU/GPU. Experiments on a public dataset (Baidu Apollo SouthBay dataset) and real-world verified the effectiveness and advantages of our approach compared with other similar algorithms.

In-memory error correction code (ECC) is a promising technique to improve the yield and reliability of high density memory design. However, the use of in-memory ECC poses a new problem to memory repair analysis algorithm, which has not been explored before. This article first makes a quantitative evaluation and demonstrates that the straightforward algorithms for memory with redundancy and in-memory ECC have serious deficiency on either repair rate or repair analysis speed. Accordingly, an optimal repair analysis algorithm that leverages preprocessing/filter algorithms, hybrid search tree, and depth-first search strategy is proposed to achieve low computational complexity and optimal repair rate in the meantime. In addition, a heuristic repair analysis algorithm that uses a greedy strategy is proposed to efficiently find repair solutions. Experimental results demonstrate that the proposed optimal repair analysis algorithm can achieve optimal repair rate and increase the repair analysis speed by up to 105 × compared with the straightforward exhaustive search algorithm. The proposed heuristic repair analysis algorithm is approximately 28 percent faster than the proposed optimal algorithm, at the expense of 5.8 percent repair rate loss. [ABSTRACT FROM AUTHOR]

Energy storage devices (ESD), such as UPS batteries, have been repurposed in datacenter as a promising tuning knob for peak power shaving and power cost reducing. However, batteries progressively aging due to irregular usage patterns, which result in less effective capacity and even pose serious threat to server availability. Nevertheless, prior proposals largely ignore the aging issues of battery which may lead to low energy availability for datacenter servers. To fill this critical void, we thoroughly investigate battery aging on a heavily instrumented prototype system over an observation period of ten months. We propose Battery Anti-Aging Treatment Plus (BAAT-P ), a novel power delivery architecture included aging management algorithms from the perspective of computing system to hide, reduce, mitigate and plan the battery aging effects for high energy availability in datacenter. Our techniques exploit diverse battery aging mechanisms and dynamic aging management algorithms to provide system-level availability guarantee for datacenter. We evaluate the BAAT-P design with a real prototype. Compared with a battery powered datacenter without aging management policies, the results show that BAAT-P can extend battery lifetime by 72 percent, reduce battery cost by 33 percent and effectively improve energy availability for datacenter servers while maintaining workload performance for the performance critical workloads. [ABSTRACT FROM AUTHOR]