Single-photon Lidar (SPL) offers unprecedented sensitivity and time resolution, which enables Satellite Laser Ranging (SLR) systems to identify space debris from distances spanning thousands of kilometers. However, existing SPL systems face limitations in distance-trajectory extraction due to the widespread and undifferentiated noise photons. In this paper, we propose a novel velocity-based sparse photon clustering algorithm, leveraging the velocity correlation of the target’s echo signal photons in the distance-time dimension, by computing and searching the velocity and acceleration of photon distance points between adjacent pulses over a period of time and subsequently clustering photons with the same velocity and acceleration. Our algorithm can extract object trajectories from sparse photon data, even in low signal-to-noise ratio (SNR) conditions. To verify our method, we establish a ground simulation experimental setup for a single-photon ranging Lidar system. The experimental results show that our algorithm can extract the quadratic track with over 99% accuracy in only tens of milliseconds, with a signal photon counting rate of 5% at -20 dB SNR. Our method provides an effective approach for detecting and sensing extremely weak signals at the sub-photon level in space.

The last decade has witnessed an explosion of research on persistent memory, which combines the low access latency of dynamic random access memory (DRAM) with the durability of secondary storage. Intel’s implementation of persistent memory, called Optane, comes close to realizing the game-changing potential of persistent memory in terms of performance; however, it also suffers from limited endurance and relies on a proprietary wear leveling mechanism to mitigate memory cell wear-out. The traditional embedded approach to wear leveling, in which the storage device itself maps logical addresses to physical addresses, can be fast and energy-efficient, but it is also relatively inflexible and can lead to missed opportunities for optimization. An alternative school of thought, exemplified by "open channel" solid state drives (SSDs), delegates responsibility for wear leveling to software, where it can be tailored to specific applications. In this research, we consider a hypothetical hardware platform where the same paradigm is applied to the persistent memory device, and ask how the wear leveling mechanism can be co-designed with synchronization structures that generate highly skewed memory access patterns. Building on the recent work of Liu and Golab, we implement an improved wear leveling atomic counter by leveraging hardware transactional memory in a novel way. Our solution is close to optimal with respect to both space complexity and measured performance.

The last decade has witnessed an explosion of research on persistent memory, covering both hardware implementations and software techniques. Research activities in this area are primarily driven by the performance benefits of persistent memory, which behaves like DRAM with respect to latency and yet provides the durability of secondary storage. These benefits can only be realized with efficient solutions to the problem of memory cell wear-out, which is one of the fundamental weaknesses of persistent memory versus DRAM, and has traditionally been addressed in hardware. In this paper, we consider the theoretical foundations of solving this problem in software, which allows for application-specific optimizations. Our main contributions are to formalize the problem, and present a novel software implementation of atomic Fetch-And-Increment (FAI) that internally uses multiple words of persistent memory to distribute wear.

Abstract Cloud data center consumes enormous amount of electrical power resulting in high operating cost and carbon dioxide emission. Virtual machine (VM) consolidation strives to reallocate VMs to the minimum number of physical machines dynamically to reduce energy consumption. An approach is proposed to solve consolidation problem of VMs toward optimization of their energy consumption. A novel algorithm is presented, and an energy consumption model is also presented to support the algorithm to find optimal solutions. Experimental results show that energy consumption can be reduced greatly by dynamic VM consolidation. In comparison with other classic algorithms, our proposed algorithm outcomes them and achieves average savings of 42% on energy consumption.