Adaptive Duty Cycling MAC Protocols Using Closed-Loop Control for Wireless Sensor Networks

The fundamental design goal of wireless sensor MAC protocols is to minimize unnecessary power consumption of the sensor nodes, because of its stringent resource constraints and ultra-power limitation. In existing MAC protocols in wireless sensor networks (WSNs), duty cycling, in which each node periodically cycles between the active and sleep states, has been introduced to reduce unnecessary energy consumption. Existing MAC schemes, however, use a fixed duty cycling regardless of multi-hop communication and traffic fluctuations. On the other hand, there is a tradeoff between energy efficiency and delay caused by duty cycling mechanism in multi-hop communication and existing MAC approaches only tend to improve energy efficiency with sacrificing data delivery delay. In this paper, we propose two different MAC schemes (ADS-MAC and ELA-MAC) using closed-loop control in order to achieve both energy savings and minimal delay in wireless sensor networks. The two proposed MAC schemes, which are synchronous and asynchronous approaches, respectively, utilize an adaptive timer and a successive preload frame with closed-loop control for adaptive duty cycling. As a result, the analysis and the simulation results show that our schemes outperform existing schemes in terms of energy efficiency and delivery delay

A theoretical model for predicting Schottky-barrier height of the nanostructured silicide-silicon junction

ABSTRACT In this work, we have performed the first-principles calculations to investigate the Schottky barrier height (SBH) of various nanostructured silicide-silicon junctions. As for the silicides, PtSi, NiSi, TiSi2, and YSi2 have been used. We find that EFiF = EFi – EF, where EFi and EF are the intrinsic Fermi level of the semiconductor part and the Fermi level of the junction, respectively, is unchanged by nanostructuring. From this finding, we suggest a model, a symmetric increase of the SBH (SI) model, to properly predict SBHs of nanostructured silicide-silicon junctions. We also suggest two measurable quantities for the experimental validation of our model. The effect of our SI model applied to nanostructures such as nanowires and ultra-thin-bodies is compared with that of the widely used previous SBH model

Spectacular Interiority in Post-Apartheid South African Literature

Near the fall of apartheid, South Africa underwent a literary transformation. No longer bound by racialized dichotomies, South African authors rejected extreme narratives (called spectacular exteriority) in favor of nuanced, analytical, and personal ones. This paper argues that the extreme, spectacular representations remain an essential part of two works of post-apartheid literature, Thirteen Cents by K. Sello Duiker and The Folly by Ivan Vladislavić—however, with a twist. Instead of crafting extreme descriptions and events of society, the authors are more concerned with crafting extreme descriptions and thoughts of the characters’ minds. Within these novels, characters experience graphic, subjective, and hallucinatory visions which call to attention the social struggles that remain on a less visible, more personal level after apartheid’s abolition

Realizing Stabilized Landing for Computation-Limited Reusable Rockets: A Quantum Reinforcement Learning Approach

The advent of reusable rockets has heralded a new era in space exploration, reducing the costs of launching satellites by a significant factor. Traditional rockets were disposable, but the design of reusable rockets for repeated use has revolutionized the financial dynamics of space missions. The most critical phase of reusable rockets is the landing stage, which involves managing the tremendous speed and attitude for safe recovery. The complexity of this task presents new challenges for control systems, specifically in terms of precision and adaptability. Classical control systems like the proportional-integral-derivative (PID) controller lack the flexibility to adapt to dynamic system changes, making them costly and time-consuming to redesign of controller. This paper explores the integration of quantum reinforcement learning into the control systems of reusable rockets as a promising alternative. Unlike classical reinforcement learning, quantum reinforcement learning uses quantum bits that can exist in superposition, allowing for more efficient information encoding and reducing the number of parameters required. This leads to increased computational efficiency, reduced memory requirements, and more stable and predictable performance. Due to the nature of reusable rockets, which must be light, heavy computers cannot fit into them. In the reusable rocket scenario, quantum reinforcement learning, which has reduced memory requirements due to fewer parameters, is a good solution.Comment: 5 pages, 5 figure