Design of Event-Triggered Asynchronous H∞ Filter for Switched Systems Using the Sampled-Data Approach

The design of networked switched systems with event-based communication is attractive due to its potential to save bandwidth and energy. However, ensuring the stability and performance of networked systems with event-triggered communication and asynchronous switching is challenging due to their time-varying nature. This paper presents a novel sampled-data approach to design event-triggered asynchronous H∞ filters for networked switched systems. Unlike most existing event-based filtering results, which either design the event-triggering scheme only or co-design the event-triggering condition and the filter, we consider that the event-triggering policy is predefined and synthesize the filter. We model the estimation error system as an event-triggered switched system with time delay and non-uniform sampling. By implementing a delay-dependent multiple Lyapunov method, we derive sufficient conditions to ensure the global asymptotic stability of the filtering error system and an H∞ performance level. The efficacy of the proposed design technique and the superiority of the filter performance is illustrated by numerical examples and by comparing the performance with a recent result

Design of Event-Triggered Asynchronous H_∞ Filter for Switched Systems Using the Sampled-Data Approach

The design of networked switched systems with event-based communication is attractive due to its potential to save bandwidth and energy. However, ensuring the stability and performance of networked systems with event-triggered communication and asynchronous switching is challenging due to their time-varying nature. This paper presents a novel sampled-data approach to design event-triggered asynchronous $\mathcal {H}_{\infty}$ filters for networked switched systems. Unlike most existing event-based filtering results, which either design the event-triggering scheme only or co-design the event-triggering condition and the filter, we consider that the event-triggering policy is predefined and synthesize the filter. We model the estimation error system as an event-triggered switched system with time delay and non-uniform sampling. By implementing a delay-dependent multiple Lyapunov method, we derive sufficient conditions to ensure the global asymptotic stability of the filtering error system and an $\mathcal {H}_{\infty}$ performance level. The efficacy of the proposed design technique and the superiority of the filter performance is illustrated by numerical examples and by comparing the performance with a recent result

New and Safe Treatment of Food Impacted in the Esophagus: A Single Center Experience of 100 Consecutive Cases

Aim. Large food bits can get stuck in the esophagus and must be removed by endoscopy. In some cases, this can be difficult or unsafe. We describe a new and safe treatment for such patients. Materials and Methods. 100 consecutive patients were referred to Akershus University Hospital with impacted food in the esophagus. In 36 patients (36%), the food passed spontaneously. In 59 (92%) of the remaining 64 patients, the food was removed by endoscopic intervention. In the last five patients, endoscopic removal was judged difficult or unsafe. These patients received the new treatment: one capsule Creon 10000 IU dissolved in 30 mL of Coca-Cola administered by a nasooesophageal tube four times daily for 2-3 days. Results. Of the 59 patients treated with endoscopic procedure, complications occurred in four (7%): three bleedings and one perforation of the esophagus. In five patients treated with Coca-Cola and Creon, the food had either passed or was soft after 2-3 days and could easily be removed. Conclusion. The treatment of choice of impacted food in the esophagus is endoscopic removal. In cases where this is difficult, we recommend treatment with Coca-Cola and Creon for 2-3 days before complications occur

Mobility-Aware Data Caching to Improve D2D Communications in Heterogeneous Networks

User Equipment (UE) is equipped with limited cache resources that can be utilized to offload data traffic through device-to-device (D2D) communications. Data caching at a UE level has the potential to significantly alleviate data traffic burden from the backhaul link. Moreover, in wireless networks, users exhibit mobility that poses serious challenges to successful data transmission via D2D communications due to intermittent connectivity among users. Users’ mobility can be exploited to efficiently cache contents by observing connectivity patterns among users. Therefore, it is crucial to develop an efficient data caching mechanism for UE while taking into account users’ mobility patterns. In this work, we propose a mobility-aware data caching approach to enhance data offloading via D2D communication. First, we model users’ connectivity patterns. Then, contents are cached in UE’ cache resources based on users’ data preferences. In addition, we also take into account signal-to-interference and noise ratio (SINR) requirements of the users. Hence, our proposed caching mechanism exploits connectivity patterns of users to perform data placement based on users’ own demands and neighboring users to enhance data offloading via cache resources. We performed extensive simulations to investigate the performance of our proposed mobility-aware data caching mechanism. The performance of our proposed caching mechanism is compared to most deployed data caching mechanisms, while taking into account the dynamic nature of the wireless channel and the interference experienced by the users. From the obtained results, it is evident that our proposed approach achieves 14%, 16%, and 11% higher data offloading gain than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of users, cache capacity, and number of contents, respectively. Moreover, we also analyzed cache hit rates, and our proposed scheme achieves 8% and 5% higher cache hit rate than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of contents and cache capacity, respectively. Hence, our proposed caching mechanism brings significant improvement in data sharing via D2D communications

A Reinforcement Learning Based Data Caching in Wireless Networks

Data caching has emerged as a promising technique to handle growing data traffic and backhaul congestion of wireless networks. However, there is a concern regarding how and where to place contents to optimize data access by the users. Data caching can be exploited close to users by deploying cache entities at Small Base Stations (SBSs). In this approach, SBSs cache contents through the core network during off-peak traffic hours. Then, SBSs provide cached contents to content-demanding users during peak traffic hours with low latency. In this paper, we exploit the potential of data caching at the SBS level to minimize data access delay. We propose an intelligence-based data caching mechanism inspired by an artificial intelligence approach known as Reinforcement Learning (RL). Our proposed RL-based data caching mechanism is adaptive to dynamic learning and tracks network states to capture users’ diverse and varying data demands. Our proposed approach optimizes data caching at the SBS level by observing users’ data demands and locations to efficiently utilize the limited cache resources of SBS. Extensive simulations are performed to evaluate the performance of proposed caching mechanism based on various factors such as caching capacity, data library size, etc. The obtained results demonstrate that our proposed caching mechanism achieves 4% performance gain in terms of delay vs. contents, 3.5% performance gain in terms of delay vs. users, 2.6% performance gain in terms of delay vs. cache capacity, 18% performance gain in terms of percentage traffic offloading vs. popularity skewness (γ), and 6% performance gain in terms of backhaul saving vs. cache capacity

A safe treatment option for esophageal bezoars

AbstractINTRODUCTIONBezoar in the esophagus is a rare condition and associated with structural or functional abnormalities of the esophagus. Endoscopy is the main tool for diagnosis and treatment for bezoar in the esophagus.PRESENTATION OF CASEHere we present a case where an endoscopic evacuation of an esophageal bezoar was unsuccessful. We treated the bezoar through a nasogastric tube using a cocktail composed of pancreatic enzymes dissolved in Coca-Cola.DISCUSSIONEndoscopy is regarded as the mainstay for the diagnosis and treatment of esophageal bezoars. However, when this approach fails, other treatment options include dissolution therapy, and surgical exploration and removal of the bezoar. Surgical removal of an esophageal bezoar is associated with a high risk of morbidity and mortality. We advocate that dissolving therapy should be the first choice of treatment when endoscopic evacuation is not possible.CONCLUSIONThis is the first report describing a successful treatment of an esophageal bezoar with a cocktail of Coca-Cola and pancreatic enzymes. It is an effective, inexpensive, and worldwide available treatment and should be considered when endoscopic evacuation fails