Research on Strategy-Based Anti-jamming Intelligent Decision Engine for Adaptive Burst Communication

Author(s):  
Kesi He ◽  
Yufan Cheng ◽  
Huanhuan Duan ◽  
Bingbing Shen ◽  
Yu Ran
Author(s):  
P. Clark ◽  
M.L Rilee ◽  
S.A. Curtis ◽  
S.A. Bailin ◽  
Steven Hall ◽  
...  

2020 ◽  
Author(s):  
Karthik Muthineni

The new industrial revolution Industry 4.0, connecting manufacturing process with digital technologies that can communicate, analyze, and use information for intelligent decision making includes Industrial Internet of Things (IIoT) to help manufactures and consumers for efficient controlling and monitoring. This work presents the design and implementation of an IIoT ecosystem for smart factories. The design is based on Siemens Simatic IoT2040, an intelligent industrial gateway that is connected to modbus sensors publishing data onto Network Platform for Internet of Everything (NETPIE). The design demonstrates the capabilities of Simatic IoT2040 by taking Python, Node-Red, and Mosca into account that works simultaneously on the device.


Author(s):  
Pawan Singh Mehra

AbstractWith huge cheap micro-sensing devices deployed, wireless sensor network (WSN) gathers information from the region and delivers it to the base station (BS) for further decision. The hotspot problem occurs when cluster head (CH) nearer to BS may die prematurely due to uneven energy depletion resulting in partitioning the network. To overcome the issue of hotspot or energy hole, unequal clustering is used where variable size clusters are formed. Motivated from the aforesaid discussion, we propose an enhanced fuzzy unequal clustering and routing protocol (E-FUCA) where vital parameters are considered during CH candidate selection, and intelligent decision using fuzzy logic (FL) is taken by non-CH nodes during the selection of their CH for the formation of clusters. To further extend the lifetime, we have used FL for the next-hop choice for efficient routing. We have conducted the simulation experiments for four scenarios and compared the propound protocol’s performance with recent similar protocols. The experimental results validate the improved performance of E-FUCA with its comparative in respect of better lifetime, protracted stability period, and enhanced average energy.


Human Affairs ◽  
2021 ◽  
Vol 31 (2) ◽  
pp. 149-164
Author(s):  
Dmytro Mykhailov

Abstract Contemporary medical diagnostics has a dynamic moral landscape, which includes a variety of agents, factors, and components. A significant part of this landscape is composed of information technologies that play a vital role in doctors’ decision-making. This paper focuses on the so-called Intelligent Decision-Support System that is widely implemented in the domain of contemporary medical diagnosis. The purpose of this article is twofold. First, I will show that the IDSS may be considered a moral agent in the practice of medicine today. To develop this idea I will introduce the approach to artificial agency provided by Luciano Floridi. Simultaneously, I will situate this approach in the context of contemporary discussions regarding the nature of artificial agency. It is argued here that the IDSS possesses a specific sort of agency, includes several agent features (e.g. autonomy, interactivity, adaptability), and hence, performs an autonomous behavior, which may have a substantial moral impact on the patient’s well-being. It follows that, through the technology of artificial neural networks combined with ‘deep learning’ mechanisms, the IDSS tool achieves a specific sort of independence (autonomy) and may possess a certain type of moral agency. Second, I will provide a conceptual framework for the ethical evaluation of the moral impact that the IDSS may have on the doctor’s decision-making and, consequently, on the patient’s wellbeing. This framework is the Object-Oriented Model of Moral Action developed by Luciano Floridi. Although this model appears in many contemporary discussions in the field of information and computer ethics, it has not yet been applied to the medical domain. This paper addresses this gap and seeks to reveal the hidden potentialities of the OOP model for the field of medical diagnosis.


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