scholarly journals A Cognitive Anycast Routing Method for Delay-Tolerant Networks

Network ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 116-131
Author(s):  
Ricardo Lent
Keyword(s):  
Space Applications ◽  
Routing Problem ◽  
Propagation Delays ◽  
Anycast Routing ◽  
Series Of Experiments ◽  
Improved Performance ◽  
Standard Contact ◽  
Delay Tolerant ◽  
Group Member ◽  
Fine Tune

A cognitive networking approach to the anycast routing problem for delay-tolerant networking (DTN) is proposed. The method is suitable for the space–ground and other domains where communications are recurrently challenged by diverse link impairments, including long propagation delays, communication asymmetry, and lengthy disruptions. The proposed method delivers data bundles achieving low delays by avoiding, whenever possible, link congestion and long wait times for contacts to become active, and without the need of duplicating data bundles. Network gateways use a spiking neural network (SNN) to decide the optimal outbound link for each bundle. The SNN is regularly updated to reflect the expected cost of the routing decisions, which helps to fine-tune future decisions. The method is decentralized and selects both the anycast group member to be used as the sink and the path to reach that node. A series of experiments were carried out on a network testbed to evaluate the method. The results demonstrate its performance advantage over unicast routing, as anycast routing is not yet supported by the current DTN standard (Contact Graph Routing). The proposed approach yields improved performance for space applications that require as-fast-as-possible data returns.

Improving Recommendation Accuracy and Diversity via Multiple Social Factors and Social Circles

2019 ◽  
pp. 132-154
Author(s):  
Yong Feng ◽  
Heng Li ◽  
Zhuo Chen ◽  
Baohua Qiang
Keyword(s):  
Social Factors ◽  
Online Social Networks ◽  
Online Information ◽  
Information Discovery ◽  
Proposed Model ◽  
Probabilistic Matrix Factorization ◽  
Series Of Experiments ◽  
Networks Analysis ◽  
Recommendation Accuracy ◽  
Improved Performance

Recommender systems have been widely employed to suggest personalized online information to simplify users' information discovery process. With the popularity of online social networks, analysis and mining of social factors and social circles have been utilized to support more effective recommendations, but have not been fully investigated. In this chapter, the authors propose a novel recommendation model with the consideration of more comprehensive social factors and topics. To further enhance recommendation accuracy, four social factors are simultaneously injected into the recommendation model based on probabilistic matrix factorization. Meanwhile, the authors explore several new methods to measure these social factors. Moreover, they infer explicit and implicit social circles to enhance the performance of recommendation diversity. Finally, the authors conduct a series of experiments on publicly available data. Experimental results show the proposed model achieves significantly improved performance over the existing models in which social information have not been fully considered.

scholarly journals Belief in Control: Voluntary Choice Enhances Subsequent Task Performance under Undefeated Choice-Outcome Causation

2021 ◽  
Author(s):  
Xiaoxiao Luo ◽  
Lihui Wang ◽  
xiaolin zhou
Keyword(s):  
Search Task ◽  
Behavioral Performance ◽  
Search Performance ◽  
Blank Screen ◽  
Subsequent Behavior ◽  
Series Of Experiments ◽  
Improved Performance ◽  
Voluntary Choice ◽  
Task Irrelevant ◽  
Subsequent Task

Humans are believed to have volition through which they act upon and change the external environment. As an exercise of volition, making a voluntary choice facilitates the subsequent behavioral performance relative to a forced choice. However, it is unclear how this facilitation is constrained by the perceived relationship between a choice and its outcome. In a series of experiments, participants were free or forced to choose one of two presented pictures. The outcome of the choice was then revealed, which could be always the chosen picture or always the unchosen picture (i.e., a confirmed choice-outcome causation), a blank screen with no picture at all (i.e., an unrevealed choice-outcome relation), the chosen or unchosen picture with equal probability (i.e., a defeated choice-outcome causation), or a third picture different from the two preceding options (again, a defeated choice-outcome causation). Participants then complete a visual search task with the task-irrelevant picture (or the blank screen) serving as a background. Results showed that the search performance was improved after a voluntary choice under both the confirmed causation and the unrevealed relation, but not under the defeated causation. Over individuals, the improved performance due to voluntary choice under confirmed causation positively correlated with the improved performance under the unrevealed relation, and with the reported belief in controlling the outcome of the choice. Our findings suggest that the exercise of volition motivates subsequent behavior, and this motivation is restricted to an “undefeated” choice-outcome causation which affords a belief in controlling the outcome by exerting volition.

scholarly journals Novel Cross-View Human Action Model Recognition Based on the Powerful View-Invariant Features Technique

2018 ◽  
Vol 10 (9) ◽  
pp. 89 ◽  
Author(s):  
Sebastien Mambou ◽  
Ondrej Krejcar ◽  
Kamil Kuca ◽  
Ali Selamat
Keyword(s):  
Human Action Recognition ◽  
Human Action ◽  
Action Identification ◽  
Memory Space ◽  
Deep Model ◽  
Action Model ◽  
Series Of Experiments ◽  
Time Required ◽  
Model Recognition ◽  
Fine Tune

One of the most important research topics nowadays is human action recognition, which is of significant interest to the computer vision and machine learning communities. Some of the factors that hamper it include changes in postures and shapes and the memory space and time required to gather, store, label, and process the pictures. During our research, we noted a considerable complexity to recognize human actions from different viewpoints, and this can be explained by the position and orientation of the viewer related to the position of the subject. We attempted to address this issue in this paper by learning different special view-invariant facets that are robust to view variations. Moreover, we focused on providing a solution to this challenge by exploring view-specific as well as view-shared facets utilizing a novel deep model called the sample-affinity matrix (SAM). These models can accurately determine the similarities among samples of videos in diverse angles of the camera and enable us to precisely fine-tune transfer between various views and learn more detailed shared facets found in cross-view action identification. Additionally, we proposed a novel view-invariant facets algorithm that enabled us to better comprehend the internal processes of our project. Using a series of experiments applied on INRIA Xmas Motion Acquisition Sequences (IXMAS) and the Northwestern–UCLA Multi-view Action 3D (NUMA) datasets, we were able to show that our technique performs much better than state-of-the-art techniques.

scholarly journals Highly Conductive Carbon Fiber-Reinforced Polymer Composite Electronic Box: Out-of-Autoclave Manufacturing for Space Applications

Fibers ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 92 ◽  
Author(s):  
Marta Martins ◽  
Rui Gomes ◽  
Luís Pina ◽  
Celeste Pereira ◽  
Olaf Reichmann ◽  
...  
Keyword(s):  
Carbon Fiber ◽  
Raw Materials ◽  
Fiber Reinforced Polymer ◽  
Thermal Dissipation ◽  
Carbon Fiber Reinforced Polymer ◽  
Fiber Reinforced ◽  
Space Applications ◽  
Reinforced Polymer ◽  
Carbon Fiber Reinforced ◽  
Improved Performance

One of the main advantages of carbon fiber-reinforced polymer (CFRP) electronic housings, when compared with traditionally used aluminum ones, is the potential for mass savings. In recent years, the power consumption of electronics has been growing, resulting in the need for higher thermal dissipation of electronic housings, requiring the use of highly thermally conductive materials. In this work, the manufacturing of a highly conductive CFRP electronic housing is reported. With a view to reducing total energy costs on manufacturing, an out-of-the autoclave manufacturing process was followed. Due to the inherent low thermal conductivity of typical raw materials for composite materials, strategies were evaluated to increase its value by changing the components used. The use of pitch-based carbon fibers was found to be a very promising solution. In addition, structural, thermal and manufacturing simulations were produced in the design phase. Improved performance was demonstrated from materials manufacturing to final breadboard testing. The results indicate potential gains of around 23% in mass reduction when compared to conventional aluminum electronic boxes. Moreover, the proposed design and the manufactured breadboard showed good compliance with mechanical and electrical requirements for spacecraft structures. The thermal balance results showed a performance slightly below to what would be expected from the detailed design.

INTEGRATED CIRCUIT CHANNEL ROUTING USING A PARETO-OPTIMAL GENETIC ALGORITHM

2012 ◽  
Vol 21 (05) ◽  
pp. 1250041
Author(s):  
THEODORE W. MANIKAS
Keyword(s):  
Integrated Circuit ◽  
Routing Algorithm ◽  
Signal Propagation ◽  
Pareto Optimal ◽  
Signal Delay ◽  
Integrated Circuit Design ◽  
Channel Routing ◽  
Routing Problem ◽  
Propagation Delays ◽  
Optimal Approach

An important part of the integrated circuit design process is the channel routing stage, which determines how to interconnect components that are arranged in sets of rows. The channel routing problem has been shown to be NP-complete, thus this problem is often solved using genetic algorithms. The traditional objective for most channel routers is to minimize total area required to complete routing. However, another important objective is to minimize signal propagation delays in the circuit. This paper describes the development of a genetic channel routing algorithm that uses a Pareto-optimal approach to accommodate both objectives. When compared to the traditional channel routing approach, the new channel router produced layouts with decreased signal delay, while still minimizing routing area.

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