14,007 research outputs found
Aircraft systems architecting: a functional-logical domain perspective
Presented is a novel framework for early systems architecture design. The framework defines data structures and algorithms that enable the systems architect to operate interactively and simultaneously in both the functional and logical domains. A prototype software tool, called AirCADia Architect, was implemented, which allowed the framework to be evaluated by practicing aircraft systems architects. The evaluation confirmed that, on the whole, the approach enables the architects to effectively express their creative ideas when synthesizing new architectures while still retaining control over the process
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
Performance evaluation of a distributed integrative architecture for robotics
The eld of robotics employs a vast amount of coupled sub-systems. These need to interact
cooperatively and concurrently in order to yield the desired results. Some hybrid algorithms
also require intensive cooperative interactions internally. The architecture proposed lends it-
self amenable to problem domains that require rigorous calculations that are usually impeded
by the capacity of a single machine, and incompatibility issues between software computing
elements. Implementations are abstracted away from the physical hardware for ease of de-
velopment and competition in simulation leagues. Monolithic developments are complex, and
the desire for decoupled architectures arises. Decoupling also lowers the threshold for using
distributed and parallel resources. The ability to re-use and re-combine components on de-
mand, therefore is essential, while maintaining the necessary degree of interaction. For this
reason we propose to build software components on top of a Service Oriented Architecture
(SOA) using Web Services. An additional bene t is platform independence regarding both
the operating system and the implementation language. The robot soccer platform as well
as the associated simulation leagues are the target domain for the development. Furthermore
are machine vision and remote process control related portions of the architecture currently
in development and testing for industrial environments. We provide numerical data based on
the Python frameworks ZSI and SOAPpy undermining the suitability of this approach for the
eld of robotics. Response times of signi cantly less than 50 ms even for fully interpreted,
dynamic languages provides hard information showing the feasibility of Web Services based
SOAs even in time critical robotic applications
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Recently, deep learning has made significant progress in the task of
sequential recommendation. Existing neural sequential recommenders typically
adopt a generative way trained with Maximum Likelihood Estimation (MLE). When
context information (called factor) is involved, it is difficult to analyze
when and how each individual factor would affect the final recommendation
performance. For this purpose, we take a new perspective and introduce
adversarial learning to sequential recommendation. In this paper, we present a
Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the
effect of context information on sequential recommendation. Specifically, our
proposed MFGAN has two kinds of modules: a Transformer-based generator taking
user behavior sequences as input to recommend the possible next items, and
multiple factor-specific discriminators to evaluate the generated sub-sequence
from the perspectives of different factors. To learn the parameters, we adopt
the classic policy gradient method, and utilize the reward signal of
discriminators for guiding the learning of the generator. Our framework is
flexible to incorporate multiple kinds of factor information, and is able to
trace how each factor contributes to the recommendation decision over time.
Extensive experiments conducted on three real-world datasets demonstrate the
superiority of our proposed model over the state-of-the-art methods, in terms
of effectiveness and interpretability
Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks
Soaring capacity and coverage demands dictate that future cellular networks
need to soon migrate towards ultra-dense networks. However, network
densification comes with a host of challenges that include compromised energy
efficiency, complex interference management, cumbersome mobility management,
burdensome signaling overheads and higher backhaul costs. Interestingly, most
of the problems, that beleaguer network densification, stem from legacy
networks' one common feature i.e., tight coupling between the control and data
planes regardless of their degree of heterogeneity and cell density.
Consequently, in wake of 5G, control and data planes separation architecture
(SARC) has recently been conceived as a promising paradigm that has potential
to address most of aforementioned challenges. In this article, we review
various proposals that have been presented in literature so far to enable SARC.
More specifically, we analyze how and to what degree various SARC proposals
address the four main challenges in network densification namely: energy
efficiency, system level capacity maximization, interference management and
mobility management. We then focus on two salient features of future cellular
networks that have not yet been adapted in legacy networks at wide scale and
thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and
device-to-device (D2D) communications. After providing necessary background on
CoMP and D2D, we analyze how SARC can particularly act as a major enabler for
CoMP and D2D in context of 5G. This article thus serves as both a tutorial as
well as an up to date survey on SARC, CoMP and D2D. Most importantly, the
article provides an extensive outlook of challenges and opportunities that lie
at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201
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