57,399 research outputs found
On the Minimization of Handover Decision Instability in Wireless Local Area Networks
This paper addresses handover decision instability which impacts negatively
on both user perception and network performances. To this aim, a new technique
called The HandOver Decision STAbility Technique (HODSTAT) is proposed for
horizontal handover in Wireless Local Area Networks (WLAN) based on IEEE
802.11standard. HODSTAT is based on a hysteresis margin analysis that, combined
with a utilitybased function, evaluates the need for the handover and
determines if the handover is needed or avoided. Indeed, if a Mobile Terminal
(MT) only transiently hands over to a better network, the gain from using this
new network may be diminished by the handover overhead and short usage
duration. The approach that we adopt throughout this article aims at reducing
the minimum handover occurrence that leads to the interruption of network
connectivity (this is due to the nature of handover in WLAN which is a break
before make which causes additional delay and packet loss). To this end, MT
rather performs a handover only if the connectivity of the current network is
threatened or if the performance of a neighboring network is really better
comparing the current one with a hysteresis margin. This hysteresis should make
a tradeoff between handover occurrence and the necessity to change the current
network of attachment. Our extensive simulation results show that our proposed
algorithm outperforms other decision stability approaches for handover decision
algorithm.Comment: 13 Pages, IJWM
A new and efficient intelligent collaboration scheme for fashion design
Technology-mediated collaboration process has been extensively studied for over a decade. Most applications with collaboration concepts reported in the literature focus on enhancing efficiency and effectiveness of the decision-making processes in objective and well-structured workflows. However, relatively few previous studies have investigated the applications of collaboration schemes to problems with subjective and unstructured nature. In this paper, we explore a new intelligent collaboration scheme for fashion design which, by nature, relies heavily on human judgment and creativity. Techniques such as multicriteria decision making, fuzzy logic, and artificial neural network (ANN) models are employed. Industrial data sets are used for the analysis. Our experimental results suggest that the proposed scheme exhibits significant improvement over the traditional method in terms of the time–cost effectiveness, and a company interview with design professionals has confirmed its effectiveness and significance
The world-wide spread of journalism convergence
Convergence is a likely destination for news media in many parts of the world, though the duration of the journey will vary from country to country. This paper defines convergence as well as it is possible to do so, traces its spread around the world, and describes some of the most common business models. It looks at the forces driving convergence, and factors common to the most successful converged operations. The paper also describes the uncertain scenario in Australia now the Howard government has announced plans to change media ownership laws. It ends with discussion about changes in curricula at journalism programs in the United States in the light of the spread of convergence.<br /
Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G
By caching content at network edges close to the users, the content-centric
networking (CCN) has been considered to enforce efficient content retrieval and
distribution in the fifth generation (5G) networks. Due to the volume,
velocity, and variety of data generated by various 5G users, an urgent and
strategic issue is how to elevate the cognitive ability of the CCN to realize
context-awareness, timely response, and traffic offloading for 5G applications.
In this article, we envision that the fundamental work of designing a cognitive
CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to
associatively learn and control the states of edge devices (such as phones,
vehicles, and base stations) and in-network resources (computing, networking,
and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework
for C-CCN in 5G, which can aggregate the idle computing resources of the
neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive
learning tasks. By leveraging artificial intelligence (AI) to jointly
processing sensed environmental data, dealing with the massive content
statistics, and enforcing the mobility control at network edges, the FEL makes
it possible for mobile users to cognitively share their data over the C-CCN in
5G. To validate the feasibility of proposed framework, we design two
FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network
acceleration, 2) enhanced mobility management. Simultaneously, we present the
simulations to show the FEL's efficiency on serving for the mobile users'
delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201
Learning models for semantic classification of insufficient plantar pressure images
Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and
effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set
learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose
an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are
introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset
of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by
using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)-
based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally,
the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained
CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition
methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H)
and time (training and evaluation). The proposed method for the plantar pressure classification task shows high
performance in most indices when comparing with other methods. The transfer learning-based method can be
applied to other insufficient data-sets of sensor imaging fields
- …