14,828 research outputs found
Mengenal pasti masalah pemahaman dan hubungannya dengan latar belakang matematik, gaya pembelajaran, motivasi dan minat pelajar terhadap bab pengawalan kos makanan di Sekolah Menengah Teknik (ert) Rembau: satu kajian kes.
Kajian ini dijalankan untuk mengkaji hubungan korelasi antara latar belakang Matematik, gaya pembelajaran, motivasi dan minat dengan pemahaman pelajar terhadap bab tersebut. Responden adalah seramai 30 orang iaitu terdiri daripada pelajar tingkatan lima kursus Katering, Sekolah Menengah Teknik (ERT) Rembau, Negeri Sembilan. Instrumen kajian adalah soal selidik dan semua data dianalisis menggunakan program SPSS versi 10.0 untuk mendapatkan nilai min dan nilai korelasi bagi memenuhi objektif yang telah ditetapkan. Hasil kajian ini menunjukkan bahawa hubungan korelasi antara gaya pembelajaran pelajar terhadap pemahaman pelajar adalah kuat. Manakala hubungan korelasi antara latar belakang Matematik, motivasi dan minat terhadap pemahaman pelajar adalah sederhana. Nilai tahap min bagi masalah pemahaman pelajar, latar belakang Matematik, gaya pembelajaran, motivasi dan minat terhadap bab Pengawalan Kos Makanan adalah sederhana. Kajian ini mencadangkan penghasilan satu Modul Pembelajaran Kendiri bagi bab Pengawalan Kos Makanan untuk membantu pelajar kursus Katering dalam proses pembelajaran mereka
Multimedia Content Distribution in Hybrid Wireless Networks using Weighted Clustering
Fixed infrastructured networks naturally support centralized approaches for
group management and information provisioning. Contrary to infrastructured
networks, in multi-hop ad-hoc networks each node acts as a router as well as
sender and receiver. Some applications, however, requires hierarchical
arrangements that-for practical reasons-has to be done locally and
self-organized. An additional challenge is to deal with mobility that causes
permanent network partitioning and re-organizations. Technically, these
problems can be tackled by providing additional uplinks to a backbone network,
which can be used to access resources in the Internet as well as to inter-link
multiple ad-hoc network partitions, creating a hybrid wireless network. In this
paper, we present a prototypically implemented hybrid wireless network system
optimized for multimedia content distribution. To efficiently manage the ad-hoc
communicating devices a weighted clustering algorithm is introduced. The
proposed localized algorithm deals with mobility, but does not require
geographical information or distances.Comment: 2nd ACM Workshop on Wireless Multimedia Networking and Performance
Modeling 2006 (ISBN 1-59593-485
Design and Analysis of SD_DWCA - A Mobility based clustering of Homogeneous MANETs
This paper deals with the design and analysis of the distributed weighted
clustering algorithm SD_DWCA proposed for homogeneous mobile ad hoc networks.
It is a connectivity, mobility and energy based clustering algorithm which is
suitable for scalable ad hoc networks. The algorithm uses a new graph parameter
called strong degree defined based on the quality of neighbours of a node. The
parameters are so chosen to ensure high connectivity, cluster stability and
energy efficient communication among nodes of high dynamic nature. This paper
also includes the experimental results of the algorithm implemented using the
network simulator NS2. The experimental results show that the algorithm is
suitable for high speed networks and generate stable clusters with less
maintenance overhead
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Distributed Clustering in Cognitive Radio Ad Hoc Networks Using Soft-Constraint Affinity Propagation
Absence of network infrastructure and heterogeneous spectrum availability in cognitive radio ad hoc networks (CRAHNs) necessitate the self-organization of cognitive radio users (CRs) for efficient spectrum coordination. The cluster-based structure is known to be effective in both guaranteeing system performance and reducing communication overhead in variable network environment. In this paper, we propose a distributed clustering algorithm based on soft-constraint affinity propagation message passing model (DCSCAP). Without dependence on predefined common control channel (CCC), DCSCAP relies on the distributed message passing among CRs through their available channels, making the algorithm applicable for large scale networks. Different from original soft-constraint affinity propagation algorithm, the maximal iterations of message passing is controlled to a relatively small number to accommodate to the dynamic environment of CRAHNs. Based on the accumulated evidence for clustering from the message passing process, clusters are formed with the objective of grouping the CRs with similar spectrum availability into smaller number of clusters while guaranteeing at least one CCC in each cluster. Extensive simulation results demonstrate the preference of DCSCAP compared with existing algorithms in both efficiency and robustness of the clusters
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