98 research outputs found
Aplikasi mudah alih bagi pembelajaran Bahasa Melayu berdasarkan kepada kaedah fonetik dan oral aural
Selaras dengan perkembangan ICT dan pengintegrasian teknologi dalam pengajaran dan pembelajaran di peringkat sekolah Malaysia, penghasilan bahan bantu mengajar seperti Koswer serta menerusi web telah banyak dibangunkan. Begitu juga dengan penggunaan Mobile Learning seperti peranti pintar berplatfom Android dan iOS yang perlu dimanfaatkan sepenuhnya agar pendidikan di Malaysia berkembang seiring dengan pendidikan negara maju. Walaupun penggunaan Mobile Learning semakin berkembang dan diintegrasikan dalam pembelajaran namun reka bentuk penyampaiannya perlu diberi perhatian terutama kepada pelajar pendidikan khas yang sering dilihat ketinggalan dalam pembelajaran mereka. Sejajar dengan matlamat pendidikan negara terhadap pengimplementasian ICT dalam pendidikan, sebuah aplikasi mudah alih (apps) pembelajaran Bahasa Melayu dibangunkan berdasarkan kepada kaedah fonetik dan oral-aural. Justeru itu kajian ini dijalankan bagi menilai keberkesanan apps dalam menyokong perkembangan kemahiran membaca mereka berdasarkan kepada Model Reka Bentuk Hannafin and Peck. Dengan menggunakan kaedah fonetik dan oral-aural yang disuntik ke dalam apps Mari Baca ini, ia juga membantu dalam perubahan tingkah laku pelajar lembam sekaligus menyokong kepada peningkatan prestasi pembelajaran mereka. Seramai 5 orang responden terlibat dalam kajian ini dalam kalangan pelajar Pendidikan Khas Bermasalah Pembelajaran (PKBP) di salah sebuah sekolah luar bandar. Dengan nilai kebolehpercayaan instrumen a = 0.835, hasil dapatan yang diperoleh menunjukkan pencapaian pelajar telah meningkat kepada tahap lebih baik. Secara keseluruhan, penggunaan apps Mari Baca dalam pembelajaran pelajar lembam telah menunjukkan perkembangan yang baik terhadap pencapaian dan kemahiran membaca serta perubahan yang positif terhadap tingkah laku mereka
Mobility models towards the performance of geographical-based route maintenance strategy in DSR
In the future, Mobile Ad hoc Networks (MANET)s are expected to be deployed in myriads of scenarios having complex node mobility and connectivity dynamics. Unfortunately, these complex movement scenarios give a tough challenge to the MANET routing protocol. We reviewed some mobility models that have different mobility characteristic, and also studied the effect of these mobility models towards the performance of geographical-based route maintenance strategy in the Dynamic Source Routing (DSR) protocol
AN ADAPTIVE LOCALIZATION SYSTEM USING PARTICLE SWARM OPTIMIZATION IN A CIRCULAR DISTRIBUTION FORM
Tracking the user location in indoor environment becomes substantial issue in recent research High accuracy and fast convergence are very important issues for a good localization system. One of the techniques that are used in localization systems is particle swarm optimization (PSO). This technique is a stochastic optimization based on the movement and velocity of particles. In this paper, we introduce an algorithm using PSO for indoor localization system. The proposed algorithm uses PSO to generate several particles that have circular distribution around one access point (AP). The PSO generates particles where the distance from each particle to the AP is the same distance from the AP to the target. The particle which achieves correct distances (distances from each AP to target) is selected as the target. Four PSO variants, namely standard PSO (SPSO), linearly decreasing inertia weight PSO (LDIW PSO), self-organizing hierarchical PSO with time acceleration coefficients (HPSO-TVAC), and constriction factor PSO (CFPSO) are used to find the minimum distance error. The simulation results show the proposed method using HPSO-TVAC variant achieves very low distance error of 0.19 mete
A new route maintenance strategy for dynamic source routing protocol
Although DSR can respond a route quickly, it yields a long delay when a route is rebuilt. This is because when source node receives RERR packet, it will try to find alternative routes from route cache. If alternative routes are not available, source node, then, will enter route discovery phase to find new routes. In this paper we introduce a new route maintenance strategy by utilizing location information. We called this new route maintenance strategy as DISTANCE (Distance baSed rouTe maintenANCE). DISTANCE works by adding another node (called bridge node) into the source list to prevent the link from failure. From the simulation result, DISTANCE improves the performance of DSR in terms of packet sending ratio and delay
Energy-aware cluster based cooperative spectrum sensing for cognitive radio sensor networks
Energy efficient spectrum sensing and data communication to extend the lifetime of cognitive radio sensor network is becoming increasingly important due to resource constraint of CR-WSN inherent from WSN. This paper presents an energy-aware clustering (EAC) algorithm that enhances spectrum sensing performance and reduces network energy consumption thereby prolonging lifetime of the network. We derived network wide energy consumption model in terms of spectrum sensing energy consumption, intra cluster and inter clusters energy consumptions, and then determined the optimal number of clusters for the network. Through numerical analysis, we evaluate the effectiveness of the proposed algorithm in terms of minimizing network wide energy consumption and improving spectrum sensing performance
A weighted hard combination scheme for cooperative spectrum sensing in cognitive radio sensor networks
Multi-user spatial sensing diversity exploration through cooperation spectrum sensing greatly improves sensing performance. However, high communication overhead and energy costs for exchanging sensing results may limit its viability in a realistic large scale resource constraint network such as cognitive radio wireless sensor networks. This paper presents a Weighted Hard Combination (WHC) scheme that combines features of both quantized and hard combining schemes to minimize energy cost for reporting sensing result and improve primary user detection performance in cooperative sensing. We evaluate the effectiveness of the scheme through simulation. Performance comparison of the WHC scheme in terms of detection performance, reporting energy cost and reporting time ratio with conventional hard combination, soft combination and quantized schemes indicates viability of the scheme. The results indicate that the WHC scheme minimizes reporting energy cost by 70% and improves detection performance by 5.6% compared to the quantized 3-bits scheme
Detection of denial of service attacks against domain name system using neural networks
In this paper we introduce an intrusion detection system for Denial of Service (DoS) attacks against Domain Name System (DNS). Our system architecture consists of two most important parts: a statistical preprocessor and a neural network classifier. The preprocessor extracts required statistical features in a shorttime frame from traffic received by the target name server. We compared three different neural networks for detecting and classifying different types of DoS attacks. The proposed system is evaluated in a simulated network and showed that the best performed neural network is a feed-forward backpropagation with an accuracy of 99%
An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks
It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach
Coverage degree in mobile nodes wireless sensor network
Coverage degree and mobility have acquired a lot of attention lately in Wireless Sensor Network (WSN). This paper shows that nodes mobility can enhance coverage degree for WSN. Analytical model is introduced to describe the coverage degree in mobile nodes wireless sensor network. MATLAB was used to build the simulator. The analytical work is validated by simulated results. Both of analytical model and simulation showing that the coverage degree has been enhanced in Mobile Nodes Wireless Sensor Network (MNWSN) as compared to static network
Robust 3D indoor positioning system based on radio map using Bayesian network
Indoor positioning remains a serious issue due to the difficulty in attaining sufficient accuracy within an indoor environment using tracking technology of low complexity. Currently, most positioning systems do not embed the off-the-shelf (OTS) system which allows mobile devices to estimate location without using any additional hardware. In this paper, we propose a robust 3D indoor positioning system that is suitable for an indoor IoT application. This system based on Bayesian network that depends on Wi-Fi signals strength. It was experimentally tested in a building with pre-deployed access points (APs). The experimental results indicate that localization accuracy of the proposed system is high with the use of a small-sized radio map
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