Throughput evaluation for the downlink scenario of co-tier interference in heterogeneous network

To extend the coverage and capacity of Heterogeneous Networks (HetNets), femtocells (HeNodeBs) has been impressive to deploy in in-house or apartment. Owing to co-channel spectrum involvement these HeNodeB sources Co-Tier interference (CTI) with neighbor HeNodeBs and users of HeNodeB (HUE) in orthogonal frequency division multiplexing Access (OFDMA). As a result, CTI is occurred which causes of system throughput degradation. This paperinvestigates the OFDMA subcarrier allocation techniques and algorithms. A Genetic Algorithm based SubcarrierAllocation (GA-SA) framework is evaluated to enhanced throughput of HeNodeB and HUE. The enhancement of the system throughput and Signal to Interference Noise Ratio (SINR) is analyzed to mitigate CTI. The system level simulation is considered to evaluate the performance of the framework. The results show that the throughput is enhanced for HUE and HeNodeB, which can mitigate the CTI in OFDMA

Recognition of isolated handwritten Arabic characters

The challenges that face the handwritten Arabic recognition are overwhelming such as different varieties of handwriting and few public databases available. Also, teaching the non-Arabic speaker at the young age is very difficult due to the unfamiliarity of the words and meanings. So, this project is focused on building a model of a deep learning architecture with convolutional neural network (CNN) and multilayer perceptron (MLP) neural network by using python programming language. This project analyzes the performance of a public database which is Arabic Handwritten Characters Dataset (AHCD). However, training this database with CNN model has achieved a test accuracy of 95.27% while training it with MLP model achieved 72.08%. Therefore, the CNN model is suitable to be used in the application device

Stress in parents of children with autism: A Malaysian experience

This study examines differences in parental stress between parents of Autism Spectrum Disorder (ASD) children (n=21) and Typically Developed (TD) children (n=41) in Malaysia. This study also compares the ages of parents of ASD children with parents of TD children with stress as a variable in these parents. Parents completed the Parental Stress Index (brief Malay version) and a socio-demographic questionnaire. Parents with ASD children were found to be significantly more stressed compared to parents of TD children (p<0.001). Significant scores were also found in the Parent-Child Dysfunctional Interaction (P-CDI) sub-scale (p<0.001) as well as Difficult Child (DC) and Parental Distress (PD) sub-scales with lower significance (p<0.05). Results also indicate that the 30-35-year-old age group among ASD parents was significantly found to be more stressed compared with parents of TD children of the same ages. Implications of the findings regarding support and intervention for families with ASD are also discussed

User-centric learning for multiple access selections

We are in the age where business growth is based on how user-centric your services or goods is. Current research on wireless system is more focused on ensuring that user could achieve optimal throughput with minimal delay, disregarding what user actually wants from the services. Looking from con-nectivity point of view, especially in urban areas these days, there are multiple mobile and wireless access that user could choose to get connected to. As people are looking toward machine automa-tion, we understand that the same could be done for allowing users to choose services based on their own requirement. This paper looks into unconventional, non-disruptive approach to provide mobile services based on user requirements. The first stage of this study is to look for user association from three new perspectives. The second stage involved utilizing a reinforcement learning algorithm known as q-learning, to learn from feedbacks to identify optimal decision in reaching user-centric requirement goal. The outcome from the proposed deployment has shown significant improvement in user association with learning aware solution © BEIESP

Outage probability analysis of Co-Tier interference in heterogeneous network

In Heterogeneous Network (HetNet), the femtocell (HeNB) has been deployed by the telecommunication industries to provide extensive coverage as well as capacity in an indoor. These HeNBs are Customer Premise Equipment (CPE) which is randomly used in co-channel with macrocell (MeNB) and causes the Co-Tier Interference (CTI) in OFDMA. The effect of CTI in OFDMA systems can lead the system throughput degradation and service disruption. Because of quick direct changing features in Rayleigh channel, it is compulsory to succeed the satisfactory performance. The signal-to-interference noise ratio (SINR) is arbitrary which drives the highest capacity to be an irregular variable. However, this paper derives the expressions of outage probabilities based on the hybrid Genetic Algorithm (GA) with biogeography based dynamic subcarrier allocation (HGBBDSA) algorithm is implemented in reducing the outage probability. The outage probability countenance is expressed for the moment-generating function of the total SINR at the receivers end. The simulation results demonstrate that the HGBBDSA can lessen the outage to 45 % than existing methods

QR code based authentication method for IoT applications using three security layers

A quick response code-based authentication method (QRAM) is proposed. QRAM is applicable for lots of internet of things (IoT) applications. QRAM aims to verify requests of such an access to IoT applications. Requests are made using a quick response code (QRC). To authenticate contents of QRC, users will scan QRC to access IoT applications. To authenticate contents of QRC, three procedures are applied. QRAM contributes to IoT automatic access systems or smart applications in terms of authentication and safety of access. QRAM is evaluated in term of security factors (e.g., authentication). Computation time of authentication procedures for several IoT applications has become a considerable issue. QRAM aims to reduce computation time consumed to authenticate each QRC. Some authentication techniques still face difficulties when an IoT application requires fast response to users; therefore, QRAM aims to enhance so to meet real-time applications. Thus, QRAM is compared to several competitive methods used to verify QRC in term of computation time. Results confirmed that QRAM is faster than other competitive techniques. Besides, results have shown a high level of complexity in term of decryption time needed to deduce private contents of QRC. QRAM also is robust against unauthorized requests of access

Vegetation Monitoring Using UAV: A Preliminary Study

Remote sensing using drone or UAV (unmanned aerial vehicle) is the current trends and this technology can provide unrevealed life altering benefits to mankind. Drones are being used in many sectors such as for military, research, agricultural and recreational means. This technology not only can reduce the time of inspection, but it is also giving many benefits such as provides real-time live video for site inspection that can help user to analyze site logistic and speeding up the overall tasks. However, vegetation monitoring using remote sensing has its own challenges in terms of processing the captured image and data. Somehow, previous research has suggested a lot of different possible algorithm that could be used for post-processing the data gathered. Nevertheless, most of the algorithm requires a specific sensor in order to get the result. The objective of this paper is to identify and verify the algorithm that is suitable to process the vegetation image. This research will use the data gathered from various area by using consumer camera and process by using Visible Atmospherically Resistant Index (VARI) indices. Finally, this research will observe the accuracy of the result analyzed using the VARI and identify the characteristic of the algorithm

Analysis of two adjacent articulation Quranic letters based on MFCC and DTW

—Reciting al-Quran in the correct way is an obligatory duty for Muslims, and therefore learning al-Quran is a continuous education until the correct recitation is achieved. It is important to learn Tajweed rules to master the recitation of Quranic verses. Moreover, mastering the pronunciation of Arabic sounds is the first and key step to achieve accurate recitation of al-Quran. The rules were guided by the Islamic Scholars in fields related to al-Quran from their knowledge and experiences. Very limited researches were found in the perspective of sciences and engineering. In this paper two Quranic letters (ذ and ز) that are articulated from adjacent points of articulation were analyzed using Mel- frequency coefficient analysis. MFCCs matrices were calculated then compared using the dynamic time warping DTW technique to calculate the similarity matrices and find the similarity distance. Results show that letters from the same point of articulation have less similarity distance compared to the letters from different point of articulation