14 research outputs found
A quality of experience approach in smartphone video selection framework for energy efficiency
No full textOnline video streaming is getting more common in the smartphone device nowadays.
Since the Corona Virus (COVID-19) pandemic hit all human across the globe in 2020,
the usage of online streaming among smartphone user are getting more vital.
Nevertheless, video streaming can cause the smartphone energy to drain quickly
without user to realize it. Also, saving energy alone is not the most significant issues
especially if with the lack of attention on the user Quality of Experience (QoE). A
smartphones energy management is crucial to overcome both of these issues. Thus, a
QoE Mobile Video Selection (QMVS) framework is proposed. The QMVS
framework will govern the tradeoff between energy efficiency and user QoE in the
smartphone device. In QMVS, video streaming will be using Dynamic Video Attribute
Pre-Scheduling (DVAP) algorithm to determine the energy efficiency in smartphone
devices. This process manages the video attribute such as brightness, resolution, and
frame rate by turning to Video Content Selection (VCS). DVAP is handling a set of
rule in the Rule Post-Pruning (RPP) method to remove an unused node in list tree of
VCS. Next, QoE subjective method is used to obtain the Mean Opinion Score (MOS)
of users from a survey experiment on QoE. After both experiment results (MOS and
energy) are established, the linear regression technique is used to find the relationship
between energy consumption and user QoE (MOS). The last process is to analyze the
relationship of VCS results by comparing the DVAP to other recent video streaming
applications available. Summary of experimental results demonstrate the significant
reduction of 10% to 20% energy consumption along with considerable acceptance of
user QoE. The VCS outcomes are essential to help users and developer deciding which
suitable video streaming format that can satisfy energy consumption and user QoE
Design and development of a small-scale 12S-14P outer rotor HEFSM
No full textSimulation, prototype experimental, and mathematical modelling is an essential process to provide sufficient evidence before a full-scale development or mass production. Hence, this study focuses on validating a small scale of 12S-14P outer-rotor hybrid excitation flux switching motor (OR-HEFSM) through simulation, experimental, and mathematical modelling. The JMAG-Designer software as finite element solver is used to design and analyse the designed geometry structure. Throughout simulation process, the rotor design with direct drive structure as illustrated in Appendix A is chosen based on optimisation process. Thus, the generated back EMF, torque, and power through simulation at a speed of 1,200 r/min is 6.58 V, 16.4 Nm, and 12.4 kW, correspondingly. The designed model has been fabricated using actual prototype analysis (APA) approach, which is involves five stages, namely 3-D design, material selection, fabrication, assembly, and experimental test. The computer-aided software of SolidWorks is used to implement the first stage of APA while the prototype structure is fabricated using a computer numerical control (CNC) machine. The prototype has been tested experimentally using a measurement tool such as Fluke Analyser and oscilloscope. The back EMF showed a good agreement between simulation and preliminary experimental results with percentage differences approximately 5.1% at a speed of, 1,200 r/min. In contrast with the prediction results based on mathematical modelling using sizing equation, the calculated back EMF, torque, and power is 7.58%, 8.6%, and 8.4% higher than simulation results, respectively. Even so, the results had proven that the concept of three-phase working principle for small-scale 12S-14P OR-HEFSM with direct drive structure remained the same for simulation, experiment, and prediction
Quality of Experience (QOE) Aware Video Attributes Determination for Mobile Streaming Using Hybrid Profiling
No full textToday, consumers use a smartphone device to display the media contents for work and entertainment purposes, as well as watching online video. Online video streaming is the main cause that consume smartphone’s energy quickly. To overcome this problem, smartphone’s energy management is crucial. Thus, a hybrid energy-aware profiler is proposed. Basically, a profiler will monitor and manage the energy consumption in the smartphone devices. The hybrid energy-aware profiler will set up a protocol preference of both the user and the device. Then, it will estimates the energy consumption in smartphone. However, saving energy alone can contribute to the Quality of Experience (QoE) neglection, thus the proposed solution takes into account the client QoE. Even though there are several existing energy-aware profilers that have been developed to manage energy use in smartphones however, most energy-aware profilers does not consider QoE at the same time. The proposed solution consider both, the performance of the hybrid energy-aware profiler is compared with the baseline energy models against a variation of content adaptation according to the pre-defined variables. Three types of variables were determined; resolution, frame rate and energy consumption in smartphone devices. In this area, QoE subjective methods based on MOS (Mean Opinion Score) are the most commonly used approaches for defining and quantifying real video quality. Nevertheless, although these approaches have been established to consistently quantify users’ amounts of approval, they do not adequately realize which are the criteria of video attribute that important. In this paper, we conducted an experiment with a certain devices to measures user’s QoE and energy usage of video attribute in smartphone devices. Our results demonstrate that the list of possible solution is a relevant and useful video attribute that satify the users
Rainfall-Runoff Modeling Using Artificial Neural Network for Batu Pahat River Basin
No full textThis research delves into the effectiveness of Artificial Neural Networks with Multilayer Perceptron (ANN-MLP) and Nonlinear AutoRegressive with eXogenous inputs (NARX) models in predicting short-term rainfall-runoff patterns in the Batu Pahat River Basin. This study aims to predict river water levels using historical rainfall and river level data for future intervals of 1, 3, and 6 hours. Data preprocessing techniques, including the management of missing values, identification of outliers, and reduction of noise, were applied to enhance the accuracy and dependability of the models. This study assessed the performance of the models for ANN-MLP and NARX by comparing their effectiveness across various forecast timeframes and evaluating their performance in different scenarios. The findings of the study revealed that the ANN-MLP model showed robust performance in short-term prediction. On the contrary, the NARX model exhibited higher accuracy, particularly in capturing intricate temporal relationships and external impacts on river behavior. The ANN-MLP produces 99% accuracy for 1-hour prediction, and NARX yields 98% accuracy with 0.3245 Root Mean Squared Error and 0.1967 Mean Absolute Error. This study makes a valuable contribution to hydrological forecasting by presenting a rigorous and precise modeling methodology
Rainfall-Runoff Modeling Using Artificial Neural Network for Batu Pahat River Basin
No full textThis research delves into the effectiveness of Artificial Neural Networks with Multilayer Perceptron (ANN-MLP) and Nonlinear AutoRegressive with eXogenous inputs (NARX) models in predicting short-term rainfall-runoff patterns in the Batu Pahat River Basin. This study aims to predict river water levels using historical rainfall and river level data for future intervals of 1, 3, and 6 hours. Data preprocessing techniques, including the management of missing values, identification of outliers, and reduction of noise, were applied to enhance the accuracy and dependability of the models. This study assessed the performance of the models for ANNMLP and NARX by comparing their effectiveness across various forecast timeframes and evaluating their performance in different scenarios. The findings of the study revealed that the ANN-MLP model showed robust performance in short-term prediction. On the
contrary, the NARX model exhibited higher accuracy, particularly in capturing intricate temporal relationships and external impacts on river behavior. The ANN-MLP produces 99% accuracy for 1-hour prediction, and NARX yields 98% accuracy with 0.3245 Root Mean Squared Error and 0.1967 Mean Absolute Error. This study makes a valuable contribution to hydrological forecasting by presenting a rigorous and precise modeling methodology
User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices
No full textيعد جدول الفديو الاكثر انتشارا اليوم. اضافة الى ذلك، وبسبب انتشار الوباء عالميا، كثير من الناس التزموا المنزل واعتمدوا على الخدمات الجدولية للاخبار والتعليم والتسلية. على اية حال، مستعمل تجربة (QoE (غير مقتنع باختيار محتوى الفديو بينما يتدفق في الاجهزة الذكية. ينزعج المستعملون بمسح نوعية الفيديو الغير متوقعة التي تحدث في اجهزتهم الذكية. في هذا البحث، نقترح مخطط لاختيار الفديو الهيكلي الذي يهدف الى زيادة قناعة مستعمل (QoE ). تم استعمال نظام الحلول الحسابية لاختيار محتوى الفديو لانشاء خريطة لاختيار الفديوالتي ترضي مستعمل نوعية الجدول الاكثراعتبارا. تصنف اختيار محتوى الفديو الى مجاميع صفات الفديو. سينخفض مستوى جدول ( VCS) بالتدريج ليعتبر اقل اختيار الفديو الذي لا يقبلها المستعمل اعتمادا على نوعية الفديو. لتقييم مستوى القناعة ، استعملنا درجة الرأي الوضيع ( MOS) لقياس تكيف قبول المستعمل اتجاه نوعية جدول الفديو. أظهرت النتائج الاخيرة بأن نظام الحلول الحسابية المقترح توضح بأن المستعمل يقتنع باختيار الفديو بواسطة تغيير صفات الفديو. Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education, and entertainment. However, when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified into video attributes groups. The level of VCS streaming will gradually decrease to consider the least video selection that users will not accept depending on video quality. To evaluate the satisfaction level, we used the Mean Opinion Score (MOS) to measure the adaptability of user acceptance towards video streaming quality. The final results show that the proposed algorithm shows that the user satisfies the video selection, by altering the video attributes
Quality of Experience (QOE) Aware Video Attributes Determination for Mobile Streaming Using Hybrid Profiling
No full textToday, consumers use a smartphone device to display the media contents for work and entertainment purposes, as well as watching online video. Online video streaming is the main cause that consume smartphone’s energy quickly. To overcome this problem, smartphone’s energy management is crucial. Thus, a hybrid energy-aware profiler is proposed. Basically, a profiler will monitor and manage the energy consumption in the smartphone devices. The hybrid energy-aware profiler will set up a protocol preference of both the user and the device. Then, it will estimates the energy consumption in smartphone. However, saving energy alone can contribute to the Quality of Experience (QoE) neglection, thus the proposed solution takes into account the client QoE. Even though there are several existing energy-aware profilers that have been developed to manage energy use in smartphones however, most energy-aware profilers does not consider QoE at the same time. The proposed solution consider both, the performance of the hybrid energy-aware profiler is compared with the baseline energy models against a variation of content adaptation according to the pre-defined variables. Three types of variables were determined; resolution, frame rate and energy consumption in smartphone devices. In this area, QoE subjective methods based on MOS (Mean Opinion Score) are the most commonly used approaches for defining and quantifying real video quality. Nevertheless, although these approaches have been established to consistently quantify users’ amounts of approval, they do not adequately realize which are the criteria of video attribute that important. In this paper, we conducted an experiment with a certain devices to measures user’s QoE and energy usage of video attribute in smartphone devices. Our results demonstrate that the list of possible solution is a relevant and useful video attribute that satify the users
Fullerene-to-MWCNT Structural Evolution Synthesized by Arc Discharge Plasma
No full textThe growth of multi-walled carbon nanotubes (MWCNTs) has been extensively studied using electron microscopy. The ex situ structural behavior was examined to investigate the growth of the MWCNTs under different environments and pressures using electron microscopy. The arc discharge plasma technique was applied to synthesize the MWCNTs by evaporating carbon through the arc plasma between two cylindrical graphite rods, with a background pressure of 10−2 to 102 mbar, inside a vacuum chamber under different ambient environments. The results showed that long MWCNT structures were successfully grown. We suggest that the mechanism involves: (i) fullerene formation; (ii) the elongation of fullerenes; and (iii) the growth of MWCNTs. Agglomeration with other structures then forms MWCNT bundles. We note that the pressure and environment in the vacuum chamber can affect the structure of the MWCNTs
Systematic Literature Review on Persuasive System Design Framework for Managing Curriculum Performance
No full textIntegrating digital resources into educational assessment has led to the widespread adoption of e-portfolios as tools for documenting and evaluating student achievement, thereby transforming traditional evaluation methods. However, the existing frameworks primarily focus on assessing academic performance, often neglecting the comprehensive monitoring of student’s co-curricular activities. To overcome current gaps in comprehensive student evaluation, this study introduces a conceptual framework incorporating persuasive system design (PSD) into an e-portfolio to facilitate efficient co-curricular performance monitoring in Malaysian secondary schools. To ensure a thorough approach to educational evaluation, it is essential to effectively monitor and manage academic and extracurricular performance to understand student progress comprehensively. By adding Physical Activity, Sports, and Co-curriculum Assessment (PAJSK) – specific categories and key PSD elements- primary task support, dialogue support, system credibility support, and social support- that are all designed to improve user engagement and system dependability in an educational environment, the framework builds on the Oinas-Kukkonen and Harijumaa PSD Model. This study adapts and discusses the persuasive design elements to meet the goals of educational assessment frameworks by comparing PSD implementation in e-health, e-tourism, e-commerce, and e-learning. The results offer an overview of developing a practical, engaging e-portfolio framework that facilitates comprehensive student evaluation, especially in educational environments focusing on co-curricular achievement
Aplikasi pembelajaran haiwan menggunakan teknik visualisasi
No full textPada masa kini, aplikasi mudah alih semakin mendapat perhatian dan populariti. Namun begitu, kandungan aplikasi pembelajaran yang dibangunkan oleh pembangun tempatan masih kurang. Oleh itu, Aplikasi Pembelajaran Haiwan Menggunakan Teknik Visualisasi untuk platform mudah alih ini dibangunkan. Aplikasi ini bertujuan untuk membenarkan kanak-kanak bermain sambil belajar menggunakan telefon mudah alih. Ini akan dapat menyelesaikan masalah kekurangan aplikasi pembelajaran di platform mudah alih. Aplikasi ini dibangunkan menggunakan model ADDIE sebagai metodologi pembangunan disebabkan ianya bersesuaian dengan konsep pembelajaran yang digunakan di dalam aplikasi ini. Model ini melibatkan 5 fasa iaitu fasa analisis, fasa reka bentuk, fasa pembangunan, fasa implementasi dan fasa penilaian. Aplikasi ini telah diuji oleh beberapa pengguna sasaran yang dipilih secara rawak. Berdasarkan pengujian, semua responden bersetuju setuju bahawa aplikasi telah berfungsi dengan baik dan memenuhi objektif pembangunan aplikasi. Kesimpulannya, matlamat projek bagi mereka bentuk kandungan sebuah aplikasi pembelajaran haiwan yang diberi nama Animalia dengan menggunakan teknik visualisasi dalam platform mudah alih adalah berjaya
