33 research outputs found
Influencing Factors on Intention And Adoption of KBZ Credit Card (Aung Pyae Sone,2023)
No full textThe study aims to identify the influencing factors on the intention and adoption of KBZ credit card usage among customers. The study used both descriptive and quantitative research methods. Primary data as well as secondary data are used in this study. A sample of 300 active KBZ credit cardholders were selected by using simple random sampling method. They were interviewed with structured questionnaire. This study explored the five factors which are perceived usefulness, perceived ease of use, knowledge on credit card, facilitating conditions, and perceived trust on payment requirements. According to the regression analysis, the results revealed that three factors which were perceived usefulness, facilitating conditions of credit cards, and perceived trust in payment requirements had significantly influence on users' intention to use KBZ credit cards. Moreover, the intention to use KBZ credit card also had a significant influence on adoption of KBZ credit card. KBZ Bank should address the accessibility issues that customers perceive with respect to credit card adoption including offering of user-friendly educational resources and features to help new users better understand credit card usage and management. Moreover, providing education on the advantages of using KBZ credit cards over social or cultural pressures to use them could help to increase customer adoption
Open-source Mobility Platform for Children with Disabilities
No full textPowered wheelchairs are integral to the development of children with disabilities. However, commercial wheelchairs are very expensive, typically costing over $10,000, to be widely incorporated into physical therapy and rehabilitation programs. In 2017-18, two Union seniors designed a low-cost power mobility device (Version 1.0) by retrofitting a Fisher-Price children toy called the Wild Thing. This device was delivered to The Kevin G. Langan School in the spring of 2018. This senior capstone design project improves Version 1.0 to create Version 2.0 and addresses challenges in the field of assistive technologies.
The challenges in the field of assistive technologies lies in the need to customize hardware and/or software on each device for children with different motor skills. Current DIY assistive wheelchairs are not easily replicable and self-diagnosable upon system malfunctions. Poor hardware and software design documentation makes maintenance difficult.
To address these challenges, this senior capstone design project transforms a one-time project into an easily replicable open-source product. For Version 2.0 to be accessible to anyone who may wish to replicate the device, a public website (https://muse.union.edu/umobility) was built as dedicated resource hub. On this site, users are able to access the construction manual of both the control system as well as the seating structure of Version 2.0
Mobile landmark recognition for information retrieval
No full textThe final year project report aims to cover the theoretical background of the existing recognition techniques, different components for recognition process and performance analysis of different combination of feature detection technique to use in Mobile Landmark Recognition application.
Content-based image retrieval has been researched upon with techniques ranging from image feature extraction, representation, indexing. Bag-of-Words framework is used in the project where each image is modeled as a collection of features plotted over a histogram. A database with 50 categories containing a total of 4000+ images is used to train and test the system. This database was built with image recognition in mind so as to ensure that it is able to simulate the real scenario where users capture image using their smartphones. Two feature detection methods and two different resolutions were used. Two feature detection methods were dense sampling based and keypoint detection based approach. 640 x 480 and 320 x 240 resolutions were used in the project. Hierarchical k-means approach is used in clustering and scalable vocabulary tree is used in machine learning.
In summary, it is proven that using dense sampling based approach performed better than keypoint detection based approach. Even reducing the resolution still gives the best result for dense sampling.Bachelor of Engineerin
Sagaing Fault slip and deformation in Myanmar observed by continuous GPS measurements
No full textAbstractThe Sagaing Fault is a major tectonic structure between the Indian Plate and Sunda Plate. The fault measures 1200 km along north–south and cuts through the centre of Myanmar. Many urban areas lie along the fault. As a result, Myanmar has established a continuous Global Positioning System (cGPS) network across the Sagaing Fault since 2011. The cGPS network consists of eight cGPS stations that form two transects across the fault. The data analysis covers a period of four years from 2011 to 2014. GAMIT, GLOBK, and TRACK software suite packages are used for GPS data processing and analysis. This study consists of two main objectives. The first objective is to analyse the Myanmar cGPS network observations in order to measure the moving rate and direction of movement for each cGPS station using GAMIT/GLOBK software packages. The second objective is to investigate the co-seismic moving rate associated with the earthquake event using TRACK kinematic positioning program. The analysis results indicate that the east side of the Sagaing Fault moves southeastward at the average rate of approximately 32–40 mm/a, whereas the west side of the fault moves northeastward at the rate of about 31–35 mm/a. For co-seismic analysis, two cGPS stations are analysed in connection with the 2012 M6.8 Thabeikkyin earthquake. These stations are located 50–60 km away from the epicentre. The GPS data analysis clearly showed that the station at the east side of the Sagaing Fault immediately moved south by 15.0 cm, whereas the station at the west side of the fault moved north by 3.0 cm. In conclusion, this study demonstrates that the Sagaing Fault's tectonic activities can be monitored by cGPS observations using geodetic processing techniques. We believe that such investigation brings contribution to better understand of the tectonic activities in Myanmar and South East Asia
Energy-Efficient Offloading and User Association in UAV-assisted Vehicular Ad Hoc Network
No full textTask offloading scheme provides opportunistic energy saving for computation-intensive on-vehicle applications. The evolution of the Vehicular Edge Computing (VEC) paradigm has contributed a vast potential that can enhance the performance of such vehicles with energy-hungry and delay-sensitive services. However, determining how much workload to compute locally or offload to the VEC server is still quite challenging. Moreover, when all the vehicles try to offload their computation tasks to the same VEC server, it leads to deterioration in the performance gain due to overburden. Recently, unmanned aerial vehicle (UAV) as the edge server has gained huge attraction due to its well maneuverability and cost efficiency. In this paper, we study the energy-efficient offloading as well as association of the vehicles between the road side unit (RSU) and UAV. First, we formulate the joint offloading and association problem. Next, we decompose the formulated mixed integer linear (MIL) problem into two subproblems and then solve them by using standard convex optimization. Finally, we compare our proposed algorithm with benchmark schemes and the numerical results demonstrate that our algorithm outperforms the benchmark solutions.</p
Experimental verification of parameters in automobile crankshaft modelling for vibration analysis
No full textIn the interest of utilized more stable automobile components at high speed for reduction the vibration of mechanical system, dynamic characteristics analysis plays a vital role in complex mechanical parts. This paper introduces a clarified approach on statistical investigation and modal analysis methodology to study, predict and accurate crankshaft natural frequencies by using design of experiment (DOE). In this research, first, simulation had been done with MSC Nastran/ Patran to find out the natural frequencies in each mode shape of crankshaft as well as the verification with experiment was carried out. In order to less inaccuracy, numerous simplified crankshaft models were created by using these as input and DOE was established to acquire precise parameters of optimized crankshaft design as the second phase. This method can be further used for the optimizing the structural parameters and would provide some value basis to qualitative measure of parameters and determination of optimized structure. In Conclusion, modal verification accuracy between experimental and simulation has improved
Deep Reinforcement Learning based Joint Spectrum Allocation and Configuration Design for STAR-RIS-Assisted V2X Communications
No full textVehicle-to-Everything (V2X) communications play a crucial role in ensuring
safe and efficient modern transportation systems. However, challenges arise in
scenarios with buildings, leading to signal obstruction and coverage
limitations. To alleviate these challenges, reconfigurable intelligent surface
(RIS) is regarded as an effective solution for communication performance by
tuning passive signal reflection. RIS has acquired prominence in 6G networks
due to its improved spectral efficiency, simple deployment, and
cost-effectiveness. Nevertheless, conventional RIS solutions have coverage
limitations. Therefore, researchers have started focusing on the promising
concept of simultaneously transmitting and reflecting RIS (STAR-RIS), which
provides 360\degree coverage while utilizing the advantages of RIS technology.
In this paper, a STAR-RIS-assisted V2X communication system is investigated. An
optimization problem is formulated to maximize the achievable data rate for
vehicle-to-infrastructure (V2I) users while satisfying the latency and
reliability requirements of vehicle-to-vehicle (V2V) pairs by jointly
optimizing the spectrum allocation, amplitudes, and phase shifts of STAR-RIS
elements, digital beamforming vectors for V2I links, and transmit power for V2V
pairs. Since it is challenging to solve in polynomial time, we decompose our
problem into two sub-problems. For the first sub-problem, we model the control
variables as a Markov Decision Process (MDP) and propose a combined double deep
Q-network (DDQN) with an attention mechanism so that the model can potentially
focus on relevant inputs. For the latter, a standard optimization-based
approach is implemented to provide a real-time solution, reducing computational
costs. Extensive numerical analysis is developed to demonstrate the superiority
of our proposed algorithm compared to benchmark schemes.Comment: 12 pages, 9 figure
Deep Reinforcement Learning based Joint Spectrum Allocation and Configuration Design for STAR-RIS-Assisted V2X Communications
No full textVehicle-to-everything (V2X) communications is pivotal for modern transportation systems, but the challenges arise in scenarios with buildings, leading to signal obstruction and limited coverage. To alleviate these challenges, reconfigurable intelligent surface (RIS) is regarded as an effective solution for communication performance by tuning passive signal reflection. RIS has acquired prominence in 6G networks due to its improved spectral efficiency, simple deployment, and cost-effectiveness. Nevertheless, conventional RIS solutions have coverage limitations. Researchers are exploring on the promising concept of simultaneously transmitting and reflecting RIS (STAR-RIS), which provides 360° coverage while utilizing the advantages of RIS technology. In this article, an STAR-RIS-assisted V2X communication system is investigated. An optimization problem is formulated to maximize the achievable data rate for vehicle-to-infrastructure (V2I) users while satisfying the latency and reliability requirements of vehicle-to-vehicle (V2V) pairs by jointly optimizing the spectrum allocation, amplitude and phase shift values of STAR-RIS elements, digital beamforming vectors for V2I links, and transmit power for V2V pairs. Since it is challenging to solve in polynomial time, we decompose our problem into two subproblems. For the first subproblem, we model the control variables as a Markov Decision Process and propose a combined double deep Q-network (DDQN) with an attention mechanism so that the model can potentially focus on relevant inputs. For the latter, a standard optimization-based approach is implemented to provide a real-time solution, reducing computational costs. Numerical results demonstrate that our solution approach outperforms the vanilla DDQN approach by 5.2%, and our proposed system outperforms the conventional RIS by 39%.</p
Aerial STAR-RIS Empowered MEC:A DRL Approach for Energy Minimization
No full textMulti-access Edge Computing (MEC) addresses computational and battery limitations in devices by allowing them to offload computation tasks. To overcome the difficulties in establishing line-of-sight connections, integrating unmanned aerial vehicles (UAVs) has proven beneficial, offering enhanced data exchange, rapid deployment, and mobility. The utilization of reconfigurable intelligent surfaces (RIS), specifically simultaneously transmitting and reflecting RIS (STAR-RIS) technology, further extends coverage capabilities and introduces flexibility in MEC. This letter explores the integration of UAV and STAR-RIS to facilitate communication between IoT devices and an MEC server. The formulated problem aims to minimize energy consumption for IoT devices and aerial STAR-RIS by jointly optimizing task offloading, aerial STAR-RIS trajectory, amplitude and phase shift coefficients, and transmit power. Given the non-convexity of the problem and the dynamic environment, solving it directly within a polynomial time frame is challenging. Therefore, deep reinforcement learning (DRL), particularly proximal policy optimization (PPO), is introduced for its sample efficiency and stability. Simulation results illustrate the effectiveness of the proposed system compared to benchmark schemes in the literature
