This paper proposes a novel approach to improve the quality of human motion data captured by a depth sensor. Depth-based motion capture (D-Mocap) data suffer significant errors due to noise, self-occlusion, interference, and other technical limitations. We aim to improve 3D joint trajectories to be more kinematically admissible and to sustain the skeleton structure more anthropometrically stable. Our research integrates nonlinear Kalman filters (KF) with Differential Evolution (DE) algorithms together to take advantage of the kinematic and anthropometric constraints respectively. Specifically, we compare three nonlinear KFs in terms of their effectiveness and accuracy, including the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the Tobit Kalman filter (TKF). Both simulated and real-world D-Mocap data are used to examine the proposed algorithm. We also show the improved accuracy of six lower-body joint angles that are often used for clinical gait assessment.

INTRODUCTION: When compared to traditional ticketing systems, mobile ticketing has several advantages, allowing ubiquitous and remote access to payments, avoiding queues and replacing notes and coins. It also allows service providers to reduce their costs and achieve operational and productivity gains. However, while some cities have implemented mobile ticketing solutions on their public transport network, the adoption of such services appears to have limited success. The causes that lead to such a low rate of use of the service are still unknown.

OBJECTIVES: This paper presents an in-depth study of the reasons that lead mobile ticketing customers to adopt or abandon this type of service and establishes a series of strategies to attract and retain customers.

METHODS: It uses the city of Porto, Portugal, as an illustrative example, where a mobile ticketing solution, called Anda, was launched. Customer complaints related to 6 months of using Anda were analysed and usability tests were carried out with real customers in the context of use.

RESULTS: This analysis allowed to identify a series of factors that lead people to adopt or abandon this type of services. Then, a series of strategies were defined and identified that allow to capture and retain mobile ticketing customers during the various stages of the mobile ticketing lifecycle: user onboarding, user engagement, user retention and user reinstall. For each of the stages of this life cycle, the main concerns to be considered were also listed, a series of tactics were defined to reverse the abandonment trend and a series of KPIs were specified to measure the efficiency of the strategies.

CONCLUSION: This paper fills an important research gap in the literature, being very useful in shaping future strategies for successful mobile ticketing solutions.

Sandbox applications can be used as anti-forensics techniques to hide important evidence in the digital forensics investigation. There is limited research on sandboxing technologies, and the existing researches on sandboxing are focusing on the technology itself. The impact of sandbox applications on live digital forensics investigation has not been systematically analysed and documented. In this study, we proposed a methodology to analyse sandbox applications on Windows systems. The impact of having standalone sandbox applications on Windows operating systems image was evaluated. Experiments were conducted to examine the artefacts of three sandbox applications: Sandboxie, BufferZone and ToolWiz Time Freeze on Windows 7, Windows Server12 R2 and Windows XP operating systems in 2018. We found that (1) only the installed applications can be found after deleting the ToolWiz Time Freeze content. Unlike Sandboxie, the data can be retrieved from the memory images even after deleting the application’s content if the system was not restated; (2) not all the sandbox applications data will be deleted after restarting the systems, e.g., BufferZone’s content can be retrieved even after restarting the system.

INTRODUCTION: With the development of artificial intelligence, facial expression recognition has become a hot topic. Facial expression recognition has been widely applied to every field of our life. How to improve the accuracy of facial emotion recognition is an important research content.

OBJECTIVES: In today's facial expression recognition, there are problems such as weak generalization ability and low recognition accuracy. Aiming to improve the current facial expression recognition problems, we propose a novel facial emotion recognition method.

METHODS: This paper focuses on the deep learning-based static face image expression recognition method, and combines transfer learning and deep residual network ResNet-101 to realize facial expression recognition.

RESULTS: The simulation results show that the overall accuracy of our method is 96.29± 0.78%.

CONCLUSION: The performance of this model is superior to the current mainstream face emotion recognition models. In the future research, we will try other methods based on deep learning.

This paper presents a comprehensive model including a wireless backhaul as a cost-effective backhaul alternative to wired backhaul for vehicular networks, a heterogeneous underlay cognitive vehicular network with multiple mobile secondary transmitters acting as mobile small cells, a mobile secondary receiver and a mobile primary user. To increase the spectrum utilization in this proposed vehicular network, multiple mobile secondary transmitters forward the signal to a mobile secondary receiver while using the same spectrum with a mobile primary user on the condition that the interference caused by secondary transmitters is tolerable at the primary user. A Bernoulli process is applied to model wireless backhaul reliability. The analytical closed-form expressions for outage probability as well as the asymptotic expression are derived to reveal the effects of backhaul reliability on the network performance over double-Rayleigh fading channels.

INTRODUCTION: The mobile application requires some information about its environment in order to the adaptation of its behaviour according to this information in a transparent manner. However, such a task usually needs the resource consumption of mobile devices.

OBJECTIVES: the main goal of this work was the design of the context aware infrastructure for the healthcare applications embedded on mobile devices. This last aims to the integration of the context awareness into the execution environment and providing a adequate methodology of the generic context manager, with the maintenance of low level the resource consumption.

METHODS: This collected the context information (medical data) thanks to the component (Context Element Monitor). When the components set acquire values of different elements context which belong the same class, these components run in the same time. it is mean the execution of its in a group way. From the state of monitors (ACTIVE or DISABLED), our system creates the new context situation. The mobile application is structurally an assembly of services. The adaptation methodology revolves around the execution manager of services according to the new created context situation. This approach ensures the best use of resources depending on the characterization of context and reduces the amount of contextual information in the adaptation processes.

RESULTS: We evaluated a memory space in three cases, since it is the most resource that is affected in the field of the mobile health (mobile patient monitoring application). For this, we estimated the consumed memory at two levels: the first is the management module "Context Manager" and the second level considers the adaptation procedure. In the Context Manager, the first and the third case were the exploited memory space was almost identical, Knowing that its consumption is varied between 1556 and 2026 KB. But in the second case, it increased and become varying between 2424 and 6848 KB. the other hand, in the adaptation mechanism, it is necessary a huge space of a memory for managing services more than 40000 kb.

CONCLUSION: Our own approach of context manager and adaptation can open the new way in the m-health applications development. This allows these applications that can exploit the great quantity and the different type of a medical data with the best use of the memory space.

Networks such as LTE-M, NB-IoT, LoRa and SigFox are being deployed for massive IoT communications. However, the Aloha protocols used for the media access are inefficient when the network size is high. As a result, a Distributed Queuing (DQ) algorithm is being proposed to replace the conventional access schemes because of its superior network performance. In this paper, we study the algorithm with no prior consideration of any underlying technology at the physical layer. We perform a steady-state analysis of the algorithm in a network where the traffic is composed of periodic and urgent packets. It was found that for high traffic from the network all the nodes were taking part in each contention. Nevertheless, for better performance in terms of access delay and packet drop rate, it was preferable to operate the algorithm in the small traffic interval. Moreover, the downlink traffic has been observed to have a significant impact on the stability of the algorithm.

INTRODUCTION: Information and Communication Technologies are deemed to be a significant influence on teenagers’ development. It is important for studies to be conducted to assess the underlying influence of ICT on the teenager age group.

OBJECTIVES: The aim of this paper is to review whether ICT influences teenagers’ skills, competencies, health and well-being based on previous studies.

METHODS: Articles from 2015 to 2020 were selected for analysis to identify the influences on teenagers.

RESULTS: Computer and mobile devices offer opportunities to improve literacy in digital skills and competencies. It is also evident that teenagers are prone to risks of getting musculoskeletal symptoms as a result of excessive use of such devices, and it also leads to unforeseen illnesses such as depression in the case of teenager’s well-being.

CONCLUSION: Adopting ICT may have beneficial implications for teenagers, but it can pose more adverse effects on their development when used excessively.

INTRODUCTION: ECG have emerged as the most acceptable and widely used technique to infer mental health status using cardiac signals thereby resolving major challenge of Mental Health Assessment protocols.

OBJECTIVES: Authors mainly aimed at identification of stressed signals to distinguish subjects exhibiting stress ECG signals.

METHODS: Authors have taken advantage of three optimization techniques namely, Genetic Algorithm (GA), Artificial Bee Colony (ABC) and improved Particle Swarm Optimization (PSO) that further improves the classification accuracy of Multi-kernel SVM.

RESULTS: The simulation analysis confer that the proposed work outperforms the existing works while demonstrating an average accuracy, precision, recall and specificity of 98.93%, 96.83%, 96.83% and 96.72%, respectively when evaluated against dataset comprising of 1000 ECG samples.

CONCLUSION: It is observed that the proposed stress prediction based on improved VMD and Improved SVM outperformed the existing work that comprised of traditional VMD and SVM.

Buddhism has a rich visual history of ancient paintings and sculpture used to evoke spiritual and archetypal concepts. One of the key figures in Mahayana Buddhism is the celestial Buddha Amitabha, also known as the Buddha of Infinite Light. Several Buddhist texts describe the “Sukhāvatī” the land that that Amitabha inhabits. The texts not only describe the physical characteristics of this environment but also the soundscape. This paper discusses the process of transcoding an image of Amitabha into sound to create an interactive sonic environment. This process explores how choosing sounds based upon the literary description, can be a way of creating a link between an image and the literary counterpart.