Mobile Multimedia Research Articles

User-Perceived Capacity: Theory, Computation, and Achievable Policies

User-perceived throughput is a novel performance metric attracting a considerable amount of recent attention because it characterizes the quality of the experience in mobile multimedia services. For instance, it gives a data rate of video streaming with which a user will not experience any lag or outage in watching video clips. However, its performance limit remains open. In this paper, we are interested in the achievable upper bound of user-perceived throughput, also referred to as the user-perceived capacity, and how to achieve it in typical wireless channels. We find that the user-perceived capacity is quite limited or even zero with channel state information at the receiver (CSIR) only. When both CSIR and channel state information at the transmitter (CSIT) are available, the user-perceived throughput can be substantially improved by power or even rate adaptation. A constrained Markov decision process (CMDP)-based approach is conceived to compute the user-perceived capacity with joint power–rate adaptation. It is rigorously shown that the optimal policy obeys a threshold-based rule with time, backlog, and channel gain thresholds. With power adaptation only, the user-perceived capacity is equal to the hard-delay-constrained capacity in our previous work and achieved by joint diversity and channel inversion.

Channel Allocation in mobile multimedia network using artificial neural networks

Capital asset control systems may enhance network performance, reduce congestion, and optimise resource allocation with the use of artificial intelligence. Using machine learning models like decision trees and neural networks allows for more informed and adaptive admission control options. These models can precisely predict how the network's quality of service (QoS) will be affected by accepting a new call. Exploring AI-enhanced CAC schemes requires a thorough analysis of various machine learning methods and their potential applications to real-time network management. To ensure that AI-based CAC implementations are actually possible in resource-limited mobile situations, it is crucial to consider their computational complexity and resource requirements. Accuracy and computational efficiency are two competing goals, and this study examines both. Finding a happy medium that can meet the stringent requirements of mobile multimedia networks is the driving force behind this research. Also covered in this study is the possibility of mixing AI-powered CAC with cutting-edge tech like 5G networks and cloud computing. Collectively, these technology and artificial intelligence (AI) have to open up new opportunities for situationally-conscious, dynamic admission manipulate. As a result, mobile multimedia networks might be a lot more efficient and adaptable. An exploration of CAC schemes is provided in this article, which draws attention to the potential game-changing impact of AI-based methods for mobile multimedia network optimisation. The increasing demand for multimedia services is creating new challenges, yet these challenges may be amenable to artificial intelligence integration into CAC systems. More adaptive and intelligent network management solutions would be possible as a result.

Autonomous and Intelligent Mobile Multimedia Cyber-Physical System with Secured Heterogeneous IoT Network

Autonomous and Intelligent Mobile Multimedia Cyber-Physical System with Secured Heterogeneous IoT Network

Mathematical Model of Heterogeneous Traffic in LTE Networks

With the introduction of multimedia mobile applications in cellular networks, effective methods have been used to increase throughput. The article defines methods for dividing cellular network resources through mathematical modeling for heterogeneous traffic. The numerical properties of the indicators of joint servicing of requests, the efficiency of joint transmission of real-time traffic and elastic data traffic have been studied. Explores key uses of traffic model resource bandwidth.

Multimedia Mobile Application to Improve the Academic Achievement of Fifth Grade Elementary School Students in Physical Education Classes

Lack of student interest in learning has an impact on low student learning outcomes. One of the factors that causes low student learning outcomes is the lack of learning media. This research aims to examine the effect of multimedia mobile applications to improve the academic achievement of fifth grade elementary school students in physical education classes. This type of research is quantitative research using an experimental design. The population in this study was 112 grade V elementary school students. This research took samples randomly using the Slovin formula. The methods used to collect data are questionnaires and tests. The instruments used to collect data were questionnaire sheets and test questions. The data analysis technique used to analyze the data is inferential statistics. The results of this research, namely the t-test results, show that there is an influence of the use of mobile Apps in learning activities. It can be concluded that the use of mobile Apps in learning activities can improve students' academic achievement in class V physical education. This research implies that the use of mobile Apps in learning activities can make it easier for students to learn.

Enhancing collaborative learning in psychoeducation through social network analysis

Abstract Social network opens up a practical new path for psychoeducation and, with the help of a multimedia mobile terminal’s network, text sentiment analysis for students’ mental health problem revelation and constructing corresponding psychoeducation model. Based on the above viewpoint, this paper proposes a semi-supervised learning method for sentiment analysis using graphs. Specifically, a graph-based semi-supervised learning classifier is constructed using the “Weibo-Weibo” relationship graph. At the same time, social network and text similarity relationships are combined. A link between annotated and unannotated texts was established, and an optimization algorithm was used to solve the model, revealing the psychological problems of students under the emotional texts of social networks, thus constructing a corresponding “four-in-one” mental health education model, which enhances the collaborative learning of students and the four major organizations in psychoeducation. The accuracy, accuracy, recall, and F1 value are outperformed by the model in this paper by 2.44%, 2.11%, 1.45%, and 2.34%, respectively. The eight questions designed around the three dimensions of collaborative learning situation, learning effectiveness, and four-in-one teaching model were highly rated by students. The four-in-one teaching model that utilizes social network sentiment analysis effectively enhances students’ collaborative learning and improves their ability to solve psychoeducation’s difficulties.

LAE-GAN: a novel cloud-based Low-light Attention Enhancement Generative Adversarial Network for unpaired text images

With the widespread adoption of mobile multimedia devices, the deployment of compute-intensive inference tasks on edge and resource-constrained devices, particularly in the context of low-light text detection, remains a formidable challenge. Existing deep learning approaches have shown limited effectiveness in restoring images for extremely dark scenes. To address these limitations, this paper presents a novel cloud-based Low-light Attention Enhancement Generative Adversarial Network for unpaired text images (LAE-GAN) for the non-paired text image enhancement task in extremely low-light conditions. In the first stage, compressed low-light images are transmitted from edge devices to a cloud server for image enhancement. The LAE-GAN, an end-to-end network comprising a Zero-DCE and AGM-net generator, is designed with a global and local discriminator structure. The initial illumination restoration of extremely low-light images is accomplished using the Zero-DCE network. To enhance text details, we propose an Enhanced Text Attention Mechanism (ETAM) that transforms text information into a comprehensive text attention mechanism across the entire network. The Sobel operator is employed to extract text edge information, while attention is focused on text region details through constraints imposed on the attention map and edge map. Additionally, an AGM-Net module is integrated to reduce noise and fine-tune illumination. In the second stage, the cloud server makes decisions based on user requirements and processes requests in parallel, scaling with the quantity of requests. In the third stage, the enhanced results are transmitted back to edge devices for text detection. Experimental results on widely used LOL and SID low-light datasets demonstrate significant improvements in both quantitative and qualitative analysis, surpassing state-of-the-art enhancement methods in terms of image restoration and text detection.

Improving QoS in mobile multimedia streaming with SCTP-PQ

The Stream Control Transmission Protocol (SCTP) is often the preferred transport layer protocol in streaming applications. This protocol combines the best aspects of Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), but also offers additional features. SCTP supports multihoming and multi-streaming applications and has a congestion mechanism like TCP. Media streaming consists of different types of frames with different levels of importance. For example, I-frames carry more information than B-frames in Moving Picture Experts Group (MPEG). Usually, MPEG frames are processed using the First-In-First-Out (FIFO) algorithm. In this paper, a four-level priority queue integrated protocol named SCTP Priority Queue (SCTP-PQ) has been developed to reduce jitter and delay in real-time multimedia streaming for mobile devices. As part of the development, priority and retransmitted packets are determined on the sending side and these packets are processed faster by using the priority queue on the receiving side. In this way, the average queue delay of priority packets on the receiving side is reduced by 90 % and the throughput values are increased by an average of 10 times. The developed protocol has been extensively tested and compared with SCTP. The results show that the SCTP-PQ outperforms the standard SCTP in terms of jitter and delay.

Soft Delivery: Survey on a New Paradigm for Wireless and Mobile Multimedia Streaming

The increasing demand for video streaming services is the key driver of modern wireless and mobile communications. Although many studies have designed digital-based delivery schemes to send video content over wireless and mobile networks, significant quality degradation, known as cliff and leveling effects, often occurs owing to fluctuating channel characteristics. In this article, we present a comprehensive summary of soft delivery, which is a new paradigm for wireless and mobile video streaming and discuss the future directions of soft delivery. Existing studies found that introducing multi-dimensional cosine transform, human vision system, and graph signal processing can make soft delivery schemes more effective in untethered immersive experiences, including virtual reality and volumetric media, than digital-based delivery schemes. In addition, this study finds that soft delivery has the potential to be a new standard to deliver deep neural network models and tactile information over wireless and mobile networks.

Edge Computing Driven Low-Light Image Dynamic Enhancement for Object Detection

We focus on performing object detection in images taken under low-light conditions, which is critical task and often occurs in mobile multimedia computing environment. Unlike former methods to obtain enhanced images before detection with variant kinds of manually designed filters, we propose an edge computing and multi-task driven framework to complete tasks of image enhancement and object detection with fast response. The proposed framework consists of two stages, namely cloud-based enhancement stage and edge-based detection stage. In cloud-based enhancement stage, we establish connection between mobile users and cloud servers to input rescaled and small-size illumination parts of lowlight images, where enhancement subnetworks are dynamically combined to output several enhanced illumination parts and corresponding weights based on low-light context of input images. During edge-based detection stage, cloud-computed weights offers informativeness information on extracted feature maps to enhance their representation abilities, which results in accurate predictions on labels and positions for objects. By applying the proposed framework in cloud computing system, experimental results show it significantly improves detection performance in mobile multimedia and low-light environment.

Mobility-aware caching strategy in an SDN-based cooperative caching network

The upsurge of mobile multimedia traffic puts a heavy burden on the cellular network, and wireless caching has emerged as a powerful technique to overcome the backhaul bottleneck and alleviate the network burden. However, most previous works ignored user mobility, thus not reaping the caching gain from user mobility and having limited practical applications. In this paper, a mobility-aware caching strategy for the software-defined network (SDN)-based network is studied. Firstly, since typical mobile user (MU) has multiple opportunities to connect with the nearby MUs and Small-base stations (SBSs), the contact times between MUs as well as between MU and SBSs are derived as the Poisson distribution and Gamma distribution. Secondly, we propose a two-tier cooperative caching strategy, where SBSs cache the rateless Fountain code encoded video blocks probabilistically and nonrepeatedly while MUs just store the whole encoded video received last time. The corresponding four-stage transmission process is analyzed, where the key intermediate step is the derivation of the service and the failing probabilities of each transmission manner. Finally, we derive the successful offloading rate and average data offloading ratio (ADOR) as performance metrics. A system optimization problem based on ADOR is formulated, and two solutions are proposed, namely, derivative-based solution (DB-Solution) and long-tail distribution approximation (LTD-Approximation). Simulation results demonstrate that the effectiveness of LTD-Approximation is similar to the DB-Solution, and the proposed caching strategy can achieve quasi-optimal performance compared with other contrast schemes.

Development Of Mobile Multimedia Module With An Ecological Awareness Based On Daily Project Learning

Education and schools have great potential to support the success of efforts to instill the value of ecological awareness amidst the problems of environmental damage. The development of mobile multimedia modules designed with the concept of a daily project-based learning approach makes ecological education more meaningful and contextual. The purpose found out the method of implementing the value of ecological awareness through the development of mobile multimedia modules for elementary school students using a daily project-based learning approach. This type of research was research and development (research and development). The development model for this multimedia module is the ADDIE (Analysis, Design, Development, Implementation, Evaluation) development model from Lee & Owens. The results of the material validation showed that the overall average value of the validation is 3.6 in a very valid category. Media validation with an average value of 3.4 categories was valid. User response showed that an average number of 3.2 indicating a positive response. The test results on students showed the value of Sig. (2 tailed) was 0.000 < 0.05. Therefore, it can be concluded that there was a difference in the average Pre-Test and Post Test, which meant there was a change that showed the effectiveness of implementing ecological awareness values through the development of mobile multimedia modules for elementary school students with a daily project-based learning approach. The results of students’ daily activities in the project also showed good and very good categories.

GEOTURISTIC POTENTIAL OF GEOLOGICAL MONUMENTS OF THE DNIPROPETROVS`K REGION

Geotourism as a modern trend in the tourism industry is gaining more and more popularity worldwide. It is the development of knowledge about the geological history of our planet, the course of processes of the formation of various rocks and minerals.
 The territory of Dnipropetrovs’k region is rich in unique, rare objects of the geological environment, including and world importance. These objects have been preserved on the earth's surface in the form of rock outcrops, which most clearly characterize the geological structure and history of the development of our planet, and are of great scientific and educational importance.
 The most unique objects of the natural heritage of Dnipropetrovs’k region, worthy of international attention, are geological monuments. Today, about 38 geological monuments of nature can be classified as geological monuments within the Dnipropetrovs’k region, among which 13 are included in the nature reserve fund of Ukraine and approved by the relevant decisions of state institutions.
 In order to determine the geotourism potential of the regions of the region, a comprehensive, point-based, monitoring assessment of geological monuments was carried out according to the following indicators: informational uniqueness, physical condition, state of improvement, transport accessibility, demand, seasonality of visits, ecological condition, recreational attractiveness, safety of inspection, density of accommodation, nature conservation status. The total number of points demonstrates the tourist attractiveness of the object.
 According to the specified criteria, the number of points for evaluating the tourist attractiveness of geological monuments varies from 15 to 28 points. Geological monuments assigned to the nature reserve fund received the highest points.
 The assessment of the spatial differentiation of indicators of tourist attractiveness of the geological monuments of the region was performed using the inverse distance weighting (IDW) interpolation method in the QGIS program. As a result, a map of the geotourism potential of the regions of the Dnipropetrovs’k region was built. Four types of zones are distinguished: with high (western part of the region, most of Kryvyi Rih region), medium (southern part of the region), low geotourism potential (the central part and a separate zone in the south of the region), and the territory where there are no geological monuments (north and east region).
 The analysis of the map makes it possible to come to the conclusion that, at present, geological monuments located in the Kryvyi Rih district are of interest for the formation of geotours. Other geological objects of Dnipropetrovs’k region should be considered as a supplement to tours of another direction, for example, based on a combination of natural, archaeological and cultural objects. This is facilitated by the fact that the majority of geological monuments are territorially connected with historical and cultural monuments, recreational locations, religious routes.
 In order to popularize geotourism sites in the region, an interactive map "Geological sights of the Dnipropetrovs’k region, classified as a nature reserve" was created on the Google Maps platform.
 An excursion route "Geological monuments of Kryvorizhye" has also been created. The excursion consists of 9 objects that reveal the entire range of the geological history of Kryvorizhye and covers the age range from the Paleoarchean to the Neoproterozoic, which is about 2 billion years, imprinted in numerous outcrops along the banks of the Ingulets and Saksagan rivers and the slopes of the streams. The tour is designed as a GPS mobile multimedia guide on the Izi.travel platform.
 Keywords: geological reminder, geotourism potential, geological tourism, Dnipropetrovsk region.

Emergence of energy optimization in MIMO-OFDM communication system with hybrid teamwork-grasshopper optimization

Abstract In the application of mobile multimedia and wireless communication system needs an enormous amount of data. Due to the usage of a huge data rate, a superior amount of energy is offered to BER, which effectively enhances the entire energy consumption rate in the system, and also, the generation of CO2 emission leads to global warming. Hence, professionals searched for energy-efficient approaches for attaining enhanced data rates in wireless communication systems. Then, professionals observed the energy efficacy as well as QoS as an essential factor in developing and validating the mobile multimedia communication system that has created superior interest in recent days. In this paper, energy-efficient models are suggested for MIMO-OFDM mobile multimedia communication systems with statistical QoS constraints. Hence, this research suggests a new MTGSO for energy optimization in the MIMO-OFDM mobile multimedia communication system to improve spectral efficiency and system capacity. Thus, the empirical result of the designed method attains better performance regarding average power constraints, achieving 29.8 %, 21.3 %, 123.8 %, and 4.2 % enhanced than SSO, FOA, GSO, and TOA. This energy-efficient hybrid algorithm for MIMO-OFDM proves its efficiency in terms of various performance measures and statistical measures.

Exploration of Chinese cultural communication mode based on the Internet of Things and mobile multimedia technology.

Image retrieval technology has emerged as a popular research area of China's development of cultural digital image dissemination and creative creation with the growth of the Internet and the digital information age. This study uses the shadow image in Shaanxi culture as the research object, suggests a shadow image retrieval model based on CBAM-ResNet50, and implements it in the IoT system to achieve more effective deep-level cultural information retrieval. First, ResNet50 is paired with an attention mechanism to enhance the network's capacity to extract sophisticated semantic characteristics. The second step is configuring the IoT system's picture acquisition, processing, and output modules. The image processing module incorporates the CBAM-ResNet50 network to provide intelligent and effective shadow play picture retrieval. The experiment results show that shadow plays on GPU can retrieve images at a millisecond level. Both the first image and the first six photographs may be accurately retrieved, with a retrieval accuracy of 92.5 percent for the first image. This effectively communicates Chinese culture and makes it possible to retrieve detailed shadow-play images.

Task Offloading for Deep Learning Empowered Automatic Speech Analysis in Mobile Edge-Cloud Computing Networks

With the explosive growth of mobile multimedia services and artificial intelligence applications involving automatic speech analysis (ASA), mobile devices are increasingly unable to handle these computation-intensive tasks generated by users due to the limited computing resource. Besides, the existing cloud computing paradigm is not capable of processing such real-time and delay-sensitive ASA tasks. In this paper, by leveraging mobile edge computing and deep learning (DL), we investigate task offloading for DL-empowered ASA in mobile edge-cloud computing networks to minimize the total time for processing ASA tasks, thereby providing an agile service response. Specifically, to accelerate the processing of ASA tasks, we decompose a convolutional neural network based encoder-decoder model and deploy the encoder at edge servers to extract the features of ASA tasks. Moreover, edge servers derive the user tolerance limit by using a linear regression model for further enhancing the quality of experience of users. Based on some certain network constraints (i.e., user association and edge servers' storage/computing capacity), we propose a low-complexity and distributed offloading framework to solve the formulated complex problem. Evaluation results demonstrate the effectiveness of the proposed framework on reducing the total time and improving the satisfaction rate of users.

Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development

— The difficulties to drive away the durian farm threatens animals such as wild boars, monkeys, foxes, and squirrels during nighttime often experienced by durian farmers. Therefore, the Pro Durian application is proposed that allows farmers to identify durian threats through a camera phone with an alert feature activation when the system detects an animal to drive away those animals. The application implements a deep learning algorithm of Convolutional Neural Network (CNN)-YOLO3in order to receive the best output results in identifying the different datasets of durian farm threats. The classification accuracies reached 80% in detecting the animal’s images. Keywords— Durian Farm, Recognition Image, TensorFlow lite, Android Studio, Convolution Neural Network

'Trials and Tribulations': The Ambivalent Influence of Temporary Accommodation on Mental Health Recovery in Chronically Homeless Adults.

Relatively few studies have explicitly examined whether and how shelter-type, temporary or emergency accommodation shapes homeless clients' personal (mental health) recovery. A transatlantic phenomenological qualitative study was conducted to examine the influence of those services on personal recovery. Eighteen chronically homeless adults with a history of serious mental illness were recruited from several temporary accommodation services in New York City (NYC), U.S., and Glasgow, Scotland. Participants completed repeat in-depth interviews and a novel one-week multimedia mobile phone diary. The interpretative phenomenological analysis (IPA) produced three overarching group experiential themes: 'everything was just starting to fall into place'; 'caught in a trap'; and 'trials and tribulations'. Collectively, the findings underscore the duality of influence of temporary accommodation on recovery. Those ambiguous spaces confronted participants with existential uncertainty, volatility and chronic boredom, but also proffered opportunities for envisioning and enacting recovery. Embarking on recovery while residing in temporary accommodation is possible, even for those enduring chronic life adversity. However, it is contingent upon enabling socio-material, affective and relational resources. Implications are discussed for theorising recovery as a contextually embedded, relational phenomenon, and for providing recovery-oriented support across the housing continuum.

Design of a 5G Multimedia Broadcast Application Function Supporting Adaptive Error Recovery

The demand for mobile multimedia streaming services has been steadily growing in recent years. Mobile multimedia broadcasting addresses the shortage of radio resources but introduces a network error recovery problem. Retransmitting multimedia segments that are not correctly broadcast can cause service disruptions and increased service latency, affecting the quality of experience perceived by end users. With the advent of networking paradigms based on virtualization technologies, mobile networks have been enabled with more flexibility and agility to deploy innovative services that improve the utilization of available network resources. This paper discusses how mobile multimedia broadcast services can be designed to prevent service degradation by using the computing capabilities provided by multiaccess edge computing (MEC) platforms in the context of a 5G network architecture. An experimental platform has been developed to evaluate the feasibility of a MEC application to provide adaptive error recovery for multimedia broadcast services. The results of the experiments carried out show that the proposal provides a flexible mechanism that can be deployed at the network edge to lower the impact of transmission errors on latency and service disruptions.

Using mobile multimedia platform for teaching radiographic diagnosis

Using mobile multimedia platform for teaching radiographic diagnosis