A Cross-Layer Optimized Scheme and Its Application in Mobile Multimedia Networks with QoS Provision

To cope with the rapid growth of multimedia applications that requires dynamic levels of quality of service (QoS), cross-layer (CL) design, where multiple protocol layers are jointly combined, has been considered to provide diverse QoS provisions for mobile multimedia networks. However, there is a lack of a general mathematical framework to model such CL scheme in wireless networks with different types of multimedia classes. In this paper, to overcome this shortcoming, we therefore propose a novel CL design for integrated real-time/non-real-time traffic with strict preemptive priority via a finite-state Markov chain. The main strategy of the CL scheme is to design a Markov model by explicitly including adaptive modulation and coding at the physical layer, queuing at the data link layer, and the bursty nature of multimedia traffic classes at the application layer. Utilizing this Markov model, several important performance metrics in terms of packet loss rate, delay, and throughput are examined. In addition, our proposed framework is exploited in various multimedia applications, for example, the end-to-end real-time video streaming and CL optimization, which require the priority-based QoS adaptation for different applications. More importantly, the CL framework reveals important guidelines as to optimize the network performance

Energy-Aware Rate and Description Allocation Optimized Video Streaming for Mobile D2D Communications

The proliferation problem of video streaming applications and mobile devices has prompted wireless network operators to put more efforts into improving quality of experience (QoE) while saving resources that are needed for high transmission rate and large size of video streaming. To deal with this problem, we propose an energy-aware rate and description allocation optimization method for video streaming in cellular network assisted device-to-device (D2D) communications. In particular, we allocate the optimal bit rate to each layer of video segments and packetize the segments into multiple descriptions with embedded forward error correction (FEC) for realtime streaming without retransmission. Simultaneously, the optimal number of descriptions is allocated to each D2D helper for transmission. The two allocation processes are done according to the access rate of segments, channel state information (CSI) of D2D requester, and remaining energy of helpers, to gain the highest optimization performance. Simulation results demonstrate that our proposed method (named OPT) significantly enhances the performance of video streaming in terms of high QoE and energy saving.

Mobile Wearable Communications

This special issue provides the latest research and development on wireless mobile wearable communications. According to a report by Juniper Research, the market value of connected wearable devices is expected to reach $1.5 billion by 2014, and the shipment of wearable devices may reach 70 million by 2017. Good examples of wearable devices are the prominent Google Glass and Microsoft HoloLens. As wearable technology is rapidly penetrating our daily life, mobile wearable communication is becoming a new communication paradigm. Mobile wearable device communications create new challenges compared to ordinary sensor networks and short-range communication. In mobile wearable communications, devices communicate with each other in a peer-to-peer fashion or client-server fashion and also communicate with aggregation points (e.g., smartphones, tablets, and gateway nodes). Wearable devices are expected to integrate multiple radio technologies for various applications' needs with small power consumption and low transmission delays. These devices can hence collect, interpret, transmit, and exchange data among supporting components, other wearable devices, and the Internet. Such data are not limited to people's personal biomedical information but also include human-centric social and contextual data. The success of mobile wearable technology depends on communication and networking architectures that support efficient and secure end-to-end information flows. A key design consideration of future wearable devices is the ability to ubiquitously connect to smartphones or the Internet with very low energy consumption. Radio propagation and, accordingly, channel models are also different from those in other existing wireless technologies. A huge number of connected wearable devices require novel big data processing algorithms, efficient storage solutions, cloud-assisted infrastructures, and spectrum-efficient communications technologies.

Vision and deaf awareness training is it app.ropriate?

Title
Visual and deaf awareness training is it app.ropriate
Purpose
Some of our most vulnerable patients have a sensory deficit. An app which focused on patients with a vision and/or hearing loss was developed for healthcare students. The intent was to embed the core values necessary for students to provide appropriate care for patients with a sensory deficit.
Setting
Queen’s University Belfast, School of Nursing and Midwifery.
Methods
Stage 1
A review of current sensory awareness training in the United Kingdom
Stage 2
Application for funding
Stage 3
Development of a teaching tool template with the essential aspects required for sensory awareness training
Stage 4
Collaboration with others: Royal National Institute for the Blind, Action on Hearing Loss, Computer technician.
Stage 5
Production and transfer of multimedia outputs onto a software application system.
Stage 6
App Piloted with a sample of lecturers (n=5), undergraduate nursing students (n=20), service users (n=5)
Stage 7
Editing
Stage 8
App made available to all undergraduate nursing students
Stage 9
App evaluation (n=300)



Results
Overall nursing students positively evaluated the app, 100% of students rated the app between good and excellent. Qualitative evidence from service users and practice partnerships was extremely positive:
"At last I feel listened too in respect to my hearing loss and empowered. I don't feel like I am complaining I am actually helping to create something which should benefit staff and all of us with a hearing or vision loss". Patient
“Very insightful into the lives of those with a disability will be so useful in practice as an aid to jog my memory". 1st year nursing student
Conclusion
It is hoped that further evaluation and implementation of the app will show an improved quality to the care delivered to those with a sensory deficit. We believe that by working in partnership with service users we have helped to create an innovative tool that benefits both staff and patients.
Financial disclosure Yes
Funding of £2700 was awarded in 2014 through the Martha McMenamin Memorial Northern Ireland Scholarship.

Belfast Soundwalks

The Belfast Soundwalks project, led by Professor Pedro Rebelo and co-ordinated by Dr Sarah Bass (Sonic Arts Research Centre) in collaboration with Belfast City Council (BCC), aims to use sonic art to engage the public through the development of a locative mobile phone app. Targeting both tourists and citizens of the city, this project aims to sonically enhance the experience of a number of areas of the city, including destinations that may not traditionally be accessed as attractions by visitors and/or disregarded or undervalued by local residents. The project will bring together a number of sonic artists/composers who will create approximately ten soundwalks around the city, while liaising with BCC to distribute the resulting app to the public in line with their tourism and cultural strategy. The project is centred on the development of smart phone apps which provide unique listening experiences associated with key places in the city. The user’s location in the city is tracked through GPS which triggers sound materials ranging from speech to environmental sound and abstract imagined sound worlds. Additionally, local community groups will be consulted in order to evaluate and reflect upon the effectiveness of the soundwalks.

The project builds on the success of the Literary Belfast app and aims to further strengthen links between Queen’s University Belfast and Belfast City Council through facilitating the dissemination of an art form not widely experienced by the general public. Through the newly created Institute for Collaborative Research in the Humanities, directed by Professor John Thompson we are articulating this project with Queen’s consortium partners, Newcastle University and Durham University.

“The Arts and Humanities Research Council (AHRC) funds world-class, independent researchers in a wide range of subjects: ancient history, modern dance, archaeology, digital content, philosophy, English literature, design, the creative and performing arts, and much more. This financial year the AHRC will spend approximately £98m to fund research and postgraduate training in collaboration with a number of partners. The quality and range of research supported by this investment of public funds not only provides social and cultural benefits but also contributes to the economic success of the UK. For further information on the AHRC, please go to: www.ahrc.ac.uk”.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

The significance of bruising in the pre-mobile infant

The majority of children in our society are loved andcherished. The occasional cases of intentional injury to a childresulting in death or significant harm evoke powerful anduncomfortable feelings (Devaney et al, 2013), and the publicoutcry may result in health and social workers facing criticism.Identifying whether an infant is at risk of abuse is a challengefor practitioners, and can be a source of stress and anxiety(Brandon et al, 2011). Bruising is a strong indicator of childabuse involving intentional injury (Kemp et al, 2014). Theincidence of bruising correlates to developmental stage, withnon-mobile infants least likely to incur bruising. Therefore, itspresence in pre-mobile infants requires immediate assessment.A search of the literature around bruising in pre-mobile infantsrevealed themes of missed opportunities for early intervention,the role of the father in the family and the significance of childdevelopment. Sharing of knowledge and expertise within themultidisciplinary team is key to safeguarding infants.

Footprints in the sand: a persistent spatial impression of fishing in a mobile groundfish assemblage

Fishing is well known to curtail the size distribution of fish populations. This paper reports the discovery of small-scale spatial patterns in length appearing in several exploited species of Celtic Sea demersal 'groundfish'. These patterns match well with spatial distributions of fishing activity, estimated from vessel monitoring records taken over a period of 6 years, suggesting that this 'mobile' fish community retains a persistent impression of local-scale fishing pressure. An individual random-walk model of fish movement best matched these exploitation 'footprints' with individual movement rates set to <35 km per year. We propose that Celtic Sea groundfish may have surprisingly low movement rates for much of the year, such that fishing impact is spatially heterogeneous and related to local fishing intensity.

A Fast Algorithm for Sparse Support Vector Machines for Mobile Computing Applications

This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.