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Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy-to-use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity. and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (&gt, =85% for sensitivity and 100% for specificity).

BackgroundThere is a need for diagnostic tests of early PD diagnosis. A subset of AI known as deep learning (DL) has shown great promise in diagnostic medical imaging, sometimes outperforming radiolo- gists by detecting patterns invisible to the human eye. Using DL, we explored whether such changes are detectable on routine PD MRI scans.MethodsWe trained a convolutional neural network to classify 138 PD and 60 control brain MRI images acquired from the Parkinson’s Progression Marker Initiative (PPMI) database. Models were assessed using k-fold cross-validation. We used Deep SHapley Additive exPlanations (DeepSHAP) to visualise the contri- bution of individual pixels to the model’s prediction.ResultsA combined dataset of axial T2 and proton density MRI images was classified with 79% accuracy and an area under the curve (AUC) of 0.86. Respectively T2 and proton density models classified cases with 81/84% accuracy and AUC of 0.83/0.88. DeepSHAP heat maps demonstrated predominant interest in midbrain slices.ConclusionOur models exhibited good diagnostic performance and demonstrated interest in PD relevant brain regions. We will validate this model in a large dataset of routinely collected NHS MRI scans, many of which predate onset of motor symptoms.

The successful development of amyloid-based biomarkers and tests for Alzheimer's disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities, we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease, suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers.

Tone-mapping-operators (TMOs) provide a useful means for converting high dynamic range (HDR) images to low dynamic range (LDR) images so that they can be viewed on standard displays, but this may influence the visual quality of experience (QoE) of the end-user. There is a need to understand the impact of TMOs to inform the choice of TMO algorithms for different displays, especially for small-screen-devices (SSDs) such as those used in mobile phones. This is important, as mobile devices are becoming the primary means of consuming multimedia contents. However, few studies have been undertaken to assess the impact of TMOs and viewing devices (especially SSDs) on the visual QoE of the user when using. In this chapter, we evaluate subjectively and objectively, the commonly used TMOs in different displays and resolutions for colour and grey-scale HDR images. Our results show that viewing devices have an influence on the TMOs performance, suggesting the need for a careful choice of TMO to enhance the viewing-QoE of the end-user. As expected, the higher resolution, the better HDR-image quality. Surprisingly, there was no significant difference between the Mean of Opinion Score (MOS) scores for colour and grey-scale images in SSDs. The device and TMOs affect QoE for colour and grey HDR-image equally. We found Shannon entropy (SE) to be a good objective measure of quality for colour and grey HDR images, suggesting that entropy may find use in automated HDR quality control assessment schemes, while; HDR-VDP-2 is a good objective measure for colour HDR image only.

Quality of experience (QoE) is a human-centric paradigm, which produces the blueprint of human-behavioral-states such as perception, emotion, cognition, and expectation. Recent advances in neurophysiological monitoring tools have facilitated the study of frequency, time, and location of neuronal activity to an unprecedented degree, as well as opened doors to a better understanding of human overall behavioral systems. Physiological signals, such as the electroencephalogram (EEG), have shown promise in revealing the subject's emotion or attention in quality assessment and the correlation of this with media service quality. This chapter proposes a novel objective QoE model for high dynamic range (HDR) images and is based on the relationship between objective (i.e. delta-beta coupling) and subjective measures (i.e. mean opinion score MOS). The analysis of the results indicate that the proposed QoE model has a strong correlation with MOS scores, hence can be effectively used in predicting the overall HDR image quality. An advantage of the model is that it is lightweight and it provides a measure of user-perceived quality, but without requiring time-consuming subjective tests. The model has potential applications in several other areas, including QoE control and optimization. Future mobile providers can benefit from applying the proposed QoE-based model to optimize users' acceptability and satisfaction for different HDR image scenarios.

Electrophysiology provides a real-time readout of neural functions and network capability in different brain states, on temporal (fractions of milliseconds) and spatial (micro, meso, and macro) scales unmet by other methodologies. However, current international guidelines do not endorse the use of electroencephalographic (EEG)/magnetoencephalographic (MEG) biomarkers in clinical trials performed in patients with Alzheimer's disease (AD), despite a surge in recent validated evidence. This position paper of the ISTAART Electrophysiology Professional Interest Area endorses consolidated and translational electrophysiological techniques applied to both experimental animal models of AD and patients, to probe the effects of AD neuropathology (i.e., brain amyloidosis, tauopathy, and neurodegeneration) on neurophysiological mechanisms underpinning neural excitation/inhibition and neurotransmission as well as brain network dynamics, synchronization, and functional connectivity, reflecting thalamocortical and corticocortical residual capacity. Converging evidence shows relationships between abnormalities in EEG/MEG markers and cognitive deficits in groups of AD patients at different disease stages. The supporting evidence for the application of electrophysiology in AD clinical research as well as drug discovery pathways warrants an international initiative to include the use of EEG/MEG biomarkers in the main multicentric projects planned in AD patients, to produce conclusive findings challenging the present regulatory requirements and guidelines for AD studies. (C) 2019 Elsevier Inc. All rights reserved.

Future softwarized 5G networks have to be robust enough so as to ensure high network reliability and services availability. 5G network architecture should make sure that any failed parts in the network are detected, restored and recovered within a permissible period of time and at the lowest achievable cost. In this paper, we propose a Multipath Protection and Link-Failure free MPTCP/SR-based SDN/NFV architecture that increases survivability, resilience, availability of services in 5G networks. We present our system model and a multiPath protection and dynamic Link- Failure free algorithm called "PathReLief" that greatly reduces the failure recovery time and avoids link congestion in MPTCP/SR SDN/NFV 5G networks. To demonstrate the effectiveness of our proposal, we compare the performance of the proposed algorithm and the conventional topology discovery mechanisms for link/node failures in POX and OpenDaylight controllers. Preliminary results show that, our approach outperforms others used in the commonly used controllers (i.e., POX and OpenDaylight), in terms of reduced failure recovery time and localization time.

Early detection of Alzheimer's disease (AD) will help researchers to better understand the disease and develop improved treatments. Recent developments have thus focused on identifying biomarkers for mild cognitive impairment due to AD (MCI) and AD during the preclinical phase. The aim of this pilot study is to determine whether exhaled volatile organic compounds (VOCs) can be used as a non-invasive method to distinguish controls from MCI, controls from AD and to determine whether there are differences between MCI and AD. The study used gas chromatography-ion mobility spectrometry (GC-IMS) techniques. Confounding factors, such as age, smoking habits, gender and alcohol consumption are investigated to demonstrate the efficacy of results. One hundred subjects were recruited including 50 controls, 25 AD and 25 MCI patients. The subject cohort was age- and gender-matched to minimise bias. Breath samples were analysed using a commercial GC-IMS instrument (G.A.S. BreathSpec, Dortmund, Germany). Data analysis indicates that the GC-IMS signal was consistently able to separate between diagnostic groups [AUC ± 95%, sensitivity, specificity], controls versus MCI: [0.77 (0.64-0.90), 0.68, 0.80], controls versus AD: [0.83 (0.72-0.94), 0.60, 0.96], and MCI versus AD: [0.70 (0.55-0.85), 0.60, 0.84]. VOC analysis indicates that six compounds play a crucial role in distinguishing between diagnostic groups. Analysis of possible confounding factors indicate that gender, age, smoking habits and alcohol consumption have insignificant influence on breath content. This pilot study confirms the utility of exhaled breath analysis to distinguish between AD, MCI and control subjects. Thus, GC-IMS offers great potential as a non-invasive, high-throughput, diagnostic technique for diagnosing and potentially monitoring AD in a clinical setting.

5G networks are set to empower new user experiences for multimedia streaming services by offering high data rates and low latency. The rapid growth of multimedia services, such as video streaming services over future softwarized 5G networks, poses great challenges for both service providers and network operators regarding service provisioning with high Quality of Experience (QoE). Network evolution towards Software Defined Networking (SDN) and Network Function Virtualization (NFV) provides new opportunities to develop innovative ways to address the challenges. In this paper, we present a novel QoE-aware management scheme based on SDN/NFV by utilizing Multi-Path-TCP/Segment-Routing (MPTCP/SR) techniques and exploring information from both the network and client. The new scheme, entitled QoEMultiSDN, provides an optimized end-to-end QoE for multimedia service delivery. QoEMultiSDN employs two novel strategies to achieve this: (a) QoE-based multisource routing and QoE optimization, and (b) multipath protection and dynamic link-failure-free mechanisms. We implemented the proposed scheme over SDN through Dynamic Adaptive Streaming over HTTP (DASH) experiments using Mininet, POX and OpenDaylight controllers, and compared its performance with MPTCP and regular TCP. Experimental results indicate that the QoEMultiSDN outperforms others in terms of system throughput, failure recovery time and the QoE of the end-user.

With the increasing number of people living with Alzheimer's disease (AD), there is a need for low-cost and easy to use methods to detect AD early to facilitate access to appropriate care pathways. Neuroimaging biomarkers (such as those based on PET and MRI) and biochemical biomarkers (such as those based on CSF) are recommended by international guidelines to facilitate diagnosis. However, neuroimaging is expensive and may not be widely available and CSF testing is invasive. Bloodbased biomarkers offer the potential for the development of a low-cost and more time efficient tool to detect AD to complement CSF and neuroimaging as blood is much easier to obtain. Although no single blood biomarker is yet able to detect AD, combinations of biomarkers (also called panels) have shown good results. However, a large number of biomarkers are often needed to achieve a satisfactory detection performance. In addition, it is difficult to reproduce reported results within and across different study cohorts because of data overfitting and lack of access to the datasets used in the studies. In this study, our focus is to identify an optimum panel (in terms of the least number of blood biomarkers to meet the specified diagnostic performance of 80% sensitivity and specificity) based on a widely accessible data set, and to demonstrate a testing methodology that reinforces reproducibility of results. Realizing a panel with reduced number of markers will have significant impact on the complexity and cost of diagnosis and potential development of cost-effective point of care devices.

This article presents independent research commissioned by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (grant reference number: RP-PG-0707-10124). Financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) (grant reference number: EP/M006301/1) and the EU Horizon 2020 (EU H2020) Marie Skłodowska-Curie Innovative Training Networks (grant reference number: 721281) is also gratefully acknowledged. Background : Up to half of patients with dementia may not receive a formal diagnosis, limiting access to appropriate services. It is hypothesised that it may be possible to identify undiagnosed dementia from a profile of symptoms recorded in routine clinical practice. Aim : The aim of this study is to develop a machine learning-based model that could be used in general practice to detect dementia from routinely collected NHS data. The model would be a useful tool for identifying people who may be living with dementia but have not been formally diagnosed. Design & setting : The study involved a case-control design and analysis of primary care data routinely collected over a 2-year period. Dementia diagnosed during the study period was compared to no diagnosis of dementia during the same period using pseudonymised routinely collected primary care clinical data. Method : Routinely collected Read-encoded data were obtained from 18 consenting GP surgeries across Devon, for 26 483 patients aged >65 years. The authors determined Read codes assigned to patients that may contribute to dementia risk. These codes were used as features to train a machine-learning classification model to identify patients that may have underlying dementia. Results: The model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively. Conclusion: The results show that routinely collected primary care data may be used to identify undiagnosed dementia. The methodology is promising and, if successfully developed and deployed, may help to increase dementia diagnosis in primary care. Publisher PDF

We report on the development of chemical vapour deposition (CVD) based graphene field effect transistor (GFET) immunosensors for the sensitive detection of Human Chorionic Gonadotropin (hCG), a glycoprotein risk biomarker of certain cancers. The GFET sensors were fabricated on Si/SiO2 substrate using photolithography with evaporated chromium and sputtered gold contacts. GFET channels were functionalized with a linker molecule to immobile anti-hCG antibody on the surface of graphene. Binding reaction of the antibody with varying concentration levels of hCG antigen demonstrated the limit of detection of the GFET sensors to be below 1pg/mL using four-probe electrical measurements. We also show annealing can significantly improve the carrier transport properties of GFETs and shift the Dirac point (Fermi level) with reduced p-doping in back-gated measurements. The developed GFET biosensors are generic and could find applications in a broad range of medical diagnostics in addition to cancer, such as neurodegenerative (Alzheimer’s, Parkinson’s and Lewy body) and cardiovascular disorders.

A new reference-free, objective, video quality prediction model that takes into account video content type to predict the quality of streamed high efficiency video coding (HEVC) encoded video sequences is proposed. Research has shown that for the same encoder settings and network quality of service (NQoS), the video quality differs for different types of video content . This indicates that, in addition to encoder settings and NQoS, there may be other key parameters that impact video quality. In this work, we hypothesized that video content type is one of the key parameters that may impact the quality of streamed videos. Based on this assertion, temporal information is extracted from the motion vector (MV) information inherent in the encoded video bitstreams and spatial information is extracted from the quantisation parameter (QP) and the number of bits (Bits) of coded intra (I) and predictive (P) frames to develop a metric that quantifies the content type of different video sequences . The content type metric is subsequently used together with encoding QP setting and network packet loss rate (PLR) to develop a reference -free objective video quality prediction model for streamed HEVC encoded video sequences. This model has an accuracy of 92% when the model predicted values of sequences not used in model derivation are compared with mean opinion score (MOS) obtained through subjective method.

We examine whether modeling of the causal dynamic relationships between frontal and occipital electroencephalogram (EEG) time-series recordings reveal reliable differentiating characteristics of Alzheimer's patients versus control subjects in a manner that may assist clinical diagnosis of Alzheimer's disease (AD). The proposed modeling approach utilizes the concept of principal dynamic modes (PDMs) and their associated nonlinear functions (ANF) and hypothesizes that the ANFs of some PDMs for the AD patients will be distinct from their counterparts in control subjects. To this purpose, global PDMs are extracted from 1-min EEG signals of 17 AD patients and 24 control subjects at rest using Volterra models estimated via Laguerre expansions, whereby the O1 or O2 recording is viewed as the input signal and the F3 or F4 recording as the output signal. Subsequent singular value decomposition of the estimated Volterra kernels yields the global PDMs that represent an efficient basis of functions for the representation of the EEG dynamics in all subjects. The respective ANFs are computed for each subject and characterize the specific dynamics of each subject. For comparison, signal features traditionally used in the analysis of EEG signals in AD are computed as benchmark. The results indicate that the ANFs of two specific PDMs, corresponding to the delta-theta and alpha bands, can delineate the two groups well.

It is widely accepted that early diagnosis of Alzheimer's disease (AD) makes it possible for patients to gain access to appropriate health care services and would facilitate the development of new therapies. AD starts many years before its clinical manifestations and a biomarker that provides a measure of changes in the brain in this period would be useful for early diagnosis of AD. Given the rapid increase in the number of older people suffering from AD, there is a need for an accurate, low-cost and easy to use biomarkers that could be used to detect AD in its early stages. Potentially, the electroencephalogram (EEG) can play a vital role in this but at present, no reliable EEG biomarker exists for early diagnosis of AD. The gradual slowing of brain activity caused by AD starts from the back of the brain and spreads out towards other parts. Consequently, determining the brain regions that are first affected by AD may be useful in its early diagnosis. Higuchi fractal dimension (HFD) has characteristics which make it suited to capturing region-specific neural changes due to AD. The aim of this study is to investigate the potential of HFD of the EEG as a biomarker which is associated with the brain region first affected by AD. Mean HFD value was calculated for all channels of EEG signals recorded from 52 subjects (20-AD and 32-normal). Then, p-values were calculated between the two groups (AD and normal) to detect EEG channels that have a significant association with AD. k-nearest neighbor (KNN) algorithm was used to compute the distance between AD patients and normal subjects in the classification. Our results show that AD patients have significantly lower HFD values in the parietal areas. HFD values for channels in these areas were used to discriminate between AD and normal subjects with a sensitivity and specificity values of 100% and 80%, respectively.

Quality of Experience (QoE) assessment of multimedia services is a challenging task and an understanding of how the user perceives quality at the physiological level would facilitate this. Physiological signals, such as the electroencephalogram (EEG), have shown promise in revealing the subject's emotion or attention in quality assessment and the correlation of this with media service quality. This paper investigated the relationships between changes in EEG features and subjective quality test scores (i.e. MOS) for High Dynamic Range (HDR) images viewed with a mobile device. Results show that changes in the gamma and beta bands correlated negatively with MOS, whereas positive correlations were observed in the alpha band. Coupling between activities in the delta and beta bands (i.e. positive correlation between power in the fast beta and slow delta frequency bands) is related to anxiety and dissatisfaction. Thus, the results suggest that increases in the degree of coupling are associated with decreases in HDR quality. This also suggests that in the HDR image QoE assessment, human emotions play a significant role. Potentially, these findings may be exploited in objective QoE perception modelling.

About 1.2 million people in Europe have Parkinson's disease (PD). PD patients often suffer from social exclusion and depression due to progressive lack of control over the disease. Eventually, most require constant care, leading to huge socioeconomic burdens. To partially tackle this issue, 7 partners in 7 countries participated in LiveWell (EU-funded project, 2012-14) to develop an innovative Web-based education, training and social community platform targeting PD patients, carers and clinicians. This paper describes LiveWell from conception to completion, presents lessons learnt over the course of the project, and highlights some identified main areas of best practice. The Project Consortium might not have had the chance to implement everything learnt in the current outputs, but these lessons and guidelines can be later used to improve LiveWell, and can also be generalised to benefit similar e-health services. The combination of appropriate online education and social communities could help patients and carers cope positively with PD, promoting social inclusion and better outcomes.

Early diagnosis of Alzheimer's Disease (AD) is widely regarded as necessary to allow treatment to be started before irreversible damage to the brain occur and for patients to benefit from new therapies as they become available. Low-cost point-of-care (PoC) diagnostic tools that can be used to routinely diagnose AD in its early stage would facilitate this, but such tools require reliable and accurate biomarkers. However, traditional biomarkers for AD use invasive cerebrospinal fluid (CSF) analysis and/or expensive neuroimaging techniques together with neuropsychological assessments. Blood-based PoC diagnostics tools may provide a more cost and time efficient way to assess AD to complement CSF and neuroimaging techniques. However, evidence to date suggests that only a panel of biomarkers would provide the diagnostic accuracy needed in clinical practice and that the number of biomarkers in such panels can be large. In addition, the biomarkers in a panel vary from study to study. These issues make it difficult to realise a PoC device for diagnosis of AD. An objective of this paper is to find an optimum number of blood biomarkers (in terms of number of biomarkers and sensitivity/specificity) that can be used in a handheld PoC device for AD diagnosis. We used the Alzheimer's disease Neuroimaging Initiative (ADNI) database to identify a small number of blood biomarkers for AD. We identified a 6-biomarker panel (which includes A1Micro, A2Macro, AAT, ApoE, complement C3 and PPP), which when used with age as covariate, was able to discriminate between AD patients and normal subjects with a sensitivity of 85.4% and specificity of 78.6%.

Advances in multimedia devices and video compression techniques and the availability of increased network bandwidth in both fixed and mobile networks has increased the proliferation of multimedia applications (e.g. IPTV, video streaming and online gaming). However, this has also posed a real challenge to network and service providers to deliver these applications with an acceptable Quality of Experience (QoE). In these multimedia applications, it is highly desirable to predict and if possible control video quality to meet such QoE and user expectations. Streamed video quality is affected by both encoding and transmission processes. The impacts of these processes are content dependent. This issue has gradually been recognised in video quality modelling research in recent years. In this paper, we carried out objective and subjective tests on video sequences to investigate the impact of video content type and encoding parameter settings on HEVC video quality. Initial results show that varying video content type and encoding parameters impact video quality. Based on the test results, we developed a content-based video quality prediction (CVQP) model that takes into account HEVC encoding parameter such as Quantization Parameter (QP) and video content type (characterised by motion activities and complexity of video sequences). We achieved an accuracy of 92 % for the test dataset when model predicted PSNR values were compared with full reference PSNR measurements. The performance of the model was also evaluated by comparing predicted PSNR with those of Double Stimulus Impairment Scale (DSIS) subjective quality ratings. Results show a good correlation between actual MOS and predicted PSNR. The proposed model could be used by content providers to determine the initial quality of videos based on QP and content type.

The frequency spectrum of the magnetoencephalogram (MEG) background activity was analysed in 15 schizophrenia (SCH) patients with predominant positive symptoms and 17 age-matched healthy control subjects using the following variables: median frequency (MF), spectral entropy (SpecEn) and relative power in delta (RP delta), theta (RP theta), lower alpha (RP alpha 1), upper alpha (RP alpha 2), beta (RP beta) and gamma (RP gamma) bands. We found significant differences between the two subject groups in the average level of MF and RP gamma in some regions of the scalp. Additionally, the MF, SpecEn, RP beta and RP gamma values of SCH patients with positive symptoms had a different dependence on age as compared with the results of control subjects, suggesting that SCH affects the way in which the brain activity evolves with age. Moreover, we also classified the MEG signals by means of a cross-validated feature selection process followed by a logistic regression. The subjects were classified with 71.3% accuracy and an area under the ROC curve of 0.741. Thus, the spectral and classification analysis of the MEG in SCH may provide insights into how this condition affects the brain activity and may help in its early detection.

Diagnosis of Alzheimer's disease (AD) is often difficult, especially early in the disease process at the stage of mild cognitive impairment (MCI). Yet, it is at this stage that treatment is most likely to be effective, so there would be great advantages in improving the diagnosis process. We describe and test a machine learning approach for personalized and cost-effective diagnosis of AD. It uses locally weighted learning to tailor a classifier model to each patient and computes the sequence of biomarkers most informative or cost-effective to diagnose patients. Using ADNI data, we classified AD versus controls and MCI patients who progressed to AD within a year, against those who did not. The approach performed similarly to considering all data at once, while significantly reducing the number (and cost) of the biomarkers needed to achieve a confident diagnosis for each patient. Thus, it may contribute to a personalized and effective detection of AD, and may prove useful in clinical settings.

With advances in wireless technology and the increase in popularity of mobile devices, more and more people now rely on mobile devices for multimedia services (such as video streaming and video calls). A mobile device can be connected and roamed to different networks in heterogeneous wireless networks. The Media Independent Handover (MIH) framework is designed by the IEEE 802.21 group to support seamless vertical handover between different networks. However, how to select an appropriate network from available ones and when to execute the handover remain the key challenges in MIH. This paper proposes a user-centric QoE-driven vertical handover (VHO) framework, based on MIH, which aims to maintain acceptable QoE of different mobile application services and to select an appropriate network based on users' preferences (e.g. on cost). Further a user-centric QoE-driven algorithm is implemented in the proposed framework. Its performance is evaluated and compared with two other VHO algorithms based on Network Simulator 2 (NS2) for video streaming services over heterogeneous networks. The preliminary results show that the proposed algorithm can maintain better QoE and at the same time, take into account user's preference on cost when compared with the other two algorithms.

The rapid increase in the number of older people with Alzheimer's disease (AD) and other forms of dementia represents one of the major challenges to the health and social care systems. Early detection of AD makes it possible for patients to access appropriate services and to benefit from new treatments and therapies, as and when they become available. The onset of AD starts many years before the clinical symptoms become clear. A biomarker that can measure the brain changes in this period would be useful for early diagnosis of AD. Potentially, the electroencephalogram (EEG) can play a valuable role in early detection of AD. Damage in the brain due to AD leads to changes in the information processing activity of the brain and the EEG which can be quantified as a biomarker. The objective of the study reported in this paper is to develop robust EEG-based biomarkers for detecting AD in its early stages. We present a new approach to quantify the slowing of the EEG, one of the most consistent features at different stages of dementia, based on changes in the EEG amplitudes (ΔEEG A ). The new approach has sensitivity and specificity values of 100% and 88.88%, respectively, and outperformed the Lempel-Ziv Complexity (LZC) approach in discriminating between AD and normal subjects.

The development of HDR imaging is seen as an important step towards improving the visual quality of experience (QoE) of the end user in many applications. In practice, Tone-mapping operators (TMOs) provide a useful means for converting a high dynamic range (HDR) image to a low dynamic range (LDR) image in order to achieve better visualization on standard displays. Although mobile devices are becoming popular, the techniques for displaying the content of HDR images on the screens of such devices are still in the early stages. While several studies have been conducted to evaluate TMOs on conventional displays, few studies have been carried out to date to evaluate TMOs on small screen displays, such as those used in mobile devices. In this paper we evaluate, using subjective and objective methods, the most popular Tone-mapping-operators in different mobile displays and resolutions under normal viewing conditions for the end-user. Preliminary results show that small screen displays (SSDs) have an impact on the performance of TMOs compared to computer displays. In general, the larger the mobile resolution, the better the subjective results. We also found clear differences between SSDs and LDRs performances. The best TMO for mobile displays is iCAM06 and for computer displays it is Photographic Reproduction.

Alzheimer's disease (AD) and other forms of dementia are one of the major public health and social challenges of our time because of the large number of people affected. Early diagnosis is important for patients and their families to get maximum benefits from access to health and social care services and to plan for the future. EEG provides useful insight into brain functions and can play a useful role as a first line of decision-support tool for early detection and diagnosis of dementia. It is non-invasive, low-cost and has a high temporal resolution. The functions of brain cells are affected by damage caused by dementia and this in turn causes changes in the features of the EEG. Information theoretic methods have emerged as a potentially useful way to quantify changes in the EEG as biomarkers of dementia. Tsallis entropy has been shown to be one of the most promising information theoretic methods for quantifying changes in the EEG. In this paper, we develop the approach further. This has yielded an enhanced performance compared to existing approaches.

Blood-based biomarkers for Alzheimer’s disease would be very valuable because blood is a more accessible biofluid and is suitable for repeated sampling. However, currently there are no robust and reliable blood-based biomarkers for practical diagnosis. In this study we used a knowledge-based protein feature pool and two novel support vector machine embedded feature selection methods to find panels consisting of two and three biomarkers. We validated these biomarker sets using another serum cohort and an RNA profile cohort from the brain. Our panels included the proteins ECH1, NHLRC2, HOXB7, FN1, ERBB2, and SLC6A13 and demonstrated promising sensitivity (>87%), specificity (>91%), and accuracy (>89%).

This article presents a new approach for the analysis of biomedical data to support the management and care of patients with Alzheimer's disease (AD). The increase in prevalence of neurodegenerative disorders such as AD has led to a need for objective means to assist clinicians with the analysis and interpretation of complex biomedical data. To this end, we propose a "Bioprofile" analysis to reveal the pattern of disease in the subject's biodata. From the Bioprofile, personal "Bioindices" that indicate how closely a subject's data resemble the pattern of AD can be derived. We used an unsupervised technique (k-means) to cluster variables of the ADNI database so that subjects are divisible into those with the Bioprofile of AD and those without it. Results revealed that there is an "AD pattern" in the biodata of most AD and mild cognitive impairment (MCI) patients and some controls. This pattern agrees with a recent hypothetical model of AD evolution. We also assessed how the Bioindices changed with time and we found that the Bioprofile was associated with the risk of progressing from MCI to AD. Hence, the Bioprofile analysis is a promising methodology that may potentially provide a complementary new way of interpreting biomedical data. Furthermore, the concept of the Bioprofile could be extended to other neurodegenerative diseases.

A novel, generic framework for musical instrument sound design is proposed. The framework uses an intelligent and perceptual based approach to address the two main problems in sound design - optimization of synthesis parameters and assessment of sound quality. A fuzzy model is used to capture and exploit knowledge of sound design from audio experts. A robust methodology, based on the ITU Perceptual Evaluation of Audio Quality (PEAQ) algorithm, is used for objective prediction of sound synthesis quality and sound quality assessment. The new framework makes it possible to automate the optimization of synthesis parameters. It also allows the designer to evaluate objectively, the perceptual impact of individual parameters on the final sound quality which is useful for benchmarking sound synthesis methods. The framework is generic and can be used for sound design for a wide range of musical instruments. We illustrate the use of the framework in pipe organ sound design. Results from this show that the new approach provides an important and useful alternative to existing methods.

The primary aim of this paper is to present a new content-based, non-intrusive quality of experience (QoE) prediction model for low bitrate and resolution (QCIF) H.264 encoded videos and to illustrate its application in video quality adaptation over Universal Mobile Telecommunication Systems (UMTS) networks. The success of video applications over UMTS networks very much depends on meeting the QoE requirements of users. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet such QoE requirements. Video quality is affected by distortions caused both by the encoder and the UMTS access network. The impact of these distortions is content dependent, but this feature is not widely used in non-intrusive video quality prediction models. In the new model, we chose four key parameters that can impact video quality and hence the QoE-content type, sender bitrate, block error rate and mean burst length. The video quality was predicted in terms of the mean opinion score (MOS). Subjective quality tests were carried out to develop and evaluate the model. The performance of the model was evaluated with unseen dataset with good prediction accuracy ( ~ 93%). The model also performed well with the LIVE database which was recently made available to the research community. We illustrate the application of the new model in a novel QoE-driven adaptation scheme at the pre-encoding stage in a UMTS network. Simulation results in NS2 demonstrate the effectiveness of the proposed adaptation scheme, especially at the UMTS access network which is a bottleneck. An advantage of the model is that it is light weight (and so it can be implemented for real-time monitoring), and it provides a measure of user-perceived quality, but without requiring time-consuming subjective tests. The model has potential applications in several other areas, including QoE control and optimization in network planning and content provisioning for network/service providers.

Network quality of service (NQoS) of IP networks is unpredictable and impacts the quality of networked multimedia services. Adaptive voice and video schemes are therefore vital for the provision of voice over IP (VoIP) services for optimised quality of experience (QoE). Traditional adaptation schemes based on NQoS do not take perceived quality into consideration even though the user is the best judge of quality. Additionally, uncertainties inherent in NQoS parameter measurements make the design of adaptation schemes difficult and their performance suboptimal. This paper presents a QoE-driven adaptation scheme for voice and video over IP to solve the optimisation problem to provide optimal QoE for networked voice and video applications. The adaptive VoIP architecture was implemented and tested both in NS2 and in an Open IMS Core network to allow extensive simulation and test-bed evaluation. Results show that the scheme was optimally responsive to available network bandwidth and congestion for both voice and video and optimised delivered QoE for different network conditions, and is friendly to TCP traffic.

The primary aim of this paper is to propose a framework for bioprofile analysis over grid to support individualized healthcare. The framework provides a basis for a ?grid-based? solution to the challenge of ?distributed bioprofile analysis? in bioprofiling. The main objectives are: 1) to propose architecture for bioprofile analysis over grid; 2) to propose a service model for bioprofile analysis over grid; and 3) to present a practical implementation of grid environment to support distributed electroencephalogram analysis for early detection of dementia, a major health and social care problem, to illustrate the work.

The relative velocity between mobile devices is one of the key factors that determine the quality of communications in Mobile Ad-hoc NETworks (MANETs). Velocity estimates are useful in various aspects of ad hoc networking (e.g., predicting link lifetime and measuring performance). Conventional ways of estimating velocity rely on the availability of positioning systems such as the Global Positioning System (GPS) or precise knowledge of the characteristics of wireless channels/signals. A recently proposed method exploits time-varying internode range information for velocity estimations, provided that the range estimates are noise free. In this paper, we propose a new range-based method for relative velocity estimations (RVEs). We derive two range-based relative velocity estimators for both sparse and dense ad hoc networks, i.e., RVEs and RVEd. In addition to being less dependent on the characteristics of the wireless channel, the proposed method is more tolerant of the multipath or nonline-of-sight (NLOS) errors contained in range measurements than the existing method. Simulation results show an excellent match between the velocity estimates given by the proposed method and the actual values in both sparse and dense network cases, regardless of the nodal speed limits or the distribution of noises. In comparison with the existing method, the proposed method is shown to achieve an improvement factor of about 33 (in terms of normalized bias) with Gaussian multipath noises of 4-m standard deviation or 20 with uniform NLOS noises of 32-m maximum standard deviation.

Jitter buffer plays an important role in Voice over IP (VoIP) applications because it provides a key mechanism for achieving good speech quality to meet technical and commercial requirements. The main objective of this paper is to propose a new, simple-to-use jitter buffer algorithm as a front-end to conventional static or adaptive jitter buffer algorithms to provide improved performance, in terms of enhanced user-perceived speech quality and reduced end-to-end delay. Supported by signal processing features, the new algorithm, the so-called Play Late Algorithm, alters the playout delay inside a speech talkspurt without introducing unnecessary extra end-to-end delay. The results show that the new algorithm achieves the best performance under different network conditions when compared to conventional static and adaptive jitter buffer algorithms. The results reported here are based on live tests and emulated network conditions on real mobile phone prototypes. The mobile phone prototypes use AMR codec and support full IP/UDP/RTP stack with IPSec function in some of the tests. The method for perceived speech quality measurement is based on the ITU-T standard for speech quality evaluation (PESQ).

About 1.2 million people in Europe have Parkinson's disease (PD). PD patients often suffer from social exclusion and depression due to progressive lack of control over the disease. Eventually, most require constant care, leading to huge socioeconomic burdens. To partially tackle this issue, 7 partners in 7 countries participated in LiveWell (EU-funded project, 2012-14) to develop an innovative Web-based education, training and social community platform targeting PD patients, carers and clinicians. This paper describes LiveWell from conception to completion, presents lessons learnt over the course of the project, and highlights some identified main areas of best practice. The Project Consortium might not have had the chance to implement everything learnt in the current outputs, but these lessons and guidelines can be later used to improve LiveWell, and can also be generalised to benefit similar e-health services. The combination of appropriate online education and social communities could help patients and carers cope positively with PD, promoting social inclusion and better outcomes.

Clinical alarm plays a critical role to alert clinician to situations that require urgent response. However, alarms are ignored or switched off. The objective of the study is to identify user response, propose response time and recommendation to improve user response. A survey was carried out in Malaysia with 324 clinicians, 279(86.1%) were registered nurses and medical assistants (MA) and 45(13.9%) were medical doctors. The findings show that 16.0% of the respondents switch off the alarm, 20.4% silence the alarm, 14.5% delayed to respond and 20% ignored the alarm. The findings indicate 78.1% of the respondent recommended less than 10sec for 'immediate respond' for high risk alarm, 72.5% recommended less than 60sec for 'prompt respond' for medium risk alarms and 43.2% recommended delayed respond for low risk alarm should not be specified. The incorporation of response time based on risk in the design of alarm can improve user alarm response.

This paper proposes a QoE-driven LTE downlink scheduling algorithm which is able to maximise the number of users per cell while maintaining an acceptable QoE for VoIP applications. The QoE-driven scheduling algorithm was implemented in LTE-Sim and its performance was compared to common LTE scheduling algorithms. The main difference between QoE-driven scheduling and existing scheduling mechanisms was prioritisation of users for resource allocation by taking into account their QoE requirements for VoIP applications. Preliminary results have shown that QoE-driven LTE downlink scheduling improved the cell capacity by 75% compared to MLWDF and 250% compared to PF and EXP-PF.

The primary aim of this paper is to present new models for objective, nonintrusive, prediction of voice quality for IP networks and to illustrate their application to voice quality monitoring and playout buffer control in VoIP networks. The contributions of the paper are threefold. First, we present a new methodology for developing perceptually accurate models for nonintrusive prediction of voice quality which avoids time-consuming subjective tests. The methodology is generic and as such it has wide applicability in multimedia applications. Second, based on the new methodology, we present efficient regression models for predicting conversational voice quality nonintrusively for four modern codecs (G.729, G.723.1, AMR and iLBC). Third, we illustrate the usefulness of the models in two main applications - voice quality prediction for real Internet VoIP traces and perceived quality-driven playout buffer optimization. For voice quality prediction, the results show that the models have accuracy close to the combined ITU PESQ/E-model method using real Internet traces (correlation coefficient over 0.98). For playout buffer optimization, the proposed buffer algorithm provides an optimum voice quality when compared to five other buffer algorithms for all the traces considered

Recently, crowdsourcing has emerged as a cheaper and quicker alternative to traditional laboratory based Quality of Experience (QoE) evaluation for video streaming services. Crowdsourcing is a process of recruiting anonymous members of the public to solve/perform different tasks without supervision. This approach of seeking solutions from the public has been enhanced by ubiquitous internet connectivity, low cost and the ease to recruit workers without geographical restrictions. Although crowdsourcing makes it possible to save cost and reach a large group of people to perform subjective video quality testing, challenges such as validity of results and the trustworthiness of workers still remain unresolved. In this paper, we attempt to address some of these challenges by developing a screening algorithm that is able to determine the validity and trustworthiness of crowdsourcing video QoE evaluation results. This algorithm is based on evaluator's extracted data (e.g. Network IP addresses, network devices and browser information, time spent on the crowdsourcing platform, date and grading patterns). To determine the performance of this algorithm, we carried out a separate controlled laboratory based subjective video quality testing. This test enables us to determine how screened crowdsourcing results correlate with lab based results. Preliminary results show that crowdsourcing results can be improved by up to 59% when the proposed screening algorithm is applied. Moreover, the results support our assertion that crowdsourcing can provide reliable video QoE measurements if proper screening is performed on the test results.

This paper presents the findings of a pilot study to provide information about the characteristics of current networks and data transfers. The main aim of the study was to infer the properties of a large number of network paths. In addition, the study produced statistics relating to the average size of a typical Web page and both under the restriction of a single‐point connection. The study was performed in two steps: trace collection followed by TCP per‐flow analysis. The trace collection used the functionality of a random link generator, combined with an automatic HTTP retrieval tool. The TCP analysis was applied to the collected traces and it involved an offline TCP per‐flow method developed in previous research.

In the past, many video quality assessment methods have been proposed to predict video quality non-intrusively from different application and/or network related parameters. One of the important parameters which have been identified to have a significant impact on video quality is the video content type. To determine video content type, researchers have used different methods such as the extraction of video motion features from the encoded bitstream, the calculation of pixel-wise differences between consecutive frames and human vision systems. In this paper, we developed a new motion amount (MA) metric for defining and determining video content type for the newly released HEVC encoding standard. This metric is based on a combination of the counting number of extracted motion vectors from an encoded bitstream, the magnitude and the complexity of video sequences. Accuracy of the metric was determined by performing a direct correlation between MA, video bitrate and Peak Signal-to-Noise Ratio (PSNR). We also performed a performance comparison with existing pixel-wise approach of defining video content type. Preliminary results show that our metric outperformed the pixel-wise approach of content type definition. Based on the new metric, we further developed an HEVC encoded video quality prediction model that takes into account the MA and the Quantization Parameter (QP). We achieved an accuracy of 96% when our model predicted PSNR values are compared with full reference PSNR measurement.

We examine whether modeling of the causal dynamic relationships between frontal and occipital electroencephalogram (EEG) time-series recordings reveal reliable differentiating characteristics of Alzheimer’s patients versus control subjects in a manner that may assist clinical diagnosis of Alzheimer’s disease (AD). The proposed modeling approach utilizes the concept of principal dynamic modes (PDMs) and their associated nonlinear functions (ANF) and hypothesizes that the ANFs of some PDMs for the AD patients will be distinct from their counterparts in control subjects. To this purpose, global PDMs are extracted from 1-min EEG signals of 17 AD patients and 24 control subjects at rest using Volterra models estimated via Laguerre expansions, whereby the O1 or O2 recording is viewed as the input signal and the F3 or F4 recording as the output signal. Subsequent singular value decomposition of the estimated Volterra kernels yields the global PDMs that represent an efficient basis of functions for the representation of the EEG dynamics in all subjects. The respective ANFs are computed for each subject and characterize the specific dynamics of each subject. For comparison, signal features traditionally used in the analysis of EEG signals in AD are computed as benchmark. The results indicate that the ANFs of two specific PDMs, corresponding to the delta–theta and alpha bands, can delineate the two groups well., Alzheimer's disease (AD) is currently the most common neurodegenerative disorder in the western world and in the future its incidence is expected to double every 20 years. Because a definitive diagnosis can be made only by autopsy and AD pathology can start years before the first symptoms, there is a need for objective, non-invasive and affordable means to support clinicians in the detection and monitoring of the disease. We present an approach for assisting the diagnosis of Alzheimer's disease via modeling the causal dynamic relationships between frontal and occipital lectroencephalogram (EEG) time-series recordings. In a controlled clinical trial of 41 subjects, the proposed input-output modeling methodology was able to differentiate consistently between AD patients and controls.