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.

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.

The paper presents novel content-aware adaptation schemes for QoE optimized HTTP-based adaptive video streaming services. In recent years, content type has been investigated and proofed to have a deep influence on user's acceptable quality of experience (QoE). However, most of the existing HTTP-based adaptive video strategies only conduct bitrate switching according to network resources without a consideration of video content types. This may cause a non-optimal network resource allocation which may not achieve the optimized QoE for delivered video streaming services. In this paper, we proposed two content-aware adaptation schemes, named as Content-aware Probe and Adapt (C-PANDA) and Content-aware Dynamic Programming (C-DP), which are able to decide the video bitrate for the next video segment, based on not only the available bandwidth, the existing buffer capacity, but also the video content type. We implemented both content-aware adaptation algorithms in player prototypes based on libdash 3.0 library. Preliminary results show that content-aware adaptation schemes achieve better QoE when compared with conventional PANDA scheme. The proposed schemes are able to optimize QoE according to contents. They can achieve higher than acceptable QoE for most attractive contents such as those with high motion intensity to improve user experience for the most interesting scenes; select same video representations for segments belonging to a scene to avoid quality oscillation; and reserve buffer resource during low motion scenes to avoid stalling for the following high motion scenes. The work will be helpful in designing QoE-aware DASH schemes in the future.

The Next Generation Networks (NGNs) were proposed to provide ubiquitous access to multimedia services over the Internet. With the best effort nature of the Internet, the quality of real time multimedia services such as voice and video can rapidly degrade due to network impairments. In this paper, we propose a novel feedback-free Early VoIP Quality Adaptation Scheme (EVQAS) which can adapt voice send bit rate (e.g. Adaptive Multi-Rate (AMR) codec mode) automatically according to the buildup of queuing delay in the network. This differs from existing adaptation methods which adapt send bit rate according to either packet loss information or Perceived Quality of Service (PQoS) metrics (e.g. Mean Opinion Score (MOS)) derived from packet loss and delay. The proposed scheme can avoid congested packet loss which has an obvious impact on perceived voice quality. We implemented this adaptation mechanism in open Android mobile and tested in Open IMS Core testbed. Preliminary results show that the early adaptation mechanism can effectively release congestion before packet loss occurs. This has increased overall perceived voice quality when compared with existing adaptation method. This lightweight feedback-free early adaptation mechanism can be easily applied to other mobile devices to improve VoIP quality in current NGN VoIP applications.

User's perceived quality of service (QoS) or quality of experience (QoE) is likely to be the major determining factor in the success of new multimedia applications over wireless/mobile networks. The primary aim of this study is to present an adaptation scheme that is QoE-driven for optimising content provisioning and network resource utilisation for video applications over wireless networks. The proposed scheme encompasses the application of a QoE-driven model for optimising content provisioning and network resource utilisation. The content provisioning is optimised by the determination of initial content quality by adapting the video sender bitrate (SBR) according to users' QoE requirement. By finding the impact of the QoS parameters on end-to-end perceptual video quality, the optimum trade-off between SBR and frame rate is found and the benefits to network providers in maximising existing network resources are demonstrated. The QoE is measured in terms of the mean opinion score. The proposed scheme makes it possible for content providers to achieve optimum streaming (with an appropriate SBR) suitable for the network and content type for a requested QoE. The scheme is also beneficial for network providers for network resource provision and planning, and therefore maximising existing network infrastructure by providing service differentiation.

The anticipated growth of IPTV makes selection of suitable congestion controllers for video-stream traffic of vital concern. Measurements of packet dispersion at the receiver provide a graded way of estimating congestion, which is particularly suited to video as it does not rely on packet loss. A closed-loop congestion controller, which dynamically adapts the bitstream output of a transcoder or video encoder to a rate less likely to lead to packet loss, is presented. The video congestion controller is based on fuzzy logic with packet dispersion and its rate of change forming the inputs. Compared with TCP emulators such as TCP-friendly rate control (TFRC) and rate adaptation protocol (RAP), which rely on packet loss for real-time congestion control, the fuzzy-logic trained system's sending rate is significantly smoother when multiple video-bearing sources share a tight link. Using a packet dispersion method similarly results in a fairer allocation of bandwidth than TFRC and RAP. These gains for video traffic are primarily because of better estimation of network congestion through packet dispersion but also result from accurate interpretation by the fuzzy-logic controller.

Intelligent congestion control is vital for encoded video streaming of a clip or film, as network traffic volatility and the associated uncertainties require constant adjustment of the bit rate. Existing solutions, including the standard transmission control protocol (TCP) friendly rate control equation-based congestion controller, are prone to fluctuations in their sending rate and may respond only when packet loss has already occurred. This is a major problem, because both fluctuations and packet loss affect the end-user's perception of the delivered video. A type-1 (T1) fuzzy logic congestion controller (FLC) can operate at video display rates and can reduce packet loss and rate fluctuations, despite uncertainties in measurements of delay arising from congestion and network traffic volatility. However, a T1 FLC employing precise T1 fuzzy sets cannot fully cope with the uncertainties associated with such dynamic network environments. A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to produce improved performance. This paper proposes an interval type-2 FLC that achieves a superior delivered video quality compared with existing traditional controllers and a T1 FLC. To show the response in different network scenarios, tests demonstrate the response both in the presence of typical Internet cross-traffic as well as when other video streams occupy a bottleneck on an All-Internet protocol (IP) network. As All-IP networks are intended for multimedia traffic, it is important to develop a form of congestion control that can transfer to them from the mixed traffic environment of the Internet. It was found that the proposed type-2 FLC, although it is specifically designed for Internet conditions, can also successfully react to the network conditions of an All-IP network. When the control inputs were subject to noise, the type-2 FLC resulted in an order of magnitude performance improvement in comparison with the T1 FLC. The type-2 FLC also showed reduced packet loss when compared with the other controllers, again resulting in superior delivered video quality. When judged by established criteria, such as TCP-friendliness and delayed feedback, fuzzy logic congestion control offers a flexible solution to network bottlenecks. These findings offer the type-2 FLC as a way forward for congestion control of video streaming across packet-switched IP networks.

Congestion control of a variable bit-rate video stream crossing the Internet is crucial to ensuring the quality of the received video. When a fuzzy-logic congestion controller (FLC) changes the sending rate of a video transcoder, it does so without feedback of packet loss, using packet dispersion instead. Compared with the well-known TFRC and RAP controllers, the FLC's sending rate is significantly smoother, allowing it to more closely take up available bandwidth at a bottleneck link. There is an accompanying order of magnitude reduction in packet losses. Due to better utilization of the available bandwidth, video quality is improved over time by several decibels in low-packet-loss conditions. The strength of the FLC solution is demonstrated by the resulting video quality when typical Web traffic forms the background traffic. The FLC avoids any risk of congestion collapse through fairness to coexisting TCP flows and is robust to changes in path delay and router buffer configuration.