This paper reviews objective methods for prognostic modelling of cancer tumours located within radiology images, a process known as radiomics. Radiomics is a novel feature transformation method for detecting clinically relevant features from radiological imaging data that are difficult for the human eye to perceive. To facilitate the detection machine learning and deep learning methods are increasingly investigated with the aim of improving patient diagnosis, treatment options and outcomes. A review of the relevant works in the expanding field of radiomics for survival prediction from cancer is provided. Research works outside the field of radiomics which define techniques that may be of future use to improve feature extraction and analysis are also reviewed. Radiomics is a rapidly advancing field of clinical image analysis with a vast potential for supporting decision making involved in the diagnosis and treatment of cancer. The realisation of this goal of more effective decision making requires significant individual and integrated expertise from domain experts in medicine, biology and computer science to allow advances in computer vision and machine learning techniques to be applied effectively. Deep learning combined with machine learning has the potential to advance the field of radiomics significantly in the years to come, provided that mechanisms for data sharing or distributed learning are established to increase the availability of data across all patient and tumour types.

The laboratory plays an important role in teaching engineering skills. An Electrical Engineering department at an Australian University implemented a reform to monitor and improve student satisfaction with the teaching laboratories. A Laboratory Manager was employed to oversee the quality of 27 courses containing instructional laboratories. Student satisfaction surveys were carried out on all relevant laboratories every year, and the data were used for continuous improvement. This paper will investigate the reforms that were implemented and outline a number of the improvements made. It also examines the program's overall impact on: (1) overall satisfaction; (2) laboratory notes; (3) learning experiences; (4) computer facilities; (5) engineering equipment; and (6) condition of the laboratory. Student satisfaction with the laboratories increased by 32% between 2007 and 2013. The results show that the laboratory notes (activity and clarity) and the quality of the equipment used are among the most influential factors on student satisfaction. In particular, it is important to have notes or resources that explain in some detail how to use and troubleshoot equipment and software used in the laboratory.

Project-based learning is a widely used pedagogical strategy in engineering education shown to be effective in fostering problem-solving, design, and teamwork skills. There are distinct benefits to be gained from giving students autonomy in determining the nature and scope of the projects that they wish to undertake, but a lack of expert guidance and of a clear direction at the outset can result in confusion, frustration, and unfulfilled goals. Moreover, engineering schools face the imperative of providing students with opportunities to engage with industry during their courses, which can be difficult to accomplish due to logistical and time constraints. This paper reports on a case study in which undergraduate students of electrical, computer, mechatronics, and telecommunications engineering interacted with representatives from industry to obtain feedback at the inception phase of their design projects. Students pitched their ideas to the industry guests at a virtual “trade fair” held within a hybrid video conferencing and three-dimensional (3-D) virtual world environment, in preparation for the assessable pitches that they had to deliver on campus to a faculty audience. Survey and assessment results attest to the participants' satisfaction as well as to the effectiveness of the approach in improving student self-efficacy and performance. The paper concludes with recommendations for engineering educators looking to implement similar initiatives and a brief outline of the authors' plans for the future.

Multimedia is ubiquitously available online with large amounts of video increasingly consumed through Web sites such as YouTube or Google Video. However, online multimedia typically limits users to visual/auditory stimulus, with onscreen visual media accompanied by audio. The recent introduction of MPEG-V proposed multi-sensory user experiences in multimedia environments, such as enriching video content with so-called sensory effects like wind, vibration, light, etc. In MPEG-V, these sensory effects are represented as Sensory Effect Metadata (SEM), which is additionally associated to the multimedia content. This paper presents three user studies that utilize the sensory effects framework of MPEG-V, investigating the emotional response of users and enhancement of Quality of Experience (QoE) of Web video sequences from a range of genres with and without sensory effects. In particular, the user studies were conducted in Austria and Australia to investigate whether geography and cultural differences affect users' elicited emotional responses and QoE.

In ultrasonic positioning systems (UPSs) chirp waveforms have attracted much attention due to its high range resolution. However, the multiple-access schemes for chirp-based UPS are limited. In its application to multiple-access ultrasonic positioning, effective waveform diversity design is a prerequisite. In a multiple-access UPS, each transmitter should transmit a unique waveform with impulse-like auto-correlation and relatively flat cross-correlations to the waveforms transmitted by other transmitters. Proposed in this paper is a methodology whereby multiple transmitters can transmit chirp signals simultaneously. The chirp waveforms are constructed by concatenating a number of linear sub-chirps of the same durations and bandwidths but different starting and stopping frequencies. This process is optimized by selecting sequences with impulse-like auto-correlations and relatively flat cross-correlations. Firstly, the efficiency of the proposed methodology is evaluated by several metrics and then, in an indoor environment, through simulations and experiments for ultrasonic positioning.

Automatic generation of metadata is an important component of multimedia search-by-content systems as it both avoids the need for manual annotation as well as minimising subjective descriptions and human errors. This paper explores the automatic attachment of basic descriptions (or dasiaTagspsila) to human motion held in a motion-capture database on the basis of a dynamic time warping (DTW) approach. The captured motion is held in the Acclaim ASF/AMC format commonly used in game and movie motion capture work and the approach allows for the comparison and classification of motion from different subjects. The work analyses the bone rotations important to a small set of movements and results indicate that only a small set of examples is required to perform reliable motion classification.