Enhancing the teaching of professional practice and key skills in engineering through the use of computer animation

This paper presents some observations on how computer animation was used in the early years of a degree program in Electrical and Electronic Engineering to enhance the teaching of key skills and professional practice. This paper presents the results from two case studies. First, in a first year course which seeks to teach students how to manage and report on group projects in a professional way. Secondly, in a technical course on virtual reality, where the students are asked to use computer animation in a way that subliminally coerces them to come to terms with the fine detail of the mathematical principles that underlie 3D graphics, geometry, etc. as well as the most significant principles of computer architecture and software engineering. In addition, the findings reveal that by including a significant element of self and peer review processes into the assessment procedure students became more engaged with the course and achieved a deeper level of comprehension of the material in the course.

Barriers to student computer usage: staff and student perceptions

Ensuring that all graduates are able to exploit new technologies is a primary goal of all UK universities and a variety of assumptions have underpinned policies designed to promote this goal, This paper explores some of these assumptions through the findings of a. longitudinal study involving a cohort of over 800 university students. The study adopted a student perspective to examine the factors affecting their use of computers over a three year period. Unsurprisingly, the results indicated that situational factors (e.g. access, training and time) influence the extent to which students use computers, but a disparity was found in the importance attributed to these factors by the academic staff, who focused on the needs of their department, and by the students, who focused on their individual needs. Results suggest that increased attention to a student perspective may lead to improved strategic planning in students' use of computers.

Multimodal Biometric Human Recognition for Perceptual Human-Computer Interaction

In this paper, a novel video-based multimodal biometric verification scheme using the subspace-based low-level feature fusion of face and speech is developed for specific speaker recognition for perceptual human--computer interaction (HCI). In the proposed scheme, human face is tracked and face pose is estimated to weight the detected facelike regions in successive frames, where ill-posed faces and false-positive detections are assigned with lower credit to enhance the accuracy. In the audio modality, mel-frequency cepstral coefficients are extracted for voice-based biometric verification. In the fusion step, features from both modalities are projected into nonlinear Laplacian Eigenmap subspace for multimodal speaker recognition and combined at low level. The proposed approach is tested on the video database of ten human subjects, and the results show that the proposed scheme can attain better accuracy in comparison with the conventional multimodal fusion using latent semantic analysis as well as the single-modality verifications. The experiment on MATLAB shows the potential of the proposed scheme to attain the real-time performance for perceptual HCI applications.

Face Recognition in Global Harmonic Subspace

In this paper, a novel pattern recognition scheme, global harmonic subspace analysis (GHSA), is developed for face recognition. In the proposed scheme, global harmonic features are extracted at the semantic scale to capture the 2-D semantic spatial structures of a face image. Laplacian Eigenmap is applied to discriminate faces in their global harmonic subspace. Experimental results on the Yale and PIE face databases show that the proposed GHSA scheme achieves an improvement in face recognition accuracy when compared with conventional subspace approaches, and a further investigation shows that the proposed GHSA scheme has impressive robustness to noise.

A Low Complexity Video Watermarking in H.264 Compressed Domain

In this paper, a new blind and readable H.264 compressed domain watermarking scheme is proposed in which the embedding/extracting is performed using the syntactic elements of the compressed bit stream. As a result, it is not necessary to fully decode a compressed video stream both in the embedding and extracting processes. The method also presents an inexpensive spatiotemporal analysis that selects the appropriate submacroblocks for embedding, increasing watermark robustness while reducing its impact on visual quality. Meanwhile, the proposed method prevents bit-rate increase and restricts it within an acceptable limit by selecting appropriate quantized residuals for watermark insertion. Regarding watermarking demands such as imperceptibility, bit-rate control, and appropriate level of security, a priority matrix is defined which can be adjusted based on the application requirements. The resulted flexibility expands the usability of the proposed method.

SVD-Based Universal Spatial Domain Image Steganalysis

This paper is concerned with the universal (blind) image steganalysis problem and introduces a novel method to detect especially spatial domain steganographic methods. The proposed steganalyzer models linear dependencies of image rows/columns in local neighborhoods using singular value decomposition transform and employs content independency provided by a Wiener filtering process. Experimental results show that the novel method has superior performance when compared with its counterparts in terms of spatial domain steganography. Experiments also demonstrate the reasonable ability of the method to detect discrete cosine transform-based steganography as well as the perturbation quantization method.