Wei Yan

Wei Yan

BRICKxAR is a novel Augmented Reality (AR) instruction method for construction toys such as LEGO. With BRICKxAR, physical LEGO construction is guided by virtual bricks. Compared with the state-of-the-art, accuracy of the virtual - physical model alignment is significantly improved through a new design of marker-based registration, which can achieve an average error less than 1mm throughout the model. Realistic object occlusion is accomplished to reveal the true spatial relationship between physical and virtual bricks. LEGO players' hand detection and occlusion are realized to visualize the correct spatial relationship between real hands and virtual bricks, and allow virtual bricks to be "grasped" by real hands. The integration of these features makes AR instructions possible for small-parts assembly, validated through a working AR prototype for constructing LEGO Arc de Triomphe, quantitative measures of the accuracies of registration and occlusions, and heuristic evaluation of AR instruction features.

Nima Karimian, Fatemeh Tehranipoor, Nikolaos Anagnostopoulos et al.

Nowadays, there is an increasing interest in the development of Autonomous Vehicles (AV). However, there are two types of attack challenges that can affect AVs and are yet to be resolved, i.e., sensor attacks and vehicle access attacks. This paper, to the best of our knowledge, is the first work that proposes a novel authentication scheme involving DRAM power-up unique features using deep Convolutional Neural Network (CNN), which can be used to implement secure access control of autonomous vehicles. Our approach consists of two parts. First, we convert raw power-up sequence data from DRAM cells into a two-dimensional (2D) format to generate a DRAM image structure. Second, we apply deep CNN to DRAM images, in order to extract unique features from each memory to classify them for authentication. To evaluate our proposed approach, we utilize data from three Commercial-Off-The-Shelf (COTS) DRAMs taken under various environmental and other conditions (high/low temperature, high/low supply voltage and aging effects). Based on our results, our proposed authentication method ``DRAMNet'' achieves 98.63% accuracy and 98.49% precision. In comparison to other state-of-the-art CNN architectures, such as the AlexNet and VGGNet models, our DRAMNet approach fares equally well or better than them.