Understanding the effect of skin mechanical properties on the friction of human finger-pads

The aim of this work is to achieve an understanding of the effect of skin mechanical properties on the friction of human finger-pads. This project primarily concentrates on gaining a more fundamental understanding of the frictional properties of skin. To achieve this, various parameters (epidermis thickness, sweat-gland counts, etc.) affecting skin friction were evaluated using an in-vivo technique, Optical Coherence Tomography (OCT) and a friction testing device. This project is also interested in investigating how those parameters alter the friction for different ages, genders, ethnicities and different contact conditions, such as moisture, temperature, loads, etc. Experimental studies were conducted to investigate the skin frictional behaviour. The findings showed that the skin friction obeys a two-term relationship. The skin friction was found to be strongly associated with its Young’s modulus. Tests on the skin structural properties showed the moisture level of the skin, skin thickness and skin morphological properties play important roles in determining the skin friction. The findings gained can be applied to explain how the skin friction varies among different participants. Further tests showed that physico-chemical properties of the skin can have a significant effect on the skin friction. The OCT system was combined with a multi-axis force plate to measure the contact area between fingers and smooth surfaces. Static measurement showed both apparent and real contact area increase with normal load following a power-law relationship. This is associated with the skin mechanical properties. The dynamic contact area was investigated using a Digital Image Correlation (DIC) method. As a finger was sliding along a flat surface, the dynamic apparent contact area was found to decrease with time

Semantic Image Segmentation via Deep Parsing Network

This paper addresses semantic image segmentation by incorporating rich information into Markov Random Field (MRF), including high-order relations and mixture of label contexts. Unlike previous works that optimized MRFs using iterative algorithm, we solve MRF by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-to-end computation in a single forward pass. Specifically, DPN extends a contemporary CNN architecture to model unary terms and additional layers are carefully devised to approximate the mean field algorithm (MF) for pairwise terms. It has several appealing properties. First, different from the recent works that combined CNN and MRF, where many iterations of MF were required for each training image during back-propagation, DPN is able to achieve high performance by approximating one iteration of MF. Second, DPN represents various types of pairwise terms, making many existing works as its special cases. Third, DPN makes MF easier to be parallelized and speeded up in Graphical Processing Unit (GPU). DPN is thoroughly evaluated on the PASCAL VOC 2012 dataset, where a single DPN model yields a new state-of-the-art segmentation accuracy.Comment: To appear in International Conference on Computer Vision (ICCV) 201

Steady-state bifurcation of FHN-type oscillator on a square domain

The Turing patterns of reaction-diffusion equations defined over a square region are more complex because of the D4-symmetry of the spatial region. This leads to the occurrence of multiple equivariant Turing bifurcations. In this paper, taking the FHN model as an example, we give a explicit calculation formula of normal form for the simple and double Turing bifurcation of the reaction-diffusion equation with Dirichlet boundary conditions and defined on a square space, and we also obtain a method for the calculation of the existence of spatially inhomogeneous steady-state solutions. This paper provides a theoretical basis for exploring and predicting the pattern formation of spatial multimode interaction

Shape-correlated statistical modeling and analysis for respiratory motion estimation

Respiratory motion challenges image-guided radiation therapy (IGRT) with location uncertainties of important anatomical structures in the thorax. Effective and accurate respiration estimation is crucial to account for the motion effects on the radiation dose to tumors and organs at risk. Moreover, serious image artifacts present in treatment-guidance images such 4D cone-beam CT cause difficulties in identifying spatial variations. Commonly used non-linear dense image matching methods easily fail in regions where artifacts interfere. Learning-based linear motion modeling techniques have the advantage of incorporating prior knowledge for robust motion estimation. In this research shape-correlation deformation statistics (SCDS) capture strong correlations between the shape of the lung and the dense deformation field under breathing. Dimension reduction and linear regression techniques are used to extract the correlation statistics. Based on the assumption that the deformation correlations are consistent between planning and treatment time, patient-specific SCDS trained from a 4D planning image sequence is used to predict the respiratory motion in the patient's artifact-laden 4D treatment image sequence. Furthermore, a prediction-driven atlas formation method is developed to weaken the consistency assumption, by integrating intensity information from the target images and the SCDS predictions into a common optimization framework. The strategy of balancing between the prediction constraints and the intensity-matching forces makes the method less sensitive to variation in the correlation and utilizes intensity information besides the lung boundaries. This strategy thus provides improved motion estimation accuracy and robustness. The SCDS-based methods are shown to be effective in modeling and estimating respiratory motion in lung, with evaluations and comparisons carried out on both simulated images and patient images

Applications of Emerging Memory in Modern Computer Systems: Storage and Acceleration

In recent year, heterogeneous architecture emerges as a promising technology to conquer the constraints in homogeneous multi-core architecture, such as supply voltage scaling, off-chip communication bandwidth, and application parallelism. Various forms of accelerators, e.g., GPU and ASIC, have been extensively studied for their tradeoffs between computation efficiency and adaptivity. But with the increasing demand of the capacity and the technology scaling, accelerators also face limitations on cost-efficiency due to the use of traditional memory technologies and architecture design. Emerging memory has become a promising memory technology to inspire some new designs by replacing traditional memory technologies in modern computer system. In this dissertation, I will first summarize my research on the application of Spin-transfer torque random access memory (STT-RAM) in GPU memory hierarchy, which offers simple cell structure and non-volatility to enable much smaller cell area than SRAM and almost zero standby power. Then I will introduce my research about memristor implementation as the computation component in the neuromorphic computing accelerator, which has the similarity between the programmable resistance state of memristors and the variable synaptic strengths of biological synapses to simplify the realization of neural network model. At last, a dedicated interconnection network design for multicore neuromorphic computing system will be presented to reduce the prominent average latency and power consumption brought by NoC in a large size neuromorphic computing system