The effect of surface roughness parameters on contact and wettability of solid surfaces

Surfaces of materials strongly affect functional properties such as mechanical, biological, optical, acoustic and electronic properties of materials, particularly at the micro/nano scale. Surface effects stem from the interplay of surface morphology and surface chemical properties. This dissertation focuses on (1) modeling the effect of surface roughness parameters on solid-solid contact and solid-liquid interaction as well as; (2) developing a surface engineering method that can generate random surfaces with desired amplitude and spatial roughness parameters for tribological and biomimetic applications.;Autocorrelation length (ACL) is a surface roughness parameter that provides spatial information of surface topography that is not included in amplitude parameters such as root-mean-square roughness. A relationship between ACL and the friction behavior of a rough surface was developed. The probability density function of peaks and the mean peak height of a profile were given as functions of its ACL. These results were used to estimate the number of contact points when a rough surface comes into contact with a flat surface, and it was shown that the larger the ACL of the rough surface, the less the number of contact points. Based on Hertzian contact mechanics, it was shown that the real area of contact increases with increasing of number of contact points. Results from microscale friction experiments (where friction force is proportional to real area of contact) on polished and etched silicon surfaces are presented to verify the analysis.;A versatile surface processing method based on electrostatic deposition of particles and subsequent dry etching was shown to be able to independently tailor the amplitude and spatial roughness parameters of the resulting surfaces. Statistical models were developed to connect process variables to the amplitude roughness parameters center line average, root mean square and the spatial parameter, autocorrelation length of the final surfaces. Process variables include particle coverage, which affected both amplitude and spatial roughness parameters, particle size, which affected only spatial parameters and etch depth, which affects only amplitude parameters. The autocorrelation length of the final surface closely followed a power law decay with particle coverage, the most significant processing parameter. Center line average, root mean square followed a nonlinear relation with particle coverage and particle size. Experimental results on silicon substrates agreed reasonably well with model predictions.;This same hybrid surface engineering process was used to demonstrate adhesion and friction reduction. Microscale adhesion and friction tests were conducted on flat (smooth) and processed silicon surfaces with a low elastic modulus thermoplastic rubber (Santoprene) probe that allowed a large enough contact area to observe the feature size effect. Both adhesion and friction force of the processed surfaces were reduced comparing to that of the flat surfaces.;The process is also used to generate superhydrophobic engineering surfaces by mimicking the structure of lotus leaves. Tunable bimodal roughness (in both micro and nano scale) and a thin hydrophobic fluorocarbon film were generated on an engineering material surface by the hybrid process. These surfaces exhibit contact angles with water of more than 160°. A geometric model was developed to related air-trapping ability of hydrophobic surfaces with hillock features to process variables (hillock diameter, etching depth and coverage) and contact angle. The model is shown to be able to predict minimum coverage of hillocks required for air-trapping on hydrophobic rough surfaces. The model predictions agree with experimental observations reasonably well. This model can particularly be extended to utilizing statistical roughness parameters to predict air-trapping for rough hydrophobic surfaces

Generating random surfaces with desired autocorrelation length

A versatile surface processing method based on electrostatic deposition of particles and subsequent dry etching is shown to be able to tailor the autocorrelation length of a random surface by varying particle size and coverage. An explicit relation between final autocorrelation length, surface coverage of the particles, particle size, and etch depth is built. The autocorrelation length of the final surface closely follows a power law decay with particle coverage, the most significant processing parameter. Experimental results on silicon substrates agree reasonably well with model predictions

Adversarial Defense via Neural Oscillation inspired Gradient Masking

Spiking neural networks (SNNs) attract great attention due to their low power consumption, low latency, and biological plausibility. As they are widely deployed in neuromorphic devices for low-power brain-inspired computing, security issues become increasingly important. However, compared to deep neural networks (DNNs), SNNs currently lack specifically designed defense methods against adversarial attacks. Inspired by neural membrane potential oscillation, we propose a novel neural model that incorporates the bio-inspired oscillation mechanism to enhance the security of SNNs. Our experiments show that SNNs with neural oscillation neurons have better resistance to adversarial attacks than ordinary SNNs with LIF neurons on kinds of architectures and datasets. Furthermore, we propose a defense method that changes model's gradients by replacing the form of oscillation, which hides the original training gradients and confuses the attacker into using gradients of 'fake' neurons to generate invalid adversarial samples. Our experiments suggest that the proposed defense method can effectively resist both single-step and iterative attacks with comparable defense effectiveness and much less computational costs than adversarial training methods on DNNs. To the best of our knowledge, this is the first work that establishes adversarial defense through masking surrogate gradients on SNNs

Method to Generate Surfaces with Desired Roughness Parameters

A surface engineering method based on the electrostatic deposition of microparticles and dry etching is described and shown to be able to independently tune both amplitude and spatial roughness parameters of the final surface. Statistical models were developed to connect process variables to the amplitude parameters (center line average and root-mean-square) and a spatial parameter (autocorrelation length) of the final surfaces. Process variables include particle coverage, which affects both amplitude and spatial roughness parameters, particle size, which affects only spatial parameters, and etch depth, which affects only amplitude parameters. Correlations between experimental data and model predictions are discussed

A noise based novel strategy for faster SNN training

Spiking neural networks (SNNs) are receiving increasing attention due to their low power consumption and strong bio-plausibility. Optimization of SNNs is a challenging task. Two main methods, artificial neural network (ANN)-to-SNN conversion and spike-based backpropagation (BP), both have their advantages and limitations. For ANN-to-SNN conversion, it requires a long inference time to approximate the accuracy of ANN, thus diminishing the benefits of SNN. With spike-based BP, training high-precision SNNs typically consumes dozens of times more computational resources and time than their ANN counterparts. In this paper, we propose a novel SNN training approach that combines the benefits of the two methods. We first train a single-step SNN(T=1) by approximating the neural potential distribution with random noise, then convert the single-step SNN(T=1) to a multi-step SNN(T=N) losslessly. The introduction of Gaussian distributed noise leads to a significant gain in accuracy after conversion. The results show that our method considerably reduces the training and inference times of SNNs while maintaining their high accuracy. Compared to the previous two methods, ours can reduce training time by 65%-75% and achieves more than 100 times faster inference speed. We also argue that the neuron model augmented with noise makes it more bio-plausible

Spiking sampling network for image sparse representation and dynamic vision sensor data compression

Sparse representation has attracted great attention because it can greatly save storage resources and find representative features of data in a low-dimensional space. As a result, it may be widely applied in engineering domains including feature extraction, compressed sensing, signal denoising, picture clustering, and dictionary learning, just to name a few. In this paper, we propose a spiking sampling network. This network is composed of spiking neurons, and it can dynamically decide which pixel points should be retained and which ones need to be masked according to the input. Our experiments demonstrate that this approach enables better sparse representation of the original image and facilitates image reconstruction compared to random sampling. We thus use this approach for compressing massive data from the dynamic vision sensor, which greatly reduces the storage requirements for event data

Self-supervised Domain-agnostic Domain Adaptation for Satellite Images

Domain shift caused by, e.g., different geographical regions or acquisition conditions is a common issue in machine learning for global scale satellite image processing. A promising method to address this problem is domain adaptation, where the training and the testing datasets are split into two or multiple domains according to their distributions, and an adaptation method is applied to improve the generalizability of the model on the testing dataset. However, defining the domain to which each satellite image belongs is not trivial, especially under large-scale multi-temporal and multi-sensory scenarios, where a single image mosaic could be generated from multiple data sources. In this paper, we propose an self-supervised domain-agnostic domain adaptation (SS(DA)2) method to perform domain adaptation without such a domain definition. To achieve this, we first design a contrastive generative adversarial loss to train a generative network to perform image-to-image translation between any two satellite image patches. Then, we improve the generalizability of the downstream models by augmenting the training data with different testing spectral characteristics. The experimental results on public benchmarks verify the effectiveness of SS(DA)2