A Single-Stage Passive Vibration Isolation System for Scanning Tunneling Microscopy

Scanning Tunneling Microscopy (STM) uses quantum tunneling effect to study the surfaces of materials on an atomic scale. Since the probe of the microscope is on the order of nanometers away from the surface, the device is prone to noises due to vibrations from the surroundings. To minimize the random noises and floor vibrations, passive vibration isolation is a commonly used technique due to its low cost and simpler design compared to active vibration isolation, especially when the entire vibration isolation system (VIS) stays inside an Ultra High Vacuum (UHV) environment. This research aims to analyze and build a single-stage passive VIS for an STM. The VIS consists of a mass-spring system staying inside an aluminum hollow tube. The mass-spring system is comprised of a circular copper stage suspended by a combination of six extension springs, and the STM stays on top of the copper stage. Magnetic damping with neodymium magnets, which induces eddy currents in the copper conductor, is the primary damping method to reduce the vibrations transferred to the mass-spring system. FEMM and MATLAB® are used to model magnetic flux density and damping coefficients from eddy current effect, which will help determine the necessary damping ratios for the VIS. Viton, which demonstrates a high compatibility with vacuum environments, will also serve as a great damping material between joints and contacts for the housing tube. Viton will be modeled as a Mooney-Rivlin hyperelastic material whose material parameters are previous studied, and Abaqus will be used as a Finite Element Analysis software to study the Viton gaskets’ natural frequencies. The natural frequencies of the aluminum hollow tube will also be investigated through Abaqus

Supervised Hashing with End-to-End Binary Deep Neural Network

Image hashing is a popular technique applied to large scale content-based visual retrieval due to its compact and efficient binary codes. Our work proposes a new end-to-end deep network architecture for supervised hashing which directly learns binary codes from input images and maintains good properties over binary codes such as similarity preservation, independence, and balancing. Furthermore, we also propose a new learning scheme that can cope with the binary constrained loss function. The proposed algorithm not only is scalable for learning over large-scale datasets but also outperforms state-of-the-art supervised hashing methods, which are illustrated throughout extensive experiments from various image retrieval benchmarks.Comment: Accepted to IEEE ICIP 201

Natural disasters and household welfare : evidence from Vietnam

As natural disasters hit with increasing frequency, especially in coastal areas, it is imperative to better understand how much natural disasters affect economies and their people. This requires disaggregated measures of natural disasters that can be reliably linked to households, the first challenge this paper tackles. In particular, a methodology is illustrated to create natural disaster and hazard maps from first hand, geo-referenced meteorological data. In a second step, the repeated cross-sectional national living standard measurement surveys (2002, 2004, and 2006) from Vietnam are augmented with the natural disaster measures derived in the first phase, to estimate the welfare effects associated with natural disasters. The results indicate that short-run losses from natural disasters can be substantial, with riverine floods causing welfare losses of up to 23 percent and hurricanes reducing welfare by up to 52 percent inside cities with a population over 500,000. Households are better able to cope with the short-run effects of droughts, largely due to irrigation. There are also important long-run negative effects, in Vietnam mostly so for droughts, flash floods, and hurricanes. Geographical differentiation in the welfare effects across space and disaster appears partly linked to the functioning of the disaster relief system, which has so far largely eluded households in areas regularly affected by hurricane force winds.Natural Disasters,Hazard Risk Management,Disaster Management,Climate Change Mitigation and Green House Gases,Adaptation to Climate Change

Selective Deep Convolutional Features for Image Retrieval

Convolutional Neural Network (CNN) is a very powerful approach to extract discriminative local descriptors for effective image search. Recent work adopts fine-tuned strategies to further improve the discriminative power of the descriptors. Taking a different approach, in this paper, we propose a novel framework to achieve competitive retrieval performance. Firstly, we propose various masking schemes, namely SIFT-mask, SUM-mask, and MAX-mask, to select a representative subset of local convolutional features and remove a large number of redundant features. We demonstrate that this can effectively address the burstiness issue and improve retrieval accuracy. Secondly, we propose to employ recent embedding and aggregating methods to further enhance feature discriminability. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art retrieval accuracy.Comment: Accepted to ACM MM 201

Rice monitoring using ENVISAT-ASAR data: preliminary results of a case study in the Mekong River Delta, Vietnam

Vietnam is one of the world’s largest rice exporting countries, and the fertile Mekong River Delta at the southern tip of Vietnam accounts for more than half of the country’s rice production. Unfortunately, a large part of rice crop growing time coincides with a rainy season, resulting in a limited number of cloud-free optical remote sensing images for rice monitoring. Synthetic aperture radar (SAR) data allows for observations independent of weather conditions and solar illumination, and is potentially well suited for rice crop monitoring. The aim of the study was to apply new generation Envisat ASAR data with dual polarization (HH and VV) to rice cropping system mapping and monitoring in An Giang province, Mekong River Delta. Several sample areas were established on the ground, where selected rice parameters (e.g. rice height and biomass) are periodically being measured over a period of 12 months. A correlation analysis of rice parameters and radar imagery values is then being conducted to determine the significance and magnitude of the relationships. This paper describes a review of the previous research studies on rice monitoring using SAR data, the context of this on-going study, and some preliminary results that provide insights on how ASAR imagery could be useful for rice crop monitoring. More work is being done to develop algorithms for mapping and monitoring rice cropping systems, and to validate a rice yield prediction model for one year cycle using time-series SAR imagery