Algorithms on Minimizing the Maximum Sensor Movement for Barrier Coverage of a Linear Domain

In this paper, we study the problem of moving $n$ sensors on a line to form a barrier coverage of a specified segment of the line such that the maximum moving distance of the sensors is minimized. Previously, it was an open question whether this problem on sensors with arbitrary sensing ranges is solvable in polynomial time. We settle this open question positively by giving an $O(n^2 \log n)$ time algorithm. For the special case when all sensors have the same-size sensing range, the previously best solution takes $O(n^2)$ time. We present an $O(n \log n)$ time algorithm for this case; further, if all sensors are initially located on the coverage segment, our algorithm takes $O(n)$ time. Also, we extend our techniques to the cycle version of the problem where the barrier coverage is for a simple cycle and the sensors are allowed to move only along the cycle. For sensors with the same-size sensing range, we solve the cycle version in $O(n)$ time, improving the previously best $O(n^2)$ time solution.Comment: This version corrected an error in the proof of Lemma 2 in the previous version and the version published in DCG 2013. Lemma 2 is for proving the correctness of an algorithm (see the footnote of Page 9 for why the previous proof is incorrect). Everything else of the paper does not change. All algorithms in the paper are exactly the same as before and their time complexities do not change eithe

Effect of contact angle hysteresis on thermocapillary droplet actuation

Open microfluidic devices based on actuation techniques such as electrowetting, dielectrophoresis, or thermocapillary stresses require controlled motion of small liquid droplets on the surface of glass or silicon substrates. In this article we explore the physical mechanisms affecting thermocapillary migration of droplets generated by surface temperature gradients on the supporting substrate. Using a combination of experiment and modeling, we investigate the behavior of the threshold force required for droplet mobilization and the speed after depinning as a function of the droplet size, the applied thermal gradient and the liquid material parameters. The experimental results are well described by a hydrodynamic model based on earlier work by Ford and Nadim. The model describes the steady motion of a two-dimensional droplet driven by thermocapillary stresses including contact angle hysteresis. The results of this study highlight the critical role of chemical or mechanical hysteresis and the need to reduce this retentive force for minimizing power requirements in microfluidic devices

Capacitive sensing of droplets for microfluidic devices based on thermocapillary actuation

The design and performance of a miniaturized coplanar capacitive sensor is presented whose electrode arrays can also function as resistive microheaters for thermocapillary actuation of liquid films and droplets. Optimal compromise between large capacitive signal and high spatial resolution is obtained for electrode widths comparable to the liquid film thickness measured, in agreement with supporting numerical simulations which include mutual capacitance effects. An interdigitated, variable width design, allowing for wider central electrodes, increases the capacitive signal for liquid structures with non-uniform height profiles. The capacitive resolution and time response of the current design is approximately 0.03 pF and 10 ms, respectively, which makes possible a number of sensing functions for nanoliter droplets. These include detection of droplet position, size, composition or percentage water uptake for hygroscopic liquids. Its rapid response time allows measurements of the rate of mass loss in evaporating droplets

A High-Q Microwave MEMS Resonator

A High-Q microwave (K band) MEMS resonator is presented, which empolys substrate integrated waveguide (SIW) and micromachined via-hole arrays by ICP process. Nonradiation dielectric waveguide (NRD) is formed by metal filled via-hole arrays and grounded planes. The three dimensional (3D) high resistivity silicon substrate filled cavity resonator is fed by current probes using CPW line. This monolithic resonator results in low cost, high performance and easy integration with planar cicuits. The measured quality factor is beyond 180 and the resonance frequency is 21GHz.It shows a good agreement with the simulation results. The chip size is only 4.7mm x 4.6mm x 0.5mm. Finally, as an example of applications, a filter using two SIW resonators is designed.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/EDA-Publishing

Gauged Q ball in a piecewise parabolic potential

Q ball solutions are considered within the theory of a complex scalar field with a gauged U(1) symmetry and a parabolic-type potential. In the thin-walled limit, we show explicitly that there is a maximum size for these objects because of the repulsive Coulomb force. The size of Q ball will increase with the decrease of local minimum of the potential. And when the two minima degenerate, the energy stored within the surface of the Q ball becomes significant. Furthermore, we find an analytic expression for gauged Q ball, which is beyond the conventional thin-walled limit.Comment: 1 figure

OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation

As deep learning methods continue to improve medical image segmentation performance, data annotation is still a big bottleneck due to the labor-intensive and time-consuming burden on medical experts, especially for 3D images. To significantly reduce annotation efforts while attaining competitive segmentation accuracy, we propose a self-learning and one-shot learning based framework for 3D medical image segmentation by annotating only one slice of each 3D image. Our approach takes two steps: (1) self-learning of a reconstruction network to learn semantic correspondence among 2D slices within 3D images, and (2) representative selection of single slices for one-shot manual annotation and propagating the annotated data with the well-trained reconstruction network. Extensive experiments verify that our new framework achieves comparable performance with less than 1% annotated data compared with fully supervised methods and generalizes well on several out-of-distribution testing sets

Segmentation, Reconstruction, and Analysis of Blood Thrombus Formation in 3D 2-Photon Microscopy Images

We study the problem of segmenting, reconstructing, and analyzing the structure growth of thrombi (clots) in blood vessels in vivo based on 2-photon microscopic image data. First, we develop an algorithm for segmenting clots in 3D microscopic images based on density-based clustering and methods for dealing with imaging artifacts. Next, we apply the union-of-balls (or alpha-shape) algorithm to reconstruct the boundary of clots in 3D. Finally, we perform experimental studies and analysis on the reconstructed clots and obtain quantitative data of thrombus growth and structures. We conduct experiments on laser-induced injuries in vessels of two types of mice (the wild type and the type with low levels of coagulation factor VII) and analyze and compare the developing clot structures based on their reconstructed clots from image data. The results we obtain are of biomedical significance. Our quantitative analysis of the clot composition leads to better understanding of the thrombus development, and is valuable to the modeling and verification of computational simulation of thrombogenesis

Doctor Imitator: Hand-Radiography-based Bone Age Assessment by Imitating Scoring Methods

Bone age assessment is challenging in clinical practice due to the complicated bone age assessment process. Current automatic bone age assessment methods were designed with rare consideration of the diagnostic logistics and thus may yield certain uninterpretable hidden states and outputs. Consequently, doctors can find it hard to cooperate with such models harmoniously because it is difficult to check the correctness of the model predictions. In this work, we propose a new graph-based deep learning framework for bone age assessment with hand radiographs, called Doctor Imitator (DI). The architecture of DI is designed to learn the diagnostic logistics of doctors using the scoring methods (e.g., the Tanner-Whitehouse method) for bone age assessment. Specifically, the convolutions of DI capture the local features of the anatomical regions of interest (ROIs) on hand radiographs and predict the ROI scores by our proposed Anatomy-based Group Convolution, summing up for bone age prediction. Besides, we develop a novel Dual Graph-based Attention module to compute patient-specific attention for ROI features and context attention for ROI scores. As far as we know, DI is the first automatic bone age assessment framework following the scoring methods without fully supervised hand radiographs. Experiments on hand radiographs with only bone age supervision verify that DI can achieve excellent performance with sparse parameters and provide more interpretability.Comment: Original Title: "Doctor Imitator: A Graph-based Bone Age Assessment Framework Using Hand Radiographs" @inproceedings{chen2020doctor, title={Doctor imitator: A graph-based bone age assessment framework using hand radiographs}, author={Chen, Jintai and Yu, Bohan and Lei, Biwen and Feng, Ruiwei and Chen, Danny Z and Wu, Jian}, booktitle={MICCAI}, year={2020}