Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS

Multi-Robot Object Transport Motion Planning with a Deformable Sheet

Using a deformable sheet to handle objects is convenient and found in many practical applications. For object manipulation through a deformable sheet that is held by multiple mobile robots, it is a challenging task to model the object-sheet interactions. We present a computational model and algorithm to capture the object position on the deformable sheet with changing robotic team formations. A virtual variable cables model (VVCM) is proposed to simplify the modeling of the robot-sheet-object system. With the VVCM, we further present a motion planner for the robotic team to transport the object in a three-dimensional (3D) cluttered environment. Simulation and experimental results with different robot team sizes show the effectiveness and versatility of the proposed VVCM. We also compare and demonstrate the planning results to avoid the obstacle in 3D space with the other benchmark planner.Comment: 8 pages, 10 figures, accepted by RAL&CASE 2022 in June 24, 202

Management of Tuberculosis in Taiwan:A Look into the Shared Responsibilities of the Government, General Public and Medical Students

Tuberculosis (TB) is a deadly infectious disease worldwide due to its high incidenceand mortality rate. Its impact on Taiwan is no exception. This paper aims to examineeffectiveness of national policies on TB control, assess public awareness, and evaluatewhether involvement of medical students would have a positive effect towards TBcontrol in Taiwan.Literatures were first reviewed to assess effectiveness of Directly Observed Treatment,Short-course (DOTS). Although there is limited improvement in treatment success inTaiwan, there is an overall positive effect.Questionnaires designed to assess public knowledge and behaviors revealedinadequate public knowledge about DOTS, which could have led to the lack ofdistinct success in the national policy. Healthcare workshops were conducted by medical students, with survey resultsshowing a significant improvement in participants’ knowledge. Thus, medicalstudents are recommended to engage in healthcare activities to effectively aid TBcontrol.