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IMPact@SUTD, a monthly update featuring the latest published research works of SUTD faculty and researchers.
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Accurate profiling of blood components in microliter with position-insensitive coplanar electrodes-based cytometry

Sensors and Actuators B: Chemical

AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot

Sensors

Support vector machines as Bayes' classifiers

Operations Research Letters

SUTD Author: Jianwei Zhong, Minhui Liang and Ye Ai

SUTD Author: Archana Semwal, Lee Ming Jun Melvin, Rajesh Elara Mohan, Balakrishnan Ramalingam, and Thejus Pathmakumar

SUTD Author: Peter L.Jackson

“Identification and counting of blood cell subtypes are essential for medical diagnosis, which is only accessible in centralized biomedical laboratories. Here, we developed a microfluidic single-cell electrical impedance measurement device that enables accurate profiling of blood components. This miniaturized device has great potential to provide point-of-care solution for blood analysis.”--- Ye Ai

“This work presents an AI-enabled mosquito surveillance and population mapping framework using our in-house-developed robot, named ‘Dragonfly’, which uses the You Only Look Once (YOLO) V4 Deep Neural Network (DNN) algorithm and a two-dimensional (2D) environment map generated by the robot.”   --- Archana Semwal

"We reformulate the problem of determining support vectors directly as an application of Bayes' classifiers rather than as the dual program to a binary geometric separation problem. The primary purpose of the reformulation is to create a simpler exposition of the support vector machines technique. A secondary advantage is that it immediately and naturally applies to multi-class classification problems where the kernel function can be normalized as a density."   --- Peter L.Jackson

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