Counting Objects in a Robotic Hand

A robot performing multi-object grasping needs to sense the number of objects in the hand after grasping. The count plays an important role in determining the robot's next move and the outcome and efficiency of the whole pick-place process. This paper presents a data-driven contrastive learning-based counting classifier with a modified loss function as a simple and effective approach for object counting despite significant occlusion challenges caused by robotic fingers and objects. The model was validated against other models with three different common shapes (spheres, cylinders, and cubes) in simulation and in a real setup. The proposed contrastive learning-based counting approach achieved above 96\% accuracy for all three objects in the real setup

Real-Time Ozone Detection Based on a Microfabricated Quartz Crystal Tuning Fork Sensor

A chemical sensor for ozone based on an array of microfabricated tuning forks is described. The tuning forks are highly sensitive and stable, with low power consumption and cost. The selective detection is based on the specific reaction of the polymer with ozone. With a mass detection limit of ∼2 pg/mm2 and response time of 1 second, the sensor coated with a polymer sensing material can detect ppb-level ozone in air. The sensor is integrated into a miniaturized wearable device containing a detection circuit, filtration, battery and wireless communication chip, which is ideal for personal and microenvironmental chemical exposure monitoring

Reversible oxygen gas sensor based on electrochemiluminescence

A novel and robust oxygen gas sensor based on electrochemiluminescence of Ru(bpy)(3)(3+/+) ion annihilation in an ionic liquid is presented. Real-time detection of environmental oxygen concentration together with selective, sensitive and reversible performance is demonstrated

Particle Pollution Estimation Based on Image Analysis.

Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction