The Device-Object Pairing Problem: Matching IoT Devices with Video Objects in a Multi-Camera Environment.

IoT technologies enable millions of devices to transmit their sensor data to the external world. The device-object pairing problem arises when a group of Internet of Things is concurrently tracked by cameras and sensors. While cameras view these things as visual "objects", these things which are equipped with "sensing devices" also continuously report their status. The challenge is that when visualizing these things on videos, their status needs to be placed properly on the screen. This requires correctly pairing visual objects with their sensing devices. There are many real-life examples. Recognizing a vehicle in videos does not imply that we can read its pedometer and fuel meter inside. Recognizing a pet on screen does not mean that we can correctly read its necklace data. In more critical ICU environments, visualizing all patients and showing their physiological signals on screen would greatly relieve nurses' burdens. The barrier behind this is that the camera may see an object but not be able to see its carried device, not to mention its sensor readings. This paper addresses the device-object pairing problem and presents a multi-camera, multi-IoT device system that enables visualizing a group of people together with their wearable devices' data and demonstrating the ability to recover the missing bounding box.

Rapid and accurate quantification of amphetamine and methamphetamine in human urine by antibody decorated magnetite nanoparticles coupled with matrix-assisted laser desorption ionization time-of-flight mass spectrometer analysis.

In this study, a novel method for the simultaneous determination and accurate quantification of abused drugs in human urine was developed. Antibody conjugated boronic acid modified magnetite nanoparticles (Fe3O4, MNPs) were prepared for the selectively purification of illicit drugs in combination with high resolution matrix-assisted laser desorption ionization time-of-flight mass spectrometer (MALDI-TOF MS) analysis. Illicit drugs, amphetamine (AM) and methamphetamine (MA), were used as model analytes to demonstrate the feasibility of our strategy. Boronic acid functionalized MNPs were first prepared via one-pot synthesis to simplify and improve the efficiency of a chemical reaction. Anti-amphetamine antibody (anti-AM antibody) and anti-methamphetamine antibody (anti-MA antibody) was conjugated onto boronic acid modified MNPs, respectively, through the formation of boronate ester bond that could maintain the correct orientation to maximally capture their antigens. The capacity of antibody conjugation to boronic acid modified MNPs was at least 24 µg antibody/mg MNPs. Antibody-conjugated MNPs were designed to specifically capture AM and MA in human urine samples, both of which can be directly eluted to MALDI target plate by adding MALDI CHCA matrix solution for the following MALDI-MS analysis. The linear range of detection of the proposed method were 25-400 ng/mL and 25-1000 ng/mL with coefficients of determination between 0.9923 and 0.9997 for AM and MA, respectively. The lowest detectable concentrations of AM and MA were 1.87 and 3.75 ng/mL, respectively. More importantly, the proposed method allows rapid and accurate quantification of AM and MA from three suspects' urine samples. The obtained results are consistent with traditional GC/MS analysis. Antibody-conjugated MNPs could thus prove to be powerful tools for important applications such as forensic science, food safety and clinical diagnosis of disease.