A surrogate-assisted measurement correction method for accurate and low-cost monitoring of particulate matter pollutants

Air pollution involves multiple health and economic challenges. Its accurate and low-cost monitoring is important for developing services dedicated to reduce the exposure of living beings to the pollution. Particulate matter (PM) measurement sensors belong to the key components that support operation of these systems. In this work, a modular, mobile Internet of Things sensor for PM measurements has been proposed. Due to a limited accuracy of the PM detector, the measurement data are refined using a two-stage procedure that involves elimination of the non-physical signal spikes followed by a non-linear correction of the responses using a multiplicative surrogate model. The correction layer is derived from the sparse and non-uniform calibration data, i.e., a combination of the measurements from the PM monitoring station and the sensor obtained in the same location over a specified (relatively short) interval. The device and the method have been both demonstrated based on the data obtained during three measurement campaigns. The proposed correction scheme improves the fidelity of PM measurements by around two orders of magnitude w.r.t. the responses for which the post-processing has not been considered. Performance of the proposed surrogate-assisted technique has been favorably compared against the benchmark approaches from the literature

Plane-wave lightcone superstring field theory

We construct the cubic interaction vertex and dynamically generated supercharges in lightcone superstring field theory for a large class half-supersymmetric D-branes in the planewave background. We show that these satisfy the plane-wave superalgebra at first order in string coupling. The cubic interaction vertex and dynamical supercharges presented here are given explicitly in terms of oscilators and can be used to compute three-point functions of open strings with endpoints on half-supersymmetric D-branes

Programmable Input Mode Instrumentation Amplifier Using Multiple Output Current Conveyors

In this paper a programmable input mode instrumentation amplifier (IA) utilising second generation, multiple output current conveyors and transmission gates is presented. Its main advantage is the ability to choose a voltage or current mode of inputs by setting the voltage of two configuration nodes. The presented IA is prepared as an integrated circuit block to be used alone or as a sub-block in a microcontroller or in a field programmable gate array (FPGA), which shall condition analogue signals to be next converted by an analogue-to-digital converter (ADC). IA is designed in AMS 0.35 µm CMOS technology and the power supply is 3.3 V; the power consumption is approximately 9.1 mW. A linear input range in the voltage mode reaches ± 1.68 V or ± 250 µA in current mode. A passband of the IA is above 11 MHz. The amplifier works in class A, so its current supply is almost constant and does not cause noise disturbing nearby working precision analogue circuits

Support for Employees with ASD in the Workplace Using a Bluetooth Skin Resistance Sensor–A Preliminary Study

The application of a Bluetooth skin resistance sensor in assisting people with Autism Spectrum Disorders (ASD), in their day-to-day work, is presented in this paper. The design and construction of the device are discussed. The authors have considered the best placement of the sensor, on the body, to gain the most accurate readings of user stress levels, under various conditions. Trial tests were performed on a group of sixteen people to verify the correct functioning of the device. Resistance levels were compared to those from the reference system. The placement of the sensor has also been determined, based on wearer convenience. With the Bluetooth Low Energy block, users can be notified immediately about their abnormal stress levels via a smartphone application. This can help people with ASD, and those who work with them, to facilitate stress control and make necessary adjustments to their work environment

A surrogate-assisted measurement correction method for accurate and low-cost monitoring of particulate matter pollutants

Air pollution involves multiple health and economic challenges. Its accurate and low-cost monitoring is important for developing services dedicated to reduce the exposure of living beings to the pollution. Particulate matter (PM) measurement sensors belong to the key components that support operation of these systems. In this work, a modular, mobile Internet of Things sensor for PM measurements has been proposed. Due to a limited accuracy of the PM detector, the measurement data are refined using a two-stage procedure that involves elimination of the non-physical signal spikes followed by a non-linear correction of the responses using a multiplicative surrogate model. The correction layer is derived from the sparse and non-uniform calibration data, i.e., a combination of the measurements from the PM monitoring station and the sensor obtained in the same location over a specified (relatively short) interval. The device and the method have been both demonstrated based on the data obtained during three measurement campaigns. The proposed correction scheme improves the fidelity of PM measurements by around two orders of magnitude w.r.t. the responses for which the post-processing has not been considered. Performance of the proposed surrogate-assisted technique has been favorably compared against the benchmark approaches from the literature