Emotion Detection Using Noninvasive Low Cost Sensors

Emotion recognition from biometrics is relevant to a wide range of application domains, including healthcare. Existing approaches usually adopt multi-electrodes sensors that could be expensive or uncomfortable to be used in real-life situations. In this study, we investigate whether we can reliably recognize high vs. low emotional valence and arousal by relying on noninvasive low cost EEG, EMG, and GSR sensors. We report the results of an empirical study involving 19 subjects. We achieve state-of-the- art classification performance for both valence and arousal even in a cross-subject classification setting, which eliminates the need for individual training and tuning of classification models.Comment: To appear in Proceedings of ACII 2017, the Seventh International Conference on Affective Computing and Intelligent Interaction, San Antonio, TX, USA, Oct. 23-26, 201

Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena

Low-Cost Biaxial Skin Sensors for Large Strain Measurement

Undergraduate Basi

Concept, modeling, and performance prediction of a low-cost, large deformable mirror

While it is attractive to integrate a deformable mirror (DM) for adaptive optics (AO) into the telescope itself rather than using relay optics within an instrument, the resulting large DM can be expensive, particularly for extremely large telescopes. A low-cost approach for building a large DM is to use voice-coil actuators connected to the back of the DM through suction cups. Use of such inexpensive voice-coil actuators leads to a poorly damped system with many structural modes within the desired bandwidth. Control of the mirror dynamics using electro-mechanical sensors is thus required for integration within an AO system. We introduce a distributed control approach, and we show that the “inner” back sensor control loop does not need to function at low frequencies, leading to significant cost reduction for the sensors. Incorporating realistic models of low-cost actuators and sensors together with an atmospheric seeing model, we demonstrate that the low-cost mirror strategy is feasible within a closed-loop AO system

Platooning with Low-Cost Sensors

Cílem toho projektu je návrh a implementace systému konvoje dvou vozidel založeném na relativní lokalizaci a sledování trajektorie z odometrii. Systém používá kvazi operační systém Robot Operating System pro komunikaci mezi vozidly a určení relativních parametrů. Určení relativní polohy vozidel je realizováno externím systémem Whycon, který zpracovává obrázky z kamery. Tato práce rovněž zkoumá možnosti použití levných základních senzorů pro zlepšení výsledků. Byl použit rozšířený Kalmánův filtr pro fúzi odometrických dat a lokalizace ze systému Whycon pro zlepšení výsledků, které produkují jednotlivé metody samostatně.The goal of this project is to design and implement a platoon system between two vehicles based on relative localization system and odometry path follower. The system utilizes quasi operating system called Robot Operating System for the purpose of communication between vehicles and computation of relative parameters. Relative localization is based on a vision-based external localization system called Whycon. Furthermore, this thesis is about investigation of what can be achieved when combining low cost sensors in order to achieve better results. Extended Kalman Filter was used to combine wheel odometries of vehicles and Whycon localization in order to overcome their individual problems

Ensemble learning of model hyperparameters and spatiotemporal data for calibration of low-cost PM2.5 sensors.

he PM2.5 air quality index (AQI) measurements from government-built supersites are accurate but cannot provide a dense coverage of monitoring areas. Low-cost PM2.5 sensors can be used to deploy a fine-grained internet-of-things (IoT) as a complement to government facilities. Calibration of low-cost sensors by reference to high-accuracy supersites is thus essential. Moreover, the imputation for missing-value in training data may affect the calibration result, the best performance of calibration model requires hyperparameter optimization, and the affecting factors of PM2.5 concentrations such as climate, geographical landscapes and anthropogenic activities are uncertain in spatial and temporal dimensions. In this paper, an ensemble learning for imputation method selection, calibration model hyperparameterization, and spatiotemporal training data composition is proposed. Three government supersites are chosen in central Taiwan for the deployment of low-cost sensors and hourly PM2.5 measurements are collected for 60 days for conducting experiments. Three optimizers, Sobol sequence, Nelder and Meads, and particle swarm optimization (PSO), are compared for evaluating their performances with various versions of ensembles. The best calibration results are obtained by using PSO, and the improvement ratios with respect to R2, RMSE, and NME, are 4.92%, 52.96%, and 56.85%, respectively

Collaborative signal and information processing for target detection with heterogeneous sensor networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield

Accuracy analysis of direct georeferenced UAV images utilising low-cost navigation sensors

Unmanned aerial vehicles (UAVs), also known as unmanned airborne systems (UAS) or remotely piloted airborne systems (RPAS), are an established platform for close range airborne photogrammetry. Compared to manned platforms, the acquisition of local remote sensing data by UAVs is a convenient and very flexible option. For the application in photogrammetry UAVs are typically equipped with an autopilot and a lightweight digital camera. The autopilot includes several navigation sensors, which might allow an automated waypoint flight and offer a systematic data acquisition of the object resp. scene of interest. Assuming a sufficient overlap between the captured images, the position (3 coordinates: x, y, z) and the orientation (3 angles: roll, pitch, yaw) of the images can be estimated within a bundle block adjustment. Subsequently, coordinates of observed points that appear in at least two images, can be determined by measuring their image coordinates or a dense surface model can be generated from all acquired images by automated image matching. For the bundle block adjustment approximate values of the position and the orientation of the images are needed. To gather this information, several methods exist. We introduce in this contribution one of them: the direct georeferencing of images by using the navigation sensors (mainly GNSS and INS) of a low-cost on-board autopilot. Beside automated flights, the autopilot offers the possibility to record the position and the orientation of the platform during the flight. These values don’t correspond directly to those of the images. To compute the position and the orientation of the images two requirements must be fulfilled. First the misalignment angles and the positional differences between the camera and the autopilot must be determined (mounting calibration). Second the synchronization between the camera and the autopilot has to be established. Due to the limited accuracy of the navigation sensors, a small number of ground control points should be used to improve the estimated values, especially to decrease the amount of systematic errors. For the bundle block adjustment the calibration of the camera and their temporal stability must be determined additionally. This contribution presents next to the theory a practical study on the accuracy analysis of direct georeferenced UAV imagery by low-cost navigation sensors. The analysis was carried out within the research project ARAP (automated (ortho)rectification of archaeological aerial photographs). The utilized UAS consists of the airplane “MAJA”, manufactured by “Bormatec” (length: 1.2 m, wingspan: 2.2 m) equipped with the autopilot “ArduPilot Mega 2.5”. For image acquisition the camera “Ricoh GR Digital IV” is utilised. The autopilot includes a GNSS receiver capable of DGPS (EGNOS), an inertial measurement system (INS), a barometer, and a magnetometer. In the study the achieved accuracies for the estimated position and orientation of the images are presented. The paper concludes with a summary of the remaining error sources and their possible corrections by applying further improvements on the utilised equipment and the direct georeferencing process