Real-time, noise and drift resilient formaldehyde sensing at room temperature with aerogel filaments

Formaldehyde, a known human carcinogen, is a common indoor air pollutant. However, its real-time and selective recognition from interfering gases remains challenging, especially for low-power sensors suffering from noise and baseline drift. We report a fully 3D-printed quantum dot/graphene-based aerogel sensor for highly sensitive and real-time recognition of formaldehyde at room temperature. By optimising the morphology and doping of the printed structures, we achieve a record-high response of 15.23 percent for 1 parts-per-million formaldehyde and an ultralow detection limit of 8.02 parts-per-billion consuming only 130 uW power. Based on measured dynamic response snapshots, we also develop an intelligent computational algorithm for robust and accurate detection in real time despite simulated substantial noise and baseline drift, hitherto unachievable for room-temperature sensors. Our framework in combining materials engineering, structural design and computational algorithm to capture dynamic response offers unprecedented real-time identification capabilities of formaldehyde and other volatile organic compounds at room temperature.Comment: Main manuscript: 21 pages, 5 figure. Supplementary: 21 pages. 13 Figures, 2 tabl

TFT & ULSIC: Interfacing large-area thin-film sensor arrays with CMOS circuits

Large-area, conformable sensing surfaces could find many applications by interfacing humans or machines users with their environment. Given the success of TFT backplanes for flat-panel displays, a promising approach is the fabrication of large integrated thin-film sensor arrays on single substrates. In thin-film technology the number of sensors can be made very large, and they can be deployed on rigid or flexible, conformably shapeable or even elastically stretchable substrates. Flat-panel displays suggest that TFT integration can be less costly than arrays made by placing and interconnecting discrete sensels. Equally important is that low-temperature thin-film technology can accommodate the diversity of materials required by the various sensor technologies. However, thin-film devices and circuits are slow. TFT circuits cannot compete directly with ULSI circuits in controlling large sensor arrays, or in signal processing and extracting the germane information from the huge number of signals that such arrays can generate. To combine the advantages of large-area integrated TFT circuits with the speed of ULSI circuits, we have been making hybrid systems that combine TFT and ULSIC [1]. Our work covers the range from thin-film device materials to subsystems implemented in thin-film technology, to co-designing and interfacing the large-area thin-film domain with the ULSIC domain. We have demonstrated systems for the sensing of mechanical strain [2], image detection [3], acoustic speaker localization [4], electro-encephalography [5], gestures [6], and patterns of pressure. Please click Additional Files below to see the full abstract

Signal and Information Processing Methods for Embedded Robotic Tactile Sensing Systems

The human skin has several sensors with different properties and responses that are able to detect stimuli resulting from mechanical stimulations. Pressure sensors are the most important type of receptors for the exploration and manipulation of objects. In the last decades, smart tactile sensing based on different sensing techniques have been developed as their application in robotics and prosthetics is considered of huge interest, mainly driven by the prospect of autonomous and intelligent robots that can interact with the environment. However, regarding object properties estimation on robots, hardness detection is still a major limitation due to the lack of techniques to estimate it. Furthermore, finding processing methods that can interpret the measured information from multiple sensors and extract relevant information is a Challenging task. Moreover, embedding processing methods and machine learning algorithms in robotic applications to extract meaningful information such as object properties from tactile data is an ongoing challenge, which is controlled by the device constraints (power constraint, memory constraints, etc.), the computational complexity of the processing and machine learning algorithms, the application requirements (real-time operations, high prediction performance). In this dissertation, we focus on the design and implementation of pre-processing methods and machine learning algorithms to handle the aforementioned challenges for a tactile sensing system in robotic application. First, we propose a tactile sensing system for robotic application. Then we present efficient preprocessing and feature extraction methods for our tactile sensors. Then we propose a learning strategy to reduce the computational cost of our processing unit in object classification using sensorized Baxter robot. Finally, we present a real-time robotic tactile sensing system for hardness classification on a resource-constrained devices. The first study represents a further assessment of the sensing system that is based on the PVDF sensors and the interface electronics developed in our lab. In particular, first, it presents the development of a skin patch (multilayer structure) that allows us to use the sensors in several applications such as robotic hand/grippers. Second, it shows the characterization of the developed skin patch. Third, it validates the sensing system. Moreover, we designed a filter to remove noise and detect touch. The experimental assessment demonstrated that the developed skin patch and the interface electronics indeed can detect different touch patterns and stimulus waveforms. Moreover, the results of the experiments defined the frequency range of interest and the response of the system to realistic interactions with the sensing system to grasp and release events. In the next study, we presented an easy integration of our tactile sensing system into Baxter gripper. Computationally efficient pre-processing techniques were designed to filter the signal and extract relevant information from multiple sensor signals, in addition to feature extraction methods. These processing methods aim in turn to reduce also the computational complexity of machine learning algorithms utilized for object classification. The proposed system and processing strategy were evaluated on object classification application by integrating our system into the gripper and we collected data by grasping multiple objects. We further proposed a learning strategy to accomplish a trade-off between the generalization accuracy and the computational cost of the whole processing unit. The proposed pre-processing and feature extraction techniques together with the learning strategy have led to models with extremely low complexity and very high generalization accuracy. Moreover, the support vector machine achieved the best trade-off between accuracy and computational cost on tactile data from our sensors. Finally, we presented the development and implementation on the edge of a real–time tactile sensing system for hardness classification on Baxter robot based on machine and deep learning algorithms. We developed and implemented in plain C a set of functions that provide the fundamental layer functionalities of the Machine learning and Deep Learning models (ML and DL), along with the pre–processing methods to extract the features and normalize the data. The models can be deployed to any device that supports C code since it does not rely on any of the existing libraries. Shallow ML/DL algorithms for the deployment on resource–constrained devices are designed. To evaluate our work, we collected data by grasping objects of different hardness and shape. Two classification problems were addressed: 5 levels of hardness classified on the same objects’ shape, and 5 levels of hardness classified on two different objects’ shape. Furthermore, optimization techniques were employed. The models and pre–processing were implemented on a resource constrained device, where we assessed the performance of the system in terms of accuracy, memory footprint, time latency, and energy consumption. We achieved for both classification problems a real-time inference (< 0.08 ms), low power consumption (i.e., 3.35 μJ), extremely small models (i.e., 1576 Byte), and high accuracy (above 98%)

Care-Chair: Opportunistic health assessment with smart sensing on chair backrest

A vast majority of the population spend most of their time in a sedentary position, which potentially makes a chair a huge source of information about a person\u27s daily activity. This information, which often gets ignored, can reveal important health data but the overhead and the time consumption needed to track the daily activity of a person is a major hurdle. Considering this, a simple and cost-efficient sensory system, named Care-Chair, with four square force sensitive resistors on the backrest of a chair has been designed to collect the activity details and breathing rate of the users. The Care-Chair system is considered as an opportunistic environmental sensor that can track each and every activity of its occupant without any human intervention. It is specifically designed and tested for elderly people and people with sedentary job. The system was tested using 5 users data for the sedentary activity classification and it successfully classified 18 activities in laboratory environment with 86% accuracy. In an another experiment of breathing rate detection with 19 users data, the Care-Chair produced precise results with slight variance with ground truth breathing rate. The Care-Chair yields contextually good results when tested in uncontrolled environment with single user data collected during long hours of study. --Abstract, page iii

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs

Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey

Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning. This opens up an opportunity to use modern machine learning tools on large-scale nonlinear monitoring and control problems. This article provides a survey of recent results with applications in the process industry.Comment: IFAC World Congress 202

Machine Analysis of Facial Expressions

No abstract

Development of an optical sensor for real-time weed detection using laser based spectroscopy

The management of weeds in agriculture is a time consuming and expensive activity, including in Australia where the predominant strategy is blanket spraying of herbicides. This approach wastes herbicide by applying it in areas where there are no weeds. Discrimination of different plant species can be performed based on the spectral reflectance of the leaves. This thesis describes the development of a sensor for automatic spot spraying of weeds within crop rows. The sensor records the relative intensity of reflected light in three narrow wavebands using lasers as an illumination source. A prototype weed sensor which had been previously developed was evaluated and redesigned to improve its plant discrimination performance. A line scan image sensor replacement was chosen which reduced the noise in the recorded spectral reflectance properties. The switching speed of the laser sources was increased by replacing the laser drivers. The optical properties of the light source were improved to provide a more uniform illumination across the viewing area of the sensor. A new opto-mechanical system was designed and constructed with the required robustness to operate the weed sensor in outdoor conditions. Independent operation of the sensor was made possible by the development of hardware and software for an embedded controller which operated the opto-electronic components and performed plant discrimination. The first revised prototype was capable of detecting plants at a speed of 10 km/h in outdoor conditions with the sensor attached to a quad bike. However, it was not capable of discriminating different plants. The final prototype included a line scan sensor with increased dynamic range and pixel resolution as well as improved stability of the output laser power. These changes improved the measurement of spectral reflectance properties of plants and provided reliable discrimination of three different broadleaved plants using only three narrow wavelength bands. A field trial with the final prototype demonstrated successful discrimination of these three different plants at 5 km/h when a shroud was used to block ambient light. A survey of spectral reflectance of four crops (sugarcane, cotton, wheat and sorghum) and the weeds growing amongst these crops was conducted to determine the potential for use of the prototype weed sensor to control spot-spraying of herbicides. Visible reflectance spectra were recorded from individual leaves using a fibre spectrometer throughout the growing season for each crop. A discriminant analysis was conducted based on six narrow wavebands extracted from leaf level spectral reflectance measured with a spectrometer. The analysis showed the potential to discriminate cotton and sugarcane fro

Non-destructive Testing in Civil Engineering

This Special Issue, entitled “Non-Destructive Testing in Civil Engineering”, aims to present to interested researchers and engineers the latest achievements in the field of new research methods, as well as the original results of scientific research carried out with their use—not only in laboratory conditions but also in selected case studies. The articles published in this Special Issue are theoretical–experimental and experimental, and also show the practical nature of the research. They are grouped by topic, and the main content of each article is briefly discussed for your convenience. These articles extend the knowledge in the field of non-destructive testing in civil engineering with regard to new and improved non-destructive testing (NDT) methods, their complementary application, and also the analysis of their results—including the use of sophisticated mathematical algorithms and artificial intelligence, as well as the diagnostics of materials, components, structures, entire buildings, and interesting case studies