High accuracy measurement of mechanical strain is critical and broadly practiced in several application areas including structural health monitoring, industrial process control, manufacturing, avionics and the automotive industry, to name a few. Strain sensors, otherwise known as strain gauges, are fueled by various nanomaterials, among which graphene has attracted great interest in recent years, due to its unique electro-mechanical characteristics. Graphene shows not only exceptional physical properties but also has remarkable mechanical properties, such as piezoresistivity, which makes it a perfect candidate for strain sensing applications. In the present review, we provide an in-depth overview of the latest studies focusing on graphene and its strain sensing mechanism along with various applications. We start by providing a description of the fundamental properties, synthesis techniques and characterization methods of graphene, and then build forward to the discussion of numerous types of graphene-based strain sensors with side-by-side tabular comparison in terms of figures-of-merit, including strain range and sensitivity, otherwise referred to as the gauge factor. We demonstrate the material synthesis, device fabrication and integration challenges for researchers to achieve both wide strain range and high sensitivity in graphene-based strain sensors. Last of all, several applications of graphene-based strain sensors for different purposes are described. All in all, the evolutionary process of graphene-based strain sensors in recent years, as well as the upcoming challenges and future directions for emerging studies are highlighted.

https://www.mdpi.com/2072-666X/13/1/119/pdf?version=1642063746

Graphene as a Piezoresistive Material in Strain Sensing Applications

The main topic of the present thesis is the improvement of fabrication processes simulation by means of the Level Set (LS) method. The LS is a mathematical approach used for evolving fronts according to a motion defined by certain laws. The main advantage of this method is that the front is embedded inside a higher dimensional function such that updating this function instead of directly the front itself enables a trivial handling of complex situations like the splitting or coalescing of multiple fronts. In particular, this document is focused on wet and dry etching processes, which are widely used in the micromachining process of Micro-Electro-Mechanical Systems (MEMS). A MEMS is a system formed by mechanical elements, sensors, actuators, and electronics. These devices have gained a lot of popularity in last decades and are employed in several industry fields such as automotive security, motion sensors, and smartphones. Wet etching process consists in removing selectively substrate material (e.g. silicon or quartz) with a liquid solution in order to form a certain structure. This is a complex process since the result of a particular experiment depends on many factors, such as crystallographic structure of the material, etchant solution or its temperature. Similarly, dry etching processes are used for removing substrate material, however, gaseous substances are employed in the etching stage. In both cases, the usage of a simulator capable of predicting accurately the result of a certain experiment would imply a significant reduction of design time and costs. There exist a few LS-based wet etching simulators but they have many limitations and they have never been validated with real experiments. On the other hand, atomistic models are currently considered the most advanced simulators. Nevertheless, atomistic simulators present some drawbacks like the requirement ofa prior calibration process in order to use the experimental data. Additionally, a lot of effort must be invested to create an atomistic model for simulating the etching process of substrate materials with different atomistic structures. Furthermore, the final result is always formed by unconnected atoms, which makes difficult a proper visualization and understanding of complex structures, thus, usually an additional visualization technique must be employed. For its part, dry etching simulators usually employ an explicit representation technique to evolve the surface being etched according to etching models. This strategy can produce unrealistic results, especially in complex situations like the interaction of multiple surfaces. Despite some models that use implicit representation have been published, they have never been directly compared with real experiments and computational performance of the implementations have not been properly analysed. The commented limitations are addressed in the various chapters of the present thesis, producing the following contributions: ? An efficient LS implementation in order to improve the visual representation of atomistic wet etching simulators. This implementation produces continuous surfaces from atomistic results. ? Definition of a new LS-based model which can directly use experimental data of many etchant solutions (such as KOH, TMAH, NH4HF2, and IPA and Triton additives) to simulate wet etching processes of various substrate materials (e.g. silicon and quartz). ? Validation of the developed wet etching simulator by comparing it to experimental and atomistic simulator results. ? Implementation of a LS-based tool which evolves the surface being etched according to dry etching models in order to enable the simulation of complex processes. This implementation is also validated experimentally. ? Acceleration of the developed wet and dry etching simulators by using Graphics Processing Units (GPUs). In this thesis, all the listed solutions require the definition of thorough studies and methodologies of design in order to develop efficient and correct implementations, including benchmark experiments against other existing models and/or experimental results.

/pdf/study-modelling-and-implementation-of-the-level-set-method-4g7ax3e911.pdf

Study, Modelling and Implementation of the Level Set Method Used in Micromachining Processes

In digital airborne electro-optical imagery, the identification of objects, particularly vehicles, has an important role in wide-area search and surveillance applications. We propose an identification and pose estimation approach based on maximising the correlation of features in an image with projections of 3D models. It has been applied to imagery collected in a controlled laboratory environment as well as imagery collected during airborne field trials. The results show good discrimination between different vehicle classes, although performance is degraded by vehicle camouflage and low-resolution imagery. Our approach is scalable, in terms of database size and feature sets, and computationally efficient.

Pose-invariant vehicle identification in aerial electro-optical imagery

A procedure for the design of digital filters with either a finite or infinite impulse response (FIR or IIR) is presented. Complex flatness constraints, incorporating the desired passband group delay, are used to specify the properties of non-linear-phase filters. Solutions are found using a linear combination of basis functions with zero or non-zero poles, for FIR or IIR filters, respectively. The procedure is used to design multidimensional filters for Lucas-Kanade optical flow.

Optimal Design of Digital IIR and FIR Filters Using Complex Flatness Constraints: A Unified Approach

This paper presents a wireless webcam based smart visual surveillance system with real-time moving object detection, classification and tracking capabilities for car burglar detection. This image processing based car detection system can handle object detection, classification and tracking in both indoor and outdoor environments. The classification algorithm makes use of the temporal tracking results and the shape of the detected objects to accurately classify objects into classes such as human, human group and vehicle. The purpose of designing a smart surveillance system that is based on the integration of motion detection and visual tracking is to achieve a better performance in helping the security officers to prevent car burglar crimes from escalating. Experimental results show that the proposed car burglar detection algorithm is able to achieve accuracy as high as 96.2% in optimum setting with routine time of 1.28 seconds.

Wireless webcam based car burglar detection system

The persistent airborne surveillance of large geographical areas is now a viable proposition. As well as providing cues to moving objects, it presents new opportunities for understanding the behaviours and motivations of people, both individually and collectively. Exploitation of these huge collections of imagery (a facet of the Big Data challenge) requires more effective tools to derive and abstract useful information to cue the analyst. This paper describes a new system which brings together a number of techniques: moving target detection; tracking; recognition and photogrammetry, to address wide area surveillance problems. We provide a first report on the demands this places on component parts and interfaces. Significantly, we adopt international interoperability standards, particularly with regard to video metadata, to constrain the solution space. We also describe new performance improvements to the video moving target indication and photogrammetry algorithms as well as analysing for the first time the performance of our integrated target model matching capability in our automated system.

Application of Detection and Recognition Algorithms to Persistent Wide Area Surveillance

This work addresses the problem of extracting semantics associated with multiple, cooperatively managed motion imagery sensors to support indexing and search of large imagery collections. The extracted semantics relate to the motion and identity of vehicles within a scene, viewed from aircraft and the ground. Semantic extraction required three steps: Video Moving Target Indication (VMTI), imagery fusion, and object recognition. VMTI used a previously published algorithm, with some novel modifications allowing detection and tracking in low frame rate, Wide Area Motion Imagery (WAMI), and Full Motion Video (FMV). Following this, the data from multiple sensors were fused to identify a highest resolution image, corresponding to each moving object. A final recognition stage attempted to fit each delineated object to a database of 3D models to determine its type. A proof-of-concept has been developed to allow processing of imagery collected during a recent experiment using a state of the art airborne surveillance sensor providing WAMI, with coincident narrower-area FMV sensors and simultaneous collection by a ground-based camera. An indication of the potential utility of the system was obtained using ground-truthed examples.

Shannon Fehlmann

Papers

Application of Detection and Recognition Algorithms to Persistent Wide Area Surveillance

Fusion of Multiple Sensor Data to Recognise Moving Objects in Wide Area Motion Imagery