A vision system for mobile maritime surveillance platforms

Mobile surveillance systems play an important role to minimise security and safety threats in high-risk or hazardous environments. Providing a mobile marine surveillance platform with situational awareness of its environment is important for mission success. An essential part of situational awareness is the ability to detect and subsequently track potential target objects.Typically, the exact type of target objects is unknown, hence detection is addressed as a problem of finding parts of an image that stand out in relation to their surrounding regions or are atypical to the domain. Contrary to existing saliency methods, this thesis proposes the use of a domain specific visual attention approach for detecting potential regions of interest in maritime imagery. For this, low-level features that are indicative of maritime targets are identified. These features are then evaluated with respect to their local, regional, and global significance. Together with a domain specific background segmentation technique, the features are combined in a Bayesian classifier to direct visual attention to potential target objects.The maritime environment introduces challenges to the camera system: gusts, wind, swell, or waves can cause the platform to move drastically and unpredictably. Pan-tilt-zoom cameras that are often utilised for surveillance tasks can adjusting their orientation to provide a stable view onto the target. However, in rough maritime environments this requires high-speed and precise inputs. In contrast, omnidirectional cameras provide a full spherical view, which allows the acquisition and tracking of multiple targets at the same time. However, the target itself only occupies a small fraction of the overall view. This thesis proposes a novel, target-centric approach for image stabilisation. A virtual camera is extracted from the omnidirectional view for each target and is adjusted based on the measurements of an inertial measurement unit and an image feature tracker. The combination of these two techniques in a probabilistic framework allows for stabilisation of rotational and translational ego-motion. Furthermore, it has the specific advantage of being robust to loosely calibrated and synchronised hardware since the fusion of tracking and stabilisation means that tracking uncertainty can be used to compensate for errors in calibration and synchronisation. This then completely eliminates the need for tedious calibration phases and the adverse effects of assembly slippage over time.Finally, this thesis combines the visual attention and omnidirectional stabilisation frameworks and proposes a multi view tracking system that is capable of detecting potential target objects in the maritime domain. Although the visual attention framework performed well on the benchmark datasets, the evaluation on real-world maritime imagery produced a high number of false positives. An investigation reveals that the problem is that benchmark data sets are unconsciously being influenced by human shot selection, which greatly simplifies the problem of visual attention. Despite the number of false positives, the tracking approach itself is robust even if a high number of false positives are tracked

ATLAS pixel detector electronics and sensors

The silicon pixel tracking system for the ATLAS experiment at the Large Hadron Collider is described and the performance requirements are summarized. Detailed descriptions of the pixel detector electronics and the silicon sensors are given. The design, fabrication, assembly and performance of the pixel detector modules are presented. Data obtained from test beams as well as studies using cosmic rays are also discussed

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

Augmented Reality Assistance for Surgical Interventions using Optical See-Through Head-Mounted Displays

Augmented Reality (AR) offers an interactive user experience via enhancing the real world environment with computer-generated visual cues and other perceptual information. It has been applied to different applications, e.g. manufacturing, entertainment and healthcare, through different AR media. An Optical See-Through Head-Mounted Display (OST-HMD) is a specialized hardware for AR, where the computer-generated graphics can be overlaid directly onto the user's normal vision via optical combiners. Using OST-HMD for surgical intervention has many potential perceptual advantages. As a novel concept, many technical and clinical challenges exist for OST-HMD-based AR to be clinically useful, which motivates the work presented in this thesis. From the technical aspects, we first investigate the display calibration of OST-HMD, which is an indispensable procedure to create accurate AR overlay. We propose various methods to reduce the user-related error, improve robustness of the calibration, and remodel the calibration as a 3D-3D registration problem. Secondly, we devise methods and develop hardware prototype to increase the user's visual acuity of both real and virtual content through OST-HMD, to aid them in tasks that require high visual acuity, e.g. dental procedures. Thirdly, we investigate the occlusion caused by the OST-HMD hardware, which limits the user's peripheral vision. We propose to use alternative indicators to remind the user of unattended environment motion. From the clinical perspective, we identified many clinical use cases where OST-HMD-based AR is potentially helpful, developed applications integrated with current clinical systems, and conducted proof-of-concept evaluations. We first present a "virtual monitor'' for image-guided surgery. It can replace real radiology monitors in the operating room with easier user control and more flexibility in positioning. We evaluated the "virtual monitor'' for simulated percutaneous spine procedures. Secondly, we developed ARssist, an application for the bedside assistant in robotic surgery. The assistant can see the robotic instruments and endoscope within the patient body with ARssist. We evaluated the efficiency, safety and ergonomics of the assistant during two typical tasks: instrument insertion and manipulation. The performance for inexperienced users is significantly improved with ARssist, and for experienced users, the system significantly enhanced their confidence level. Lastly, we developed ARAMIS, which utilizes real-time 3D reconstruction and visualization to aid the laparoscopic surgeon. It demonstrates the concept of "X-ray see-through'' surgery. Our preliminary evaluation validated the application via a peg transfer task, and also showed significant improvement in hand-eye coordination. Overall, we have demonstrated that OST-HMD based AR application provides ergonomic improvements, e.g. hand-eye coordination. In challenging situations or for novice users, the improvements in ergonomic factors lead to improvement in task performance. With continuous effort as a community, optical see-through augmented reality technology will be a useful interventional aid in the near future

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Allosteric ligands for the pharmacologically dark receptors GPR68 and GPR65

At least 120 non-olfactory G protein-coupled receptors in the human genome are ”orphans” for which endogenous ligands are unknown, and many have no selective ligands, hindering elucidation of their biological functions and clinical relevance. Among these is GPR68, a proton receptor that lacks small molecule modulators for probing its biology. Yeast-based screens against GPR68 identified the benzodiazepine drug lorazepam as a non-selective GPR68 positive allosteric modulator. Over 3000 GPR68 homology models were refined to recognize lorazepam in a putative allosteric site. Docking 3.1 million molecules predicted new GPR68 modulators many of which were confirmed in functional assays. One potent GPR68 modulator—ogerin– suppressed recall in fear conditioning in wild-type, but not in GPR68 knockout mice. The same approach led to the discovery of allosteric agonists and negative allosteric modulators for GPR65. Combining physical and structure-based screening may be broadly useful for ligand discovery for understudied and orphan GPCRs