Low-Cost Inventions and Patents

Inventions have led to the technological advances of mankind. There are inventions of all kinds, some of which have lasted hundreds of years or even longer. Low-cost technologies are expected to be easy to build, have little or no energy consumption, and be easy to maintain and operate. The use of sustainable technologies is essential in order to move towards a greater global coverage of technology, and therefore to improve human quality of life. Low-cost products always respond to a specific need, even if no in-depth analysis of the situation or possible solutions has been carried out. It is a consensus in all industrialized countries that patents have a decisive influence on the organization of the economy, as they are a key element in promoting technological innovation. Patents must aim to promote the technological development of countries, starting from their industrial situations

Autonomous mobility scooters as assistive tools for the elderly

The aim of this research is to investigate the development of an autonomous navigation system that could be used as an assistive tool for elderly and disabled people in their activities of daily living. The navigation environment is an urban environment and the platform is a Mobility Scooter (MoS). To achieve this aim, a differentially steered MoS was modifed to receive motion commands from a computer and outfitted with onboard sensors that included a Global Positioning System (GPS) receiver and two 2D planar laser range sensors. Perception methods were developed to detect the presence of an outdoor pedestrian walkway. These methods achieved this by processing the range data produced by the laser sensors to identify features that are typically found around walkways like curbs, low vegetation, walls and barriers. A method that utilises GPS localisation information to plan and navigate a route in an outdoor urban environment was also developed. Extensive experimental work was conducted to test the accuracy, repeatability and usefulness of the sensory devices. The developed perception methodologies were evaluated in real world environments while the navigation algorithms were predominantly tested in virtual environments. A navigation system that plans a route in an urban environment and follows it using behaviours arranged in a hierarchy is presented and shown to have the ability to safely navigate an MoS along an outdoor pedestrian path

Radiological and Nuclear Threat Detection Using Small Unmanned Aerial Systems

The aim of this research is to demonstrate the feasibility of remotely sensing nuclear and radiological threat materials by leveraging recent advances in radiation detectors, unmanned systems, and contextual sensors. The broad intent is to get detectors out of the hands of humans and onto semi-autonomous systems for a wide range of use cases. The search for special nuclear material is one specific mission area where radiation detectors employed on small unmanned aerial systems could provide significant operational value by exploiting the advantages that remote access enables: improved collection time, decreased source-to-detector distance, and reduced unintentional shielding. The goals of this study are fivefold: (1) assess current capabilities for directed search and substantiate the improvement that an unmanned approach would provide, (2) expand the understanding of the background radiation environment to include building rooftops, (3) establish system requirements and map out the parameter space of trade-offs (i.e., trade space) based on an analysis of current sensor and platform capabilities, (4) investigate and optimize search methods, and (5) identify and characterize additional mission areas for further investigation.To achieve these five goals, we started by identifying boundary conditions for signal collection time, source-to-detector distance (i.e., standoff), and intervening material attenuation for three different search modes: vehicle-mounted standoff detection, rotary-wing aerial detection, and small unmanned aerial system-based remote detection. The objective of this analysis was to calculate the theoretical reduction in detector area required to achieve the same minimum detectable activity of a Cs-137 source for a given detector material. We found that measuring from the rooftop with just 50 cm2 of detector area should detect smaller activity sources than 10,000 cm2 in a vehicle-borne approach or 5,000 cm2 in an aerial helicopter-borne approach.Our next objective was to characterize the background radiation environment sensed from the rooftops of light industrial buildings. We conducted a measurement campaign across fifteen buildings varying in geographic location, size, shape, height, wall construction, and roofing material. We discovered the variation in the background radiation ranged up to ±50% when analyzing contributions from seven prominent background peaks. Across a single building, this variation ranged 25–40% for contributions from potassium, uranium, and thorium. We also examined the attenuation of radiation by roofing materials both in simulation and experiment. We found that typical roof construction attenuates 1461 keV gamma-rays by approximately 50% when passing normal to the roof and continues to increase as the incident angle between the source and the detector increases. This observation directly influenced our approach to developing an optimal search scheme.With knowledge of the background and consideration of threat signatures, we then initiated an effort to develop a system architecture and design a sensor suite capable of detecting relevant threats in the anticipated environment. We employed established requirements analysis techniques to frame the development of a system that will provide tangible operational value to the user. We examined the trade space for platforms and sensors in terms of size, weight, power, cost, and visibility profile. Although our survey of capabilities is a snapshot in time, it lays the foundation for future analysis of alternatives. We recommend a platform that can move both through the air and on the ground and suggest further exploration of tube-launched systems for several military mission areas employing radiation sensors. For detectors, we recommend room temperature semi-conductors: cadmium zinc telluride for gamma-ray spectroscopy and lithium-backfilled etched-silicon diodes for neutron detection. Technologies such as real-time kinematic positioning, solid-state light depth and ranging, and thermal infrared cameras warrant further study as auxiliary contextual sensors in the system.Assuming an overmatched system is attainable, we then constructed a method to select advantageous measurement locations and developed techniques to optimize a search pattern. We devised a nonlinear programming routine and applied threshold cuts to reduce the time to converge to a near-optimal solution. We also explored several parameters that might be used as the objective quantity depending on the mission requirements and intelligence assessment.Finally, with the intent of removing humans from the task of operating detectors in elevated radiation areas, we sought to expand our inquiry to seven additional military mission areas. We briefly examined a historical vignette where unmanned radiation detection assets would have provided considerable value, summarized the general operational conditions, assessed the impact that remote detection might have on the speed, accuracy, fidelity, safety, or feasibility of a given mission, and identified unique challenges that might arise in developing a materiel solution. These additional areas are ripe for exploration and contribution from the broader community of researchers

Book of Abstracts

Book of Abstracts of the 5th European Turfgrass Society Conference, held in Salgados | Albufeira | Portugal. 6th - 8th, June, 2016.info:eu-repo/semantics/publishedVersio

Book of Abstracts

Book of Abstracts of the 5th European Turfgrass Society Conference, held in Salgados | Albufeira | Portugal. 6th - 8th, June, 2016.info:eu-repo/semantics/publishedVersio

RoboArch: Architectural Modelling for Robotic Applications

Robotic systems are being employed in a diverse range of applications, with both the scale and complexity of their software increasing through having to operate in unstructured environments and to provide higher levels of autonomy. In addition, the need for robotic systems to be verified grows as robots are used in applications where they can have significant safety implications. Verification of even small robotic systems software is a challenging problem. Therefore, additional techniques are required to enable the practitioners to produce verified robotic systems. The use of model-driven engineering and domain-specific languages (DSLs) have proven useful in the development of complex systems in other areas so applying them to the field of robotics can contribute to the goal of building reliable and safe systems. In this thesis we present RoboArch, a notation for describing the architectures and patterns of robotic systems software supported by the formally defined semantics of RoboChart. RoboChart is a DSL for modelling the behaviour of robot software controllers using state machines. We describe RoboArch from the top-down. First, we examine the role of robotics software architectures in the development of robotic systems by reviewing five robotics architectures, and five DSLs. Next, for the layered architectural pattern, the RoboArch notation is introduced; we provide a metamodel, well-formedness conditions, and transformation rules to RoboChart. Further, we characterise two patterns: reactive skills and subsumption, which can be used by a layer. Finally, we discuss a tool and its implementation for the evaluation of RoboArch and automation of the rules as model transformations. We use a case study of a small obstacle avoidance system to demonstrate: the application of the reactive skills pattern using RoboArch and the expected properties of the architecture that can be proven using the generated RoboChart model CSP semantics

ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL VOC (per-category comparisons in Table 3, distribution of localization difficulty in Fig 16), a list of queries used for obtaining object detection images (Appendix C), and some additional reference