Design of an Automated Machine that Projects Ultraviolet Rays for the Safety of Food Products for Supermarkets

Abstract—This research presents the design and control of a machine for use in shopping centers and markets, which has been designed using mechatronic systems and artificial intelligence. In this research, a control is established for each moment of use, thus being more autonomous and efficient. The development of the project shows that the action of a reconnaissance camera with artificial intelligence before the disinfection chamber and mechanized pivoting arms at the outlet improves the autonomy of the machine. Also, a control panel was added that regulates the selection and control process in case of inconveniences; In addition, the type C UV disinfection chamber is covered with ABS polymer to prevent the rays from going outside. From the above, the mechatronic system implemented will improve the quality and disinfection time of the products in supermarkets

QuickSAT/Autonomy: A Framework of Autonomy APIs and System for Small Satellites to Support Tactical Intelligence, Surveillance, and Reconnaissance and Vehicle Health Management Functions

The QuickSAT/Autonomy is a framework of autonomy APIs and system with flight computer designed for cubesats to support Tactical Intelligence, Surveillance, and Reconnaissance (ISR) functions plus generic autonomy related functions such as vehicle health management. The system allows the cubesat through a range of sensors such as a hyperspectral camera to collect data, “perceive” key select information, and alert to the ground what is “sensed”, relaying only the required critical data. Operators can later download larger data sets as needed. This will allow the streamlining what is communicated to the ground in a timely manner. Communications is a critical bottleneck in small satellites such as cubesats – on board autonomy can rapidly assist in ISR functions. Distributed systems using formation flying satellites can use synergy of autonomous payloads on-board of different satellites instead of a multiplying effect of constellations in order to enhance coverage. The open framework allows the QuickSAT/Autonomy System with our LinkStar avionics system is to provide a flexible vehicle management system, a range of autonomy tools, global communications, and accurate and rapid tracking of 1U and larger cubesats and small satellites, providing exceptional situational awareness and ISR support. QuickSAT/Autonomy provides a database architecture to support the knowledge base built on the ground and during flight, a Bayesian Network learning framework, and supporting Apache Airflow based tools to name a few of the on-board Artificial Intelligence/Machine Learning (ML) functions within QuickSAT/Autonomy. The heart of the LinkStar avionics system is provided by either the BeagleBone Black AI module for Cubesats and power constrained vehicles OR the Xilinx Zynq UltraScale+ MPSoC chipbased FRNCS computer for larger Cubesats and Small Satellites, a system that combines a quad core ARM-53 processor and Zynq-7000 FPGA. LinkStar supports I2C, I2C multiplexer, Space Wire, serial, USB and CAN allowing for a range of sensors to be connected to the QuickSAT/Autonomy architecture; other data buses can be easily infused into the design. LinkStar also includes smart power management software set of APIs, software rad-hardening tools, and QuickSAT/Xen hypervisor to enable multiple, secure operating systems on the processor. The board design is a PC104 format with full support for the Cubesat bus. The integrated flight computer hosts the QuickSAT/VMS system providing vehicle control, communications, and instrument management functions. QuickSAT/VMS provides a web based interface for easy vehicle configuration, system testing, and management. Supporting APIs are provided with the system. For this presentation we will present the framework and the overall architecture

Governance of artificial intelligence and personal health information

Peer-reviewed journal article: Winter, J. S., & Davidson, E. (2019). “Governance of artificial intelligence and personal health information.” Digital Policy, Regulation and Governance (DPRG), 21(3), 280-290. Special issue on “Artificial Intelligence: Beyond the hype?” doi:10.1108/DPRG-08-2018-0048Purpose – This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning innovations in health care. Risks to privacy and justice/equity are discussed, along with potential solutions. Design/methodology/approach – This conceptual paper highlights the scale and scope of PHI data consumed by deep learning algorithms and their opacity as novel challenges to health data governance. Findings – This paper argues that these characteristics of machine learning will overwhelm existing data governance approaches such as privacy regulation and informed consent. Enhanced governance techniques and tools will be required to help preserve the autonomy and rights of individuals to control their PHI. Debate among all stakeholders and informed critique of how, and for whom, PHI-fueled health AI are developed and deployed are needed to channel these innovations in societally beneficial directions. Social implications – Health data may be used to address pressing societal concerns, such as operational and system-level improvement, and innovations such as personalized medicine. This paper informs work seeking to harness these resources for societal good amidst many competing value claims and substantial risks for privacy and security. Originality/value – This is the first paper focusing on health data governance in relation to AI/machine learning. Keywords – Big data, Governance, Artificial intelligence, Deep learning, Personal health informatio

Double elevation: Autonomous weapons and the search for an irreducible law of war

What should be the role of law in response to the spread of artificial intelligence in war? Fuelled by both public and private investment, military technology is accelerating towards increasingly autonomous weapons, as well as the merging of humans and machines. Contrary to much of the contemporary debate, this is not a paradigm change; it is the intensification of a central feature in the relationship between technology and war: Double elevation, above one's enemy and above oneself. Elevation above one's enemy aspires to spatial, moral, and civilizational distance. Elevation above oneself reflects a belief in rational improvement that sees humanity as the cause of inhumanity and de-humanization as our best chance for humanization. The distance of double elevation is served by the mechanization of judgement. To the extent that judgement is seen as reducible to algorithm, law becomes the handmaiden of mechanization. In response, neither a focus on questions of compatibility nor a call for a 'ban on killer robots' help in articulating a meaningful role for law. Instead, I argue that we should turn to a long-standing philosophical critique of artificial intelligence, which highlights not the threat of omniscience, but that of impoverished intelligence. Therefore, if there is to be a meaningful role for law in resisting double elevation, it should be law encompassing subjectivity, emotion and imagination, law irreducible to algorithm, a law of war that appreciates situated judgement in the wielding of violence for the collective

An Evaluation Schema for the Ethical Use of Autonomous Robotic Systems in Security Applications

We propose a multi-step evaluation schema designed to help procurement agencies and others to examine the ethical dimensions of autonomous systems to be applied in the security sector, including autonomous weapons systems

Investigating the use of unmanned plant machinery on construction sites

The UK Construction Sector has been estimated to contribute 8% of the UK’s GDP [1]. The worldwide recession has forced construction companies to introduce and adopt cost saving measures to increase productivity. Several robotic building systems are in development for the Construction Sector such as the PERI’s Automatic Climbing System [2] and Brokk’s remote-controlled demolition machines [3], but there has been little implementation on live sites. Construction sites by their very nature are dynamically changing environments, so if human input was removed entirely, a robot would need a high level of awareness of the current state of the building project in order to navigate and carry out its task

Autonomous Systems as Legal Agents: Directly by the Recognition of Personhood or Indirectly by the Alchemy of Algorithmic Entities

The clinical manifestations of platelet dense (δ) granule defects are easy bruising, as well as epistaxis and bleeding after delivery, tooth extractions and surgical procedures. The observed symptoms may be explained either by a decreased number of granules or by a defect in the uptake/release of granule contents. We have developed a method to study platelet dense granule storage and release. The uptake of the fluorescent marker, mepacrine, into the platelet dense granule was measured using flow cytometry. The platelet population was identified by the size and binding of a phycoerythrin-conjugated antibody against GPIb. Cells within the discrimination frame were analysed for green (mepacrine) fluorescence. Both resting platelets and platelets previously stimulated with collagen and the thrombin receptor agonist peptide SFLLRN was analysed for mepacrine uptake. By subtracting the value for mepacrine uptake after stimulation from the value for uptake without stimulation for each individual, the platelet dense granule release capacity could be estimated. Whole blood samples from 22 healthy individuals were analysed. Mepacrine incubation without previous stimulation gave mean fluorescence intensity (MFI) values of 83±6 (mean ± 1 SD, range 69–91). The difference in MFI between resting and stimulated platelets was 28±7 (range 17–40). Six members of a family, of whom one had a known δ-storage pool disease, were analysed. The two members (mother and son) who had prolonged bleeding times also had MFI values disparate from the normal population in this analysis. The values of one daughter with mild bleeding problems but a normal bleeding time were in the lower part of the reference interval