Abstract
The W. M. Keck Observatory Adaptive Optics (AO) facilities have been operating with a Field Programmable Gate Array (FPGA) based real time controller (RTC) since 2007. The RTC inputs data from various AO wavefront and tip/tilt sensors; and corrects image blurring from atmospheric turbulence via deformable and tip/tilt mirrors. Since its commissioning, the Keck I and Keck II RTCs have been upgraded to support new hardware such as pyramid wavefront and infrared tip-tilt sensors. However, they are reaching the limits of their capabilities in terms of processing bandwidth and the ability to interface with new hardware. Together with the Keck All-sky Precision Adaptive optics (KAPA) project, a higher performance and a more reliable RTC is needed to support next generation capabilities such as laser tomography and sensor fusion. This paper provides an overview of the new RTC system, developed with our contractor/collaborators (Microgate, Swinburne University of Technology and Australian National University), and the initial on-sky performance. The upgrade includes an Interface Module to interface with the wavefront sensors and controlled hardware, and a Graphical Processing Unit (GPU) based computational engine to meet the system's control requirements and to provide a flexible software architecture to allow future algorithms development and capabilities. The system saw first light in 2021 and is being commissioned in 2022 to support single conjugate laser guide star (LGS) AO, along with a more sensitive EMCCD camera. Initial results are provided to demonstrate single NGS & LGS performance, system reliability, and the planned upgrade for four LGS to support laser tomography.

Abstract
This research proposes positioning obstacle detection sensors by multirotor unmanned aerial vehicles (UAVs) dedicated to detailed inspections in high voltage towers. Different obstacle detection sensors are analyzed to compose a multisensory architecture in a multirotor UAV. The representation of the beam pattern of the sensors is modeled in the CoppeliaSim simulator to analyze the sensors' coverage and detection performance in simulation. A multirotor UAV is designed to carry the same sensor architecture modeled in the simulation. The aircraft is used to perform flights over a deactivated electrical tower, aiming to evaluate the detection performance of the sensory architecture embedded in the aircraft. The results obtained in the simulation were compared with those obtained in a real scenario of electrical inspections. The proposed method achieved its goals as a mechanism to early evaluate the detection capability of different previously characterized sensor architectures used in multirotor UAV for electrical inspections.

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Abstract
The paper presents the work carried out at the HCI Engineering Education workshop, organised by IFIP working groups 2.7/13.4 and 13.1. It describes four case studies of projects and exercises used in Human-Computer Interaction Engineering courses. We propose a common framework for presenting the case studies and describe the four case studies in detail. We then draw conclusions on the differences between the presented case studies that highlight the diversity and multidisciplinary aspects to be taught in a Human-Computer Interaction Engineering course. As future work, we plan to create a repository of case studies as a resource for teachers.

Abstract
The 3rd International Congress on Blockchain and Applications 2021 will be held in Salamanca from 6 to 8 of October. This annual congress will reunite blockchain and artificial intelligence (AI) researchers, who will share ideas, projects, lectures, and advances associated with those technologies and their application domains. Among the scientific community, blockchain and AI are seen as a promising combination that will transform the production and manufacturing industry, media, finance, insurance, e-government, etc. Nevertheless, there is no consensus with schemes or best practices that would specify how blockchain and AI should be used together. Combining blockchain mechanisms and artificial intelligence is still a particularly challenging task. The BLOCKCHAIN’21 congress is devoted to promoting the investigation of cutting-edge blockchain technology, to exploring the latest ideas, innovations, guidelines, theories, models, technologies, applications and tools of blockchain and AI for the industry, and to identifying critical issues and challenges those researchers and practitioner must deal with in the future research. We want to offer researchers and practitioners the opportunity to work on promising lines of research and to publish their developments in this area. The technical program has been diverse and of high quality, and it focused on contributions to both, well-established and evolving areas of research. More than 44 papers have been submitted to 38 from over 20 different countries (Canada, France, Germany, India, Ireland, Italy, Jordan, Luxembourg, Malaysia, Malta, Morocco, Netherlands, Oman, Portugal, Slovenia, Spain, Sweden, United Arab Emirates, and USA). We would like to thank all the contributing authors, the members of the Program Committee, the sponsors (IBM, Indra, EurAI, AEPIA, AFIA, APPIA, and AIR Institute), and the Organizing Committee for their hard and highly valuable work. We are especially grateful for the funding supporting by project “XAI - XAI - Sistemas Inteligentes Auto Explicativos creados con Módulos de Mezcla de Expertos,” ID SA082P20, financed by Junta Castilla y León, Consejería de Educación, and FEDER funds. Their work contributed to the success of the BLOCKCHAIN’21 event and, finally, the Local Organization Members and the Program Committee Members for their hard work, which was essential for the success of BLOCKCHAIN’21.

Abstract
The development of foot ulcers is associated with the Diabetic Foot (DF), which is a problem detected in patientswith Diabetes Mellitus (DM). Several studies demonstrate that thermography is a technique that can be used to identify and monitor the DF problems, thus helping to analyze the possibility of ulcers arising, as tissue inflammation causes temperature variation. There is great interest in developing methods to detect abnormal plantar temperature changes, since healthy individuals generally show characteristic patterns of plantar temperature variation and that the plantar temperature distribution of DF tissues does not followa specific pattern, so temperature variations are difficult to measure. In this sequel, a methodology, that uses thermograms to analyze the diversity of thermal changes that exist in the plant of a foot and classifies it as being from an individual with possibility of ulcer arising or not, is presented in this paper. Therefore, the concept of clustering is used to propose binary classifiers with different descriptors, obtained using two clustering algorithms, to predict the risk of ulceration in a foot. Moreover, for each descriptor, a numerical indicator and a classification thresholder are presented. In addition, using a combination of two different descriptors, a hybrid quantitative indicator is presented. A public dataset (containing 90 thermograms of the sole of the foot healthy people and 244 of DM patients) was used to evaluate the performance of the classifiers; using the hybrid quantitative indicator and the k-means clustering, the following metrics were obtained: Accuracy = 80%, AUC = 87% and F-measure = 86%.