Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs

Low-resolution infrared (IR) array sensors offer a low-cost, low-power, and privacy-preserving alternative to optical cameras and smartphones/wearables for social distance monitoring in indoor spaces, permitting the recognition of basic shapes, without revealing the personal details of individuals. In this work, we demonstrate that an accurate detection of social distance violations can be achieved processing the raw output of a 8x8 IR array sensor with a small-sized Convolutional Neural Network (CNN). Furthermore, the CNN can be executed directly on a Microcontroller (MCU)-based sensor node. With results on a newly collected open dataset, we show that our best CNN achieves 86.3% balanced accuracy, significantly outperforming the 61% achieved by a state-of-the-art deterministic algorithm. Changing the architectural parameters of the CNN, we obtain a rich Pareto set of models, spanning 70.5-86.3% accuracy and 0.18-75k parameters. Deployed on a STM32L476RG MCU, these models have a latency of 0.73-5.33ms, with an energy consumption per inference of 9.38-68.57µJ

Teaching Hardware Reverse Engineering: Educational Guidelines and Practical Insights

Since underlying hardware components form the basis of trust in virtually any computing system, security failures in hardware pose a devastating threat to our daily lives. Hardware reverse engineering is commonly employed by security engineers in order to identify security vulnerabilities, to detect IP violations, or to conduct very-large-scale integration (VLSI) failure analysis. Even though industry and the scientific community demand experts with expertise in hardware reverse engineering, there is a lack of educational offerings, and existing training is almost entirely unstructured and on the job. To the best of our knowledge, we have developed the first course to systematically teach students hardware reverse engineering based on insights from the fields of educational research, cognitive science, and hardware security. The contribution of our work is threefold: (1) we propose underlying educational guidelines for practice-oriented courses which teach hardware reverse engineering; (2) we develop such a lab course with a special focus on gate-level netlist reverse engineering and provide the required tools to support it; (3) we conduct an educational evaluation of our pilot course. Based on our results, we provide valuable insights on the structure and content necessary to design and teach future courses on hardware reverse engineering

Low-cost Efficient Wireless Intelligent Sensor (LEWIS) for Engineering, Research, and Education

Sensors have the capability of collecting engineering data and quantifying environmental changes, activities, or phenomena. Civil engineers lack of knowledge in sensor technology. Therefore, the vision of smart cities equipped with sensors informing decisions has not been realized to date. The cost associated with data acquisition systems, laboratories, and experiments restricts access to sensors for wider audiences. Recently, sensors are becoming a new tool in education and training, giving learners real-time information that can reinforce their confidence and understanding of scientific or engineering new concepts. However, the electrical components and computer knowledge associated with sensors are still a challenge for civil engineers. If sensing technology costs and use are simplified, sensors could be tamed by civil engineering students. The researcher developed, fabricated, and tested an efficient low-cost wireless intelligent sensor (LEWIS) aimed at education and research, named LEWIS1. This platform is directed at learners connected with a cable to the computer but has the same concepts and capabilities as the wireless version. The content of this paper describes the hardware and software architecture of the first prototype and their use, as well as the proposed new LEWIS1 (LEWIS1 beta) that simplifies both hardware and software, and user interfaces. The capability of the proposed sensor is compared with an accurate commercial PCB sensor through experiments. The later part of this paper demonstrates applications and examples of outreach efforts and suggests the adoption of LEWIS1 beta as a new tool for education and research. The authors also investigated the number of activities and sensor building workshops that has been done since 2015 using the LEWIS sensor which shows an ascending trend of different professionals excitement to involve and learn the sensor fabrication.Comment: 19 pages, 17 figures, 7 table

Internet of Things. Information Processing in an Increasingly Connected World

This open access book constitutes the refereed post-conference proceedings of the First IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2018, held at the 24th IFIP World Computer Congress, WCC 2018, in Poznan, Poland, in September 2018. The 12 full papers presented were carefully reviewed and selected from 24 submissions. Also included in this volume are 4 WCC 2018 plenary contributions, an invited talk and a position paper from the IFIP domain committee on IoT. The papers cover a wide range of topics from a technology to a business perspective and include among others hardware, software and management aspects, process innovation, privacy, power consumption, architecture, applications

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

Effect of incremental pattern transformation strategy on academic achievement, job task performance and learning satisfaction among vocational trainees

Acquisition of conceptual and procedural knowledge is often challenging especially when the content deals with symbolic representations whereas not limited to memorizing the symbol but also requires a learner to have multiple factual knowledge associated with the real representation. The aim of this study was to test the effect of using incremental pattern transformation materials (iOST) – theoretically grounded materials - on conceptual and procedural knowledge acquisition among vocational trainees. The iOST was designed to enhance trainees’ learning of symbolic representations in electrical circuit diagrams. The quasi-experimental design method was used with 110 vocational trainees who were taking the vehicle air conditioning course from two vocational training centres. Trainees were divided into three groups, two treatment groups (assigned to paper-based iOST and animation-based iOST) and one control group. The duration of study was six weeks. Pre-test and post-test were used for assessing academic achievement, while practical test and a questionnaire were used for job task performance and learning satisfaction respectively. The ANCOVA, Chi-Square Test and Mann-Whitney U Test were used to test for differences between groups on academic achievement, job task performance and learning satisfaction respectively while the Spearman's rank-order correlation method was used to assess associations between learning satisfaction, conceptual and procedural knowledge. The results show that the experimental groups are better on academic achievement (effect size = .329) and job task performance (effect size = .657) with both groups exhibiting high learning satisfaction. The findings indicate that the iOST materials (irrespective of media) are effective in promoting learning of symbolic representation and support the acquisition of conceptual and procedural knowledge for better task performance. The findings also suggest that, appropriately designed learning materials can support learning and job performance

Toward next-generation engineering education: A case study of an engineering capstone project based on BIM technology in MEP systems

Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.Supporting Information is available online at: https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fcae.22448&file=cae22448-sup-0001-Appendices.docx .To respond to the digital building environment and Industry 4.0 in building information modeling (BIM)-based mechanical, electrical, and plumbing (MEP) systems this study developed a new approach to engineering capstone projects. This article reports on a case study of the development of the innovative capstone project for engineering majors, which is based on team-based learning (TBL) combined with the 360-degree evaluation feedback method to cultivate students' BIM competency. The data collection and analysis involves a combination of qualitative and quantitative methods, which aims to evaluate students' learning outcomes and their BIM competency. The results indicate that TBL, combined with the 360-degree evaluation feedback in the capstone project, can be highly effective in improving graduates' BIM competency. This study discusses the interoperability of BIM software tools, problems in the process of data exchange, and provides suggestions for improving the course and BIM team collaboration. The research has identified how professional capabilities for students can be enhanced, enabled through a capstone project, and educators are able to deploy the BIM course to develop engineers that closely meet industry needs. The study provides the case for using the capstone project to improve and cultivate engineering students' BIM competency in MEP systems. This study has provided a new paradigm for applying TBL and 360-degree evaluation feedback to engineering education.2020 UK-China-BRI (Vietnam/Indonesia) Education Partnership Initiative; Emerging Engineering Education Research and Practice Project of Ministry of Education of the People's Republic of China. Grant Number: E-GKRWJC20202914; Shanxi Province Higher Education Teaching Reform Project. Grant Number: 19BZ016; National Social Science Fund projects. Grant Number: 20BJY010; National Social Science Fund Post-financing projects. Grant Number: 19FJYB017; Education Funding of Master of Engineering Management in China. Grant Number: 2017-ZX-004; Shaanxi Social Science Fund. Grant Number: 2017S004; List of Key Science and Technology Projects in China's Transportation Industry in 2018-International Science and Technology Cooperation Project. Grant Numbers: 2018-GH-006, 2019-MS5-100; Special Fund for Graduate Student Education Reform of Central College, Chang'an University. Grant Numbers: 300103190943, 300103187091, 300103190413, 300103190018, 300103302301, 300111002005, 310628161406; Fundamental Research for Education Reform of Central College, Chang'an University. Grant Numbers: 300103292806, 300104292305, 300104292304, 300104292308, 300104282301, 300104282318, 300104282323, 310623172904, 310623171003, 310623171633; Fundamental Research for Funds for the Central Universities (Humanities and Social Sciences), Chang'an University. Grant Numbers: 300102231641, 300102230612, 300102281669

Microgrid Energy Management using Weather Forecasts: Case Study, Discussion and Challenges

The main objective of this study is to demonstrate the integration of weather forecasts which can lead to a significant reduction in energy costs and carbon emissions while ensuring the reliability of the microgrid operation. By serving a small area or a particular building, the incorporation of weather forecasts can considerably increase the efficiency of microgrid energy management. The planning and operation of microgrids can be greatly improved by using weather predictions, which give useful information about upcoming weather conditions. By forecasting future energy demand and supply based on meteorological conditions, Microgrid Energy Management (MEM) is utilized to optimize the energy management decisions in microgrid systems. Making better choices regarding energy generation, storage, and consumption may be aided by the incorporation of weather forecasts, which can offer a more precise and trustworthy estimate of the energy demand and supply. This strategy can result in increased energy efficiency, decreased energy prices, and decreased carbon emissions, all of which are important goals for contemporary power systems. A promising approach for raising energy effectiveness and lowering greenhouse gas emissions in contemporary power networks is MEM. The incorporation of weather forecasts into MEM can improve decision-making regarding energy management by giving a better insight of future energy demand and supply. This essay examines the advantages and disadvantages of using weather forecasts in MEM through the presentation of a case example. By providing valuable information about future weather conditions, weather forecasts this review explain the Optimized Renewable Energy Integration, Improved Energy Storage Utilization, Load Shifting and Demand Response, Efficient Grid Management for reducing reliance on fossil fuels and lowering energy cost and carbon emissions. In order to address the issues related with MEM employing weather forecasts, this study offers potential fixes for increasing the accuracy of weather forecasts and emphasizes the necessity for more research in this area

Energy Systems Integration: Economics of a New Paradigm

Energy Systems Integration (ESI) is an emerging paradigm emanating from a whole system perspective of the energy sector. It is based on a holistic view in which the main energy carriers are integrated to achieve horizontal synergies and efficiencies at all levels. The energy system may in turn integrate with other infrastructure sectors such as water, transport, and telecommunications to meet the demand for a broad range of energy and essential services. It also implies that energy security, sustainability, and equity objectives can be balanced more effectively. There is already progress in the technical aspects of ESI. However, such systems require not only physical solutions but they also need economic, regulatory, and policy frameworks to ensure efficient performance over time. Thus, it is important to better understand the economic features of integrated energy systems. To our knowledge this aspect is barely addressed in the literature on ESI. This paper does not attempt to survey the technical literature on the topic but to describe some of its relevant economic features. We discuss selected aspects that relate to industrial organisation, regulation, business economics, and technology. Finally, we offer some early considerations and policy recommendations

Design and Evaluation of a Hardware System for Online Signal Processing within Mobile Brain-Computer Interfaces

Brain-Computer Interfaces (BCIs) sind innovative Systeme, die eine direkte Kommunikation zwischen dem Gehirn und externen Geräten ermöglichen. Diese Schnittstellen haben sich zu einer transformativen Lösung nicht nur für Menschen mit neurologischen Verletzungen entwickelt, sondern auch für ein breiteres Spektrum von Menschen, das sowohl medizinische als auch nicht-medizinische Anwendungen umfasst. In der Vergangenheit hat die Herausforderung, dass neurologische Verletzungen nach einer anfänglichen Erholungsphase statisch bleiben, die Forscher dazu veranlasst, innovative Wege zu beschreiten. Seit den 1970er Jahren stehen BCIs an vorderster Front dieser Bemühungen. Mit den Fortschritten in der Forschung haben sich die BCI-Anwendungen erweitert und zeigen ein großes Potenzial für eine Vielzahl von Anwendungen, auch für weniger stark eingeschränkte (zum Beispiel im Kontext von Hörelektronik) sowie völlig gesunde Menschen (zum Beispiel in der Unterhaltungsindustrie). Die Zukunft der BCI-Forschung hängt jedoch auch von der Verfügbarkeit zuverlässiger BCI-Hardware ab, die den Einsatz in der realen Welt gewährleistet. Das im Rahmen dieser Arbeit konzipierte und implementierte CereBridge-System stellt einen bedeutenden Fortschritt in der Brain-Computer-Interface-Technologie dar, da es die gesamte Hardware zur Erfassung und Verarbeitung von EEG-Signalen in ein mobiles System integriert. Die Architektur der Verarbeitungshardware basiert auf einem FPGA mit einem ARM Cortex-M3 innerhalb eines heterogenen ICs, was Flexibilität und Effizienz bei der EEG-Signalverarbeitung gewährleistet. Der modulare Aufbau des Systems, bestehend aus drei einzelnen Boards, gewährleistet die Anpassbarkeit an unterschiedliche Anforderungen. Das komplette System wird an der Kopfhaut befestigt, kann autonom arbeiten, benötigt keine externe Interaktion und wiegt einschließlich der 16-Kanal-EEG-Sensoren nur ca. 56 g. Der Fokus liegt auf voller Mobilität. Das vorgeschlagene anpassbare Datenflusskonzept erleichtert die Untersuchung und nahtlose Integration von Algorithmen und erhöht die Flexibilität des Systems. Dies wird auch durch die Möglichkeit unterstrichen, verschiedene Algorithmen auf EEG-Daten anzuwenden, um unterschiedliche Anwendungsziele zu erreichen. High-Level Synthesis (HLS) wurde verwendet, um die Algorithmen auf das FPGA zu portieren, was den Algorithmenentwicklungsprozess beschleunigt und eine schnelle Implementierung von Algorithmusvarianten ermöglicht. Evaluierungen haben gezeigt, dass das CereBridge-System in der Lage ist, die gesamte Signalverarbeitungskette zu integrieren, die für verschiedene BCI-Anwendungen erforderlich ist. Darüber hinaus kann es mit einer Batterie von mehr als 31 Stunden Dauerbetrieb betrieben werden, was es zu einer praktikablen Lösung für mobile Langzeit-EEG-Aufzeichnungen und reale BCI-Studien macht. Im Vergleich zu bestehenden Forschungsplattformen bietet das CereBridge-System eine bisher unerreichte Leistungsfähigkeit und Ausstattung für ein mobiles BCI. Es erfüllt nicht nur die relevanten Anforderungen an ein mobiles BCI-System, sondern ebnet auch den Weg für eine schnelle Übertragung von Algorithmen aus dem Labor in reale Anwendungen. Im Wesentlichen liefert diese Arbeit einen umfassenden Entwurf für die Entwicklung und Implementierung eines hochmodernen mobilen EEG-basierten BCI-Systems und setzt damit einen neuen Standard für BCI-Hardware, die in der Praxis eingesetzt werden kann.Brain-Computer Interfaces (BCIs) are innovative systems that enable direct communication between the brain and external devices. These interfaces have emerged as a transformative solution not only for individuals with neurological injuries, but also for a broader range of individuals, encompassing both medical and non-medical applications. Historically, the challenge of neurological injury being static after an initial recovery phase has driven researchers to explore innovative avenues. Since the 1970s, BCIs have been at one forefront of these efforts. As research has progressed, BCI applications have expanded, showing potential in a wide range of applications, including those for less severely disabled (e.g. in the context of hearing aids) and completely healthy individuals (e.g. entertainment industry). However, the future of BCI research also depends on the availability of reliable BCI hardware to ensure real-world application. The CereBridge system designed and implemented in this work represents a significant leap forward in brain-computer interface technology by integrating all EEG signal acquisition and processing hardware into a mobile system. The processing hardware architecture is centered around an FPGA with an ARM Cortex-M3 within a heterogeneous IC, ensuring flexibility and efficiency in EEG signal processing. The modular design of the system, consisting of three individual boards, ensures adaptability to different requirements. With a focus on full mobility, the complete system is mounted on the scalp, can operate autonomously, requires no external interaction, and weighs approximately 56g, including 16 channel EEG sensors. The proposed customizable dataflow concept facilitates the exploration and seamless integration of algorithms, increasing the flexibility of the system. This is further underscored by the ability to apply different algorithms to recorded EEG data to meet different application goals. High-Level Synthesis (HLS) was used to port algorithms to the FPGA, accelerating the algorithm development process and facilitating rapid implementation of algorithm variants. Evaluations have shown that the CereBridge system is capable of integrating the complete signal processing chain required for various BCI applications. Furthermore, it can operate continuously for more than 31 hours with a 1800mAh battery, making it a viable solution for long-term mobile EEG recording and real-world BCI studies. Compared to existing research platforms, the CereBridge system offers unprecedented performance and features for a mobile BCI. It not only meets the relevant requirements for a mobile BCI system, but also paves the way for the rapid transition of algorithms from the laboratory to real-world applications. In essence, this work provides a comprehensive blueprint for the development and implementation of a state-of-the-art mobile EEG-based BCI system, setting a new benchmark in BCI hardware for real-world applicability