Is Europe in the Driver's Seat? The Competitiveness of the European Automotive Embedded Systems Industry

This report is one of a series resulting from a project entitled ¿Competitiveness by Leveraging Emerging Technologies Economically¿ (COMPLETE), carried out by JRC-IPTS. Each of the COMPLETE studies illustrates in its own right that European companies are active on many fronts of emerging and disruptive ICT technologies and are supplying the market with relevant products and services. Nevertheless, the studies also show that the creation and growth of high tech companies is still very complex and difficult in Europe, and too many economic opportunities seem to escape European initiatives and ownership. COMPLETE helps to illustrate some of the difficulties experienced in different segments of the ICT industry and by growing potential global players. This report reflects the findings of a study conducted by Egil Juliussen and Richard Robinson, two senior experts from iSuppli Corporation on the Competitiveness of the European Automotive Embedded Software industry. The report starts by introducing the market, its trends, the technologies, their characteristics and their potential economic impact, before moving to an analysis of the competitiveness of the corresponding European industry. It concludes by suggesting policy options. The research, initially based on internal expertise and literature reviews, was complemented with further desk research, expert interviews, expert workshops and company visits. The results were ultimately reviewed by experts and also in a dedicated workshop. The report concludes that currently ICT innovation in the automotive industry is a key competence in Europe, with very little ICT innovation from outside the EU finding its way into EU automotive companies. A major benefit of a strong automotive ICT industry is the resulting large and valuable employment base. But future maintenance of automotive ICT jobs within the EU will only be possible if the EU continues to have high levels of product innovation.JRC.DDG.J.4-Information Societ

Design and Estimation of an AUV Portable Intelligent Rescue System Based on Attitude Recognition Algorithm

This research is based on the attitude sensing algorithm to design a portable intelligent rescue system for autonomous underwater vehicles (AUVs). To lower the possibility of losing the underwater vehicle and reduce the difficulty of rescuing, when an AUV intelligent rescue system (AIRS) detects the fault of AUVs and they could not be reclaimed, AIRS can pump carbon dioxide into the airbag immediately to make the vehicle resurface. AIRS consists of attitude sensing module, double-trigger inflator mechanism, and activity recognition algorithm. The sensing module is an eleven-DOF sensor that is made up of a six-axis inertial sensor, a three-axis magnetometer, a barometer, and a thermometer. Furthermore, the signal calibration and extended Kalman filter (SC-EKF) is proposed to be used subsequently to calibrate and fuse the data from the sensing module. Then, the attitude data are classified with the principle of feature extraction (FE) and backpropagation network (BPN) classifier. Finally, the designed double-trigger inflator can be triggered not only by electricity but also by water damage when the waterproof cabin is severely broken. With the AIRS technology, the safety of detecting and investigating the use AUVs can be increased since there is no need to send divers to engage in the rescue mission under water

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学位の種別:課程博士University of Tokyo(東京大学

Dynamics of a Threshold Shock Sensor: Combining Bi-stability and Triboelectricity

A proof of concept of a triboelectric threshold shock sensor and its characterization are presented. Shock sensors are used in many applications in the automotive, shipping and other industries, mainly to determine if acceleration thresholds are met. Many shock sensors are only mechanical, so the only way to know if the threshold has been reached is to physically check the device. There are noticeable advantages of using triboelectric transduction and bi-stability to create a shock sensor. By combining a buckled-beam structure and a triboelectric generator, we created a proof of concept of a tunable threshold shock sensor. The sensor generates a voltage peak only if the base acceleration is beyond a threshold. In addition, the sensor produces voltage proportional to the base acceleration beyond the threshold acceleration. This means the output signal provides more information about the strength of the shock that the device experiences. The sensor concept is illustrated for a threshold shock of 3.26g, but the threshold can be tuned by increasing the compressive axial force of the buckled beam. Increasing this axial force increases the threshold shock the sensor can detect. Thus, the combined system is a tunable threshold shock sensor with enhanced functionality. We presented a mathematical model that captures important observations of the experiments and can be used as a design tool for more precise, high-resolution triboelectric shock sensors

Emerging policy problems related to ubiquitous computing

Peer-reviewed journal articleThis paper provides an overview of the human-centered vision of Ubiquitous Computing and draws on research examining slowly emerging problems over a long-term time frame in the emerging Ubiquitous Computing environment. A six-phase process employing scenario planning, electronic focus groups, and problem assessment surveys harnessed the insight of 165 individuals from diverse backgrounds and regions throughout the State of Hawaii. Distinct differences were found between the problem identification of specialists (policymakers and systems designers) and non-specialists (everyday citizens), and there were significant differences found in the problem assessment between groups. The greatest differences in both phases emerged from social and psychological issues related to the emerging Ubiquitous Computing environment. It is argued that in addition to enormous technical changes, Ubiquitous Computing will serve to blur sociotechnical boundaries throughout the environment, challenging existing distinctions between humans and machine intelligences. As the potential for extending human capabilities via computing and communications technology is actualized in coming decades, what it means to be human will be a major source of public policy conflicts, and the early identification of problems related to these changes is essential in order to mitigate their impacts and socially negotiate a more desirable future

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

Medical semiconductor sensors: a market perspective on state-of-the-art solutions and trends

The aim of this Master Thesis is to analyse the worldwide state-of-the art market solutions and trends in semiconductor sensors within medical applications; specially magnetic and pressure sensors, with the intention of developing a potential entry plan of Infineon Technologies AG into this market. For that purpose, a fit between a top-down and bottom-up qualitative and quantitative estimation of the medical semiconductor sensor’s market size has been made; with application units, sensor volumes and sensor revenues, with a horizontal scope of five years. Once understood the existing market, some insight into the competitive landscape is provided, where the key suppliers are analysed in terms of product portfolio and revenue share estimates, on an application basis. And also, a spotlight on innovation and trends at three levels – healthcare, medical devices and medical semiconductor sensors – is presented, to forecast a possible evolution of the fore-mentioned market. The research that has been conducted is based on three main sources of information; internal contacts (i.e. within Infineon), external contacts (most of them through internal references) and internet research. Access to market research company’s reports and interviews has been particularly helpful, to complement extensive internet research. Outcomes of this study indicate that the global medical semiconductor magnetic sensor market reveals low revenue potential; as most of the applications are yet innovation fields. Reed switch replacement in battery-powered medical devices can be an opportunity for magnetic switches. However, this project suggests that there is a key investment opportunity: magnetic beads for viral detection with spintronics sensors. The global medical semiconductor pressure sensor market seems a fairly mature market; the gross part of the revenue comes from blood pressure measurement. Blood pressure measurement might be an opportunity for existing automotive semiconductor pressure sensor products. Furthermore, this report suggests that the future of blood pressure measurement might tend towards implantable pressure sensors, with a non-significantly different technological basis. To conclude, this report unveils certain business opportunities for Infineon’s semiconductor magnetic and pressure sensor products; and puts special focus on the development of derivative products to pioneer the commercialization of innovative medical applications, with a forecasted huge revenue potential

Wearable Sensor Gait Analysis for Fall Detection Using Deep Learning Methods

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide range of gait variations in older. Choosing the appropriate sensor and placing it in the most suitable location are essential components of a robust real-time fall detection system. This dissertation implements various detection models to analyze and mitigate injuries due to falls in the senior community. It presents different methods for detecting falls in real-time using deep learning networks. Several sliding window segmentation techniques are developed and compared in the first study. As a next step, various methods are implemented and applied to prevent sampling imbalances caused by the real-world collection of fall data. A study is also conducted to determine whether accelerometers and gyroscopes can distinguish between falls and near-falls. According to the literature survey, machine learning algorithms produce varying degrees of accuracy when applied to various datasets. The algorithm’s performance depends on several factors, including the type and location of the sensors, the fall pattern, the dataset’s characteristics, and the methods used for preprocessing and sliding window segmentation. Other challenges associated with fall detection include the need for centralized datasets for comparing the results of different algorithms. This dissertation compares the performance of varying fall detection methods using deep learning algorithms across multiple data sets. Furthermore, deep learning has been explored in the second application of the ECG-based virtual pathology stethoscope detection system. A novel real-time virtual pathology stethoscope (VPS) detection method has been developed. Several deep-learning methods are evaluated for classifying the location of the stethoscope by taking advantage of subtle differences in the ECG signals. This study would significantly extend the simulation capabilities of standard patients by allowing medical students and trainees to perform realistic cardiac auscultation and hear cardiac auscultation in a clinical environment

Development of a Wireless Mobile Computing Platform for Fall Risk Prediction

Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. By performing a thorough investigation of existing systems and using predictive analytics, we built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of a fall before it happens. The major focus of this dissertation has been to develop and implement this unique system to help predict the risk of falls. We used built-in sensors --accelerometer and gyroscope-- in smartphones and a sensor embedded smart-shoe. The smart-shoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. The interactions between these sensors and the user resulted in distinct challenges for this research while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work makes several contributions to current and future generation mobile computing research