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Autonomous navigation is one of the main areas of research in mobile robots and intelligent connected vehicles. In this context, we are interested in presenting a general view on robotics, the progress of research, and advanced methods related to this field to improve autonomous robots’ localization. We seek to evaluate algorithms and techniques that give robots the ability to move safely and autonomously in a complex and dynamic environment. Under these constraints, we focused our work in the paper on a specific problem: to evaluate a simple, fast and light SLAM algorithm that can minimize localization errors. We presented and validated a FastSLAM 2.0 system combining scan matching and loop closure detection. To allow the robot to perceive the environment and detect objects, we have studied one of the best deep learning technique using convolutional neural networks (CNN). We validate our testing using the YOLOv3 algorithm.
Power line communications (PLC) reuse the existing power-grid infrastructure for the transmission of data signals. As power line the communication technology does not require a dedicated network setup, it can be used to connect a multitude of sensors and Internet of Things (IoT) devices. Those IoT devices could be deployed in homes, streets, or industrial environments for sensing and to control related applications. The key challenge faced by future IoT-oriented narrowband PLC networks is to provide a high quality of service (QoS). In fact, the power line channel has been traditionally considered too hostile. Combined with the fact that spectrum is a scarce resource and interference from other users, this requirement calls for means to increase spectral efficiency radically and to improve link reliability. However, the research activities carried out in the last decade have shown that it is a suitable technology for a large number of applications. Motivated by the relevant impact of PLC on IoT, this paper proposed a cooperative spectrum allocation in IoT-oriented narrowband PLC networks using an iterative water-filling algorithm.
An autonomous vehicle is a robotic vehicle with decision and action capability capable of performing assigned tasks without or with minimal human intervention. Autonomous cars have been in development for many years. The Society of Automotive Engineers (SAE International) published in 2014 a classification in five levels of driving automation, with level 0 corresponding to completely manual driving, and level 5 to an ideal dream where the vehicle would be able to navigate entirely autonomously for all missions and in all environments. This work addressed the navigation of an autonomous vehicle in general. We focus on one of the most complex scenarios of the road network and crossing of road intersections. In this paper, the critical features of autonomous intelligent vehicles are reviewed. Furthermore, the associated problems are presented, and the most advanced solutions are derived. This article aims to allow a novice in this field to understand the different facets of localization and perception problems for autonomous vehicles.
Rotating machinery occupies a predominant place in many industrial applications. However, rotating machines are often encountered with severe vibration problems. The measurement of these machines’ vibrations signal is of particular importance since it plays a crucial role in predictive maintenance. When the vibrations are too high, they often cause fatigue failure. They announce an unexpected stop or break and, consequently, a significant loss of productivity or an attack on the personnel’s safety. Therefore, fault identification at early stages will significantly enhance the machine’s health and significantly reduce maintenance costs. Although considerable efforts have been made to master the field of machine diagnostics, the usual signal processing methods still present several drawbacks. This paper examines the rotating machinery condition monitoring in the time and frequency domains. It also provides a framework for the diagnosis process based on machine learning by analyzing the vibratory signals.
In today’s education, healthcare, and manufacturing sectors, organizations and information societies are discussing new enhancements to corporate structure and process efficiency using digital platforms. These enhancements can be achieved using digital tools. Industry 5.0 and Society 5.0 give several potentials for businesses to enhance the adaptability and efficacy of their industrial processes, paving the door for developing new business models facilitated by digital platforms. Society 5.0 can contribute to a super-intelligent society that includes the healthcare industry. In the past decade, the Internet of Things, Big Data Analytics, Neural Networks, Deep Learning, and Artificial Intelligence (AI) have revolutionized our approach to various job sectors, from manufacturing and finance to consumer products. AI is developing quickly and efficiently. We have heard of the latest artificial intelligence chatbot, ChatGPT. OpenAI created this, which has taken the internet by storm. We tested the effectiveness of a considerable language model referred to as ChatGPT on four critical questions concerning “Society 5.0”, “Healthcare 5.0”, “Industry,” and “Future Education” from the perspectives of Age 5.0.
In this work, a web-based software architecture and framework for management and diagnosis of large amounts of medical data in an ophthalmologic reading center is proposed. Data management for multi-center studies requires merging of standing data and repeatedly gathered clinical evidence such as vital signs and raw data. If ophthalmologic questions are involved the data acquisition is often provided by non-medical staff at the point of care or a study center, whereas the medical finding is mostly provided by an ophthalmologist in a specialized reading center. The study data such as participants, cohorts and measured values are administrated at a single data center for the entire study. Since a specialized reading center maintains several studies, the medical staff must learn the different data administration for the different data center. With respect to the increasing number and sizes of clinical studies, two aspects must be considered. At first, an efficient software framework is required to support the data management, processing and diagnosis by medical experts at the reading center. In the second place, this software needs a standardized user-interface that has not to be trained/taylore /adapted for each new study. Furthermore different aspects of quality and security controls have to be included. Therefore, the objective of this work is to establish a multi purpose ophthalmologic reading center, which can be connected to different data centers via configurable data interfaces in order to treat various topics simultaneously.
Personalized remote healthcare monitoring is in continuous development due to the technology improvements of sensors and wearable electronic systems. A state of the art of research works on wearable sensors for healthcare applications is presented in this work. Furthermore, a state of the art of wearable devices, chest and wrist band and smartwatches available on the market for health and sport monitoring is presented in this paper. Many activity trackers are commercially available. The prices are continuously reducing and the performances are improving, but commercial devices do not provide raw data and are therefore not useful for research purposes.
Integrierte Schaltkreise (IC) sind ein integraler Bestandteil vieler Geräte wie zum Beispiel Smartphones, Computer oder Fernseher. Auf den Schaltkreisen werden immer mehr Funktionen integriert. Um die Arbeit auch zukünftig in gegebener Zeit bewältigen zu können, bedarf es daher einer Möglichkeit für die gleichzeitige Zusammenarbeit der Entwickler. Unter dem Arbeitstitel eCEDA (eCollaboration for Electronic Design Automation) wird ein Konzept für eine Webanwendung entwickelt, die die Echtzeitkollaboration von Entwicklern im Chipentwurf ermöglichen soll. Dieses Konzept sowie verschiedene Aspekte der Kollaboration werden in dieser Arbeit behandelt.
The impact of stress of every human being has become a serious problem. Reported impact on persons are a higher rate or health disorders like heart problems, obesity, asthma, diabetes, depressions and many others. An individual in a stressful situation has to deal with altered cognition as well as an affected decision making skill and problem solving. This could lead to a higher risk for accidents in dynamic environments such as automotive. Different papers faced the estimation as well as prediction of drivers’ stress level during driving. Another important question is not only the stress level of the driver himself, but also the influence on and of a group of other drivers in the near area. This paper proposes a system, which determines a group of drivers in a near area as clusters and it derives or computes the individual stress level. This information will be analyzed to generate a stress map, which represents a graphical view about road section with a higher stress influence. Aggregated data can be used to generate navigation routes with a lower stress influence as well as recommend driving behavior to decrease stress influenced driving as well as improve road safety.
Urban platforms are essential for smart and sustainable city planning and operation. Today they are mostly designed to handle and connect large urban data sets from very different domains. Modelling and optimisation functionalities are usually not part of the cities software infrastructure. However, they are considered crucial for transformation scenario development and optimised smart city operation. The work discusses software architecture concepts for such urban platforms and presents case study results on the building sector modelling, including urban data analysis and visualisation. Results from a case study in New York are presented to demonstrate the implementation status.