scholarly journals Rise of the Autonomous Machines

Computer ◽  
2022 ◽  
Vol 55 (1) ◽  
pp. 64-73
Author(s):  
Shaoshan Liu ◽  
Jean-Luc Gaudiot
Keyword(s):  
2020 ◽  
Vol 35 (1) ◽  
Author(s):  
Willem Gravett

The development of artificial intelligence has the potential to transform lives and work practices, raise efficiency, savings and safety levels, and provide enhanced levels of services. However, the current trend towards developing smart and autonomous machines with the capacity to be trained and make decisions independently holds not only economic advantages, but also a variety of concerns regarding their direct and indirect effects on society as a whole. This article examines some of these concerns, specifically in the areas of privacy and autonomy, state surveillance, and bias and algorithmic transparency. It concludes with an analysis of the challenges that the legal system faces in regulating the burgeoning field of artificial intelligence.


Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1178
Author(s):  
Bo Sun ◽  
Bo Tan ◽  
Wenbo Wang ◽  
Elena Simona Lohan

The 5G network is considered as the essential underpinning infrastructure of manned and unmanned autonomous machines, such as drones and vehicles. Besides aiming to achieve reliable and low-latency wireless connectivity, positioning is another function provided by the 5G network to support the autonomous machines as the coexistence with the Global Navigation Satellite System (GNSS) is typically supported on smart 5G devices. This paper is a pilot study of using 5G uplink physical layer channel sounding reference signals (SRSs) for 3D user equipment (UE) positioning. The 3D positioning capability is backed by the uniform rectangular array (URA) on the base station and by the multiple subcarrier nature of the SRS. In this work, the subspace-based joint angle-time estimation and statistics-based expectation-maximization (EM) algorithms are investigated with the 3D signal manifold to prove the feasibility of using SRSs for 3D positioning. The positioning performance of both algorithms is evaluated by estimation of the root mean squared error (RMSE) versus the varying signal-to-noise-ratio (SNR), the bandwidth, the antenna array configuration, and multipath scenarios. The simulation results show that the uplink SRS works well for 3D UE positioning with a single base station, by providing a flexible resolution and accuracy for diverse application scenarios with the support of the phased array and signal estimation algorithms at the base station.


Author(s):  
Carmen C. Mayorga-Martinez ◽  
Jan Vyskočil ◽  
Filip Novotný ◽  
Martin Pumera

Light powered self-propelled 2D-material MXene-based sandwitch micromachines degrade high-energy exposives on a go.


Computer ◽  
2021 ◽  
Vol 54 (4) ◽  
pp. 66-69
Author(s):  
Shaoshan Liu ◽  
Jean-Luc Gaudiot ◽  
Hironori Kasahara

2018 ◽  
Vol 161 ◽  
pp. 03014 ◽  
Author(s):  
Vladimir Serebrenny ◽  
Madin Shereuzhev ◽  
Ivan Metasov

Agriculture is the extremely important and developing economic movement in all times. Automation of agricultural machines occurs by different ways. One way is through the creation of specialized technical solutions for the required technological processes, another way is the construction of automatic agricultural machines, including mobile ones. The state of modern technology allows to create autonomous machines. The agriculture robotization trends are the high precision and unmanned farming. The article considers the issues of robotization of agricultural machinery. Stages of robotization of agricultural mobile machines were analyzed. The factors affecting the autonomous movement of mobile agrorobots were shown.


2020 ◽  
Author(s):  
Yazhou Li ◽  
Yahong Rosa Zheng

This paper presents two methods, tegrastats GUI version jtop and Nsight Systems, to profile NVIDIA Jetson embedded GPU devices on a model race car which is a great platform for prototyping and field testing autonomous driving algorithms. The two profilers analyze the power consumption, CPU/GPU utilization, and the run time of CUDA C threads of Jetson TX2 in five different working modes. The performance differences among the five modes are demonstrated using three example programs: vector add in C and CUDA C, a simple ROS (Robot Operating System) package of the wall follow algorithm in Python, and a complex ROS package of the particle filter algorithm for SLAM (Simultaneous Localization and Mapping). The results show that the tools are effective means for selecting operating mode of the embedded GPU devices.


2021 ◽  
Author(s):  
Britta Hale ◽  
Douglas L. Van Bossuyt ◽  
Nikolaos Papakonstantinou ◽  
Bryan O’Halloran

Abstract Fuelled by recent technological advances, Machine Learning (ML) is being introduced to safety and security-critical applications like defence systems, financial systems, and autonomous machines. ML components can be used either for processing input data and/or for decision making. The response time and success rate demands are very high and this means that the deployed training algorithms often produce complex models that are not readable and verifiable by humans (like multi layer neural networks). Due to the complexity of these models, achieving complete testing coverage is in most cases not realistically possible. This raises security threats related to the ML components presenting unpredictable behavior due to malicious manipulation (backdoor attacks). This paper proposes a methodology based on established security principles like Zero-Trust and defence-in-depth to help prevent and mitigate the consequences of security threats including ones emerging from ML-based components. The methodology is demonstrated on a case study of an Unmanned Aerial Vehicle (UAV) with a sophisticated Intelligence, Surveillance, and Reconnaissance (ISR) module.


Author(s):  
Gur Emre Guraksin

Along with the rise of artificial intelligence (AI), there are many different research fields gaining importance. Because of the growing amount of data and needs for immediate access to information for dealing with the problems, different types of research fields take place within the scientific community. Internet of things (IoT) is one of them, and it enables devices to communicate with each other in order to form a general network of physical, working devices. The objective of this chapter in this manner is to provide a general discussion of using nature-inspired techniques of AI to form the future of biomedical engineering over IoT. Because it is often thought that the medical services of the future will be based on autonomous machines supported with AI and IoT, discussing such a topic by considering biomedical engineering applications will be good for the related literature.


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