Cognitive robotics and engineering strive to develop brain-robot interfaces (BRI) that enable effective collaboration between robots and humans. The continued advancements in neuroscience, robotics, and machine learni...
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ISBN:
(纸本)9798350385731;9798350385724
Cognitive robotics and engineering strive to develop brain-robot interfaces (BRI) that enable effective collaboration between robots and humans. The continued advancements in neuroscience, robotics, and machine learning are projected to broaden the scope of BRI applications, ultimately improving human-robot cooperation. A major area of focus for BRI-based system development involves mapping brain activities to control actions through motor imagery (MI) based technologies such as electroencephalogram (EEG). In this paper, a novel technique for classifying different tasks involving MI of upper limb movements from EEG signals is presented. To emphasize the inter-related information within various bands of EEG signals, ratio of band power (RBP)-based features are proposed for categorizing different upper limb-based MI tasks. The combination of these proposed features and an optimized KNN classifier produced remarkable accuracy in classifying diverse MI tasks from EEG signals. This approach was compared with recent methods applied to the same dataset, illustrating its superiority in performance. Furthermore, a comparison between the proposed RBP-based features and conventional band power-based features underscored the effectiveness of RBP-based attributes for classifying MI-related tasks. The improved effectiveness of the proposed approach could lead to progress in BRI-based systems development across a range of applications.
Increased flight time and advancedsensors are making Micro Aerial Vehicles (MAVs) easier to use, facilitating their widespread adoption in fields such as precision agriculture or environmental monitoring. However, cu...
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ISBN:
(纸本)9798350361087;9798350361070
Increased flight time and advancedsensors are making Micro Aerial Vehicles (MAVs) easier to use, facilitating their widespread adoption in fields such as precision agriculture or environmental monitoring. However, current applications are limited mainly to passive visual observation from far above;to enable the next generation of aerial robot applications, MAVs must begin to directly physically interact with objects in the environment, such as placing and collecting sensors. Enabling these applications for a wide spectrum of end-users is only possible if the mechanism is safe and easy to use, without overburdening the user with complex integration, complicated control, or overwhelming and convoluted feedback. To this end we propose a self-sufficient passive payload system to enable both the deployment and retrieval of sensors for agriculture. This mechanism can be simply mechanically attached to a commercial, off-the-shelf MAV, without requiring further electrical or software integration. The user-centric design and mechanical intelligence of the system facilitates ease of use through simplified control with targeted perceptual feedback. The usability of the system is validated quantitatively and qualitatively in a user study demonstrating sensor deployment and collection. All participants were able to deploy and collect at least four sensors both within 10 minutes in visual line-of-sight and within 12 minutes in beyond visual line-of-sight, after only three minutes of practice. Enabling MAVs to physically interact with their environment will usher in the next stage of MAV utility and applications. Complex tasks, such as sensor deployment and retrieval, can be realized relatively simply, by relying on a mechanically passive system designed with the user in mind, these payloads can enable such applications to be more widely available and inclusive to end-users.
Intelligent vehicle path recognition and control is a hot topic for research and application. In this paper, an intelligent vehicle path recognition and control scheme with visual sensors as the main focus and multipl...
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Superconductor-based magnetic levitation struggles with an inherently non-stiff and lightly damped coupling between superconductor and levitation module. This means a magnet that is levitating above a superconductor c...
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The human-computer interaction has evolved with GUIs, while laptops and desktops still rely on traditional input methods due to the prohibitive costs of large touch screens. We propose a novel virtual mouse system usi...
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This paper constructs an intelligent home monitoring system based on NI wireless sensor network. The system consists of multiple sensors to collect temperature and humidity, smoke, theft and other environmental data. ...
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Modern urban development depends on optimizing cognitive sensor communications in smart cities, which is the focus of this investigation. In the era of expanding sensor networks, efficient data transmission is paramou...
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We present a system which grows and manages a network of remote viewpoints during the natural installation cycle for a newly installed camera network or a newly deployed robot fleet. No explicit notion of camera posit...
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We present a system which grows and manages a network of remote viewpoints during the natural installation cycle for a newly installed camera network or a newly deployed robot fleet. No explicit notion of camera position or orientation is required, neither global i.e. relative to a building plan - nor local - i.e. relative to an interesting point in a room. Furthermore, no metric relationship between viewpoints is required. Instead, we leverage our prior work in effective remote control without extrinsic or intrinsic calibration and extend it to the multi-camera setting. In this, we memorise, from simultaneous robot detections in the tracker thread, soft pixel-wise topological connections between viewpoints. We demonstrate our system with repeated autonomous traversals of workspaces connected by a network of six cameras across a productive office environment.
Smart technologies have revolutionized home automation, enhancing the way people interact with the environment. This study introduces an innovative home automation system that combines voice and gesture recognition te...
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Indoor environmental comfort has become increasingly important, necessitating occupant-centric systems that provide personalized comfort. This trend is particularly notable in light of the increasing frequency of extr...
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ISBN:
(纸本)9783031600111;9783031600128
Indoor environmental comfort has become increasingly important, necessitating occupant-centric systems that provide personalized comfort. This trend is particularly notable in light of the increasing frequency of extreme weather events associated with global climate change. This paper proposes a novel framework integrating real-time occupant feedback, multi-sensor data fusion, online modeling, and intelligent sensor technologies to dynamically tailor indoor microenvironments. The framework collects diverse data on built environment and personal health using environmental sensors and wearable devices. It employs online machine learning algorithms to analyze the database and automatically adjust environmental conditions in real-time to match occupants' preferences. In implementing this framework, advanced encryption are utilized to enable swift, localized data processing while preserving privacy. Multi-sensor fusion techniques are leveraged to integrate heterogeneous sensor data into an accurate assessment of occupant comfort. The user interface facilitates occupant feedback to continuously refine the system's reinforcement learning model. By personalizing comfort in a responsive, privacy-aware manner, this framework is expected to enhance occupant well-being and satisfaction, potentially enabling significant energy savings by avoiding overcooling and overheating. The framework represents an innovative application of smart and computing technologies, including deep learning and data fusion, to advance beyond static environmental setpoints. In anticipation of testing, it shows promise in revolutionizing occupant-centric comfort, fostering the creation of more adaptive and resilient indoor spaces.
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