High-frequency ultraweak magnetic field measurements are essential. We demonstrate a scalar rubidium atomic magnetometer with effective measurement bandwidth of kilohertz and sensitivity of subpicotesla operating at a...
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High-frequency ultraweak magnetic field measurements are essential. We demonstrate a scalar rubidium atomic magnetometer with effective measurement bandwidth of kilohertz and sensitivity of subpicotesla operating at a temperature of 365 K in an ambient field of 50 mu T. The Faraday rotation signal is enhanced by a more simplified optical multipass cell compared to previous works. We developed an approach of continuous or burst sampling mode to maximize the frequency capability of the system, and propose a scheme of precise control pulse series as well as an analytical method of data processing. Experimental results show that high contrast atomic spin projection noise and free induction decay signal are observed, and a sampling rate of 2 kHz along with the sensitivity of 0.7 pT/Hz(1/2) between 20 and 1000 Hz have been achieved. It would help further research in physics and biomedical investigations that need high bandwidth magnetometers or sensor arrays in the earth's field.
Fluorescence imaging is a powerful tool to detect the presence of processing.agents, e.g., oils, lubricants, and organic coatings on metal surfaces. This imaging technique can be used in production e. g., to determine...
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Radar sensors operating in the mmWave frequency range face challenges when used as indoor perception and imaging devices, primarily due to noise and multipath signal distortions. These distortions often impair the sen...
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ISBN:
(纸本)9798350329216;9798350329209
Radar sensors operating in the mmWave frequency range face challenges when used as indoor perception and imaging devices, primarily due to noise and multipath signal distortions. These distortions often impair the sensors' ability to accurately perceive and image the indoor environment. Nevertheless, this sensor offers distinct advantages over camera and LiDAR sensors. This encompasses the estimation of object reflectivity, known as radar cross-section (RCS), and the ability to penetrate through objects that are thin or have low reflectivity. This results in a 'through-the-wall' sensing capability. Due to the aforementioned disadvantages, most research in the field of imaging radar tends to exclude indoor areas. We introduce a machine learning-based mmWave MIMO FMCW imaging radar object classifier designed to identify small, hand-sized objects in indoor settings, utilizing only radar IQ samples as input. This system achieves 97-99% accuracy on our test set and maintains approximately 50% accuracy even under challenging conditions, such as increased background noise and occlusion of sample objects, without the need for adjusting training or pre-processing. This demonstrates the robustness of our approach and offers insights into what needs to be improved in the future to achieve generalization and very high accuracy even in the presence of significant indoor perturbations.
robotic lightweight-exoskeletons show great potential to enhance human motion capabilities for the disabled, elderly, and healthy. Active human-robotic interfaces with integrated actuation and sensing allow high quali...
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ISBN:
(纸本)9798350303278
robotic lightweight-exoskeletons show great potential to enhance human motion capabilities for the disabled, elderly, and healthy. Active human-robotic interfaces with integrated actuation and sensing allow high quality measurements and an optimal fit when worn. We present a compact Compliant Actuator-sensor Unit (CASU) that can be embedded in most interfaces. Based on shape memory alloy strips in a compliant parallel configuration, this actuator-sensor unit is able to provide a controllable force impact. The sensing of interaction forces through the user's clothing is enabled using a thin layer based capacitive force sensor. The unit was characterized in a test setup and attached to a subject's limb. We could demonstrate controllable actuation in the force range of up to 62N with effective force offsets of 13.7N from arbitrary contact force requiring less than 10W. The unit showed reliable sensing, revealing characteristic patterns of basic limb movements. The actuation controls the initial contact forces and enables robust interaction force monitoring and can thereby facilitate further signalprocessing. For future work, we will miniaturize the unit further and investigate compensation of interface displacements during activities of daily living in a user study.
This paper introduces the Artificial Intelligence-Driven Energy-efficient Analog-to-Learning system (AIDEAL), an AI-enabled in-sensor computing platform designed to enhance energy efficiency in edge devices through in...
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This paper introduces the Artificial Intelligence-Driven Energy-efficient Analog-to-Learning system (AIDEAL), an AI-enabled in-sensor computing platform designed to enhance energy efficiency in edge devices through in situ data processing. AIDEAL extracts task-relevant features from analog inputs, significantly reducing the data volume for digitization and transmission, by utilizing analog compute-in-memory (ACIM). AIDEAL demonstrates substantial benefits across various applications including image reconstruction, image classification, and object detection. Notably, it achieves 60.2%-80.2% reduction in sensor energy for image classification compared to traditional methods of digitizing and transmitting full input data. We also examine AIDEAL under different feature reduction rates and variations in voltage, and temperature in ACIM. Furthermore, we introduce two algorithmic strategies, feature restoration (FR) and hamming weight based energy aware quantization (HEQA), which enhance accuracy and energy efficiency. These methods result in less than 2% accuracy drop and 37% energy savings compared to baseline, respectively.
Robust object pose tracking plays an important role in robot manipulation, but it is still an open issue for quickly moving targets as motion blur and low frequency detection can reduce pose estimation accuracy even f...
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ISBN:
(纸本)9781665491907
Robust object pose tracking plays an important role in robot manipulation, but it is still an open issue for quickly moving targets as motion blur and low frequency detection can reduce pose estimation accuracy even for state-of-the-art RGB-D-based methods. An event-camera is a low-latency vision sensor that can act complementary to RGB-D. Specifically, its sub-millisecond temporal resolution can be exploited to correct for pose estimation inaccuracies due to low frequency RGB-D based detection. To do so, we propose a dual Kalman filter: the first filter estimates an object's velocity from the spatio-temporal patterns of "events", the second filter fuses the tracked object velocity with a low-frequency object pose estimated from a deep neural network using RGB-D data. The full system outputs high frequency, accurate object poses also for fast moving objects. The proposed method works towards low-power robotics by replacing high-cost GPU-based optical flow used in prior work with event-cameras that inherently extract the required signal without costly processing. The proposed algorithm achieves comparable or better performance when compared to two state-of-the-art 6-DoF object pose estimation algorithms and one hybrid event/RGB-D algorithm on benchmarks with simulated and real data. We discuss the benefits and trade-offs for using the event-camera and contribute algorithm, code, and datasets to the community. The code and datasets are available at https://***/event-driven-robotics/Hybrid-object-tracking-with-events-and-frames.
The sense of touch is essential for locating buried objects when vision-based approaches are limited. We present an approach for tactile perception when sensorized robot fingertips are used to directly interact with g...
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The sense of touch is essential for locating buried objects when vision-based approaches are limited. We present an approach for tactile perception when sensorized robot fingertips are used to directly interact with granular media particles in teleoperated systems. We evaluate the effects of linear and nonlinear classifier model architectures and three tactile sensor modalities (vibration, internal fluid pressure, fingerpad deformation) on the accuracy of estimates of fingertip contact state. We propose an architecture called the Sparse-Fusion Recurrent Neural Network (SF-RNN) in which sparse features are autonomously extracted prior to fusing multimodal tactile data in a fully connected RNN input layer. The multimodal SF-RNN model achieved 98.7% test accuracy and was robust to modest variations in granular media type and particle size, fingertip orientation, fingertip speed, and object location. Fingerpad deformation was the most informative modality for haptic exploration within granular media while vibration and internal fluid pressure provided additional information with appropriate signalprocessing. We introduce a real-time visualization of tactile percepts for remote exploration by constructing a belief map that combines probabilistic contact state estimates and fingertip location. The belief map visualizes the probability of an object being buried in the search region and could be used for planning.
In our digital world, everything around us needs to be processed to reduce human effort and make modern life easier, more practical, more convenient, and more sustainable. Nevertheless, today there are major problems ...
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ISBN:
(数字)9798331529437
ISBN:
(纸本)9798331529444
In our digital world, everything around us needs to be processed to reduce human effort and make modern life easier, more practical, more convenient, and more sustainable. Nevertheless, today there are major problems as energy and water scarcity. Our scop is to design a robot uses solar energy panel fixed above our it for maintaining continuous charging to generate clean electrical power and store it in a lithium battery, this stored electricity used later for running robotic systems for efficient irrigation in big scale farms to conserve water consumption and precise crop monitoring, using many inputs data (sensor) as soil moisture sensor which consist of with two copper tubes inputs to control whether the soil is wet or dry. more sensors are used as temperature sensor and color senser. Where all sensors connected to the microcontroller (Arduino processor) which used to activate or shut down the irrigation pump and light ON /OFF Red LED or Green LED after detecting the level of crop maturity, when the microcontroller receives the signal from the color sensors, plus the temperature of weather considered in all data processing. thus, the robot can then detect crop needs more water or ready for growth up to harvesting time.
The past decades have seen the rapid development of tactile sensors in material, fabrication, and mechanical structure design. The advancement of tactile sensors has heightened the expectation of sensor functions, and...
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The past decades have seen the rapid development of tactile sensors in material, fabrication, and mechanical structure design. The advancement of tactile sensors has heightened the expectation of sensor functions, and thus put forward a higher demand for data processing. However, conventional analysis techniques have not kept pace with the tactile sensor development and still suffer from some severe drawbacks, like cumbersome models, poor efficiency, and expensive costs. Machine learning, with its prominent ability for big data analysis and fast processing.speed, can offer many possibilities for tactile data analysis. Herein, the machine learning techniques employed for processing.tactile signals are reviewed. Supervised learning and unsupervised learning for analog signals are covered, and processing.spike signals with machine learning are summarized. Furthermore, the applications in robotic tactile perception and human activity monitoring are presented. Finally, the current challenges and future prospects in sensors, data, algorithms, and benchmarks are discussed.
This paper presents a non-destructive metal classification system, which is based on magnetic induction and mutual impedance measurement technology. The detection system mainly consists of a signal source, a power amp...
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ISBN:
(数字)9798350375909
ISBN:
(纸本)9798350375916
This paper presents a non-destructive metal classification system, which is based on magnetic induction and mutual impedance measurement technology. The detection system mainly consists of a signal source, a power amplifier, an ADC acquisition module and a dual-coil sensor. In the dual-coil sensor, the primary coil is excited by low-frequency current, and it then produce a changing magnetic field. In the secondary coil, electromotive force is induced. The current and induced electromotive force are measured through ADC acquisition. And then, mutual impedance of the dual-coil sensor can be obtained by signalprocessing. The metal material, which is placed between the dual-coil sensor, can influence the measured mutual impedance, and this can be utilized in classification. A prototype was manufactured, and the measured and simulated results are also provided to demonstrate the validity of this non-destructive metal classification system. Additionally, for enhancing the robustness of the system, an RNN model is investigated to alleviate the influence of variations in metal material size.
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