This tutorial introduces participants to the fundamentals and advanced concepts of integrating sensor and actor systems within the Internet of Things (IoT) through hands-on experience with small, portable ad-hoc netwo...
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Deep Neural Network (DNN) accelerators are increasingly integrated into sensing applications, such as wearables and sensor networks, to provide advanced in-sensor processing capabilities. Given wearables' strict s...
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ISBN:
(纸本)9798350337570
Deep Neural Network (DNN) accelerators are increasingly integrated into sensing applications, such as wearables and sensor networks, to provide advanced in-sensor processing capabilities. Given wearables' strict size and power requirements, minimizing the area and energy consumption of DNN accelerators is a critical concern. In that regard, computing DNN models in the time domain is a promising architecture, taking advantage of both technology scaling friendliness and efficiency. Yet, time-domain accelerators are typically not fully digital, limiting the full benefits of time-domain computation. In this work, we propose a time-domain multiply and accumulate (MAC) circuitry enabling an all-digital with a small size and low energy consumption to target in-sensor processing. The proposed MAC circuitry features a simple and efficient architecture without dependencies on analog non-idealities such as leakage and charge errors. It is implemented in 22nm FD-SOI technology, occupying 35 mu m x 35 mu m while supporting multi-bit inputs (8-bit) and weights (4-bit). The power dissipation is 46.61 mu W at 500MHz, and 20.58 mu W at 200MHz. Combining 32 MAC units achieves an average power efficiency, area efficiency and normalized efficiency of 0.45 TOPS/W and 75GOPS/mm(2), and 14.4 1b-TOPS/W.
Molecularly imprinted electrochemical sensors (MI-ECSs) are a significant advancement in analytical techniques, especially for water quality monitoring (WQM). These sensors utilize molecular imprinting to create polym...
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Molecularly imprinted electrochemical sensors (MI-ECSs) are a significant advancement in analytical techniques, especially for water quality monitoring (WQM). These sensors utilize molecular imprinting to create polymer matrices that exhibit high specificity and affinity for target analytes. MI-ECSs integrate molecularly imprinted polymers (MIPs) with electrochemical transducers (ECTs), enabling the selective recognition and quantification of contaminants. Their design features template-shaped cavities in the polymer that mimic the functional groups, shapes, and sizes of target analytes, resulting in enhanced binding interactions and improved sensor performance in complex water environments. The fabrication of MI-ECSs involves selecting suitable monomeric units (monomers) and crosslinkers, using a target analyte as a template, polymerizing, and then removing the template to expose the imprinted sites. advanced methodologies, such as electropolymerization and surface imprinting, are used to enhance their sensitivity and reproducibility. MI-ECSs offer considerable benefits, including high selectivity, low detection limits, rapid response times, and the potential for miniaturization and portability. They effectively assess and detect contaminants, like (toxic) heavy metals (HMs), pesticides, pharmaceuticals, and pathogens, in water systems. Their ability for real-time monitoring makes them essential for ensuring water safety and adhering to regulations. This paper reviews the architecture, principles, and fabrication processes of MI-ECSs as innovative strategies in WQM and their application in detecting emerging contaminants and toxicants (ECs and Ts) across various matrices. These ECs and Ts include organic, inorganic, and biological contaminants, which are mainly anthropogenic in origin and have the potential to pollute water systems. Regarding this, ongoing advancements in MI-ECS technology are expected to further enhance the analytical capabilities and performances
In the realm of indoor robotics, navigation poses a critical challenge across service robots, humanoids, and warehouse automation. Existing techniques like line following, RFID tracking, and Aruco marker-based systems...
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Recently, rehabilitation robotics has demonstrated the ability to improve the methodologies significantly. Robotic devices can provide accuracy, repeatability, high-dose and task-specific training, and real-time feedb...
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ISBN:
(纸本)9798331516246;9798331516239
Recently, rehabilitation robotics has demonstrated the ability to improve the methodologies significantly. Robotic devices can provide accuracy, repeatability, high-dose and task-specific training, and real-time feedback. These features are essential in rehabilitation, focusing on the upper limb and the wrist, as a joint needed to perform most activities of daily living (ADLs) and thus crucial to maintaining a high quality of life. This is particularly important considering the demand for effective rehabilitation solutions due to frequent motor impairments related to neurological or orthopedic issues. This review focuses on robotic devices for wrist rehabilitation, exploring their characteristics with an eye toward significant and appealing features for commercialization. We explored relevant features of these devices such as a robust and human-centered mechanical design, an appropriate actuation system, and an advancedsensor network integrated with a sophisticated control architecture to monitor and optimize human-machine interactions. We considered usability factors as ergonomics, ease of use, and adaptability to the needs of different users, for clinical applications and broad market adoption. This work reviews existing robotic devices for wrist rehabilitation, comparing their key features to evaluate the current state of the field. It aims to identify which devices best meet the necessary criteria and highlight issues that still require further engineering research.
In contemporary autonomous driving systems relying on sensor fusion, traditional digital processors encounter challenges associated with analogue-to-digital conversion and iterative vector-matrix operations, which are...
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In contemporary autonomous driving systems relying on sensor fusion, traditional digital processors encounter challenges associated with analogue-to-digital conversion and iterative vector-matrix operations, which are encumbered by limitations in terms of response time and energy consumption. In this study, we present an analogue Kalman filter circuit based on molybdenum disulfide (MoS2) memtransistor, designed to accelerate sensor fusion for precise localization in autonomous vehicle applications. The nonvolatile memory characteristics of the memtransistor allow for the storage of a fixed Kalman gain, which eliminates the data convergence and thus accelerates the processing speeds. Additionally, the modulation of multiple conductance states by the gate terminal enables fast adaptability to diverse autonomous driving scenarios by tuning multiple Kalman filter gains. Our proposed analogue Kalman filter circuit accurately estimates the position coordinates of target vehicles by fusing sensor data from light detection and ranging (LiDAR), millimeter-wave radar (Radar), and camera, and it successfully solves real-word problems in a signal-free crossroad intersection. Notably, our system achieves a 1000-fold improvement in energy efficiency compared to that of digital circuits. This work underscores the viability of a memtransistor for achieving fast, energy-efficient real-time sensing, and continuous signal processing in advancedsensor fusion technology.
Unmanned Aerial Vehicles (UAVs) are increasingly integrated in various applications. The urgent need for accurate navigation algorithms is emphasized in various applications. In this paper, we propose an integrated fr...
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ISBN:
(纸本)9789819755936;9789819755943
Unmanned Aerial Vehicles (UAVs) are increasingly integrated in various applications. The urgent need for accurate navigation algorithms is emphasized in various applications. In this paper, we propose an integrated framework focusing on robust attitude and position/velocity estimation for UAV navigation using Kalman filtering employing Kalman filtering (KF) technique and chi-square test based sensor fusion quality assessment. Our approach integrates sensor data through a filter-based approach, and Kalman filter handles attitude estimation and position/velocity estimation. The modal architecture enhances robustness and facilitates seamless integration of multiple sensors while prioritizing reduced attitude estimation and position/velocity estimation. Accurate attitude estimation is also prioritized to reduce the risk in case of deviation. Accurate attitude estimation is also prioritized to reduce the risk of deviation in case of higher order state estimators. Experimental validation on the "SHENG PROUAV" model proves the effectiveness of our framework and demonstrates accurate attitude estimation results.
This research addresses the critical need for reliable sensorsystems in the aviation industry and the challenges associated with timely and accurate fault detection. We propose a novel method for enhancing fault dete...
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This paper presents the development and evaluation of an embroidery textile tactile sensor, designed for advanced pressure mapping applications. Utilizing a unique embroidery technique with conductive threads and inco...
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A novel vector displacement sensor based on femtosecond laser direct writing gratings and waveguides in a seven-core fiber was proposed. Avoiding the use of fan-in and fan-out devices, such a vector displacement senso...
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