The impact of hot carrier injection (HCI) on the performance of standard and low-VT FinFETs are investigated and benchmarked with each other. For this investigation, these FinFETs were fabricated with various gate len...
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Suitably tailored forms of spatiotemporal modulation in electronic circuit networks have been recently employed to overcome fundamental challenges in modern electronic systems, including breaking reciprocity, squeezin...
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Suitably tailored forms of spatiotemporal modulation in electronic circuit networks have been recently employed to overcome fundamental challenges in modern electronic systems, including breaking reciprocity, squeezing the footprint of high-Q resonators, and overcoming the delay-bandwidth limit. Rotating patterns of temporal modulations have been used to synthesize angular momentum, which replaces magnetic bias to break reciprocity in integrated circuits. However, this approach is limited by trade-offs between modulation speed, footprint, and bandwidth of operation. Rotating switching patterns in commutated capacitor networks also enables compact filters and quasielectrostatic wave propagation, overcoming the delay-bandwidth limit. In this paper, we combine these mechanisms in an integrated-circuit ring that synthetically rotates in two dimensions, realizing an effective helicoidal motion that provides ultrabroadband quasielectrostatic nonreciprocal responses fitting within a theoretically infinitesimal size. We also analyze the impact of modulation signal noise on time-modulated nonreciprocal components and unveil the role of a dynamic noise mechanism based on which the noise level increases in the presence of a strong signal passing through the component, along with methods to mitigate this effect. We experimentally verify these principles in a three-port integrated circulator based on a 65-nm CMOS process that operates from dc to 1 GHz with a miniaturization factor of 2 × 106.
EEG and fMRI are complementary, noninvasive technologies for investigating human brain function. These modalities have been used to uncover large-scale functional networks and their disruptions in clinical populations...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
EEG and fMRI are complementary, noninvasive technologies for investigating human brain function. These modalities have been used to uncover large-scale functional networks and their disruptions in clinical populations. Given the high temporal resolution of EEG and high spatial resolution of fMRI, integrating these modalities can provide a more holistic understanding of brain activity. This work explores a multimodal source decomposition technique for extracting shared modes of temporal variation between fMRI BOLD signals and EEG spectral power fluctuations in the resting state. The resulting components are then compared between patients with focal epilepsy and controls, revealing multimodal network differences between groups.
Amorphous indium gallium zinc oxide (a-IGZO) has recently made significant advancement as a key material for electronic component design owing to its compatibility with complementary metal oxide semiconductor technolo...
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Current methods for quantifying osteoarthritis severity have limited resolution and accessibility. Patient-recorded outcome measures such as the Knee Injury and Osteoarthritis Outcome Score (KOOS) capture symptom seve...
Current methods for quantifying osteoarthritis severity have limited resolution and accessibility. Patient-recorded outcome measures such as the Knee Injury and Osteoarthritis Outcome Score (KOOS) capture symptom severity, but are subjectively reported and have little correlation with quantifiable metrics of disease such as Kellgren-Lawrence x-ray grade or MRI findings. Knee acoustic emissions (KAEs) offer a convenient, noninvasive option for quantifying joint health. Here, we use machine learning and wearable design to create an interpretable two-stage algorithm for combining KAEs and KOOS scores into an objective, more accessible method of quantifying disease severity. Our algorithm successfully discriminated between early and late-stage osteoarthritis (balanced accuracy = 85%, ROC-AUC = 0.88). The addition of KAEs improved classification of osteoarthritis severity over the use of KAEs (balanced accuracy = 53%, ROC-AUC = 0.786) or KOOS scores alone (balanced accuracy = 63%, ROC-AUC = 0.593). The findings suggest that KAEs combined with patient-recorded metrics can be used to make a more objective and accessible metric for digitally monitoring knee joint health.
The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of healt...
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Bilevel optimization has become a powerful framework in a variety of machine learning applications including signal processing, meta-learning, hyperparameter optimization, reinforcement learning and network architectu...
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Induction motors can be operated as induction generators when additional capacitors are added to the stator terminals. Capacitors connected to induction generators can generate voltage and can provide reactive power. ...
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DRL has emerged as a promising approach for mobile robot navigation in unknown environments without a prior map. However, the performance of DRL methods for this task varies greatly, depending on the choice of algorit...
DRL has emerged as a promising approach for mobile robot navigation in unknown environments without a prior map. However, the performance of DRL methods for this task varies greatly, depending on the choice of algorithm, state representation, and training procedure. In this paper we explore various cutting-edge DRL algorithms, such as policy-, value-, and actor-critic-based approaches. Our results demonstrate the effectiveness of the ranging sensor approach, which achieves robust navigation policies capable of generalizing to unseen virtual environments with a high success rate. We combine Behavior Cloning with Imitation Learning to expedite the training process, leveraging expert demonstrations and reinforcement learning. Our methodology enables faster training while enhancing the learning efficiency and performance of the robot, obtaining better results in terms of crash and success rate, and being able to reach a cumulative reward of approximately 12000.
Path planning is a crucial component of autonomous navigation and frequently demands different priorities such as path length, safety, or energy consumption, with the latter being particularly important in the context...
Path planning is a crucial component of autonomous navigation and frequently demands different priorities such as path length, safety, or energy consumption, with the latter being particularly important in the context of unmanned autonomous vehicles. In many applications, the agent may have to react to environment shifting. Algorithms such as geometric and dynamic programming as well as techniques such as artificial potential fields have been employed to tackle this local planning problem. In recent years, machine learning has gained more evidence in many research fields due to its flexibility and generalization capabilities. In this study, we propose a Q-learning-based approach to local planning, which weighs three crucial factors- path length, safety, and energy consumption- that can be freely adjusted by the user to suit its application’s needs. The performance of the proposed method was tested in simulated static and dynamic scenarios as well as benchmarked with a baseline approach. The results show that it can perform well in both kinds of environments without struggling with the commom pitfalls of other local planning algorithms.
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