| 1. |
- Abbafati, Cristiana, et al.
(författare)
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- 2020
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Huang, Jiayu, et al.
(författare)
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A Reconfigurable Near-Sensor Processor for Anomaly Detection in Limb Prostheses
- 2024
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Ingår i: IEEE Transactions on Biomedical Circuits and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 1932-4545 .- 1940-9990. ; , s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a reconfigurable near-sensor anomaly detection processor to real-time monitor the potential anomalous behaviors of amputees with limb prostheses. The processor is low-power, low-latency, and suitable for equipment on the prostheses and comprises a reconfigurable Variational Autoencoder (VAE), a scalable Self-Organizing Map (SOM) Array, and a window-size-adjustable Markov Chain, which can implement an integrated miniaturized anomaly detection system. With the reconfigurable VAE, the proposed processor can support up to 64 sensor sampling channels programmable by global configuration, which can meet the anomaly detection requirements in different scenarios. A scalable SOM array allows for the selection of different sizes based on the complexity of the data. Unlike traditional time accumulation-based anomaly detection methods, the Markov Chain is utilized to detect time-series-based anomalous data. The processor is designed and fabricated in a UMC 40-nm LP technology with a core area of 1.49 mm2 and a power consumption of 1.81 mW. It achieves real-time detection performance with 0.933 average F1 Score for the FSP dataset within 24.22 s, and 0.956 average F1 Score for the SFDLA-12 dataset within 30.48 s, respectively. The energy dissipation of detection for each input feature is 43.84 nJ with the FSP dataset, and 55.17 nJ with the SFDLA-12 dataset. Compared with ARM Cortex-M4 and ARM Cortex-M33 microcontrollers, the processor achieves energy and area efficiency improvements ranging from 257×, 193× and 11×, 8×, respectively. IEEE
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| 3. |
- Li, Jiayu, et al.
(författare)
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Charge transport and electron-hole asymmetry in low-mobility graphene/hexagonal boron nitride heterostructures
- 2018
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Ingår i: Journal of Applied Physics. - : AIP Publishing. - 0021-8979 .- 1089-7550. ; 123:6
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Tidskriftsartikel (refereegranskat)abstract
- Graphene/hexagonal boron nitride (G/h-BN) heterostructures offer an excellent platform for developing nanoelectronic devices and for exploring correlated states in graphene under modulation by a periodic superlattice potential. Here, we report on transport measurements of nearly 0°-twisted G/h-BN heterostructures. The heterostructures investigated are prepared by dry transfer and thermally annealing processes and are in the low mobility regime (approximately 3000 cm2 V-1s-1 at 1.9 K). The replica Dirac spectra and Hofstadter butterfly spectra are observed on the hole transport side, but not on the electron transport side, of the heterostructures. We associate the observed electron-hole asymmetry with the presence of a large difference between the opened gaps in the conduction and valence bands and a strong enhancement in the interband contribution to the conductivity on the electron transport side in the low-mobility G/h-BN heterostructures. We also show that the gaps opened at the central Dirac point and the hole-branch secondary Dirac point are large, suggesting the presence of strong graphene-substrate interaction and electron-electron interaction in our G/h-BN heterostructures. Our results provide additional helpful insight into the transport mechanism in G/h-BN heterostructures.
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| 4. |
- Li, Jiayu, et al.
(författare)
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Electron-Hole Symmetry Breaking in Charge Transport in Nitrogen-Doped Graphene
- 2017
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Ingår i: ACS Nano. - : American Chemical Society (ACS). - 1936-0851 .- 1936-086X. ; 11:5, s. 4641-4650
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Tidskriftsartikel (refereegranskat)abstract
- Graphitic nitrogen-doped graphene is an excellent platform to study scattering processes of massless Dirac Fermions by charged impurities, in which high mobility can be preserved due to the absence of lattice defects through direct substitution of carbon atoms in the graphene lattice by nitrogen atoms. In this work, we report on electrical and magnetotransport measurements of high-quality graphitic nitrogen-doped graphene. We show that the substitutional nitrogen dopants in graphene introduce atomically sharp scatters for electrons but long-range Coulomb scatters for holes and, thus, graphitic nitrogen-doped graphene exhibits clear electron-hole asymmetry in transport properties. Dominant scattering processes of charge carriers in graphitic nitrogen-doped graphene are analyzed. It is shown that the electron-hole asymmetry originates from a distinct difference in intervalley scattering of electrons and holes. We have also carried out the magnetotransport measurements of graphitic nitrogen-doped graphene at different temperatures and the temperature dependences of intervalley scattering, intravalley scattering, and phase coherent scattering rates are extracted and discussed. Our results provide an evidence for the electron-hole asymmetry in the intervalley scattering induced by substitutional nitrogen dopants in graphene and shine a light on versatile and potential applications of graphitic nitrogen-doped graphene in electronic and valleytronic devices.
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| 5. |
- Yuan, Shuai, et al.
(författare)
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Tunable metal hydroxide-organic frameworks for catalysing oxygen evolution
- 2022
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Ingår i: Nature Materials. - : Springer Science and Business Media LLC. - 1476-1122 .- 1476-4660. ; 21:6, s. 673-680
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Tidskriftsartikel (refereegranskat)abstract
- The oxygen evolution reaction is central to making chemicals and energy carriers using electrons. Combining the great tunability of enzymatic systems with known oxide-based catalysts can create breakthrough opportunities to achieve both high activity and stability. Here we report a series of metal hydroxide–organic frameworks (MHOFs) synthesized by transforming layered hydroxides into two-dimensional sheets crosslinked using aromatic carboxylate linkers. MHOFs act as a tunable catalytic platform for the oxygen evolution reaction, where the π–π interactions between adjacent stacked linkers dictate stability, while the nature of transition metals in the hydroxides modulates catalytic activity. Substituting Ni-based MHOFs with acidic cations or electron-withdrawing linkers enhances oxygen evolution reaction activity by over three orders of magnitude per metal site, with Fe substitution achieving a mass activity of 80 A g−1catalyst at 0.3 V overpotential for 20 h. Density functional theory calculations correlate the enhanced oxygen evolution reaction activity with the MHOF-based modulation of Ni redox and the optimized binding of oxygenated intermediates.
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| 6. |
- Zheng, Daniel J., et al.
(författare)
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Linker-Dependent Stability of Metal-Hydroxide Organic Frameworks for Oxygen Evolution
- 2023
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Ingår i: Chemistry of Materials. - : American Chemical Society (ACS). - 0897-4756 .- 1520-5002. ; 35:13, s. 5017-5031
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Tidskriftsartikel (refereegranskat)abstract
- Metal-organic frameworks (MOFs) are periodic organic-inorganicmaterials that have garnered considerable attention for electrocatalyticapplications due to their wide tunability. Metal-hydroxide organicframeworks (MHOFs), a subset of MOFs that combine layered metal hydroxideswith organic ligands of various & pi;-& pi; stacking energy,have shown promising catalytic functions, such as for the oxygen evolutionreaction (OER). The long-term electrochemical stability of these materialsfor the OER is unfortunately not well understood, which is criticalto design practical devices. In this study, we investigated how Ni-basedMHOFs composed of two linkers with different & pi;-& pi;interaction strength (terephthalate; L1 and azobenzene-4,4 & PRIME;-dicarboxylate;L4) change as a function of cycle number and potential for the OER.All MHOFs tested showed significant increases in the number of electrochemicallyactive Ni sites and OER activity when cycled. MHOFs constructed usingthe linkers with stronger & pi;-& pi; stacking energy (L4)were observed to remain intact in bulk with only near-surface transformationsto NiOOH2-x -like phases, whereasMHOFs with linkers of weaker & pi;-& pi; stacking energy(L1) showed complete reconstruction to NiOOH2-x -like phases. This was confirmed using X-ray diffraction,X-ray absorption spectroscopy, and electron microscopy. Further, insitu characterization using Raman and UV-vis revealed thatthe presence of stable linkers within the MHOF structure suppressesthe Ni2+/Ni(3+& delta;)+ redox process. We furtheridentify NiOOH2-x as the OER activephase, while the MHOF phase serves as a precatalyst. We further proposea detailed mechanism for the phase transformation, which providesvaluable insights into the future challenges for the design of bothstable and catalytically active MOF-based materials for water oxidation.
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| 7. |
- Zhu, Zikai, et al.
(författare)
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Using a VAE-SOM architecture for anomaly detection of flexible sensors in limb prosthesis
- 2023
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Ingår i: Journal of Industrial Information Integration. - : Elsevier BV. - 2452-414X .- 2467-964X. ; 35
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Tidskriftsartikel (refereegranskat)abstract
- Flexible wearable sensor electronics, combined with advanced software functions, pave the way toward increasingly intelligent healthcare devices. One important application area is limb prosthesis, where printed flexible sensor solutions enable efficient monitoring and assessing of the actual intra-socket dynamic operation conditions in clinical and other more natural environments. However, the data collected by such sensors suffer from variations and errors, leading to difficulty in perceiving the actual operational conditions. This paper proposes a novel method for detecting anomalies in the data that are collected for measuring the intra-socket dynamic operation conditions by printed flexible wearable sensors. A discrete generative model based on Variational AutoEncoder (VAE) is used first to encode the collected multi-variant time-series data in terms of latent states. After that, a clustering method based on the Self-Organizing Map (SOM) is used to acquire discrete and interpretable representations of the VAE encoded latent states. An adaptive Markov chain is utilized to detect anomalies by quantifying state transitions and revealing temporal dependencies. The contributions of the proposed architecture conclude as follows: (1) Using the VAE-SOM hybrid model to regularize the continues data as discrete states, supporting interpreting the operational data to analytic models. (2) Employing adaptive Markov chains to generalize the transitions of these states, allowing to model the complex operational conditions. Compared with benchmark methods, our architecture is validated via two public datasets and achieves the best F1 scores. Moreover, we measure the run-time performance of this lightweight architecture. The results indicate that the proposed method performs low computational complexity, facilitating the applications on real-life productions.
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