448 research outputs found

    Shape VQ-based adaptive predictive lossless image coder

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    A new shape adaptive predictive lossless image coder is proposed. Three classes of block shapes are delineated with associated “optimum” predctors. Each image is partitioned into sub-blocks that are classified into one of the three classes using vector quantisation. The encoder then employs the predictor corresponding to the class of the block under consideration. Performance evaluation of the proposed coder in comparison with four other lossless coders includmg lossless JPEG indicates its superiority

    Environment-independent mmWave Fall Detection with Interacting Multiple Model

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    The ageing society brings attention to daily elderly care through sensing technologies. The future smart home is expected to enable in-home daily monitoring, such as fall detection, for seniors in a non-invasive, non-cooperative, and non-contact manner. The mmWave radar is a promising candidate technology for its privacy-preserving and non-contact manner. However, existing solutions suffer from low accuracy and robustness due to environment dependent features. In this paper, we present FADE (\underline{FA}ll \underline{DE}tection), a practical fall detection radar system with enhanced accuracy and robustness in real-world scenarios. The key enabler underlying FADE is an interacting multiple model (IMM) state estimator that can extract environment-independent features for highly accurate and instantaneous fall detection. Furthermore, we proposed a robust multiple-user tracking system to deal with noises from the environment and other human bodies. We deployed our algorithm on low computing power and low power consumption system-on-chip (SoC) composed of data front end, DSP, and ARM processor, and tested its performance in real-world. The experiment shows that the accuracy of fall detection is up to 95\%

    MaskClustering: View Consensus based Mask Graph Clustering for Open-Vocabulary 3D Instance Segmentation

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    Open-vocabulary 3D instance segmentation is cutting-edge for its ability to segment 3D instances without predefined categories. However, progress in 3D lags behind its 2D counterpart due to limited annotated 3D data. To address this, recent works first generate 2D open-vocabulary masks through 2D models and then merge them into 3D instances based on metrics calculated between two neighboring frames. In contrast to these local metrics, we propose a novel metric, view consensus rate, to enhance the utilization of multi-view observations. The key insight is that two 2D masks should be deemed part of the same 3D instance if a significant number of other 2D masks from different views contain both these two masks. Using this metric as edge weight, we construct a global mask graph where each mask is a node. Through iterative clustering of masks showing high view consensus, we generate a series of clusters, each representing a distinct 3D instance. Notably, our model is training-free. Through extensive experiments on publicly available datasets, including ScanNet++, ScanNet200 and MatterPort3D, we demonstrate that our method achieves state-of-the-art performance in open-vocabulary 3D instance segmentation. Our project page is at https://pku-epic.github.io/MaskClustering

    MIPS-Fusion: Multi-Implicit-Submaps for Scalable and Robust Online Neural RGB-D Reconstruction

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    We introduce MIPS-Fusion, a robust and scalable online RGB-D reconstruction method based on a novel neural implicit representation -- multi-implicit-submap. Different from existing neural RGB-D reconstruction methods lacking either flexibility with a single neural map or scalability due to extra storage of feature grids, we propose a pure neural representation tackling both difficulties with a divide-and-conquer design. In our method, neural submaps are incrementally allocated alongside the scanning trajectory and efficiently learned with local neural bundle adjustments. The submaps can be refined individually in a back-end optimization and optimized jointly to realize submap-level loop closure. Meanwhile, we propose a hybrid tracking approach combining randomized and gradient-based pose optimizations. For the first time, randomized optimization is made possible in neural tracking with several key designs to the learning process, enabling efficient and robust tracking even under fast camera motions. The extensive evaluation demonstrates that our method attains higher reconstruction quality than the state of the arts for large-scale scenes and under fast camera motions

    Si-based anode materials for lithium rechargeable batteries

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    Silicon is a very promising candidate to replace graphite as the anode in Li-ion batteries because of its very high theoretical capacity, relatively low cost and low toxicity. However, it has not yet made its way into commercial cells. This review highlights recent progress on Si-based anode materials for lithium rechargeable batteries

    Synthesis and electrochemical properties of WO3/C for lithium ion batteries

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    WO3/C nanorods were prepared by a combination of hydrothermal synthesis method and the solid phase reaction method, using (NH4)10H2(W2O7)6, H2C2O4·2H2O and glucose(carbon source) as raw materials. The effects of different proportions of glucose on the morphologies and electrochemical properties of the final products were systematically investigated. The results showed that the WO3/C nanorods prepared with the 10 wt.% glucose as carbon source exhibited the highest reversible specific capacity (807 mAh g-1) at current density of 50 mA g-1 and the best cycle performances among all samples. Besides, it behaved good rate performance. It indicated that WO3/C nanorods could be promising electrode materials for lithium ion battery application

    Ball-milled FeP/graphite as a low-cost anode material for the sodium-ion battery

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    Phosphorus is a promising anode material for sodium batteries with a theoretical capacity of 2596 mA h g-1. However, phosphorus has a low electrical conductivity of 1 x 10-14 S cm-1, which results in poor cycling and rate performances. Even if it is alloyed with conductive Fe, it still delivers a poor electrochemical performance. In this article, a FeP/graphite composite has been synthesized using a simple, cheap, and productive method of low energy ball-milling, which is an efficient way to improve the electrical conductivity of the FeP compound. The cycling performance was improved significantly, and when the current density increased to 500 mA g-1, the FeP/graphite composite could still deliver 134 mA h g-1, which was more than twice the capacity of the FeP compound alone. Our results suggest that by using a low-energy ball-milling method, a promising FeP/graphite anode material can be synthesized for the sodium battery

    A germanium/single-walled carbon nanotube composite paper as a free-standing anode for lithium-ion batteries

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    Paper-like free-standing germanium (Ge) and single-walled carbon nanotube (SWCNT) composite anodes were synthesized by the vacuum filtration of Ge/SWCNT composites, which were prepared by a facile aqueous-based method. The samples were characterized by X-ray diffraction, field emission scanning electron microscopy, and transmission electron microscopy. Electrochemical measurements demonstrate that the Ge/SWCNT composite paper anode with the weight percentage of 32% Ge delivered a specific discharge capacity of 417 mA h g−1 after 40 cycles at a current density of 25 mA g−1, 117% higher than the pure SWCNT paper anode. The SWCNTs not only function as a flexible mechanical support for strain release, but also provide excellent electrically conducting channels, while the nanosized Ge particles contribute to improving the discharge capacity of the paper anode

    Binder-free and carbon-free 3D porous air electrode for Li-O2 batteries with high efficiency, high capacity, and long life

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    Pt-Gd alloy polycrystalline thin film is deposited on 3D nickel foam by pulsed laser deposition method serving as a whole binder/carbon-free air electrode, showing great catalytic activity enhancement as an efficient bifunctional catalyst for the oxygen reduction and evolution reactions in lithium oxygen batteries. The porous structure can facilitate rapid O2 and electrolyte diffusion, as well as forming a continuous conductive network throughout the whole energy conversion process. It shows a favorable cycle performance in the full discharge/charge model, owing to the high catalytic activity of the Pt-Gd alloy composite and 3D porous nickel foam structure. Specially, excellent cycling performance under capacity limited mode is also demonstrated, in which the terminal discharge voltage is higher than 2.5 V and the terminal charge voltage is lower than 3.7 V after 100 cycles at a current density of 0.1 mA cm−2. Therefore, this electrocatalyst is a promising bifunctional electrocatalyst for lithium oxygen batteries and this depositing high-efficient electrocatalyst on porous substrate with polycrystalline thin film by pulsed laser deposition is also a promising technique in the future lithium oxygen batteries research
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