This paper introduces a novel adaptation approach for first-order pre- and de-emphasis filters, an essential tool in many speech and audio codecs to increase coding efficiency and perceived quality. The proposed zero-...
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Internet service providers (ISPs) have been using Machine Learning (ML)-based network traffic classification in recent years primarily to dynamically adjust their networks to the growing needs of their customers. Even...
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This research introduces a novel, real-time, non-invasive, non-destructive, and accurate method for battery mon-itoring using magnetic field mapping. This technique detects stratification in flooded Lead Acid Batterie...
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A two-layered high Gain and wide Bandwidth Metamaterial Antenna working at 26 GHz for mm-Wave applications is presented in this paper. The antenna has an overall compact size of 12.4 x 12.4 x 8.38 mm3. It is realized ...
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The size of spatial receptive fields, from the early 3×3 convolutions in VGGNet to the recent 7×7 convolutions in ConvNeXt, has always played a critical role in architecture design. In this paper, we propose...
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The size of spatial receptive fields, from the early 3×3 convolutions in VGGNet to the recent 7×7 convolutions in ConvNeXt, has always played a critical role in architecture design. In this paper, we propose a Mixture of Receptive Fields (MoRF) instead of using a single receptive field. MoRF contains the combinations of multiple receptive fields with different sizes, e.g., convolutions with different kernel sizes, which can be regarded as experts. Such an approach serves two functions: one is to select the appropriate receptive field according to the input, and the other is to expand the network capacity. Furthermore, we also introduce two types of routing mechanisms, hard routing and soft routing to automatically select the appropriate receptive field experts. In the inference stage, the selected receptive field experts are merged via re-parameterization to maintain a similar inference speed compared to the single receptive field. To demonstrate the effectiveness of MoRF, we integrate the MoRF concept into multiple architectures, e.g., ResNet and ConvNeXt. Extensive experiments show that our approach outperforms the baselines in image classification, object detection, and segmentation tasks without significantly increasing the inference time. Copyright 2024 by the author(s)
DC fast charging is a key enabler for removing barriers to the widespread adoption of electric vehicles (EVs), offering rapid recharging solutions that alleviate range anxiety. However, at capacity-constrained fast ch...
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In this work, a two-port Multiple Input Multiple Output (MIMO) antenna with a metamaterial surface for mm-Wave 5G MIMO Applications is proposed. The two-port MIMO antenna has a compact size of 10 x 20 x 0.78 mm3 and i...
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Detecting low Radar Cross Section (RCS) targets like UAVs demands advanced radar technologies with high resolution and sensitivity. This study evaluates Beamforming and MIMO radar technologies for their effectiveness ...
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In this paper, we present a cooperative integrated sensing and communication (ISAC) framework in cell-free multiple-input multiple-output (MIMO) systems, where multiple access points (APs), under the control of a cent...
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This paper introduces a new three-phase AC-AC converter with galvanic isolation, reduced power conversion stages and isolated DC ports. The new converter combines two dual active half-bridge (DAHB) converters with two...
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