75 research outputs found
On a Peculiar Shaped Concretionary Structure from the Stone Quarry of Mt. Mitakigamori in Haruno-cho, Agawa-gun. Kochi Prefecture, Shikoku, Japan
The peculiar-shaped concretionary structure from the Cretaceous Hayama Formation is described and discussed. It is considered valuable for interpretation of the upper and under surface of strata
Practical Commercial 5G Standalone (SA) Uplink Throughput Prediction
While the 5G New Radio (NR) network promises a huge uplift of the uplink
throughput, the improvement can only be seen when the User Equipment (UE) is
connected to the high-frequency millimeter wave (mmWave) band. With the rise of
uplink-intensive smartphone applications such as the real-time transmission of
UHD 4K/8K videos, and Virtual Reality (VR)/Augmented Reality (AR) contents,
uplink throughput prediction plays a huge role in maximizing the users' quality
of experience (QoE). In this paper, we propose using a ConvLSTM-based neural
network to predict the future uplink throughput based on past uplink throughput
and RF parameters. The network is trained using the data from real-world drive
tests on commercial 5G SA networks while riding commuter trains, which
accounted for various frequency bands, handover, and blind spots. To make sure
our model can be practically implemented, we then limited our model to only use
the information available via Android API, then evaluate our model using the
data from both commuter trains and other methods of transportation. The results
show that our model reaches an average prediction accuracy of 98.9\% with an
average RMSE of 1.80 Mbps across all unseen evaluation scenarios
Learned Image Compression with Mixed Transformer-CNN Architectures
Learned image compression (LIC) methods have exhibited promising progress and
superior rate-distortion performance compared with classical image compression
standards. Most existing LIC methods are Convolutional Neural Networks-based
(CNN-based) or Transformer-based, which have different advantages. Exploiting
both advantages is a point worth exploring, which has two challenges: 1) how to
effectively fuse the two methods? 2) how to achieve higher performance with a
suitable complexity? In this paper, we propose an efficient parallel
Transformer-CNN Mixture (TCM) block with a controllable complexity to
incorporate the local modeling ability of CNN and the non-local modeling
ability of transformers to improve the overall architecture of image
compression models. Besides, inspired by the recent progress of entropy
estimation models and attention modules, we propose a channel-wise entropy
model with parameter-efficient swin-transformer-based attention (SWAtten)
modules by using channel squeezing. Experimental results demonstrate our
proposed method achieves state-of-the-art rate-distortion performances on three
different resolution datasets (i.e., Kodak, Tecnick, CLIC Professional
Validation) compared to existing LIC methods. The code is at
https://github.com/jmliu206/LIC_TCM.Comment: Accepted by CVPR2023 (Highlight
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