Variational Speech Waveform Compression to Catalyze Semantic Communications

We propose a novel neural waveform compression method to catalyze emerging speech semantic communications. By introducing nonlinear transform and variational modeling, we effectively capture the dependencies within speech frames and estimate the probabilistic distribution of the speech feature more accurately, giving rise to better compression performance. In particular, the speech signals are analyzed and synthesized by a pair of nonlinear transforms, yielding latent features. An entropy model with hyperprior is built to capture the probabilistic distribution of latent features, followed with quantization and entropy coding. The proposed waveform codec can be optimized flexibly towards arbitrary rate, and the other appealing feature is that it can be easily optimized for any differentiable loss function, including perceptual loss used in semantic communications. To further improve the fidelity, we incorporate residual coding to mitigate the degradation arising from quantization distortion at the latent space. Results indicate that achieving the same performance, the proposed method saves up to 27% coding rate than widely used adaptive multi-rate wideband (AMR-WB) codec as well as emerging neural waveform coding methods

Wireless Deep Speech Semantic Transmission

In this paper, we propose a new class of high-efficiency semantic coded transmission methods for end-to-end speech transmission over wireless channels. We name the whole system as deep speech semantic transmission (DSST). Specifically, we introduce a nonlinear transform to map the speech source to semantic latent space and feed semantic features into source-channel encoder to generate the channel-input sequence. Guided by the variational modeling idea, we build an entropy model on the latent space to estimate the importance diversity among semantic feature embeddings. Accordingly, these semantic features of different importance can be allocated with different coding rates reasonably, which maximizes the system coding gain. Furthermore, we introduce a channel signal-to-noise ratio (SNR) adaptation mechanism such that a single model can be applied over various channel states. The end-to-end optimization of our model leads to a flexible rate-distortion (RD) trade-off, supporting versatile wireless speech semantic transmission. Experimental results verify that our DSST system clearly outperforms current engineered speech transmission systems on both objective and subjective metrics. Compared with existing neural speech semantic transmission methods, our model saves up to 75% of channel bandwidth costs when achieving the same quality. An intuitive comparison of audio demos can be found at https://ximoo123.github.io/DSST

Low-cost flexible plasmonic nanobump metasurfaces for label-free sensing of serum tumor marker.

The use of plasmonic metasurface for sensing has great potential on label-free detection of human tumor markers, which could benefit clinical examination. In this work, we adopt nanoimprint and plasma etching to optimize the nanofabrication for low-cost flexible plasmonic metasurface sensors with gold nanobump arrays, which enable facile surface bio-functionality, high sensitivity and simple optical measurement in the visible range. A high bulk refractive index sensitivity of 454.4 nm/RIU is achieved for the prototype plasmonic metasurface sensors at the wavelengths from 620 nm to 720 nm. The rapid quantitative tumor marker sensing of carcinoembryonic antigen in human serum samples from less than 10 ng/mL to more than 87 ng/mL is achieved, which demonstrates good agreement with the conventional chemiluminescence immunoassay system and sufficiently covers the threshold tumor marker concentration of 20 ng/mL for early cancer prediction. Our method is capable of low-cost high-throughput manufacturing for flexible lightweight plasmonic metasurface sensors, which will facilitate wide applications on portable biomedical sensing devices for future point-of-care diagnosis and mobile healthcare

Low-cost flexible plasmonic nanobump metasurfaces for label-free sensing of serum tumor marker

Abstract(#br)The use of plasmonic metasurface for sensing has great potential on label-free detection of human tumor markers, which could benefit clinical examination. In this work, we adopt nanoimprint and plasma etching to optimize the nanofabrication for low-cost flexible plasmonic metasurface sensors with gold nanobump arrays, which enable facile surface bio-functionality, high sensitivity and simple optical measurement in the visible range. A high bulk refractive index sensitivity of 454.4 nm/RIU is achieved for the prototype plasmonic metasurface sensors at the wavelengths from 620 nm to 720 nm. The rapid quantitative tumor marker sensing of carcinoembryonic antigen in human serum samples from less than 10 ng/mL to more than 87 ng/mL is achieved, which demonstrates good agreement with the conventional chemiluminescence immunoassay system and sufficiently covers the threshold tumor marker concentration of 20 ng/mL for early cancer prediction. Our method is capable of low-cost high-throughput manufacturing for flexible lightweight plasmonic metasurface sensors, which will facilitate wide applications on portable biomedical sensing devices for future point-of-care diagnosis and mobile healthcare

Model for the prediction of potato impact damage depth

A model was developed to predict the impact damage depth of potato. The model consisted of a series of differential equations derived from the force equilibrium on a differential element, which, respectively, governs the relationships among the potato collision displacement and initial collision speed, natural angular frequency of undamped system, and damping ratio. For the collision displacement of potato impact with the rod, the study used the impact test rig and acceleration acquisition system to measure the corresponding values under different experimental conditions. Combined with the determinate experimental data, the mentioned parameters of the model were obtained. According to the model, the obtained maximum value of potato collision displacement was treated as the prediction value of damage depth. The results showed both the experimental values and prediction values of potato damage depth increased with the increment of initial height, and the difference between experimental values and prediction values was less than 8.8%. To evaluate the significant factor of impact damage depth of potato, this study selected the tuber temperature, initial height, tuber mass, and impact material as experiment factors in the orthogonal tests and the order of influencing factors was found to be as follows: initial height > tuber mass > tuber temperature > impact material

Research on Energy-saving Performance of Low-energy Consumption Green Buildings

In view of the shortcomings of the current green building evaluation system, this paper present a new evaluating index for the energy saving performance of low-energy green buildings: CEC, and this paper evaluated and analyzed the energy-saving performance of green building by using CEC index

Infrared and Visible Image Fusion Method Based on a Principal Component Analysis Network and Image Pyramid

The aim of infrared (IR) and visible image fusion is to generate a more informative image for human observation or some other computer vision tasks. The activity-level measurement and weight assignment are two key parts in image fusion. In this paper, we propose a novel IR and visible fusion method based on the principal component analysis network (PCANet) and an image pyramid. Firstly, we use the lightweight deep learning network, a PCANet, to obtain the activity-level measurement and weight assignment of IR and visible images. The activity-level measurement obtained by the PCANet has a stronger representation ability for focusing on IR target perception and visible detail description. Secondly, the weights and the source images are decomposed into multiple scales by the image pyramid, and the weighted-average fusion rule is applied at each scale. Finally, the fused image is obtained by reconstruction. The effectiveness of the proposed algorithm was verified by two datasets with more than eighty pairs of test images in total. Compared with nineteen representative methods, the experimental results demonstrate that the proposed method can achieve the state-of-the-art results in both visual quality and objective evaluation metrics

Observing Individuals and Behavior of Hainan Gibbons (<i>Nomascus hainanus</i>) Using Drone Infrared and Visible Image Fusion Technology

The Hainan gibbon (Nomascus hainanus) is one of the most endangered primates in the world. Infrared and visible images taken by drones are an important and effective way to observe Hainan gibbons. However, a single infrared or visible image cannot simultaneously observe the movement tracks of Hainan gibbons and the appearance of the rainforest. The fusion of infrared and visible images of the same scene aims to generate a composite image which can provide a more comprehensive description of the scene. We propose a fusion method of infrared and visible images of the Hainan gibbon for the first time, termed Swin-UetFuse. The Swin-UetFuse has a powerful global and long-range semantic information extraction capability, which is very suitable for application in complex tropical rainforest environments. Firstly, the hierarchical Swin Transformer is applied as the encoder to extract the features of different scales of infrared and visible images. Secondly, the features of different scales are fused through the l1-norm strategy. Finally, the Swing Transformer blocks and patch-expanding layers are utilized as the decoder to up-sample the fusion features to obtain the fused image. We used 21 pairs of Hainan gibbon datasets to perform experiments, and the experimental results demonstrate that the proposed method achieves excellent fusion performance. The infrared and visible image fusion technology of drones provides an important reference for the observation and protection of the Hainan gibbons