High Absorption Electromagnetic Wave Properties of Composite CoFeO3 Synthesized by Simple Mechanical Alloying

Electronic equipment demand is strongly correlated to the electromagnetic wave interference (EMI), which causes severe effects on human health. Microwave absorbing materials (MAMs) are one method to protect human health from EMI. Cobalt nanoparticles show high performance as MAMs. Here, we have synthesized CoFeO3 by simple mechanical alloying for increased multiple reflections, interfacial polarization, magnetic domain loss, electron spin loss, internal resonance, hoping electron, conductive loss, and multiple scattering for improved absorption of EMI waves. We determined the electronic properties from the Quantum Espresso (QE) and corresponding results are discussed. The metallic character comes from the d-state of transition metal atoms Fe (II) and Co which are sufficiently large in magnitude in the Fermi level of band structure and density of state (DOS) distribution. Crystallite size in the range of 13.6 to 18.7 nm with surface morphology shows irregular shapes of the particles. For CoFeO3 as MAMs, we found that the reflection loss (RL) is -55 dB (lower than the previous reported -43.2 dB) at 10-11 GHz for a thickness of 8 mm, indicating that this study shows high potential of CoFeO3 as an alternative composite for MAMs applications.

Englishization in Danish higher education

This chapter offers a longitudinal overview of Englishization in Danish higher education, tracing its conceptualization from critical to constructive. In the initial stages, English was viewed sceptically, with concerns over domain loss, equity, quality of education, and consequences for the national language and culture. Such concerns led to a joint Nordic language policy promoting parallel language use, that is, a balanced use of English and the national language. In Denmark, this concept has been particularly salient, with all Danish universities having some sort of parallel language policy (Hultgren, 2014b). Recently, more constructive stances have replaced the concerns, perhaps recognizing that the continued expansion of Englishization is inevitable. Today, numerous Danish initiatives advance practical solutions to secure a smooth implementation of English medium of instruction.

Neural-Network-Based Cross-Channel Intra Prediction

To reduce the redundancy among different color channels, e.g., YUV, previous methods usually adopt a linear model that tends to be oversimple for complex image content. We propose a neural-network-based method for cross-channel prediction in intra frame coding. The proposed network utilizes twofold cues, i.e., the neighboring reconstructed samples with all channels, and the co-located reconstructed samples with partial channels. Specifically, for YUV video coding, the neighboring samples with YUV are processed by several fully connected layers; the co-located samples with Y are processed by convolutional layers; and the proposed network fuses the twofold cues. We observe that the integration of twofold information is crucial to the performance of intra prediction of the chroma components. We have designed the network architecture to achieve a good balance between compression performance and computational efficiency. Moreover, we propose a transform domain loss for the training of the network. The transform domain loss helps obtain more compact representations of residues in the transform domain, leading to higher compression efficiency. The proposed method is plugged into HEVC and VVC test models to evaluate its effectiveness. Experimental results show that our method provides more accurate cross-channel intra prediction compared with previous methods. On top of HEVC, our method achieves on average 1.3%, 5.4%, and 3.8% BD-rate reductions for Y, Cb, and Cr on common test sequences, and on average 3.8%, 11.3%, and 9.0% BD-rate reductions for Y, Cb, and Cr on ultra-high-definition test sequences. On top of VVC, our method achieves on average 0.5%, 1.7%, and 1.3% BD-rate reductions for Y, Cb, and Cr on common test sequences.

Frequency-domain loss function for deep exposure correction of dark images

We address the problem of exposure correction of dark, blurry and noisy images captured in low-light conditions in the wild. Classical image-denoising filters work well in the frequency space but are constrained by several factors such as the correct choice of thresholds and frequency estimates. On the other hand, traditional deep networks are trained end to end in the RGB space by formulating this task as an image translation problem. However, that is done without any explicit constraints on the inherent noise of the dark images and thus produces noisy and blurry outputs. To this end, we propose a DCT/FFT-based multi-scale loss function, which when combined with traditional losses, trains a network to translate the important features for visually pleasing output. Our loss function is end to end differentiable, scale-agnostic and generic; i.e., it can be applied to both RAW and JPEG images in most existing frameworks without additional overhead. Using this loss function, we report significant improvements over the state of the art using quantitative metrics and subjective tests.