Semantic Communications with Explicit Semantic Base for Image Transmission

Semantic communications, aiming at ensuring the successful delivery of the meaning of information, are expected to be one of the potential techniques for the next generation communications. However, the knowledge forming and synchronizing mechanism that enables semantic communication systems to extract and interpret the semantics of information according to the communication intents is still immature. In this paper, we propose a semantic image transmission framework with explicit semantic base (Seb), where Sebs are generated and employed as the knowledge shared between the transmitter and the receiver with flexible granularity. To represent images with Sebs, a novel Seb-based reference image generator is proposed to generate Sebs and then decompose the transmitted images. To further encode/decode the residual information for precise image reconstruction, a Seb-based image encoder/decoder is proposed. The key components of the proposed framework are optimized jointly by end-to-end (E2E) training, where the loss function is dedicated designed to tackle the problem of nondifferentiable operation in Seb-based reference image generator by introducing a gradient approximation mechanism. Extensive experiments show that the proposed framework outperforms state-of-art works by 0.5 - 1.5 dB in peak signal-to-noise ratio (PSNR) w.r.t. different signal-to-noise ratio (SNR)

Sensitivity analysis of machine components thermal properties effects on winding temperature

This paper investigates the sensitivity analysis of winding temperature to key parameters in electrical machine thermal design. With a validated 3D thermal model based on an existing 75kW traction machine for an electric vehicle, the methodology of the sensitivity analysis study is conducted and presented. Finally, further research and practical guidelines on reducing the peak temperature of electrical machines are proposed

Equivalent slot thermal conductivity and back-iron extension effects on machine cooling

Back-iron Extension (BIE) is an effective thermal management technique which reduces the winding temperatures by projecting part of the back iron into the center of slot, thereby shortening the heat transfer path between the coil and back iron. Based on an existing concentrated-wound traction motor, this paper investigates the effects of equivalent slot thermal conductivity of coil on the optimal back iron extension geometry and temperature reduction

Harmonization of multi-site functional MRI data with dual-projection based ICA model

Modern neuroimaging studies frequently merge magnetic resonance imaging (MRI) data from multiple sites. A larger and more diverse group of participants can increase the statistical power, enhance the reliability and reproducibility of neuroimaging research, and obtain findings more representative of the general population. However, measurement biases caused by site differences in scanners represent a barrier when pooling data collected from different sites. The existence of site effects can mask biological effects and lead to spurious findings. We recently proposed a powerful denoising strategy that implements dual-projection (DP) theory based on ICA to remove site-related effects from pooled data, demonstrating the method for simulated and in vivo structural MRI data. This study investigates the use of our DP-based ICA denoising method for harmonizing functional MRI (fMRI) data collected from the Autism Brain Imaging Data Exchange II. After frequency-domain and regional homogeneity analyses, two modalities, including amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), were used to validate our method. The results indicate that DP-based ICA denoising method removes unwanted site effects for both two fMRI modalities, with increases in the significance of the associations between non-imaging variables (age, sex, etc.) and fMRI measures. In conclusion, our DP method can be applied to fMRI data in multi-site studies, enabling more accurate and reliable neuroimaging research findings

Dynamic UAV Swarm Collaboration for Multi-Targets Tracking under Malicious Jamming: Joint Power, Path and Target Association Optimization

In this paper, the multi-target tracking (MTT) with an unmanned aerial vehicle (UAV) swarm is investigated in the presence of jammers, where UAVs in the swarm communicate with each other to exchange information of targets during tracking. The communication between UAVs suffers from severe interference, including inter-UAV interference and jamming, thus leading to a deteriorated quality of MTT. To mitigate the interference and achieve MTT, we formulate a interference minimization problem by jointly optimizing UAV's sub-swarm division, trajectory, and power, subject to the constraint of MTT, collision prevention, flying ability, and UAV energy consumption. Due to the multiple coupling of sub-swarm division, trajectory, and power, the proposed optimization problem is NP-hard. To solve this challenging problem, it is decomposed into three subproblems, i.e., target association, path plan, and power control. First, a cluster-evolutionary target association (CETA) algorithm is proposed, which involves dividing the UAV swarm into the multiple sub-swarms and individually matching these sub-swarms to targets. Second, a jamming-sensitive and singular case tolerance (JSSCT)-artificial potential field (APF) algorithm is proposed to plan trajectory for tracking the targets. Third, we develop a jamming-aware mean field game (JA-MFG) power control scheme, where a novel cost function is established considering the total interference. Finally, to minimize the total interference, a dynamic collaboration approach is designed. Simulation results validate that the proposed dynamic collaboration approach reduces average total interference, tracking steps, and target switching times by 28%, 33%, and 48%, respectively, comparing to existing baselines.Comment: 14 pages, 17 figure

Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression

To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscillatory brain patterns were determined by permutation correlation analysis between individual time courses of Fourier-ICA components and musical features. We found that (1) three components, including a beta sensorimotor network, a beta auditory network and an alpha medial visual network, were involved in music processing among most healthy subjects; and that (2) one alpha lateral component located in the left angular gyrus was engaged in music perception in most individuals with MDD. The proposed method allowed the statistical group comparison, and we found that: (1) the alpha lateral component was activated more strongly in healthy subjects than in the MDD individuals, and that (2) the derived frequency-dependent networks of musical feature processing seemed to be altered in MDD participants compared to healthy subjects. The proposed pipeline appears to be valuable for studying disrupted brain oscillations in psychiatric disorders during naturalistic paradigms.Peer reviewe

Noninvasive prenatal diagnosis of 21-Hydroxylase deficiency using target capture sequencing of maternal plasma DNA.

Here, we aimed to validate a noninvasive method using capture sequencing for prenatal diagnosis of congenital adrenal hyperplasia due to 21-Hydroxylase deficiency (21-OHD). Noninvasive prenatal diagnosis (NIPD) of 21-OHD was based on 14 plasma samples collected from 12 families, including four plasma sample collected during the first trimester. Targeted capture sequencing was performed using genomic DNA from the parents and child trios to determine the pathogenic and wild-type alleles associated with the haplotypes. Maternal plasma DNA was also sequenced to determine the fetal inheritance of the allele using hidden Markov model-based haplotype linkage analysis. The effect of fetal DNA fraction and sequencing depth on the accuracy of NIPD was investigated. The lower limit of fetal DNA fraction was 2% and the threshold mean sequence depth was 38, suggesting potential advantage if used in early gestation. The CYP21A2 genotype of the fetus was accurately determined in all the 14 plasma samples as early as day 1 and 8 weeks of gestation. Results suggest the accuracy and feasibility of NIPD of 21-OHD using a small target capture region with a low threshold for fetal DNA fraction and sequence depth. Our method is cost-effective and suggests diagnostic applications in clinical practice

The single factor experiment of the non-linear tube in abrasive flow machining

In order to obtain high quality of special channel surface and improve overall performance of machine or parts, this paper regarded the non linear tube-nozzle as the research object, and the single factor experiment was performed in the critical process parameter of abrasive flow machining(AFM) with self-developed abrasive medium, to study the relationship between process parameters and channel surface of microstructure and the influence of process parameters on the workpiece surface quality. The results show that abrasive flow technology can obviously improve surface quality of the non-linear tube, and has important practical value to improve the stability and the functional performance of the non-linear tube. The results can provide technical support for the deep research in the theory of abrasive flow machining

A Review of Carbon Emissions from Electrical Machine Materials

As the world embarks on a global mission to tackle climate change, reducing carbon represents a key challenge given the escalating global warming. The U.K. is among many other nations that are determined to decarbonise all sectors and strive to achieve a net zero carbon target by 2050. While much attention has been paid to improving performance and reducing carbon emissions in electrical machines, the current research landscape focuses mainly on the thermal and electromagnetic facets. Surprisingly, carbon emissions from the production stage, especially those related to raw material consumption, remain a largely unexplored area. This paper wishes to shed light on a neglected dimension by providing a comprehensive review of carbon emissions in the manufacture of electrical machines, thus contributing significantly to the wider discourse on carbon emission reduction by comparing the carbon emission values associated with various materials commonly used for the main components of these machines. A further case study is included to assess and explore the impact of material alterations on a synchronous machine, from a carbon emission perspective. A reliable material guide will provide engineers at the design stage with the critical insight needed to make informed material selection decisions, highlighting the critical role of carbon emission values beyond conventional thermal and electromagnetic considerations, achieving sustainable and environmentally conscious electrical machine design