Resource Allocation for Capacity Optimization in Joint Source-Channel Coding Systems

Benefited from the advances of deep learning (DL) techniques, deep joint source-channel coding (JSCC) has shown its great potential to improve the performance of wireless transmission. However, most of the existing works focus on the DL-based transceiver design of the JSCC model, while ignoring the resource allocation problem in wireless systems. In this paper, we consider a downlink resource allocation problem, where a base station (BS) jointly optimizes the compression ratio (CR) and power allocation as well as resource block (RB) assignment of each user according to the latency and performance constraints to maximize the number of users that successfully receive their requested content with desired quality. To solve this problem, we first decompose it into two subproblems without loss of optimality. The first subproblem is to minimize the required transmission power for each user under given RB allocation. We derive the closed-form expression of the optimal transmit power by searching the maximum feasible compression ratio. The second one aims at maximizing the number of supported users through optimal user-RB pairing, which we solve by utilizing bisection search as well as Karmarka' s algorithm. Simulation results validate the effectiveness of the proposed resource allocation method in terms of the number of satisfied users with given resources.Comment: 6 pages, 6 figure

MIMO Precoding Design with QoS and Per-Antenna Power Constraints

Precoding design for the downlink of multiuser multiple-input multiple-output (MU-MIMO) systems is a fundamental problem. In this paper, we aim to maximize the weighted sum rate (WSR) while considering both quality-of-service (QoS) constraints of each user and per-antenna power constraints (PAPCs) in the downlink MU-MIMO system. To solve the problem, we reformulate the original problem to an equivalent problem by using the well-known weighted minimal mean square error (WMMSE) framework, which can be tackled by iteratively solving three subproblems. Since the precoding matrices are coupled among the QoS constraints and PAPCs, we adopt alternating direction method of multipliers (ADMM) to obtain a distributed solution. Simulation results validate the effectiveness of the proposed algorithm

Semantic-aware Transmission for Robust Point Cloud Classification

As three-dimensional (3D) data acquisition devices become increasingly prevalent, the demand for 3D point cloud transmission is growing. In this study, we introduce a semantic-aware communication system for robust point cloud classification that capitalizes on the advantages of pre-trained Point-BERT models. Our proposed method comprises four main components: the semantic encoder, channel encoder, channel decoder, and semantic decoder. By employing a two-stage training strategy, our system facilitates efficient and adaptable learning tailored to the specific classification tasks. The results show that the proposed system achieves classification accuracy of over 89\% when SNR is higher than 10 dB and still maintains accuracy above 66.6\% even at SNR of 4 dB. Compared to the existing method, our approach performs at 0.8\% to 48\% better across different SNR values, demonstrating robustness to channel noise. Our system also achieves a balance between accuracy and speed, being computationally efficient while maintaining high classification performance under noisy channel conditions. This adaptable and resilient approach holds considerable promise for a wide array of 3D scene understanding applications, effectively addressing the challenges posed by channel noise.Comment: submitted to globecom 202

Seismic response analysis on shear lag effect of continuous curved box girder with three spans

Shear lag effect of continuous curved box girder with three spans under seismic excitation is studied in this paper. Firstly, spatial shell finite element model is founded by ANSYS, and EL-centro seismic wave is chosen as seismic excitation. Secondly, the shear lag effect at different cross sections are investigated with dynamic time-history analysis, the results show that under seismic excitation there is prominent shear lag effect in continuous curved box girder, the maximum shear lag coefficient is 3.02, shear lag effect is severe, shear lag effect at mid-span cross sections are prominent than support cross sections, and inside peak shear lag coefficients are generally greater than outside. Finally, the numeric results are compared with the experimental results from a vibration table testing, which shows great consistencies

CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation

Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress, there are still some challenges for these methods, such as indistinguishable weak defects and defect-like interference in the background. To address these issues, we propose a transformer network with multi-stage CNN (Convolutional Neural Network) feature injection for surface defect segmentation, which is a UNet-like structure named CINFormer. CINFormer presents a simple yet effective feature integration mechanism that injects the multi-level CNN features of the input image into different stages of the transformer network in the encoder. This can maintain the merit of CNN capturing detailed features and that of transformer depressing noises in the background, which facilitates accurate defect detection. In addition, CINFormer presents a Top-K self-attention module to focus on tokens with more important information about the defects, so as to further reduce the impact of the redundant background. Extensive experiments conducted on the surface defect datasets DAGM 2007, Magnetic tile, and NEU show that the proposed CINFormer achieves state-of-the-art performance in defect detection

Long-term postoperative quality of life in childhood survivors with cerebellar mutism syndrome

BackgroundTo investigate the long-term quality of life (QoL) of children with cerebellar mutism syndrome (CMS) and explore the risk factors for a low QoL.ProcedureThis cross-sectional study investigated children who underwent posterior fossa surgery using an online Pediatric Quality of Life Inventory questionnaire. CMS and non-CMS patients were included to identify QoL predictors.ResultsSixty-nine patients were included (male, 62.3%), 22 of whom had CMS. The mean follow-up time was 45.2 months. Children with CMS had a significantly lower mean QoL score (65.3 vs. 83.7, p < 0.001) and subdomain mean scores (physical; 57.8 vs. 85.3, p < 0.001; social: 69.5 vs. 85.1, p = 0.001; academic: p = 0.001) than those without CMS, except for the emotional domain (78.0 vs. 83.7, p = 0.062). Multivariable analysis revealed that CMS (coefficient = −14.748.61, p = 0.043), chemotherapy (coefficient = −7.629.82, p = 0.013), ventriculoperitoneal (VP) shunt placement (coefficient = −10.14, p = 0.024), and older age at surgery (coefficient = −1.1830, p = 0.007) were independent predictors of low total QoL scores. Physical scores were independently associated with CMS (coefficient = −27.4815.31, p = 0.005), VP shunt placement (coefficient = −12.86, p = 0.025), and radiotherapy (coefficient = −13.62, p = 0.007). Emotional score was negatively associated with age at surgery (coefficient = −1.92, p = 0.0337) and chemotherapy (coefficient = −9.11, p = 0.003). Social scores were negatively associated with male sex (coefficient = −13.68, p = 0.001) and VP shunt placement (coefficient = −1.36, p = 0.005), whereas academic scores were negatively correlated with chemotherapy (coefficient = −17.45, p < 0.001) and age at surgery (coefficient = −1.92, p = 0.002). Extent of resection (coefficient = 13.16, p = 0.021) was a good predictor of higher academic scores.ConclusionCMS results in long-term neurological and neuropsychological deficits, negatively affecting QoL, and warranting early rehabilitation

CD8(+) T Cells Involved in Metabolic Inflammation in Visceral Adipose Tissue and Liver of Transgenic Pigs

Anti-inflammatory therapies have the potential to become an effective treatment for obesity-related diseases. However, the huge gap of immune system between human and rodent leads to limitations of drug discovery. This work aims at constructing a transgenic pig model with higher risk of metabolic diseases and outlining the immune responses at the early stage of metaflammation by transcriptomic strategy. We used CRISPR/Cas9 techniques to targeted knock-in three humanized disease risk genes, GIPR(dn) , hIAPP and PNPLA3(I148M) . Transgenic effect increased the risk of metabolic disorders. Triple-transgenic pigs with short-term diet intervention showed early symptoms of type 2 diabetes, including glucose intolerance, pancreatic lipid infiltration, islet hypertrophy, hepatic lobular inflammation and adipose tissue inflammation. Molecular pathways related to CD8(+) T cell function were significantly activated in the liver and visceral adipose samples from triple-transgenic pigs, including antigen processing and presentation, T-cell receptor signaling, co-stimulation, cytotoxicity, and cytokine and chemokine secretion. The similar pro-inflammatory signaling in liver and visceral adipose tissue indicated that there might be a potential immune crosstalk between the two tissues. Moreover, genes that functionally related to liver antioxidant activity, mitochondrial function and extracellular matrix showed distinct expression between the two groups, indicating metabolic stress in transgenic pigs' liver samples. We confirmed that triple-transgenic pigs had high coincidence with human metabolic diseases, especially in the scope of inflammatory signaling at early stage metaflammation. Taken together, this study provides a valuable large animal model for the clinical study of metaflammation and metabolic diseases.Peer reviewe