Parallel Modules Research Articles

Evaluation of the effect of disposal of landfill leachate in a sewage treatment plant composed of stabilization ponds

This study aimed to evaluate the combined treatment system of domestic sewage and leachate by serial stabilization ponds in a Wastewater Treatment Plant (WWTP). The WWTP had two parallel pond modules, one receiving (module II—pond M10) and the other not receiving leachate (module I—pond M5). Physical, chemical, and biological monitoring parameters collected from January 2017 to December 2021 were evaluated. High reducing efficiency for ammonium nitrogen was found, with an average of 95.7 ± 2.5%. BOD5 and COD can be considered statistically different for a 95% confidence level in the two pond modules. The phytoplankton community was composed of 46 taxa distributed in four taxonomic classes: 6 belonging to the Cyanobacteria group (13%), 22 to the Green Algae group (48%), 4 to the Diatoms group (9%), and 14 to the Phytoflagellate group (30%). No ecotoxicity was detected for the evaluated pond effluent samples. Thus, although the insertion of leachate impacted the quality of the final effluent of the ponds and the organic matter, it may not have affected the effluents’ ecotoxicity. This research helps to understand the impacts of long-term leachate insertion in a real treatment system using stabilization ponds for combined treatment. In addition to the usual efficiency evaluation parameters, ecotoxicological tests and phytoplankton are also evaluated.

Baseline-free assisted lamb wave-based damage detection in CFRP composites using graph convolutional networks and Transformer models

Carbon fiber-reinforced polymer (CFRP) composites are widely used in aerospace but are susceptible to damage, threatening structural safety. Thus, developing an effective structural health monitoring system is essential. This study presents CFRP-former, a novel deep learning framework that detects damage in CFRP composites by constructing spatio-temporal representation graphs of Lamb waves (LWs). The CFRP-former comprises three key modules: graph convolutional neural network (GCNN), a multi-layer perceptron (MLP), and a Transformer Encoder. The GCNN models the topological relationships between LW nodes and sensors. The MLP reduces data dimensionality while preserving key information for subsequent analysis. Meanwhile, the Transformer Encoder, employing multi-head attention mechanisms, captures global time-series patterns in the LW signals. Additionally, the CFRP-former framework includes a parallel module that separately predicts both the size and location of damage. Experiments using only four sensors show that CFRP-former achieves high accuracy in detecting and quantifying damage, with robustness confirmed through ablation tests.

A hybrid CNN-transformer network: Accurate and efficient semantic segmentation of crops and weeds on resource-constrained embedded devices

Weed control plays a crucial role in agricultural production. The utilization of advanced vision algorithms on intelligent weeding robots enables the autonomous and efficient resolution of weed-related issues. Vision transformers are highly sensitive to plant texture and shape, but their computational cost is too high. Consequently, we propose a novel hybrid CNN-transformer network for the semantic segmentation of crops and weeds on Resource-Constrained Embedded Devices. Our network follows an encoder–decoder structure, incorporating the proposed concat extended downsampling block in the encoder, which increases inference speed by reducing memory access time and improves the accuracy of feature extraction. For global semantic extraction, we introduce the proposed Parallel input transformer semantic enhancement module, which employs a shared transformer block to increase the computation rate. Additionally, Global-Local semantic fusion block mitigates the semantic gap problem well. To fully utilize the transformer’s ability to process plant texture and shape, we employ the fusion enhancement block in the decoder, thus minimizing the loss of feature information. Segmentation results on three publicly benchmark datasets show that our network outperforms the commonly used CNN-based, transformer-based, and hybrid CNN-transformer-based methods in terms of segmentation accuracy. Moreover, our network comprises only 0.1887M parameters and 0.2145G floating-point operations. We also evaluate the inference speed on an NVIDIA Jetson Orin NX embedded system, which result for inference single image 28.28 msec, and achieving a detection speed of 35.36 FPS. The experimental results highlight that our network maintains the best inference speed and exhibits the strongest segmentation performance on resource-constrained embedded systems.

LPI Radar Waveform Recognition Based on Hierarchical Classification Approach and Maximum Likelihood Estimation

The importance of information gathering is emphasized to minimize casualties and economic losses in warfare. Through electronic warfare, which utilizes electromagnetic waves, it is possible to discern the enemy’s intentions and respond accordingly, thereby leading the battle advantageously. Consequently, related research is actively underway. The development of various radar signal modulation techniques has revealed limitations in the existing modulation recognition methods, necessitating the development of distinguishing features to overcome these limitations. This paper proposes and analyzes distinguishing features that can differentiate various modulation schemes. Eleven distinguishing features were employed, and twenty-two types of modulated signals, including analog, digital, and composite modulation, were classified using hierarchical classification approach and maximum likelihood estimation (MLE). The proposed method achieves a recognition performance of 99.76% at an SNR of 20 dB and 98.45% at an SNR of 8 dB.

A multi‐path residual network for image denoising based on edge prior information

AbstractEdge‐preserving denoising is important in image analysis. However, most existing methods suffer from smoothing and loss of high frequency detail at the edges. Aiming at this issue, a multi‐path residual network using edge prior information is proposed. Specifically, the edge is first recovered by a newly designed parallel edge extraction module. Then it is used as a priori to generate affine transform parameters to guide the weight distribution between noise and edge, so that the shallow feature extraction pays more attention to preserving edges. In the deep feature extraction stage, a multi‐scale attention unit is designed to provide the spatial neighbourhood information of the feature points to filter and activate the deep features, which further enhances the ability of the network to extract fine‐grained noisy features. Finally, by taking the difference between the noisy image and the noise feature obtained with residual learning, the denoised image is produced. Extensive experiments show that the PSNR is improved by 0.00–0.78 dB on grey image denoising and 0.00–2.69 dB on colour image denoising compared with others. The denoised image has a better ability to preserve image edges, high frequency details and visual perception.

Effect of module configurations on the performance of parallel-connected lithium-ion battery modules

To meet the power and energy of battery storage systems, lithium-ion batteries have to be connected in parallel to form various battery modules. However, different single module collector configurations (SCCs) and unavoidable interconnect resistances lead to inhomogeneous currents and state-of-charge (SoC) within the module, thereby significantly reducing the module lifetime and safety. Therefore, the correlation of SCCs to module inhomogeneous currents and SoC are worth investigating. Based on mathematical permutations and combinations calculations, all SCCs are firstly defined and obtained in this paper. Then, two parallel modules are developed in Matlab/Simscape based on fresh cell and degradation cell separately, which are used to quantify the influence of all SCCs. Furthermore, multiple module collector configurations (MCCs) and Urban Dynamometer Driving Schedule (UDDS) working condition are considered in the analysis. Results indicate that both module collectors connected to same edge cell of module cause the most inhomogeneity. The maximum SoC difference under the UDDS is only 46 % of the 0.5C. Moreover, MCCs can reduce inhomogeneous currents and SoC within the parallel module, while not all MCCs perform better homogeneous performance than SCCs. Finally, the effect of changes in SCCs on degradation module inhomogeneous currents and SoC is verified as same as for fresh cells parallel module. Based on the above results, the optimal single module collector configuration of the N cells parallel module is obtained through mathematical analysis, which can greatly improve the currents and SoC homogeneity of the module.

Image segmentation of tunnel water leakage defects in complex environments using an improved Unet model

Computer vision technology provides an intelligent means for detecting tunnel water leakage areas. However, the accuracy of defect feature extraction and segmentation is limited by factors such as insufficient lighting and environmental interference inside tunnels. To address the problem, this paper proposes a tunnel water leakage area segmentation network model called Customized Side Guided-Unet (CSG-Unet), using Unet as the baseline model. The main contributions are: (1) To improve the accuracy of water leakage area extraction, a customized side guided term is introduced to direct the net’s attention to the changes in light and shade within the image. A parallel attention network module is designed to extract internal information from the guided term. Subsequently, a strengthened channel attention module aggregates the guided term and the original information to achieve accurate segmentation of water leakage areas; (2) To address the scarcity of tunnel water leakage area datasets, a basic dataset is constructed by collecting data from open-source datasets and manually gathered data in tunnels. On this basis, perspective transformation is used to change the camera viewpoint, gaussian noise is randomly added to the images in the dataset to simulate images taken in dimly lit scenes, thereby expanding the dataset and enhancing the network’s generalization. The CSG-Unet network was trained using the constructed training set, achieving a mean Intersection over Union (mi IoU) of 85.54%, a mean Dice coefficient (mi Dice) of 85.26%, and a mean Pixel Accuracy (mi PA) of 90.85%. Compared to its baseline network, U-Net (tiny), these metrics show an improvement of over 3.2% in each indicator. Finally, a visual comparison between the improved network and the baseline network further confirms that the proposed model can effectively adapt to the segmentation of water leakage areas in complex environments.

Early diagnosis of Alzheimer’s disease using a group self-calibrated coordinate attention network based on multimodal MRI

Convolutional neural networks (CNNs) for extracting structural information from structural magnetic resonance imaging (sMRI), combined with functional magnetic resonance imaging (fMRI) and neuropsychological features, has emerged as a pivotal tool for early diagnosis of Alzheimer’s disease (AD). However, the fixed-size convolutional kernels in CNNs have limitations in capturing global features, reducing the effectiveness of AD diagnosis. We introduced a group self-calibrated coordinate attention network (GSCANet) designed for the precise diagnosis of AD using multimodal data, including encompassing Haralick texture features, functional connectivity, and neuropsychological scores. GSCANet utilizes a parallel group self-calibrated module to enhance original spatial features, expanding the field of view and embedding spatial data into channel information through a coordinate attention module, which ensures long-term contextual interaction. In a four-classification comparison (AD vs. early MCI (EMCI) vs. late MCI (LMCI) vs. normal control (NC)), GSCANet demonstrated an accuracy of 78.70%. For the three-classification comparison (AD vs. MCI vs. NC), it achieved an accuracy of 83.33%. Moreover, our method exhibited impressive accuracies in the AD vs. NC (92.81%) and EMCI vs. LMCI (84.67%) classifications. GSCANet improves classification performance at different stages of AD by employing group self-calibrated to expand features receptive field and integrating coordinated attention to facilitate significant interactions among channels and spaces. Providing insights into AD mechanisms and showcasing scalability for various disease predictions.

Local-global feature fusion network for hyperspectral image classification

ABSTRACT Hyperspectral images (HSIs) are rich in spatial and spectral information, making them crucial for accurate classification. While existing convolutional neural network (CNN) methods have shown promise in HSI classification, they primarily focus on local features and struggle with establishing long-range dependencies. To address these limitations, we propose a Local-Global Feature Fusion Network (LGFFN). LGFFN effectively learns joint local-global information from HSIs. Firstly, we employ a group-general convolution parallel module (GGCPM) to extract both local and global spectral features. Next, we utilize parallel branches: a CNN branch for capturing fine-grained spatial information and an improved transformer encoder (ITE) branch for capturing long-distance spatial-spectral features and exploiting semantic information. Finally, we elegantly integrate the diverse features obtained from these branches to obtain classification results. We validate our method on four datasets and compare it with six state-of-the-art hyperspectral classification methods. Our experimental results demonstrate that LGFFN achieves high classification accuracy, outperforming existing methods.

Optimization of steel plate quality inspection driven by PscSE and SPPFELAN

AbstractBased on the improved YOLOv8n, a steel plate defect detection and recognition method is proposed to address the high labor costs and workload of traditional tasks. SPPFELAN processes inputs in parallel to enhance computational efficiency by executing multiple pooling operations simultaneously. The parallel feature fusion module PscSE, using a mixed‐dimension SE attention mechanism (scSE), captures global and channel‐related information better, improving characterization capability. The EIOU loss function addresses the ambiguous aspect ratio definition of CIOU loss, enhancing detection accuracy and accelerating convergence. Results show the YOLOv8n‐PscSE‐SPPFELAN model achieves 76.9% mAP@0.5 on the Northeastern University steel plate dataset, a 4.6% improvement over the original YOLOv8n, with a computation amount of 7.7 GFLOPs, reducing resource usage and greatly improving detection speed.

Dual-phase interface engineering via parallel modulation strategy for highly reversible Zn metal batteries

The reversibility and stability of aqueous Zn metal batteries (AZMBs) are largely limited by Zn dendrites and interfacial parasitic reactions. Herein, we propose a parallel modulation strategy to boost the reversibility of the Zn anode by introducing N,N,N’,N’-tetramethylchloroformamidinium hexafluorophosphate (TCFH) as an additive in the electrolyte. TCFH is composed of PF6− and TN+ with opposite charges. PF6− can spontaneously induce the in-situ generation of ZnF2 solid electrolyte interface (SEI) on the anode, which can improve the transport kinetics of Zn2+ at the interface, thus promoting the rapid and uniform deposition of Zn as well as inhibiting the growth of dendrites. In addition, TN+ is enriched at the anode surface during Zn deposition through the anchoring effect, which brings a reconfiguration of the ion/molecule distribution. The anchored-TN+ reduces the concentrations of H2O and SO42−, sufficiently restraining the parasitic reaction. Thanks to the dual-phase interface engineering constructed of PF6− and TN+ in parallel, the symmetric cell with the proposed electrolyte survives long cycling stability over 750 h at 20 mA cm−2, 10 mAh cm−2. This study offers a distinct viewpoint to the multidimensional optimization of Zn anodes for high-performance AZMBs.

병렬 운전을 통한 4kW급 인공위성용 아날로그 LLC 공진형 컨버터 개발

A modular analog LLC resonant converter capable of delivering high power for aerospace applications with zero voltage switching turn on of power semiconductors has been proposed. The LLC converter utilizes a half bridge configuration, with top and bottom switches designed by paralleling two low capacity power semiconductors suitable for use in space, thereby increasing the current capacity to enable an output of up to 1.2kW. Each LLC converter module takes in 100V power and outputs 300V in a step up DC/DC converter configuration, designed to operate with regulated voltage and current. This paper describes the operating principles and steady state analysis of the proposed DC/DC converter. To validate its effectiveness, a prototype was constructed and tested. The tests yielded a max efficiency of 95.3% and an output of approximately 4kW, demonstrating the feasibility and output performance of high efficiency, high power supplies through load sharing of multiple modules in parallel.

Enhancing Skin Disease Diagnosis with TFFNet: A Two-Stream Feature Fusion Network Integrating CNNs and Self Attention Block

The skin of an individual serves as the primary defense mechanism for safe guarding vital organs in the body. Although this barrier effectively protects internal organs from a variety of threats, it is still prone to damage from viral, fungal, or dust-related illnesses. Even minor skin injuries possess the potential to escalate into more severe and hazardous conditions. A prompt and precise skin disease diagnosis becomes crucial in expediting the healing process for individuals grappling with skin-related issues. The objective of this study is to develop a system based on Convolutional Neural Network (CNN) that can accurately identify various skin diseases. The proposed architecture, known as TFFNet (Two-Stream Feature Fusion Network), integrates two simultaneous modules featuring a Self-Attention (SA) block. We employ Self Attention-Convolutional Neural Networks (SACNNs) and Depthwise Separable Convolution (DWSC) to establish a diagnostic system for skin diseases. In this method, two separate CNN models are joined together, and two parallel modules (M1 and M2) are added. This greatly reduces the total number of trainable parameters. In comparison to other deep learning methods outlined in existing literature, the proposed CNN exhibits a notably lower number of learned parameters, specifically around 7 million for classification purposes. The skin disease classification was carried out on three datasets—ISIC2016, ISIC2017, and HAM10000. The model achieved testing accuracies of 89.70%, 90.52%, and 90.12% on each respective dataset.

Quarry rock reef design features influence fish assemblage structure across a systematically heterogenous restoration reef

To restore an area of temperate rocky-reef degraded by sedimentation, scour, and burial, a large quarry rock reef, the Palos Verdes Restoration Reef (PVR), was built with a heterogenous design including high relief elements intended to increase fish biomass productivity and support a diverse reef community. The replicated design features provide a unique opportunity to study the effects of reef design on fish habitat use patterns. To determine how submodule scale habitat features are associated with variation in the assemblage structure of eleven focal fish species on the PVR we conducted diver-operated stereo-video surveys on all 18 PVR modules 9–13 months after construction. The highest mean densities of most focal fish species and highest total fish densities were observed on high and medium-relief reef submodules and their adjacent ecotones positioned on the offshore sides or ends of modules. These included the most abundant species on the PVR, the zooplanktivorous Blacksmith (Chromis punctipinnis), as well as the fishery species Kelp Bass (Paralabrax clathratus) and California Sheephead (Bodianus pulcher). On the inshore side of parallel modules, the reef and ecotone transects on low and medium relief submodules exhibited the lowest total mean fish densities, and consistently lower mean focal fish species densities. Focal fish species assemblages also differed between the reef and sand-rock ecotone transects. Reef-resident planktivorous fishes likely contribute to reef primary and secondary productivity through consumer mediated nutrient transport and are an important consideration in restoration reef design. Future reef restoration designs should consider incorporating replicated heterogeneous design features including the placement of higher relief elements relative to shore and current patterns as a special consideration for providing habitat for planktivorous reef-resident fishes.

RF crosstalk mitigation via floating shields in parallel silicon traveling-wave modulators

High-density integration of electro-optic (E-O) elements in photonic integrated circuits (PICs) offers compact, low-cost high-capacity transceiver modules. Tighter component spacing increases compactness, but leads to crosstalk that can degrade performance. We investigate and characterize the crosstalk between silicon traveling-wave Mach-Zehnder modulators (TW-MZMs) laid out in parallel in PICs. We propose the use of floating shield strips to reduce E-O crosstalk between these TW-MZMs. We show via simulation and experiment that these strips can increase shoreline bandwidth density by nearly 50%. The improvement in E-O crosstalk covers frequencies as high as 60 GHz.

Denoising Diffusion Implicit Model for Camouflaged Object Detection

Camouflaged object detection (COD) is a challenging task that involves identifying objects that closely resemble their background. In order to detect camouflaged objects more accurately, we propose a diffusion model for the COD network called DMNet. DMNet formulates COD as a denoising diffusion process from noisy boxes to prediction boxes. During the training stage, random boxes diffuse from ground-truth boxes, and DMNet learns to reverse this process. In the sampling stage, DMNet progressively refines random boxes to prediction boxes. In addition, due to the camouflaged object’s blurred appearance and the low contrast between it and the background, the feature extraction stage of the network is challenging. Firstly, we proposed a parallel fusion module (PFM) to enhance the information extracted from the backbone. Then, we designed a progressive feature pyramid network (PFPN) for feature fusion, in which the upsample adaptive spatial fusion module (UAF) balances the different feature information by assigning weights to different layers. Finally, a location refinement module (LRM) is constructed to make DMNet pay attention to the boundary details. We compared DMNet with other classical object-detection models on the COD10K dataset. Experimental results indicated that DMNet outperformed others, achieving optimal effects across six evaluation metrics and significantly enhancing detection accuracy.

Efficiency enhancement of photovoltaic-thermoelectric generator hybrid module by heat dissipating technique

Exploring substantial solar irradiation and recuperating excess heat generated during photovoltaic energy conversion is a critical issue. The efficiency of photovoltaic systems (PV) is significantly depend on the increased operating temperatures encountered by solar radiation. One conceivable option for improving the conversion of solar energy is to integrate a photovoltaic (PV) panel with a thermal-electric generator (TEG) material module to create a hybrid system. This study proposed a parallel PV-TEG hybrid module that effectively harvests the maximum solar energy spectrum while maximizing the use of heat generated by the thermoelectric material to improve the overall system efficiency. The proposed module consists of a photovoltaic unit, thermoelectric material module and passive cooling of fluid channels. The aim of this work was to develop a PV-TEG hybrid system and create an energy simulation model in a MATLAB environment to analyze the model's performance under various operational conditions by applying both theoretical and experimental approaches. Findings showed considerable concurrence. At 13:00, when the PV surface temperature was 54 °C, the PV efficiency reached to its lowest value of 12.0 %. Nevertheless, the highest TEG efficiency recorded was 4.7 % at 12:00 h. The efficiency of the TEG module was significantly affected by weather conditions, inlet cooling water temperature, and fluid flow rate in comparison to both the PV efficiency and the thermal efficiency.

Parallel gain modulation mechanisms set the resolution of color selectivity in human visual cortex.

Color discrimination is fundamental to human behavior. We find bananas by coarsely searching for yellow but then differentiate nuances of yellow to pick the best exemplars. How does the brain adjust the resolution of color selectivity to our changing needs? Here, we analyze the brain magnetic response in the human visual cortex to show that color selectivity is adaptively set by coarse- and fine-resolving processes running in parallel at different hierarchical levels. Those include a gain enhancement in the higher-lever cortex of color units tuned away from the target to resolve very similar colors and a coarsely resolving gain enhancement in the mid-level cortex of units tuned to the target. Our findings suggest that attention operates on a form of multiresolution representation of color at different levels in the visual hierarchy, which keeps selectivity adaptive to a changing resolution context.

Advancing high-resolution remote sensing: a compact and powerful approach to semantic segmentation

ABSTRACT Deep learning (DL)-based approaches are notable for their ability to establish feature associations without relying on physical constraints, unlike traditional strategies that are complex and dependent on expert experience. However, three main challenges hinder the versatility of semantic segmentation models. First, the targets in these images are dense and exist at varying spatial scales, which imposes higher demands on the model for accurate segmentation across scales. Second, the segmentation of small targets in the images is often overlooked, leading to a compromise between fine segmentation and model efficiency. Lastly, the data-intensive nature of remote sensing images and the resource-intensive operations of large-scale networks impose significant communication and computation burdens on edge devices, which may not have sufficient resources to handle them effectively. To address these challenges, this paper proposes a lightweight semantic segmentation method for remote sensing images to achieve high-precision segmentation for multi-scale targets while maintaining low computational complexity. The main components include: (1) embedding the inverted residual block structure to minimize the number of model parameters and computational costs; (2) introducing the parallel irregular space pyramid pooling module to efficiently aggregate multi-scale contextual information for fine-grained recognition of small targets; and (3) embedding transfer learning into the encoder-decoder structure to speed up the convergence rate and improve multi-scale feature fusion capability, thereby reducing semantic information loss. The proposed lightweight method has been extensively tested on real-world high-resolution remote sensing datasets. It achieved PA, MPA, MIoU, and FWIoU scores of 87.90%, 75.76%, 66.29%, and 78.81% on the Vaihingen dataset; 87.03%, 85.31%, 74.85%, and 77.54% on the Potsdam dataset; and 95.37%, 83.33%, 75.70%, and 91.31% on the Aeroscapes dataset. Compared to other popular semantic segmentation models, the proposed method achieved the highest values in all four evaluation indicators, demonstrating its effectiveness and superiority.

Exploring refined dual visual features cross-combination for image captioning

For current image caption tasks used to encode region features and grid features Transformer-based encoders have become commonplace, because of their multi-head self-attention mechanism, the encoder can better capture the relationship between different regions in the image and contextual information. However, stacking Transformer blocks necessitates quadratic computation through self-attention to visual features, not only resulting in the computation of numerous redundant features but also significantly increasing computational overhead. This paper presents a novel Distilled Cross-Combination Transformer (DCCT) network. Technically, we first introduce a distillation cascade fusion encoder (DCFE), where a probabilistic sparse self-attention layer is used to filter out some redundant and distracting features that affect attention focus, aiming to obtain more refined visual features and enhance encoding efficiency. Next, we develop a parallel cross-fusion attention module (PCFA) that fully exploits the complementarity and correlation between grid and region features to better fuse the encoded dual visual features. Extensive experiments conducted on the MSCOCO dataset demonstrate that our proposed DCCT method achieves outstanding performance, rivaling current state-of-the-art approaches.