Parallel Modules Research Articles

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning.

This paper introduces a new technique for automatic modulation classification (AMC) in Cognitive Radio (CR) networks. The method employs a straightforward classifier that utilizes high-order cumulant for training. It focuses on the statistical behavior of both analog modulation and digital schemes, which have received limited attention in previous works. The simulation results show that the proposed method performs well with different signal-to-noise ratios (SNRs) and channel conditions. The classifier's performance is superior to that of complex deep learning methods, making it suitable for deployment in CR networks' end units, especially in military and emergency service applications. The proposed method offers a cost-effective and high-quality solution for AMC that meets the strict demands of these critical applications.

Lightweight Spatial-Temporal Contextual Aggregation Siamese Network for Unmanned Aerial Vehicle Tracking

Benefiting from the powerful feature extraction capability of deep learning, the Siamese tracker stands out due to its advanced tracking performance. However, constrained by the complex backgrounds of aerial tracking, such as low resolution, occlusion, similar objects, small objects, scale variation, aspect ratio change, deformation and limited computational resources, efficient and accurate aerial tracking is still difficult to realize. In this work, we design a lightweight and efficient adaptive temporal contextual aggregation Siamese network for aerial tracking, which is designed with a parallel atrous module (PAM) and adaptive temporal context aggregation model (ATCAM) to mitigate the above problems. Firstly, by using a series of atrous convolutions with different dilation rates in parallel, the PAM can simultaneously extract and aggregate multi-scale features with spatial contextual information at the same feature map, which effectively improves the ability to cope with changes in target appearance caused by challenges such as aspect ratio change, occlusion, scale variation, etc. Secondly, the ATCAM adaptively introduces temporal contextual information to the target frame through the encoder-decoder structure, which helps the tracker resist interference and recognize the target when it is difficult to extract high-resolution features such as low-resolution, similar objects. Finally, experiments on the UAV20L, UAV123@10fps and DTB70 benchmarks demonstrate the impressive performance of the proposed network running at a high speed of over 75.5 fps on the NVIDIA 3060Ti.

Research on fault diagnosis of industrial materials based on hybrid deep learning model

Abstract Bearing fault detection is becoming more and more important in industrial development, and deep learning image processing technology provides a new solution for this. In this study, ResNet50 is used to replace VGG-16 as the feature extraction network of Faster R-CNN, and feature pyramid network (FPN) and parallel attention module (PAM) are introduced to achieve higher detection accuracy and speed. The experimental validation was conducted with the Case Western Reserve University bearing dataset using a three-fold cross-validation and compared with Yolov5, FPN, and the original Faster R-CNN model. The experimental results show that the accuracy of the proposed bearing image fault detection method is 78.6%, the accuracy is 77.4%, and the recall rate is 76.9%, which can locate and identify bearing faults more accurately. Future work could focus on further optimizing the model structure to enhance detection performance, strengthening the model’s generalization ability to meet the detection requirements of different types of bearing faults.

Real‐world image deblurring using data synthesis and feature complementary network

AbstractMany learning‐based approaches to image deblurring have received increasing attention in recent years. However, the models trained on existing synthetic datasets do not generalize well to real‐world blur, resulting in undesirable artifacts and residual blur. This work attempts to address this problem from two aspects: training data synthesis and network architecture. To narrow the domain gap between synthetic and real domains, a realistic blur synthesis pipeline to generate high‐quality blurred data is proposed. Since the blur is non‐uniform and has different scales and degrees, a parallel feature complementary module to fully exploit the local and non‐local information, which improves the feature representation and helps the network to perceive the non‐uniform blur, is developed. In addition, a spatial Fourier reconstruction block to facilitate correct detail recovery in the spatial and Fourier domains is introduced. Based on these two designs, an effective encoder–decoder network for deblurring is designed. Extensive experiments demonstrate the validity and superiority of the proposed blur synthesis method and deblurring network. In particular, the proposed deblurring network can achieve superior or comparable performance to Restormer, while saving 70% of network parameters and 53% of floating point operations (FLOPs).

EEG emotion recognition based on Ordinary Differential Equation Graph Convolutional Networks and Dynamic Time Wrapping

Graph Convolutional Network (GCN) has been extensively utilized to extract relations among electroencephalography (EEG) electrode channels for its strong ability to handle non-Euclidean data. However, GCN still has some issues when it comes to extracting features from EEG signals: (1) GCN with more layers may experience over-smoothing, restricting its ability to mine longer dependency relations. (2) At the moment, most GCNs used to process EEG signals construct adjacency matrices by Euclidean distance, only considering the correlations on the feature domain while ignoring changes of signals over the entire time window. To address the issues above, we introduce an Ordinary Differential Equation (ODE) based GCN, which can perfectly eliminate the over-smoothing problem of the traditional GCN. Besides, we also propose a method based on Dynamic Time Wrapping (DTW) algorithm to construct an adjacency matrix in the time domain. To handle adjacency matrices calculated by Euclidean distance and DTW distance respectively, we apply a temporal–spatial model composed of two parallel modules each containing an ODE-based GCN and Long short-term memory neural networks (LSTM) network in turn. We conducted experiments on three public datasets. The results show that our methods have achieved an improvement of 2.19%/2.77%/2.13%/2.01% on Arousal/Valence/Dominance/Liking on DEAP dataset, 1.43% on SEED dataset and 3.06%/3.27% on Arousal/Valence on DREAMER dataset compared with state-of-the-art (SOTA) baseline methods. It demonstrates that our method can effectively approve the performance to handle the relations between EEG channels. The premise of the ODE-based GCN is that signal changes of all EEG channels should be continuous rather than abrupt. We believe that it conforms to the EEG mode, as it is activated by the same emotion stimulation while being collected.

Heterogeneous density-based clustering with a dual-functional memristive array.

In the big data era, the requirement for data clustering methods that can handle massive and heterogeneous datasets with varying distributions increases. This study proposes a clustering algorithm for data sets with heterogeneous density using a dual-mode memristor crossbar array for data clustering. The array consists of a Ta/HfO2/RuO2 memristor operating in analog or digital modes, controlled by the reset voltage. The digital mode shows low dispersion and a high resistance ratio, and the analog mode enables precise conductance tuning. The local outlier factor is introduced to handle a heterogeneous density, and the required Euclidean and K-distances within the given dataset are calculated in the analog mode in parallel. In the digital mode, clustering is performed based on the connectivity among data points after excluding the detected outliers. The proposed algorithm boasts linear time complexity for the entire process. Extensive evaluations of synthetic datasets demonstrate significant improvement over representative density-based algorithms, and the datasets with heterogeneous density are clustered feasibly. Finally, the proposed algorithm is used to cluster the single-molecule localization microscopy data, demonstrating the feasibility of the suggested method for real-world problems.

Investigating the performance of soft robotic adaptive feet with longitudinal and transverse arches.

Biped robots usually adopt feet with a rigid structure that simplifies walking on flat grounds and yet hinders ground adaptation in unstructured environments, thus jeopardizing stability. We recently explored in the SoftFoot the idea of adapting a robotic foot to ground irregularities along the sagittal plane. Building on the previous results, we propose in this paper a novel robotic foot able to adapt both in the sagittal and frontal planes, similarly to the human foot. It features five parallel modules with intrinsic longitudinal adaptability that can be combined in many possible designs through optional rigid or elastic connections. By following a methodological design approach, we narrow down the design space to five candidate foot designs and implement them on a modular system. Prototypes are tested experimentally via controlled application of force, through a robotic arm, onto a sensorized plate endowed with different obstacles. Their performance is compared, using also a rigid foot and the previous SoftFoot as a baseline. Analysis of footprint stability shows that the introduction of the transverse arch, by elastically connecting the five parallel modules, is advantageous for obstacle negotiation, especially when obstacles are located under the forefoot. In addition to biped robots' locomotion, this finding might also benefit lower-limb prostheses design.

Remote sensing image semantic segmentation network based on multi-scale feature enhancement fusion

Semantic segmentation is a crucial method for recognizing and classifying objects in high-resolution remote sensing images (HRRSIs). However, due to the problems of varying target scale and difficulty in determining the edges of small-scale targets in remote sensing images, traditional semantic segmentation models perform poorly. To address this issue, we propose a multi-scale feature enhancement network (MFENet) to improve the segmentation accuracy of small-scale objects in HRRSIs. MFENet considers the differences between objects of different scales and selects more suitable receptive fields to enhance the extraction of multi-scale semantic features. We propose a composite atrous multi-scale feature fusion (CAMFF) module to enhance the extraction of spatial detail and semantic information of features at different scales. In addition, we propose an improved composite atrous spatial pyramid pooling (C-ASPP) module to enhance the network feature extraction capability across multiple scales. We also propose a network structure that combines the C-ASPP module with the efficient channel attention (ECA) module in parallel, which performs better to extract contextual information. Our experimental evaluations on the Potsdam and Vaihingen datasets demonstrate the effectiveness of our Network, It F1 score reaching 93.33% and 94.66% respectively.

Investigation on Analog and Digital Modulations Recognition Using Machine Learning Algorithms

Investigation on Analog and Digital Modulations Recognition Using Machine Learning Algorithms

Research on cross-modal emotion recognition based on multi-layer semantic fusion.

Multimodal emotion analysis involves the integration of information from various modalities to better understand human emotions. In this paper, we propose the Cross-modal Emotion Recognition based on multi-layer semantic fusion (CM-MSF) model, which aims to leverage the complementarity of important information between modalities and extract advanced features in an adaptive manner. To achieve comprehensive and rich feature extraction from multimodal sources, considering different dimensions and depth levels, we design a parallel deep learning algorithm module that focuses on extracting features from individual modalities, ensuring cost-effective alignment of extracted features. Furthermore, a cascaded cross-modal encoder module based on Bidirectional Long Short-Term Memory (BILSTM) layer and Convolutional 1D (ConV1d) is introduced to facilitate inter-modal information complementation. This module enables the seamless integration of information across modalities, effectively addressing the challenges associated with signal heterogeneity. To facilitate flexible and adaptive information selection and delivery, we design the Mask-gated Fusion Networks (MGF-module), which combines masking technology with gating structures. This approach allows for precise control over the information flow of each modality through gating vectors, mitigating issues related to low recognition accuracy and emotional misjudgment caused by complex features and noisy redundant information. The CM-MSF model underwent evaluation using the widely recognized multimodal emotion recognition datasets CMU-MOSI and CMU-MOSEI. The experimental findings illustrate the exceptional performance of the model, with binary classification accuracies of 89.1% and 88.6%, as well as F1 scores of 87.9% and 88.1% on the CMU-MOSI and CMU-MOSEI datasets, respectively. These results unequivocally validate the effectiveness of our approach in accurately recognizing and classifying emotions.

Study on Condition Monitoring Method Based on Magnetic Flux Density for Multi-chips Parallel IGBT Modules

Study on Condition Monitoring Method Based on Magnetic Flux Density for Multi-chips Parallel IGBT Modules

A Current and Output Power Balance Method Applied in IPT System with Multiple Parallel Pickup Modules for Rail Transit

A Current and Output Power Balance Method Applied in IPT System with Multiple Parallel Pickup Modules for Rail Transit

A Lightweight Ultrasound Image Denoiser Using Parallel Attention Modules and Capsule Generative Adversarial Network

The quality of ultrasound (US) imaging has been constrained by its limited contrast and resolution, inherent speckle noise, and the presence of other artifacts. Existing traditional and deep learning-based US denoising approaches have many limitations, such as reliance on manual parameter configurations, poor performance for unknown noise levels, the requirement for a large number of training data, and high computational expense. To address these challenges, we propose a novel Generative Adversarial Network (GAN) based denoiser. Capsule networks are utilized in both the generator and discriminator of the proposed GAN to capture intricate sparse features with less complexity. In addition, bias components are removed from all neurons of the generator to handle the unknown noise levels. A parallel attention module is also included in the proposed model to further enhance denoising performance. The proposed approach is trained in a semi-supervised manner and can thus be trained with fewer labeled images. Experimental evaluation on publicly available HC18 and BUSI datasets showed that the proposed approach achieved state-of-the-art denoising performance, with PSNR values of 33.86 and 34.16, and SSIM indices of 0.91 and 0.90, respectively. Moreover, experiments showed that the proposed approach is lightweight and more than twice as fast as similar denoisers.

MSDANet: A multi-scale dilation attention network for medical image segmentation

Deep learning shows excellent performance in medical image segmentation. However, a pooling operation in its encoding stage leads to feature loss and the ability of multi-scale contextual information extraction is insufficient. In addition, there is a semantic gap between low-level features and high-level features in the encoder–decoder. Inspired by an U-shaped network (UNet), this work proposes a multi-scale dilation attention network (MSDANet) for medical image segmentation. Specifically, it is mainly composed of a parallel dilation pooling module (PD), a multi-scale channel attention mechanism (MSCA), a multilayer perceptron squeeze and excitation module (MSE), a big kernel convolution module (BC), and a skip feature pyramid module (SFP). The proposed PD reduces the loss of subtle features during downsampling. In order to improve the extraction of effective features, MSCA, MSE and BC are used in encoding and decoding stages. In the skip connection stage, SPF is used to reduce the difference between the low-level and high-level semantic information to accelerate the network learning. We validate the proposed model on two publicly available medical image datasets. The results show that MSDANet has superior performance on several evaluation metrics compared to state-of-the-art methods. Code is available at https://github.com/1999luan/MSDANet.

Traffic Sign Detection Algorithm Based on Improved Yolox

This paper proposes a novel PVF-YOLO model to extract the multi-scale traffic sign features more effectively during car driving. Firstly, the original convolution module is replaced with the Omni-Dimensional convolution (ODconv) and the feature information obtained from the shallow feature layer is incorporated into the network. Secondly, this paper proposes a parallel structure block module for capturing multi-scale features. This module uses the Large Kernel Attention (LKA) and Visual Multilayer Perceptron (Visual MLP) to capture the information generated by the network model. It enhances the representation ability of feature maps. Next, in the process of training, the gradient concentration algorithm is used to optimize the initial Stochastic Gradient Descent (SGD). Under the condition of real-time detection, it improves the detection accuracy. Finally, to improve the robustness of the model, this paper conducts extensive experiments. Tsinghua-Tencent 100K (TT100K), Changsha University of Science and Technology CCTSDB (CSUST Chinese Traffic Sign Detection Benchmark) are used as the training data set. It verifies that the PVF-YOLO method proposed in this paper enhances the detection ability of traffic signs of different scales, and the detection speed and accuracy are better than the original model.

PARTNERING TO IMPROVE CARE: STRENGTHENING THE FAMILY CAREGIVER AND HOME CARE WORKER RELATIONSHIP

Abstract Family caregivers and home care workers provide essential daily care for millions of older adults and individuals with disabilities across the United States—and together they constitute the backbone of home and community-based services (HCBS). In many cases, these unpaid and paid caregivers work closely together and share common knowledge, skills, experiences, and challenges. Yet the overlap and relationship between them remains relatively understudied. This session will describe the importance of strengthening the family caregiver/home care worker dyad and discuss promising developments in research, policy, and practice. We will then highlight a pilot training intervention designed to improve the shared knowledge and interpersonal competencies of family caregivers and home care workers. The intervention— Partnering to Improve Care: Direct Care Workers and Families—was developed by PHI (a national nonprofit research, public education, and consulting organization) in partnership with the Florida Pioneer Network, Home Instead, and Florida State University through the North and Central Florida Geriatrics Workforce Enhancement Partnership (GWEP). Informed by the PHI Coaching Approach®, the intervention consists of five parallel online training modules for family caregivers and for home care workers that utilize adult learner-centered teaching methods and reflect input from home care stakeholders. The modules portray common scenarios that family caregivers and home care workers face and aim to build communication and relationship skills that facilitate collaborative problem-solving and the provision of person-centered care. We will present key findings on the feasibility and acceptability of this intervention, discuss next steps in this research, and highlight broader policy and practice recommendations.

Unveiling shadows: A comprehensive framework for insider threat detection based on statistical and sequential analysis

With the increasing importance of internal information security, detecting insider threats has become a critical issue to safeguard organizations' information systems. However, most of the previous studies either overlook temporal relationships or have difficulty attaining accurate performance. One of the primary factors contributing to this challenge is their approach, which lacks a holistic perspective. To our knowledge, none of these studies has considered the integration of statistical and sequential information in addressing this issue. Therefore, we propose a comprehensive framework for insider threat detection based on statistical and sequential analysis to address this challenge. Leveraging the strengths of both statistical analysis and sequential analysis, we deploy an efficient implementation for analyzing and modeling user data based on convolutional attention and a transformer encoder, referred to as CATE. First, user behavior logs are consolidated from diverse sources and preprocessed into a suitable format for subsequent analysis. Then, two parallel analysis modules analyze user data in two different dimensions. The analysis modules are entirely constructed using a neural network for its high adaptability and efficient integration of information from distinct dimensions. Specifically, a subnetwork structure based on convolutional attention is designed to effectively learn statistical information, while a separate subnetwork structure based on transformers is tailored for learning sequential information. Finally, we perform a series of solid experiments utilizing the publicly available CERT dataset to evaluate our framework's effectiveness and robustness in detecting insider threats and identifying malicious scenarios.

Operation characteristic research on organic Rankine cycle-free piston linear generator (ORC-FPLG) based on multi-module combination

Free piston linear generator (FPLG) presents great potential in organic Rankine cycle (ORC) system to replace traditional expanders because of low friction loss, simple structure and good sealing. Drawing on the concept of multi-cylinder engines, ORC-FPLG simulation model coupled with vehicle have been established based on multi-module combination with GT-suite/Simulink. Then, the operation characteristics in parallel mode, series mode and different intake distribution modes are analyzed. And the influence of engine parameters, and the energy-saving potential under road conditions are explored. The results show that: there exists an optimal module number in parallel mode to maximize net output power and thermal efficiency. When the number of modules is 4, the maximum net output power and thermal efficiency are 11.19 kW and 7.73%. The more parallel modules in series mode is, the higher net output power and thermal efficiency is, which is more conducive to improve output performance. Net power output and thermal efficiency gradually increase with engine speed and fuel injection. There exists an optimal injection timing and exhaust timing to maximize net power output and thermal efficiency. Compared to urban road conditions, ORC-FPLG system under suburban conditions achieves better energy recovery results with waste heat recovery efficiency of 2.22%.

Breaking Current Balance Enhancement for Parallel IGBT Modules in DC Circuit Breaker

Breaking Current Balance Enhancement for Parallel IGBT Modules in DC Circuit Breaker

Constructing comprehensive and discriminative representations with diverse attention for occluded person re-identification

Occluded person re-identification (Re-ID) is a challenging task that aims to match occluded person images to holistic ones across different camera views. Feature diversity is crucial for achieving high-performance Re-ID. Previous methods rely on additional annotations or hand-crafted rules to achieve feature diversity, which are inefficient or infeasible for occluded Re-ID. To address this, we propose the Diverse Attention Net (DANet) which utilizes attention mechanism to achieve diverse feature mining. Specifically, DANet incorporates a pair of complementary Diverse Parallel Attention Modules (DPAM), which, under the attention decorrelation constraint (ADC), help the model automatically capture diverse discriminative features in a global scope. Additionally, we propose an Efficient Transformer layer that can seamlessly integrate with the proposed DPAM and synergistically enhance the capability to handle occlusions. The resulting DANet construct a set of comprehensive representations that encode diverse discriminative features. Extensive experiments demonstrate DANet achieves state-of-the-art performance on both occluded and holistic ReID benchmarks.