Convolutional Filters Research Articles

ConciseCarNet: convolutional neural network for parking space classification

The car is a mode of transportation that brings numerous benefits to the community. As a result, the growth of vehicles is increasing, which has a negative impact. Some of the negative impacts include noise, air pollution, traffic congestion, and the need for parking spaces. Drivers that drive around looking for parking places increase the negative impact as well as boredom and even worry for the driver. Therefore, the driver needs this information on the availability of parking spaces. A convolutional neural network (CNN) using a camera is one of the best methods that can be used to solve this problem. We built a more efficient CNN architecture for classifying parking spaces, which was named ConciseCarNet. ConciseCarNet uses 33 and 11 convolution filters, which cause fewer parameters than previous architectures. ConciseCarNet has two branches, each with a different branch structure. This branch is designed to generate additional feature variations, which will help improve the accuracy. Based on testing, the accuracy of ConciseCarNet2x outperforms the accuracy of mAlexnet, Carnet, EfficientParkingNet, and you look once (YOLO)+MobilNet architectures, which is 99.37%. ConciseCarNet has fewer parameters, file sizes, and floating point operations (FLOPs) compared to other architectures.

Quality-aware fuzzy min-max neural networks for dynamic brain network analysis and its application to schizophrenia identification

Dynamic functional connections (dFCs) refer to the observed phenomenon that functional connectivity changes over a short time, which has become a pioneering method in the diagnosis of brain diseases. However, current dynamic brain network analysis methods are insufficient to address the fuzzy information of brain networks caused by the quality issues of rs-fMRI data, and the uncertainty arising from inconsistent data quality across different windows due to missing values and noise in individual windows. These results in unreliable integration of multiple windows. To this end, this paper proposes a dynamic brain network analysis method based on quality-aware fuzzy min–max neural networks (QFMMNet). The individual window of dFCs is treated as a view, and we define three convolution filters to extract features from the brain network under the multi-view learning framework, thereby obtaining multi-view evidence for dFCs. We design the multi-view fuzzy min–max neural networks (MVFMM) based on fuzzy sets to deal with the fuzzy information of the brain network, which takes evidence as input patterns to generate hyperboxes and serves as the classification layer of each view. A quality-aware ensemble module is introduced to deal with uncertainty, which employs Dempster-Shafer Theory (D-S theory) to directly model the uncertainty and evaluate the dynamic quality-aware weighting of each view. At the decision level of model, a weighted fusion strategy is employed for multi-view fusion based on the dynamic quality-aware weighting, thereby the quality of each view is fully considered to improve the trustworthiness of the decision. We verified the effectiveness of QFMMNet through comparison with advanced algorithms on three real schizophrenia datasets, and the results show that QFMMNet significantly improved the classification performance of brain disease diagnosis tasks. The accuracy on the Huaxi, COBRE, and Xiangya datasets reached 87.14%, 86.67%, and 85.27%, respectively.

Enhancing 3D video watching experiences: Tackling compression and 3D warping distortions in synthesized view with perceptual guidance

In 3D video systems, synthesized videos are typically rendered using view synthesis technology, mainly Depth Image Based Rendering (DIBR) technology, and suffer from both compression and 3D warping artifacts, which may degrade the perceptual quality of 3D video. Taking into account human perceptual characteristics towards synthesized views, wherein individuals readily discern DIBR distortion, such as cracks and irregular stretching, more attention should be paid to addressing DIBR distortion for Synthesized View Quality Enhancement (SVQE). In this paper, we propose a Distortion Map-guided Asymmetrical encoder–decoder restoration Network for SVQE, termed DMANet, which prioritizes human perceptual factors while maintaining a delicate balance between effectiveness and efficiency. Specifically, to consider the perceptual characteristics, a distortion-aware module is introduced by embedding the predicted DIBR distortion into the restoration network through multi-scale feature embedding, and collaborates with the DIBR distortion prediction loss to focus more on the DIBR-distorted regions. Meanwhile, to promote the efficiency of the U-shape network, an asymmetrical encoder–decoder restoration network is proposed, where the encoder progressively integrates both transformer and CNN modules for facilitating local–global feature extraction, while the decoder is configured with only the CNN module. Furthermore, hybrid transformer-based modules incorporate channel attention interaction and convolutional filters to fully exploit the channel-wise global modeling ability of self-attention while preserving local details. Substantial experimental results show that the proposed DMANet can outperform SOTA SVQE methods and is comparable to SOTA image restoration methods with fewer model parameters, flops, and shorter running time.

End-to-End CNN conceptual model for a biometric authentication mechanism for ATM machines

People commonly use automated teller machines, also known as ATMs, in contemporary culture. The use of ATMs for currency withdrawal is becoming increasingly widespread in the modern world. Furthermore, attacks and break-ins have increased, highlighting the need for an ATM that is more secure and equipped with extra safety measures. The existing method of confirming these transactions through credit cards and ATMs carries a high level of risk. Card trapping, cash larceny, shoulder surfing, and ATM skimming are among the examples. The proposed system, which is based on biometrics and deep convolutional neural networks, can effectively resolve the problems associated with conventional ATM cards and PINs. The proposed method relies on face biometrics, which offers a novel technique to address the current challenge. This article presents a novel end-to-end multiscale attention-based light-weighted deep convolutional neural network (MA-LW-DCNN) framework for enhancing the accuracy of recognizing faces. The multiscale attention mechanism in face recognition enhances feature extraction by applying convolution filters at various scales, allowing the network to capture detailed and global facial features. It generates an attention map by normalizing characteristic maps and refining them using activation functions, improving recognition accuracy by focusing on essential face regions. The suggested system is assessed using a synthetic dataset against conventional machine and deep learning-based face recognition techniques. The experiment results for different individuals demonstrate an accuracy rate of around 99.84% in authenticating test samples. The proposed approach produced significantly improved evaluation measures precision, recall, and loss by 0.99, 0.99, and 0.16, respectively.

Continuous Generative Neural Networks: A Wavelet-Based Architecture in Function Spaces

In this work, we present and study Continuous Generative Neural Networks (CGNNs), namely, generative models in the continuous setting: the output of a CGNN belongs to an infinite-dimensional function space. The architecture is inspired by DCGAN, with one fully connected layer, several convolutional layers and nonlinear activation functions. In the continuous L 2 setting, the dimensions of the spaces of each layer are replaced by the scales of a multiresolution analysis of a compactly supported wavelet. We present conditions on the convolutional filters and on the nonlinearity that guarantee that a CGNN is injective. This theory finds applications to inverse problems, and allows for deriving Lipschitz stability estimates for (possibly nonlinear) infinite-dimensional inverse problems with unknowns belonging to the manifold generated by a CGNN. Several numerical simulations, including signal deblurring, illustrate and validate this approach.

Multimodal Biometric Authentication System for Military Weapon Access: Face and ECG Authentication

Unimodal or Single factor biometric systems refer to biometric systems that employ only one form of biometric data to authenticate an individual’s identity. These kinds of biometrics are susceptible to higher error rates and security vulnerabilities because it relays on a single trait for authentication. To overcome this, multimodal biometrics method is proposed. Multi-modal biometric system can authenticate more than once and some advantages include; highaccuracy, low error rate, and large population coverage. These biometrics systems increase integrity and privacy since it will contain several biometric features of every customer. So, here designed a multimodal biometrics project utilizing deep learning to enhance authentication security by combining face and Electrocardiogram(ECG) signals. VGG-16 model, a deep learning architecture used to capture complex patterns in accurate individual identification with both ECG and Facial data. The high-resolution convolutional filters capture the intricate details of the face and ECG waveform, ensuring high accuracy in distinguishing different individuals.

Dynamic evidence fusion neural networks with uncertainty theory and its application in brain network analysis

Deep learning has demonstrated significant potential and advantages, achieving notable success in the medical field, particularly in the application of brain network analysis. However, most models ignore the uncertainty caused by inconsistent view quality and fail to effectively leverage the potential correlations and temporal sequences present in multi-view data, preventing neural networks from fully showcasing their strengths. To this end, this paper proposes dynamic evidence fusion neural networks (DEF-NNs) with uncertainty theory, and applies it to brain network analysis. Our model is established within a multi-view learning framework that considers the functional connections under each window as a view. We employ a dynamic evidence learning module to capture the evidence for each time window of the dynamic brain network, utilizing three distinct convolutional filters to extract feature maps. Then, a dynamic evidence fusion mechanism is designed and a dynamic trust function is constructed according to the temporal nature of dFC data. The evidence generated by multiple windows is fused at the decision level of classification, dealing with the uncertainty caused by inconsistent view quality and improving the classification performance. We verified the effectiveness of DEF-NNs through comparison with advanced algorithms on three schizophrenia datasets, and the results show that DEF-NNs significantly improved the classification performance of brain disease diagnosis tasks.

GDCNet: Graph Enrichment Learning via Graph Dropping Convolutional Networks.

Graph convolutional networks (GCNs) have been widely studied to address graph data representation and learning. In contrast to traditional convolutional neural networks (CNNs) that employ many various (spatial) convolution filters to obtain rich feature descriptors to encode complex patterns of image data, GCNs, however, are defined on the input observed graph G(X,A) and usually adopt the single fixed spatial convolution filter for graph data feature extraction. This limits the capacity of the existing GCNs to encode the complex patterns of graph data. To overcome this issue, inspired by depthwise separable convolution and DropEdge operation, we first propose to generate various graph convolution filters by randomly dropping out some edges from the input graph A . Then, we propose a novel graph-dropping convolution layer (GDCLayer) to produce rich feature descriptors for graph data. Using GDCLayer, we finally design a new end-to-end network architecture, that is, a graph-dropping convolutional network (GDCNet), for graph data learning. Experiments on several datasets demonstrate the effectiveness of the proposed GDCNet.

Synchronization-Inspired Interpretable Neural Networks.

Synchronization is a ubiquitous phenomenon in nature that enables the orderly presentation of information. In the human brain, for instance, functional modules such as the visual, motor, and language cortices form through neuronal synchronization. Inspired by biological brains and previous neuroscience studies, we propose an interpretable neural network incorporating a synchronization mechanism. The basic idea is to constrain each neuron, such as a convolution filter, to capture a single semantic pattern while synchronizing similar neurons to facilitate the formation of interpretable functional modules. Specifically, we regularize the activation map of a neuron to surround its focus position of the activated pattern in a sample. Moreover, neurons locally interact with each other, and similar ones are synchronized together during the training phase adaptively. Such local aggregation preserves the globally distributed representation nature of the neural network model, enabling a reasonably interpretable representation. To analyze the neuron interpretability comprehensively, we introduce a series of novel evaluation metrics from multiple aspects. Qualitative and quantitative experiments demonstrate that the proposed method outperforms many state-of-the-art algorithms in terms of interpretability. The resulting synchronized functional modules show module consistency across data and semantic specificity within modules.

Artificial intelligence assisted common maternal fetal planes prediction from ultrasound images based on information fusion of customized convolutional neural networks.

Ultrasound imaging is frequently employed to aid with fetal development. It benefits from being real-time, inexpensive, non-intrusive, and simple. Artificial intelligence is becoming increasingly significant in medical imaging and can assist in resolving many problems related to the classification of fetal organs. Processing fetal ultrasound (US) images increasingly uses deep learning (DL) techniques. This paper aims to assess the development of existing DL classification systems for use in a real maternal-fetal healthcare setting. This experimental process has employed two publicly available datasets, such as FPSU23 Dataset and Fetal Imaging. Two novel deep learning architectures have been designed in the proposed architecture based on 3-residual and 4-residual blocks with different convolutional filter sizes. The hyperparameters of the proposed architectures were initialized through Bayesian Optimization. Following the training process, deep features were extracted from the average pooling layers of both models. In a subsequent step, the features from both models were optimized using an improved version of the Generalized Normal Distribution Optimizer (GNDO). Finally, neural networks are used to classify the fused optimized features of both models, which were first combined using a new fusion technique. The best classification scores, 98.5 and 88.6% accuracy, were obtained after multiple steps of analysis. Additionally, a comparison with existing state-of-the-art methods revealed a notable improvement in the suggested architecture's accuracy.

Lightweight concrete crack detection based on spiking neural networks

PurposeMost existing methods for concrete crack detection are based on deep learning techniques such as convolutional neural networks. However, these models, due to their large memory footprint, high power consumption and insufficient feature extraction capabilities, face challenges in mobile applications. To address these issues, this paper proposes a lightweight spiking neural network detection model.Design/methodology/approachThis model achieves fast and accurate crack detection. Firstly, the Gabor-Spiking (GS) module preprocesses input images, extracting texture features and edge features of crack images through Gabor filter convolution modules and spiking convolution modules, respectively. Next, the multiscale residual (MR) module is designed, composed of convolutional layers and residual modules of various scales, to process the fused features and perform crack detection.FindingsExperimental results demonstrate that the model’s size can be reduced to 4.6 MB, achieving accuracy improvements to 87.3 and 96.4% on the SDNET and OCD datasets, respectively.Originality/valueThis paper proposes a lightweight spiking neural network detection model based on the GS module for edge texture feature fusion and the MR module for crack detection.

Suppression of interference fringes in wavelength modulation spectroscopy based on convolutional neural networks

To mitigate interference fringes in wavelength modulated spectroscopy (WMS), we introduce a new filter based on convolutional neural networks (New CNNF). This filter effectively suppresses both high and low-frequency variations of interference fringes, including those with envelopes. In striving for comprehensive interference fringe suppression across the entire frequency spectrum, while accommodating diverse cavity length conditions, we confront the challenge of data sparsity. To address this, we introduce a pioneering methodology: finely segmenting the dataset through concentration column density normalization, thereby achieving notable noise reduction. Through the construction and training of the New CNNF model, it was found to exhibit superior performance compared to traditional filtering algorithms, particularly under low signal-to-noise ratio (SNR) conditions.Following processing with the novel convolutional neural network filters (New CNNF), the sample's signal-to-noise ratio (SNR) improved by 26.70 dB, increasing from the original −10.11 dB. When the etalon lengths were 2 cm and 100 cm, the goodness of fit between the predicted second harmonic signal amplitude by New CNNF and the corresponding label concentration reached 0.9995 and 0.9999, respectively. Experimental results demonstrate that New CNNF effectively suppresses high and low-frequency variations and enveloped interference fringes in the TDLAS-WMS system, thereby enhancing the accuracy and stability of methane concentration measurements. CH4 transitions at λ = 1.654 μm were selected to validate this approach. Our method shows promising application prospects and can be extended to the sensing of other gas molecules.

Brief Study on Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision and pattern recognition, offering state-of-the-art performance in tasks such as image classification, object detection, and segmentation. This study explores the architecture, training strategies, and applications of CNNs, focusing on their ability to automatically learn spatial hierarchies of features through layers of convolutional filters. The research evaluates various CNN architectures, including Lent, Alex Net, and ResNet, highlighting their improvements in accuracy and efficiency. Additionally, the study investigates advanced techniques such as data augmentation, transfer learning, and regularization methods (e. g, dropout and batch normalization), which enhance the model's generalization capabilities. Through empirical experiments, we demonstrate the effectiveness of CNNs in real-world scenarios, including medical image analysis, autonomous driving, and facial recognition. The study concludes with a discussion of the future trends of CNNs, particularly in the context of deep learning optimization, hardware acceleration, and integration with emerging technologies like edge computing and quantum machine learning.

Multi-objective topological design considering functionally graded materials and coated fiber reinforcement

This study presents a multi-objective topology optimization method tailored to structures fabricated from functionally graded materials (FGMs), coated FGMs, and coated fiber-reinforced composite materials (FRCMs) with fixed fiber thickness. The design objective is the simultaneous minimization of elastic and thermal compliance. The material properties of these composite materials were derived to generate datasets using the representative volume element method under periodic boundary conditions. Subsequently, machine learning modules were developed based on the datasets to combine with the design process. The multi-objective optimization problem was addressed using the weighted sum method ensuring the generation of the Pareto front. The adaptive weighting strategy is employed to avoid biased results toward a single objective function. To define the coated boundaries within the design domain, image post-processing techniques such as convolution filters, interpolation schemes, and erosion methods were employed on the material layout information of the optimized FGM structures. Through numerical examples, optimized material layouts for coated assemblies incorporating FGMs and FRCMs are presented, with the performance verified through objective function values.

Local and global mixture network for image inpainting

In general, CNN-based inpainting can recover local patterns effectively using convolutional filters, but it may not exploit global correlation fully. On the other hand, transformer-based inpainting can fill in large holes faithfully based on global correlation, rather than local one. In this paper, we propose a novel image inpainting algorithm, called local and global mixture (LGM), to take advantage of the strengths of both approaches and compensate for their weaknesses. The LGM network comprises the local inpainting network (LIN) and the global inpainting network (GIN) in parallel, which are based on convolutional layers and transformer blocks, respectively, and exchange their intermediate results with each other. Furthermore, we develop an error propagation model with a continuous error mask, updated in LIN but used in both LIN and GIN to provide more reliable inpainting results. Extensive experiments demonstrate that the proposed LGM algorithm provides excellent inpainting performance, which indicates the efficacy of the parallel combination of LIN and GIN and the effectiveness of the error propagation model.

Mesh Convolution With Continuous Filters for 3-D Surface Parsing.

Geometric feature learning for 3-D surfaces is critical for many applications in computer graphics and 3-D vision. However, deep learning currently lags in hierarchical modeling of 3-D surfaces due to the lack of required operations and/or their efficient implementations. In this article, we propose a series of modular operations for effective geometric feature learning from 3-D triangle meshes. These operations include novel mesh convolutions, efficient mesh decimation, and associated mesh (un)poolings. Our mesh convolutions exploit spherical harmonics as orthonormal bases to create continuous convolutional filters. The mesh decimation module is graphics processing unit (GPU)-accelerated and able to process batched meshes on-the-fly, while the (un)pooling operations compute features for upsampled/downsampled meshes. We provide an open-source implementation of these operations, collectively termed Picasso. Picasso supports heterogeneous mesh batching and processing. Leveraging its modular operations, we further contribute a novel hierarchical neural network for perceptual parsing of 3-D surfaces, named PicassoNet++. It achieves highly competitive performance for shape analysis and scene segmentation on prominent 3-D benchmarks. The code, data, and trained models are available at https://github.com/EnyaHermite/Picasso.

Enhancing Breast Cancer Detection through Progressive Convolutional Neural Networks with Adaptive Kernel Pooling

Breast cancer is one of the leading causes of mortality among women worldwide, making early detection a crucial component in reducing fatality rates. Traditional diagnostic methods often rely on manual interpretation of mammograms or histopathological images, which can be time-consuming and prone to human error. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs), have demonstrated significant potential in automating and enhancing breast cancer detection. Breast cancer detection remains a critical challenge in medical diagnostics, where early identification can significantly improve patient outcomes. Leveraging advanced deep learning techniques, this research proposes a novel approach by integrating Progressive Convolutional Neural Networks (PCNNs) with Adaptive Kernel Pooling (AKP) for enhanced image classification. The model architecture dynamically adjusts convolutional filter sizes across layers to capture both coarse and fine-grained features, while AKP adapts pooling operations based on spatial dimensions, optimizing feature retention. Through rigorous experimentation on a large breast cancer histopathological dataset, the proposed method achieved an impressive accuracy of 98%, demonstrating its potential in real-world clinical applications. The high accuracy indicates the model's robustness in differentiating between malignant and benign tissues. This approach not only enhances detection precision but also addresses the challenge of feature loss during pooling, a common issue in traditional Convolutional Neural Networks (CNNs). Additionally, this study contributes to the growing field of medical imaging by offering an adaptable and scalable architecture that could be employed across various medical diagnostics beyond breast cancer detection. The proposed PCNN-AKP framework sets a new benchmark for automated cancer detection models, providing a pathway for future innovations in the use of deep learning for healthcare diagnostics.

Reproducible and Interpretable Machine Learning-Based Radiomic Analysis for Overall Survival Prediction in Glioblastoma Multiforme

Purpose: To develop and validate an MRI-based radiomic model for predicting overall survival (OS) in patients diagnosed with glioblastoma multiforme (GBM), utilizing a retrospective dataset from multiple institutions. Materials and Methods: Pre-treatment MRI images of 289 GBM patients were collected. From each patient’s tumor volume, 660 radiomic features (RFs) were extracted and subjected to robustness analysis. The initial prognostic model with minimum RFs was subsequently enhanced by including clinical variables. The final clinical–radiomic model was derived through repeated three-fold cross-validation on the training dataset. Performance evaluation included assessment of concordance index (C-Index), integrated area under curve (iAUC) alongside patient stratification into low and high-risk groups for overall survival (OS). Results: The final prognostic model, which has the highest level of interpretability, utilized primary gross tumor volume (GTV) and one MRI modality (T2-FLAIR) as a predictor and integrated the age variable with two independent, robust RFs, achieving moderately good discriminatory performance (C-Index [95% confidence interval]: 0.69 [0.62–0.75]) with significant patient stratification (p = 7 × 10−5) on the validation cohort. Furthermore, the trained model exhibited the highest iAUC at 11 months (0.81) in the literature. Conclusion: We identified and validated a clinical–radiomic model for stratification of patients into low and high-risk groups based on OS in patients with GBM using a multicenter retrospective dataset. Future work will focus on the use of deep learning-based features, with recently standardized convolutional filters on OS tasks.

Inferring ECG Waveforms from PPG Signals with a Modified U-Net Neural Network.

There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the hospital environment. However, the continuous monitoring of ECG signals via mobile devices is challenging, as it requires users to keep their fingers pressed on the device during data collection, making it unfeasible in the long term. On the other hand, the PPG does not contain this limitation. However, the medical knowledge to diagnose these anomalies from this sign is limited by the need for familiarity, since the ECG is studied and used in the literature as the gold standard. To minimize this problem, this work proposes a method, PPG2ECG, that uses the correlation between the domains of PPG and ECG signals to infer from the PPG signal the waveform of the ECG signal. PPG2ECG consists of mapping between domains by applying a set of convolution filters, learning to transform a PPG input signal into an ECG output signal using a U-net inception neural network architecture. We assessed our proposed method using two evaluation strategies based on personalized and generalized models and achieved mean error values of 0.015 and 0.026, respectively. Our method overcomes the limitations of previous approaches by providing an accurate and feasible method for continuous monitoring of ECG signals through PPG signals. The short distances between the infer-red ECG and the original ECG demonstrate the feasibility and potential of our method to assist in the early identification of heart diseases.

A visual measurement method for grinding surface roughness combining filter and branch convolution network

ABSTRACT The visual measurement method of grinding surface roughness is mainly predicted by designing image feature indicators. In contrast, the self-extraction method of grinding surface features based on deep learning has problems such as noise, low resolution and poor perception of details. This paper proposes a visual measurement method for grinding surface roughness to address these problems by combining filters and branching convolutional networks. The method adopts a two-branch convolutional neural network, inputs the images using filter denoising and the original grinding workpiece surface images simultaneously, self-extracts roughness correlation features and fuses them to achieve feature enhancement, effectively improves the recognition accuracy and generalisation ability of the model, and verifies the robustness of the model in the case of rusting on the workpiece surface. The experimental results show that the method can effectively solve the problem of the grinding process’s weak and difficult recognition of surface roughness feature information and provide a basis for automated visual online measurement of grinding surface roughness.