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Municipal papers and polyethylene terephthalate (PET) wastes are primary constituents of municipal solid waste (MSW) which necessitates effective disposal. Co-pyrolysis is an efficient transformation technology for the production of valuable gas and liquid fuels. Therefore, co-pyrolysis characteristics, kinetics, gas and liquid products of cellulose and PET mixtures were investigated in a two-stage reactor by multiple characterizations coupling with deep learning approach. The results indicated that there exhibited positive interaction for cellulose and PET co-pyrolysis in terms of mass difference (δ m) and mass loss rate difference (δ r) between experimental and calculated values. The thermal decomposition process and synergistic effects (δ m and δ r) were accurately predicted by deep learning approaches (R 2 =0.9823–0.9993) using temperature, catalyst, mixing ratio and heating rate variables. Feature importance revealed that the temperature, catalyst, heating rate and mixing ratio were in the descending order to affect the remaining mass from TG curves. And the temperature played a predominant role in synergistic effects. The Flynn-Wall-Ozawa (FWO) method was more suitable for fitting the pyrolysis process to obtain activation energy (E a) with higher R 2 (0.94714–0.99640) compared with Kissinger-Akahira-Sunose (KAS) method. When the mixing ratio of cellulose and PET was 1:1, the E a was significantly reduced to 80.73 KJ/mol, and further reduced to 73.04 KJ/mol by introducing the catalyst, as compared to E a of individual cellulose (178.95 KJ/mol) and PET (198.34 KJ/mol). Co-pyrolysis of cellulose and PET led to a reduction of carbohydrates, furans, alcohols, ketones, and phenols, while the content of acids was remarkably increased which was consistent with the observation in FTIR that the absorption peak of C＝O became sharp and strong. The introduction of ZSM-11 resulted in an increase in aromatics and a decrease in acids content owing to aromatization and deoxygenation. This work is expected to offer beneficial guidance for the efficient disposal of MSW and the application of deep learning methods. [Display omitted] • The interactive pathway for catalytic co-pyrolysis of cellulose and PET was proposed. • The activation energy reached the lowest 73.04 KJ/mol when the mixing ratio was 1:1. • Optimized convolutional neural network predicted synergistic effects with R 2 > 0.99. • Feature importance for TG was temperature > catalyst > heating rate > mixing ratio. [ABSTRACT FROM AUTHOR]

Offshore wind-hydrogen systems operate in harsh marine environments for extended periods, posing risks of low accessibility and high failure rates. This paper proposes a data-driven fault detection framework for offshore wind-hydrogen systems, aiming to promptly detect potential faults based on the system's physical parameters. The research establishes a comprehensive model based on the operating principles of offshore wind-hydrogen systems. The study uses fault injection techniques to simulate common partial faults in offshore wind-hydrogen systems and collects relevant physical parameters (such as wind turbine power generation, energy storage battery charging/discharging current, and electrolyzer hydrogen production rate). A fault detection model was constructed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to enable precise detection of potential faults in the system. The established model provides theoretical support for designing, optimizing, and operating offshore wind-hydrogen systems, reducing reliance on actual experiments and lowering project risks and costs. Introducing artificial faults can evaluate the system's performance in different fault scenarios. This paper proposes a CNN-BiLSTM fault detection method that achieves automatic feature extraction and high-precision classification of time-series fault data, confirming the practicality and feasibility of offshore wind-hydrogen systems. This investigation realizes the integration of the offshore wind-hydrogen system model and deep learning, as well as a fully automated learning process from data to fault detection. By calculating and comparing results, it is verified that the framework has a high accuracy of 96.9%. This innovative research offers new methods and perspectives for enhancing the reliability and efficiency of fault detection in offshore wind-hydrogen systems. • A comprehensive multi-system simulation model for offshore wind-hydrogen systems is established. • Fault injection is employed to acquire data samples for offshore wind-hydrogen systems. • Utilizing the CNN-BiLSTM method, a high-accuracy deep learning fault detection model is crafted. • A simulation model-based data-driven deep learning framework for fault detection is devised in this study. [ABSTRACT FROM AUTHOR]

An Election is a method of selection of individuals to hold the public office in democracy. Ballot is basically a piece of paper that is used to cast vote during election. In ballot paper voting system each voter uses a ballot paper which is not shared and basically it is a paper printed with the name and symbols of the candidates. The Electronic Voting Machine is basically a memory recorder which records the vote casted by the voters. In this paper, main advantages of E-voting systems for country is highlighted. For constructing E-voting systems, every countries need to do great attention to Verification and Validation requirements. In this research, E-voting scheme with face recognition using deep learning technique is proposed. The process of casting vote is accomplished by blockchain technology and blind signature mechanism. The main objective of the proposed scheme is to explore the positive effects of security and safety in online voting system. • The Election is a method of selection of individuals to hold the public office in democracy. • The Electronic Voting Machine is basically a memory recorder which records the vote casted by the voters. • The aim of the research is to E-voting scheme with face recognition using deep learning technique is proposed. • The main objective of the proposed scheme is to explore the positive effects of security and safety in online voting system. [ABSTRACT FROM AUTHOR]

Cancer is a condition in which abnormal cells uncontrollably split and damage the body tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical images play an indispensable role in detecting various cancers; however, manual interpretation of these images by radiologists is observer-dependent, time-consuming, and tedious. An automatic decision-making process is thus an essential need for cancer detection and diagnosis. This paper presents a comprehensive survey on automated cancer detection in various human body organs, namely, the breast, lung, liver, prostate, brain, skin, and colon, using convolutional neural networks (CNN) and medical imaging techniques. It also includes a brief discussion about deep learning based on state-of-the-art cancer detection methods, their outcomes, and the possible medical imaging data used. Eventually, the description of the dataset used for cancer detection, the limitations of the existing solutions, future trends, and challenges in this domain are discussed. The utmost goal of this paper is to provide a piece of comprehensive and insightful information to researchers who have a keen interest in developing CNN-based models for cancer detection. [ABSTRACT FROM AUTHOR]

Deep neural networks have achieved outstanding performance in computer vision tasks. Convolutional Neural Networks (CNNs) typically operate in the spatial domain with raw images, but in practice, images are usually stored and transmitted in their compressed representation where JPEG is one of the most widely used encoder. Also, these networks are computationally intensive and slow. This paper proposes performing the learning and inference processes in the compressed domain in order to reduce the computational complexity and improve the speed of popular CNNs. For this purpose, a novel graph-based frequency channel selection method is proposed to identify and select the most important frequency channels. The computational complexity is reduced by retaining the important frequency components and discarding the insignificant ones as well as eliminating the unnecessary layers of the network. Experimental results show that the modified ResNet-50 operating in the compressed domain is up to 70% faster than the spatial-based traditional ResNet-50 while resulting in similar classification accuracy. Moreover, this paper proposes a preprocessing step with partial encoding to improve the resilience to distortions caused by low-quality encoded images. Finally, we show that training a network with highly compressed data can achieve a good classification accuracy with up to 93% reduction in the storage requirements of the training data. [ABSTRACT FROM AUTHOR]

Both the ABO and Rhesus (Rh) blood groups play crucial roles in blood transfusion medicine. Herein, we report a simple and low-cost paper-based analytical device (PAD) for phenotyping red blood cell (RBC) antigens. Using this Rh typing format, 5 Rh antigens on RBCs can be simultaneously detected and macroscopically visualized within 12 min. The proposed Rh phenotyping relies on the presence or absence of hemagglutination in the sample zones after immobilizing the antibodies targeting each Rh antigen. The PAD was optimized in terms of filter paper type, antibodies, and distance of the visualization zone. In this study, the optimal conditions were Whatman filter paper Grade 4; anti-D, –C, -E, -c, and -e antibodies; RBC suspension of 30%; and a visualization zone of 1 cm above the sample zone. The accuracy of simultaneously phenotyping the five Rh RBC antigens in the blood samples (n = 4692) was 99.19%, comparable with the accuracy of the gold-standard tube method used by blood bank laboratories in several regions of Thailand. Furthermore, decision making based on this method can be assisted by deep learning. After implementing a two-stage objective detection algorithm (YOLO v4-tiny) and classification model (DenseNet-201), the ambiguous images (n = 48) were interpreted with 100% accuracy. The PAD integrated with customized-region convolutional neural networks can reduce the interpretation discrepancies in RBC antigen phenotyping in any laboratory. [Display omitted] • Five Rh antigens typing PADs combined with deep learning algorithm for an assisted human-decision. • Rh antigens typing PADs were tested with real blood samples and compared with the standard tube method. • Dataset of photographs following Rh PADs testing were trained and evaluated by the in-house deep learning platform CiRA CORE. • Misinterpretation caused by weak or ambiguous reactions was solved by the CiRA CORE platform. [ABSTRACT FROM AUTHOR]

In this paper, we address the image denoising problem in presence of speckle degradation typically arising in ultra-sound images. Variational methods and Convolutional Neural Networks (CNNs) are considered well-established methods for specific noise types, such as Gaussian and Poisson noise. Nonetheless, the advances achieved by these two classes of strategies are limited when tackling the de-speckle problem. In fact, variational methods for speckle removal typically amounts to solve a non-convex functional with the related issues from the convergence viewpoint; on the other hand, the lack of large datasets of noise-free ultra-sound images has not allowed the extension of the state-of-the-art CNN denoiser methods to the case of speckle degradation. Here, we aim at combining the classical variational methods with the predictive properties of CNNs by considering a weighted total variation regularized model; the local weights are obtained as the output of a statistically inspired neural network that is trained on a small and composite dataset of natural and synthetic images. The resulting non-convex variational model, which is minimized by means of the Alternating Direction Method of Multipliers (ADMM) is proven to converge to a stationary point. Numerical tests show the effectiveness of our approach for the denoising of natural and satellite images. • An hybrid strategy for the despeckling problem combining convolutional neural networks and variational methods is proposed. • The proposed neural architecture is statistically-inspired and not requires the training on a large data-set of images. • A proof of convergence to a stationary point for the alternating direction method is given. [ABSTRACT FROM AUTHOR]

Timely and effective fault diagnosis is crucial to guarantee the reliability and durability of proton exchange membrane fuel cell (PEMFC). Segmented cell technology has been proven to be a powerful tool for exploring the internal characteristics of fuel cells, yet it is seldom applied for fault diagnosis. This paper proposes a data-driven fault diagnosis method that combines segmented cell and deep learning technology for PEMFC water management fault diagnosis. The dynamic characteristics of the current distribution of the fuel cell under different levels of faults are explored with segmented cell technology. A dual-input convolutional neural network is applied to combine current distribution data and sensor data for a more comprehensive system status for fault diagnosis. The results show that the model can efficiently identify flooding and drying faults with more than 98.5% accuracy, faster and more accurately than other algorithms. This work highlights the significance of segmented cell technology for the comprehension of water management inside PEMFC and provides a new concept for PEMFC fault diagnosis. [Display omitted] • The segmented cell technology is applied to investigate the current distribution during flooding and drying process. • A dual-input CNN is adopted to establish a fault diagnosis model for PEMFC water management. • The proposed method exhibits excellent diagnostic performance, with an accuracy rate of over 98.5%. [ABSTRACT FROM AUTHOR]

The large number of near-Earth asteroids (NEAs) has greatly impacted human space activities and Earth security. However, detecting NEAs in astronomical images with complex, varying backgrounds is still extremely challenging. In this paper, we propose a deep segmentation assisted asteroid tracking algorithm, termed DSAT, to construct a possible pipeline for faint NEA tracking in astronomical images. First, the single-frame object detection problem is converted to a segmentation problem, enabling robust extraction of faint potential moving objects. Then, a multiframe motion prior-based moving object tracking algorithm is proposed to find real NEAs. We further propose a distance tolerance criterion to help DSAT achieve effective tracking in practical situations when detection has partially failed. Finally, the pipeline is tested with both simulated and real astronomical images at different SNRs and in crowded fields. The results showed that our pipeline has the potential to detect and track faint NEAs in complex backgrounds. Our code is publicly available at https://github.com/zhenhongdu/DeepSegAsteroidTracker. [ABSTRACT FROM AUTHOR]

• Introduced the Siamese 4D-Alznet, which combines five distinct blocks of CNNs. • We proposed three custom TL models incorporating frozen and replacing layers. • Our methods prove 95.07% binary classification performance in terms of accuracy. • All techniques performed best for NC, EMCI, LMCI, and AD classification. Alzheimer's disease (AD) is the general form of dementia, leading to a progressive neurological disorder characterized by memory loss due to brain cell damage. Artificial Intelligence (AI) assists in the early identification and prediction of AD patients, determining future risks and benefits for radiologists and doctors to save time and cost. Since deep learning (DL) approaches work well with massive datasets and have recently become helpful for AD detection, there remains an area for improvement in automating detection performance. Present approaches somehow addressed the challenges of limited annotated data samples for binary classification. This contrasts with prior state-of-the-art techniques, which were constrained by their incapacity to capture abstract-level information. In this paper, we proposed a Siamese 4D-AlzNet model comprised of four parallel convolutional neural network (CNN) streams (Five CNN layer blocks) and customized transfer learning models (Frozen VGG-19, Frozen VGG-16, and customized AlexNet). Siamese 4D-AlzNet was vertically and horizontally stored, and the spatial features were passed to the final layer for classification. For experiments, T1-weighted MRI images comprised of four distinct subject classes, normal control (NC), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), and AD, have been employed. Our proposed models achieved outstanding accuracy, with a remarkable 95.05% accuracy distinguishing between normal and AD subjects. The performance across remaining binary class pairs consistently exceeded 90%. We thoroughly compared our model with the latest methods using the same dataset as our reference. Our proposed model improved NC-AD and MCI-AD classification accuracy by 2% 7%. [ABSTRACT FROM AUTHOR]

• Proposing physics-informed DCNN for HFI-SCB temperature field reconstruction. • Proposing Deep MC-QR method to quantify data uncertainty caused by noise. • Proposing HFI-SCB reliability analysis method based on interval multilevel BN. Temperature field reconstruction is essential for analyzing the reliability of a high-density functionally integrated satellite circuit board (HFI-SCB). As a representative deep learning model, the deep convolutional neural network (DCNN) is a powerful tool for reconstructing the HFI-SCB temperature field. However, DCNN needs a lot of labeled data to learn its parameters, which is contrary to the fact that actual satellite engineering can only acquire noisy unlabeled data. Thus, this paper proposes an unsupervised method, i.e., the physics-informed deep Monte Carlo quantile regression method, for reconstructing the HFI-SCB temperature field and quantifying the data uncertainty caused by sensor noise. The proposed method combines a DCNN with known physics knowledge to reconstruct an accurate HFI-SCB temperature field using only monitoring point temperatures. Besides, the proposed method can quantify the data uncertainty by the Monte Carlo quantile regression. Based on the reconstructed temperature field and the quantified data uncertainty, this paper builds an interval multilevel Bayesian Network to analyze the HFI-SCB reliability. Two case studies are used to validate the proposed method. [ABSTRACT FROM AUTHOR]

Hydrogen attack is the major failure cause of hydrogen equipment breakdown maintenance. Especially, common problems like cracks frequently occur but are challenging to find while operating, presenting an issue for safety and production. In the process of hydrogen damage evolution, a method for defect state recognition is proposed in this paper. Acoustic emission (AE) technology is used for inspecting the entire hydrogen charging process. The characteristic parameters including the counts and duration of the AE signals are first preprocessed, and the current damage states such as the dislocation propagation, and the occurrence of cracks are identified. Then, a deep learning convolutional neural network is used to create a hydrogen defect recognition (HDR) model with the input of a short-time Fourier transform for the feature vector extraction of various damage status AE signals. Finally, the hydrogen defect recognition experiment revealed that HDR is better in classification accuracy at 98.37% due to dislocation propagation and cracks identification. The study can provide an online damage recognition approach for damage state early warning and evaluation to guarantee hydrogen equipment safety operation. • Monitor the hydrogen damage process using acoustic emission detection technology. • Characteristics of the acoustic sources in hydrogen damage process are studied. • Neural network is used for hydrogen damage signal recognition and diagnosis. • STFT transform image is used as the neural network model input. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks have been popularly applied in single-image deraining recently. Nevertheless, as the network becomes deeper, it is easy to cause training over-fitting and performance saturation, particularly in the case of insufficient training data. In this paper, we report the design of a new network, namely parallel deraining convolutional neural network (PARDNet), for single-image deraining. Specifically, PARDNet adopts two parallel residual sub-networks based on different receptive fields to extract more comprehensive characteristics of the rain streaks, as well as decrease the depth of the network. The hybrid dilated convolution is employed to enlarge the sub-network's receptive field to capture more context information. The efficient channel attention module is integrated into the proposed PARDNet to capture rain streaks more effectively and preserve more background details. Furthermore, to facilitate the network training, the residual learning is also fused into PARDNet in a holistic way. Extensive experiments on synthetic and real-world rainy image datasets demonstrate the superiority of PARDNet for single-image deraining. • We propose a new parallel deep network framework for single-image deraining. • The hybrid dilated convolution is employed to capture more useful context information. • The efficient channel attention is adopted to extract more rain streaks effectively. • The framework we developed achieves better deraining performance on three test datasets. [ABSTRACT FROM AUTHOR]

• The deep learning model can better extract the deeper features of the multi-point deformation sequence. • MVMD algorithm reduces the complexity of multi-point displacement sequence. • The model considers the spatiotemporal characteristics of dam deformation, the prediction accuracy is obviously improved. It is difficult for single measuring point model to synchronously characterize the spatial correlation of deformation sequence of concrete dam and the similarity of displacements of different measuring points. Considering the limitation of conventional model in dealing with spatial-temporal data structure, In this study, a deep learning technique for multi-point and multi-output deformation prediction of concrete dams is proposed, which is based on Multivariate Variational Mode Decomposition (MVMD) and three-dimensional convolutional neural network (3D-CNN) combined model. Firstly, the multi-point sequence is decomposed into several relatively stable sub-sequences with different frequency scales by MVMD, which reduces the complexity of the multi-point displacement sequence. Then, In order to enhance the model 's ability to fit the time and spatial features of the sequence, the 3D-CNN model is used to predict the decomposed components and reconstruct the displacement data of the measuring points. This study focuses on the principle, process and calculation steps of the proposed model, and verifies the proposed model through the deformation data of three typical measuring points. The test results show that the prediction model proposed in this paper is an effective method for dam deformation prediction. [ABSTRACT FROM AUTHOR]

In recent years, many image denoising methods have been proposed based on convolutional neural networks (CNNs). While these methods have shown continuous performance improvement by introducing various mechanisms and structures, their computational cost tends to become increasingly expensive, owing to the resulting complex network architectures. This paper aims at winning the trade-off between computational efficiency and denoising performance for CNN-based image denoisers. Towards this end, we draw inspirations from traditional variational models with wavelet analysis operators for CNN architecture design. A model-inspired CNN is proposed with four key modules: iterative encoding-decoding units inspired by the iterative denoising process, directional convolutions inspired by the separable wavelet filters, inception modules inspired by the multi-scale analysis of wavelets, and stage-wise connections inspired by the adding noise back operation. In experiments, our CNN shows high computational efficiency in both training and test, with competitive results to state-of-the-art approaches. • A neural network inspired by the key ingredients of wavelet analysis model. • A sequential encoding-decoding network for image denoising. • A deep image denoiser well balancing effectiveness and computational efficiency. [ABSTRACT FROM AUTHOR]

• This paper presents a novel learning-based framework offering short temporal spanning to support dynamic scenes. • The framework has two concatenated modules to remove geometric distortions and to refine micro details, respectively. • We exploit complex-valued convolutions extracting phase information, altered by atmospheric turbulence. • The proposed method efficiently mitigates the atmospheric turbulence distortion and outperforms existing methods. Atmospheric turbulence distorts visual imagery and is always problematic for information interpretation by both human and machine. Most well-developed approaches to remove atmospheric turbulence distortion are model-based. However, these methods require high computation and large memory making real-time operation infeasible. Deep learning-based approaches have hence gained more attention but currently work efficiently only on static scenes. This paper presents a novel learning-based framework offering short temporal spanning to support dynamic scenes. We exploit complex-valued convolutions as phase information, altered by atmospheric turbulence, is captured better than using ordinary real-valued convolutions. Two concatenated modules are proposed. The first module aims to remove geometric distortions and, if enough memory, the second module is applied to refine micro details of the videos. Experimental results show that our proposed framework efficiently mitigates the atmospheric turbulence distortion and significantly outperforms existing methods. [ABSTRACT FROM AUTHOR]

The increasing availability of remote sensing data allows dealing with spatial-spectral limitations by means of pan-sharpening methods. However, fusing inter-sensor data poses important challenges, in terms of resolution differences, sensor-dependent deformations and ground-truth data availability, that demand more accurate pan-sharpening solutions. In response, this paper proposes a novel deep learning-based pan-sharpening model which is termed as the double-U network for self-supervised pan-sharpening (W-NetPan). In more details, the proposed architecture adopts an innovative W-shape that integrates two U-Net segments which sequentially work for spatially matching and fusing inter-sensor multi-modal data. In this way, a synergic effect is produced where the first segment resolves inter-sensor deviations while stimulating the second one to achieve a more accurate data fusion. Additionally, a joint loss formulation is proposed for effectively training the proposed model without external data supervision. The experimental comparison, conducted over four coupled Sentinel-2 and Sentinel-3 datasets, reveals the advantages of W-NetPan with respect to several of the most important state-of-the-art pan-sharpening methods available in the literature. The codes related to this paper will be available at https://github.com/rufernan/WNetPan. [ABSTRACT FROM AUTHOR]

Neural networks suffer performance degradation when the source and target data lie in a different distribution hampering direct deployment of the model to diverse target domains. To this end, domain generalization (DG) aims to generalize the model well to an unknown target domain by utilizing multiple source domains. This paper proposes two simple swapping mechanisms, texture and channel swapping (TCX), for DG. Texture swapping augments the source dataset by replacing textures of an image with other textures in source dataset to alleviate the texture bias problem in convolution neural networks (CNNs). Furthermore, channel swapping switches channels of input feature vectors of the classifier along with its labels to encourage the model to utilize more channels when classifying an image. Together, we expect the model to learn less domain-specific features but more generalized class-specific features resulting in better domain generalization performance. We demonstrate the effectiveness of our approach with state-of-the-art results on three domain generalization benchmarks. [ABSTRACT FROM AUTHOR]

Remote sensing imagery allows temporal and large-scale observation of the Earth, and advanced techniques such as deep learning have been developed to deal with the massive data. As a result, by combining remote sensing with deep learning, it is possible to realize change detection, which can reveal the change in land cover and land use. Recent work has focused on semantic change detection (SCD), which simultaneously locates where the changes took place while identifying what objects changed between the different periods. Deep learning based SCD methods are typically based on a multi-task structure, which combines the classification and binary change detection (BCD). In this structure, the change region proposals are derived from the transformation (e.g., concatenation, differencing) of multi-temporal images, but after being processed by a convolutional neural network, there can be an incomplete response in the spatio-temporal features. Because of the high similarity of the color, texture, and geometric information of the ground objects in temporal high-resolution remote sensing imagery, this reduces the distinguishability of features and makes it difficult to identify changed areas. Meanwhile, there is less interaction between the BCD and classification, resulting in semantic inconsistency in changed areas. In this paper, the temporal-agnostic change region proposal network (TCRPN) is proposed, which is based on exploring the saliency of the changed regions in single-temporal images to highlight the changed areas in the spatio-temporal features and improve the response to change. As the changed areas can be the salient regions in the single-temporal images, a single-image salient area extractor (SSAE) is proposed to extract the salient areas from the different temporal images, obtaining salient change region candidates. As the change-related salient regions are temporally agnostic, temporal saliency fusion is used to integrate the candidates extracted from the single-temporal images to obtain a more reliable salient area map, where the salient area map is used to enhance the spatio-temporal features. Meanwhile, a shortcut is proposed to further make the change representation more distinguishable and suppress incorrectly highlighted regions. Directly utilizing multi-temporal images for change region proposal can reduce the change information loss and increase the interaction between the classification and BCD tasks to obtain better spatio-temporal features. The TCRPN is simple and flexible, and can be plugged into any multi-task SCD network. We selected four advanced multi-task SCD methods as the baseline, and added the TCRPN in their encoder or decoder part to conduct comprehensive experiments on three large-scale SCD datasets. The experimental results confirmed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Multitask learning (MTL) aims at beneficial joint solving of multiple prediction problems by sharing information across different tasks. However, without adequate consideration of interdependencies, MTL models are prone to miss valuable information. In this paper, we introduce a novel deep MTL architecture that specifically encodes cross-task interdependencies within the setting of multiple image classification problems. Based on task-wise interim class label probability predictions by an intermediately supervised hard parameter sharing convolutional neural network, interdependencies are inferred in two ways: i) by directly stacking label probability sequences to the image feature vector (i.e. , multitask stacking), and ii) by passing probability sequences to gated recurrent unit-based recurrent neural networks to explicitly learn cross-task interdependency representations and stacking those to the image feature vector (i.e. , interdependency representation learning). The proposed MTL architecture is applied as a tool for generic multi-criteria building characterization using street-level imagery related to risk assessments toward multiple natural hazards. Experimental results for classifying buildings according to five vulnerability-related target variables (i.e., five learning tasks), namely height , lateral load-resisting system material , seismic building structural type , roof shape, and block position are obtained for the Chilean capital Santiago de Chile. Our MTL methods with cross-task label interdependency modeling consistently outperform single task learning (STL) and classical hard parameter sharing MTL alike. Even when starting already from high classification accuracy levels, estimated generalization capabilities can be further improved by considerable margins of accumulated task-specific residuals beyond +6% κ. Thereby, the combination of multitask stacking and interdependency representation learning attains the highest accuracy estimates for the addressed task and data setting (up to cross-task accuracy mean values of 88.43% overall accuracy and 84.49% κ). From an efficiency perspective, the proposed MTL methods turn out to be substantially favorable compared to STL in terms of training time consumption. [ABSTRACT FROM AUTHOR]

• Providing an in-depth and comprehensive review related to underwater object detection. • Extensively exploring the correlation between underwater image enhancement and underwater object detection. • Analyzing the impact and augmentation forms of underwater image enhancement in underwater object detection. • Discussing the challenges, future trends and applications of underwater object detection. Underwater object detection is one of the most challenging research topics in computer vision technology. The complex underwater environment makes underwater images suffer from high noise, low visibility, blurred edges, low contrast and color deviation, which brings significant challenges to underwater object detection tasks. In underwater object detection tasks, traditional object detection methods often perform poorly in terms of accuracy and generalization capabilities. Underwater object detection requires accurate, stable, generalizable, real-time and lightweight detection models, for which many researchers have proposed various underwater object detection techniques based on deep learning. Although many outstanding results have been achieved on underwater object detection over the years, the research status of underwater object detection techniques are still lack of unified induction, and some existing problems need to be further probed from the latest perspective. In addition, previous reviews lack analysis on the relationship between underwater image enhancement and object detection. Therefore, this paper provides a comprehensive review of the current research challenges, future development trends, and potential applications of underwater object detection techniques. More importantly, this paper has explored the internal relationship between underwater image enhancement and object detection, and analyzed the possible implementation manners of underwater image enhancement in the object detection task in order to further enhance its benefits. The experiments show the performances of current underwater image enhancement and state-of-the-art object detection algorithms, point out their limitations, and indicate that there is not a strict positive correlation between underwater image enhancement and the accuracy improvement of object detection. The domain shift caused by underwater image enhancement cannot be ignored. This paper can be regarded as a guide for future works on underwater object detection. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide. Over this period, the economic and healthcare consequences of COVID-19 infection in survivors of acute COVID-19 infection have become apparent. During the course of the pandemic, computer analysis of medical images and data have been widely used by the medical research community. In particular, deep-learning methods, which are artificial intelligence (AI)-based approaches, have been frequently employed. This paper provides a review of deep-learning-based AI techniques for COVID-19 diagnosis using chest radiography and computed tomography. Thirty papers published from February 2020 to March 2022 that used two-dimensional (2D)/three-dimensional (3D) deep convolutional neural networks combined with transfer learning for COVID-19 detection were reviewed. The review describes how deep-learning methods detect COVID-19, and several limitations of the proposed methods are highlighted. [ABSTRACT FROM AUTHOR]

Identification of human actions from video has gathered much attention in past few years. Most of the computer vision tasks such as Health Care Activity Detection, Suspicious Activity detection, Human Computer Interactions etc. are based on the principle of activity detection. Automatic labelling of activity from videos frames is known as activity detection. With the introduction of deep networks, the process of activity detection is clustered into two groups known as hand-crafted feature based approach and automatic feature extraction approach. This paper focuses on various approaches used in recent literature based on traditional and automatic approach. Moreover, hierarchy for different approaches under them such as space based, motion based, genetic based, fuzzy based, dictionary based are discussed. With introduction of Convolutional Neural Networks and Recurrent Neural Networks, automatic learning capability from input modality makes them first choice to be implemented for activity recognition. In this paper various approaches have been analyzed according to methodology, accuracy, classifier and datasets. [ABSTRACT FROM AUTHOR]

Estimating the remaining useful life (RUL) of proton exchange membrane fuel cells (PEMFCs) is key to predicting and increasing their durability. This paper investigates six deep learning techniques – long short-term memory (LSTM) networks, gated recurrent units (GRU), 1-D convolutional neural network (CNN)-LSTMs, 1D–CNN–GRUs, 1D–CNN–Bidirectional-LSTMs, and 1D–CNN–Bidirectional-GRUs – for RUL and long-term degradation trend prediction for a five-cell PEMFC stack operated with and without current ripples. The hyperparameters of all models are optimized for minimum root-mean-square error (RMSE) of the stack voltage using tree-structured Parzen estimators (TPE) with hyperband pruning. The obtained prediction results are benchmarked using the coefficient of determination (R2), RMSE, and mean absolute percentage error (MAPE) in stack voltage predictions and relative error (RE) in RUL estimates. The comparative analysis for models trained on 50% datasets reveals that the best R2, RMSE, MAPE, and RE of 98.87E-2, 1.258E-3, 0.285E-1, and 0.0425E-1 are offered by the LSTM model for the stack without current ripples. Further, for the stack with current ripples, the best R2, RMSE, MAPE, and RE of 99.482E-2, 1.77E-3, 0.444E-1, and 0.089E-1 are exhibited by the 1D–CNN–Bidirectional-GRU model. The LSTM model also reveals excellent transfer learning performance on the stack with current ripples with the best R2, RMSE, MAPE, and RE of 98.91E-2, 2.455E-3, 0.362E-1, and 0.044E-1, respectively. [Display omitted] • Six deep learning models are developed for PEMFC RUL and degradation prediction. • Hyperparameters of each model are optimized using TPE for static and dynamic loading. • Static loading models exhibited excellent transfer learning performance. • All tuned models performed superior to recent reported approaches in RMSE. • LSTM and 1D–CNN–Bidirectional-GRU demonstrated best results trained on 50% data. [ABSTRACT FROM AUTHOR]

Pipeline leakage and water deposits can cause serious consequences, such as environmental pollution, safety accidents, and economic losses. Therefore, effective detection of these flaws is of critical importance. Currently, most of the detection methods rely heavily on experienced inspectors and specialized equipment, which is labor-intensive and costly. To this end, this paper presents a one-dimensional residual convolutional neural network (1D-ResNet) based percussion method, for detecting pipeline leakage and water deposit. The proposed method uses sound produced by tapping the pipe as input to 1D-ResNet, which can directly extract features from the audio signal, avoiding hand-crafted feature extraction process. The effectiveness of the proposed method is validated through experiments, showing strong performance in pipeline fault detection. Furthermore, the 1D-ResNet method exhibits better classification performance and stronger noise robustness compared to other methods. In summary, this study presents a novel approach for the detection of pipeline leakage and deposit through the innovative introduction of 1D-ResNet deep learning technology, which has significant application prospects. [ABSTRACT FROM AUTHOR]

During the last few years, many deep learning (DL) methods have been proposed for the classification of hyperspectral images (HSI), and the final results show that these models have great potential. However, most existing deep models only consider the original spectral HSI datacube, excluding other sources of information such as textural and geometrical features. In addition to their computational complexity, most DL approaches cannot effectively consider local nonlinear relationships between features. In order to address these issues, this paper develops a new DL approach called MLKD-CNN, which uses multifeature local kernel descriptors (MLKDs) and convolutional neural networks (CNN) to classify HSIs. This method produces local kernel descriptors (LKDs) of spectral, extended morphological profiles, and Gabor features in the feature generation step. A simple CNN is then used to classify MLKD generated from spatial and spectral features. The proposed MLKD considers local nonlinear relationships of both spectral and spectral features, making them capable of achieving high accuracy levels while reducing the computational burden of deep methods. The experiments on four benchmark HSIs show that the proposed MLKD-CNN outperforms some recent HSI classification methods in terms of accuracy and computation time. [ABSTRACT FROM AUTHOR]

• Develop a dataset of Mars scenes from numerous raw Mars images. • Compare performance of three common deep learning models to select the appropriate one. • Conduct transfer learning and data augmentation to enhance selected model's generation capacity. • Deep learning can realize autonomous, real-time Martian terrain classification. Scene information plays a crucial role in motion control, attitude perception, and path planning for wheeled planetary rovers (WPRs). Terrain recognition is the fundamental component of scene recognition. Due to the rich information, visual sensors are usually used in terrain classification. However, teleoperation delay prevents WPRs from using visual information efficiently. End-to-end learning method of deep learning (DL) that does not need complex image preprocessing was proposed to deal with this issue. This paper first built a terrain dataset (consists of loose sand, bedrock, small rock, large rock, and outcrop) using real Mars images to directly support You Only Look Once (YOLOv5) to test its performance on terrain classification. Because the capability of end-to-end training scheme is positively correlated with dataset, the performance of YOLOv5 can be significantly improved by exploiting orders of magnitude more data. The best combination of hyperparameters and models was achieved by slightly tuning YOLOv5, and data augmentation was also applied to optimize its accuracy. Furthermore, its performance was compared with two other end-to-end network architectures. Deep learning algorithms can be used in the future planetary exploration missions, such as WPRs autonomy improvement, traversability analysis, and avoiding getting trapped. [ABSTRACT FROM AUTHOR]

Improving the accuracy of photovoltaic (PV) power forecasting is crucial to ensure more effective use of energy resources. Improvements are especially important for regions for which historical solar radiation data do not exist. This paper proposes a cyber-secure forecasting model called federated deep learning (FDL) to forecast PV power generation in various regions across Iran. The training process in each client is done by a convolutional neural network (CNN). Then, a generalizable global supermodel is generated based on the features extracted in each client, which has the ability to generalize and forecast for regions where there is no training data. Preserve data privacy and ideal performance against cyber-attacks are prominent features of the proposed method. The use of the proposed method is illustrated with a case study for Iran. The proposed FDL network is designed with 9 clients and three different scenarios were developed to test the robustness of the suggested method. In the first scenario, the PV power generation forecasting is done via the proposed technique and other conventional methods. The performance accuracy (R 2) of the generated global supermodel in this scenario for PV power generation forecasting in the regions of Khomein, Meybod, Varzaneh, Taleghan, and Shiraz are obtained as 0.981, 0.989, 0.986, 0.983, and 0.987 respectively. However, it was observed that other conventional deep learning-based models such as CNN and long short-term memory were not able to provide any forecasting for these regions. The second scenario models the scaling attack as a specific pattern of false data injection attack, to evaluate the performance of forecasting models against the data integrity attack. In the third scenario, cyber-attack detection is performed based on data visualization and image processing procedures. The results presented in different scenarios emphasize the high accuracy and generalizability of the global cyber-secure supermodel in PV power generation forecasting in different regions of Iran. [ABSTRACT FROM AUTHOR]

Objective: Tissue mechanical properties are valuable markers for tissue characterization, aiding in the detection and staging of pathologies. Shear wave elastography (SWE) offers a quantitative assessment of tissue mechanical characteristics based on the SW propagation profile, which is derived from the SW particle motion. Improving the signal-to-noise ratio (SNR) of the SW particle motion would directly enhance the accuracy of the material property estimates such as elasticity or viscosity. Methods: In this paper, we present a 3-D multi-resolution convolutional neural network (MRCNN) to perform improved estimation of the SW particle velocity V z. Additionally, we propose a novel approach to generate training data from real acquisitions, providing high SNR ground truth target data, one-to-one paired to inputs that are corrupted with real-world noise and disturbances. Discussion: By testing the network on in vitro data acquired from a commercial breast elastography phantom, we show that the MRCNN outperforms Loupas' autocorrelation algorithm with an improved SNR of 4.47 dB for the V z signals, a two-fold decrease in the standard deviation of the downstream elasticity estimates, and a two-fold increase in the contrast-to-noise ratio of the elasticity maps. The generalizability of the network was further demonstrated with a set of ex vivo porcine liver data. Conclusion: The proposed MRCNN outperforms the standard autocorrelation method, in particular in low SNR regimes. [ABSTRACT FROM AUTHOR]

Domain shift is the problem in which trained models fail to maintain their performance when they confront new test domains. Cross-scene classification, a technique developed to overcome this challenge, has attracted significant research interest in the field of remote sensing (RS). By exploring the correlation between the source domain and the target domain, relevant models can have better generalization ability regarding the target domain. Nevertheless, Domain Adaptation (DA), as the main technique for cross-scene classification, necessarily requires access to the target samples to assist the model training, a condition that is difficult to satisfy in real-world applications. Domain Generalization (DG) has attracted increasing research attention in recent years. Given one or several source domain(s), DG tends to learn models that can perform well when dealing with unseen (inaccessible) target domains. DG can better deal with out-of-distribution generalization with fewer restrictions, making it a good fit for cross-scene classification. Notably, little research has been conducted on domain generalization in the field of remote sensing. Thus, we refer to this type of method as cross-scene generalization. Recent studies have shown that convolutional neural networks have a strong bias towards recognizing textures rather than shapes. Accordingly, we proposed a Style and Content Separation Network (SCSN) for RS image cross-scene generalization in this paper, which can improve the generalizability and discriminative capability. The Style and Content Separation (SCS) module uses instance normalization to obtain the content information and thereby ensure better generalization ability. Moreover, the residual feature, which contains the style information, can supplement the feature representations after refinement. We further proposed a separation loss to constrain the style and content separation process. Experimental results and relevant analysis demonstrate the effectiveness of the proposed SCSN on cross-scene generalization tasks. Code is available at https://github.com/WHUzhusihan96/SCSN. [ABSTRACT FROM AUTHOR]

• This paper proposes a filter pruning approach for CNN model compression by transferring the knowledge of unimportant filters to the filters of higher importance. • Before pruning unimportant filters, a custom regularizer is utilized for knowledge transfer, which increases the gap between L1-norms of important and unimportant filters. • The effect of the penalty imposed in the custom regularizer is analyzed to justify the need for knowledge transfer before pruning. • In order to validate the robustness of the proposed framework across different CNN architectures, we experiment with five popular CNNs, namely, LeNet-5, VGG-16, ResNet-56, ResNet-110, and ResNet-50. • Experiments are performed on three benchmark datasets MNIST, CIFAR-10, and ImageNet. • An improvement over the baseline in terms of accuracy is observed even after removing 95.15%, 62.28%, and 62.39% of the Floating Point OPerations (FLOPs) from architectures LeNet-5, ResNet-56, and ResNet-110, respectively. As the deep learning models have been widely used in recent years, there is a high demand for reducing the model size in terms of memory and computation without much compromise in the model performance. Filter pruning is a very widely adopted strategy for model compression. The existing filter pruning methods identify the unimportant filters and prune them without worrying about information loss. They try to recover the same by fine-tuning the remaining filters, limiting their performance. In this paper, we tackle this problem by utilizing the knowledge from unimportant filters before pruning to minimize information loss. First, the proposed method identifies the unimportant and important filters by exploiting the lower and higher importance, respectively, using the L 1 -norm of filters. Next, the proposed custom UFKT-Reg regularizer ( R ufkt ) transfers the knowledge from unimportant filters before pruning to remaining filters, notably to a fixed number of important filters. Hence, the proposed method minimizes information loss due to the removal of unimportant filters. The experiments are conducted using the three benchmark datasets, including MNIST, CIFAR-10, and ImageNet. The proposed filter pruning method outperforms many recent state-of-the-art filter pruning methods. An improvement over the baseline in terms of accuracy is observed even after removing 95.15%, 62.28%, and 62.39% of the Floating Point OPerations (FLOPs) from architectures LeNet-5, ResNet-56, and ResNet-110, respectively. After pruning 53.25% of FLOPS from ResNet-50, only 1.02% and 0.47% of drops are observed in top-1 and top-5 accuracies, respectively. The code used in this paper will be publicly available at (https://github.com/sarvanichinthapalli/UFKT). [ABSTRACT FROM AUTHOR]

In current deep learning architectures, each of the deeper layers in networks tends to contain hundreds of unorganized neurons which makes it hard for humans to understand how they interact with each other. By organizing the neurons using correlation as the criteria, humans can observe how clusters of neighbouring neurons interact with each other. Research in Explainable Artificial Intelligence (XAI) aims to all alleviate the black-box nature of current AI methods and make them understandable by humans. In this paper, we extend our previous algorithm for XAI in deep learning, called Locality Guided Neural Network (LGNN). LGNN preserves locality between neighbouring neurons within each layer of a deep network during training. Motivated by Self-Organizing Maps (SOMs), the goal is to enforce a local topology on each layer of a deep network such that neighbouring neurons are highly correlated with each other. Our algorithm can be easily plugged into current state of the art Convolutional Neural Network (CNN) models to make the neighbouring neurons more correlated. A cluster of neighbouring neurons activating for a class makes the network both quantitatively and qualitatively more interpretable when visualized, as we show through our experiments. This paper focuses on image processing with CNNs, but can theoretically be applied to any type of deep learning architecture. In our experiments, we train VGG and WRN networks for image classification on CIFAR100 and Imagenette. Our experiments analyse different perceptible clusters of activations in response to different input classes. [ABSTRACT FROM AUTHOR]

• An overview of deep learning algorithms used in medical image segmentation is presented. • More than 150 papers applying deep learning to different medical applications are summarised. • Challenges and future directions in medical image segmentation are discussed. Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical images. They have been used extensively for medical image segmentation as the first and significant components of the diagnosis and treatment pipeline. Medical image segmentation is efficiently addressed by many types of deep neural networks, such as convolutional neural networks, fully convolutional network recurrent networks, adversarial networks, and U-shaped networks. This paper reviews the major DL models and applications pertinent to medical image segmentation and summarizes over 150 contributions to the field. Brief overviews of articles are provided by application area: anatomical structures such as organs, bones, and vessels, and abnormalities such as lesions and calcification. Moreover, we discuss current challenges and suggest directions for future research. [ABSTRACT FROM AUTHOR]

[Display omitted] Feature based image matching has been a research focus in photogrammetry and computer vision for decades, as it is the basis for many applications where multi-view geometry is needed. A typical feature based image matching algorithm contains five steps: feature detection, affine shape estimation, orientation assignment, description and descriptor matching. This paper contains innovative work in different steps of feature matching based on convolutional neural networks (CNN). For the affine shape estimation and orientation assignment, the main contribution of this paper is twofold. First, we define a canonical shape and orientation for each feature. As a consequence, instead of the usual Siamese CNN, only single branch CNNs needs to be employed to learn the affine shape and orientation parameters, which turns the related tasks from supervised to self supervised learning problems, removing the need for known matching relationships between features. Second, the affine shape and orientation are solved simultaneously. To the best of our knowledge, this is the first time these two modules are reported to have been successfully trained together. In addition, for the descriptor learning part, a new weak match finder is suggested to better explore the intra-variance of the appearance of matched features. For any input feature patch, a transformed patch that lies far from the input feature patch in descriptor space is defined as a weak match feature. A weak match finder network is proposed to actively find these weak match features; they are subsequently used in the standard descriptor learning framework. The proposed modules are integrated into an inference pipeline to form the proposed feature matching algorithm. The algorithm is evaluated on standard benchmarks and is used to solve for the parameters of image orientation of aerial oblique images. It is shown that deep learning feature based image matching leads to more registered images, more reconstructed 3D points and a more stable block geometry than conventional methods. The code is available at https://github.com/Childhoo/Chen_Matcher.git. [ABSTRACT FROM AUTHOR]

The goal of semantic segmentation is to segment the input image according to semantic information and predict the semantic category of each pixel from a given label set. With the gradual intellectualization of modern life, more and more applications need to infer relevant semantic information from images for subsequent processing, such as augmented reality, autonomous driving, video surveillance, etc. This paper reviews the state-of-the-art technologies of semantic segmentation based on deep learning. Because semantic segmentation requires a large number of pixel-level annotations, in order to reduce the fine-grained requirements of annotation and reduce the economic and time cost of manual annotation, this paper studies the works on weakly-supervised semantic segmentation. In order to enhance the generalization ability and robustness of the segmentation model, this paper investigates the works on domain adaptation in semantic segmentation. Many types of sensors are usually equipped in some practical applications, such as autonomous driving and medical image analysis. In order to mine the association between multi-modal data and improve the accuracy of the segmentation model, this paper investigates the works based on multi-modal data fusion semantic segmentation. The real-time performance of the model needs to be considered in practical application. This paper analyzes the key factors affecting the real-time performance of the segmentation model and investigates the works on real-time semantic segmentation. Finally, this paper summarizes the challenges and promising research directions of semantic segmentation tasks based on deep learning. [ABSTRACT FROM AUTHOR]

• A novel general cascaded convolutional neural network (CNN) reading method for pointer reading is presented. • Proposes PMA method, which can increase the data samples significantly without introducing redundant background information. • Proposes ADFPN for feature fusion, learns branch importance, performs fusion at various levels. Enhances fusion efficiency. • Construct a dataset for pointer meter readings that contains images of common pointer meters in substations. Pointer meters continue to serve as the primary monitoring instruments within substations. To solve the issue of poor model accuracy and low reading accuracy when reading multiple types of pointer meters, this paper proposes a universal cascaded convolutional neural network for accurate reading of various types of pointer instruments. The proposed detection method consists of two stages: instrument digital positioning and instrument pointer fitting. In the digital(figure of a number) positioning stage, an instrument data augmentation method for "large background and small target" is proposed to improve the model's sensitivity to digital position and reduce the interference of redundant background information to the model. In addition, an adaptive feature fusion pyramid structure is proposed for more effective feature fusion and enhanced fuzzy small target recognition. Compared with the baseline model, the method introduced in this study realizes a 0.47 % improvement in target detection accuracy. In our proposed dataset, the average recognition accuracy for the digital position location phase is 98.65 %,the average reading errors in square instruments, circular instruments, and instruments with uneven scales are 0.76 %, 0.73 %, and 0.67 % respectively. The proposed universal detection method in this paper achieves ultra-high precision and can be applied to reading pointer instruments of different types and scales. Compared with existing pointer meter reading methods, the proposed detection method showcases superior simplicity and effectiveness. Furthermore, it showcases a reduced measurement error, thereby bolstering its practical utility in industrial contexts. [ABSTRACT FROM AUTHOR]

[Display omitted] • Integrating deep learning with 2DCOS for wheat flour spectral classification. • The improved EfficientNet model achieved 100% accuracy in wheat flour identification. • Comparison of deep learning and machine learning methods in spectral analysis. • Transforms spectra into images, applicable beyond wheat flour. Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduces an innovative method for wheat flour recognition, combining deep learning (DL) with Two-dimensional correlation spectrum (2DCOS). In this investigation, 316 samples from four distinct types of wheat flour were collected using a near-infrared (NIR) spectrometer, and the raw spectra of each sample underwent preprocessing employing diverse methods. The discrete generalized 2DCOS algorithm was applied to generate 3792 2DCOS images from the preprocessed spectral data. We trained a deep learning model tailored for flour 2DCOS images – EfficientNet. Ultimately, this DL model achieved 100% accuracy in identifying wheat flour within the test set. The findings demonstrate the viability of directly transforming spectra into two-dimensional images for species recognition using 2DCOS and DL. Compared to the traditional stoichiometric method Partial Least Squares Discriminant Analysis (PLS_DA), machine learning methods Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and deep learning methods one-dimensional convolutional neural network (1DCNN) and residual neural network (ResNet), the model proposed in this paper is better suited for wheat flour identification, boasting the highest accuracy. This study offers a fresh perspective on wheat flour type identification and successfully integrates the latest advancements in deep learning with 2DCOS for spectral type identification. Furthermore, this approach can be extended to the spectral identification of other products, presenting a novel avenue for future research in the field. [ABSTRACT FROM AUTHOR]

In recent studies, deep learning methodologies have shown significant promise in crack detection. However, their practical implementation faces challenges due to the intricate diversity of structural surfaces and the inherent narrowness of cracks. To mitigate these problems, this paper introduces SegFormer, an efficient semantic segmentation model with hierarchical Transformer, for crack detection on concrete and asphalt surfaces in multiple scenarios. The combination of Cross-Entropy (CE) and Dice loss functions is employed to enhance the detection of fine cracks. Additionally, the paper presents an evaluation framework and discusses metrics for assessing crack segmentation results to provide a more precise and comprehensive analysis of model performance. Experimental results indicate that SegFormer outperforms Convolutional Neural Networks (CNNs) such as FCN, U-Net, and DeepLabV3 utilizing different backbones. Notably, the integration of multiple loss functions contributes to a more stable training process, expedites convergence, and yields enhanced results compared to models utilizing individual loss functions. • SegFormer is introduced for fine crack detection in various scenarios. • The combination of CE and Dice loss functions enhances crack detection. • A comprehensive model evaluation framework is presented. • Metrics for fine crack segmentation assessment are discussed. [ABSTRACT FROM AUTHOR]

In personal identity recognition systems, detecting a person's age, gender, and language using voice signal characteristics is a crucial issue, especially because of the importance of security considerations. Age, gender, and language classification problems are important in signal processing because they are used to analyze and understand human behavior, interactions, and preferences. This can be especially useful in the fields of human-computer interaction, psychology, and social science research. In this paper, a new system for detecting a speaker's age, gender, and language based on deep learning models is presented. Deep learning models have shown great efficacy in various fields of signal processing. For this paper, a range of deep models were tested, including convolutional neural networks (CNNs), recurrent neural network (RNN), and a fine-tuning ResNet34 architecture. Additionally, techniques such as transfer learning were applied to improve the efficiency of the proposed system. The input voice signals are preprocessed by applying the spectro-temporal transform to obtain additional features that can be fed to the ResNet34 model, which is designed specifically for the task of voice signal processing. The dataset used in this paper was sourced from the Mozilla common voice initiative, which is dedicated to advancing speech recognition and language identification technologies. The performance of the proposed algorithm was evaluated in the presence of Gaussian noise to determine its robustness. The experimental results demonstrated that the proposed algorithm significantly outperformed basic algorithms and other deep neural networks in terms of age and gender recognition from voice signals. [ABSTRACT FROM AUTHOR]

• We proposed a delineating field parcel model (TSANet) based on satellite image time series. • TSANet learn the relevance of spatial-spectral-temporal feature representation. • TSANet is robust across space and time. • TSANet performs better than pervious methods. • The time series data from the main growing period has a greater impact. Satellite image time series (SITS), such as Sentinel-2 imagery, plays a crucial role in the delineation of agricultural fields by reducing the impacts of ambiguities due to the spatial arrangement of field boundaries. Existing delineate field parcel models rely extensively on spatial features derived from single-date imagery. However, several studies have exploited the potential of SITS to effectively tackle the complexities associated with the intrinsic consistency between agricultural fields and their boundaries. This paper proposes a novel Two-Stream Attention convolutional neural network (TSANet) to capture the subtle difference between agricultural fields from SITS. Specifically, a field temporal semantic stream is introduced to adaptively leverage the significance of spatial-spectral-temporal feature representation associated with the location of agricultural parcels, especially where transitions in crop types take place. Considering the consistency between field parcels and their boundaries, we developed a field boundary prediction stream to enhance the extraction of edge features, particularly for the extraction of small and irregular agricultural parcels. Moreover, a field parcel refining block is employed to further enhance the geometric accuracy of agricultural fields. We conducted experiments on Sentinel-2 images from the Netherlands. Results showed that our approach produced a better layout of agricultural fields, with an average F1-score of 0.91 than the existing 3D-UNet, U-TEA, and BiConvLSTM. In addition, through the analysis of both quantitative and qualitative results, the stronger robustness of the model compared to other algorithms has been verified by temporal transfer and large-scale spatial prediction. We compared the difference between SITS and the corresponding composite images, which further verified the influence of temporal variation on the proposed approach. This paper provides a general guide for delineating agricultural parcels using SITS. [ABSTRACT FROM AUTHOR]

Soybean is a critical source of food, protein, and oil, and thus has received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod-counting algorithm capable of performing effectively in real-field conditions remains a significant challenge. This paper presents a pioneering work of accurate soybean pod counting utilizing unmanned aerial vehicle (UAV) images captured from actual soybean fields in Michigan, USA. Specifically, this paper presents SoybeanNet, a novel point-based counting network that harnesses powerful transformer backbones for simultaneous soybean pod counting and localization with high accuracy. In addition, a new dataset of UAV-acquired images for soybean pod counting was created and open-sourced, consisting of 113 drone images with more than 260k manually annotated soybean pods. The images are taken from an altitude of approximately 13 ft, with angles between 53 and 58 degrees, under natural lighting conditions. Through comprehensive evaluations, SoybeanNet demonstrates superior performance over five state-of-the-art approaches when tested on the collected images. Remarkably, SoybeanNet achieves a counting accuracy of 84.51% when tested on the testing dataset, attesting to its efficacy in real-world scenarios. The publication also provides both the source code and the labeled soybean dataset, offering a valuable resource for future research endeavors in soybean pod counting and related fields. • Created and open-sourced the 1st UAV-acquired image dataset for soybean pod counting. • Pioneered the first study on precise soybean pod counting utilizing UAV images. • Demonstrated SoybeanNet's superiority over 5 state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Geological reports house a wealth of geological domain knowledge and expert experience knowledge, and the efficient extraction of geological entity relations from these texts holds great significance in constructing a geological knowledge graph. To address challenges such as overlapping entity relations, data imbalance, and the limited incorporation of contextual features in existing deep learning-based entity relation extraction methods, this paper proposes a relational graph convolutional network pointer model based on the pre-trained model of Robustly Optimize Bidirectional Encoder Representation from Transformers Pre-training Approach (RoBERTa). Specifically, our approach tackles the issue of overlapping entity relationships by identifying the primary entity and the subordinate entity associated with a specific relationship. Additionally, by incorporating a relationship graph convolutional network, we can capture multiple relationships among entities. Moreover, we leverage the Biaffine layer to accurately handle entity annotation boundaries, thereby enhancing the precision of entity classification. To address the issue of imbalanced data, we adopt the Focal Loss function during experiments. Furthermore, we construct a geological entity relation dataset comprising 24 types of relationships, which is generated by collecting seven regional geological reports and employing semi-automatic annotation techniques. Finally, the proposed algorithm was tested and comparison with several baseline models, the results demonstrate the superior performance of the proposed model in this paper. • In this paper, a geological entity relationship dataset is constructed. • In this paper, we propose a convolutional pointer network model for relational maps based on the pre-trained RoBERTa model and validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Food safety is an important public health issue, and deep learning (DL) algorithms can provide powerful tools and methods for food safety and authenticity detection. Compared with chemometric algorithms and traditional machine learning algorithms, the performances of DL algorithms are improved in many aspects. By learning and analyzing a large amount of data, DL models can improve the efficiency and accuracy of food safety and authenticity detection, helping to ensure the public health and safety. This paper reviews some commonly used chemometric algorithms, traditional machine learning algorithms, and popular DL algorithms. Among them, special attentions are paid to convolutional neural network (CNN), fully convolutional network (FCN) and generative adversarial network (GAN). Moreover, the auxiliary effect of GAN on CNN is highlighted. Finally, this paper revisits recent applications of DL algorithms in the field of food safety and authenticity detection, and prospects the challenges and future directions of DL algorithms in this field. Although DL has made many achievements in the field of food safety and authenticity detection, there is still a great potential for development. For example, the data augmentation function of GAN can assist CNN to obtain more training samples, thus improving the recognition rate. In addition, multimodal neural network (MNN) or multimodal attention network (MAN) can be also used to achieve the fusion of data from different modalities to further improve the robustness and accuracy of DL algorithms. [Display omitted] • Some popular DL algorithms in food safety and authenticity detection are introduced. • The improvements of DL algorithms to chemometric and ML algorithms are summarized. • Recent applications of DL algorithms in food safety and authenticity detection are reviewed. • Future prospects of DL algorithms in food safety and authenticity detection are discussed. [ABSTRACT FROM AUTHOR]

Because the carbon load inside a diesel particulate filters (DPF) affects the DPF regeneration, and the carbon load recognition is significant for the particulate matter (PM) emission control. It is necessary to investigate an on-board DPF carbon load recognition method because the carbon load cannot be directly measured by sensors. Aiming to build a DPF carbon load prediction model adopting the deep learning method, this paper proposes a DPF carbon load identification model based on different experimental parameters using a layered one dimension convolutional neural network (1D-CNN) method. To improve data validity, this paper adopts two data-processing methods. The data pre-processing adopts data splicing method to complete the construction of the original sample set, and the data after-processing uses wavelet packet transform method to establish the feature sample sets. The model adopts the optimal feature dataset constructed by three input parameters, i.e., temperature difference, pressure difference, and exhaust mass flow, and has both high training accuracy and test accuracy above 90 %. The pressure difference is the most important influencing input parameter, and the three-parameter sample set (Δ T + Δ P + Q) has great recognition accuracy and good model stability with the high training accuracy and test accuracy as well as less iteration. • A heavy-duty diesel engine was tested under various conditions of carbon load inside DPF. • The data processing was done by two methods: data splicing and wavelet packet transform. • We proposed a DPF carbon load identification model based on a one-dimension CNN method. • The model adopts 3 parameters, and the 3-parameter sample set has optimal performance. • The 1D-CNN model has both high training accuracy and test accuracy above 90 %. [ABSTRACT FROM AUTHOR]

Groundwater level (GWL) prediction is important for ecological protection and resource utilization; it helps in formulating policies for artificial groundwater recharge, modifying the number of extraction wells, etc., and can support sustainable human development as well as inform water resource management decisions. However, climate change, anthropogenic impacts, and the complex coupling between surface water and groundwater increase the difficulty of predicting groundwater levels. The model proposed in this paper combines external data as well as multiple models. The method leverages long and short-term memory (LSTM) and convolutional neural network (CNN) models, combined with secondary modal decomposition and slime mould algorithm (SMA), together with an adaptive weight module (AWM). The study applies this method to predict GWL for three different hydrological conditions in China, specifically for the Jinan Baotu Spring, Heihu Spring, and Zhongtianshe watershed of Taihu Lake. A comparison of metrics such as mean absolute error and Nash efficiency coefficient for single and hybrid models shows that the model in this paper is more advantageous than the single model and other hybrid models. The interpretability of the model is enhanced by SHAP values that demonstrate the degree of contribution of the input variables. This paper uses SHAP analyses to identify the key drivers affecting groundwater levels. These factors must be detected in order to develop groundwater resource protection measures. [Display omitted] • Multivariate fusion data including hydrology and meteorology are used as model input. • A secondary modal decomposition module for historical groundwater level data was utilized. • The neural network hyperparameters are optimized using the slime mould algorithm. • Aggregate subsets of prediction with adaptive modules rather than linear summation. • The interpretable SHAP model measures the degree of influence of external variables. [ABSTRACT FROM AUTHOR]

Recently, many efforts have been concentrated on land use land cover (LULC) classification due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and climate change. However, various aspects have attracted hyperspectral imaging due to there being informative discriminative features, such as spectral-spatial features. To this end, this paper is a comprehensive and systematic review of LULC classification using hyperspectral images by reviewing four significant research investigations. Moreover, the four investigations have addressed the following points: (1) the main components of the hyperspectral imaging, the modes of hyperspectral imaging with data acquisition methods, and the intrinsic differences between hyperspectral image and multispectral image, (2) the role of machine learning in LULC classification, and the standard deep learning methods: Convolution Neural Network (CNN), Stacked Autoencoder (SAE), Deep Belief Network (DBN), Recurrent Neural Network (RNN), and Generative Adversarial Network (GAN), (3) the standard benchmark hyperspectral datasets and the evaluation criteria, (4) the main challenges of LULC classification with the possible solutions for the limited training samples issue, the promising future directions, and finally the recent applications for LULC classification. [ABSTRACT FROM AUTHOR]

• Substituting the FFNs of the transformer encoders with locality modules enriches the local contexts in multi-object tracking. • The quantitative and qualitative research shows the strength of locality in transformer-based MOT systems. • The performance of pedestrian tracking is boosted when training the model with a mixture of detection and tracking datasets. Multi-object tracking (MOT) is a critical task in various domains, such as traffic analysis, surveillance, and autonomous vehicles. The joint-detection-and-tracking paradigm has been extensively researched, which is faster and more convenient for training and deploying over the classic tracking-by-detection paradigm while achieving state-of-the-art performance. This paper explores the possibilities of enhancing the MOT system by leveraging the prevailing convolutional neural network (CNN) and a novel vision transformer technique Locality. There are several deficiencies in the transformer adopted for computer vision tasks. While the transformers are good at modeling global information for a long embedding, the locality mechanism, which learns the local features, is missing. This could lead to negligence of small objects, which may cause security issues. We combine the TransTrack MOT system with the locality mechanism inspired by LocalViT and find that the locality-enhanced system outperforms the baseline TransTrack by 5.3% MOTA on the MOT17 dataset. [ABSTRACT FROM AUTHOR]

Recently, super-resolution (SR) techniques based on deep learning have taken more and more attention, aiming to improve the images and videos resolutions. Most of the SR methods are related to other fields of computer vision such as image classification, image segmentation, and object detection. Based on the success of the image SR task, many image SR surveys are introduced to summarize the recent work in the image SR domains. However, there is no survey to summarize the SR models for the lightweight image SR domain. In this paper, we present a comprehensive survey of the state-of-the-art lightweight SR models based on deep learning. The SR techniques are grouped into six major categories: include convolution, residual, dense, distillation, attention, and extremely lightweight based models. Also, we cover some other issues related to the SR task, such as benchmark datasets and metrics for performance evaluation. Finally, we discuss some future directions and open problems, that may help other community researchers in the future. [Display omitted] • Lightweight image super-resolution based on deep learning models are reviewed. • The network design, loss function, framework, and training datasets are considered. • The network design structure of the reviewed models are categorized into six types. • The future research directions of lightweight image super-resolution are indicated. [ABSTRACT FROM AUTHOR]