When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition With Limited Data

An effective lossless compression method for attitude data with implementation on FPGA

The attitude angles of the drilling tool serve as crucial information for transmitting Measurement While Drilling (MWD) data, enabling the optimization of drilling performance and ensuring tool safety. However, the real-time transmission and processing of attitude data pose a significant challenge, especially with the increasing prevalence of horizontal and directional drilling. To accurately and promptly obtain the attitude data, this paper proposes a lossless compression method based on Huffman coding, called Adaptive Frame Prediction Huffman Coding (AFPHC). This approach leverages the slowly varying characteristics of MWD tool attitude data, employing frame residual prediction to reduce data volume and selecting optimal bit widths for encoding transmission data. By using real-world drilling data, the proposed method is implemented on a Verilog HDL on a Xilinx field-programmable gate array (FPGA) circuit. Simulation and experiment results show that compression ratios provided by the proposed method for the inclination, azimuth, and toolface angles reach up to 4.02 times, 3.98 times, and 1.48 times, respectively, outperforming several existing methods.

Attention-Guided Learning With Feature Reconstruction for Skin Lesion Diagnosis Using Clinical and Ultrasound Images

Skin lesion is one of the most common diseases, and most categories are highly similar in morphology and appearance. Deep learning models effectively reduce the variability between classes and within classes, and improve diagnostic accuracy. However, the existing multi-modal methods are only limited to the surface information of lesions in skin clinical and dermatoscopic modalities, which hinders the further improvement of skin lesion diagnostic accuracy. This requires us to further study the depth information of lesions in skin ultrasound. In this paper, we propose a novel skin lesion diagnosis network, which combines clinical and ultrasound modalities to fuse the surface and depth information of the lesion to improve diagnostic accuracy. Specifically, we propose an attention-guided learning (AL) module that fuses clinical and ultrasound modalities from both local and global perspectives to enhance feature representation. The AL module consists of two parts, attention-guided local learning (ALL) computes the intra-modality and inter-modality correlations to fuse multi-scale information, which makes the network focus on the local information of each modality, and attention-guided global learning (AGL) fuses global information to further enhance the feature representation. In addition, we propose a feature reconstruction learning (FRL) strategy which encourages the network to extract more discriminative features and corrects the focus of the network to enhance the model's robustness and certainty. We conduct extensive experiments and the results confirm the superiority of our proposed method. Our code is available at: https://github.com/XCL-hub/AGFnet.

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Single-event effects (SEE) are an important index of radiation resistance for fully depleted silicon on insulator (FDSOI) devices. The research into traditional FDSOI devices is based on simulation software, which is time consuming, requires a large amount of calculation, and has complex operations. In this paper, a prediction method for the SEE of FDSOI devices based on deep learning is proposed. The characterization parameters of SEE can be obtained quickly and accurately by inputting different particle incident conditions. The goodness of fit of the network curve for predicting drain transient current pulses can reach 0.996, and the accuracy of predicting the peak value of drain transient current and total collected charge can reach 94.00% and 96.95%, respectively. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.10 × 10² and 1.38 × 10³ times, respectively. This method can significantly reduce the computational cost, improve the simulation speed, and provide a new feasible method for the study of the single-event effect in FDSOI devices.

Marker-free characterization of full-length transcriptomes of single live circulating tumor cells

The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor in the enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypes relative to the primary tumor. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from peripheral blood. Most CTC detection methods based on single-cell RNA sequencing (scRNA-seq) use a mix of size selection, marker-based white blood cell (WBC) depletion, and antibodies targeting tumor-associated antigens. However, the majority of these methods either miss out on atypical CTCs or suffer from WBC contamination. We present unCTC, an R package for unbiased identification and characterization of CTCs from single-cell transcriptomic data. unCTC features many standard and novel computational and statistical modules for various analyses. These include a novel method of scRNA-seq clustering, named deep dictionary learning using k-means clustering cost (DDLK), expression-based copy number variation (CNV) inference, and combinatorial, marker-based verification of the malignant phenotypes. DDLK enables robust segregation of CTCs and WBCs in the pathway space, as opposed to the gene expression space. We validated the utility of unCTC on scRNA-seq profiles of breast CTCs from six patients, captured and profiled using an integrated ClearCell FX and Polaris workflow that works by the principles of size-based separation of CTCs and marker-based WBC depletion.

Single event effects prediction of MOSFET device using deep learning

Single event effect (SEE) is an important problem in the reliability research of integrated circuits. The study of SEE of traditional MOSFET devices is mainly based on simulation software, which is characterized by slow simulation speed, large computation and time-consuming. In this paper, a SEE research method based on deep learning is proposed. The method relies on 28 nm MOSFET. The complete drain transient current pulse, transient current peak value and total collected charge can be obtained in a short time by inputting relevant parameters that affect the SEE. The accuracy of the network for predicting transient current peak and total collected charge is 96.95% and 97.53% respectively, and the mean goodness of fit of the network for predicting the drain transient current pulse curve is 0.985. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.89 × 10³and 1.50 × 10³times respectively. This method has good prediction effect and provides a new possibility for the study of SEE.

Cross-Corpus Speech Emotion Recognition Based on Transfer Learning and Multi-Loss Dynamic Adjustment

In this paper, we do research on cross-corpus speech emotion recognition (SER), in which the training and testing speech signals come from different speech corpus. The mismatched feature distribution between the training and testing sets makes many classical algorithms unable to achieve better results. To deal with this issue, a transfer learning and multi-loss dynamic adjustment (TLMLDA) algorithm is initiatively proposed in this paper. The proposed algorithm first builds a novel deep network model based on a deep auto-encoder and fully connected layers to improve the representation ability of features. Subsequently, global domain and subdomain adaptive algorithms are jointly adopted to implement features transfer. Finally, dynamic weighting factors are constructed to adjust the contribution of different loss functions to prevent optimization offset of model training, which effectively improve the generalization ability of the whole system. The results of simulation experiments on Berlin, eNTERFACE, and CASIA speech corpora show that the proposed algorithm can achieve excellent recognition results, and it is competitive with most of the state-of-the-art algorithms.

Deep Learning Algorithm-Based Ultrasound Image Information in Diagnosis and Treatment of Pernicious Placenta Previa

This study was to explore the value of the deep dictionary learning algorithm in constructing a B ultrasound scoring system and exploring its application in the clinical diagnosis and treatment of pernicious placenta previa (PPP). 60 patients with PPP were divided into a low-risk group (severe, implantable) and high-risk group (adhesive, penetrating) according to their clinical characteristics, B ultrasound imaging characteristics, and postpartum pathological examination results. Under PPP ultrasonic image information using the deep learning algorithm, the B ultrasound image diagnostic scoring system was established to predict the depth of various types of placenta accreta. The results showed that the cut-off values of severe, implantable, adhesive, and penetrating types were <2.3, 2.3-6.5, 6.5-9, and ≥9 points, respectively; there were significant differences in the termination of pregnancy and neonatal birth weight between the two groups (P < 0.05); the positive predictive value, negative predictive value, and false positive rate of ultrasound images based on the deep dictionary learning algorithm for PPP were 95.33%, 94.89%, and 3.56%, respectively. Thus, the ultrasound image diagnostic scoring system based on the deep learning algorithm has an important predictive role for PPP, which can provide a more targeted diagnosis and treatment plan for patients in clinical practice and improve the prediction and treatment efficiency.

Hyperspectral Image Classification via Deep Structure Dictionary Learning

The construction of diverse dictionaries for sparse representation of hyperspectral image (HSI) classification has been a hot topic over the past few years. However, compared with convolutional neural network (CNN) models, dictionary-based models cannot extract deeper spectral information, which will reduce their performance for HSI classification. Moreover, dictionary-based methods have low discriminative capability, which leads to less accurate classification. To solve the above problems, we propose a deep learning-based structure dictionary for HSI classification in this paper. The core ideas are threefold, as follows: (1) To extract the abundant spectral information, we incorporate deep residual neural networks in dictionary learning and represent input signals in the deep feature domain. (2) To enhance the discriminative ability of the proposed model, we optimize the structure of the dictionary and design sharing constraint in terms of sub-dictionaries. Thus, the general and specific feature of HSI samples can be learned separately. (3) To further enhance classification performance, we design two kinds of loss functions, including coding loss and discriminating loss. The coding loss is used to realize the group sparsity of code coefficients, in which within-class spectral samples can be represented intensively and effectively. The Fisher discriminating loss is used to enforce the sparse representation coefficients with large between-class scatter. Extensive tests performed on hyperspectral dataset with bright prospects prove the developed method to be effective and outperform other existing methods.

Artificial Intelligence Algorithm-Based MRI in Evaluating the Treatment Effect of Acute Cerebral Infarction

The study is aimed at exploring the application of artificial intelligence algorithm-based magnetic resonance imaging (MRI) in the diagnosis of acute cerebral infarction, expected to provide a reference for diagnosis and effect evaluation of acute cerebral infarction. In this study, 80 patients diagnosed with suspected acute cerebral infarction per Diagnostic Criteria for Cerebral Infarction were selected as the research subjects. MRI images were reconstructed by deep dictionary learning to improve their recognition ability. At the same time, the same diagnostic operation was performed by Computed Tomography (CT) images to compare with MRI. The results of the interalgorithm comparison showed the image reconstruction effect of the deep dictionary learning model is significantly better than SAE reconstruction, single-layer dictionary reconstruction model, and KAVD reconstruction. After comparison, the results of MRI based on artificial intelligence algorithm and CT evaluation were statistically significant (P < 0.05). In the lesion image, the diameter of MRI lesions (3.81 ± 0.77 cm) based on artificial intelligence algorithm and the diameter of lesions in CT (3.66 ± 1.65 cm) also had significant statistical significance (P < 0.05). The results showed that MRI based on deep learning was more sensitive than CT imaging for diagnosis and evaluation of patients with acute cerebral infarction, with only 1 case misdiagnosed. The rate of disease detection and lesion image quality had a higher improvement. The results can provide effective support for the clinical application of MRI based on artificial intelligence algorithm in the diagnosis of acute cerebral infarction.

A Feature-Enriched Deep Convolutional Neural Network for JPEG Image Compression Artifacts Reduction and its Applications

The amount of multimedia data, such as images and videos, has been increasing rapidly with the development of various imaging devices and the Internet, bringing more stress and challenges to information storage and transmission. The redundancy in images can be reduced to decrease data size via lossy compression, such as the most widely used standard Joint Photographic Experts Group (JPEG). However, the decompressed images generally suffer from various artifacts (e.g., blocking, banding, ringing, and blurring) due to the loss of information, especially at high compression ratios. This article presents a feature-enriched deep convolutional neural network for compression artifacts reduction (FeCarNet, for short). Taking the dense network as the backbone, FeCarNet enriches features to gain valuable information via introducing multi-scale dilated convolutions, along with the efficient 1 ×1 convolution for lowering both parameter complexity and computation cost. Meanwhile, to make full use of different levels of features in FeCarNet, a fusion block that consists of attention-based channel recalibration and dimension reduction is developed for local and global feature fusion. Furthermore, short and long residual connections both in the feature and pixel domains are combined to build a multi-level residual structure, thereby benefiting the network training and performance. In addition, aiming at reducing computation complexity further, pixel-shuffle-based image downsampling and upsampling layers are, respectively, arranged at the head and tail of the FeCarNet, which also enlarges the receptive field of the whole network. Experimental results show the superiority of FeCarNet over state-of-the-art compression artifacts reduction approaches in terms of both restoration capacity and model complexity. The applications of FeCarNet on several computer vision tasks, including image deblurring, edge detection, image segmentation, and object detection, demonstrate the effectiveness of FeCarNet further.

CSI Amplitude Fingerprinting for Indoor Localization with Dictionary Learning

With the rapid growth of the demand for location services in the indoor environment, fingerprint-based indoor positioning has attracted widespread attention due to its high-precision characteristics. This paper proposes a double-layer dictionary learning algorithm based on channel state information (DDLC). The DDLC system includes two stages. In the offline training stage, a two-layer dictionary learning architecture is constructed for the complex conditions of indoor scenes. In the first layer, for the input training data of different regions, multiple sub-dictionaries are generated corresponding to learning, and non-coherent promotion items are added to emphasize the discrimination between sparse coding in different regions. The second-level dictionary learning introduces support vector discriminant items for the fingerprint points inside each region, and uses Max-margin to distinguish different fingerprint points. In the online positioning stage, we first determine the area of the test point based on the reconstruction error, and then use the support vector discriminator to complete the fingerprint matching work. In this experiment, we selected two representative indoor positioning environments, and compared the DDLC with several existing indoor positioning methods. The results show that DDLC can effectively reduce positioning errors, and because the dictionary itself is easy to maintain and update, the characteristic of strong anti-noise ability can be better used in CSI indoor positioning work.

Projective Double Reconstructions Based Dictionary Learning Algorithm for Cross-Domain Recognition

Dictionary learning plays a significant role in the field of machine learning. Existing works mainly focus on learning dictionary from a single domain. In this paper, we propose a novel projective double reconstructions (PDR) based dictionary learning algorithm for cross-domain recognition. Owing the distribution discrepancy between different domains, the label information is hard utilized for improving discriminability of dictionary fully. Thus, we propose a more flexible label consistent term and associate it with each dictionary item, which makes the reconstruction coefficients have more discriminability as much as possible. Due to the intrinsic correlation between cross-domain data, the data should be reconstructed with each other. Based on this consideration, we further propose a projective double reconstructions scheme to guarantee that the learned dictionary has the abilities of data itself reconstruction and data crossreconstruction. This also guarantees that the data from different domains can be boosted mutually for obtaining a good data alignment, making the learned dictionary have more transferability. We integrate the double reconstructions, label consistency constraint and classifier learning into a unified objective and its solution can be obtained by proposed optimization algorithm that is more efficient than the conventional l1 optimization based dictionary learning methods. The experiments show that the proposed PDR not only greatly reduces the time complexity for both training and testing, but also outperforms over the stateof- the-art methods.