Vector Quantization Research Articles

Lossless Recompression of Vector Quantization Index Table for Texture Images Based on Adaptive Huffman Coding Through Multi-Type Processing

With the development of the information age, all walks of life are inseparable from the internet. Every day, huge amounts of data are transmitted and stored on the internet. Therefore, to improve transmission efficiency and reduce storage occupancy, compression technology is becoming increasingly important. Based on different application scenarios, it is divided into lossless data compression and lossy data compression, which allows a certain degree of compression. Vector quantization (VQ) is a widely used lossy compression technology. Building upon VQ compression technology, we propose a lossless compression scheme for the VQ index table. In other words, our work aims to recompress VQ compression technology and restore it to the VQ compression carrier without loss. It is worth noting that our method specifically targets texture images. By leveraging the spatial symmetry inherent in these images, our approach generates high-frequency symbols through difference calculations, which facilitates the use of adaptive Huffman coding for efficient compression. Experimental results show that our scheme has better compression performance than other schemes.

Ensemble LVQ Model for Photovoltaic Line-to-Line Fault Diagnosis Using K-Means Clustering and AdaGrad

Line-to-line (LL) faults are one of the most frequent short-circuit conditions in photovoltaic (PV) arrays which are conventionally detected and cleared by overcurrent protection devices (OCPDs). However, OCPDs are shown to face challenges when detecting LL faults under critical detection conditions, i.e., low mismatch levels and/or high fault impedance values. This occurs due to insufficient fault current, thus leaving the LL faults undetected and leading to power losses and even catastrophic fire hazards. To compensate for OCPD deficiencies, recent studies have proposed modern artificial intelligence (AI)-based methods. However, various limitations can still be witnessed even in AI-based methods, such as (i) most of the models requiring a massive training dataset, (ii) critical fault detection conditions not being taken into consideration, (iii) models not being accurate enough when dealing with critical conditions, etc. To this end, the present paper proposes a learning vector quantization (LVQ)-based ensemble learning model in which three LVQs are individually trained to detect and classify LL faults in PV arrays. The initial LVQ vectors are determined using the k-means clustering method, and the learning rate is optimized by the adaptive gradient (AdaGrad) optimizer. The training and testing datasets are collected according to the PV array’s current–voltage (I–V) characteristic curve, and several features are extracted based on the Canberra and chi-squared distance techniques. The model utilizes a small training dataset, considers various critical detection conditions for LL faults—such as different mismatch levels and fault impedance values—and the final experimental results show that the model achieves an impressive average accuracy of 99.26%.

SVQ-MAE: an efficient speech pre-training framework with constrained computational resources

Self-supervised learning for speech pre-training models has achieved remarkable success in acquiring superior speech contextual representations by learning from unlabeled audio, excelling in numerous downstream speech tasks. However, the pre-training of these models necessitates significant computational resources and training duration, presenting a high barrier to entry into the realm of pre-training learning. In our efforts, by amalgamating the resource-efficient benefits of the generative learning model, Masked Auto Encoder, with the efficacy of the vector quantization method in discriminative learning, we introduce a novel pre-training framework: Speech Vector Quantization Masked Auto Encoder (SVQ-MAE). Distinct from the majority of SSL frameworks, which require simultaneous construction of speech contextual representations and mask reconstruction within an encoder-only module, we have exclusively designed a decoupled decoder for pre-training SVQ-MAE. This allows the additional decoupled decoder to undertake the mask reconstruction task solely, reducing the learning complexity of pretext tasks and enhancing the encoder’s efficiency in extracting speech contextual representations. Owing to this innovation, by using only 4 GPUs, SVQ-NAE can achieve high performance comparable to wav2vec 2.0, which requires 64 GPUs for training. In the Speech Processing Universal Performance Benchmark, SVQ-MAE surpasses wav2vec 2.0 in both keyword spotting and emotion recognition tasks. Furthermore, in cross-lingual ASR for Mandarin, upon fine-tuning on AISHELL-1, SVQ-MAE achieves a Character Error Rate of 4.09%, outperforming all supervised ASR models.

Machine learning-based identification and validation of immune-related biomarkers for early diagnosis and targeted therapy in diabetic retinopathy

The early diagnosis of diabetic retinopathy (DR) is challenging, highlighting the urgent need to identify new biomarkers. Immune responses play a crucial role in DR, yet there are currently no reports of machine learning (ML) algorithms being utilized for the development of immune-related molecular markers in DR. Based on the datasets GSE102485 and GSE160306, differentially expressed genes (DEGs) were screened using Weighted Gene Co-expression Network Analysis (WGCNA). Five ML algorithms including Bayesian, Learning Vector Quantization (LVQ), Wrapper (Boruta), Random Forest (RF), and Logistic Regression were employed to select immune-related genes associated with DR (DR.Sig). Seven ML algorithms including Naive Bayes (NB), RF, Support Vector Machine (SVM), AdaBoost Classification Trees (AdaBoost), Boosted Logistic Regressions (LogitBoost), K-Nearest Neighbors (KNN), and Cancerclass were utilized to construct a predictive model for DR. The relationship between DR.Sig genes and immune cells was analyzed using single-sample Gene Set Enrichment Analysis (ssGSEA). Additionally, drug sensitivity prediction of DR.Sig genes and molecular docking were performed. Through the utilization of 5 ML algorithms, 6 immune-related biomarkers closely related to the occurrence of DR were identified, including FCGR2B, CSRP1, EDNRA, SDC2, TEK, and CIITA. The DR predictive model constructed based on these 6 DR.Sig genes using the Cancerclass algorithm demonstrated superior predictive performance compared to 4 previously published DR-related biomarkers. In vivo and in vitro experiments also provided strong validation of the expression of the 6 genes in DR. Positive correlations were observed between these genes and 22 types of immune cells. Molecular docking results revealed that CSRP1, EDNRA, and TEK exhibited the highest affinities with the small molecule compounds etoposide, FR-139317, and camptothecin, respectively. The models constructed based on various ML algorithms can effectively predict the occurrence of DR events and hold potential for targeted drug therapies, providing a basis for the early diagnosis and targeted treatment of DR.

An efficient infinity norm minimization algorithm for under-determined inverse problems

The problem of solving under-determined systems of linear equations with minimum peak magnitude (ℓ∞ norm) has numerous applications in signal processing. These include Peak-to-Average Power Ratio (PAPR) reduction in MIMO-OFDM systems, vector quantization, approximate nearest neighbor search, optimal control in robotics, and power grid optimization. Several methods have been proposed to address this problem, but they often face limitations in computational speed or representation accuracy. Some methods also impose constraints on the frame matrix, such as restrictions on the type of its entries or its aspect ratio. In this paper, we present the Fast Iterative Peak Shrinkage Algorithm (FIPSA), which iterates over feasible solutions to consistently reduce peak magnitude and provably converge to near-optimal solutions. Our experimental results, conducted across various frame matrix types and aspect ratios, demonstrate that FIPSA consistently achieves near-minimal ℓ∞ norm values. In addition, it operates 1.3 to 7.3 times faster than previous methods, while maintaining an average representation error of 10−15. Notably, these advancements are achieved without imposing any constraints on the frame matrix.

Quantization‐Based Latin Hypercube Sampling for Dependent Inputs With an Application to Sensitivity Analysis of Environmental Models

ABSTRACTNumerical models are essential for comprehending intricate physical phenomena in different domains. To handle their complexity, sensitivity analysis, particularly screening is crucial for identifying influential input parameters. Kernel‐based methods, such as the Hilbert‐Schmidt Independence Criterion (HSIC), are valuable for analyzing dependencies between inputs and outputs. Implementing HSIC requires data from the original model, which leads to the need of efficient sampling strategies to limit the number of costly numerical simulations. While, for independent input variables, existing sampling methods like Latin Hypercube Sampling (LHS) are effective in estimating HSIC with reduced variance, incorporating dependence is challenging. This article introduces a novel LHS variant, quantization‐based LHS (QLHS), which leverages Voronoi vector quantization to address dependent inputs. The method provides good coverage of the range of variations in the input variables. The article outlines expectation estimators based on QLHS in various dependency settings, demonstrating their unbiasedness. The method is applied to several models of growing complexities, first on simple examples to illustrate the theory, then on more complex environmental hydrological models, when the dependence is known or not, and with more and more interactive processes and factors. The last application is on the digital twin of a French vineyard catchment (Beaujolais region) to design a vegetative filter strip and reduce water, sediment, and pesticide transfers from the fields to the river. QLHS is used to compute HSIC measures and independence tests, demonstrating its usefulness, especially in the context of complex models.

Event-triggered and quantised feedback stabilisation of switched networked control systems

In order to conserve network resources, this paper investigates the event-triggered control and quantised feedback stabilisation of switched networked control systems by introducing the vector quantizer technique. The state information undergoes quantisation and is transmitted to the controller after being sampled by the event-triggering technology. The designed event-triggered mechanism relies on quantised state values, and the generation of each quantised value also depends on the event-triggered condition. Addressing the asynchronous issue between the controller and the subsystem, we provide a sufficient condition for ensuring exponential stability of the closed-loop system and outline the controller gain design method by employing the multiple Lyapunov function and average dwell time methods. Besides, we demonstrate the explicit exclusion of the Zeno phenomenon in the event-triggered mechanism. Finally, the viability of our approach is demonstrated through simulation examples.

Quantized kernel recursive q-Rényi-like algorithm

This paper introduces the kernel recursive q-Rényi-like (KRqRL) algorithm, based on the q-Rényi kernel function and the kernel recursive least squares (KRLS) algorithm. To reduce the computational complexity and memory requirements of the KRqRL algorithm, an online vector quantization (VQ) method is employed to quantize the network size to a codebook size, resulting in the quantized KRqRL (QKRqRL) algorithm. This paper provides a detailed analysis of the convergence and computational complexity of the QKRqRL algorithm. In the simulation experiments, the network size of each algorithm is reduced to 25% of its original size. The performance of the QKRqRL algorithm is evaluated in terms of convergence speed, prediction error, and computation time under non-Gaussian noise conditions. Finally, the QKRqRL algorithm is further validated using sunspot data, demonstrating its superior stability and online prediction performance.

Prediction of surface roughness at the machining of densified wood surfaces

ABSTRACT In this research, modeling and interpretation of the effects of the machining parameters on surface roughness (Rz) using artificial neural networks (ANN) and analysis methods (MLP, LVQ and Taguchi Design analysis) werw studied. Black poplar (Populus nigra) was selected as the experimental material. Specimens, which were transversal densified by a thermo-mechanical (TM) process at 0%, 20% and 40% compression ratios. The densified wood was processed at 1000, 1500 and 2000 mm/min feed speeds and in 12,000, 15,000, and 18,000 rpm rotation speeds in a CNC machine by using two different cutters. Rz surface-roughness values were measured. The modeling of Rz with artificial neural networks (ANN) is discussed. While 97% success was achieved in the classification made with multilayer perceptron (MLP) in estimating Rz, this success rate is 100% in the classification made with learning vector quantization (LVQ). MLP and LVQ are operations in the MATLAB R2019b software. Taguchi values were compared with the experimental data results, it was seen that the MSE value was 4.6%, the MAE value was 1.8%, and the RMSE value was 2.1%. These results showed that the presented models can accurately determine the appropriate parameters for the desired Rz. The prediction results and presented models can be applied for targeted industrial applications.

Adaptive Neighbors Graph Learning for Large-Scale Data Clustering using Vector Quantization and Self-Regularization

In traditional adaptive neighbors graph learning (ANGL)-based clustering, the time complexity is more than O(n2), where n is the number of data points, which is not scalable for large-scale data problems in real applications. Subsequently, ANGL adds a balance regularization to its objective function to avoid the sparse over-fitting problem in the learned similarity graph matrix. Still, the regularization may leads to many weak connections between data points in different clusters. To address these problems, we propose a new fast clustering method, namely, Adaptive Neighbors Graph Learning for Large-Scale Data Clustering using Vector Quantization and Self-Regularization (ANGL-LDC), to perform vector quantization (VQ) on original data and feed the obtained VQ data as the input in the n×n similarity graph matrix learning. Hence, the n×n similarity graph matrix learning problem is simplified to weighted m×m(m≪n) graph learning problem, where m is the number of distinct points and weight is the duplicate times of distinct points in VQ data. Consequently, the time complexity of ANGL-LDC is much lower than that of ANGL. At the same time, we propose a new ANGL objective function with a graph connection self-regularization mechanism, where the ANGL-LDC objective function will get an infinity value if the value of one graph connection is equal to 1. Therefore, ANGL-LDC naturally avoids obtaining the sparse over-fitting problem since we need to minimize the value of ANGL-LDC’s objective function. Experimental results on synthetic and real-world datasets demonstrate the scalability and effectiveness of ANGL-LDC.

Breast Tumor Diagnosis Based on Molecular Learning Vector Quantization Neural Networks.

DNA nanotechnology plays a crucial role in precise cancer medicine. Currently, molecular logic circuits are applied to detect tumor-specific biomarkers and control the release of therapeutic drugs. However, these systems lack self-learning capabilities for intelligent diagnostics in biological samples, and their data processing capabilities are limited. Here, a molecular learning vector quantization neural network (LVQNN) model based on DNA strand displacement (DSD) technology for breast tumor diagnosis is developed. Compared to previous work, the molecular LVQNN boasts powerful computing abilities, handling high-dimensional data for intelligent cancer diagnosis. To verify the feasibility and versatility of the network, two distinct typical datasets are selected: one from a single source with cell morphology data from 569 cases, and a more extensive one spanning different populations and ages, with miRNA gene expression data from 1881 cases. By using the molecular LVQNN, diagnostic experiments are conducted on 50 and 120 public individuals from these two datasets, respectively, achieving accuracy rates of 94% and 97.5%. This study demonstrates that the LVQNN model exhibits significant advantages in breast cancer diagnosis and enhances diagnostic accuracy while introducing new approaches for intelligent cancer diagnosis, anticipated to bring significant breakthroughs and application prospects to precise cancermedicine.

Systematization of sunflower genotypes based on seed phenotypic characteristics using neural networks

In recent years, various datasets related to the phenotyping of sunflower genotypes have become increasingly accessible. However, one of the key challenges remains the efficient and accurate prediction of phenotypes based on genotypes in the context of climate change. Analyzing phenotypes at different levels of organization and detecting connections between phenotypes and genotypes require the integration and processing of large, diverse, and often noisy datasets. Machine learning offers a broad arsenal of methods and approaches for identifying predictive patterns in such data. Therefore, the research aimed to develop a methodology for the systematization of sunflower genotypes based on seed phenotypic characteristics using the data vector quantization method and neural networks. The study revealed the phenotypic characteristics of sunflower seeds from various genotypes selected by the Institute of Oilseed Crops of NAAS, grown in the southern Steppe of Ukraine, including seed length, width, thickness, seed mass, kernel mass, and seed coat cracking force. For this purpose, appropriate laboratory equipment was developed, including two modules for determining the morphological and rheological properties of seeds. The developed methodology for the systematization of sunflower genotypes based on seed phenotypic characteristics includes the following steps: measuring the characteristics of sunflower seeds from various samples (parental components); studying the mutual correlation of characteristics; conducting hierarchical cluster analysis of the data using the Ward's method; determining the optimal number of groups; performing k-means clustering using the vector quantization method; determining the correspondence of ranges of characteristics to the group; training a neural network to group the data by samples and created groups; verifying the adequacy of the neural network on test data. The developed methodology was tested, and the MLP 30-15-3 neural network for grouping data by samples and created groups of sunflower seeds was developed in the Statistica software package. The network's training efficiency was 99.4%, and such of testing and validation was 95.6% and 96.7%, respectively.

One-index vector quantization based adversarial attack on image classification

To improve storage and transmission, images are generally compressed. Vector quantization (VQ) is a popular compression method as it has a high compression ratio that suppresses other compression techniques. Despite this, existing adversarial attack methods on image classification are mostly performed in the pixel domain with few exceptions in the compressed domain, making them less applicable in real-world scenarios. In this paper, we propose a novel one-index attack method in the VQ domain to generate adversarial images by a differential evolution algorithm, successfully resulting in image misclassification in victim models. The one-index attack method modifies a single index in the compressed data stream so that the decompressed image is misclassified. It only needs to modify a single VQ index to realize an attack, which limits the number of perturbed indexes. The proposed method belongs to a semi-black-box attack, which is more in line with the actual attack scenario. We apply our method to attack three popular image classification models, i.e., Resnet, NIN, and VGG16. On average, 55.9 % and 77.4 % of the images in CIFAR-10 and Fashion MNIST, respectively, are successfully attacked, with a high level of misclassification confidence and a low level of image perturbation.

VQ-NeRF: Neural Reflectance Decomposition and Editing With Vector Quantization.

We propose VQ-NeRF, a two-branch neural network model that incorporates Vector Quantization (VQ) to decompose and edit reflectance fields in 3D scenes. Conventional neural reflectance fields use only continuous representations to model 3D scenes, despite the fact that objects are typically composed of discrete materials in reality. This lack of discretization can result in noisy material decomposition and complicated material editing. To address these limitations, our model consists of a continuous branch and a discrete branch. The continuous branch follows the conventional pipeline to predict decomposed materials, while the discrete branch uses the VQ mechanism to quantize continuous materials into individual ones. By discretizing the materials, our model can reduce noise in the decomposition process and generate a segmentation map of discrete materials. Specific materials can be easily selected for further editing by clicking on the corresponding area of the segmentation outcomes. Additionally, we propose a dropout-based VQ codeword ranking strategy to predict the number of materials in a scene, which reduces redundancy in the material segmentation process. To improve usability, we also develop an interactive interface to further assist material editing. We evaluate our model on both computer-generated and real-world scenes, demonstrating its superior performance. To the best of our knowledge, our model is the first to enable discrete material editing in 3D scenes.

A maritime image dehazing algorithm based on improved VQGAN

ABSTRACT Maritime image dehazing faces challenges as existing methods struggle to handle different concentrations of sea fog, and the treatment of details in dehazed results is often suboptimal. This paper proposes a maritime image dehazing algorithm based on the VQGAN mode. Specifically, since VQGAN is capable of generating high-quality discrete codebooks, we input the haze-free maritime dataset into VQGAN for pre-training to obtain a high-quality codebook prior. During the vector quantisation stage,high-quality vectors from the discrete codebook undergo distance calculations with the upsampled vectors containing haze.This process replaces the vectors containing haze with the high-quality vectors. To address the issue of residual fog, the authors introducing the FAM-UNet encoder.To tackle distortion issues, the authors introduced the Pyramid, Cascading, and Deformable Convolution Feature Alignment Module (PCD) into the original model. The experimental results indicate that our model can effectively restore clear images, providing assistance for subsequent advanced computer vision tasks..

Semantic-empowered visible light communication for image transmission based on deep convolutional generative adversarial network

In this paper, a semantic communication-based scheme was proposed to tackle the optimization challenge of transmission efficiency and link stability in indoor visible light communication (VLC) systems utilizing light-emitting diodes for image transmission. The semantic model, established by deep convolutional generative adversarial network (DCGAN) and vector quantization method, can effectively extract the essential characteristics of images. In addition, indoor VLC channel models including line-of-sight (LOS) and non-line-of-sight (NLOS) links are established in a 5*5*3 room, while incorporating noise interference encountered during signal transmission into the training process of the semantic model to enhance its anti-interference capability. Besides, the performance disparity between the semantic VLC system and the conventional VLC system using Better Portable Graphics (BPG) is assessed across various modulation formats, transmission distances, and receiving locations. The simulation results demonstrate that the semantic VLC system effectively enhances link stability and achieves a signal-to-noise ratio (SNR) gain exceeding 6 dB. Additionally, the semantic VLC system achieves superior performance compared to the conventional VLC system employing convolutional code at a rate of 1/2, without incurring additional bits consumption for error correction. Moreover, the practicality of the semantic VLC system is experimentally validated on an indoor physical testbed online enabled by the field programmable gate array (FPGA) with up to 1 Gbps data rate. In comparison with existing schemes, the semantic VLC system effectively reduces communication overhead by 40% while maintaining similar received image quality. Furthermore, it significantly enlarges the communication coverage of the VLC system by a factor of two without necessitating any hardware modifications and ensures stable transmission of images in high-speed scenarios.

Comparison of support vector machines (SVMs) and the learning vector quantization (LVQ) techniques for geological domaining: a case study from Darehzar porphyry copper deposit, SE Iran

Comparison of support vector machines (SVMs) and the learning vector quantization (LVQ) techniques for geological domaining: a case study from Darehzar porphyry copper deposit, SE Iran

Frame-level nonverbal feature enhancement based sentiment analysis

Multimodal Sentiment Analysis (MSA) comprehensively utilizing data from multiple modalities to obtain more accurate sentiment attribute, has important applications in other fields, such as social media analysis, user experience evaluation and medical health, etc. It is worth noting that previous studies have paid little attention to the inconsistency of the initial representation granularity between verbal (textual) and nonverbal (acoustic and visual) modalities. As a result, the imbalanced emotional information between them complicates the interaction process, and ultimately affects the model’s performance. To solve this problem, this paper proposes a Frame-level Nonverbal feature Enhancement Network (FNENet) to improve performance on MSA by reducing the gap and integrating asynchronous affective information between modalities. Specifically, Vector Quantization (VQ) is applied to nonverbal modalities to reduce the granularity differences and improve the performance of the model. Additionally, nonverbal information is integrated through the Sequence Fusion mechanism (SF) into a pre-trained language model to enhance the textual representation, which benefits the word-level semantic expression according to the asynchronous affective cues preserved in unaligned frame-level nonverbal features. Extensive experiments on three benchmark datasets demonstrate that FNENet significantly outperforms baseline methods. It indicates that our model has potential application on MSA.

An Innovative Recompression Scheme for VQ Index Tables

As we move into the digital era, the pace of technological advancement is accelerating rapidly. Network traffic often becomes congested during the transmission of large data volumes. To mitigate this, data compression plays a crucial role in minimizing transmitted data. Vector quantization (VQ) stands out as a potent compression technique where each image block is encoded independently as an index linked to a codebook, effectively reducing the bit rate. In this paper, we introduce a novel scheme for recompressing VQ indices, enabling lossless restoration of the original indices during decoding without compromising visual quality. Our method not only considers pixel correlations within each image block but also leverages correlations between neighboring blocks, further optimizing the bit rate. The experimental results demonstrated the superior performance of our approach over existing methods.

A Novel Method of Using Machine Learning Techniques to Protect Clouds Against Distributed Denial of Service (DDoS) Attacks.

The term "cloud computing" describes a method of providing hardware- and software-based services over the internet. This allows users to access their data and apps from any device. The benefits of cloud computing include scalability, virtualization, access to user assets, lower infrastructure costs, and flexibility. However, one drawback is that it is susceptible to distributed denial of service attacks, which occur when multiple computer systems collaborate to target a particular resource, website, or server. Distributed denial of service (DDoS) attacks present a serious risk to computer networks and constitute a major cyber security challenge. This results in a denial of service for end users, as a result of false connection requests, a flood of messages, and twisted packets causing the system to slow down or even crash. Real people and services cannot access cloud computing. The issue of machine learning algorithms for identifying distributed denial of service (DDoS) attacks is explored in this article. In order to identify and defend cloud systems from harmful assaults, we developed a new machine learning approach in this work that is based on transfer learning. This study offers NSL-KDD datasets and two methodologies. There are two types of filters available: the Learning Vector Quantization (LVQ) Filters and the Principal Component Analysis (PCA) method, which reduces dimensionality. The features selected from each method were pooled using Decision Tree (DT), Naïve Bayes (NB), and Support Vector Machine (SVM) to detect distributed denial of service attacks (DDoS). We contrasted the results of several classifications. In terms of attack detection, LVQ-based DT performed better results as compared to other methods.