Coding Of Vectors Research Articles

Analyzing the Integration Interpretation of Civics and Family Poetry Culture in University Language Programs Based on Deep Learning Models

Abstract In this paper, firstly, we start from the imagery in the poem, excavate the resources of the ideology and politics in the university language poetry, and put forward the feasible path of the integration of the ideology and politics of the university language course and the poetry culture of the family training. The research on poetry culture in university language is conducted using a deep learning model. Based on the deep learning model, keywords in the poetry text are extracted and extended, taking into account the issue of word splitting and lexical annotation. The sequence coding of word vectors is needed so as to construct a poetry generation model based on RNNPG, and then the research and analysis on the concept of ideology and politics of the poetry culture in the university language are carried out. The results show that in terms of model performance, the RNNPG model is 1.17 overall, and the overall generation effect is more ideal compared to other single discriminator generation models. The acceptance level of sophomores is generally above 0.29, which is better than that of freshmen, and the teaching of ancient poems under the perspective of “curriculum politics” is especially necessary to improve their cultural taste and aesthetic interest. This study is conducive to the personalized development of students and the formation of correct concepts.

МЕТОД ОБРОБКИ ТРАНСФОРМАНТ НА ОСНОВІ ЛОКАЛЬНО-ПОЗИЦІЙНОГО КОДУВАННЯ З АДАПТИВНОЮ ОСНОВОЮ

The presence of an imbalance caused by an insufficient level of performance of modern and promising infocommunication technologies with respect to the information intensity of bit streams is shown. It is substantiated that a decrease in the level of imbalance is organized as a result of an increase in the efficiency of information processing technologies. It is substantiated that at the moment the JPEG platform is the basic concept for building compression technologies. In accordance with this, it is proposed to organize the further development of methods for processing video resources using individual components of the JPEG platform in the direction of increasing the integrity of information while ensuring the required level of its availability. The development of a syntactic representation of a significant microsegment of a video frame in spectral space based on local-positional coding of structural vectors of significant transformant components and lengths of zero components with the subsequent formation of uniform codograms, the positioning of which is determined by the dynamic ranges of the corresponding code values ​​using structural bases, is presented. It is shown that the created video resource encoding methods based on the developed concept of processing significant segments provide a reduction in the delivery time of information under the conditions of the required level of its integrity, i.e. an increase in the level of information security is achieved

A supervised sparsity-based wavelet feature for bearing fault diagnosis

This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients; (2) design a structured dictionary that combines the signal characteristics and class information; (3) use the dictionary to implement the sparse coding of the WPT vectors, which can be solved by basis pursuit (BP) and (4) calculate the SSWF from the sparse coefficients. During the process, WPT can detect the fault occurrence of the bearing signal. Sparse coding based on a structured dictionary can find a robust representation of the signal and at the same time, integrate the class information. Therefore, SSWF is able to stably and discriminatively reflect different fault types, which indicates its potential in bearing fault diagnosis. Experiments on two bearing cases are conducted to verify the advantages of SSWF in the detection of bearing faults.

On the Computational Intractability of Exact and Approximate Dictionary Learning

The efficient sparse coding and reconstruction of signal vectors via linear observations has received a tremendous amount of attention over the last decade. In this context, the automated learning of a suitable basis or overcomplete dictionary from training data sets of certain signal classes for use in sparse representations has turned out to be of particular importance regarding practical signal processing applications. Most popular dictionary learning algorithms involve NP-hard sparse recovery problems in each iteration, which may give some indication about the complexity of dictionary learning but does not constitute an actual proof of computational intractability. In this technical note, we show that learning a dictionary with which a given set of training signals can be represented as sparsely as possible is indeed NP-hard. Moreover, we also establish hardness of approximating the solution to within large factors of the optimal sparsity level. Furthermore, we give NP-hardness and non-approximability results for a recent dictionary learning variation called the sensor permutation problem. Along the way, we also obtain a new non-approximability result for the classical sparse recovery problem from compressed sensing.

A New Coding Mode for Hybrid Video Coders Based on Quantized Motion Vectors

The rate allocation tradeoff between motion vectors and transform coefficients has a major importance when it comes to efficient video compression. This paper introduces a new coding mode for an H.264/AVC-like video coder, which improves the management of this resource allocation. The proposed technique can be used within any hybrid video encoder allowing a different coding mode for any macroblock. The key tool of the new mode is the lossy coding of motion vectors, obtained via quantization: while the transformed motion-compensated residual is computed with a high-precision motion vector, the motion vector itself is quantized before being sent to the decoder, in a rate/distortion optimized way. Several problems have to be faced with in order to get an efficient implementation of the coding mode, especially the coding and prediction of the quantized motion vectors, and the selection and encoding of the quantization steps. This new coding mode improves the performance of the hybrid video encoder over several sequences at different resolutions.

Visually Lossless Accuracy of Motion Vector in Overcomplete Wavelet-based Scalable Video Coding

In this paper, we propose a visually lossless accuracy model for scalable coding of motion vectors in overcomplete wavelet-based scalable video codec. By exploiting theory of stationary random process, we first estimate motion compensation errors in spatial domain due to inaccurate motion vectors. We then extend the results to overcomplete wavelet domain, and further derive the errors caused by fraction-pixel motion vectors. Finally, combining with a visibility model of wavelet coefficient errors, we propose a novel algorithm to estimate the accuracy threshold of motion vectors with which motion compensation errors will be invisible. Experimental results show that our algorithm is effective in estimating visually lossless accuracy threshold of motion vectors. The proposed algorithm can also be used in scalable motion estimation in wavelet domain. It can accelerate motion estimation speed by stopping halfway at the accuracy that will not cause any visible errors.

Set-based SAT-solving

The 3-SAT problem is one of the most important and interesting NP-complete problems with many applications in different areas. In several previous papers we showed the use of ternary vectors and set-theoretic considerations as well as binary codings and bit-parallel vector operations in order to solve this problem. Lists of orthogonal ternary vectors have been the main data structure, the intersection of ternary vectors (representing sets of binary solution candidates) was the most important set theoretic operation. The parallelism of the solution process has been established on the register level, i.e. related to the existing hardware, by using a binary coding of the ternary vectors, and it was also possible to transfer the solution process to a hierarchy of processors working in parallel. This paper will show further improvements of the existing algorithms which are easy to understand and result in very efficient algorithms and implementations. Some examples will be presented that will show the recent results.

Optimality of KLT for High-Rate Transform Coding of Gaussian Vector-Scale Mixtures: Application to Reconstruction, Estimation, and Classification

The Karhunen-Loeacuteve transform (KLT) is known to be optimal for high-rate transform coding of Gaussian vectors for both fixed-rate and variable-rate encoding. The KLT is also known to be suboptimal for some non-Gaussian models. This paper proves high-rate optimality of the KLT for variable-rate encoding of a broad class of non-Gaussian vectors: Gaussian vector-scale mixtures (GVSM), which extend the Gaussian scale mixture (GSM) model of natural signals. A key concavity property of the scalar GSM (same as the scalar GVSM) is derived to complete the proof. Optimality holds under a broad class of quadratic criteria, which include mean-squared error (MSE) as well as generalized f-divergence loss in estimation and binary classification systems. Finally, the theory is illustrated using two applications: signal estimation in multiplicative noise and joint optimization of classification/reconstruction systems

Resolution of Binary Coding of Real-Valued Vectors by Hyperrectangular Receiptive Fields

Schemes of binary coding of real-valued vectors are considered. Hyperrectangular receptive fields such as RSC and Prager randomly distributed fields and CMAC regular fields are analyzed. Theoretical and experimental estimations of resolving power (resolution) of codes and the comparative analysis of coding schemes are presented.

Frames, graphs and erasures

Two-uniform frames and their use for the coding of vectors are the main subject of this paper. These frames are known to be optimal for handling up to two erasures, in the sense that they minimize the largest possible error when up to two frame coefficients are set to zero. Here, we consider various numerical measures for the reconstruction error associated with a frame when an arbitrary number of the frame coefficients of a vector are lost. We derive general error bounds for two-uniform frames when more than two erasures occur and apply these to concrete examples. We show that among the 227 known equivalence classes of two-uniform (36, 15)-frames arising from Hadamard matrices, there are 5 that give smallest error bounds for up to 8 erasures.

Motion and texture rate-allocation for prediction-based scalable motion-vector coding

Modern video coding applications require data transmission over variable-bandwidth wired and wireless network channels to a variety of terminals, possibly having different screen resolutions and available computing power. Scalable video coding technology is needed to optimally support these applications. Recently proposed wavelet-based video codecs employing spatial-domain motion-compensated temporal filtering (SDMCTF) provide quality, resolution and frame-rate scalability while delivering compression performance comparable to that of H.264, the state-of-the-art in single-layer video coding. These codecs require quality-scalable coding of the motion vectors to support a large range of bit-rates with optimal compression efficiency. In this paper, the practical use of prediction-based scalable motion-vector coding in the context of scalable SDMCTF-based video coding is investigated. Extensive experimental results demonstrate that, irrespective of the employed motion model, our prediction-based scalable motion-vector codec (MVC) systematically outperforms state-of-the-art wavelet-based solutions for both lossy and lossless compression. A new rate-distortion optimized rate-allocation strategy is proposed, capable of optimally distributing the available bit-budget between the different frames and between the texture and motion information, making the integration of the scalable MVC into a scalable video codec possible. This rate-allocation scheme systematically outperforms heuristic approaches previously employed in the literature. Experiments confirm that by using a scalable MVC, lower bit-rates can be attained without sacrificing motion-estimation efficiency and that the overall coding performance at low rates is significantly improved by a better distribution of the available rate between texture and motion information. The only downside of scalable motion-vector coding is a slight performance loss incurred at high bit-rates.

Coding of variable dimension speech spectral vectors using weighted nonsquare transform vector quantization

This paper addresses the problem associated with variable-dimension vector quantization, and presents a new quantization technique that combines a variable-size nonsquare transform (NST) with a fixed-dimension vector quantizer. We show that all linear dimension conversion methods can be treated as special cases of a general approach for linear dimension conversion formulated as NST. By incorporating the speech perceptual properties, we introduce a technique called weighted nonsquare transform vector quantization (WNSTVQ) for the quantization of speech spectral vectors. We show that the total perceptual weighted distortion can be separated into the weighted modeling distortion, which is solely determined by the choice of transforms in WNSTVQ and the weighted quantizer distortion. We discuss the factors that influence the performance of the WNSTVQ system and provide a complexity analysis for two WNSTVQ implementations. Finally, experimental results are presented to show that the WNSTVQ system has the ability to trade performance for computational complexity and memory storage by selecting suitable transforms and/or the length of fixed-dimension vectors.

Average optimal vector transform for VQ-based image and video compression

We present an average sense optimal vector transform (AOVT) for vector quantization (VQ)-based coding of square block vectors. We introduce new notions of average intervector correlations and exploit the new structure of the intervector correlation matrix in deriving our AOVT. We present the results of applying our AOVT to many real images and demonstrate the superiority of the AOVT in terms of better preservation of the intravector correlation in the transform domain. We also present coding results on a variety of images to demonstrate the fact that the above-mentioned advantage of AOVT translates into coding gains since the performance of VQ increases if the transform domain vectors have more intravector dependencies.

Hierarchical 2-D mesh representation, tracking, and compression for object-based video

This paper proposes methods for designing, tracking and coding hierarchical two-dimensional (2-D) content-based mesh representations. The design procedure consists of constructing a fine-to-coarse hierarchy of Delaunay meshes, using image- and shape-based criteria for mesh geometry simplification. Hierarchical tracking employs a coarse-to-fine strategy with mesh-based motion vector optimization. We introduce new techniques to maintain the initial mesh hierarchy and topology during tracking by imposing certain constraints at each stage of the procedure. The hierarchical compression technique is based on a nearest neighbor ordering of mesh node points. This ordering serves to identify the mesh boundary nodes as well as establish spatial predictors for differential coding of node coordinates and motion vectors. The proposed hierarchical mesh representation, which has applications in object-based video manipulation, investing, and compression, provides improved tracking performance (compared to a nonhierarchical representation) and allows progressive (scalable) transmission of the object geometry (including shape) and motion information, as well as variable level-of-detail rendering. Experimental results are presented to compare the tracking and compression performance of hierarchical versus nonhierarchical mesh representations and to demonstrate the tradeoff between image quality and mesh bit rate for 2-D mesh-based video object rendering.

Optical flow-based motion compensation algorithm for very low-bit-rate video coding

In this article, we propose an efficient compression algorithm for very low-bit-rate video applications. The algorithm is based on (a) an optical-flow motion estimation to achieve more accurate motion prediction fields; (b) discrete cosine transformation (DCT) coding of the motion vectors from the optical-flow estimation to reduce the motion overheads; and (c) an adaptive threshold technique to match optical flow motion prediction and minimize the residual errors. Unlike the classic block-matching based DCT video coding schemes in MPEG-1/2 and H.261/3, the proposed algorithm uses optical flow for motion compensation and the DCT is applied to the optical flow field instead of predictive errors. Thresholding techniques are used to treat different regions to complement optical flow technique and to efficiently code residual data. While maintaining a comparable peak signal-to-noise ratio (PSNR) and computational complexity with that of ITU-T H.263/TMN5, the reconstructed video frames of the proposed coder are free of annoying blocking artifacts, and hence visually much more pleasant. The computer simulation are conducted to show the feasibility and effectiveness of the algorithm. Results at 11 kbps are presented which can be used for videophone applications in the existing public switched telephone network (PSTN). © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 230–237, 1998

Optimal binary vector quantization via enumeration of covering codes

Binary vector quantization (BVQ) refers to block coding of binary vectors under a fidelity measure. Covering codes were studied as a means of lattice BVQ. But a further source coding problem hidden in the equivalence of covering codes has seemingly eluded attention. Given a d-dimensional hypercube (code space), equivalent covering codes of the same covering radius but of different codewords have different expected BVQ distortions for a general probability mass function. Thus one can minimize, within the code equivalence, the expected distortion over all different covering codes. This leads a two-stage optimization scheme for BVQ design. First we use an optimal covering code to minimize the maximum per-vector distortion at a given rate. Then under the minmax constraint, we minimize the expected quantization distortion. This minmax constrained BVQ method (MCBVQ) controls both the maximum and average distortions, and hence improves subjective quality of compressed binary images, MCBVQ also avoids poor local minima that may trap the generalized Lloyd method. The [7,4] Hamming code and [8,4] extended Hamming code are found to be particularly suitable for MCBVQ on binary images. An efficient and simple algorithm is introduced to enumerate all distinct [7,4] Hamming/[8,4] extended Hamming codes and compute the corresponding expected distortions in optimal MCBVQ design. Furthermore, MCBVQ using linear covering codes has a compact codebook and a fast syndrome-encoding algorithm.

Error-resilience improvement for block-transform video coders

An algorithm is presented to enhance the resilience of block-based video-coding algorithms against channel errors. The error-resilience algorithm described suggests the use of fixed length coding to alleviate the effect of Huffman coding on the synchronisation of the decoder under erroneous conditions. The proposed mechanism modifies the order of transmission of the fixed-length video parameters to increase the chance of their arrival. Synchronisation words are sent at fixed-length intervals within the bit stream to reduce the possibility of the decoder falling on similar bit patterns within the bit stream. To limit the effect of error accumulation, the motion prediction process is halted and the differential coding of motion vectors is not applied. FEC techniques are applied on some error-sensitive segments of the reordered video bitstream. The effectiveness of the proposed error-resilience algorithm is evidenced by both subjective and objective results.

Feedforward associative memory switched-capacitor artificial neural networks

Observed spatiotemporal firings of biological neurons have lead many researchers to believe that the rate of firings of these biological neurons is what conveys neuronal information in the brain. In this paper we seek to highlight parallels between biological neurons and observed effects in real neurons, with artificial neurons implemented as switched-capacitor structures. One such effect is the heavy use of lateral inhibition observed in the brain that is often modeled by winner-take-all analog circuits. This paper introduces a novel winner-take-all circuit using switched capacitors that truly mimics this effect seen in biological systems. In addition, we show how switched-capacitor structures can also cater to both binary and bipolar coding of input data vectors, as required by many artificial neural network paradigms today. Applications of switched-capacitors artificial neural networks to pattern recognition and character recognition problems using feedforward associative neural networks are also discussed, and two examples are provided. Simulations using both HSPICE and SWITCAP2 confirm all our expectations.

Identification and Synthesis of Linear Sequential Machines

This paper considers the identification and synthesis of linear sequential machines from their state transition tables. Necessary and sufficient conditions for linearity are derived which form the basis of identification tests. A sufficient condition leads to a method for coding the system's state vectors in a fashion consistent with linearity but which does not entail trial and error. The coding process is analytic in nature and allows the coding of state vectors independently of the coding or linearity of the output table. Both the Moore and Mealy models are considered in deriving coding procedures for the input and output vectors.