However , a compact encoding scheme has not been discussed for MR-RePair . We propose a practical encoding method for MR-RePair and show its effectiveness through comparative experiments . Moreover , we extend MR-RePair to run-length context free grammar and design a novel variant for it called RL-MR-RePair . In case an area to be noted is designated at a portion of an image , various operations exploiting the designation are realized on the decoding side . In an image encoding device ( 100 ) , a ROI setting unit ( 20 ) sets a ROI area in the image . An entropy encoding unit ( 14 ) encodes the image in entropy . Disclosed is an image encoding device which includes ; a first prediction parameter determination unit ( 53 ) which selects , from a basic set , prediction parameters for each prediction unit belonging to a first group ; a second prediction parameter determination unit ( 55 ) which selects prediction parameters for each prediction unit belonging to a second group from a reduced set that comprises prediction parameters numbering fewer than the prediction parameters included in the basic set and that includes at least some of the prediction parameters selected by the first prediction parameter determination unit ( 53 ) ; and a prediction parameter encoding unit ( 243 ) which encodes whether the first prediction parameter determination unit ( 53 ) selected any prediction parameters and whether the second prediction parameter determination unit ( 55 ) selected any prediction parameters . Growing evidence suggests that age-related deficits in associative memory are alleviated when the to-be-associated items are semantically related . Here we investigate whether this beneficial effect of semantic relatedness is paralleled by spatio-temporal changes in cortical EEG dynamics during incidental encoding . Young and older adults were presented with faces at a particular spatial location preceded by a biographical cue that was either semantically related or unrelated . In a subsequent surprise recognition memory test , lures of a novel category , another exemplar , the same object in a different state , or exactly the same object were presented . Lure recognition performance was above chance , suggesting that incidental encoding resulted in reliable memory formation . Critically , performance for state lures was worse than for exemplars , which was driven by a greater similarity of state as opposed to exemplar foils to the original objects . The sentence tokens are morphosyntactically annotated , words are lemmatised and English-Slovene pairs of term candidates are marked up and linked . The corpus is distributed in the canonical TEI encoding , in the so called vertical format used by the (no)Sketch Engine and CWB concordancers , and as plain text files . Each distribution format also contains a file with thesis metadata . In successive refinement source coding one first describes the given source using a few bits of information , and then subsequently improves the description of the same source when more information is supplied . Sequential coding differs from successive refinement in that the second-stage encoding involves describing a correlated source as opposed to improving the description of the same source . We introduce the notion of a coupled fidelity criterion to quantify perceived distortion in certain applications of sequential coding . The present invention relates to a sound signal encoder and a sound signal decoder , especially a vector quantizing equipment and a vector quantizing method used wherein . The vector quantizing equipment of the invention encodes the target vector through the multi-stage vector quantizing , and comprises the following components : a first quantizing unit which uses a first code vector stored in the first codebook and the target vector or the forecasting error vector of the target vector to execute a first distance calculation and obtains a first stage encoding with the result calculated by the first distance ; an amplitude storing unit which is used for storing the scalar relevant with the encoding of the first stage ; and a second quantizing unit which a third code vector by multiplying the second code vector stored in the second codebook by the scalar ; the second distance calculation is executed by the decoding vector obtained by the first-stage encoding and the third code vector ; and the second-stage encoding is obtained with the result calculated by the second distance . Of those tested , Carryover-12 is preferred because it is both effective at compression and fast at decompression . A novel technique , Sigma Encoding prior to compression , is proposed and tested . Sigma Encoding utilizes a parameterized dictionary to reduce the number of bits necessary to store an integer . This method shows an about 0.3 bit per integer improvement over Carryover-12 while costing only about 3 extra clock cycles per integer to decompress . The partial attribute value of each instance data object is randomly set to 0 , the incomplete object is simulated as the input of the filling automatic encoder , Constructs the data and the instance prototype to train the network parameter . Examples of automatic encoding machine were shown in Figure 1 . y Based on this , this paper presents a classification method of hyperspectral images based on deep learning . Firstly , the spectral dimension of the hyperspectral data is reduced using automatic encoding machine , then convolutional neural network is used as the classifier , and the pixel and its neighborhood pixels are classified together as the input of the classifier , so as to realize the hyperspectral image classification with spectral space . The proposed model provides choices from among different trade-off solutions . The self adaptive B*tree coded AMOSA combines the novel cooling schedule , B*tree encoding , improved neighborhood search procedure , self adaptive local search , and the AMOSA . In B*tree coded AMOSA , the solution is represented using a B*tree . Systems and methodologies are described based on the peer-to-peer encoding for peer discovery signals from the network to facilitate identifying peers . For example , a time-direct signaling for dividing the frequency resource into a plurality of segments may communicate an identifier within a peer discovery interval is utilized ; Thus , a particular segment selected for transmission can signal a portion of the identifier , while the remainder can be signaled based upon tones communicated within the selected segment , the . In this article , we describe NetMoVie a new architecture for the management of adaptive streaming based on the RTP standard . NetMoVie optimizes the bandwidth by providing a hybrid encoding solution and also reduces the time needed to change the quality delivered to the clients . One is reduction of the number of variables and transitions in the k-model by distinguishing the temporal operator EX from the others . The other is simplification of the transformation of formulae by using uniform path encoding instead of a disjunction of all paths needed in the k-model . With these improvements , for an ACTL formula , the length of the final encoding of the formula in the worst case is reduced . Our solution outperforms the fastest Zlib version by a factor of 2.6 and higher ( in compression time ) yet demonstrates only a negligible drop-off in terms of compression ratio . The basic building blocks for our solution are a fast LZ77 compressor ( the LZ4 package ) and a standard Huffman encoding package ( Zlib ) . In the paper we describe how a non-trivial combination constructed around these building blocks achieves the aforementioned performance and compatibility . Our key idea is k-bit compaction , in which the n-bit multiplier is divided into n/k digits in 2k -nary 's , then the multiplicand is multiplied with each digit into a middle-product . And our second idea is oneminus-one encoding for the redundant binary representation . We 've compared 2-bit , 3-bit and 4-bit compactions . It also belongs to the same complexity class as many CAD problems dealing with sequential circuits , which makes it a natural candidate for encoding such problems . This work proposes a succinct QBF encoding for modeling sequential circuit behavior . The encoding is parametrized and further compression is achieved using time-frame windowing . Because of the differences between language model and NLU task objectives , the problem of lack of concern on some key words exists . Thus in this paper , we propose a method called reverse mapping bytepair encoding , which maps named-entity information and other word-level linguistic features back to subwords during the encoding procedure of bytepair encoding ( BPE ) . We employ this method to the Generative Pre-trained Transformer ( OpenAI GPT ) ( Radford et al. , 2018 ) by adding a weighted linear layer after the embedding layer . The goal of this paper is to present a more efficient way to automatically construct appropriate neural network topologies as well as their initial weight settings . Our approach combines evolutionary algorithms and genetic programming techniques and is based on a new network encoding schema where instead of a string like encoding the graph representation of neural nets is used . This way of " encoding " reduces the computational expense and leads to a greater variety of network topologies . We reduce the problem of serializability to satisfiability of a formula in First-Order Logic ( FOL ) , which allows us to harness the power of existing SMT solvers . We provide rules to automatically construct the FOL encoding from programs written in SQL ( allowing loops and conditionals ) and express consistency specifications as FOL formula . In addition to detecting bounded serializability violations , we also provide two orthogonal schemes to reason about unbounded executions by providing sufficient conditions ( again , in the form of FOL formulae ) whose satisfiability implies the absence of anomalies in any arbitrary execution . Sinusoidal coding plays an important role in low bit-rate audio coding . This paper considers frequency-differential encoding of the sinusoidal model parameters as an alternative to time-differential encoding . For a given signal frame , the parameters of each sinusoidal component may be encoded either differentially relative to other components in the same frame , or directly , i.e. , without differential encoding . A method to start the jump or avoidance of a pair of operations transcoding carried out in series through a first trans-coder , arranged together with a first communication terminal on a local side of a communication network and by a second trans-coder , arranged together with a second communication terminal on a distant network communication side , the method characterized by comprising receiving from the distant side , in a first initial request to bypass or bypass operations being transcoding , information about an encoding format being used at that time on the far side , about encoding capabilities of the distant side , and about a version of a bypassing protocol that provides support distally , and transmitting to the remote side in a second initial request to bypass transcoding operations , information about a trained encoding is being used at that time on the local side , about encoding capabilities of the local side and about a version of a bypassing protocol it supports the local side , and initiate an immediate optimization format encoding only if the local version and the remote version equal or exceed a specific version to allow , on one or both sides , a change of communication format being used at that time , before starting the avoidance operations trans-coding . Thus , RF encoding might allow omitting the use of all main field gradients , alleviating acoustic noise and other main field gradient-related problems . This study investigates the potential of RF transmit encoding using spatially nonlinear RF fields generated by an eight-channel parallel transmit system equipped with the corresponding transmit array . Appropriate spatial encoding functions are determined iteratively by randomized superposition of the array element sensitivities and judging their performance . Then we concatenate all five TED / TEDx development and test corpus to form a test set containing 6750 sentence pairs . We apply byte - pair encoding Sennrich2016NeuralMT to transform the texts to subword level so that the vocabulary has a size of 20 K for each language . For evaluation , we report tokenized BLEU using “ multi - bleu.perl ” . For instance , we might view machine translation in this way , where in particular we attempt to generate English sentences from ( corresponding ) French sentences . Seq2seq models are part of the broader class of “ encoder - decoder ” models , which first use an encoding model to transform a source object into an encoded representation . Many different sequential ( and non - sequential ) encoders have proven to be effective for different source domains . The LSTM language model oracle achieved an upper bound of 72.8 % accuracy on the test set . Matching Networks with a simple encoding model achieve 32.4 % , 36.1 % , 38.2 % accuracy on the task with examples in the set , respectively . Future work should explore combining parametric models such as an LSTM - LM with non - parametric components such as the Matching Networks explored here . proposed denoising autoencoder ( DAE ) that learns the data distribution in a supervised learning fashion . Both DBN and DAE learn a low dimensional representation ( encoding ) for each data instance and generate it from a decoding network . Recently , variational autoencoder ( VAE ) that combines deep learning with statistical inference intended to represent a data instance in a latent hidden space , while still utilizing ( deep ) neural networks for non - linear mapping . In this paper , we introduce a novel neural network architecture called Multi - layer Embedding with Memory Network ( MEMEN ) for machine reading task . In the encoding layer , we employ classic skip - gram model to the syntactic and semantic information of the words to train a new kind of embedding layer . We also propose a memory network of full - orientation matching of the query and passage to catch more pivotal information . It can also utilize the pre - processing tool , e.g. named entity recognizer , part - of - speech recognizer , lexical parser and coreference identifier etc . , to incorporate more lexical and syntactical information into the feature vector . Encoding Layer encodes the representations by incorporating the context information or enriching the representation with desirable features for future use . For instance , a model can adopt bidirectional recurrent neural network to model the temporal interaction on both direction , recursive neural network ( also known as TreeRNN ) to model the compositionality and the recursive structure of language , or self - attention to model the long - term dependency on sentence . Instead of summation adopted by Parikh et al . , ESIM proposes to compute both max and average pooling and feeds the concatenate fixed length vector to the final classifier : a fully connected multi - layer perceptron . Figure [ reference ] shows a high - level view of the ESIM architecture , where the bottom LSTM1 layer of Figure [ reference ] is the input encoding layer , the middle part with LSTM2 layer is the local inference layer , the upper part is the inference composition layer . ‘ ‘ confusion network ’ ’ representation of the hypotheses of the ASR ( see Figure [ reference ] ) . A confusion network is a graph encoding , for each speech segment in an utterance , the competing decoding hypotheses along with their posterior probability , thus providing a richer output than the - best decoding hypothesis . In particular , confusion networks in conjunction with NLU systems typically improve end - to - end performance in speech - to - meaning tasks . The contributions of this paper can be summarized as follows . We decompose the problem of MRC with unanswerable questions into three sub - tasks and combine them into a unified model , which uses the shared encoding and interaction layers . Thus , the three - tasks can be trained simultaneously in an end - to - end fashion . However , we found that if we trained each of the character - based and word - based encodings with their own loss , and combined them using an additional meta - BiLSTM model , we obtained optimal performance . In the meta - BiLSTM model , we concatenate the output , for each word , of its context sensitive character and word - based encodings , and put this through another BiLSTM to create an additional combined context sensitive encoding . This is followed by a final MLP whose output is passed to a linear layer for tag prediction . For determining whether one sentence entails another it can be a good strategy to check for entailment or contradiction of individual word - and phrase - pairs . To encourage such behavior we employ neural word - by - word attention similar to , [ reference ] and [ reference ] . The difference is that we do not use attention to generate words , but to obtain a sentence - pair encoding from fine - grained reasoning via soft - alignment of words and phrases in the premise and hypothesis . In our case , this amounts to attending over the first LSTM 's output vectors of the premise while the second LSTM processes the hypothesis one word at a time , thus generating attention weight - vectors Significant amount of work has been done in order to overcome this limitation . Predictive Sparse Decomposition ( PSD ) is a successful variant of sparse coding , which uses a predictor to approximate the sparse representation and solves the sparse coding computationally expensive encoding step . In this paper , we aim to solve some of the above - mentioned problems . We were surprised by the extent of the improvement obtained by reversing the words in the source sentences . We conclude that it is important to find a problem encoding that has the greatest number of short term dependencies , as they make the learning problem much simpler . In particular , while we were unable to train a standard RNN on the non - reversed translation problem ( shown in fig . We obtain a of 91.89 for the CoNLL - 2003 English shared task , which significantly outperforms a collection of highly competitive baseline models . We also present an ablation study showing the importance of auto - encoding , over using features as either inputs or outputs alone , and moreover , show including the autoencoder components reduces training requirements to 60 % , while retaining the same predictive accuracy . section : Introduction The data collection in these IR tasks is mostly in a multifield categorical form , for example , [ Weekday = Tuesday , Gender = Male , City = London ] , which is normally transformed into high - dimensional sparse binary features via onehot encoding [ reference ] . They come in two flavours , ( i ) those which use input images at a few scales and corresponding deep feature extraction networks , and ( ii ) those which combine feature maps from different layers of a single deep architecture . The common idea is to use features extracted at multiple scales to provide both local and global context and the using feature maps of the early encoding layers retain more high frequency detail leading to sharper class boundaries . Some of these architectures are difficult to train due to their parameter size . The sentence encoder compresses each source sentence into a vector representation and the classifier makes a three - way classification based on the two vectors of the two source sentences . The model follows the ‘ encoding - based rule ’ , i.e. , the encoder will encode each source sentence into a fixed length vector without any information or function based on the other sentence ( e.g. , cross - attention or memory comparing the two sentences ) . In order to fully explore the generalization of the sentence encoder , the same encoder is applied to both the premise and the hypothesis with shared parameters projecting them into the same space . _ title _ ’ ’ version also includes the actual movie title in the key . This representation can be combined with center encoding . Note that this representation is inherently specific to datasets in which there is an apparent or meaningful title for each document . Let denote the number of words in the sequence , the dimension of the embeddings , and the word embeddings corresponding to words in the sequence . We produce a global encoding of using a global bidirectional LSTM . where is the dimension of the LSTM state . subsection : Question Hierarchy Given the 1 - hot encoding of the question words , we first embed the words to a vector space ( learnt end - to - end ) to get . To compute the phrase features , we apply 1 - D convolution on the word embedding vectors . Huffman coding , as an efficient and simple variable length coding , is used in source coding . But the existing Huffman coding algorithm efficiency is not high , but application is also limited , therefore , this paper proposes an adaptive Huffman coding algorithm , this algorithm with other Huffman encoding compared with high efficiency , wide application range . The refined model is then used to seed the initial population of an immune inspired multi-objective optimization algorithm in the third stage to obtain a set of fuzzy models with improved transparency . To tackle the problem of simultaneously optimizing the structure and parameters , a variable length coding scheme is adopted to improve the efficiency of the search . The proposed modeling approach is applied to a real data set from the steel industry . The two problems are both formulated as integer fractional programming problems and the optimal solutions are derived with in-depth analysis . For both problems , numerical results show that the proposed variable-length coding scheme with the optimal amount of energy to be transferred or optimal number of channel use outperforms the coding strategy in an existing work . In the irreversible conversion process , the components Cb and Cr of pixel data are thinned out by way of a reduction process and subsequently interpolated based on the component Y of pixel data by way of an expansion process , while some bits of pixel data are reduced by way of a quantization process . The reversible compressive coding process performs a predictive coding process and a variable-length coding process on each pixel data selected in a raster-scanning sequence , thus producing compressive coded data . This makes it possible to decode both reversible compressive coded data and irreversible compressive coded data in units of lines . By the addition of an orthogonal transformation process and the result and the motion compensated reference image quantization process and inverse quantization processing and inverse orthogonal transform processing of the prediction residual , the reference image stored in the memory is generated . The orthogonal transform process and a quantization process and a variable length coding process of the prediction residuals is compressed moving picture coding apparatus that encodes video output signal is generated . Reference picture screen in the reference image A inside the video display screen , B , includes a screen outside the reference image D of the outer C and the image display screen , off-screen reference image D screen in the reference image A , B , similar C a plurality of reference image a and is generated based on the positional relationship B. Systems.- Document Generation and Picture Retrieval.- FLORES : A JAVA Based Image Database for Ornamentals.- Pictorial Portrait Indexing Using View-Based Eigen-Eyes.- Image Retrieval Using Fuzzy Triples.- Image Compression.- Variable-Bit-Length Coding : An Effective Coding Method.- Block-Constrained Fractal Coding Scheme for Image Retrieval.- Efficient Algorithms for Lossless Compression of 2D/3D Images.- Virtual Environments.- LucentVision(TM ) : A System for Enhanced Sports Viewing.- Building 3D Models of Vehicles for Computer Vision.- Integrating Applications into Interactive Virtual Environments.- Recognition Systems.- Structural Sensitivity for Large-Scale Line-Pattern Recognition.- Complex Visual Activity Recognition Using a Temporally Ordered Database.- Image Database Assisted Classification.- A Visual Processing System for Facial Prediction.- Semi-interactive Structure and Fault Analysis of (111)7x7 Silicon Micrographs.- Using Wavelet Transforms to Match Photographs of Individual Sperm Whales Identified by the Contour of the Trailing Edge of the Fluke.- From Gaze to Focus of Attention.- Automatic Interpretation Based on Robust Segmentation and Shape-Extraction.- A Pre-filter Enabling Fast Frontal Face Detection.- Visualization Systems.- A Technique for Generating Graphical Abstractions of Program Data Structures.- Visual Presentations in Multimedia Learning : Conditions that Overload Visual Working Memory.- Visualization of Spatial Neuroanatomical Data.- Visualization of the Cortical Potential Field by Medical Imaging Data Fusion.- Applying Visualization Research Towards Design . We propose a new coding scheme for lossless compression . This scheme , variable-bit-length coding , stores different grayscale values using a different number of bits . The compression performance of variable-bit-length coding coupled with a preprocessing method called remapping is compared with the compression performance of well known coding methods such as Huffman coding , arithmetic coding , and LZW coding . On the bechmark suite of 10 images used by us , remapping with variable-bit-length coding obtained maximum compression more often than did any other coding method . The development of an efficient compression scheme to process the Arabic language represents a difficult task . This paper employs the dynamic Huffman coding on data compression with variable length bit coding , on the Arabic language . Experimental tests have been performed on both Arabic and English text . The present invention is to correctly decode the data encoded by the variable-length encoding method having improved compression efficiency . The variable length coding method is a variable length coding method of coding with reference to the parameter table for each predetermined unit of data , and includes the following steps . Parameter encoding parameters to be used in encoding for encoding the information to view the ◎ obtained parameter table in the parameter initialization step ◎ initialization to set an initial value table parameter table information encoding step ◎ each of the sub data constituting the data of a predetermined unit a parameter acquiring step ◎ sub-data obtained from the table with reference to the encoding parameters acquired by placing in a position to be obtained before the sub-data encoding step data of an encoded the ◎ encoded information a predetermined unit to variable-length coding output information encoding data arrangement step The NxM data are read out by a predetermined sequence and converted to symbol data . The variable length coding part codes the symbol data by variable length . A coding control part has logics for selecting the scan pattern and provides the scan pattern selecting information to the switching part . In the paper a lossless compression method is proposed , tailored specifically for DTM data . The method involves discarding redundant data , performing differential coding , Variable Length Value coding , and finally compression using LZ77 or PPM algorithm . We present the results of experiments performed on real-world hydrographic data , which prove the validity of the proposed approach . Then , the resolution conversion of the disturbance is carried out in the orthogonal transform domain ( frequency domain ) with respect to the inverse weighted video signal in the resolution converter 16 . The The video signal after then , the resolution conversion is weighted in the weighting ( W ) section 18 , and quantized by the quantization ( Q ) unit 19 is variable-length variable length coding by the encoder ( VLC ) unit 20 , a second format It is output as the digital signals ( MPEG video signal ) . We propose a new decoding algorithm for interleaved Reed-Solomon Codes that attempts to correct all " interleaved " codewords simultaneously . In particular , interleaved encoding gives rise to multi-dimensional curves and more specifically to a variation of the Polynomial Reconstruction Problem , which we call Simultaneous Polynomial Reconstruction . We present and analyze a novel probabilistic algorithm that solves this problem . We have chosen to work in the bit stream domain in order to simplify the data embedding process in the case in which the original data are already in a compressed form . Thanks to the exploitation of an interleaved encoding technique and of some space and frequency masking considerations , we have been able to reduce the BER of the original algorithm . Some results underlining the improvements we have been able to obtain thanks to our technique are shown . The first measuring mode uses conventional phase-shift sequences in order to achieve highly accurate measurements under controlled conditions . The second measuring mode uses a new interleaved encoding approach for phase-shift patterns , which enables robust measurements even in the presence of relative motion between object and sensor . An example for this type of motion is the movement of the endoscope tip by hand . The transmission apparatus is disclosed . The transmission apparatus in the basis of the parity check matrix information bits , first parity bits and second parity bit interleaver and , interleaved LDPC code for interleaving encoder , LDPC codeword to generate the LDPC codeword comprising constellation mapping the word sex shop also includes a mapper . In order to improve the channel error correcting capacity of tactical data link and reduce decoding delay , a new concatenating method for RS codes and LDPC codes is proposed , which is presented by interleaving encoding and iterative decoding . A technique of interleaving encoding is adapted to enhance their relativity , at the same time , a combination of hard decision decoding for RS codes and iterative soft decision decoding is employed to guarantee sufficient decoding capability with lower decoding delay . Simulation results show that this method can obtain significant performance with proper decoding delay . The injected current through a pair of surface electrodes induces a magnetic flux density distribution inside the imaging object , which results in additional magnetic flux density . To measure the magnetic flux density signal in MREIT , the phase difference approach in an interleaved encoding scheme cancels out the systematic artifacts accumulated in phase signals and also reduces the random noise effect by doubling the measured magnetic flux density signal . For practical applications of in vivo MREIT , it is essential to reduce the scan duration maintaining spatial-resolution and sufficient contrast . The compressed data is sensitive to channel interference , where then the channel error correction coding is introduced to enhance the channel anti-jamming characteristics . To overcome it , a source-channel combined coding scheme is presented in which both multi-resolution over-sampling is used for source coding and RS and interleaver coding is used for channel coding . The " exclusive or " linear code can be encoded and decoded . Further , the present invention is the use of " exclusive or " code series does not require interleaver encoder , so no processing corresponding to the interleaving delay time , the input information bits can be coded and transmitted . In this paper we compared the wavelet based algorithms such are Embedded Zero tree Wavelet ( EZW ) , Set partitioning in Hierarchical Trees ( SPIHT ) and spatial orientation tree Wavelet ( STW ) . These algorithms are compared in terms of Compression Ratio ( CR ) , Peak Signal to Noise Ratio ( PSNR ) , Bit per pixels and Mean Square Error ( MSE ) . The effects of image contents and its corresponding compression ratios , filter orders of various wavelet functions and number of decompositions are observed . The outcomes of the image compression techniques such as Wavelet Difference Reduction ( WDR ) , Set Partitioning In Hierarchical Tree ( SPIHT ) , Embedded Zero Wavelet ( EZW ) , Adaptively Scanned Wavelet Difference Reduction ( ASWDR ) , Spatial Orientation-tree Wavelet ( STW ) were compared and tabulated by using the various parameters such as Mean Square Error ( MSE ) , Peak Signal to Noise Ratio ( PSNR ) and ( Bits Per Pixel ) BPP values from the image which was reconstructed . In this paper , an Improved Embedded Zero Wavelet ( IEZW ) is proposed and compared with other image compression techniques . All techniques of image compression basically divided into two parts , spatial domain compression technique and frequency domain compression technique . In frequency domain techniques there are numerous techniques like Fourier Transform , Discrete Wavelet Transform ( DWT ) and Discrete Cosine Transform ( DCT ) etc . after converting the image into frequency domain transformation , it uses several encoding technique like Embedded Zero Tree ( EZW ) coding , SPIHT ( Set Partitioning in Hierarchical Tree ) , ASWDR ( Adaptively Scanned Wavelet Difference Reduction ) WDR ( Wavelet Difference Reduction ) and STW ( Spatial orientation Tree Wavelet ) etc . These encoding schemes are also known as Progressive Coefficients Significance Methods ( PCSM ) . We then propose an alternative approach to face verification via SR . Specifically , we propose to use explicit SR encoding on local image patches rather than the entire face . The obtained sparse signals are pooled via averaging to form multiple region descriptors , which are then concatenated to form an overall face descriptor . Due to the deliberate loss spatial relations within each region ( caused by averaging ) , the resulting descriptor is robust to misalignment & various image deformations . Within the proposed framework , we evaluate several SR encoding techniques : l1-minimisation , Sparse Autoencoder Neural Network ( SANN ) , and an implicit probabilistic technique based on Gaussian Mixture Models . Thorough experiments on AR , FERET , exYaleB , BANCA and ChokePoint datasets show that the proposed local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods , in both verification and closed-set identification problems . Due to the deliberate loss spatial relations within each region ( caused by averaging ) , the resulting descriptor is robust to misalignment & various image deformations . Within the proposed framework , we evaluate several SR encoding techniques : l1-minimisation , Sparse Autoencoder Neural Network ( SANN ) , and an implicit probabilistic technique based on Gaussian Mixture Models . Thorough experiments on AR , FERET , exYaleB , BANCA and ChokePoint datasets show that the proposed local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods , in both verification and closed-set identification problems . Accordingly , in this paper , we propose a novel method aimed at overcoming the challenges associated with facial age estimation . First , we propose a novel age encoding method , referred to as ‘ Soft-ranking ’ , which encodes two important properties of facial age , < inline-formula > < tex-math notation="LaTeX">${i.e.}$ < /tex-math></inline-formula > , the ordinal property and the correlation between adjacent ages . Therefore , Soft-ranking provides a richer supervision signal for training deep models . Moreover , we carefully analyze existing evaluation protocols for age estimation , finding that the overlap in identity between the training and testing sets affects the relative performance of different age encoding methods . Moreover , we achieve state-of-the-art performance on four most popular age databases , < inline-formula > < tex-math notation="LaTeX">${i.e.}$ < /tex-math></inline-formula > , Morph II , AgeDB , CLAP2015 , and CLAP2016 .
However , a compact encoding scheme has not been discussed for MR-RePair . We propose a practical encoding method for MR-RePair and show its effectiveness through comparative experiments . Moreover , we extend MR-RePair to run-length context free grammar and design a novel variant for it called RL-MR-RePair .
In case an area to be noted is designated at a portion of an image , various operations exploiting the designation are realized on the decoding side . In an image encoding device ( 100 ) , a ROI setting unit ( 20 ) sets a ROI area in the image . An entropy encoding unit ( 14 ) encodes the image in entropy .
Disclosed is an image encoding device which includes ; a first prediction parameter determination unit ( 53 ) which selects , from a basic set , prediction parameters for each prediction unit belonging to a first group ; a second prediction parameter determination unit ( 55 ) which selects prediction parameters for each prediction unit belonging to a second group from a reduced set that comprises prediction parameters numbering fewer than the prediction parameters included in the basic set and that includes at least some of the prediction parameters selected by the first prediction parameter determination unit ( 53 ) ; and a prediction parameter encoding unit ( 243 ) which encodes whether the first prediction parameter determination unit ( 53 ) selected any prediction parameters and whether the second prediction parameter determination unit ( 55 ) selected any prediction parameters .
Growing evidence suggests that age-related deficits in associative memory are alleviated when the to-be-associated items are semantically related . Here we investigate whether this beneficial effect of semantic relatedness is paralleled by spatio-temporal changes in cortical EEG dynamics during incidental encoding . Young and older adults were presented with faces at a particular spatial location preceded by a biographical cue that was either semantically related or unrelated .
In a subsequent surprise recognition memory test , lures of a novel category , another exemplar , the same object in a different state , or exactly the same object were presented . Lure recognition performance was above chance , suggesting that incidental encoding resulted in reliable memory formation . Critically , performance for state lures was worse than for exemplars , which was driven by a greater similarity of state as opposed to exemplar foils to the original objects .
The sentence tokens are morphosyntactically annotated , words are lemmatised and English-Slovene pairs of term candidates are marked up and linked . The corpus is distributed in the canonical TEI encoding , in the so called vertical format used by the (no)Sketch Engine and CWB concordancers , and as plain text files . Each distribution format also contains a file with thesis metadata .
In successive refinement source coding one first describes the given source using a few bits of information , and then subsequently improves the description of the same source when more information is supplied . Sequential coding differs from successive refinement in that the second-stage encoding involves describing a correlated source as opposed to improving the description of the same source . We introduce the notion of a coupled fidelity criterion to quantify perceived distortion in certain applications of sequential coding .
The present invention relates to a sound signal encoder and a sound signal decoder , especially a vector quantizing equipment and a vector quantizing method used wherein . The vector quantizing equipment of the invention encodes the target vector through the multi-stage vector quantizing , and comprises the following components : a first quantizing unit which uses a first code vector stored in the first codebook and the target vector or the forecasting error vector of the target vector to execute a first distance calculation and obtains a first stage encoding with the result calculated by the first distance ; an amplitude storing unit which is used for storing the scalar relevant with the encoding of the first stage ; and a second quantizing unit which a third code vector by multiplying the second code vector stored in the second codebook by the scalar ; the second distance calculation is executed by the decoding vector obtained by the first-stage encoding and the third code vector ; and the second-stage encoding is obtained with the result calculated by the second distance .
Of those tested , Carryover-12 is preferred because it is both effective at compression and fast at decompression . A novel technique , Sigma Encoding prior to compression , is proposed and tested . Sigma Encoding utilizes a parameterized dictionary to reduce the number of bits necessary to store an integer . This method shows an about 0.3 bit per integer improvement over Carryover-12 while costing only about 3 extra clock cycles per integer to decompress .
The partial attribute value of each instance data object is randomly set to 0 , the incomplete object is simulated as the input of the filling automatic encoder , Constructs the data and the instance prototype to train the network parameter . Examples of automatic encoding machine were shown in Figure 1 . y
Based on this , this paper presents a classification method of hyperspectral images based on deep learning . Firstly , the spectral dimension of the hyperspectral data is reduced using automatic encoding machine , then convolutional neural network is used as the classifier , and the pixel and its neighborhood pixels are classified together as the input of the classifier , so as to realize the hyperspectral image classification with spectral space .
The proposed model provides choices from among different trade-off solutions . The self adaptive B*tree coded AMOSA combines the novel cooling schedule , B*tree encoding , improved neighborhood search procedure , self adaptive local search , and the AMOSA . In B*tree coded AMOSA , the solution is represented using a B*tree .
Systems and methodologies are described based on the peer-to-peer encoding for peer discovery signals from the network to facilitate identifying peers . For example , a time-direct signaling for dividing the frequency resource into a plurality of segments may communicate an identifier within a peer discovery interval is utilized ; Thus , a particular segment selected for transmission can signal a portion of the identifier , while the remainder can be signaled based upon tones communicated within the selected segment , the .
In this article , we describe NetMoVie a new architecture for the management of adaptive streaming based on the RTP standard . NetMoVie optimizes the bandwidth by providing a hybrid encoding solution and also reduces the time needed to change the quality delivered to the clients .
One is reduction of the number of variables and transitions in the k-model by distinguishing the temporal operator EX from the others . The other is simplification of the transformation of formulae by using uniform path encoding instead of a disjunction of all paths needed in the k-model . With these improvements , for an ACTL formula , the length of the final encoding of the formula in the worst case is reduced .
Our solution outperforms the fastest Zlib version by a factor of 2.6 and higher ( in compression time ) yet demonstrates only a negligible drop-off in terms of compression ratio . The basic building blocks for our solution are a fast LZ77 compressor ( the LZ4 package ) and a standard Huffman encoding package ( Zlib ) . In the paper we describe how a non-trivial combination constructed around these building blocks achieves the aforementioned performance and compatibility .
Our key idea is k-bit compaction , in which the n-bit multiplier is divided into n/k digits in 2k -nary 's , then the multiplicand is multiplied with each digit into a middle-product . And our second idea is oneminus-one encoding for the redundant binary representation . We 've compared 2-bit , 3-bit and 4-bit compactions .
It also belongs to the same complexity class as many CAD problems dealing with sequential circuits , which makes it a natural candidate for encoding such problems . This work proposes a succinct QBF encoding for modeling sequential circuit behavior . The encoding is parametrized and further compression is achieved using time-frame windowing .
Because of the differences between language model and NLU task objectives , the problem of lack of concern on some key words exists . Thus in this paper , we propose a method called reverse mapping bytepair encoding , which maps named-entity information and other word-level linguistic features back to subwords during the encoding procedure of bytepair encoding ( BPE ) . We employ this method to the Generative Pre-trained Transformer ( OpenAI GPT ) ( Radford et al. , 2018 ) by adding a weighted linear layer after the embedding layer .
The goal of this paper is to present a more efficient way to automatically construct appropriate neural network topologies as well as their initial weight settings . Our approach combines evolutionary algorithms and genetic programming techniques and is based on a new network encoding schema where instead of a string like encoding the graph representation of neural nets is used . This way of " encoding " reduces the computational expense and leads to a greater variety of network topologies .
We reduce the problem of serializability to satisfiability of a formula in First-Order Logic ( FOL ) , which allows us to harness the power of existing SMT solvers . We provide rules to automatically construct the FOL encoding from programs written in SQL ( allowing loops and conditionals ) and express consistency specifications as FOL formula . In addition to detecting bounded serializability violations , we also provide two orthogonal schemes to reason about unbounded executions by providing sufficient conditions ( again , in the form of FOL formulae ) whose satisfiability implies the absence of anomalies in any arbitrary execution .
Sinusoidal coding plays an important role in low bit-rate audio coding . This paper considers frequency-differential encoding of the sinusoidal model parameters as an alternative to time-differential encoding . For a given signal frame , the parameters of each sinusoidal component may be encoded either differentially relative to other components in the same frame , or directly , i.e. , without differential encoding .
A method to start the jump or avoidance of a pair of operations transcoding carried out in series through a first trans-coder , arranged together with a first communication terminal on a local side of a communication network and by a second trans-coder , arranged together with a second communication terminal on a distant network communication side , the method characterized by comprising receiving from the distant side , in a first initial request to bypass or bypass operations being transcoding , information about an encoding format being used at that time on the far side , about encoding capabilities of the distant side , and about a version of a bypassing protocol that provides support distally , and transmitting to the remote side in a second initial request to bypass transcoding operations , information about a trained encoding is being used at that time on the local side , about encoding capabilities of the local side and about a version of a bypassing protocol it supports the local side , and initiate an immediate optimization format encoding only if the local version and the remote version equal or exceed a specific version to allow , on one or both sides , a change of communication format being used at that time , before starting the avoidance operations trans-coding .
Thus , RF encoding might allow omitting the use of all main field gradients , alleviating acoustic noise and other main field gradient-related problems . This study investigates the potential of RF transmit encoding using spatially nonlinear RF fields generated by an eight-channel parallel transmit system equipped with the corresponding transmit array . Appropriate spatial encoding functions are determined iteratively by randomized superposition of the array element sensitivities and judging their performance .
Then we concatenate all five TED / TEDx development and test corpus to form a test set containing 6750 sentence pairs . We apply byte - pair encoding Sennrich2016NeuralMT to transform the texts to subword level so that the vocabulary has a size of 20 K for each language . For evaluation , we report tokenized BLEU using “ multi - bleu.perl ” .
For instance , we might view machine translation in this way , where in particular we attempt to generate English sentences from ( corresponding ) French sentences . Seq2seq models are part of the broader class of “ encoder - decoder ” models , which first use an encoding model to transform a source object into an encoded representation . Many different sequential ( and non - sequential ) encoders have proven to be effective for different source domains .
The LSTM language model oracle achieved an upper bound of 72.8 % accuracy on the test set . Matching Networks with a simple encoding model achieve 32.4 % , 36.1 % , 38.2 % accuracy on the task with examples in the set , respectively . Future work should explore combining parametric models such as an LSTM - LM with non - parametric components such as the Matching Networks explored here .
proposed denoising autoencoder ( DAE ) that learns the data distribution in a supervised learning fashion . Both DBN and DAE learn a low dimensional representation ( encoding ) for each data instance and generate it from a decoding network . Recently , variational autoencoder ( VAE ) that combines deep learning with statistical inference intended to represent a data instance in a latent hidden space , while still utilizing ( deep ) neural networks for non - linear mapping .
In this paper , we introduce a novel neural network architecture called Multi - layer Embedding with Memory Network ( MEMEN ) for machine reading task . In the encoding layer , we employ classic skip - gram model to the syntactic and semantic information of the words to train a new kind of embedding layer . We also propose a memory network of full - orientation matching of the query and passage to catch more pivotal information .
It can also utilize the pre - processing tool , e.g. named entity recognizer , part - of - speech recognizer , lexical parser and coreference identifier etc . , to incorporate more lexical and syntactical information into the feature vector . Encoding Layer encodes the representations by incorporating the context information or enriching the representation with desirable features for future use . For instance , a model can adopt bidirectional recurrent neural network to model the temporal interaction on both direction , recursive neural network ( also known as TreeRNN ) to model the compositionality and the recursive structure of language , or self - attention to model the long - term dependency on sentence .
Instead of summation adopted by Parikh et al . , ESIM proposes to compute both max and average pooling and feeds the concatenate fixed length vector to the final classifier : a fully connected multi - layer perceptron . Figure [ reference ] shows a high - level view of the ESIM architecture , where the bottom LSTM1 layer of Figure [ reference ] is the input encoding layer , the middle part with LSTM2 layer is the local inference layer , the upper part is the inference composition layer .
‘ ‘ confusion network ’ ’ representation of the hypotheses of the ASR ( see Figure [ reference ] ) . A confusion network is a graph encoding , for each speech segment in an utterance , the competing decoding hypotheses along with their posterior probability , thus providing a richer output than the - best decoding hypothesis . In particular , confusion networks in conjunction with NLU systems typically improve end - to - end performance in speech - to - meaning tasks .
The contributions of this paper can be summarized as follows . We decompose the problem of MRC with unanswerable questions into three sub - tasks and combine them into a unified model , which uses the shared encoding and interaction layers . Thus , the three - tasks can be trained simultaneously in an end - to - end fashion .
However , we found that if we trained each of the character - based and word - based encodings with their own loss , and combined them using an additional meta - BiLSTM model , we obtained optimal performance . In the meta - BiLSTM model , we concatenate the output , for each word , of its context sensitive character and word - based encodings , and put this through another BiLSTM to create an additional combined context sensitive encoding . This is followed by a final MLP whose output is passed to a linear layer for tag prediction .
For determining whether one sentence entails another it can be a good strategy to check for entailment or contradiction of individual word - and phrase - pairs . To encourage such behavior we employ neural word - by - word attention similar to , [ reference ] and [ reference ] . The difference is that we do not use attention to generate words , but to obtain a sentence - pair encoding from fine - grained reasoning via soft - alignment of words and phrases in the premise and hypothesis . In our case , this amounts to attending over the first LSTM 's output vectors of the premise while the second LSTM processes the hypothesis one word at a time , thus generating attention weight - vectors
Significant amount of work has been done in order to overcome this limitation . Predictive Sparse Decomposition ( PSD ) is a successful variant of sparse coding , which uses a predictor to approximate the sparse representation and solves the sparse coding computationally expensive encoding step . In this paper , we aim to solve some of the above - mentioned problems .
We were surprised by the extent of the improvement obtained by reversing the words in the source sentences . We conclude that it is important to find a problem encoding that has the greatest number of short term dependencies , as they make the learning problem much simpler . In particular , while we were unable to train a standard RNN on the non - reversed translation problem ( shown in fig .
We obtain a of 91.89 for the CoNLL - 2003 English shared task , which significantly outperforms a collection of highly competitive baseline models . We also present an ablation study showing the importance of auto - encoding , over using features as either inputs or outputs alone , and moreover , show including the autoencoder components reduces training requirements to 60 % , while retaining the same predictive accuracy . section : Introduction
The data collection in these IR tasks is mostly in a multifield categorical form , for example , [ Weekday = Tuesday , Gender = Male , City = London ] , which is normally transformed into high - dimensional sparse binary features via onehot encoding [ reference ] .
They come in two flavours , ( i ) those which use input images at a few scales and corresponding deep feature extraction networks , and ( ii ) those which combine feature maps from different layers of a single deep architecture . The common idea is to use features extracted at multiple scales to provide both local and global context and the using feature maps of the early encoding layers retain more high frequency detail leading to sharper class boundaries . Some of these architectures are difficult to train due to their parameter size .
The sentence encoder compresses each source sentence into a vector representation and the classifier makes a three - way classification based on the two vectors of the two source sentences . The model follows the ‘ encoding - based rule ’ , i.e. , the encoder will encode each source sentence into a fixed length vector without any information or function based on the other sentence ( e.g. , cross - attention or memory comparing the two sentences ) . In order to fully explore the generalization of the sentence encoder , the same encoder is applied to both the premise and the hypothesis with shared parameters projecting them into the same space .
_ title _ ’ ’ version also includes the actual movie title in the key . This representation can be combined with center encoding . Note that this representation is inherently specific to datasets in which there is an apparent or meaningful title for each document .
Let denote the number of words in the sequence , the dimension of the embeddings , and the word embeddings corresponding to words in the sequence . We produce a global encoding of using a global bidirectional LSTM . where is the dimension of the LSTM state .
subsection : Question Hierarchy Given the 1 - hot encoding of the question words , we first embed the words to a vector space ( learnt end - to - end ) to get . To compute the phrase features , we apply 1 - D convolution on the word embedding vectors .
Huffman coding , as an efficient and simple variable length coding , is used in source coding . But the existing Huffman coding algorithm efficiency is not high , but application is also limited , therefore , this paper proposes an adaptive Huffman coding algorithm , this algorithm with other Huffman encoding compared with high efficiency , wide application range .
The refined model is then used to seed the initial population of an immune inspired multi-objective optimization algorithm in the third stage to obtain a set of fuzzy models with improved transparency . To tackle the problem of simultaneously optimizing the structure and parameters , a variable length coding scheme is adopted to improve the efficiency of the search . The proposed modeling approach is applied to a real data set from the steel industry .
The two problems are both formulated as integer fractional programming problems and the optimal solutions are derived with in-depth analysis . For both problems , numerical results show that the proposed variable-length coding scheme with the optimal amount of energy to be transferred or optimal number of channel use outperforms the coding strategy in an existing work .
In the irreversible conversion process , the components Cb and Cr of pixel data are thinned out by way of a reduction process and subsequently interpolated based on the component Y of pixel data by way of an expansion process , while some bits of pixel data are reduced by way of a quantization process . The reversible compressive coding process performs a predictive coding process and a variable-length coding process on each pixel data selected in a raster-scanning sequence , thus producing compressive coded data . This makes it possible to decode both reversible compressive coded data and irreversible compressive coded data in units of lines .
By the addition of an orthogonal transformation process and the result and the motion compensated reference image quantization process and inverse quantization processing and inverse orthogonal transform processing of the prediction residual , the reference image stored in the memory is generated . The orthogonal transform process and a quantization process and a variable length coding process of the prediction residuals is compressed moving picture coding apparatus that encodes video output signal is generated . Reference picture screen in the reference image A inside the video display screen , B , includes a screen outside the reference image D of the outer C and the image display screen , off-screen reference image D screen in the reference image A , B , similar C a plurality of reference image a and is generated based on the positional relationship B.
Systems.- Document Generation and Picture Retrieval.- FLORES : A JAVA Based Image Database for Ornamentals.- Pictorial Portrait Indexing Using View-Based Eigen-Eyes.- Image Retrieval Using Fuzzy Triples.- Image Compression.- Variable-Bit-Length Coding : An Effective Coding Method.- Block-Constrained Fractal Coding Scheme for Image Retrieval.- Efficient Algorithms for Lossless Compression of 2D/3D Images.- Virtual Environments.- LucentVision(TM ) : A System for Enhanced Sports Viewing.- Building 3D Models of Vehicles for Computer Vision.- Integrating Applications into Interactive Virtual Environments.- Recognition Systems.- Structural Sensitivity for Large-Scale Line-Pattern Recognition.- Complex Visual Activity Recognition Using a Temporally Ordered Database.- Image Database Assisted Classification.- A Visual Processing System for Facial Prediction.- Semi-interactive Structure and Fault Analysis of (111)7x7 Silicon Micrographs.- Using Wavelet Transforms to Match Photographs of Individual Sperm Whales Identified by the Contour of the Trailing Edge of the Fluke.- From Gaze to Focus of Attention.- Automatic Interpretation Based on Robust Segmentation and Shape-Extraction.- A Pre-filter Enabling Fast Frontal Face Detection.- Visualization Systems.- A Technique for Generating Graphical Abstractions of Program Data Structures.- Visual Presentations in Multimedia Learning : Conditions that Overload Visual Working Memory.- Visualization of Spatial Neuroanatomical Data.- Visualization of the Cortical Potential Field by Medical Imaging Data Fusion.- Applying Visualization Research Towards Design .
We propose a new coding scheme for lossless compression . This scheme , variable-bit-length coding , stores different grayscale values using a different number of bits . The compression performance of variable-bit-length coding coupled with a preprocessing method called remapping is compared with the compression performance of well known coding methods such as Huffman coding , arithmetic coding , and LZW coding . On the bechmark suite of 10 images used by us , remapping with variable-bit-length coding obtained maximum compression more often than did any other coding method .
The development of an efficient compression scheme to process the Arabic language represents a difficult task . This paper employs the dynamic Huffman coding on data compression with variable length bit coding , on the Arabic language . Experimental tests have been performed on both Arabic and English text .
The present invention is to correctly decode the data encoded by the variable-length encoding method having improved compression efficiency . The variable length coding method is a variable length coding method of coding with reference to the parameter table for each predetermined unit of data , and includes the following steps . Parameter encoding parameters to be used in encoding for encoding the information to view the ◎ obtained parameter table in the parameter initialization step ◎ initialization to set an initial value table parameter table information encoding step ◎ each of the sub data constituting the data of a predetermined unit a parameter acquiring step ◎ sub-data obtained from the table with reference to the encoding parameters acquired by placing in a position to be obtained before the sub-data encoding step data of an encoded the ◎ encoded information a predetermined unit to variable-length coding output information encoding data arrangement step
The NxM data are read out by a predetermined sequence and converted to symbol data . The variable length coding part codes the symbol data by variable length . A coding control part has logics for selecting the scan pattern and provides the scan pattern selecting information to the switching part .
In the paper a lossless compression method is proposed , tailored specifically for DTM data . The method involves discarding redundant data , performing differential coding , Variable Length Value coding , and finally compression using LZ77 or PPM algorithm . We present the results of experiments performed on real-world hydrographic data , which prove the validity of the proposed approach .
Then , the resolution conversion of the disturbance is carried out in the orthogonal transform domain ( frequency domain ) with respect to the inverse weighted video signal in the resolution converter 16 . The The video signal after then , the resolution conversion is weighted in the weighting ( W ) section 18 , and quantized by the quantization ( Q ) unit 19 is variable-length variable length coding by the encoder ( VLC ) unit 20 , a second format It is output as the digital signals ( MPEG video signal ) .
We propose a new decoding algorithm for interleaved Reed-Solomon Codes that attempts to correct all " interleaved " codewords simultaneously . In particular , interleaved encoding gives rise to multi-dimensional curves and more specifically to a variation of the Polynomial Reconstruction Problem , which we call Simultaneous Polynomial Reconstruction . We present and analyze a novel probabilistic algorithm that solves this problem .
We have chosen to work in the bit stream domain in order to simplify the data embedding process in the case in which the original data are already in a compressed form . Thanks to the exploitation of an interleaved encoding technique and of some space and frequency masking considerations , we have been able to reduce the BER of the original algorithm . Some results underlining the improvements we have been able to obtain thanks to our technique are shown .
The first measuring mode uses conventional phase-shift sequences in order to achieve highly accurate measurements under controlled conditions . The second measuring mode uses a new interleaved encoding approach for phase-shift patterns , which enables robust measurements even in the presence of relative motion between object and sensor . An example for this type of motion is the movement of the endoscope tip by hand .
The transmission apparatus is disclosed . The transmission apparatus in the basis of the parity check matrix information bits , first parity bits and second parity bit interleaver and , interleaved LDPC code for interleaving encoder , LDPC codeword to generate the LDPC codeword comprising constellation mapping the word sex shop also includes a mapper .
In order to improve the channel error correcting capacity of tactical data link and reduce decoding delay , a new concatenating method for RS codes and LDPC codes is proposed , which is presented by interleaving encoding and iterative decoding . A technique of interleaving encoding is adapted to enhance their relativity , at the same time , a combination of hard decision decoding for RS codes and iterative soft decision decoding is employed to guarantee sufficient decoding capability with lower decoding delay . Simulation results show that this method can obtain significant performance with proper decoding delay .
The injected current through a pair of surface electrodes induces a magnetic flux density distribution inside the imaging object , which results in additional magnetic flux density . To measure the magnetic flux density signal in MREIT , the phase difference approach in an interleaved encoding scheme cancels out the systematic artifacts accumulated in phase signals and also reduces the random noise effect by doubling the measured magnetic flux density signal . For practical applications of in vivo MREIT , it is essential to reduce the scan duration maintaining spatial-resolution and sufficient contrast .
The compressed data is sensitive to channel interference , where then the channel error correction coding is introduced to enhance the channel anti-jamming characteristics . To overcome it , a source-channel combined coding scheme is presented in which both multi-resolution over-sampling is used for source coding and RS and interleaver coding is used for channel coding .
The " exclusive or " linear code can be encoded and decoded . Further , the present invention is the use of " exclusive or " code series does not require interleaver encoder , so no processing corresponding to the interleaving delay time , the input information bits can be coded and transmitted .
In this paper we compared the wavelet based algorithms such are Embedded Zero tree Wavelet ( EZW ) , Set partitioning in Hierarchical Trees ( SPIHT ) and spatial orientation tree Wavelet ( STW ) . These algorithms are compared in terms of Compression Ratio ( CR ) , Peak Signal to Noise Ratio ( PSNR ) , Bit per pixels and Mean Square Error ( MSE ) .
The effects of image contents and its corresponding compression ratios , filter orders of various wavelet functions and number of decompositions are observed . The outcomes of the image compression techniques such as Wavelet Difference Reduction ( WDR ) , Set Partitioning In Hierarchical Tree ( SPIHT ) , Embedded Zero Wavelet ( EZW ) , Adaptively Scanned Wavelet Difference Reduction ( ASWDR ) , Spatial Orientation-tree Wavelet ( STW ) were compared and tabulated by using the various parameters such as Mean Square Error ( MSE ) , Peak Signal to Noise Ratio ( PSNR ) and ( Bits Per Pixel ) BPP values from the image which was reconstructed . In this paper , an Improved Embedded Zero Wavelet ( IEZW ) is proposed and compared with other image compression techniques .
All techniques of image compression basically divided into two parts , spatial domain compression technique and frequency domain compression technique . In frequency domain techniques there are numerous techniques like Fourier Transform , Discrete Wavelet Transform ( DWT ) and Discrete Cosine Transform ( DCT ) etc . after converting the image into frequency domain transformation , it uses several encoding technique like Embedded Zero Tree ( EZW ) coding , SPIHT ( Set Partitioning in Hierarchical Tree ) , ASWDR ( Adaptively Scanned Wavelet Difference Reduction ) WDR ( Wavelet Difference Reduction ) and STW ( Spatial orientation Tree Wavelet ) etc . These encoding schemes are also known as Progressive Coefficients Significance Methods ( PCSM ) .
We then propose an alternative approach to face verification via SR . Specifically , we propose to use explicit SR encoding on local image patches rather than the entire face . The obtained sparse signals are pooled via averaging to form multiple region descriptors , which are then concatenated to form an overall face descriptor . Due to the deliberate loss spatial relations within each region ( caused by averaging ) , the resulting descriptor is robust to misalignment & various image deformations . Within the proposed framework , we evaluate several SR encoding techniques : l1-minimisation , Sparse Autoencoder Neural Network ( SANN ) , and an implicit probabilistic technique based on Gaussian Mixture Models . Thorough experiments on AR , FERET , exYaleB , BANCA and ChokePoint datasets show that the proposed local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods , in both verification and closed-set identification problems .
Due to the deliberate loss spatial relations within each region ( caused by averaging ) , the resulting descriptor is robust to misalignment & various image deformations . Within the proposed framework , we evaluate several SR encoding techniques : l1-minimisation , Sparse Autoencoder Neural Network ( SANN ) , and an implicit probabilistic technique based on Gaussian Mixture Models . Thorough experiments on AR , FERET , exYaleB , BANCA and ChokePoint datasets show that the proposed local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods , in both verification and closed-set identification problems .
Accordingly , in this paper , we propose a novel method aimed at overcoming the challenges associated with facial age estimation . First , we propose a novel age encoding method , referred to as ‘ Soft-ranking ’ , which encodes two important properties of facial age , < inline-formula > < tex-math notation="LaTeX">${i.e.}$ < /tex-math></inline-formula > , the ordinal property and the correlation between adjacent ages . Therefore , Soft-ranking provides a richer supervision signal for training deep models . Moreover , we carefully analyze existing evaluation protocols for age estimation , finding that the overlap in identity between the training and testing sets affects the relative performance of different age encoding methods . Moreover , we achieve state-of-the-art performance on four most popular age databases , < inline-formula > < tex-math notation="LaTeX">${i.e.}$ < /tex-math></inline-formula > , Morph II , AgeDB , CLAP2015 , and CLAP2016 .
Practical Repetition-Aware Grammar Compression Image encoding method and device, and image decoding method and device Image encoding device, image decoding device, and data structure Effects of semantic relatedness on age-related associative memory deficits: The role of theta oscillations The lower bounds of massive memory: Investigating memory for object details after incidental encoding Corpus of Academic Slovene (MSc/MA theses) KAS-mag 1.0 Sequential coding of correlated sources Sound encoder and sound decoder Sigma encoded inverted files Research on incomplete data mining and filling algorithm during depth learning process 基于深度学习的高光谱图像分类方法 A Classification Method for Hyperspectral Imagery Based on Deep Learning A multiobjective optimization tool for Very Large Scale Integrated nonslicing floorplanning Peer-coding method for communicating an identifier within a peer discovery in a peer network-to Optimization of wireless bandwidth utilization Improved Bounded Model Checking for the Universal Fragment of CTL A Fast Implementation of Deflate The Fastest Multiplier on FPGAs with Redundant Binary Representation Robust QBF Encodings for Sequential Circuits with Applications to Verification, Debug, and Test Improving Natural Language Understanding by Reverse Mapping Bytepair Encoding Evolving Neural Network Structures by Means of Genetic Programming Automated Detection of Serializability Violations under Weak Consistency Schemes for optimal frequency-differential encoding of sinusoidal model parameters Elusion of transcoding operations in a communication network. RF encoding using a multielement parallel transmit system. Compressing Word Embeddings via Deep Compositional Code Learning Sequence-to-Sequence Learning as Beam-Search Optimization Matching Networks for One Shot Learning SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension Natural Language Inference over Interaction Space Attention Boosted Sequential Inference Model Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces U-Net: Machine Reading Comprehension with Unanswerable Questions Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings Reasoning about Entailment with Neural Attention No more meta-parameter tuning in unsupervised sparse feature learning Sequence to Sequence Learning with Neural Networks Evaluating the Utility of Hand-crafted Features in Sequence Labelling Product-Based Neural Networks for User Response Prediction SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Shortcut-Stacked Sentence Encoders for Multi-Domain Inference Key-Value Memory Networks for Directly Reading Documents Global-Locally Self-Attentive Dialogue State Tracker Hierarchical Question-Image Co-Attention for Visual Question Answering Analysis and Research on Adaptive Huffman Coding An immune algorithm based fuzzy predictive modeling mechanism using variable length coding and multi-objective optimization allied to engineering materials processing Rate-Optimal Coding Design in Joint Transfer of Energy and Information Compressive coding device and decoding device Compression moving picture coding apparatus, compressed motion decoding device, the compressed moving picture coding method and the compressed moving decoding method Visual information and information systems : Third International Conference, VISUAL '99, Amsterdam, The Netherlands, June 2-4, 1999 : proceedings Variable-Bit-Lengthn Coding: An Effective Coding Method Efficient Data Compression Scheme using Dynamic Huffman Code Applied on Arabic Language Variable length encoding method, variable length decoding method, storage medium, variable length encoding device, variable length decoding device, and bit stream Coding and decoding method and apparatus using plural scanning patterns Lossless Compression Method for Digital Terrain Model of Seabed Shape Method for converting digital signal and apparatus for converting digital signal Decoding of Interleaved Reed Solomon Codes over Noisy Data Data hiding in the bit stream domain for MPEG-2 coded video sequences exploiting space and frequency masking A 3D measuring endoscope for hand-guided operation Transmitter and signal processing method thereof A Method of Interleaving Encoding and Iterative Decoding for RS Codes and LDPC Codes Optimization of magnetic flux density for fast MREIT conductivity imaging using multi-echo interleaved partial fourier acquisitions Design of Joint Source and Chnnel Coding 'Not or' codes and serial connected encoder/decoder therewith Comparative Analysis on Medical Images using SPIHT, STW and EZW Image Compression Based on Improved Embedded Zero Image Comparison with Different Filter Banks On Improved PCSM Code On robust face recognition via sparse coding: the good, the bad and the ugly On Robust Face Recognition via Sparse Encoding: the Good, the Bad, and the Ugly Soft-Ranking Label Encoding for Robust Facial Age Estimation
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