Although PDAs and cell phones are low resource devices , which makes it prohibitive to load and render entire virtual environments , they can still render images with relative ease . Based on this idea , this paper proposes a streaming system which relies on an image-based rendering approach , and is composed of several modules : a packetization scheme for images , an image-based rendering approach based on view morphing and its corresponding RTP payload format , and finally a bandwidth feedback mechanism and rate control . This paper illustrates some of the problems faced in this area , and provides a first step towards their solutions . The phasor measurement unit ( PMU ) measurements are streamed in real-time from the RTDS runtime environment to MATLAB for real-time visualization and estimation . To consider the data corruption scenario in the streaming process , a bi-modal distribution containing two normal distributions with different weights and variances are added to the measurements as the noise component . The performances of both UKF and EnKF are then compared for by calculating the mean-squared-errors ( MSEs ) between the actual and estimated states . Power consumption of video streaming systems has become a major concern , especially in battery-powered devices , such as video sensors . Power is usually dissipated in each one of the major phases of the streaming process : capturing , encoding , and transmission . This paper develops models for power consumption in each of these phases and validates them with extensive experiments , focusing primarily on H.264 video encoding . We demonstrate our proposal by operating on a recent publicly available 100 million image crowdsourced photo collection containing images geographically distributed throughout the entire world . Results illustrate that our streaming-based approach does not compromise model completeness , but achieves unprecedented levels of efficiency and scalability . The implementation results from the two case studies are compared on the basis of achieved performance and programmability . One of the Epiphany implementations demonstrates the usefulness of the architecture for the streaming based algorithm ( the auto focus criterion calculation ) by achieving a speedup of 8.9x over a sequential implementation on a state-of-the-art general-purpose processor of a later silicon technology generation and operating at a 2.7x higher clock speed . On the other case study , a highly memory-intensive algorithm ( fast factorized back projection ) , the Epiphany architecture shows a speedup of 4.25x . This paper presents our Multi-GPU SpMV implementation to improve CSR-Based SpMV performance . We make use of multiple GPUs to jointly complete SpMV computations and adopt streamed approach to increase concurrency to further improve SpMV performance . We evaluate performance of our Multi-GPU SpMV on a collection of fourteen sparse matrices and demonstrate the effectiveness of our proposed approach in performance improvement on a large-scale cluster . This MISD type of configuration provides multiple operations per data item fetched from memory . One realization of this streamed approach uses FPGAs . We 'll discuss both the general memory problem and some results based on work at Maxeler using FPGAs for acceleration . All components were designed according to the functional requirement of video surveillance system . The " streaming IN " component receives the video data from the networked video cameras and delivers them to the " storage client " . It also manages the bottleneck of the network to smooth the data stream . This study attempts to construct a predictive network between the computational features and semantic features of pulmonary nodules using online feature selection and causal structure learning . In this paper , we exploit the causal discovery based on the streaming feature algorithm and causal discovery with symmetrical uncertainty based on the streaming feature algorithm . Different from the traditional learning methods that usually obtain all computational features in advance and then select the optimal subset of features from the computational features , the proposed approach integrates online streaming feature selection with causal structure learning . The critical challenges in this integration include : 1 ) the dynamic selection of computational features and 2 ) how to evaluate the feature subsets and implement causal structure learning . In addition , considering that building a causal structure network is a time-consuming process , we improve the process by using support vector machines based on the streaming feature algorithm . The experimental results show that our proposed algorithms improve on other traditional feature selection algorithms and ensemble learning algorithm without feature selection with regard to learning accuracy . We have implemented and evaluated several enhanced mechanisms and show how to provide support for more efficient memory usage and reduction of user/kernel space switches for content download and streaming applications . In particular , we are able to reduce the CPU usage by approximately 27 % compared to the best approach without kernel modifications , by removing copy operations and system calls for a streaming scenario in which RTP headers must be added to stored data for sequence numbers and timing . When the client experiences streaming difficulties , it is necessary to perform adaptation . In the proposed streaming scenario , the ideal set of views that are pre-determined by the server according to the overall MVV reconstruction quality constraint is truncated from the delivered MVV stream . The discarded views are then reconstructed using the low-overhead additional metadata , which is calculated by the server and delivered to the receiver . In our architecture , the messages are being received through streaming and are loaded into the Big Data DB so that they can be processed after in a fast and efficient way by the SQL Batches . For the implementation part , a streaming bus can be used like Kafka and for the Big Data DB a column Big Data DB . SQL Batches module ( constructs the final tree and coarse grain view in the Processing area ) We proceed by defining models for the streaming media system and the random , lossy , packet delivery channel . Our streaming system model buffers media at the client , and combats packet losses with deadline-constrained automatic repeat request ( ARQ ) . For the channel , we define a two-state Markov model that features state-dependent packet loss probability . Sa gestion est totalement distribuée entre les nœuds de la grappe . Les données sont distribuées et rapatriées des nœ uds d’après une stratégie « StreamingRaid » , procurant ainsi un système fiable . Nous avons mesuré expérimentalement les performances de ce système en nombre de clients supportés et nous avons évalué le coût de la stratégie de tolérance aux pannes . We demonstrate the scalability of our algorithm by building representations of web - scale graphs , ( such as YouTube ) using a parallel implementation . Moreover , we describe the minimal changes necessary to build a streaming version of our approach . The rest of the paper is arranged as follows . section : Streaming One interesting variant of this method is a streaming approach , which could be implemented without knowledge of the entire graph . In this variant small walks from the graph are passed directly to the representation learning code , and the model is updated directly . Sentences can be viewed as purposed walks through an appropriately designed language network , and language models like SkipGram are designed to capture this behavior . This approach can be combined with the streaming variant ( Section 4.4.1 ) to train features on a continually evolving network without ever explicitly constructing the entire graph . Maintaining representations with this technique could enable web - scale classification without the hassles of dealing with a web - scale graph . At last , image score of class can be obtained by the sum over all proposals : . The interpretation of the two streams framework is as follows . The is the probability of proposal belonging to class . This paper presents an human sensing ( HS ) system based on Hidden Markov Models ( HMMs ) for classifying physical activities : walking , walking-upstairs , walking-downstairs , sitting , standing and lying down . The system includes a feature extractor ( developed by the authors and presented in a previous work ) , an HMMs training module and an HAR module . All experiments have been done using a publicly available dataset named UCI Human Activity Recognition Using Smartphones . This is then followed by knowledge transfer from offline training to the online tracking process . Online tracking involves a classification neural network which is constructed from the encoder part of the trained autoencoder as a feature extractor and an additional classification layer . Both the feature extractor and the classifier can be further tuned to adapt to appearance changes of the moving object . In particular , a model-based spatial process decomposition is developed where the random weights are unique to individual patients for component functions common across patients . Model fitting and selection are based on maximum likelihood , while feature extractions are via optimal prediction of the underlying true image . A simulation study evaluates the properties of the proposed methodology and for illustration , a cancer image data set is analyzed and QIBs are extracted in association with a clinical endpoint . Computation of texture information for image classification and pattern recognition is a highly complex activity . Much of this complexity is related to feature extractions where computational cost is related with the tonal levels and resolution of the images . One of the more used methods is the Gray Level Occurrence Matrix ( GLCM ) that present quadratic time complexity in relation to of gray levels . Our system is clearly described , modular in nature , replicable on any Desktop , and demonstrated with extensive experiments , backed by insightful analysis on different Convolutional Neural Networks ( CNNs ) , as stand-alone and fused with others . With a large corpus of unconstrained , real-world video data , we examine the usefulness of different CNN models as features extractors for modeling high-level events , i.e. , pre-trained CNNs that differ in architectures , training data , and number of outputs . For each CNN , we use 1-fps from all training exemplar to train one-vs-rest SVMs for each event . Arabic Sign Language Recognition field became widespread because of its difficult nature and numerous details . Most researchers employed different input sensors , features extractors , and classifiers on static and dynamic data . These different ways were customized and employed in our previous work in the Arabic Sign Language Recognition field . A topdown architecture with lateral connections is developed for building high - level semantic feature maps at all scales . This architecture , called a Feature Pyramid Network ( FPN ) , shows significant improvement as a generic feature extractor in several applications . Using FPN in a basic Faster R - CNN system , our method achieves state - of - the - art singlemodel results on the COCO detection benchmark without bells and whistles , surpassing all existing single - model entries including those from the COCO 2016 challenge winners . DIAT uses an adversarial loss to learn the mapping from to , where and are face images in two different domains and , respectively . This method has a regularization term on the mapping as to preserve identity features of the source image , where is a feature extractor pretrained on a face recognition task . CycleGAN also uses an adversarial loss to learn the mapping between two different domains and . As shown in the last column of Table [ reference ] , our method is slightly slower than R - FCN , which also reaches a real - time speed ( 8.2 fps or 9.8 fps without context ) and achieves the best trade - off between accuracy and speed . We argue that the sharing process of feature extraction between two branches and the design of lightweight RoI - wise subnetwork after RoI pooling both greatly reduce the model complexity . As shown in Table [ reference ] , we also compared our method with other state - of - the - art single model . Here we propose offline Malayalam handwritten character recognition using probabilistic simplified fuzzy ARTMAP ( PSFAM ) . PSFAM is a combination of SFAM and PNN ( Probabilistic Neural Network ) . After preprocessing stage , scanned image is segmented into line images . In order to deal with the low utilization rate of chemical fertilizer , the soil needs to be precisely classified . Traditional neural network is not applicable to the precise judgements of soil classification , SFAM model is constructed based on improved adaptive genetic algorithm , and then applied to the soil testing and classification , which achieved good results . By combining this two layers , SFAM is capable to perform classification very efficiently and giving very high performances . Lastly , the SFAM model is applied to different simulations . The simulation results obtained for the three different datasets : Iris , Wisconsin breast cancer and wine dataset prove that SFAM model has better performance results than other models for these classification applications . Fuzzy ARTMAP is a supervised learning algorithm utilising two fuzzy ART modules and an associated mapping network . A simplified version of fuzzy ARTMAP ( SFAM ) was designed by incorporating a simplification of the match tracking concept on unsupervised fuzzy ART paradigms . The proposed simplified version consists of only one fuzzy ART module and an associated mapping network . This work extends the simplified fuzzy ARTMAP ( SFAM ) to a complex-valued ( CV-SFAM ) version which is able to work with spatio-temporal data produced in receptive fields of visual cortex . The CV-SFAM ’s ability for incremental learning distinguishes CV-SFAM from other complex-valued neural networks , which provides the ability to preserve learned data while learning new samples . The extracted features , which form the feature vector , are passed as input to a Simplified Fuzzy ARTMAP ( SFAM ) artificial neural network ( ANN ) classifier . The SFAM classifier compares the input data with the trained data and finds the nearest prototype from the database that ` resonates ' with the input pattern . The labels or recognized characters are assigned their corresponding Unicode code points and displayed using appropriate fonts . Details of SFAM components are discussed . A complete outline of the SFAM methodology is provided , along with discussion of key points and frequent examples . The PESFAM achieves good accuracy based on the twofold cross-validation results . In addition , the effectiveness of the proposed ensemble SFAM is delineated in sonar target differentiation . The experiments demonstrate the potential of PESFAM classifiers in offering an optimal solution to the data-ordering problem of SFAM implementation and also as an intelligent classification tool in mobile robot application . This paper proposes an algorithm developed in MATLAB to detect such faults and faulty phase in SPWM inverter by carrying out analyses on motor currents captured from a model equivalent to such situations implemented in PSIM simulation software . Some features have been extracted using Fast Fourier Transforms ( FFTs ) on Park 's Vector Modulus ( PVM ) of three phase motor current profiles & finally Simplified Fuzzy Art-Map ( SFAM ) classifier has been employed to detect faulty switch and degree or level of fault . The obtained results provide a satisfactory level of accuracy . Characteristics of PD signal for pattern recognition and classification were determined by using statistical and fractal methods from relationship among the voltage phase angle , the discharge magnitude and the repeated existing of partial discharges . The simplified fuzzy ARTMAP ( SFAM ) was used as artificial intelligent technique for pattern recognition and classification . PDs characteristic quantities were used as input parameters for Simplified Fuzzy ARTMAP to train pattern recognition and classification system . In this paper , pattern classifiers of spatial obstacle avoidance situations using Neural Networks ( NN ) , Fuzzy Logic ( FL ) , Genetic Algorithms ( GA ) and Adaptive Resonance Theory ( ART ) individually or in combination are suggested . These classifiers are based on supervised learning and adaptation paradigms as Gradient Back-Propagation ( GBP ) , FL , GA and Simplified Fuzzy ArtMap ( SFAM ) resulting in NN/GBP and FL as Intelligent Systems ( IS ) and in NN/GA , NN/GA-GBP , NN-FL/GBP and NN-FL-ART/SFAM as Hybrid Intelligent Systems ( HIS ) . Afterwards , a synthesis of the suggested pattern classifiers is presented where their results and performances are discussed as well as the Field Programmable Gate Array ( FPGA ) architectures , characterized by their high flexibility and compactness , for their implementation . The presentation order of training patterns to a simplified fuzzy ARTMAP ( SFAM ) neural network affects the classification performance . The common method to solve this problem is to use several simulations with training patterns presented in random order , where voting strategy is used to compute the final performance . In this paper , we propose a Moiré pattern Removal Neural Network ( MopNet ) to solve this problem . All core components of MopNet are specially designed for unique properties of moire patterns , including the multi-scale feature aggregation addressing complex frequency , the channel-wise target edge predictor to exploit imbalanced magnitude among colour channels , and the attribute-aware classifier to characterize the diverse appearance for better modelling Moiré patterns . Quantitative and qualitative experimental comparison validate the state-of-the-art performance of MopNet . In contrast to previous methods that simply combined pedestrian predictions on feature maps with different resolution , we propose a scale-aware hierarchical detection network for pedestrian detection under large scale variations . First , we introduce a cross-scale features aggregation module to accomplish feature augmentation for pedestrian representation through merging the lateral connection , the top-down path and bottom-up path . Specifically , the cross-scale features aggregation module can adaptively fuse hierarchical features to enhance feature pyramid representation for robust semantic and accurate localization . Further , we design a scale-aware hierarchical detection network to effectively integrate multiscale pedestrian detection into a unified framework by adaptively perceiving the augmented feature level for special-scale pedestrian detection . Multi-Scale Feature Aggregation Network With Wavelet Structure Similarity Loss Function For Single Image Dehazing . Single image dehazing is the ill-posed two-dimensional signal reconstruction problem . subsubsection : Implementation Details Throughout the experiments , the sizes of the RGB image , the semantic saliency prediction , inputs to the discriminator and inputs to the generator are fixed as ; the sizes of the instance - agnostic parsing prediction , instance - aware clustering prediction , inputs to the discriminator , inputs to the generator , inputs to the discriminator and instance location map are fixed as ; the channel number of the pixel - wise instance location map is fixed as , incorporating two corner points of the associated bounding box ; the constraint factors are empirically fixed as and , respectively ; the generator is initialized with FCN - 8s by replacing the last layer with a new convolutional layer with kernel size , pretrained on PASCAL - VOC - 2011 and finetuned on the target dataset ; the generator is initialized with WS - ResNet by eliminating the spatial pooling layers , increasing the strides of the first convolutional layers up to 2 in B , eliminating the top - most global pooling layer and the linear classifier , and adding two new convolutional layers with kernel sizes and , pretrained on ImageNet and PASCAL - VOC - 2012 , and finetuned on the target dataset ; the generator is initialized with the same backbone architecture and pre - trained weights with ( which are learned separately ) , by further augmenting it with two sibling branches for pixel - wise instance location map prediction and instance number prediction , where the first branch utilizes a MSFU ( three convolutional layers with kernal sizes for specific scale adaption ) ended with a convolutional layer with kernel size for multi - scale feature aggregation and a final convolutional layer with kernel size for location regression and the second branch utilizes the feature maps downsampled by 8 concatenated with the feature maps from the first branch ended with a global pooling layer , a hidden 512 - way fully - connected layer and a final 1 - way fully - connected layer for instance number regression ; the three discriminators ( which are learned separately ) are all initialized with a VGG - 16 by adding a new convolutional layer at the very begining with kernel size for input adaption , and replacing the last layer with a new 1 - way fully - connected layer activated by sigmoid , pre - trained on ImageNet and finetuned on the target dataset ; the newly added layers are randomly initialized by drawing weights from a zero - mean Gaussian distribution with standard deviation ; we employ an off - the - shelf clustering method to obtain the instance - aware clustering map ; the dropout ratio is empirically fixed as ; the weight decay and batch size are fixed as and , respectively ; We use an initial learning rate of for pre - trained layers , and for newly added layers in all our experiments ; we decrease the learning rate to of the previous one after 20 epochs and train the network for roughly 60 epochs one after the other ; the proposed network is implemented based on the publicly available TensorFlow platform , which is trained using Adam ( ) on four NVIDIA GeForce GTX TITAN X GPUs with 12 G memory ; the same training setting is utilized for all our compared network variants ; we evaluate the testing time by averaging the running time for images on the target set on NVIDIA GeForce GTX TITAN X GPU and Intel Core i7 - 4930 K CPU@3.40GHZ ; our NAN can rapidly process one image in about 1 second , which compares much favorably to other state - of - the - art approaches , as the current state - of - the - art methods rely on region proposal preprocessing and complex processing steps . subsubsection : Evaluation Metrics
Although PDAs and cell phones are low resource devices , which makes it prohibitive to load and render entire virtual environments , they can still render images with relative ease . Based on this idea , this paper proposes a streaming system which relies on an image-based rendering approach , and is composed of several modules : a packetization scheme for images , an image-based rendering approach based on view morphing and its corresponding RTP payload format , and finally a bandwidth feedback mechanism and rate control . This paper illustrates some of the problems faced in this area , and provides a first step towards their solutions .
The phasor measurement unit ( PMU ) measurements are streamed in real-time from the RTDS runtime environment to MATLAB for real-time visualization and estimation . To consider the data corruption scenario in the streaming process , a bi-modal distribution containing two normal distributions with different weights and variances are added to the measurements as the noise component . The performances of both UKF and EnKF are then compared for by calculating the mean-squared-errors ( MSEs ) between the actual and estimated states .
Power consumption of video streaming systems has become a major concern , especially in battery-powered devices , such as video sensors . Power is usually dissipated in each one of the major phases of the streaming process : capturing , encoding , and transmission . This paper develops models for power consumption in each of these phases and validates them with extensive experiments , focusing primarily on H.264 video encoding .
We demonstrate our proposal by operating on a recent publicly available 100 million image crowdsourced photo collection containing images geographically distributed throughout the entire world . Results illustrate that our streaming-based approach does not compromise model completeness , but achieves unprecedented levels of efficiency and scalability .
The implementation results from the two case studies are compared on the basis of achieved performance and programmability . One of the Epiphany implementations demonstrates the usefulness of the architecture for the streaming based algorithm ( the auto focus criterion calculation ) by achieving a speedup of 8.9x over a sequential implementation on a state-of-the-art general-purpose processor of a later silicon technology generation and operating at a 2.7x higher clock speed . On the other case study , a highly memory-intensive algorithm ( fast factorized back projection ) , the Epiphany architecture shows a speedup of 4.25x .
This paper presents our Multi-GPU SpMV implementation to improve CSR-Based SpMV performance . We make use of multiple GPUs to jointly complete SpMV computations and adopt streamed approach to increase concurrency to further improve SpMV performance . We evaluate performance of our Multi-GPU SpMV on a collection of fourteen sparse matrices and demonstrate the effectiveness of our proposed approach in performance improvement on a large-scale cluster .
This MISD type of configuration provides multiple operations per data item fetched from memory . One realization of this streamed approach uses FPGAs . We 'll discuss both the general memory problem and some results based on work at Maxeler using FPGAs for acceleration .
All components were designed according to the functional requirement of video surveillance system . The " streaming IN " component receives the video data from the networked video cameras and delivers them to the " storage client " . It also manages the bottleneck of the network to smooth the data stream .
This study attempts to construct a predictive network between the computational features and semantic features of pulmonary nodules using online feature selection and causal structure learning . In this paper , we exploit the causal discovery based on the streaming feature algorithm and causal discovery with symmetrical uncertainty based on the streaming feature algorithm . Different from the traditional learning methods that usually obtain all computational features in advance and then select the optimal subset of features from the computational features , the proposed approach integrates online streaming feature selection with causal structure learning . The critical challenges in this integration include : 1 ) the dynamic selection of computational features and 2 ) how to evaluate the feature subsets and implement causal structure learning . In addition , considering that building a causal structure network is a time-consuming process , we improve the process by using support vector machines based on the streaming feature algorithm . The experimental results show that our proposed algorithms improve on other traditional feature selection algorithms and ensemble learning algorithm without feature selection with regard to learning accuracy .
We have implemented and evaluated several enhanced mechanisms and show how to provide support for more efficient memory usage and reduction of user/kernel space switches for content download and streaming applications . In particular , we are able to reduce the CPU usage by approximately 27 % compared to the best approach without kernel modifications , by removing copy operations and system calls for a streaming scenario in which RTP headers must be added to stored data for sequence numbers and timing .
When the client experiences streaming difficulties , it is necessary to perform adaptation . In the proposed streaming scenario , the ideal set of views that are pre-determined by the server according to the overall MVV reconstruction quality constraint is truncated from the delivered MVV stream . The discarded views are then reconstructed using the low-overhead additional metadata , which is calculated by the server and delivered to the receiver .
In our architecture , the messages are being received through streaming and are loaded into the Big Data DB so that they can be processed after in a fast and efficient way by the SQL Batches . For the implementation part , a streaming bus can be used like Kafka and for the Big Data DB a column Big Data DB . SQL Batches module ( constructs the final tree and coarse grain view in the Processing area )
We proceed by defining models for the streaming media system and the random , lossy , packet delivery channel . Our streaming system model buffers media at the client , and combats packet losses with deadline-constrained automatic repeat request ( ARQ ) . For the channel , we define a two-state Markov model that features state-dependent packet loss probability .
Sa gestion est totalement distribuée entre les nœuds de la grappe . Les données sont distribuées et rapatriées des nœ uds d’après une stratégie « StreamingRaid » , procurant ainsi un système fiable . Nous avons mesuré expérimentalement les performances de ce système en nombre de clients supportés et nous avons évalué le coût de la stratégie de tolérance aux pannes .
We demonstrate the scalability of our algorithm by building representations of web - scale graphs , ( such as YouTube ) using a parallel implementation . Moreover , we describe the minimal changes necessary to build a streaming version of our approach . The rest of the paper is arranged as follows .
section : Streaming One interesting variant of this method is a streaming approach , which could be implemented without knowledge of the entire graph . In this variant small walks from the graph are passed directly to the representation learning code , and the model is updated directly .
Sentences can be viewed as purposed walks through an appropriately designed language network , and language models like SkipGram are designed to capture this behavior . This approach can be combined with the streaming variant ( Section 4.4.1 ) to train features on a continually evolving network without ever explicitly constructing the entire graph . Maintaining representations with this technique could enable web - scale classification without the hassles of dealing with a web - scale graph .
At last , image score of class can be obtained by the sum over all proposals : . The interpretation of the two streams framework is as follows . The is the probability of proposal belonging to class .
This paper presents an human sensing ( HS ) system based on Hidden Markov Models ( HMMs ) for classifying physical activities : walking , walking-upstairs , walking-downstairs , sitting , standing and lying down . The system includes a feature extractor ( developed by the authors and presented in a previous work ) , an HMMs training module and an HAR module . All experiments have been done using a publicly available dataset named UCI Human Activity Recognition Using Smartphones .
This is then followed by knowledge transfer from offline training to the online tracking process . Online tracking involves a classification neural network which is constructed from the encoder part of the trained autoencoder as a feature extractor and an additional classification layer . Both the feature extractor and the classifier can be further tuned to adapt to appearance changes of the moving object .
In particular , a model-based spatial process decomposition is developed where the random weights are unique to individual patients for component functions common across patients . Model fitting and selection are based on maximum likelihood , while feature extractions are via optimal prediction of the underlying true image . A simulation study evaluates the properties of the proposed methodology and for illustration , a cancer image data set is analyzed and QIBs are extracted in association with a clinical endpoint .
Computation of texture information for image classification and pattern recognition is a highly complex activity . Much of this complexity is related to feature extractions where computational cost is related with the tonal levels and resolution of the images . One of the more used methods is the Gray Level Occurrence Matrix ( GLCM ) that present quadratic time complexity in relation to of gray levels .
Our system is clearly described , modular in nature , replicable on any Desktop , and demonstrated with extensive experiments , backed by insightful analysis on different Convolutional Neural Networks ( CNNs ) , as stand-alone and fused with others . With a large corpus of unconstrained , real-world video data , we examine the usefulness of different CNN models as features extractors for modeling high-level events , i.e. , pre-trained CNNs that differ in architectures , training data , and number of outputs . For each CNN , we use 1-fps from all training exemplar to train one-vs-rest SVMs for each event .
Arabic Sign Language Recognition field became widespread because of its difficult nature and numerous details . Most researchers employed different input sensors , features extractors , and classifiers on static and dynamic data . These different ways were customized and employed in our previous work in the Arabic Sign Language Recognition field .
A topdown architecture with lateral connections is developed for building high - level semantic feature maps at all scales . This architecture , called a Feature Pyramid Network ( FPN ) , shows significant improvement as a generic feature extractor in several applications . Using FPN in a basic Faster R - CNN system , our method achieves state - of - the - art singlemodel results on the COCO detection benchmark without bells and whistles , surpassing all existing single - model entries including those from the COCO 2016 challenge winners .
DIAT uses an adversarial loss to learn the mapping from to , where and are face images in two different domains and , respectively . This method has a regularization term on the mapping as to preserve identity features of the source image , where is a feature extractor pretrained on a face recognition task . CycleGAN also uses an adversarial loss to learn the mapping between two different domains and .
As shown in the last column of Table [ reference ] , our method is slightly slower than R - FCN , which also reaches a real - time speed ( 8.2 fps or 9.8 fps without context ) and achieves the best trade - off between accuracy and speed . We argue that the sharing process of feature extraction between two branches and the design of lightweight RoI - wise subnetwork after RoI pooling both greatly reduce the model complexity . As shown in Table [ reference ] , we also compared our method with other state - of - the - art single model .
Here we propose offline Malayalam handwritten character recognition using probabilistic simplified fuzzy ARTMAP ( PSFAM ) . PSFAM is a combination of SFAM and PNN ( Probabilistic Neural Network ) . After preprocessing stage , scanned image is segmented into line images .
In order to deal with the low utilization rate of chemical fertilizer , the soil needs to be precisely classified . Traditional neural network is not applicable to the precise judgements of soil classification , SFAM model is constructed based on improved adaptive genetic algorithm , and then applied to the soil testing and classification , which achieved good results .
By combining this two layers , SFAM is capable to perform classification very efficiently and giving very high performances . Lastly , the SFAM model is applied to different simulations . The simulation results obtained for the three different datasets : Iris , Wisconsin breast cancer and wine dataset prove that SFAM model has better performance results than other models for these classification applications .
Fuzzy ARTMAP is a supervised learning algorithm utilising two fuzzy ART modules and an associated mapping network . A simplified version of fuzzy ARTMAP ( SFAM ) was designed by incorporating a simplification of the match tracking concept on unsupervised fuzzy ART paradigms . The proposed simplified version consists of only one fuzzy ART module and an associated mapping network .
This work extends the simplified fuzzy ARTMAP ( SFAM ) to a complex-valued ( CV-SFAM ) version which is able to work with spatio-temporal data produced in receptive fields of visual cortex . The CV-SFAM ’s ability for incremental learning distinguishes CV-SFAM from other complex-valued neural networks , which provides the ability to preserve learned data while learning new samples .
The extracted features , which form the feature vector , are passed as input to a Simplified Fuzzy ARTMAP ( SFAM ) artificial neural network ( ANN ) classifier . The SFAM classifier compares the input data with the trained data and finds the nearest prototype from the database that ` resonates ' with the input pattern . The labels or recognized characters are assigned their corresponding Unicode code points and displayed using appropriate fonts .
Details of SFAM components are discussed . A complete outline of the SFAM methodology is provided , along with discussion of key points and frequent examples .
The PESFAM achieves good accuracy based on the twofold cross-validation results . In addition , the effectiveness of the proposed ensemble SFAM is delineated in sonar target differentiation . The experiments demonstrate the potential of PESFAM classifiers in offering an optimal solution to the data-ordering problem of SFAM implementation and also as an intelligent classification tool in mobile robot application .
This paper proposes an algorithm developed in MATLAB to detect such faults and faulty phase in SPWM inverter by carrying out analyses on motor currents captured from a model equivalent to such situations implemented in PSIM simulation software . Some features have been extracted using Fast Fourier Transforms ( FFTs ) on Park 's Vector Modulus ( PVM ) of three phase motor current profiles & finally Simplified Fuzzy Art-Map ( SFAM ) classifier has been employed to detect faulty switch and degree or level of fault . The obtained results provide a satisfactory level of accuracy .
Characteristics of PD signal for pattern recognition and classification were determined by using statistical and fractal methods from relationship among the voltage phase angle , the discharge magnitude and the repeated existing of partial discharges . The simplified fuzzy ARTMAP ( SFAM ) was used as artificial intelligent technique for pattern recognition and classification . PDs characteristic quantities were used as input parameters for Simplified Fuzzy ARTMAP to train pattern recognition and classification system .
In this paper , pattern classifiers of spatial obstacle avoidance situations using Neural Networks ( NN ) , Fuzzy Logic ( FL ) , Genetic Algorithms ( GA ) and Adaptive Resonance Theory ( ART ) individually or in combination are suggested . These classifiers are based on supervised learning and adaptation paradigms as Gradient Back-Propagation ( GBP ) , FL , GA and Simplified Fuzzy ArtMap ( SFAM ) resulting in NN/GBP and FL as Intelligent Systems ( IS ) and in NN/GA , NN/GA-GBP , NN-FL/GBP and NN-FL-ART/SFAM as Hybrid Intelligent Systems ( HIS ) . Afterwards , a synthesis of the suggested pattern classifiers is presented where their results and performances are discussed as well as the Field Programmable Gate Array ( FPGA ) architectures , characterized by their high flexibility and compactness , for their implementation .
The presentation order of training patterns to a simplified fuzzy ARTMAP ( SFAM ) neural network affects the classification performance . The common method to solve this problem is to use several simulations with training patterns presented in random order , where voting strategy is used to compute the final performance .
In this paper , we propose a Moiré pattern Removal Neural Network ( MopNet ) to solve this problem . All core components of MopNet are specially designed for unique properties of moire patterns , including the multi-scale feature aggregation addressing complex frequency , the channel-wise target edge predictor to exploit imbalanced magnitude among colour channels , and the attribute-aware classifier to characterize the diverse appearance for better modelling Moiré patterns . Quantitative and qualitative experimental comparison validate the state-of-the-art performance of MopNet .
In contrast to previous methods that simply combined pedestrian predictions on feature maps with different resolution , we propose a scale-aware hierarchical detection network for pedestrian detection under large scale variations . First , we introduce a cross-scale features aggregation module to accomplish feature augmentation for pedestrian representation through merging the lateral connection , the top-down path and bottom-up path . Specifically , the cross-scale features aggregation module can adaptively fuse hierarchical features to enhance feature pyramid representation for robust semantic and accurate localization . Further , we design a scale-aware hierarchical detection network to effectively integrate multiscale pedestrian detection into a unified framework by adaptively perceiving the augmented feature level for special-scale pedestrian detection .
Multi-Scale Feature Aggregation Network With Wavelet Structure Similarity Loss Function For Single Image Dehazing . Single image dehazing is the ill-posed two-dimensional signal reconstruction problem .
subsubsection : Implementation Details Throughout the experiments , the sizes of the RGB image , the semantic saliency prediction , inputs to the discriminator and inputs to the generator are fixed as ; the sizes of the instance - agnostic parsing prediction , instance - aware clustering prediction , inputs to the discriminator , inputs to the generator , inputs to the discriminator and instance location map are fixed as ; the channel number of the pixel - wise instance location map is fixed as , incorporating two corner points of the associated bounding box ; the constraint factors are empirically fixed as and , respectively ; the generator is initialized with FCN - 8s by replacing the last layer with a new convolutional layer with kernel size , pretrained on PASCAL - VOC - 2011 and finetuned on the target dataset ; the generator is initialized with WS - ResNet by eliminating the spatial pooling layers , increasing the strides of the first convolutional layers up to 2 in B , eliminating the top - most global pooling layer and the linear classifier , and adding two new convolutional layers with kernel sizes and , pretrained on ImageNet and PASCAL - VOC - 2012 , and finetuned on the target dataset ; the generator is initialized with the same backbone architecture and pre - trained weights with ( which are learned separately ) , by further augmenting it with two sibling branches for pixel - wise instance location map prediction and instance number prediction , where the first branch utilizes a MSFU ( three convolutional layers with kernal sizes for specific scale adaption ) ended with a convolutional layer with kernel size for multi - scale feature aggregation and a final convolutional layer with kernel size for location regression and the second branch utilizes the feature maps downsampled by 8 concatenated with the feature maps from the first branch ended with a global pooling layer , a hidden 512 - way fully - connected layer and a final 1 - way fully - connected layer for instance number regression ; the three discriminators ( which are learned separately ) are all initialized with a VGG - 16 by adding a new convolutional layer at the very begining with kernel size for input adaption , and replacing the last layer with a new 1 - way fully - connected layer activated by sigmoid , pre - trained on ImageNet and finetuned on the target dataset ; the newly added layers are randomly initialized by drawing weights from a zero - mean Gaussian distribution with standard deviation ; we employ an off - the - shelf clustering method to obtain the instance - aware clustering map ; the dropout ratio is empirically fixed as ; the weight decay and batch size are fixed as and , respectively ; We use an initial learning rate of for pre - trained layers , and for newly added layers in all our experiments ; we decrease the learning rate to of the previous one after 20 epochs and train the network for roughly 60 epochs one after the other ; the proposed network is implemented based on the publicly available TensorFlow platform , which is trained using Adam ( ) on four NVIDIA GeForce GTX TITAN X GPUs with 12 G memory ; the same training setting is utilized for all our compared network variants ; we evaluate the testing time by averaging the running time for images on the target set on NVIDIA GeForce GTX TITAN X GPU and Intel Core i7 - 4930 K CPU@3.40GHZ ; our NAN can rapidly process one image in about 1 second , which compares much favorably to other state - of - the - art approaches , as the current state - of - the - art methods rely on region proposal preprocessing and complex processing steps . subsubsection : Evaluation Metrics
ISPCell: An Interactive Image-Based Streaming Protocol for Wireless Cellular Networks Decentralized Dynamic State Estimation with Bimodal Gaussian Mixture Measurement Noise Aggregate power consumption modeling of live video streaming systems Reconstructing the World* in Six Days *(As Captured by the Yahoo 100 Million Image Dataset) Energy-Efficient Synthetic-Aperture Radar Processing on a Manycore Architecture Multi-GPU implementation and performance optimization for CSR-based sparse matrix-vector multiplication Keynote Speech: Avoiding the Memory Bottleneck through Structured Arrays An Analysis of Big Video Data with Cloud Computing in Ubiquitous City Semantic Features Prediction for Pulmonary Nodule Diagnosis Based on Online Streaming Feature Selection Assessment of Linux' Data Path Implementations for Download and Streaming HTTP Adaptive Multiview Video Streaming Construction of a Temporal Tree and its Coarse Grain View in a Streaming Big Data Context Adaptive media playout for low-delay video streaming over error-prone channels Conception et performances d’un système de stockage vidéo distribué et tolérant aux pannes DeepWalk: online learning of social representations Multiple Instance Detection Network with Online Instance Classifier Refinement Human activity monitoring based on hidden Markov models using a smartphone Learning a Deep Compact Image Representation for Visual Tracking Spatial Process Decomposition for Quantitative Imaging Biomarkers Using Multiple Images of Varying Shapes Comparing the Use of Sum and Difference Histograms and Gray Levels Occurrence Matrix for Texture Descriptors Pre-trained D-CNN models for detecting complex events in unconstrained videos Arabic sign language recognition with 3D convolutional neural networks Feature Pyramid Networks for Object Detection StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation CoupleNet: Coupling Global Structure with Local Parts for Object Detection Malayalam Offline Handwritten Recognition Using Probabilistic Simplified Fuzzy ARTMAP Agricultural Soil Testing and Classification Based on the Improved Genetic Neural Network Algorithm Usage of Simplified Fuzzy ARTMAP for improvement of classification performances Parallel implementation of the fuzzy ARTMAP neural network paradigm on a hypercube A Novel Complex-Valued Fuzzy ARTMAP for Sparse Dictionary Learning Malayalam online handwriting recognition system: A simplified fuzzy ARTMAP approach Software Function Allocation Methodology Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation Spurious resistance identification in SPWM inverter circuit feeding power to induction motor Applications of simplified fuzzy ARTMAP to partial discharge classification and pattern recognition Soft Computing Based Pattern Classifiers for the Obstacle Avoidance Behavior of Intelligent Autonomous Vehicles (IAV) Using genetic algorithm to select the presentation order of training patterns that improves simplified fuzzy ARTMAP classification performance Mop Moiré Patterns Using MopNet Scale-Aware Hierarchical Detection Network for Pedestrian Detection Y-Net: Multi-Scale Feature Aggregation Network With Wavelet Structure Similarity Loss Function For Single Image Dehazing Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing
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MWCN MMSys ICPP IPDPS KDD '14 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS IJCNN 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) ISI ICONIP (1) ICCV ICASSP ArXiv
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