1672 results about "Network output" patented technology

A method is provided for synchronizing the playback of a digital audio broadcast on a plurality of network output devices by inserting an audio waveform sample in an audio stream of the digital audio broadcast. The method includes the steps of outputting a first unique signal as part of an audio signal which has unique identifying characteristics and is regularly occurring, outputting a second unique signal so that the time between the first and second unique signals must be significantly greater than the latency between sending and receiving devices, and coordinating play of audio by an audio waveform sample assuring the simultaneous output of the audio signal from multiple devices. An algorithm in hardware, software, or a combination of the two identifies the audio waveform sample in the audio stream. The digital audio broadcast from multiple receivers does not present to a listener any audible delay or echo effect.

A method is provided for synchronizing the playback of a digital audio broadcast on a plurality of network output devices using a microphone near a source, embedded control codes, and the audio patterns from the network output devices. An optional, additional manual adjustment method relies on a graphical user interface for adjustment and audible pulses from the devices which are to be synchronized. Synchronization of the audio is accomplished with clock synchronization of the network output devices. The digital audio broadcast from multiple receivers does not present to a listener any audible delay or echo effect.

A method is provided for synchronizing the playback of a digital audio broadcast on a plurality of network output devices by inserting a control track pulse in an audio stream of the digital audio broadcast. The method includes the steps of outputting a first control track pulse as part of an audio signal which has unique identifying characteristics and is regularly occurring, outputting a second control track pulse so that the time between the first and second control track pulses must be significantly greater than the latency between sending and receiving devices, and coordinating play of audio at the time of the occurrence of the transmission of the second control track pulse assuring the simultaneous output of the audio signal from multiple devices. The control track pulses have a value unique from any other portion of the audio stream. The digital audio broadcast from multiple receivers does not present to a listener any audible delay or echo effect.

The invention provides a method and system for fault-tolerant communication. It utilizes three wide area networks, including the cell phone network, the internet and the telephone network (PSTN). The method and system monitors the wide-area networks and sends warning messages if they cannot be accessed from a local site. Primary failure conditions relate to the access and use of the three wide-area networks. Secondary failure conditions include power outages. The possible fault conditions include: ‘telephone out’, ‘internet out’, ‘wireless radio out’, ‘power out’. If these failure conditions are detected, the method and system alerts the user and redirects voice, text message, and data traffic via a detour over a different wide-area network in order to avoid that failure.

A method and system has been disclosed that routs information over one of multiple networks in a fault-tolerant manner. In case of network failure, detour routers allow the start-detour routers to switch the information flow over another network to an end-detour router, where the information flow is switched back to the originally-intended network. Cell radios are utilized, as well as telephones and the internet to provide redundancy and fail-over under ‘telephone out’, ‘internet out’, ‘cell phone out’ conditions. The method has particular strengths in supporting communication even with dynamically assigned addresses, thus assuring that remote users receive monitoring and alarm information in a timely manner.

The invention provides a calculation apparatus and method for an accelerator chip accelerating a deep neural network algorithm. The apparatus comprises a vector addition processor module, a vector function value calculator module and a vector multiplier-adder module, wherein the vector addition processor module performs vector addition or subtraction and/or vectorized operation of a pooling layer algorithm in the deep neural network algorithm; the vector function value calculator module performs vectorized operation of a nonlinear value in the deep neural network algorithm; the vector multiplier-adder module performs vector multiplication and addition operations; the three modules execute programmable instructions and interact to calculate a neuron value and a network output result of a neural network and a synaptic weight variation representing the effect intensity of input layer neurons to output layer neurons; and an intermediate value storage region is arranged in each of the three modules and a main memory is subjected to reading and writing operations. Therefore, the intermediate value reading and writing frequencies of the main memory can be reduced, the energy consumption of the accelerator chip can be reduced, and the problems of data missing and replacement in a data processing process can be avoided.

Devices, systems, and methods for detecting and preventing unauthorized access to computer networks. Devices include a server enabled with an application that interacts with a counter-part PC application to determine whether input devices of the PC have been active within a predetermined time. Methods include providing a subscription-based service for PC users to determine whether unauthorized network output activity has occurred from a respective user's PC.

A neural network classifier provides the ability to separate and categorize multiple arbitrary and previously unknown audio sources down-mixed to a single monophonic audio signal. This is accomplished by breaking the monophonic audio signal into baseline frames (possibly overlapping), windowing the frames, extracting a number of descriptive features in each frame, and employing a pre-trained nonlinear neural network as a classifier. Each neural network output manifests the presence of a pre-determined type of audio source in each baseline frame of the monophonic audio signal. The neural network classifier is well suited to address widely changing parameters of the signal and sources, time and frequency domain overlapping of the sources, and reverberation and occlusions in real-life signals. The classifier outputs can be used as a front-end to create multiple audio channels for a source separation algorithm (e.g., ICA) or as parameters in a post-processing algorithm (e.g. categorize music, track sources, generate audio indexes for the purposes of navigation, re-mixing, security and surveillance, telephone and wireless communications, and teleconferencing).

The task of relevance score assignment to a set of items onto which an artificial neural network is applied is obtained by redistributing an initial relevance score derived from the network output, onto the set of items by reversely propagating the initial relevance score through the artificial neural network so as to obtain a relevance score for each item. In particular, this reverse propagation is applicable to a broader set of artificial neural networks and/or at lower computational efforts by performing same in a manner so that for each neuron, preliminarily redistributed relevance scores of a set of downstream neighbor neurons of the respective neuron are distributed on a set of upstream neighbor neurons of the respective neuron according to a distribution function.

The invention relates to a pedestrian recognition and tracking method based on an accelerated area Convolutional Neural Network. Firstly, training and testing data set are preprocessed according to the requirements through a robot with an infrared camera to acquire a training dataset and a testing dataset at night, and then, actual target position labeling is conducted on all training and testing photos and is recorded to a sample file; then, the accelerated area Convolutional Neural Network is constructed, the accelerated area Convolutional Neural Network is trained by using the training dataset, and the final probability belonging to a pedestrian area and a bounding box of the area are calculated out from network output by the usage of a non-maximum suppression algorithm; the accuracy of the network is tested by the usage of the testing dataset, and a network model consistent with the requirements is obtained; photos collected by the robot at night are input to an accelerated area Convolutional Neural Network model, and the probability belonging to the pedestrian area and the bounding box of the area are online output by a model in real time. According to the pedestrian detection and tracking method based on the accelerated area Convolutional Neural Network, a pedestrian in an infrared image can be effectively recognized, and real-time tracking for a pedestrian target in an infrared video can be achieved.

A multistage interconnect network (MIN) capable of supporting massive parallel processing, including point-to-point and multicast communications between processor modules (PMs) which are connected to the input and output ports of the network. The network is built using interconnected switch nodes arranged in 2 [log_b N] stages, wherein b is the number of switch node input/output ports, N is the number of network input/output ports and [log_b N] indicates a ceiling function providing the smallest integer not less than log_b N. The additional stages provide additional paths between network input ports and network output ports, thereby enhancing fault tolerance and lessening contention.

A technique for controlling a loudspeaker system with an artificial neural network includes filtering, with a deconvolution filter, a measured system response of a loudspeaker and a reverberant environment in which the loudspeaker is disposed to generate a filtered response, wherein the measured system response corresponds to an audio input signal applied to the loudspeaker while the loudspeaker is disposed in the reverberant environment. The techniques further include generating, via a neural network model, an initial neural network output based on the audio input signal, comparing the initial neural network output to the filtered response to determine an error value, and generating, via the neural network model, an updated neural network output based on the audio input signal and the error value.

The invention proposes an image semantic segmentation method based on a super-pixel edge and a full convolutional network, so that a technical problem of low accuracy in the existing image semantic segmentation method is solved. The method comprises: a training sample set, a testing sample set, and a verification sample set are constructed; a full convolutional network outputting a pixel-level semantic mark is trained, tested, and verified; semantic segmentation is carried out on a to-be-segmented image by using the verified full convolutional network outputting a pixel-level semantic mark to otain a pixel-level semantic mark; BSLIC sub-pixel segmentation is carried out on the to-be-segmented image; and semantic marking is carried out on BSLIC super-pixels by using the pixel-level semantic mark to obtain a semantic segmentation result with combination of the super-pixel edge and the high-level semantic information outputted by the full convolutional network. Therefore, the original full convolutional network segmentation accuracy is kept and the segmentation accuracy of the small edge is improved, so that the image segmentation accuracy is enhanced. The image semantic segmentation method can be applied to classification, identification, and tracking occasions requiring target detection.

The invention discloses a 3D convolutional neural network sign language identification method integrated with multi-modal data. The 3D convolutional neural network sign language identification methodintegrated with multi-modal data includes the steps: constructing a deep neural network, respectively performing characteristic extraction on an infrared image and a contour image of a gesture from the spatial dimension and the time dimension of a video, and integrating two network output based on different data formats to perform final classification of sign language. The 3D convolutional neuralnetwork sign language identification method integrated with multi-modal data can accurately extract the limb movement track information in two different data formats, can effectively reduce the computing complexity of a model, uses a deep learning strategy to integrate the classification results of two networks, and effectively solves the problem that single classifier encounters error classification because of data loss, so as to enable the model to have relatively higher robustness for illumination and background noise of different scenes.

The invention discloses a generative adversarial network-based pixel-level portrait cutout method and solves the problem that massive data sets with huge making costs are needed to train and optimizea network in the field of machine cutout. The method comprises the steps of presetting a generative network and a judgment network of an adversarial learning mode, wherein the generative network is adeep neural network with a jump connection; inputting a real image containing a portrait to the generative network for outputting a person and scene segmentation image; inputting first and second image pairs to the judgment network for outputting a judgment probability, and determining loss functions of the generative network and the judgment network; according to minimization of the values of theloss functions of the two networks, adjusting configuration parameters of the two networks to finish training of the generative network; and inputting a test image to the trained generative network for generating the person and scene segmentation image, randomizing the generated image, and finally inputting a probability matrix to a conditional random field for further optimization. According tothe method, a training image quantity is reduced in batches; and the efficiency and the segmentation precision are improved.

The invention discloses an automatic microseismic signal arrival time picking method based on a depth belief neural network. According to the method, each microseismic record is sampled according unified fixed dimensions, the signal arrival time of partial records is manually picked and is taken as the label information of the corresponding records; the information-picked records and labels of the information-picked records are taken as a total data set during network construction, including a training data set, a verification data set and a test data set; through inputting the data to the depth belief neural network for training and testing, the depth belief neural network is constructed; the actually-acquired to-be-processed data is inputted to the trained network model to carry out microseismic signal identification and automatic arrival time picking, and the network output is an arrival time point of the microseismic data.

The invention discloses a driving scene classification method based on a convolution neural network, and the method comprises the following steps: collecting a road environment video image; carrying out the classification of a traffic scene, and building a traffic scene recognition database; extracting sample images of different driving scenes from the traffic scene recognition database, carryingout the feature extraction and multiple convolution training of the sample images through a deep convolution neural network, carrying out the rasterization of pixels, connecting the pixels to form a vector, inputting the vector into a conventional neural network, obtaining convolution neural network output, and achieving the deep learning of different driving scenes; carrying out the parameter optimization of a network structure of the built convolution neural network, obtaining a trained convolution neural network classifier, carrying out the adjustment of a traffic scene recognition model, and selecting an optimal mode as the standard of the traffic scene recognition model; carrying out the real-time collection of the image of a to-be-detected traffic scene, and inputting the image intothe traffic scene recognition model for the recognition of a road environment scene.

The invention provides a video classification method, a model training method and device thereof, and electronic equipment. The training method comprises the following steps: extracting initial features of a plurality of video frames through a convolutional neural network; extracting final features of the plurality of video frames from the initial features through a recurrent neural network; inputting the final feature into an output network, and outputting a prediction result of the multi-frame video frame; determining a loss value of the prediction result through a preset loss prediction function; and training the initial model according to the loss value until parameters in the initial model converge to obtain a video classification model. According to the method, the convolutional neural network and the recurrent neural network are combined, so that the operand can be greatly reduced, and the model training and recognition efficiency is improved; and meanwhile, the association information between the video frames can be considered in the feature extraction process, so that the extracted features can accurately represent the video types, and the accuracy of video classificationis improved.

The invention discloses an egocentric vision in-the-air hand-writing and in-the-air interaction method based on a cascade convolution nerve network. The method comprises steps of S1: obtaining training data; S2: designing a depth convolution nerve network used for hand detection; S3: designing a depth convolution nerve network used for gesture classification and finger tip detection; S4: cascading a first-level network and a second-level network, cutting a region of interest out of a foreground bounding rectangle output by the first-level network so as to obtain a foreground region including a hand, and then using the foreground region as the input of the second-level convolution network for finger tip detection and gesture identification; S5: judging the gesture identification, if it is a single-finger gesture, outputting the finger tip thereof and then carrying out timing sequence smoothing and interpolation between points; and S6: using continuous multi-frame finger tip sampling coordinates to carry out character identification. The invention provides an integral in-the-air hand-writing and in-the-air interaction algorithm, so accurate and robust finger tip detection and gesture classification are achieved, thereby achieving the egocentric vision in-the-air hand-writing and in-the-air interaction.

The invention discloses a road crack identification method based on deep learning. The method comprises the following steps that firstly, road crack images are collected, a training set is established, and a convolutional neural network is constructed to extract features in the images to generate a feature map; faster R-CNN model is trained, the model comprises an RPN network, an RoI Pooling network and a full connection layer which are connected in sequence, the RPN network obtains a detection target and an image background and obtains a candidate box position; and then candidate regions aregenerated, the RoI Pooling network outputs a RoI feature map with a fixed size, the feature map generated by the convolutional neural network and the RoI feature map are integrated, the object category of the detection target is discriminated, and the accurate position of the object is returned. Finally, a to-be-identified road image is input into the trained Faster R-CNN model, and whether the image is a road crack image or not is judged. The method has the advantages of high detection speed and high recognition accuracy.

A multi-route routing method based on network coding in wireless network includes the following steps: 1) routing discovering from originating nodes to object nodes; 2) after the routing discovering stage is finished, comparing route conversion incomes of each route, and originating nodes selecting the optimum W routes therein for data transmission. Compared with the prior technology, the invention proposes a multi-route routing method based on network coding in wireless network, dynamically transmits data packets on multiple routes according to reliability and coding opportunity of routes, dynamically creates rather than only waiting for coding opportunity through conversion transmission path, and multiple routes share network flow quantity loads, and maximize the route conversion, accordingly improving network output.

The invention discloses a multi-modal emotion recognition method based on a fusion attention network. The method comprises: extracting high-dimensional features of three modes of text, vision and audio, and aligning and normalizing according to the word level; then, inputting the signals into a bidirectional gating circulation unit network for training; extracting state information output by the bidirectional gating circulation unit network in the three single-mode sub-networks to calculate the correlation degree of the state information among the multiple modes; calculating the attention distribution of the plurality of modalities at each moment; wherein the state information is the weight parameter of the state information at each moment; and weighting and averaging state information ofthe three modal sub-networks and the corresponding weight parameters to obtain a fusion feature vector as input of the full connection network, a to-be-identified text, inputting vision and audio intothe trained bidirectional gating circulation unit network of each modal, and obtaining final emotion intensity output. According to the method, the problem of weight consistency of all modes during multi-mode fusion can be solved, and the emotion recognition accuracy under multi-mode fusion is improved.

The invention discloses a neonatal pain expression classification method based on a CNN (convolutional neural network). The method comprises the following steps: firstly, neonatal pain expression images are acquired, the images are classified by professional medical staff according to calm, cry, slight pain and intense pain level by level, and a neonatal pain expression image library is established; secondly, the CNN containing one data layer, three convolutional layers, two full connection layers and one classification layer is constructed; then samples in the neonatal pain expression image library are taken as data input of the CNN, iterative training is performed on the CNN with a back propagation algorithm, and optimizing training is performed on global parameters, so that a network output loss function value is reduced and converged; finally, neonatal pain expression test samples are input and then recognized and classified with the CNN, expression recognition of neonatal infants in calm, cry, slight pain and intense pain states is realized, and one novel method and way is provided for evaluating neonatal pain degree.

The embodiment of the invention provides a data processing method and system based on a neural network and federated learning. The method comprises the following steps: determining an encryption and decryption neural network according to a joint modeling task, wherein the encryption and decryption neural network comprises M encryption neural networks and a decryption neural network; training the encryption and decryption neural network according to randomly generated training data until the encryption and decryption neural network converges; sending the M trained encrypted neural networks to Mclients, so that each client determines encrypted data by taking owned local data and model parameters of the joint modeling task as input data of each encrypted neural network; and receiving encrypted data output by the encrypted neural network of each client, inputting the encrypted data into the decrypted neural network, and determining gradient update data of the model parameters. According to the method provided by the embodiment of the invention, the joint modeling is realized while the data privacy is ensured, the operation is simple and convenient, and the data processing efficiency is improved.