3119results about How to "Easy to classify" patented technology

In the systems and methods of the present invention a multifocal multiphoton imaging system has a signal to noise ratio (SNR) that is reduced by over an order of magnitude at imaging depth equal to twice the mean free path scattering length of the specimen. An MMM system based on an area detector such as a multianode photomultiplier tube (MAPMT) that is optimized for high-speed tissue imaging. The specimen is raster-scanned with an array of excitation light beams. The emission photons from the array of excitation foci are collected simultaneously by a MAPMT and the signals from each anode are detected using high sensitivity, low noise single photon counting circuits. An image is formed by the temporal encoding of the integrated signal with a raster scanning pattern. A deconvolution procedure taking account of the spatial distribution and the raster temporal encoding of collected photons can be used to improve decay coefficient. We demonstrate MAPMT-based MMM can provide significantly better contrast than CCD-based existing systems.

A mobile communication terminal includes a display unit comprising a plurality of displays, one of which being formed as a touch panel, and a controller for analyzing a signal inputted through the touch panel to determine whether movement distance of data satisfies pre-set conditions, and outputting a control signal for displaying the corresponding data through a different display region according to the corresponding result.

Methods, apparatuses and systems that facilitate the classification of web services network traffic. In one implementation, the present invention provides an automated mechanism that facilitates synchronization of a web services traffic classification database with the current or latest definitions of one to a plurality of web services of interest. In one implementation, the present invention processes interface definitions corresponding to a given Web service to construct a traffic classification configuration for the Web service, including one or more traffic classes and corresponding matching rules or attributes for each traffic class. In one implementation, the present invention automatically creates traffic classes and matching rules that allow for differentiation between the operations supported by a Web service. Implementations of the present invention provide a mechanism allowing for classification of Web services network traffic on a granular basis to enhance network monitoring and analysis tasks, as well as network control functions, such as bandwidth management, security and other functions.

Techniques for controlling distribution of information from a secure domain by automatically detecting outgoing messages which violate security policies corresponding to the secure domain. Semantic models are constructed for one or more message categories and for the outgoing messages. The semantic model of an outgoing message is compared with the semantic models of the message categories to determine a degree of similarity between the semantic models. The outgoing message is classified based on the degree of similarity obtained from the comparison. A determination is made, based on the classification of the outgoing message, if distribution of the outgoing message would violate a security policy for the secure domain. Distribution of the outgoing message is allowed if no security policy is violated.

The invention discloses a fundus image retinal vessel segmentation method and a fundus image retinal vessel segmentation system based on deep learning. The fundus image retinal vessel segmentation method comprises the steps of performing data amplification on a training set, enhancing an image, training a convolutional neural network by using the training set, segmenting the image by using a convolutional neural network segmentation model to obtain a segmentation result, training a random forest classifier by using features of the convolutional neural network, extracting a last layer of convolutional layer output from the convolutional neural network, using the convolutional layer output as input of the random forest classifier for pixel classification to obtain another segmentation result, and fusing the two segmentation results to obtain a final segmentation image. Compared with the traditional vessel segmentation method, the fundus image retinal vessel segmentation method uses the deep convolutional neural network for feature extraction, the extracted features are more sufficient, and the segmentation precision and efficiency are higher.

A novel solution for fast packet classification includes a novel data structure to store classifier rules which enables fast packet classification, which structure employs bitmaps for each field of the incoming packet for which classification is desired. A fast packet classification algorithm using the novel data structure allows the matching rule with the highest priority to be quickly obtained. A novel rule update algorithm allows new classifier rules to be added into the data structure incrementally. In one practical implementation of a classification engine employing the structures and algorithms of the present invention, a configurable processor with customized instructions is used to accelerate packet classification.

Whether or not the CW radar is utilized for through-the-wall detection, additional one or more sensors are used with the CW radar to confirm the motion detection result or to in fact turn on the CW radar once motion or the physical presence of an individual has been sensed, thereby to provide confirmation of a less-reliable sensor with the use of the more reliable CW radar. Thus, the addition of other sensors provides lower power consumption, lower false alarm rates and better discrimination and classification of moving objects.

Methods, apparatuses and systems directed to the classification of encrypted network traffic. In one implementation, the present invention facilitates the classification of network traffic that has been encrypted according to a dynamically-created encryption mechanism involving a handshake between two end-systems, such as the SSL and TLS protocols. In one implementation, the present invention observes and analyzes attributes of the handshake between two nodes to enhance the classification of network traffic. In one embodiment, the enhanced classification mechanisms described herein operate seamlessly with other Layer 7 traffic classification mechanisms that operate on attributes of the packets themselves. Implementations of the present invention can be incorporated into a variety of network devices, such as traffic monitoring devices, packet capture devices, firewalls, and bandwidth management devices.

A device comprising a voice activity detector for detecting voice activity in a speech signal using digital data formed on the basis of samples of an audio signal. The voice activity detector comprises a first element adapted to examine whether the signal has a highpass nature. The voice activity detector also comprises a second element adapted to examine the frequency spectrum of the signal. The voice activity detector is adapted to provide an indication of speech when the first element has determined that the signal has a highpass nature or the second element has determined that the signal does not have a flat frequency response.

In a method of cognitive communication for non-interfering transmission, wherein the improvement comprises the step of conducting radio scene analysis to find not just the spectrum holes or White spaces; but also to use the signal classification, machine learning and prediction information to learn more things about the existing signals and its underlying protocols, to find the Gray space, hence utilizing the signal space, consisting of space, time, frequency (spectrum), code and location more efficiently.In a method of cognitive jamming where smart and energy efficient jamming techniques are suggested based on sensing, classification and machine learning of the existing signals.

A surface inspection system and method is provided which detects defects such as particles or pits on the surface of a workpiece, such as a silicon wafer, and also distinguishes between pit defects and particle defects. The surface inspection system comprises an inspection station for receiving a workpiece and a scanner positioned and arranged to scan a surface of the workpiece at the inspection station. The scanner includes a light source arranged to project a beam of P-polarized light and a scanner positioned to scan the P-polarized light beam across the surface of the workpiece. The system further provides for detecting differences in the angular distribution of the light scattered from the workpiece and for distinguishing particle defects from pit defects based upon these differences.

A network protection method is provided. The network protection method may include receiving a Domain Name System (DNS) request, logging the DNS request, classifying the DNS request based on an analysis of a DNS name associated with the DNS request, taking a security action based on the classification, analyzing network traffic after taking the security action, and providing substantially real-time feedback associated with the network traffic to improve future DNS request classifications. The method may further include receiving a DNS response and logging the DNS response. The analysis of the DNS name may include receiving DNS data related to the DNS name from a plurality of sources, receiving reputation data related to the plurality of sources, scoring each of the plurality of sources based on the reputation data, and aggregating the DNS data related to the DNS name based on the scoring.

A method for sorting mail to a case having a plurality of slots is described, wherein each slot corresponds to a destination. The method includes steps of loading a mail piece to be sorted into a delivery robot, determining for the mail piece a destination slot the mail piece is to be delivered to, moving the delivery robot along a rail disposed at the front of the case near the slots into proximity with an open end of the destination slot, inserting the mail piece from the delivery robot into the associated slot, and returning the delivery robot to a loading station whereby the cycle may be repeated. Such a method, when using a large number of robots moving along a common rail system, can sort a large volume of mail in carrier delivery order.

In a method of cognitive communication a system for generating non-interfering transmission, includes conducting radio scene analysis to find grey space using external signal parameters for incoming signal analysis without having to decode incoming signals.

A method and apparatus for robustly enhanced Class of Service (COS) at the application layer permits highly flexible privilege based access and enables implementation of complex policies and rules for classification and differentiation of services. Differentiation facilitates categorization of traffic to permit flexible design and implementation of multiple Class of Service levels.

The subject invention relates to facilitating text detection. The invention employs a boosted classifier and a transductive classifier to provide accurate and efficient text detection systems and / or methods. The boosted classifier is trained through features generated from a set of training connected components and labels. The boosted classifier utilizes the features to classify the training connected components, wherein inferred labels are conveyed to a transductive classifier, which generates additional properties. The initial set of features and the properties are utilized to train the transductive classifier. Upon training, the system and / or methods can be utilized to detect text in data under text detection, wherein unlabeled data is received, and connected components are extracted therefrom and utilized to generate corresponding feature vectors, which are employed to classify the connected components using the initial boosted classifier. Inferred labels are utilized to generate properties, which are utilized along with the initial feature vectors to classify each connected component using the transductive classifier.

The invention discloses a method for recognizing a road traffic sign for an unmanned vehicle, comprising the following steps of: (1) changing the RGB (Red, Green and Blue) pixel value of an image to strengthen a traffic sign feature color region, and cutting the image by using a threshold; (2) carrying out edge detection and connection on a gray level image to reconstruct an interested region; (3) extracting a labeled graph of the interested region as a shape feature of the interested region, classifying the shape of the region by using a nearest neighbor classification method, and removing a non-traffic sign region; and (4) graying and normalizing the image of the interested region of the traffic sign, carrying out dual-tree complex wavelet transform on the image to form a feature vector of the image, reducing the dimension of the feature vector by using a two-dimension independent component analysis method, and sending the feature vector into a support vector machine of a radial basis function to judge the type of the traffic sign of the interested region. By using the method, various types of traffic signs in a running environment of the unmanned vehicle can be stably and efficiently detected and recognized.

The invention discloses an image classification method based on a semi-supervised self-paced learning cross-task deep network. The method includes the steps of randomly selecting a small amount of labeling samples from the whole image data set, reserving the labels, and remaining all the samples as unlabelled samples having the real labels to be unknown in the whole process, wherein the weight ofthe labeled samples is constant to be one in the training process, the weight of the unlabelled samples is initialized to be zero, and only the labeled samples are used as a training set in the initial process; S2, training a cross-task deep network by the training set; S3, according to the trained cross-task deep network, predicting the pseudo labels of all the unlabelled samples, and giving a corresponding weight of each unlabelled sample; S4, according to a self-paced learning normal form, selecting an unlabelled sample with a high confidence degree, and adding to the training set; and S5,repeating the steps S2-S4 until the cross-task deep network performance is saturated or reaches a preset cycle number. According to the method, the human design feature is not needed to be input, andthe classification can be realized by directly inputting the original image.

A method and apparatus for intelligent Internet searching, the apparatus residing as a software application on a user's computer (the client). A single subject database of sources of directly and indirectly accessible content is stored on the client and accessed by the application. The database also necessary information for searching each source. Preferably, the database is updated at a regular interval. Multiple simultaneous hidden database searches may be performed by the application by linking the client to the appropriate database access pages on the network and forwarding the user's desired search information. Preferably, search results are updated and compared to highlight new information.

Method for preparing classified advertisements for publication in printed media, comprising the steps of: capturing (112) at least the textual content (30) of each classified advertisement expressed in natural language, automatically classifying and extracting (120-124) a plurality of data units (480) out of said textual content (30) and storing each data unit into a corresponding field of a record (48) in an electronic database (9), using said database for determining the textual content, the layout and / or the position of the classified advertisement in said printed media.

A local-neighborhood Laplacian Eigenmap (LNLE) algorithm is provided for methods and systems for semi-supervised learning on manifolds of data points in a high-dimensional space. In one embodiment, an LNLE based method includes building an adjacency graph over a dataset of labelled and unlabelled points. The adjacency graph is then used for finding a set of local neighbors with respect to an unlabelled data point to be classified. An eigen decomposition of the local subgraph provides a smooth function over the subgraph. The smooth function can be evaluated and based on the function evaluation the unclassified data point can be labelled. In one embodiment, a transductive inference (TI) algorithmic approach is provided. In another embodiment, a semi-supervised inductive inference (SSII) algorithmic approach is provided for classification of subsequent data points. A confidence determination can be provided based on a number of labeled data points within the local neighborhood. Experimental results comparing LNLE and simple LE approaches are presented.

A system for disposing of medical waste is generally configured to sort waste items into a plurality of disposable containers according to applicable rules and regulations governing the handling and / or disposal of such items. In some embodiments, a system comprises sorting stations, each of which houses a number of disposable containers. Each station can identify an item of waste, determine the most appropriate container for the item, and facilitate disposal of the item in the appropriate container.