15267 results about "Test set" patented technology

A region of skin, other than the fingertips, is stimulated. After stimulation, an opening is created in the skin (e.g., by lancing the skin) to cause a flow of body fluid from the region. At least a portion of this body fluid is transported to a testing device where the concentration of analyte (e.g., glucose) in the body fluid is then determined. It is found that the stimulation of the skin provides results that are generally closer to the results of measurements from the fingertips, the traditional site for obtaining body fluid for analyte testing.

A sports training and testing apparatus for at least one athlete (50), said apparatus including a control unit (11) adapted to implement a predetermined protocol; a plurality of remote units (12) for providing a series of stimuli for said at least one athlete in accordance with the protocol; at least one sensor (14) for providing to said control unit feedback information associated with said at least one athlete's response to the stimuli; and a communications network (10) providing communications between the control unit and the plurality of remote units including said at least one sensor (14).

The present invention is a method, circuit and system for determining a reference voltage. Some embodiments of the present invention relate to a system, method and circuit for establishing a set of operating reference cells to be used in operating (e.g. reading) cells in an NVM block or array. As part of the present invention, at least a subset of cells of the NVM block or array may be read using each of two or more sets of test reference cells, where each set of test reference cells may generate or otherwise provide reference voltages at least slightly offset from each other set of test reference cells. For each set of test reference cells used to read at least a subset of the NVM block, a read error rate may be calculated or otherwise determined. A set of test reference cells associated with a relatively low read error rate may be selected as the set of operating reference cells to be used in operating (e.g. reading) other cells, outside the subset of cells, in the NVM block or array. In a further embodiment, the selected set of test reference cells may be used to establish an operating set of reference cells having reference voltages substantially equal to those of the selected test set.

Heart valve leaflet selection methods and apparatuses which subject individual leaflets to loads and measure the resulting deflection to more reliably group leaflets of similar physical characteristics for later assembly in prosthetic heart valves. The deflection testing may be accomplished using a variety of test set ups which are designed to impart a load on the leaflet which simulates the actual loading within a heart valve. The results from a number of deflection tests are used to categorize individual leaflets, which data can be combined with other data regarding the characteristics of the leaflet to better select leaflets for assembly into a multi-leaflet heart valve. In one embodiment, the deflection test is combined with an intrinsic load test, and leaflets having similar deflection and intrinsic load values used in the same heart valve. One apparatus for testing the leaflets includes a frame for securing the arcuate cusp of the leaflet while the straight coapting edge remains free, to simulate the actual leaflet mounting configuration within the heart valve prosthesis. The frame may include a lower portion having a recess for the leaflet and plurality of receptor holes around the peripheral edge of the recess, and an upper portion having a plurality of needles which extend downward through the leaflet and into the receptor holes and secure the edges of the leaflet.

An apparatus and method provide non-intrusive in-service testing of audio/video synchronization testing without using traditional audio marker tones. The network includes an A/V synchronous test signal generator which injects video and audio markers into the video and audio non-intrusively and routes the two signals into a switch where they are switched into a channel for encoding and transmission via the ATM network. At the distant end the signal is decoded and routed by a switch into the A/V test generator and measurement set where the markers are detected and the A/V skew calculated, after which the audio and video are routed to the subscriber. The A/V test set signal generator includes a Video Blanking Interval (VBI) test signal generator and a white noise generator, the former injecting a marker into the video signal and the later injecting an audio marker into the audio signal. The video marker is injected into the VBI and broadband, background audio noise to measure the delay between the audio and video components of a broadcast. The marking of the audio is accomplished by gradually injecting white noise into the audio channel until the noise level is 6 dB above the noise floor of the audio receiver. As a precursor A/V sync signal, a small spectrum of the white noise is notched or removed. This signature precludes inadvertent recognition of program audio noise as the audio marker.

Characterization of carotid atherosclerosis and classification of plaque into symptomatic or asymptomatic along with the risk score estimation are key steps necessary for allowing the vascular surgeons to decide if the patient has to definitely undergo risky treatment procedures that are needed to unblock the stenosis. This application describes a statistical (a) Computer Aided Diagnostic (CAD) technique for symptomatic versus asymptomatic plaque automated classification of carotid ultrasound images and (b) presents a cardiovascular stroke risk score computation. We demonstrate this for longitudinal Ultrasound, CT, MR modalities and extendable to 3D carotid Ultrasound. The on-line system consists of Atherosclerotic Wall Region estimation using AtheroEdge™ for longitudinal Ultrasound or Athero-CTView™ for CT or Athero-MRView from MR. This greyscale Wall Region is then fed to a feature extraction processor which computes: (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability. The output of the Feature Processor is fed to the Classifier which is trained off-line from the Database of similar Atherosclerotic Wall Region images. The off-line Classifier is trained from the significant features from (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability, selected using t-test. Symptomatic ground truth information about the training patients is drawn from cross modality imaging such as CT or MR or 3D ultrasound in the form of 0 or 1. Support Vector Machine (SVM) supervised classifier of varying kernel functions is used off-line for training. The Atheromatic™ system is also demonstrated for Radial Basis Probabilistic Neural Network (RBPNN), or Nearest Neighbor (KNN) classifier or Decision Trees (DT) Classifier for symptomatic versus asymptomatic plaque automated classification. The obtained training parameters are then used to evaluate the test set. The system also yields the cardiovascular stroke risk score value on the basis of the four set of wall features.

The invention relates to the field of computer vision recognition and transfer learning, and provides a small sample and zero sample image classification method based on metric learning and meta-learning, which comprises the following steps of: constructing a training data set and a target task data set; selecting a support set and a test set from the training data set; respectively inputting samples of the test set and the support set into a feature extraction network to obtain feature vectors; sequentially inputting the feature vectors of the test set and the support set into a feature attention module and a distance measurement module, calculating the category similarity of the test set sample and the support set sample, and updating the parameters of each module by utilizing a loss function; repeating the above steps until the parameters of the networks of the modules converge, and completing the training of the modules; and enabling the to-be-tested picture and the training picture in the target task data set to sequentially pass through a feature extraction network, a feature attention module and a distance measurement module, and outputting a category label with the highestcategory similarity with the test set to obtain a classification result of the to-be-tested picture.

The present invention is directed to a method for detecting unknown malicious code, such as a virus, a worm, a Trojan Horse or any combination thereof. Accordingly, a Data Set is created, which is a collection of files that includes a first subset with malicious code and a second subset with benign code files and malicious and benign files are identified by an antivirus program. All files are parsed using n-gram moving windows of several lengths and the TF representation is computed for each n-gram in each file. An initial set of top features (e.g., up to 5500) of all n-grams IS selected, based on the DF measure and the number of the top features is reduced to comply with the computation resources required for classifier training, by using features selection methods. The optimal number of features is then determined based on the evaluation of the detection accuracy of several sets of reduced top features and different data sets with different distributions of benign and malicious files are prepared, based on the optimal number, which will be used as training and test sets. For each classifier, the detection accuracy is iteratively evaluated for all combinations of training and test sets distributions, while in each iteration, training a classifier using a specific distribution and testing the trained classifier on all distributions. The optimal distribution that results with the highest detection accuracy is selected for that classifier.

A test apparatus includes digital modulators provided in increments of multiple channels. A baseband signal generator performs retiming of data input as a modulation signal for the in-phase (quadrature) component, using a timing signal the timing of which can be adjusted, thereby generating a baseband signal. A driver generates a multi-value digital signal having a level that corresponds to the baseband signal output from the baseband signal generator. A multiplier amplitude-modulates a carrier signal with the multi-value digital signal. An adder sums the output signals of the multipliers.

This invention involves the testing device of analytic chip with multiple reactors, which includes a). one-way-cleaning system for one-way-cleaning of remnant sample in said reactor; or/and b).fluidity-decreasing system for fluidity-decreasing of remnant sample; or/and c). sample spotting system for sample subjecting by spotting. With the said testing device, analytic chip testing can be easily conducted with high integration, high efficiency and safety.

An apparatus for the automated testing of seeds includes a testing device having a testing stage, for analyzing a seed delivered to the testing stage; and a conveyor for automatically individually conveying each of a plurality of seeds in a tray between individual compartments in the tray and the testing stage of the testing device.

The invention belongs to the technical field of image processing and computer vision, and relates to a novel biomedical image automatic segmentation method based on a U-net network structure, including dividing a biomedical data set into a training set and a test set, and normalizing the test set and augmented test set; inputting the images of the training set into the improved U-net network model, and generating a classification probability map by output image passing through a softmax layer; calculating the error between classification probability diagram and gold standard by a centralized loss function, and obtaining the weight parameters of network model by a gradient backpropagation method; entering the images in the test set into the improved U-net network model, and outputting the image to generate a classification probability map through the softmax layer; according to the class probability in the classification probability graph, obtaining the segmentation result graph of theimage. The invention solves the problems that simple samples in the image segmentation process contribute too much to the loss function to learn difficult samples well.

Degradation of the lubricant due to contamination of the lubricant with wear particles or moisture, which greatly affects the service life of rolling bearings, can be detected in a cost-effective manner through the use of a resonance frequency band signal or high-frequency signal of an accelerometer, and the service life of a rolling bearing can be estimated with high precision at an early stage on the basis of the detected state of the wear particles and lubricant.

Provided is a method comprising baseline data acquisition means for obtaining vibration signals by using an accelerometer 4 and using a testing device to acquire resonance frequency band signals detectable at the highest sensitivity, for each specification such as model number, manufacturer name, and other specifications for a rolling bearing 3 as pertains to the relationship between the state of wear particle penetration in a rolling bearing 3 and the vibration/bearing service life, and to lubricant degradation and vibration/bearing service life; measurement means whereby an accelerometer 4 is used to obtain vibration signals for the rolling bearing 3 whose remaining service life is being assessed and which resides on a fan, a pump, or another rotating device 1, 2, for the purpose of measuring resonance frequency band signals detectable at the highest sensitivity; and determination means for estimating the state of wear particle penetration and the state of lubricant degradation of the diagnostic rolling bearing 3, and computing the remaining service life of the diagnostic rolling bearing 3 by using measurement values obtained by the measurement means, determination results of the bearing specification determination means, and data obtained by the baseline data acquisition means.

An image processing method for selecting a subset of n (where n is two or more) images representative of a test set of images comprises the step of: detecting a degree of similarity between images in the test set of images, so that the subset of images comprises those n images having substantially the lowest similarity with other images in the set.

The invention discloses a high-resolution dictionary based sparse representation image super-resolution reconstruction method. The method comprises the following steps of: (1) constructing a high-resolution brightness image library; (2) generating a sample training set; (3) learning an over-complete dictionary; (4) primarily establishing a high-resolution image brightness space; (5) establishing an image sample test set; (6) updating the high-resolution image brightness space; (7) calculating a weight sparse matrix; (8) reupdating the high-resolution image brightness space; (9) judging whether to repeat execution; and (10) outputting a high-resolution image. The high-resolution over-complete dictionary learned by the invention can be applied to different amplification factors. Sparse representation, non-local prior and data fidelity constraint are fully utilized, so that local information and global information can be comprehensively utilized. The method has higher super-resolution capacity; and the reconstructed image is closer to an actual image.

The invention relates to a specific modal semantic space modeling-based cross-modal similarity learning method. The method comprises the following steps of: 1, establishing a cross-modal database which comprises data of multiple modal types, and dividing data in the database into a training set, a test set and a validation set; 2, aiming at each modal type in the cross-modal database, constructinga semantic space aiming at the specific modal, and projecting data of the other modal types to the semantic space so as to obtain a cross-modal similarity aiming at the specific modal; 3, fusing cross-modal similarities, aiming at specific modals, obtained from different modal semantic spaces to obtain a final cross-modal similarity; and 4, calculating a similarity between a query example and a query target by taking any modal type in the test set as a query modal and taking another modal type as a target modal, and obtaining a related result list of data of the target modal according to thesimilarity. According to the method, the correctness of cross-modal retrieval can be improved.

The invention relates to a convolutional neural network-based unmanned air vehicle to-ground specific target recognition method. The method comprises the following steps of: (1) labeling to-ground specific target data sets acquired by an unmanned air vehicle according to categories, and dividing the to-ground specific target data sets into training sets, verification sets and test sets according to proportions; (2) setting convolutional neural network model training parameters, starting training and obtaining an optimal solution of a convolutional neural network model according to the trainingcondition; (3) changing a depth of the convolutional neural network model, restarting the training in the step (2) to obtain an optimal convolutional neural network model; (4) testing the test set soas to obtain recognition correctness; and (5) applying convolutional neural network model parameters, correctness of which satisfies requirements, to a practical scene of an unmanned air vehicle to-ground specific target, so as to recognize an image target acquired by the unmanned aerial vehicle.

A method of performing work on a communication system in accordance with a work assignment includes a step of identifying a data file associated with the work assignment, the data file including subscription information and network location information. The method also includes a step of preparing at least a first set of test parameters for performing a network test, the first set of parameters based on, at least in part, the subscription information and the network location information. The test parameters are then communicated to a test device. Thereafter, the test device performs a first test using the first set of test parameters.

The invention discloses a prediction model training method, a data monitoring method and device, equipment and a medium. The prediction model training method comprises the steps that time-stamping iscarried out on original service data, and the data are divided according to a preset deadline to acquire training service data carrying a time sequence state; the training service data are divided into a training set and a test set according to a preset ratio; a training set is used to train a long short term memory cycle neural network model to acquire an original prediction model; and the testset is used to test the original prediction model to acquire a target prediction model. The prediction model training method has the advantages of high time sequence and high accuracy in predicting.

The invention relates to a system and a method for testing computer software, in particular to a system and a method for on-computer test of mobile application software. The system comprises a server side and a client side, wherein the server side creates and transfers a test-case set to a test-set management unit for running the test set and establishes communication connection with a communication service unit to wait for a client request, and the client side downloads a test packet and executes test. The device supports the on-computer test in actual hardware environment and Java running environment of mobile phones so as to obtain realer running states. The device can automatically generate test cases including test drivers or pile-calling codes and the like, and can improve test coverage rate and error detecting capability.

A partial stroke testing system for online testing of emergency shut-off valve, said system is designed for implementation on an emergency shut-off valve with a main solenoid with manual reset, main solenoid valve, quick exhaust valve and a pneumatic actuator connected to a source of pressurized air supply for opening and closing the said emergency shut-off valve and the said shut-off valve normally movable between a fully open and fully closed position. The system also include control means programmed into the plant emergency shutdown system controller for initiating electrical signal for initiating a test and for enhancing the bleed rate from the said pneumatic actuator in the event of a emergency trip signal. Test means for testing the said emergency shut-off valve without fully closing the emergency shut-off valve in response to signal from the said control means is included in the system. The said test means, controlled by the said control means, include a second solenoid and a second solenoid valve for bleeding off pressurized air to thereby move the said emergency shut-off valve from full opened position to partially closed position. Means for limiting the movement of said emergency shut-off valve to a partially closed position because of the bleeding of pressurized air is included in the system. The system also includes an isolation valve for isolating the said test means for maintenance purpose.