97 results about "Noise sensitivity" patented technology

A touch sensor includes conductive elements of substantially concave shape to enable detection of multiple simultaneous touches in at least two directions, with reduced noise sensitivity and enhanced accuracy. The shapes of the conductive elements may be similar, or may be alternating, complementary shapes that cover substantially all of the sensor area. The conductive elements physically interact with adjacent elements in such a way that the area covered by a touch changes monotonically from overlapping substantially all of one element to overlapping substantially all of an adjacent element as the touch area is moved from one element to the other element along a line between the centers of those adjacent elements. Such monotonic change of touch overlap area may occur simultaneously in two orthogonal directions. Connections from internally positioned conductive elements to a touch controller may be made to pass through other conductive elements.

A sensor interface includes on-chip relaxation oscillator circuit and a PLL that operate cooperatively to generate a highly accurate clock signal on-chip using low-power components. A photodiode generates a current signal based on an optical signal that is representative of a sensor signal. An ADC that operates based on the highly accurate clock signal generates a digital signal based on the current signal generated by the photodiode, and a processor processed the digital signal to estimate sensor data within the sensor signal. Examples of characteristics that may be sensed can include environmental characteristics (e.g., temperature, humidity, barometric pressure, etc.) and/or biomedical characteristics (e.g., body temperature, heart rate, respiratory rate, blood pressure, etc.). In desired, an amplifier processes the photodiode-provided current signal before it is provided to the ADC. Also, one or more CDACs that generate feedback currents may be used to reduce noise sensitivity of the sensor interface.

The invention discloses a low-noise dual-polarization interference optic fiber gyroscope belonging to the technical field of communication. The optic fiber gyroscope comprises a light source, two linear polarized light generating circuits, two circulators, a polarizing beam splitter/combiner, a 3 multiplied by 3 polarization-preserving coupler, two photoelectric detectors, a phase modulator and apolarization-preserving optical fiber ring, wherein the light source is connected with input ends of the two linear polarized light generating circuits through optical fiber; two output ends of the two linear polarized light generating circuits are respectively connected with the optical fiber of two ports at one side of the polarizing beam splitter/combiner through the circulator; the port at theother side of the polarizing beam splitter/combiner is connected with the polarization-preserving optical fiber ring through the 3 multiplied by 3 polarization-preserving coupler; and the polarization-preserving optical fiber ring and the polarization-preserving coupler are connected with the phase modulator through optical fiber. Compared with the prior art, the optic fiber gyroscope realizes the inhibition of noise sensitivity by using the symmetry of the 3 multiplied by 3 polarization-preserving coupler and meanwhile is characterized by simple structure, low cost, small volume, high sensitivity and wide application range.

A magnetic flowmeter for measuring flow of a process fluid includes a coil configured to apply a magnetic field to the process fluid. Electrodes are arranged to sense a fluid voltage signal related to the applied magnetic field and fluid flow. An analog to digital converter provides a digital output comprising a plurality of digital samples related to the sensed fluid voltage signal. Computation circuitry receives the digitized samples and responsively provides an output related to flow.

The invention discloses a method for extracting a rough image edge based on an anisotropism Gaussian directional derivative filter. The method comprises the steps that an image is smoothed by the adoption of the discretization anisotropism Gaussian directional derivative filter, then the gradient magnitude corresponding to the maximum gradient direction of pixels of the smoothed image is calculated, and finally the rough edge is extracted by means of gradient correlation. The anisotropism Gaussian directional derivative filter is utilized for processing the image, the good noise inhibiting effect is achieved, gray level change information in each direction can be well extracted, the problems of edge contour shifting and noise sensitivity brought by Gaussian scale factors are solved, and the novel edge extracting method is provided for a corner detection algorithm based on the edge contour.

The invention belongs to the field of a radar imaging technology, and discloses a compression perception multilayer ISAR imaging method based on BP optimization, for mainly solving the problems of noise sensitivity, high single-layer compression perception optimization time complexity and difficult sparse control parameters by use of conventional ISAR imaging. The specific realization process is as follows: preprocessing original echo data; determining an orientation resolution step factor sigma; initializing an encoding matrix M1 and a decoding matrix W2; performing training by use of a BP algorithm to optimize an encoding process and a decoding process; reconstructing an ISAR image by use of a compression perception theory; performing reverse Fourier transformation on orientation, and inputting obtained echoes into a next layer, and carrying out the training by use of the same principle; and outputting a high-resolution ISAR image at a final layer. According to the invention, the convergence speed is fast, high-resolution imaging can be realized under the conditions of a low signal-to-noise ratio and a small echo quantity, and the method can be applied to classification and identification of ISAR image objects.

The invention discloses a low-power-consumption phase-locked loop frequency synthesizer, comprising a frequency rough tuning loop, a frequency fine tuning loop, an annular oscillator and a tuning varactor; the tuning varactor is provided with a first tuning terminal and a second tuning terminal, the first tuning terminal is connected with an output node of the annular oscillator, the second tuningterminal is connected with the frequency fine tuning loop, and the frequency rough tuning loop is connected with the annular oscillator. The phase-locked loop frequency synthesizer disclosed by the invention has the advantages that the frequency rough tuning loop selects a target sub frequency band with the center working frequency tending to target frequency from numerous sub frequency bands ofthe annular oscillator, thereby realizing frequency rough tuning; after the obtained target sub frequency band is subjected to rough tuning, a variable tuning voltage is generated and output by virtueof the frequency fine tuning loop, and the tuning varactor is driven to be continuously tuned in the target sub frequency band range; and voltage-frequency tuning gain of a voltage-controlled oscillator is reduced, thus noise sensitivity of the phase-locked loop frequency synthesizer on a control voltage is reduced.

The invention provides a watermark embedding method, a watermark detecting method and a device thereof. The watermark embedding method comprises the steps of: a step 1: acquiring an original image with size of M0*N0, a first chaotic sequence with length of M0, and a second chaotic sequence with length of N0; a step 2: acquiring a chaotic watermark sequence with length of M0*N0 according to the first chaotic sequence and the second chaotic sequence; step 3: partitioning the original image for acquiring image subblocks, performing discrete cosine DCT transformation on the image subblocks, and embedding the chaotic watermark sequence with length of M0*N0 into image subblocks with frequency coefficient smaller than a first threshold for obtaining a watermark image; and step 4: weighing the watermark image and the original image through a noise sensitivity coefficient, thereby obtaining a final objective image. The watermark embedding method and the device according to the invention have advantages of: high robustness in a watermark-embedded image, high hiding degree and high decryption difficulty.

The invention discloses a synthetic aperture radar image classification method based on two-dimensional principal component analysis and convolution neural network, which mainly solves the problems oflow classification accuracy, poor robustness, high complexity and high noise sensitivity in the prior art. The realization scheme is as follows: 1. selecting training and test samples, and amplify them; 2. carrying out two-dimensional principal component analysis is carried out for each sample in the training sample set and the test sample set to obtain the training sample set and the test sampleset after dimension reduction; 3. constructing the convolution neural network model and training it with the training sample after dimension reduction; 4. loading the trained model weights into the constructed convolution neural network, and inputting the reduced dimension test samples into the convolution neural network to complete the classification of the original SAR images. The invention reduces model training complexity and sensitivity to noise, improves model robustness and classification accuracy, and can be used for target detection.

The invention discloses a multi-magnetic target position detection method based on database feature matching. The method comprises the following steps: (1) calculating a magnetic field vector and a gradient tensor which are generated by randomly combining all magnetic beacons and then superposing at each measuring point position; (2) constructing a magnetic field vector index desk and a magnetic field gradient tensor index desk; (3) the carrier measures magnetic field vector and gradient tensor data on the flight path in real time; (4) calculating an actual measurement magnetic field vector matrix L2 norm value and an actual measurement magnetic field gradient tensor matrix L2 norm value according to the actual measurement magnetic field vector and the gradient tensor data; and directly performing matching positioning and confirmation optimization in the magnetic field vector index desk and the magnetic field gradient tensor index desk by using a dichotomy to obtain a final magnetic beacon combination situation, and determining the number of magnetic beacons and beacon position information corresponding to the magnetic beacon combination situation. According to the method, the defects that a traditional magnetic beacon positioning inversion technology only needs the known number of magnetic sources, and is high in dependence on measurement point distribution and high in noise sensitivity can be overcome.

The invention discloses a medical ultrasonic image line detection method, which is invented for overcoming the defects of necessary manual operation, bad noiseproof performance, tremendous calculated amount and no real-time detection in the prior art. The method comprises the following steps: (1) the area of interest is chosen; (2) the current frame images are sampled; (3) the marginal point of each sample is found out; and (4) the found marginal point is calculated through random sampling consensus algorithm and a line is chosen to be the detected line according to the calculation result. Through the method, the calculated amount of the medical ultrasonic image line detection processing is reduced greatly, the noiseproof performance is strengthened, noise sensitivity is reduced, and the influence of speckle noise on the detection result is eliminated well and the real-time and automatic detection on the video frequency ultrasonic image can be realized.

The invention discloses a signal transient element sparse representation detection method and device. The signal transient element sparse representation detection method comprises the steps that input signals are subjected to analog/digital conversion to obtain detection signals; the optimal wavelet atomic library and the minimum optimization equation are set up for the detection signals; an optimization iteration method is set up to solve the minimum optimization equation according to the optimal wavelet atomic library, and the sparse representation coefficient of the detection signals on the optimal wavelet atomic library is determined; according to the sparse representation coefficient, the time of occurrence of the transient elements in the detection signals is determined; according to the time of occurrence of transient elements in the detection signals, the cycle of the transient elements in the detection signals which contain periodic transient elements is determined. The method is used for detecting and extracting the transient elements in the signals, the result representation is concise, and noise sensitivity is low.

The invention provides an algorithm system applicable to image classification and image retrieval. The algorithm mainly comprises image modeling and and image matching modules. During image modeling, for improving characteristic description capability, a deep convolution neural network is utilized to extract depth characteristics of an image for the first time, and the hybrid Gauss model is utilized for modeling to represent the image on the basis; during image matching, for two problems of efficiency of a soil moving distance algorithm and noise sensitivity, the accurate, efficient and improved soil moving distance algorithm is proposed, Gauss noise and sparse constraints are additionally considered in an algorithm design process, moreover, through the improved designed soil moving distance algorithm, design defects of a geodesic distance measurement algorithm in the prior art are solved, after a Riemannian geometric structure of Gauss distribution is considered, three types of geodesic distance measurement modes are designed, Gauss distribution distances can be accurately measured.

The invention discloses a sign language recognition method based on a median filter and a Hu moment vector, comprising the following steps: Step 1: Acquire a color image of the sign language to be recognized; Step 2: Perform normalization processing on the acquired color image; Step 3 : Perform grayscale transformation processing on the color image; Step 4: Perform median filter processing on the grayscale image; Step 5: Extract the moment invariant of the processed sign language image to obtain the feature vector of the image to be matched; Step 6: Calculate the image to be matched The Euclidean distance between the image and the feature vector of the sign language standard image; Step 7: Recognize sign language. The invention reduces the sensitivity of the image to be recognized to light and noise through image preprocessing such as median filtering and grayscale processing. The Hu feature vector of the image describes the sign language information as a whole, and overcomes the lack of edge information caused by image segmentation. The combination of filtering and Hu moment vector to recognize sign language can effectively improve the effect of sign language recognition and increase the accuracy of sign language recognition.

The invention discloses a high-precision real-time amplitude measurement device based on self-mixing interference. The device comprises a laser light source, a beam splitter, a photoelectric detector,a reflector, a signal processing unit, a data processing display unit and a measurement subject, wherein the laser light source, the beam splitter, the photoelectric detector, the measurement subjectand the reflector are sequentially connected via an optical path; and the input end of the signal processing unit is connected with the photoelectric detector, and the output end of the same is connected with the data processing unit. The invention also discloses a high-precision real-time amplitude measurement method based on self-mixing interference. According to the device and method providedby the invention, a reference arm of a traditional laser interferometer is not needed, a single-arm optical path is easy to collimate, and the noise sensitivity of the system to the outside is reduced. A self-mixing interference principle is used, so that no specific requirement on coherence of the laser light source is needed, and the freedom of choice of the laser is large. Meanwhile, a gain parameter of the device is controllable, and a measurable minimum amplitude value can reach nanoscale; a signal spectrum feature value is acquired in a frequency domain, stripes do not need to be counted, and real-time wide-range measurement is easy to implement.

The invention provides a phase noise measurement device and measurement method based on laser frequency discrimination and cross correlation processing. A laser phase lock cross correlation processingphase noise measurement device comprises two identical independent measurement links, each of which comprises a laser source module, a delay line module, a photoelectric mixing module and a photoelectric conversion module, and in addition, a to-be-tested signal module and a data collection and cross correlation algorithm processing module are shared by the both links. By adopting the phase noisemeasurement device and measurement method provided by the invention, the bottom noise of the measurement links can be reduced, the noise sensitivity can be improved, and the link measurement accuracycan be improved.

The invention discloses a landslide identification method based on combination of a symmetric deep network and multi-scale pooling, and the method comprises the steps: constructing a training set image pair and a test set image pair through employing a large-size high-resolution dual-temporal remote sensing image; giving preprocessing algorithm parameters and network operation parameters; generating a difference image corresponding to the training set by using the training set image pair, and generating a difference image corresponding to the test set by using the test set image pair; carryingout multivariable morphological reconstruction on the difference image to remove noise and non-landslide areas; preprocessed images are input into the deep network MP-designed by the invention; in the SDNN, carrying out network model training until the network converges; input of pre-processed test images to a network MP- In the SDNN, a landslide identification result is output, the problems thatan existing landslide identification method depends on a traditional feature descriptor, threshold selection is carried out, and a landslide area is difficult to accurately identify due to noise sensitivity can be solved, the identification precision of a real landslide area is improved, and the method has the advantages of being high in identification precision, rapid and effective.

The invention provides a target positioning method and a system of a cavity mirror image. The storage medium comprises: S100, acquiring a video captured by a cavity mirror lens; S200, according to thetime and color of each frame image in the video, selecting a key frame image from the video; S300, inputting each key frame image into a preset trained YOLO target detection model to obtain a plurality of images with target positioning frames and target class identifiers; S400, synthesizing the plurality of images with the target positioning frame and the target category identifier to obtain thetarget positioning video; Wherein the training process of the YOLO target detection model at least comprises the following steps of: The K-centers clustering method is used for clustering the training sample dataset. The K-Centers clustering can effectively improve the noise sensitivity, so as to improve the picture quality of the target location video.