49 results about "Linear coding" patented technology

The invention provides a building identification method and system based on three-dimensional point cloud, and particularly relates to the technical field of computer vision and three-dimensional target identification, and the method comprises the steps: carrying out the undersampling, oversampling and data enhancement preprocessing of inputted building roof point cloud data, and carrying out the T-net network space alignment to output the aligned data. Then, an NEWPCT model is obtained by improving a PCT model, aligned data are input into the NEWPCT model, a point cloud feature matrix is generated through a linear coding layer, query, key and value matrixes are obtained through linear transformation, multi-head attention output is calculated by introducing an offset matrix, intermediate correlation features are obtained after splicing and fusion, and a global feature vector is generated through global maximum pooling; and finally, inputting the vector into a classifier, and outputting a building roof type, and the method solves the technical problems of geometric transformation sensitivity and local feature loss while improving the precision and robustness of three-dimensional point cloud building identification, thereby effectively improving the identification precision and robustness.

The invention provides a multi-keyword private information retrieval method and system based on inverted indexes, and belongs to the field of information security and privacy computing. The method comprises the following steps that: a server splits an original database into a reverse index part of a keyword-index set and a key value pair part of an index-data value; mapping the index set into an integer by utilizing Godel coding; processing the data by adopting probability batch coding and binary random linear coding and publishing a hash function; the client maps a multi-keyword query into a bucket by using a hash function to generate a query vector, and the query vector is sent to the server after being subjected to fully homomorphic encryption; the server performs homomorphic calculation on a matching result in a ciphertext state and returns the result; and the client obtains a matching index set after decryption verification, and then initiates a second round of query to obtain final data. According to the method, symmetric privacy protection of multi-keyword non-primary key query in a single-server environment is realized, leakage of query content and database information is effectively prevented, and private information retrieval security is improved.

The invention discloses a wide-range absolute type linear displacement sensor based on photoelectric linear coding and a measuring method. The upper surface of a sizing base body is provided with a row of excitation electrodes and covered with a light absorption insulating layer covering all the excitation electrodes, and the light absorption insulating layer is provided with two columns of reflective patterns; and the two columns of reflective patterns are different and coprime in quantity. The area corresponding to the unit length of each reflective pattern changes in a function relation mode in the length direction. The lower surface of the movable ruler base body is provided with an induction electrode, a light-emitting module and two rows of photoelectric sensors, the two rows of photoelectric sensors directly face the two rows of reflective patterns, and the number of each row of photoelectric sensors is two. The light emitting module sends light beams to the two columns of reflective patterns, and the photoelectric sensor collects light intensity reflected by the reflective patterns and outputs obtained photoelectric signals to the signal processing circuit. The photoelectric sensing principle is adopted for rough measurement, the transverse size of the sensor can be greatly reduced, fine measurement and rough measurement do not interfere with each other, the measurement precision is high, and implementation is easy.

The invention discloses a statistical linear coding active interference elimination method for f-OFDM (Frequency-Orthogonal Frequency Division Multiplexing). In order to solve the problems that in the prior art, CCs optimization solution needs to be carried out on each sending data symbol of a target sub-band once, the calculation complexity and the time delay are obviously higher than those of a linear calculation process, and an existing AIC algorithm is not suitable for scenes with limited calculation capacity and cruising capacity and sensitive to time delay. According to the method, active interference elimination is divided into an optimization solution part and a linear calculation part; by redesigning an AIC algorithm structure, an optimization solution process is moved out of a signal sending processing flow, and only one time of optimization solution of a linear coding matrix is needed in total. Each sending data symbol only needs to be multiplied by the linear coding matrix to obtain the CCs. Matrix multiplication belongs to linear calculation, and compared with nonlinear calculation of optimization solution, calculation complexity and time delay are greatly reduced.

The three-dimensional point cloud-based building recognition method and system provided in this application specifically relate to the technical fields of computer vision and three-dimensional target recognition. The method first performs undersampling, oversampling and data enhancement preprocessing on the input building roof point cloud data, and then outputs the aligned data through T-net network spatial alignment. Then, the NEW_PCT model is obtained by improving the PCT model, and the aligned data is input into the NEW_PCT model. The point cloud feature matrix is ​​generated through the linear coding layer, and the query, key, and value matrices are obtained through linear transformation. The bias matrix is ​​introduced to calculate the multi-head attention output, and the intermediate correlation features are obtained after splicing and fusion. The global feature vector is generated through global maximum pooling. Finally, the vector is input into the classifier to output the building roof type. This method solves the technical problems of how to overcome the geometric transformation sensitivity and local feature loss while improving the accuracy and robustness of three-dimensional point cloud building recognition, thereby effectively improving the recognition accuracy and robustness.

The present invention relates to a signal design technique resistant to phase distortion and may provide a method of a transmitter, the aspect comprising the steps of: modulating a sequence of source bits to be transmitted to a receiver to generate a first block of symbols; inserting at least one pilot into the first symbol block to generate a second symbol block; performing cumulative linear coding on the second symbol block to generate a third symbol block; performing real phase conversion on the third symbol block to generate a first signal converted to a phase value; adding a cyclic prefix (CP) to the first signal to generate a second signal; and wirelessly transmitting the second signal to the receiver.

This application provides a natural language processing method, apparatus, device, and computer-readable storage medium; relating to the field of artificial intelligence technology, the method includes: obtaining a hyperbolic word vector sequence based on a preset word vector table; performing attention encoding on the current hyperbolic word vector to obtain attention encoding features corresponding to the current hyperbolic word vector; performing a linear transformation on the attention encoding features through a preset Lorentz transformation function to obtain linear encoding features corresponding to the attention encoding features; the preset Lorentz transformation function is obtained by training a linear transformation matrix model that satisfies preset constraints; the preset constraints are used to ensure that the output feature vector obtained by the linear transformation matrix model after processing the input feature vector in the hyperbolic space is located in the hyperbolic space; and obtaining the target processing result corresponding to the sentence to be processed based on the linear encoding features. This application can improve the stability and efficiency of natural language processing using hyperbolic neural networks.

The application discloses an image semantic segmentation method applied to an electronic device comprising an image semantic segmentation model, wherein the image semantic segmentation model comprises a down-sampling module and a sparse multi-layer perception module; the image semantic segmentation method comprises the following steps: acquiring an original image, extracting a down-sampled feature map of the original image by using the down-sampling module; performing linear coding on the down-sampled feature map to obtain a linear coding feature map; performing feature extraction on the linear coding feature map by using the sparse multi-layer perception module to obtain an output feature; training the image semantic segmentation model by using the output feature, and performing an image semantic segmentation task by using the trained image semantic segmentation model; and the application can balance the speed and accuracy of image semantic segmentation, so that the image semantic segmentation operation has a faster speed and higher accuracy. The application also discloses an image semantic segmentation device, an electronic device and a storage medium, which have the above beneficial effects.

The invention relates to an uplink passive multiple access method and system based on ODMA, and the method comprises the following steps: S1, at a user side, for each active user, coding data sent by the user according to the characteristics of uplink URA, the transmission characteristics of ODMA and the linear coding characteristics of polarization codes; and S2, decoding the received information at the base station by using a multi-user detection algorithm, an ODMA transmission mode codebook and a polarization code decoding criterion so as to obtain information source information. Simulation results show that compared with other uplink URA schemes based on ODMA, the system has better decoding performance and higher energy efficiency.

The embodiment of the invention relates to the field of artificial intelligence, and provides an epilepsy electroencephalogram signal detection method and device based on frequency attention and a pulse recurrent neural network and a storage medium, and the method comprises the steps: obtaining an epilepsy electroencephalogram signal to be detected; performing datum line following coding on the epilepsy electroencephalogram signal based on a grid search algorithm to obtain a coded epilepsy electroencephalogram signal; constructing a bidirectional pulse recurrent neural network model based on a preset adaptive pulse neuron model; the bidirectional pulse recurrent neural network model is used as a reference model, the reference model is adjusted based on a frequency attention mechanism and a pulse full-connection linear coding mechanism, and an improved model is constructed; and inputting the epilepsy electroencephalogram signal into the trained improved model to output a detection result associated with the epilepsy electroencephalogram signal. By adopting the method, the model calculation performance can be improved on the basis of reducing the energy consumption, and the detection effect on the epilepsy electroencephalogram signals is improved.

The present invention provides an SRAM PUF key extraction method based on correlation judgment, comprising: an initial registration phase: powering on the SRAM, configuring and initializing variables; calculating the exclusive OR of a data block and its cyclic shifts of different points to obtain the number of cyclic shift points and the number of negatively correlated bits when the maximum negative correlation occurs; selecting N valid data blocks whose maximum negatively correlated bit numbers are greater than a set threshold; using N bits of hidden random numbers to control whether the valid data blocks are cyclically shifted according to the number of their maximum negatively correlated cyclic shift points; linearly encoding a randomly generated PUF output random number; performing an exclusive OR operation on the encoding result and the hidden random number to obtain auxiliary data; a reconstruction phase: powering on the SRAM, initializing variables, and obtaining a valid data block based on the auxiliary data selection; restoring an approximate random number of the hidden random number using the valid data block and the auxiliary data; performing an exclusive OR operation on the approximate random number and the auxiliary data to obtain a codeword before error correction; and performing linear encoding on the codeword to correct errors and restore the PUF output random number.

The embodiment of the invention relates to the technical field of three-dimensional graphic rendering, and discloses a mixed rendering method based on voxel coding. According to the method, for a three-dimensional scene in which multiple rendering strategies coexist, voxel-level division is carried out through the scene, space coding of nodes is calculated based on finite element grids, and ordered arrangement and linear coding of the nodes in the three-dimensional space are achieved. And by using the codes, constructing a code-driven scene block scheduling mechanism, and binding corresponding rendering strategies (such as neural rendering, point cloud rendering and traditional graphic rendering) to different coding areas. In the rendering stage, a proper rendering mode can be selected according to observation positions, equipment capabilities or application requirements, and unified management and mixed scheduling of multiple rendering modes are realized by means of a unified spatial coding index. The method is suitable for meta-universe construction, digital twinborn visualization and scene organization in a complex three-dimensional graphic system, and the scheduling efficiency and space consistency in a large-scale environment are improved.

Disclosed in the present application is a trend analysis and assessment method for industrial time-series data. The method comprises: collecting industrial data over a period of time, and verifying and pre-processing the industrial data, so as to obtain an industrial time-series data set; performing feature extraction on the industrial time-series data set, so as to acquire feature data of the industrial time-series data set; forming a plurality of data intervals in view of the number of intervals preset by a target user; assigning approximate values to industrial time-series data, wherein each approximate value is the upper limit value or lower limit value of a data interval within which the industrial time-series data falls, so as to form an approximate data set; and in view of the magnitude relationship between consecutive pieces of data in the approximate data set, acquiring linear encoded data sets, and determining a data trend on the basis of adjacent data in adjacent linear encoded data sets. When using the method, a target user only needs to preset the number of intervals, and the precision of trend analysis and assessment can be controlled by means of the number of intervals, thereby reducing the impact of data noise on a trend analysis and assessment result, and improving the stability of the method with regard to trend analysis and assessment.

This application discloses a verifiable computation method based on polynomial commitments, relating to the field of information verification technology. The method includes linearly encoding the coefficient vector of a target polynomial and generating a corresponding binding commitment based on hierarchical hash commitments, sending the binding commitment to a verification end; in response to a random challenge initiated by the verification end, generating an intermediate vector based on the random challenge and the coefficient vector; in response to multiple random numbers sent by the verification end, obtaining a first verification value if the first proof from the proof end is confirmed to be successful through the binding commitment; obtaining a second verification value if the second proof from the proof end is confirmed to be successful through the binding commitment; and confirming the correctness of the evaluation result of the target polynomial at a specified point by evaluating the basic polynomial commitment if the first and second verification values ​​are equal. This application effectively reduces the overall complexity and communication overhead of information verification.

The invention discloses an information physical control system spoofing attack detection method based on linear coding, and the method comprises the steps: building an information physical control system model for deploying multiple types of sensors, and enabling the system to carry out the data collection through an intelligent sensor; establishing an attack detection enhancement scheme of linear coding, and introducing a spoofing attack model; establishing an optimization problem for balancing the detection performance and the estimation performance; solving an optimal linear coding operator based on a convex optimization theory; and a detection statistic is selected, and a detection threshold and an alarm strategy are set, so that the spoofing attack is detected. According to the spoofing attack detection scheme based on optimization of the cyber-physical control system, the correlation between data is amplified through linear coding, so that high-performance detection of the spoofing attack is realized. On the basis of the optimized coding design, the detection performance is ensured, meanwhile, the influence on the system estimation performance is reduced, and the problems that an existing spoofing attack detection method has certain limitation and is poor in applicability are effectively solved.

The invention relates to the technical field of data transmission and the Internet of Things, in particular to a laboratory data transmission method, device and system based on the Internet of Things and a storage medium. According to the laboratory data transmission method based on the Internet of Things, firstly, a data interface is set, and laboratory data is uploaded to an Internet of Things cloud platform through the data interface; the data interface can be a computer interface isolated from a public network, namely, the data interface does not have a data editing function, so that the data is prevented from being tampered in the uploading process. According to the method, to-be-uploaded data of a laboratory is divided into fixed-length data and non-fixed-length data, and the data length and content of the non-fixed-length data are determined by experiment content; linear coding is carried out on the fixed-length data and the non-fixed-length data; the linear coding format of the non-fixed-length data is {the number of parameters and the content of each parameter}. According to the invention, the defect that laboratory data is limited to a local area network and cannot be remotely accessed is overcome, and a security interface and a data structure are combined, so that multiple pieces of laboratory data can be accessed through the Internet of Things under the condition of ensuring data security.

The invention discloses a data synchronization method and system based on medical consumables, and relates to the technical field of data synchronization, and the method comprises the steps: collecting monitoring target list data of medical equipment, carrying out the arrangement and structuring of the sampled data, forming an independent data group, extracting the collected data of different sensor data items, and forming an organization matrix; and carrying out linear coding on the organization matrix by using a random linear network coding (RLNC) method, generating a redundant coding packet data matrix by using matrix multiplication, and carrying out homomorphic encryption on coding packet data. According to the method, data protection and calculation operation are seamlessly combined through homomorphic encryption, encryption monitoring data can be directly processed, and encryption potential features are generated through combination of time random features and mapping hidden features, so that the hidden space features can express time dependence features and global features of random coding at the same time; and the multi-dimensional characteristic fusion capability in a medical consumable monitoring scene is enhanced.

The invention provides a concrete crack leakage damage assessment method and system, and relates to the technical field of damage assessment, and the method comprises the steps: carrying out the detection scanning of the whole region of concrete, and determining a detection spectrum; deploying a classification encoder at a front-end interface, performing aiming frame reconstruction to determine a crack aiming frame set, introducing second-order classification based on a crack mode-leakage mode, and determining a detection data packet; introducing an environmental erosion-fatigue load to carry out secondary linear coding, deploying an evaluation encoder, returning a detection data packet, triggering the evaluation encoder to carry out an evolution decision under code element collision, and determining a damage code; and performing decoding processing as a damage evaluation result and performing terminal display early warning. The technical problem that in the prior art, concrete cracks are generated due to multi-factor coupling, the crack damage assessment process is tedious, and the damage assessment efficiency is poor is solved, damage encoding is carried out through global scanning in combination with the assessment encoder, and the accuracy and efficiency of damage assessment are improved.

The invention discloses a bearing device residual life prediction model and method based on a TCN and a decay diagram, and belongs to the technical field of industrial equipment state monitoring and fault prediction diagnosis. Comprising an input processing unit, a local time period coding module, a TCN module based on a difference graph, a decay graph module and a prediction output module which are connected in sequence. The input processing unit receives information collected by the sensor and splices the information to obtain input data; the local time period coding module is used for performing time period segmentation and linear coding and outputting a local time period set coding result; outputting decay characteristics in local time periods through a TCN module based on a difference graph; outputting an adjacent matrix of a final time period through a decay diagram module; and a non-negative integer bearing residual life is output through the prediction output module. By adopting the bearing device residual life prediction model and method based on the TCN and the decay diagram, an integrated dynamic modeling framework from signal input to life output is formed, and meanwhile, working condition changes are adapted to strengthen early decay recognition.

The embodiment of the present application provides a touch chip and an electronic device, the touch chip comprises at least one analog front-end processing unit, each analog front-end processing unit comprises: an encoding module, configured to perform linear encoding on detection signals from a plurality of sensing electrodes based on a preset encoding matrix, to generate a plurality of encoded detection signals of the plurality of sensing electrodes; a plurality of analog front-end circuits, respectively configured to process the plurality of encoded detection signals of the plurality of sensing electrodes, to generate a plurality of encoded digital signals of the plurality of sensing electrodes; and a decoding module, configured to perform linear decoding on the encoded digital signals of the plurality of sensing electrodes based on a preset decoding matrix, to generate a digital detection signal of each sensing electrode in the plurality of sensing electrodes; wherein the product of the preset encoding matrix and the preset decoding matrix is a unit matrix, and the maximum value of diagonal elements in the covariance matrix of the preset decoding matrix is less than 1.

A method for performing encoding format conversion with integer arithmetic operations and a system performing the method are provided. The system includes a target device that adopts a power-management bus (PMBus), and operates an embedded system that is only capable of integer arithmetic operation without any floating-point unit. In the method, a floating-point arithmetic encoding format value is inputted. After a logarithm value of the floating-point value is obtained by extracting an exponent value from a binary representation of the floating-point value, a new exponent value can be obtained through a control flow. A new mantissa value is then calculated according to the floating-point value, the exponent value and the new exponent value. A new value that is a linear encoding format value converted from the floating-point arithmetic encoding format value is generated by combining the new exponent value and the new mantissa value.

The application discloses a method, system, terminal and storage medium for predicting a metabolic pathway high-yield gene mutation combination, and relates to the technical field of bioinformatics. The method comprises the following steps: determining a unique non-zero integer identifier of each state type of each gene in a metabolic pathway through a preset non-zero coding mechanism; determining a mutation combination to be predicted according to each gene and state type in the metabolic pathway; converting the mutation combination to be predicted into a non-zero integer representation vector based on the unique non-zero integer identifier, and performing linear coding and position coding to generate a combination feature matrix; inputting the combination feature matrix into a trained yield prediction model to obtain a predicted yield; or inputting the combination feature matrices of a plurality of mutation combinations to be predicted into a plurality of trained yield prediction models to obtain a predicted yield ranking result output by each yield prediction model, and determining a plurality of candidate verified mutation combinations from the mutation combinations to be predicted according to the predicted yield ranking result.

The invention discloses a method and a device used in a node of Internet of Things communication in wireless communication. The node receives the first information block; a first PRDCH is sent, and the first information block configures the first PRDCH; the first PRDCH adopts an OOK (On-Off Keying); the first PRDCH carries a data information block and a physical layer control information block, the first bit sequence is a bit sequence obtained by performing linear coding and bit repetition on information bits included in the physical layer control information block, and the second bit sequence is a bit sequence obtained by performing linear coding on information bits included in the data information block; the target number of repetition times is the number of repetition times of bits subjected to bit repetition, and the target number of repetition times is equal to the ratio of the code rate of the data information block transmitted on the first PRDCH to the code rate of the physical layer control information block transmitted on the first PRDCH; the first bit sequence and the second bit sequence are commonly used for generating the first PRDCH. The resource utilization rate is increased.

One or more methods, devices, and / or systems can provide techniques for linear encoding channel state information (CSI) compression for multi-transmission / reception point (mTRP). This can provide improvements to wireless communication systems, for example, by improving resilience against transmission errors and mismatches, joint or separate compression of channel matrices from different TRPs, and / or fallback to legacy CSI reporting operations in mTRP CSI compression.

This invention discloses a trilinear coding system and a method for learning video-language representations. The method comprises the following steps: S1. Using educational video and television datasets as data samples, the data samples are divided into video segments; S2. Using a dense subtitle generation model to extract dense subtitles for the video segments; extracting the original ASR transcripts; and using the dense subtitles for each segment as supplementary text to the original ASR transcripts; S3. Using the video segments, original ASR transcripts, and dense subtitles as input to the trilinear coding system to obtain a video-language representation; and S4. Using the video-language representation to perform multimodal information exchange between video and text. The method focuses on intermodal mutual information; using dense subtitles as supplementary text, the dense subtitles are automatically extracted, achieving video-text matching.

The application relates to a nonlinear encoding imaging method and system, relates to the field of nuclear magnetic resonance, and solves the problem that an image cannot be directly obtained through Fourier transform for image reconstruction under nonlinear encoding. The method comprises the following steps: acquiring a uniform ring-distributed sub-encoding field of coil units around a Z axis, so that the current amplitude of all the coil units is distributed on a sine curve in a period, and a static encoding field is generated through superposition; performing sine modulation on the current amplitude of the coil units, so that the current amplitude of all the coil units changes in the sine period, a dynamic encoding field is generated; a nuclear magnetic resonance signal generated by the dynamic encoding field and excitation is received through a radio frequency receiving array coil group, additional spatial encoding information provided by the sensitivity distribution of the radio frequency receiving array coil group is analyzed, a target image is acquired, and reconstruction is performed. The application has the following effects: when the encoding field is a nonlinear encoding field, the rapid imaging of a nuclear magnetic resonance image can be effectively ensured.

The invention discloses a customs commodity classification method and system based on multiple features of a table, and relates to the technical field of customs commodity classification. Comprising the steps that tabular customs commodity declaration data are acquired, and the data comprise numerical features, category features and text features; piecewise linear coding PLE is adopted for numerical value features, integer mapping is adopted for category features to be combined with an embedded layer, and RoBERTa pre-training model is adopted for text features to be combined with a linear transformation layer to be processed; stacking the three types of feature embedding vectors into an embedding matrix, and inputting the embedding matrix into a model based on a Transform architecture to complete multi-feature deep fusion; generating an adversarial sample, constructing a positive and negative sample pair, and combining cross entropy loss and contrast loss to train a model; and inputting to-be-classified tabular customs commodity declaration data into the trained model, and outputting a coordination system code corresponding to the commodity. According to the invention, the time cost and labor cost of manual auditing can be reduced, the error rate of manual classification can be effectively reduced, and the fairness and rationality of tax collection are ensured.

The invention discloses a verifiable calculation method based on a polynomial commitment, and relates to the technical field of information verification, and the method comprises the steps: carrying out the linear coding of a coefficient vector of a target polynomial, generating a corresponding binding commitment based on a hierarchical hash commitment, and transmitting the binding commitment to a verification end; in response to a random challenge initiated by the verification end, generating an intermediate vector based on the random challenge and the coefficient vector; in response to the plurality of random numbers sent by the verification end, obtaining a first verification value under the condition that the first certification of the certification end is determined to pass through the binding commitment; obtaining a second verification value under the condition that the second certification of the certification end is determined to pass through the binding commitment; and under the condition that the first verification value is equal to the second verification value, determining the correctness of the evaluation result of the target polynomial on the specified point through evaluation verification committed by the basic polynomial. The method and the device have the effect of reducing the overall complexity of information verification and the communication overhead.

The application discloses a bearing device residual life prediction model and method based on TCN and decay graph, and belongs to the technical field of industrial equipment state monitoring and fault prediction and diagnosis. The model comprises sequentially connected input processing units, local time period coding modules, TCN modules based on differential graphs, decay graph modules and prediction output modules. The input processing units receive information collected by sensors and splice input data. The local time period coding modules are used for time period division and linear coding and output local time period set coding results. Decay characteristics between local time periods are output through the TCN modules based on differential graphs. Adjacency matrices of final time periods are output through the decay graph modules. Non-negative integer bearing residual life is output through the prediction output modules. The bearing device residual life prediction model and method based on TCN and decay graph are used to form an integrated dynamic modeling framework from signal input to life output, adapt to working condition changes and strengthen early decay identification.