31 results about "Gaussian sampling" patented technology

The invention discloses a machine learning driven vehicle frame lightweight design method, which comprises the following steps: (1) performing vehicle frame parametric modeling in CATIA, constructing a vehicle frame modal analysis model and a stiffness analysis model in ABAQUS, and deducing a mathematical model of vehicle frame lightweight design; (2) performing Latin hypercube sampling and discrete Gaussian sampling on continuous and discrete parameters to generate a frame population, and establishing a database; (3) designing a variable type driven hybrid variation mechanism to obtain a candidate frame population; (4) constructing a radial basis function machine learning model, and deducing a comprehensive minimum statistical lower limit function to screen an optimal candidate frame; and (5) performing parametric modeling and simulation analysis on the optimal candidate frame, updating the frame population and the database, returning to the step (3) until the optimal frame meets the design requirement, and outputting the optimal frame. According to the method, adaptive variation strategies and screening functions are designed according to the vehicle frame parameter characteristics, and the vehicle frame lightweight design effect is good.

The application relates to a dynamic self-adaptive unmanned aerial vehicle path planning method based on RRT*, which solves the technical problems that the existing unmanned aerial vehicle path planning method based on the RRT* algorithm has slow convergence speed, a high degree of path tortuosity, poor smoothness and to-be-improved efficiency; the method generates sampling points through adaptive Gaussian sampling according to adaptive target bias probability, uses an intelligent variable step length mechanism to use different step lengths in different exploration periods, and further obtains an optimized path by introducing Pareto evaluation of the degree of path tortuosity. The path planned by the application has fast convergence speed, low degree of path tortuosity and good smoothness.

The invention relates to a lattice-based public key prefix broadcast searchable encryption method and device, equipment and a medium, and the method comprises the steps: a sender integrates public keys of all target receivers to obtain an aggregated public key, carries out the hash processing of each to-be-encrypted keyword field in sequence, so as to construct a corresponding keyword vector, completes the extension, and carries out the encryption of the keyword vector; generating a broadcast ciphertext and uploading the broadcast ciphertext to a cloud server; the receiver calls a lattice base sampling algorithm to generate a private key lattice base corresponding to the aggregated public key based on the private key of the receiver, the aggregated public key and Gaussian sampling parameters in system public parameters; the receiver generates a search trap door according to the to-be-searched keyword information with the prefix search, and sends the search trap door to the cloud server; and the cloud server calculates a verification value according to the broadcast ciphertext and the search trap door, and returns a corresponding search result to the target receiver according to the verification value. According to the invention, sharp increase of storage overhead in a large-scale user scene can be avoided.

The invention relates to the technical field of probability calculation and mixed signal coprocessors, in particular to a self-calibration self-adaptive sampling system and method for Gaussian sampling and matrix inversion, and the technical scheme is characterized by comprising the steps of 1, task definition and input modeling, 2, matrix compilation mapping and discrete quantization, and 3, matrix compilation mapping and discrete quantization. The method comprises the following steps of 1, carrying out controllable noise injection and effective temperature setting, 4, carrying out on-line self-calibration and error compensation closed loop, 5, carrying out adaptive equalization / sampling control and stop criterion setting, and 6, outputting a result and carrying out system deployment. According to the method, the advantages of high throughput and low energy consumption of thermodynamic calculation are kept, and meanwhile, through online self-calibration and self-adaptive sampling control, controllable output distribution precision, verifiable errors and deployable and extensible Gaussian sampling and matrix inversion operators are achieved.

This application belongs to the field of signal denoising technology and discloses a training method, apparatus, device, and storage medium for a denoising neural network. The method includes the following steps: acquiring a noisy signal and inputting it into an initial neural network to obtain a sampling sequence; performing Gaussian sampling on the sampling sequence to obtain a noise signal; obtaining the original signal based on the noisy signal and the noise signal; calculating the error of the initial neural network based on the original signal; determining whether the error is less than a preset error threshold; if not, returning to the training step; if yes, ending the training and using the initial neural network as the denoising neural network. This application can obtain a denoising neural network without acquiring a clean signal, realizing unsupervised learning and having extremely high applicability in engineering applications.

The application discloses an Internet of Things identity authentication method based on a lightweight Falcon signature, and the Internet of Things comprises a device, an edge gateway and a cloud platform, and comprises the following steps: initializing the device to generate a Falcon-512 key pair, performing sparse compression on the Falcon-512 key pair, and performing fragmented encryption storage; the Falcon-512 key pair comprises a private key sk and a public key pk; sending a registration request CSR to a cloud certificate authority CA through the device to obtain a device certificate Cert; collecting communication data Data through the device, calculating a hash value H(Data||Timestamp) of the communication data Data, wherein Timestamp is a time stamp; inputting the hash value H into an iterative FFT, mapping and storing the hash value H through a plurality of butterfly operations in the iterative FFT to obtain an intermediate result C in the FFT; wherein, based on the independence of the butterfly operation in the iterative FFT, a plurality of butterfly operations are processed in parallel through the SIMD instruction of the ARM Cortex-M processor; and a random integer z conforming to a discrete Gaussian distribution is generated through Gaussian sampling.

The disclosed embodiments relate to a method and apparatus for delay prediction, an electronic device, and a storage medium, and relate to the field of computer technology. The delay prediction method includes: performing mixed Gaussian sampling on a target model corresponding to a target operation to obtain multiple subnet network structures of the target model; performing a convolution operation on the multiple subnet network structures to perform delay prediction, determining a predicted result of the target model's delay information, and performing the target operation on a processing object based on the predicted result. The technical solutions in the disclosed embodiments can improve the prediction results of the target model's delay information and achieve universality.

The application discloses a method for predicting the residual life of wind power rolling bearings by using incremental mapping and recursive estimation, and relates to renewable energy equipment state monitoring. First, a multi-band envelope demodulation two-dimensional spectrum diagram is obtained by transforming a vibration acceleration signal, a spectrum diagram variation is calculated and normalized, a health index and a normalized degradation increment are obtained. Second, a channel attention module is used to extract features from two dimensions of the two-dimensional spectrum diagram, and a two-dimensional convolutional neural network is designed to learn the nonlinear mapping of the spectrum diagram and the degradation increment. Third, a recursive deep learning model is established, model weight Gaussian sampling is combined, the posterior probability of future health indexes is calculated, and rolling prediction is realized. Finally, the residual life is predicted according to a failure threshold. The application has the advantages that a health index based on a multi-band envelope demodulation two-dimensional spectrum diagram variation increment is constructed, a two-dimensional convolutional neural network containing a channel attention module is designed, recursive deep learning and weight Gaussian sampling are fused, and the prediction accuracy of the residual life of wind power bearings is improved.

The invention relates to a dynamic self-adaptive unmanned aerial vehicle path planning method based on RRT *. The technical problems that an existing unmanned aerial vehicle path planning method based on an RRT * algorithm is low in convergence speed, a generated path is high in tortuosity degree and poor in smoothness, and efficiency needs to be improved are solved. Sampling points are generated through self-adaptive Gaussian sampling according to self-adaptive target offset probability selection, different step lengths are used in different exploration periods by adopting an intelligent variable step size mechanism, and Pareto evaluation on path tortuosity degree is introduced to further obtain an optimal path. The path planned by the method is high in convergence speed, and the generated path is low in tortuosity and good in smoothness.

The embodiment of the invention discloses an information prediction method, a training method of an information prediction model and a related device. The method comprises the following steps: encoding input information to obtain an encoding memory; hidden variable probability distribution is extracted for the coded memory, Gaussian sampling processing is carried out on the coded memory according to the hidden variable probability distribution, continuous hidden variables are obtained, and the continuous hidden variables represent hidden semantics corresponding to the input information; a plurality of discrete hidden variables are obtained, the continuous hidden variables and the discrete hidden variables are mixed, a plurality of mixed hidden variables are obtained, and the discrete hidden variables represent hidden semantics corresponding to the prediction results; and performing decoding processing according to the plurality of mixed hidden variables and the coding memory to obtain a plurality of prediction results. The embodiment of the invention can be suitable for application scenes meeting low-resource and time-limited conditions at the same time, and can ensure the diversity and accuracy of prediction results.

The application discloses a millimeter wave movable array beam training method based on quantum correlation simulated annealing, and relates to the technical field of wireless communication.The application proposes a quantum correlation simulated annealing (QCSA) algorithm aiming at the high-dimensional, non-convex and strongly coupled beam-array position joint optimization problem caused by the movable array, and adopts an alternating optimization framework to decompose the joint optimization problem into iterative solving of the transmitting and receiving subspaces; in each subspace optimization, the physical correlation between parameters is learned through Gaussian sampling based on an adaptive covariance matrix, the global search capability is enhanced by using an acceptance criterion containing a thermodynamic term and a quantum tunneling term to escape from a local optimum, and a periodic oscillation thermal scheduling strategy is adopted to dynamically balance exploration and utilization, so that the application has the characteristics of low overhead, fast convergence and high robustness, and is suitable for 6G millimeter wave and terahertz movable array MIMO systems.

The invention discloses a multi-working-condition tool wear prediction method based on reciprocal Gaussian sampling element learning, and relates to the technical field of numerical control machining process monitoring, and the method comprises the steps: obtaining monitoring signals in a tool cutting process under different historical working conditions; feature screening is carried out for any historical working condition; initializing parameters of a task learner, a meta learner and a reciprocal Gaussian sampling mechanism; dividing the screened features into a support set and a query set based on an initialized reciprocal Gaussian sampling mechanism; obtaining the total support loss of the historical working condition on the support set; parameters of a task learner are updated based on the total support loss of the historical working conditions; obtaining the total query loss of the historical working condition on the query set; updating parameters of the meta-learner and parameters of a reciprocal Gaussian sampling mechanism according to the sum of the total query losses of all the historical working conditions; and the tool wear value is predicted based on the monitoring signals collected in real time under the current working condition and the final parameters of the meta-learner. According to the invention, the tool wear can be accurately measured.

The invention discloses a mapping method between an incident ray coordinate and an emergent ray coordinate when a collimated elliptical Gaussian beam is converted into a collimated elliptical flat-topped beam and the size of an elliptical light spot is changed, and belongs to the field of laser shaping. The method for calculating the mapping relation between the output light coordinates and the input light coordinates comprises the following steps of: 1, utilizing the law of conservation of energy to ensure that the energy surrounded in an input surface ellipse is equal to the energy surrounded in an output surface ellipse; solving a mapping relation between the ellipse semi-major axis xiout and the ellipse semi-minor axis eta out of the emergent light beam and the ellipse semi-major axis xiin and the ellipse semi-minor axis eta in of the incident light beam; 2, solving a mathematical expression of a mapping relation between output light coordinates and input light coordinates: determining an included angle theta between a connecting line between a point (xin, yiin) and the center of an ellipse and an X axis according to the point (xin, yiin) on the ellipse circumferences of a semi-major axis xiin and a semi-minor axis eta in on an input surface, and solving an emergent point coordinate (xout, yout) corresponding to an incident point (xin, yiin) according to the theta, xiout and eta out; and 3, light intensity statistical simulation verification: carrying out Gaussian sampling on the coordinates (xin, yiin) of one million light rays, and carrying out distribution statistical simulation on all (xin, yiin) and (xout, yiout) by using matlab programming, so as to display the conversion from an elliptical Gaussian beam to an elliptical flat-topped beam.

The application provides an acoustic metamaterial design method based on a PGN model, which not only overcomes the one-to-many mapping problem from spectral response to structural parameters in the traditional acoustic metamaterial design process, but also can significantly reduce the overall computing time and improve the design efficiency by predicting the solution immediately after the training phase. The design method first constructs a data set including the structural parameters of the acoustic metamaterial and the corresponding spectral response; then in a cascaded manner of a reverse GRU model in front and a forward DNN model pre-trained by the data set in back, a PGN model is constructed, and the PGN model is trained by the data set; wherein the candidate meta-structure obtained by Gaussian sampling of the output of the reverse GRU model is used as the input of the pre-trained forward DNN model; finally, the customized spectrum is input into the trained PGN model, and the Gaussian distribution predicted by the PGN model is probabilistically sampled to generate the structural parameters meeting the conditions.

The invention provides a virtual navigator path planning method based on SAC-RRT *, which innovatively introduces formation geometric envelope parameters to map multi-agent entity constraints into virtual navigator geometric constraints, replaces traditional RRT uniform sampling with non-deterministic Gaussian sampling of an SAC algorithm, and realizes adaptive step length extension and formation-level collision detection in combination with a Critic network. Meanwhile, the path cost optimization quantity of RRT rewiring is creatively converted into rewards to be fed back to the SAC network to complete backward fusion, network training is enhanced in cooperation with a real and virtual dual-experience playback strategy, formation trafficability factors are fused in a state space, a multi-dimensional reward function optimization decision is designed, and finally, the formation trafficability is improved through three times of B spline curve smoothing processing. The multi-agent formation path planning considering safety, optimality and convergence efficiency is realized, and the problems that a traditional method ignores formation geometric constraints, sampling blindness is large, and reinforcement learning and sampling algorithm fusion is not deep are effectively solved.

The invention discloses a lattice-based fine-grained attribute encryption method supporting user and attribute double revocation, and belongs to the technical field of information security and cryptography. The method comprises the following steps: S1, initializing a system; s2, public key distribution; s3, generating a private key; s4, encrypting and uploading the data; s5, requesting and decrypting data; s6, releasing the user revocation list; and S7, distributing the attribute revocation list. According to the method, fine-grained access control on encrypted data in a cloud environment is realized, user revocation and attribute revocation are supported at the same time, the attribute revocation does not need to re-execute a discrete Gaussian sampling algorithm to generate a user private key, and ciphertext updating is realized by cloud service without participation of an encryptor; the user revocation only needs to regenerate the ciphertext component related to the user revocation by the encryption party, and any user private key does not need to be updated; according to the double revocation mechanism, the system calculation amount and the communication burden are effectively reduced, and low-overhead and dynamic fine-grained authority management facing the cloud environment is realized.

The invention discloses a polynomial discrete Gaussian sampling system in RNS-CKKS homomorphic encryption. The polynomial discrete Gaussian sampling system comprises three functional modules: a random number generation and buffer module, a parallel sampling module and a parallel modulo module. According to the invention, the cooperative work of the three functional modules is utilized, the random number generation overhead is hidden through the asynchronous pipeline architecture, and the resource utilization rate is improved; the memory layout is optimized to support continuous memory access and vectorization operation; by designing a unified parameter interface and an error control strategy, multiple sampling algorithms are integrated, the precision is ensured, multi-granularity parallelization is realized, and the calculation efficiency is maximized. The system is a high-performance and extensible discrete Gaussian sampling framework, and solves the technical problems of serial computation, low memory access efficiency, poor algorithm flexibility and the like in the prior art.

The present application belongs to the technical field of control system optimization, and particularly relates to a slippers fitting alignment parameter adaptive optimization method based on production data, comprising: obtaining historical alignment data and pre-training a variational autoencoder; constructing a binary classification training sample set for each failure mode, generating a linear failure boundary hyperplane, and encoding the failure parameter clustering center into a latent space failure representation center; calculating the alignment evaluation value, signed orthogonal distance and shortest orthogonal distance of the parameter individual, and obtaining the fitness value through a safety penalty factor; selecting a normal and excellent individual to update the variational autoencoder; performing Gaussian sampling in the latent space and performing correction away from the failure representation center to decode and generate a predicted offspring; merging and screening the population and iterating to output the optimal slippers alignment parameter. The present application can construct a failure boundary and guide the search away from the risk area.

The application discloses an irregular roadway wall surface fitting method, which comprises the following steps: firstly, collecting laser pixel planes offline: static measurement of the data of a dug roadway wall surface, point cloud splicing, generation of an effective laser data matrix, establishment of a laser fitting plane coordinate system, generation of an effective laser pixel plane, maximum error calculation and saving; secondly, real-time measurement of a roadway wall surface normal vector and distance: loading of the effective laser pixel plane, generation of effective ultrasonic array data, fitting of a space plane under the ultrasonic array coordinate system, average error calculation, when the average error is smaller than the maximum error, calculation and saving of the normal vector; otherwise, Gaussian sampling is carried out, a new fitting plane is generated, and the distance from the ultrasonic array center to the plane is calculated. The application can realize accurate estimation of the normal vector of the irregular roadway wall surface and the distance from the measuring device to the plane, can provide relative position and attitude information in the roadway section for a heading machine, has good use effect, and is convenient to popularize and use.

The application discloses a three-dimensional human behavior recognition method under a small sample condition, and belongs to the cross technical field of digital image processing and machine learning. The application utilizes the statistical distribution information of base class feature vectors and the cosine similarity of new class sample data in a feature space to guide the feature distribution calibration of new classes and base classes; utilizes multi-dimensional Gaussian sampling to sample the new class samples after distribution calibration, and solves the overfitting problem caused by the small training samples of the small sample three-dimensional human behavior recognition; the application utilizes short fragment small voxel 3DV to extract effective and more behavior fine-grained information; meanwhile, visual Transformer is introduced, global receptive field and attention mechanism thereof are utilized for time sequence feature fusion, the network can pay attention to the human behavior fragments with the most discriminability, invalid behavior fragment interference is reduced, the feature extraction capability of the original three-dimensional dynamic voxel algorithm is further improved, and the distinguishing capability of the network for similar actions is improved.

The invention belongs to the technical field of information security, and particularly discloses a Gaussian noise-free attribute base fully homomorphic encryption and decryption method and system based on LWR. The method comprises the following steps: dividing an attribute list of a user based on a universal attribute set of an encryption system, and generating a main public key MPK and a main secret key MSK of the encryption system; generating a corresponding user key according to the user attribute list, the main public key MPK and the main key MSK; encrypting the message by using the main public key and the user attribute list to generate a ciphertext; and decrypting the ciphertext by using the main public key MPK and the user key of the encryption system, and recovering the original message. By adopting the technical scheme, the dependence on Gaussian sampling is eliminated based on the LWR problem, and the size of the ciphertext is remarkably reduced, so that the efficiency and the compactness are improved.

The application discloses a first-view incremental behavior recognition method based on a multi-modal adapter, and relates to the field of video image processing.The application aims to transfer the time sequence perception ability in the old task recognition model to the current task recognition model through a time sequence distillation loss, and realizes the incremental recognition of the classification layer to multiple tasks by combining a modal balance adapter, feature Gaussian sampling of the old task recognition model and feature Gaussian sampling of the current task recognition model.First, the multi-modal time sequence perception adapter is inserted in the form of a residual before and after the forward network layer of all the Transformer encoders of the Vit-B / 16 model pre-trained on the frozen ImageNet dataset, so as to realize the extraction of time sequence information from the features of the visual modal, acceleration modal and gyroscope modal in the Transformer encoder, and ensure that the time sequence information of the current task recognition model and the old task recognition model are as close as possible through the time sequence distillation loss.Finally, the feature Gaussian distribution of the current task is constructed by calculating the feature mean and variance of the current task recognition model, and the feature Gaussian sampling of multiple tasks is realized in combination with the feature Gaussian distribution of the old task recognition model, the modal balance adapter is inserted in front of the classification layer of the current task, and the classification layer of the current task is trained again by using the sampling features and the modal balance adapter, so as to improve the recognition ability of the classification layer to each task.

The invention discloses an unmanned aerial vehicle path planning method based on an RRT improved algorithm. The method comprises the following steps: establishing a four-rotor unmanned aerial vehicle mathematical model; determining a working space; establishing an ellipsoid sampling domain; setting a sampling strategy; sampling by adopting a Gaussian sampling method; expanding by adopting an artificial potential field algorithm; obtaining a group of path points and an initial path; and obtaining a final smooth path. According to the method, random sampling is replaced by a Gaussian sampling strategy, and different sampling frequencies are selected according to different obstacle densities; a sampling domain is limited, sampling points during sampling are limited in an ellipsoid sampling domain, redundant sampling points are greatly reduced, an adaptive step length and an artificial potential field expansion strategy are added, and a more flexible expansion strategy is made under the condition of facing different obstacle densities; and difference smoothing is carried out on a split-line-shaped initial path generated by using the improved RRT algorithm by using a cubic B-spline interpolation method, so that the efficiency and quality of a final path are improved, and the generated final path is enabled to better conform to kinematics and dynamics characteristics of the unmanned aerial vehicle.

The invention discloses an identity-based signature method, which comprises the following steps of: initializing a key generation center, and generating a main public key mpk and a main private key msk; for any user identity id belongs to {0, 1} *, the key generation center generates a private key skid of the user id by using the main private key msk; generating a signature for the message mu belongs to {0, 1} by using the main public key mpk, the user identity id and the private key skid; and verifying the signature by using the main public key mpk and the user identity id. The invention further discloses a signature system for implementing the signature method, computer equipment and a storage medium. According to the method, a new primary image sampling technology is used in the process of calculating the private key for the user, and the introduced noise is smaller, so that the method is more suitable for the design of an identity-based signature algorithm; in combination with an ellipsoid Gaussian sampling technology, the designed identity-based signature algorithm has higher efficiency and safety.

The invention discloses an identity signature method based on Falcon, and relates to the field of post quantum cryptography and information security. According to the scheme, on the basis of an NTRU lattice structure, a Falcon short signature algorithm is combined with an identity-based cryptographic mechanism, certificate-free identity binding signature and traceable authentication are achieved, a system is composed of a key generation center PKG, a signer and a verifier, and the method mainly comprises five core steps of system initialization, key generation, signature generation, signature verification and identity traceability. In system initialization, the PKG generates a master key according to a Falcon parameter and issues a public parameter; in the key generation process, deriving a discrete Gaussian distribution polynomial by using a master key, a user identity and a random entropy, and constructing a user exclusive short private key and a corresponding public key through an NTRU equation; in the signature stage, a challenge is generated by adopting a message, a random salt value and identity hash together, a short vector is generated by using discrete Gaussian sampling, and a structured signature is formed after compression.

The application discloses a task decomposition type image document denoising method based on GAN, adopts a DTNS algorithm based on conditional GAN, and divides the denoising process of an image format document into two parts of discovering an interference area and eliminating the interference area; wherein the discovering the interference area is to find all noise areas represented by watermarks and seals in the image, the eliminating the interference area is to remove the noise areas, and information in the image is restored to a state before being added with noise. The application proposes a DTNS algorithm based on conditional GAN, by decomposing the denoising task into two parts of watermark discovery and watermark removal, the task difficulty of directly removing the watermark through the GAN is greatly reduced, the network complexity is reduced, and by using Gaussian sampling in a high-dimensional structure feature space in the overlapping area, the phenomenon of losing the text contour of the overlapping area can be slowed down, so that the noise in the document image can be removed better.

The application provides a gadget matrix-based lattice cryptogram original image sampling method, and relates to the field of lattice cryptogram, and the method comprises the following steps: inputting the parameters of a trapdoor and a target discrete Gaussian distribution; calculating a perturbation vector for subsequent concealment of trapdoor information; updating the center parameter of the target discrete Gaussian distribution; sampling a discrete Gaussian distribution with a new center in a gadget matrix; mapping the sampling result of the gadget matrix with the trapdoor and combining the perturbation to obtain an original image vector. The application makes the gadget sampling output a spherical Gaussian distribution by adding a secondary perturbation, and designs an integer discrete Gaussian sampler based on a lookup table method, which takes into account the efficiency of gadget matrix sampling and original image sampling. In the online phase, only one rejection sampling and k-1 lookup table methods are required, which effectively makes up for the insufficient efficiency of the existing gadget matrix sampling, improves the performance of the lattice cryptogram original image sampling, and provides a more practical and flexible solution for distributed cryptographic systems and computing-restricted devices.

The invention provides an enterprise credit scoring method and system based on improved SMOTE, and the method comprises the steps: carrying out the grouping of all features in a preprocessed enterprise sample according to business logic, and obtaining a plurality of feature groups; solving the group elastic net objective function by adopting a near-end gradient method, and screening out an optimal feature group from the plurality of feature groups according to a solving result; target default samples are screened out, the target default samples are screened and summarized according to the mahalanobis distance, and a target default sample set is obtained; in the kernel principal component space, selecting a neighbor sample according to the kernel weight, and performing Gaussian sampling according to local statistical characteristics of the selected seed sample and the neighbor sample to synthesize a virtual target default sample; and inputting the financial information and the non-financial information of the balanced training sample set as independent variables and the credit score as a dependent variable into logistic regression for training to obtain an enterprise credit scoring model. According to the invention, the accuracy of enterprise credit prediction can be improved.

The invention discloses an ellipsoid Gaussian sampling-based LWE dual attack method. The method comprises the following steps of: constructing a provable dual attack framework based on ellipsoid Gaussian sampling; an ellipsoid Gaussian sampler is constructed for sampling, and a vector list W is generated; integrating a mode switching technology into an ellipsoid dual attack framework, guessing a secret vector s by using attack statistics, and recovering the secret vector s into complete sguess; and parameter selection and optimization are carried out, and the total attack complexity is minimized. By introducing the ellipsoid Gaussian sampling and mode switching technology, the attack complexity is reduced, and the attack efficiency is improved.

The application provides a lightweight and efficient encryption / decryption coprocessor and method based on an RLWE assumption, and a good balance between performance and resource utilization is achieved. The application develops a lightweight and efficient RLWE cryptographic coprocessor based on a Schoolbook algorithm, the time complexity of Schoolbook polynomial multiplication is greatly reduced by improving the parallelism of Schoolbook polynomial multiplication, a CDT Gaussian sampler is used for key generation, and redundant data in the structure of the classical CDT Gaussian sampler is further compressed, so that the storage resources are significantly saved. Therefore, the application achieves a good balance between performance and resource utilization, has high hardware efficiency, and can greatly improve the resource utilization.