13 results about "Discrete transformation" patented technology

The invention provides a photoacoustic image reconstruction model training method and device, equipment and a medium. Comprises: obtaining a model training sample; inputting the model training sample into a to-be-trained photoacoustic image reconstruction model; calling a discrete transformation layer to carry out discrete two-dimensional wavelet transformation on the sparse photoacoustic image to obtain a low-frequency feature map and a high-frequency feature map; calling a high-frequency channel attention layer to perform feature capture on the high-frequency feature map in the vertical direction and the horizontal direction to obtain a high-frequency attention feature map; calling a low-frequency channel attention layer to process the low-frequency feature map to obtain a low-frequency attention feature map; performing image reconstruction on the high-frequency attention feature map, the low-frequency attention feature map and the sparse photoacoustic image to obtain a reconstructed image; calculating a loss value of a to-be-trained photoacoustic image reconstruction model based on the reconstructed image and the dense sampling photoacoustic image serving as a true value; and obtaining a final photoacoustic image reconstruction model under the condition that the loss value is within a preset range.

Embodiments of the present specification provide methods of storing or reading twiddle factors in a target transformation. The target transform is a discrete transform or an inverse transform thereof that transforms an input coefficient sequence into an output coefficient sequence based on a twiddle factor. The method comprises the following steps: determining a target twiddle factor to be stored, wherein the power of the target twiddle factor is a result of a product modular target value of a first factor of a first digit and a second factor of a second digit; the target twiddle factor is stored in a two-stage storage mode, and the two-stage storage mode comprises the steps that the target twiddle factor is stored at a first address in a twiddle factor storage device, and the twiddle factor storage device comprises a first number of first storage units, the first number is the number of different modular multiplication results generated by the factor of the first digit and the factor of the second digit; storing an index value directed to the first address at a second address in an index memory, where the index memory includes a target number of second memory cells, the second address corresponding to a power of the target twiddle factor; the target value is greater than the first number.

A black box code search model backdoor attack method based on learnable discrete code transformation comprises the steps that a learnable backdoor generator is constructed, and malicious codes with backdoors are generated on the premise that internal parameters of a damaged model are not accessed through the learnable discrete code transformation. Firstly, an agent model capable of simulating victim model behaviors is trained through query-response data; secondly, on the proxy model, a discrete selection process is differentiable by using a re-parameterization sampling technology, and a backdoor generator is trained in combination with a multi-objective loss function so as to realize effectiveness and concealment of backdoor implantation; according to the method, the limitation of a black box is successfully bypassed by constructing the proxy model, and a new possibility is provided for an attacker. A backdoor generation process is integrated into a differentiable training framework through learnable discrete transformation, so that end-to-end learnability is realized, and an attack strategy can be automatically optimized.

The invention discloses a satellite image compression method based on linear frequency modulation, and the method comprises the steps: S1, obtaining a satellite image, and carrying out the partitioning of the satellite image through employing a self-adaptive partitioning method, so as to enable different projective transformations in the satellite image to be separated, and obtaining a plurality of image blocks; s2, inputting all the image blocks into a trained neural network model, and predicting to obtain chirp transformation parameters of each image block; s3, performing discrete chirp transformation on each image block according to the chirp transformation parameter corresponding to each image block so as to realize transformation from an image domain to a chirp frequency spectrum domain; s4, in the chirp frequency spectrum domain, the amplitude of the frequency component is quantized, so that continuous frequency spectrum coefficients are mapped into a discrete set; and S5, compressing quantization results of all the image blocks in the step S4 by adopting a lossless algorithm, and then encoding the compressed spectrum data by adopting an encoder to complete compression of the satellite image.

The application discloses a thermal superstructure manufacturing method, device and system based on discrete transformation thermology, and belongs to the technical field of heat conduction and heat flow regulation. The method comprises the following steps: carrying out discrete simplex grid dissection on a design domain to obtain a grid, and specifying a hard constraint for an internal function boundary according to a regulation function; defining a piecewise linear mapping on the grid and solving by two-stage optimization, wherein a negative Jacobian determinant is punished in the first stage to eliminate folding to obtain a bijective mapping, and anisotropy is inhibited by minimizing conformal distortion in the second stage; and finally, the thermal conductivity tensor field of the physical domain is derived by using a Jacobian matrix J, microstructure matching and filling are carried out, and additive manufacturing is adopted. The application solves the problems of folding and extreme parameters which are prone to occur in complex geometric transformation thermology design, can realize efficient heat management and omnidirectional heat regulation under any complex shape, and the error of experimental verification is less than 5 percent.

The present application relates to an autonomous landing guidance method for an aircraft based on Radon transform in an unknown environment. The method includes: obtaining semantic segmentation information, depth information and slope information of a monocular RGB image; gridding the monocular RGB image, calculating a regional evaluation index based on the semantic segmentation information, depth information and slope information in the grid, normalizing the regional evaluation index and mapping it to the grid position to obtain a two-dimensional grid scene information map; converting the two-dimensional grid scene information map into a binary image according to a threshold, performing connectivity detection on the binary image to obtain multiple candidate regions; performing discrete Radon transform on each candidate region to screen the optimal landing area that meets the landing requirements of the aircraft; constructing a local coordinate system on the optimal landing area, solving the relative position and posture between the local coordinate system and the camera coordinate system, and guiding the aircraft to complete autonomous landing. The use of this method can improve the efficiency and reliability of autonomous landing guidance of aircraft in unknown scenes.

The present invention discloses a satellite image compression method based on chirp, which includes: S1 obtaining a satellite image and performing block division on the satellite image by using an adaptive block division method to separate different projective transformations in the satellite image and obtain a plurality of image blocks; S2 inputting all the image blocks into a trained neural network model to predict the chirp transform parameters of each image block; S3 performing discrete chirp transform on each image block according to the chirp transform parameters corresponding to each image block to realize the conversion from the image domain to the chirp frequency spectrum domain; S4 in the chirp frequency spectrum domain, quantizing the amplitude of the frequency components to map the continuous spectrum coefficients into a discrete set; S5 compressing the quantization results of all the image blocks in step S4 by using a lossless algorithm, and then encoding the compressed spectrum data by using an encoder to complete the compression of the satellite image.

The application discloses an ADS-B spoofing attack detection method based on a discrete cosine patch attention mechanism, and comprises the following steps: step 1, screening and cleaning the collected ADS-B signal sequence, retaining continuous flight data with time sequence characteristics for network training; step 2, dividing the continuous flight data into a plurality of time windows according to channels, and extracting frequency domain features from the time windows by using a cosine discrete transformation; step 3, combining the time windows with frequency domain features and the time windows with time features, inputting the obtained time windows with frequency domain and time domain features into a self-attention mechanism to obtain instance-level features; step 4, normalizing the instance-level features by using anti-instance regularization; step 5, measuring the deviation of the normalized features from normal mode features learned in the training phase; step 6, summarizing the measured deviation, calculating the overall anomaly score of each signal segment, and then classifying the signals by using the anomaly score.

The invention discloses a color image blind watermarking method based on discrete Hankel transformation in order to better and effectively protect the digital copyright of a color image in the Internet. The method comprises the following steps: firstly, carrying out dimensionality reduction on a watermark image, and encrypting a layered watermark image by utilizing a Lorenz chaotic mapping encryption algorithm; a color host image is also subjected to dimensionality reduction and layering preprocessing and is divided into non-overlapping image blocks. After discrete Hankel transformation is carried out on an image block, watermark information is embedded into the image according to the embedding sequence of a blue channel, a red channel and a green channel, and the algorithm has good performance in the aspects of robustness, safety, invisibility and the like.

The present invention discloses an aircraft trajectory optimization method combining Birkhoff interpolation and LGL pseudo-spectral method, and uses the pseudo-spectral method in the numerical solution method to solve the aircraft trajectory optimization problem model. In view of the problem that the differential matrix condition number is high due to the discretization interpolation problem in the process of solving the optimization problem by the pseudo-spectral method, the condition number of the differential matrix in the pseudo-spectral method is greatly reduced by introducing Birkhoff interpolation, thereby reducing the solution time of the aircraft trajectory optimization problem model. The present invention introduces a genetic algorithm in the bionic algorithm to pre-solve the nonlinear programming problem after the discretization transformation of the pseudo-spectral method, and then uses the result of the genetic algorithm solution as the initial value of the sequential quadratic programming algorithm solution, thereby avoiding the divergence of the sequential quadratic programming algorithm due to the sensitivity of the initial value, improving the solution efficiency of the aircraft trajectory optimization problem model by the pseudo-spectral method discretization, and finally improving the trajectory optimization efficiency of the aircraft and the accuracy of problem solving.

A decoder includes circuits configured to receive a bitstream; determine a first, second, and third region of a current block; and, according to a geometric partitioning mode, decode the current block. Related devices, systems, techniques, and articles are also described. The decoder can determine, from a signal contained in the bitstream, a type of encoding transformation to decode the first, second, and / or third regions. The type of encoding transformation characterizes at least one inverse block cosine discrete transformation and one inverse adaptive block cosine discrete transformation. The decoding of the current block can include using the determined transformation type as the inverse transformation for each of the regions.

In order to solve the limitation of a traditional transform domain watermarking technology in the aspect of resisting traditional attacks and geometric attacks, the invention discloses a robust watermarking scheme based on discrete Hankel Transform (DHT), the robust watermarking scheme is remarkably different from a one-dimensional quantization index modulation method susceptible to noise interference, four-dimensional feature vectors of DHT coefficients are projected to a four-dimensional feature space, and the four-dimensional quantization index modulation method based on the DHT is obtained. The inherent stability and error correction capability of the high-dimensional quantization lattice are utilized, so that the resistance to noise interference and geometric attack is enhanced, and high-dimensional quantization index modulation is realized. The method not only has good invisibility and robustness, but also can effectively solve the problem of weak digital watermark resistance of the color image, and is suitable for occasions of digital media copyright protection.

The invention belongs to the technical field of JADCE algorithms, and relates to an efficient and accurate JADCE algorithm based on model-driven deep learning, the key of GFRA is to realize accurate joint activity detection and channel estimation, and due to sporadic traffic of Internet of Things equipment, the problem can be regarded as a typical sparse signal recovery problem. In order to solve the problem, a traditional compressed sensing algorithm can be utilized, but the defects of high calculation complexity and lack of robustness for different sensing matrixes exist. The invention provides a memory-enhanced iterative shrinkage threshold algorithm network framework for JADCE, and the performance of SSRe is effectively improved by learning discrete transformation and enhancing residual domain information interaction. Particularly, a high-throughput short-term memory mechanism is introduced into an expanded multi-stage deep network, so that the information richness and interpretability of the network are ensured. Simulation results show that the provided network has significant advantages in the aspects of detection and estimation performance, and the existing model-driven deep expansion network.