314 results about "Low complexity" patented technology

The present invention provides a method for low-complex estimation of Direction of Arrival (DoA) and Time of Arrival (ToA) in multi-antenna-based communication comprising detecting overlapped paths between a Mobile Station (MS) and a Base Station (BS) operating under said multi-antenna-based communication with scatters present therein involving channel paths represented as Channel Impulse Response (CIR) defined with channel signatures including delay and angle steering vectors, decoupling the channel paths in delay and angle-domain and separately estimate the DoA and ToA of each path with a low computational complexity which is based on one-dimensional low-index based rotation methodology and finally pairing the DoA and the ToA.

An apparatus comprising an interface and a processor. The interface may be configured to receive pixel data of an environment. The processor may be configured to process the pixel data arranged as video frames, generate a luminance distribution map of the video frames in response to a low-pass filter operation, determine a plurality of defogging intensity weights for the luminance distribution map, perform adaptive smoothing to each of the plurality of defogging intensity weights, and generate defogged video frames in response to the video frames and the plurality of defogging intensity weights with the adaptive smoothing. The plurality of defogging intensity weights may each correspond to one of a plurality of luminance intervals of the luminance distribution map. The adaptive smoothing may be configured to prevent brightness differences in the defogged video frames.

The invention provides a live broadcast long video editing method and device based on memory perception collaboration, and relates to the technical field of image processing and video editing. Voice, subtitles and picture actions are analyzed in a linkage mode on a unified time grid, low-complexity characterization of a long video is achieved through an efficient perception module, and the effect of editing the long video is achieved. Cross-fragment core characters, events and semantic clues are precipitated through long-term memory of an entity center, and current perception is fed back through memory retrieval so as to enhance consistency and robustness; on the basis, a three-mode fusion wonderness score is generated, interval optimization and boundary refinement with duration budget constraint are executed, and finally, an editing decision list is output and quickly spliced into a high-quality finely-cut video. According to the invention, the problems of large quantity of long-time live broadcast materials, low efficiency of manual reviewing and editing and easy leakage of wonderful fragments are solved, and the later production efficiency and the quality of the live broadcast content are remarkably improved.

A reduced number of interleaved encoded bits is obtained by encoding input bits by a polar code and interleaving a subset of the encoded bits obtained by the encoding. The polar code comprises a number of bit indices for placing bit values before encoding, including a first set of bit indices for placing values of the input bits and a second set of bit indices for placing a predetermined bit value. The first set of bit indices comprises a first subset of bit indices and a second subset of bit indices. The subset of the encoded bits corresponds to the second subset of the bit indices, and is for reducing the number of the encoded bits. An input bit value placed on both a first bit index in the first subset and a second bit index in the second subset, and the second bit index is based on the interleaving.

The invention discloses a dynamic beam hopping and resource allocation method for NGSO satellite security communication, and belongs to the field of sixth-generation mobile communication security communication and wireless resource allocation. According to the method, significant non-uniformity of distribution of ground flow requirements in a time domain and a space domain is considered, and a satellite-ground security communication network dynamic beam hopping and resource allocation problem model is constructed by considering ground user flow requirements and composition elements and channel characteristics of an NGSO multi-beam satellite network. Through joint optimization of satellite hopping beam scheduling, power resource allocation and auxiliary interference unmanned aerial vehicle deployment strategies, service requirements of different users and ground eavesdropping environments are dynamically adapted, and the safety throughput and queue delay fairness of the system are improved. According to the method, mixed integer linear programming modeling is adopted, and a low-complexity approximation algorithm is combined, so that the method not only has optimality guarantee, but also can be deployed and operated in an actual satellite communication system, and thus unification of secure communication and efficient resource scheduling is realized.

The application provides a uniform plane array FDA-MIMO three-dimensional target positioning method and system based on complete decoupling atomic norm minimization. The method converts the problem into a semi-positive programming solution by constructing a low-dimensional observation matrix and using complete decoupling atomic norm, and realizes joint estimation of distance, pitch angle and azimuth angle through Vandermonde decomposition. Compared with high complexity algorithms (VANM, etc.), the application significantly reduces the calculation amount by decomposing the Toeplitz matrix, and improves the single snapshot precision by using the multi-slice constraint mechanism. Compared with low complexity algorithms (3D-DANM), the application introduces an auxiliary variable matrix to restore the distance-angle coupling structure, avoids the performance loss caused by excessive relaxation, and significantly improves the estimation accuracy and multi-target resolution capability while ensuring the calculation efficiency.

The invention belongs to the field of signal processing algorithms, and provides a phase loss sparse low-rank signal recovery method based on convex programming. According to the method, through convex relaxation and anchor regression, a convex optimization frame based on an original parameter space is provided for the first time aiming at the problem of simultaneous sparse low-rank phase loss matrix signal recovery, the frame is used for recovering a simultaneous sparse low-rank matrix signal from phase loss observation data, and the frame works in an original space domain of an unknown variable. And aiming at the convex frame, a low-complexity alternating direction multiplier minimum method is developed. The sparse low-rank phase loss signal recovery method is a first sparse low-rank phase loss matrix signal recovery method based on convex optimization and in an original parameter space, and the calculation complexity is very low.

This application relates to the field of fintech and discloses a dynamic hybrid parsing and deterministic verification system and method for fuzzy bond instructions. The system constructs structured data through an instruction preprocessing module; a routing decision module calculates complexity scores based on structure, semantics, and business characteristics, and generates path selection instructions using a resource-aware dual-threshold strategy; a hybrid parsing module responds to instructions, employing a rule engine to handle low-complexity tasks and a large-scale model with enhanced retrieval to handle highly ambiguous tasks; a fallback verification module performs confidence-weighted integration and logical consistency checks, and repairs anomalies through issuer set operations and edit distance matching; a mapping generation module constructs standard query statements using an abstract syntax tree; and a closed-loop optimization module dynamically updates parameters based on user interaction feedback. This invention effectively balances computational efficiency and parsing accuracy, eliminates model illusion risks, and achieves adaptive evolution of the system.

The present embodiments disclose devices, methods, apparatuses and computer readable storage media of low complexity beamforming. The method comprises determining, at a network device, a channel covariance matrix and at least one signature vector, the at least one signature vector characterizing features of a signal associated with the terminal device; constructing one or more beam sets based on the channel covariance matrix and the at least one signature vector; selecting a predefined number of beams from the one or more beam sets; and performing a transmission to the terminal device based on a target beamforming matrix determined at least based on the predefined number of beams.

A method and system of training an explainable deep learning model includes receiving an input dataset that can be complex. The input dataset is provided to a deep learning model for feature extraction. In an exemplary embodiment, the deep learning model generates an unentangled latent space of features from the feature extraction. The features can include semantically meaningful data that is then provided to a low complexity learning model. The low complexity learning model generates an output based on a specified task (e.g., classification or regression). As a low complexity learning model, the data output from the deep learning model is believed to be inherently explainable.

The application discloses a kind of Φ-OTDR system coherence fading inhibition method and device based on spatial resolution internal redundant data, specifically: first, simulate random one-dimensional continuous signal and noise waveform, and carry out channel replication expansion, add different random noise to the waveform of each channel, obtain multi-channel signal and noise data set;Then set the structure, parameter, activation function, loss function and optimizer of time sequence neural network model, data set is sent into model and is trained;Then judge whether the model error meets the requirements, if it does not meet the requirements, adjust the structure, parameter, activation function, loss function and optimizer of model, retrain the model;If it meets the requirements, the actual data detected by Φ-OTDR system is processed and packed, and is sent into the trained model for prediction, to obtain the signal characteristics distributed along the whole optical fiber.The application has low complexity, reduces the influence of coherence fading noise on Φ-OTDR system, and improves the transmission ability of the system.

A method of decoding video data includes updating a coefficient statistics value based on one or more transform coefficients of a transform block (TB), wherein updating the coefficient statistics value includes, for each respective transform coefficient of the one or more transform coefficients of the TB: performing a derivation process to determine a temporary value, wherein the derivation process is determined based at least in part on which of a plurality of encoding procedures was used to encode the respective transform coefficient, the plurality of encoding procedures including a context-based procedure to encode the respective transform coefficient and encode the respective transform coefficient as an absolute value; and setting the coefficient statistics value to an average of the coefficient statistics value and the temporary value; determining a history value based on the coefficient statistics value; determining a rice parameter for a particular transform coefficient of the TB.

This invention relates to a beamforming and resource allocation method based on communication-aware joint optimization, applied to hierarchical coding communication scenarios. The method includes the following steps: Step 1, initializing the beamforming matrix and rate allocation vector; Step 2, solving the perception optimization subproblem based on the current beamforming matrix and rate allocation vector to obtain a new beamforming matrix; Step 3, solving the communication optimization subproblem based on the current beamforming matrix and rate allocation vector to obtain a new rate allocation vector, and determining whether convergence has occurred. If not, proceed to Step 2; if yes, proceed to Step 4; Step 4, solving the communication-aware joint optimization problem based on the current beamforming matrix and rate allocation vector to obtain the final beamforming matrix and the final rate allocation vector, thus realizing beamforming and resource allocation. Compared with existing technologies, this invention has advantages such as large capacity, high speed, and low complexity.

The application relates to the technical field of computing power allocation, and provides a computing power allocation method and system supported by an edge collaboration of a cloud computing platform, which comprises an architecture of deploying a complete dialogue model by a center node, carrying a light node model by an edge node, and configuring a semantic recognition module by a local node, the local node generates a conversation constraint set through a semantic recognition and semantic compression algorithm, long conversation model omission of user early demands is avoided, and dialogue accuracy is improved. In addition, a semantic complexity coefficient is obtained by analyzing the constraint set, the edge node collects self-computing power related parameters to evaluate node states, divides computing power rich and tight nodes, the computing power tight node processes low complexity tasks according to the self-computing power margin, the rest of the tasks are scheduled to the center node or adjacent computing power rich nodes, the tasks are distributed according to the complexity coefficient, the edge node computing power is fully utilized, and the overall computing power utilization efficiency is improved.

The invention discloses a low-complexity unequal error protection method based on two-stage cascading, and relates to the technical field of communication. Comprising the following steps: constructing a multi-stage coded modulation system with a two-stage cascade structure; respectively coding the information bits with different importance degrees based on a two-stage cascade structure of the multi-stage coding modulation system to form differentiated coding data streams; designing an optimal constellation diagram through constellation geometric shaping according to the transmission requirements of the differentiated coded data streams after coding; performing constellation mapping on the obtained differentiated coded data stream according to the optimal constellation diagram, and performing signal transmission on the mapped constellation symbols; and after the transmitted constellation symbols are received, decoding processing is performed on the constellation symbols by adopting a multi-stage decoding strategy. According to the method, the decoding complexity is greatly reduced on the premise that the error code performance is not lost.

This invention discloses a method and system for LEO satellite communication resource allocation based on dynamic weighted graph segmentation, relating to the field of satellite communication technology. Addressing the co-channel interference problem in large-scale MIMO satellite systems, this invention models the resource allocation optimization problem to maximize system sum rate as a partitioning optimization problem on a complete user graph. The weights of the edges in the graph are configured to represent the potential interference cost when two users reuse the same time-frequency resource. The optimization objective is to maximize the sum of edge weights between different partition groups. A dynamic weighted graph segmentation algorithm is employed, utilizing physical layer channel quality index feedback to introduce auxiliary weight variables. The edge weights of the graph are dynamically updated iteratively, and graph segmentation is performed until the algorithm converges. Finally, time-frequency resource allocation is completed based on the graph segmentation results. This invention can achieve near-optimal solution performance with low complexity and fast convergence speed, significantly improving the sum rate in highly dynamic satellite communication environments.

The invention provides a data compression method and device, electronic equipment and a chip system, and the method comprises the steps: carrying out the diagonalization matrix transformation of original conjugate symmetric positive definite matrix data, and determining the transformed target matrix data; the diagonal element data in the target matrix data are stored by adopting a first bit width, the non-diagonal element data in the target matrix data are stored by adopting a second bit width, and the first bit width is larger than the second bit width. According to the method, most information in matrix data is concentrated on diagonal elements through low-complexity matrix transformation, and secondary information is reserved on non-diagonal elements. And in combination with differential bit width quantization storage, diagonal elements in the target matrix data are subjected to high bit width quantization storage, and non-diagonal elements in the target matrix data are subjected to low bit width quantization storage. Therefore, on the premise of ensuring low precision loss and low complexity, the conjugate symmetric positive definite matrix data can be effectively compressed, so that the hardware storage overhead is reduced.

The application discloses a deep learning magnetotelluric data inversion method and device based on sub-region coding, and the method comprises the following steps: dividing an inversion region of magnetotelluric data into multiple coding sub-regions; constructing a training data set according to prior information of the multiple coding sub-regions; training a deep learning model by using the training data set to generate coding with prior regions, and generating coding without prior regions by using direct mapping, so as to re-parameterize a geoelectric model and obtain a re-parameterization result; and optimizing the coding based on the re-parameterization result to realize magnetotelluric data inversion. The application can flexibly embed prior information with different complexities, degrees of certainty and spatial ranges; can adaptively improve inversion effect according to the quality of the prior information; can strengthen the reconstruction resolution in a specific region or direction; and has low complexity and low training calculation amount.

A reduced number of interleaved encoded bits is obtained by encoding input bits by a polar code and interleaving a subset of the encoded bits obtained by the encoding. The polar code comprises a number of bit indices for placing bit values before encoding, including a first set of bit indices for placing values of the input bits and a second set of bit indices for placing a predetermined bit value. The first set of bit indices comprises a first subset of bit indices and a second subset of bit indices. The subset of the encoded bits corresponds to the second subset of the bit indices, and is for reducing the number of the encoded bits. An input bit value placed on both a first bit index in the first subset and a second bit index in the second subset, and the second bit index is based on the interleaving.

The invention discloses an RTP equipment temperature control method and device based on feedforward decoupling. A heating model is obtained based on input and output response data fitting of a heating area of RTP equipment; selecting an element with the maximum lag time in diagonal elements of the heating model as a reference basis, constructing a diagonal target transfer function matrix containing time delay characteristics, and ensuring that the lag time of each diagonal element is not less than the actual physical lag time of a corresponding channel; a feedforward decoupling controller model is solved based on the heating model, a direct control loop matrix and a feedback control loop matrix are obtained through calculation, the direct control loop matrix and the feedback control loop matrix are deployed in a temperature control system, and closed-loop temperature control is conducted on the multiple heating areas of the RTP equipment in combination with a PID controller. According to the method, the advantages of low complexity and quick response of the feedforward decoupling algorithm are combined, the physical realizability problem of the decoupler is solved by introducing the target transfer function matrix, and high-precision multi-region temperature synchronous control is realized.

The invention discloses a low earth orbit satellite network deterministic multicast routing method based on a time expansion graph, which relates to satellite and communication technologies, and comprises the following steps: constructing a time expansion graph to represent time slot characteristics, dynamic network topology and time-varying network resources of a low earth orbit satellite network; pruning the original time expansion graph according to the working characteristics of the CSQF mechanism and multicast service transmission requirements, and providing a solution space for deterministic multicast routing calculation; and by taking the pruned and enhanced time extension graph as input, calculating a deterministic multicast route from a source node, and selecting a transmission edge with sufficient capacity and a storage edge conforming to a CSQF mechanism hop by hop until all virtual nodes are reached, so that the obtained route has the maximum bottleneck capacity on the premise of meeting the multicast service time delay upper limit requirement. According to the method, a low-complexity algorithm is designed, the link with the optimal resource and the transmission time slot with the time delay guarantee are selected hop by hop in the graph, and the multicast tree with the time attribute is constructed, so that end-to-multi-end deterministic guarantee is provided.

This invention provides a low-complexity multi-channel denoising and noise reduction method based on Kalman filtering, comprising: acquiring a signal and preprocessing the acquired signal to obtain a signal in the short-time Fourier domain; calculating a multi-channel noise covariance matrix; estimating multi-channel autoregressive parameters using the delayed, noiseless reverberant signal estimated in the previous frame and the acquired signal in the current frame, and determining the variance value of the Kalman state noise based on the sound source change detection result of the previous frame; estimating a noiseless reverberant signal using the estimated autoregressive parameters, the acquired signal in the current frame, and the estimated multi-channel noise covariance matrix; delaying the estimated noiseless reverberant signal and calculating the estimated noiseless late reverberant signal using the autoregressive coefficients; subtracting the noiseless late reverberant signal from the noiseless reverberant signal to obtain the desired direct sound and early reverberant signal. This invention reduces computational complexity and enables real-time applications in embedded products.

The present invention proposes a system (100) for locating a low complexity device, such as a UE, by combining wireless (radar) sensing (for timing) and uplink / sidelink pilot transmission (for angle / identification), in which a wireless (radar) sensing waveform / signal (120) is embedded with data representing a code / data (121), which can be detected and decoded by a sensed target UE (112), and for generating an uplink / sidelink transmission, the uplink / sidelink transmission also comprising the code / data (121) or comprising data that can be derived from the embedded code / data (121).

This application discloses a signal transmission method, apparatus, device, and storage medium, belonging to the field of communications. The method is executed by a first device and includes: transmitting a first signal or a first channel, wherein the first signal or first channel is generated based on at least one data bit and a first sequence; wherein the first sequence includes one or more sequences in a spreading sequence, the spreading sequence being generated based on at least one of the following sequences: an m-sequence, a Gold sequence, and a Walsh sequence. The first signal / first channel spread by the first sequence can improve anti-interference capability, transmission reliability, and transmission efficiency while maintaining good characteristics of low complexity and low power consumption.

The application discloses a multi-target parameter estimation method and system based on an OTFS sensing shared waveform, relates to the technical field of communication and sensing integration, and first jointly adopts two representation methods of a discrete DD domain sensing channel and a continuous DD domain sensing channel and corresponding DD domain echo signal modes, and describes a problem model suitable for multi-target sensing and fractional delay Doppler estimation; when a target is sensed, a BCS-ML algorithm in the application estimates target parameters in two stages, estimates integer delay Doppler in the first stage, and estimates fractional delay Doppler in the second stage, the model adopted by the application and the algorithm proposed by the application realize low-complexity parameter estimation and high-precision parameter estimation of multi-target, and break through the limitations of a traditional OTFS sensing parameter estimation algorithm.

The invention particularly relates to a parameter adaptive hybrid disturbance serial cancellation (SC) decoding method for polarization codes. Aiming at the problems of disturbance parameter fixation and rigidity, lack of channel adaptability and dependence on off-line parameter search in the existing hybrid disturbance serial cancellation (HPSC) decoding method, the method comprises the following steps: firstly, establishing a linear mapping relation between disturbance noise power and channel noise power to realize automatic adaptation of a channel environment; then, the check characteristic of a cyclic redundancy check code is used as a detection indicator of a local optimal trap, intelligent switching is carried out between adaptive random disturbance and adaptive biased disturbance, and a bias adjustment strategy dynamically changing along with the number of iterations and the reliability of a received symbol is introduced in the biased disturbance stage; therefore, parameter-adjustment-free high-performance decoding is realized on the premise of keeping low complexity of SC. Simulation results show that compared with a traditional HPSC decoding method with fixed parameters, the method provided by the invention has better block error rate performance, and has lower average decoding iterations in a high signal-to-noise ratio region.

The invention relates to the technical field of wireless communication, in particular to a double decision D2D access algorithm based on user QoS and system performance. According to the double-decision D2D access algorithm based on the user QoS and the system performance, firstly, interference between cellular users is eliminated through zero-forcing precoding; on this basis, an iterative access process including interference sorting and double judgment is executed: a candidate D2D pair with the minimum sum of squares of channel gains for all current served users is selected from non-accessed D2D in each round; then carrying out first judgment to ensure that the signal to interference plus noise ratios of all cellular users after access are not lower than the QoS thresholds of the cellular users; and second judgment is carried out after passing, the total rate of the system after access is calculated and verified to be strictly greater than the rate before access, and the D2D is accessed and the system state is updated if the double judgment is passed. According to the method, on the premise of strictly guaranteeing the QoS of the cellular users, the total capacity of the system is effectively improved in a low-complexity mode, and the D2D access number and the overall performance of the network can be intelligently balanced.

The application discloses a low-complexity rough road echo time delay estimation method based on sparse frequency sampling, and is used for solving the problems of high calculation complexity and poor estimation accuracy of existing ground penetrating radar echo time delay estimation technology. In the application, a grouping sparse frequency sampling method is adopted, the data dimension is reduced through sparse sampling, and the calculation complexity is greatly reduced. Meanwhile, cross-correlation detection is adopted to solve the time domain ambiguity caused by sparse sampling. In addition, a time delay iteration screening method is adopted, the preliminary time delay estimation value result is further iterated and screened, false time delay estimation values can be effectively avoided from being mistaken for real values, and higher estimation accuracy can be obtained.

The application discloses a carrier recursive estimation method and device based on frequency dynamic partitioning, and belongs to the technical field of communication signal processing. The method comprises the following steps: a modulation signal is generated by a sending end, and Mth power demodulation processing is performed on a received signal by a receiving end; initial frequency partitioning is performed within a given frequency offset range, the period amplitudes corresponding to the end points of each partition are calculated, and the end points corresponding to the maximum and the second maximum are selected as the search range of the next recursion; the frequency offset estimation interval is narrowed through multiple recursions, finally, the frequency offset estimation value is obtained by using the end point corresponding to the maximum period amplitude of the last recursion, and the phase offset estimation value is obtained by using the phase information thereof; and finally, carrier compensation is performed on the received signal. Through dynamic frequency partitioning and recursive search, high-precision frequency offset and phase offset estimation in a high-dynamic and low-latency scene are realized, and the method has the advantages of fast estimation speed, high precision and low complexity.

A memory die estimates a bit error rate (BER) of a fuzzy program operation and uses the BER to determine whether to decode fuzzy program data on the memory die for a fine program operation. A controller on the die receives data from a storage device and generates parity pages based on the data. The controller performs a fuzzy program operation to write the data to memory cells in a block. The controller separates states in the memory cells into categories based on values of the parity pages associated with the states. The controller also performs a soft bit sensing operation and estimates a BER of the memory cells based on the soft bit sensing operation. Based on the BER, the memory controller performs on-die decoding of fuzzy program data with the parity pages or transfers the fuzzy program data to the storage device for decoding.