918 results about "Low complexity" patented technology

The invention discloses a power load prediction method based on a two-channel cross attention network, and belongs to the technical field of time sequence prediction in deep learning. The method comprises the following steps of obtaining a power load data set, performing preprocessing on the power load data set, decomposing power load data into a plurality of local components by applying VMD, iteratively optimizing VMD parameters through an APSO algorithm, and screening a subsequence with the lowest complexity to be combined with original features to serve as model input; and then constructing a dual-channel cross attention network model, processing time sequence data by using a parallel mechanism of a BiLSTM channel and a Transform channel, enhancing the feature expression ability through an improved cross attention layer, and performing power load prediction by using an MLP fused KAN network. According to the method, the time sequence prediction of the power load is carried out by constructing the two-channel cross attention network, the prediction precision and robustness are high, and the accuracy of load time sequence prediction is remarkably improved.

The invention discloses a water quality time sequence prediction method of an SSA-VMD-LSTM-XGBoost hybrid model, and belongs to the technical field of water quality monitoring and prediction. Comprising the following steps: (1) data preparation and preprocessing; (2) optimizing the water quality time sequence decomposition of the VMD based on SSA: optimizing a penalty factor and a modal number of the VMD by adopting a sparrow search algorithm (SSA), and decomposing the water quality time sequence into a plurality of sub-components with high stability and low complexity by utilizing the optimized VMD; (3) construction and training of an LSTM-XGBoost hybrid prediction model: constructing a hybrid prediction model fusing long-short term memory (LSTM) and extreme gradient boost (XGBoost), inputting a high-frequency component into the LSTM model, inputting a low-frequency component into the XGBoost model, and finally performing superposition and integration on prediction results of the models; and (4) multi-component prediction result integration and performance verification. According to the method, adaptive optimization of VMD parameters is realized through SSA, the feature extraction and time sequence modeling capability is improved by combining the advantages of LSTM and XGBoost, and the prediction precision and stability of the water quality time sequence are effectively improved.

The invention provides an answer generation method, a knowledge question-answering system and electronic equipment, and belongs to the technical field of artificial intelligence. According to the method, through multi-dimensional feature extraction, features in different dimensions can be extracted for subsequent complexity classification judgment. The complexity of subsequent processing can be reduced by screening out the core feature which can best reflect the complexity of the problem from the multi-dimensional feature set. And aggregating each core feature in the core feature set by using an adaptive aggregation strategy to obtain an aggregated feature vector, inputting the aggregated feature vector into a hierarchical complexity shunt, and for a query problem with a lower complexity level, directly outputting the complexity level by a lightweight classification layer. And for the query problem with a relatively high complexity level, the complexity level is output by the deep classification layer, so that the accuracy and efficiency of complexity classification can be ensured. And finally, according to the complexity level corresponding to the query question, a corresponding retrieval and answer generation strategy is selected, so that the flexibility and efficiency of answer generation can be improved.

The invention discloses an efficient optical flow estimation method and device based on Mama, and the method comprises the steps: carrying out the normalization and size alignment of two adjacent frames of images, and extracting the dense features of a fixed down-sampling rate through a shared weight convolution encoder; the two-frame features are sent to a multi-level feature enhancement module, an intra-frame modeling unit and a cross-frame interaction unit are cascaded and matched with channel reforming and residual error correction, and enhanced features are obtained; constructing a four-dimensional cost body on a low resolution, performing probability normalization along a target coordinate dimension, weighting a target coordinate grid according to a probability to obtain a corresponding coordinate, and subtracting the corresponding coordinate from a source coordinate to obtain an initial optical flow; and carrying out attention-guided space fusion on the initial optical flow and context and local correlation, sending the fused optical flow to a differential Mama-based autoregressive refinement module, carrying out iterative updating according to a small number of fixed steps, recovering to a target resolution through convex combination up-sampling, and outputting a final optical flow. According to the method, the optical flow field can be accurately estimated under the conditions of low complexity and low time delay.

The invention discloses a low-complexity decoding method of an LDPC-Hadamard code based on a prototype graph, and belongs to the field of digital signal processing. The implementation method comprises the following steps of: performing LDPC (Low Density Parity Check) coding on information bits, and coding a constraint relationship of check nodes of the LDPC code into a Hadamard code to obtain a prototype graph of the LDPC-Hadamard code; after coded code words are sent and received through a channel, a PVN node and an HCN node are initialized by using the received information; carrying out Hadamard decoding by using a symbol-by-symbol maximum posterior probability decoding algorithm, reducing decoding complexity by using approximate operation of DFHT, and transmitting information obtained after decoding to a PVN node; summing the updated HCN node information and the last iteration information, and judging and decoding by using a summing result; and performing decoding judgment on the PVN information, and performing decoding. According to the method, LDPC coding, Hadamard coding and Max-Log approximate calculation methods are combined, the decoding complexity is reduced, and the stability and reliability of a communication system are enhanced.

The invention discloses a configurable number-theory transformation parallel computing acceleration method and device for a post quantum cryptographic algorithm, and relates to the technical field of cryptographic algorithms. The number theory transformation is a calculation bottleneck in lattice-based post-quantum cryptography and PQC; a hardware accelerator specially designed for number theory transformation (NTT) is an effective solution for improving the execution speed. At present, a mainstream design neglects the influence of the scale of a calculation array, so that the design of an NTT calculation circuit is poor in flexibility and low in memory utilization rate, and a two-dimensional reconfigurable number theory transformation acceleration circuit is provided; the method is applied to a key generation stage, an encryption stage and a decryption stage of the post-quantum cryptographic algorithm. The NTT reconfigurable computing circuit provided by the invention can keep the utilization rate of hardware resources, and is suitable for mobile terminal equipment with limited resources; the NTT circuit adopts a low-complexity memory mapping scheme, so that the address control logic is greatly simplified, and the hardware overhead is reduced.

Systems and methods for resource conservation based on query complexity are disclosed. An input query is received (e.g., via a chat interface) and provided to a response classifier, which is a machine-learning classifier that is trained to classify input queries with complexity scores that indicate how difficult it is likely to be for an artificial intelligence (AI) model to generate a response to the input query. If the complexity score exceeds a threshold score, the input query is provided to a first AI model (e.g., a relatively high-complexity AI model having a large number of parameters, relatively long response latencies, and / or other performance characteristics). If the complexity score does not exceed the threshold score, the input query is provided to a second AI model (e.g., a lower-complexity AI model having fewer parameters, shorter response latencies, and / or other differences in performance characteristics relative to the first AI model).

The invention discloses a power adaptive digital pre-distortion method, system and device based on sparse GRU and a medium. The method comprises the steps that input and output signals under different powers are collected, a composite feature vector is constructed through amplitude normalization and phase alignment, and a pre-distortion training target is generated in combination with indirect learning; constructing a sparse GRU neural network, introducing L1 regularization constraint, and obtaining a sparse pre-distortion model through joint optimization of mean square error and sparse constraint; partial weight updating is realized based on power grading, and weight fusion output is carried out during power interval switching so as to complete power adaptive optimization; pruning and compressing the sparse GRU model and then deploying the sparse GRU model in a pre-distortion module to compensate the nonlinearity of the power amplifier; according to the method, the model complexity is reduced through rarefaction, the dynamic working condition stability is improved through a power adaptive mechanism, pruning compression adapts to low-power-consumption hardware, high precision, low complexity and adaptive capacity are considered, and the method is suitable for the low-power-consumption and high-performance requirements of a broadband wireless communication system.

The present invention provides a ray tracing-based method for indoor multi-base station location optimization in a millimeter wave frequency band, comprising: constructing an indoor millimeter wave network model; determining an indoor millimeter wave network model optimization constraint condition; constructing a cost function of multi-base station location deployment on the basis of the constraint condition; determining the initial location of each base station in the indoor millimeter wave network model; and by taking the initial location of each base station as a starting point, determining an optimal location of each base station by means of axial search together with pattern search. According to the present invention, while maintaining low complexity and the advantages of a global optimization algorithm, an indoor multi-base station optimization problem can be solved under the constraint condition that base station locations are necessarily within a feasible interval, and by taking the path loss and signal-to-noise ratio as optimization parameters, a good balance can be achieved between the accuracy of optimization results and the complexity of optimization algorithms for the multi-base station deployment optimization problem, so that the optimized base station locations can provide high-quality signal coverage for an indoor millimeter wave network.

The invention discloses a hybrid heterogeneous cloud workflow scheduling method based on reinforcement learning, and belongs to the technical field of cloud computing. The method comprises the following steps: aiming at a cloud workflow scheduling problem, by taking minimization of completion time as a target and taking cost and resources as constraints, a three-dimensional collaborative constraint model is constructed, and the cost constraints comprise server-free function budget and virtual machine budget; integrating the hyper-heuristic framework into a reinforcement learning algorithm; an improved reinforcement learning algorithm is adopted to solve the cloud workflow scheduling problem, optimal execution resources are selected, and an optimal scheduling scheme is obtained; and performing real-time scheduling according to the optimal scheduling scheme, and introducing a deviation feedback mechanism to monitor an execution error in real time. According to the method, the workflow scheduling problem is decomposed into a closed-loop optimization process of state perception and action decision, dynamic environment perception, multi-target tradeoff and online strategy optimization are deeply fused, the global search function is achieved, local optimization can be achieved, the algorithm complexity is low, and robustness is high.

The invention belongs to the technical field of communication, and particularly relates to a multiplication active code decoding method based on an SO-ORBGRAND decoder. The SO-ORBGRAND decoder outputs soft information by calculating a correct posterior probability of a conjecture code word sequence, and high-performance decoding is realized through soft information exchange and optimization processing between the row decoder and the column decoder. In the iteration process, a method for judging the validity of the code word through the check matrix is designed by utilizing the polarization characteristic of the multiplication active code, so that the iteration is supported to jump out in advance to reduce the complexity. The invention provides a multiplication active code decoding algorithm based on an SO-ORBGRAND decoder, and compared with various non-system multiplication active code decoding algorithms and the performance of traditional long-code polarization codes, the method has remarkable advantages in error correction performance; a method for judging the validity of the code word through the check matrix by utilizing the polarization characteristic design of the multiplication positive code is provided, the complexity is reduced while the judgment success rate is improved, and the implementation overhead is reduced.

In an aspect, digital communication coding methods (or simply coding methods) are provided, said methods comprising providing families of quasi-cyclic low-density parity check, LDPC, codes each represented by a single equation. Said single equation is derived from a geometry whose nature implies good error correcting properties for each of the LDPC codes and allows quasi-cyclic parity check matrices to be constructed for geometric LDPC codes of exceedingly long length, up to and exceeding four hundred thousand bits. The construction of such check matrices is not possible by any other known method. The LDPC codes have different lengths and different rates and the families of LDPC codes are provided with a guarantee of minimum distance. The geometric and quasi-cyclic properties of the LDPC codes allow low complexity decoding with very low frame error rates. Systems, computing systems and computer programs suitable to perform such coding methods are also provided.

The invention provides a parallel policy determination method, electronic equipment, a storage medium and a program product. The method comprises the following steps: determining cluster communication information, model calculation information and model resource occupation information according to information of a cluster used when a large model is trained and a structure of the large model; according to the cluster communication information and the model calculation information, modeling training time required by large model training to obtain a training time model; according to the model resource occupation information, carrying out modeling on resources occupied by large model training to obtain a resource occupation model; and according to the training time model and the resource occupation model, determining a target parallel strategy from multiple groups of parallel strategies as a parallel strategy used during large model training. Thus, by constructing the training time model and the resource occupancy model, the optimal parallel strategy can be automatically screened out from the multiple groups of parallel strategies, manual intervention is not needed, and therefore efficiency can be improved, and complexity can be reduced.

The invention discloses a space-air-ground integration-oriented full-decoupling network non-feedback transmission and resource dynamic allocation method. A solution is provided for space-air-ground multi-node cooperation resource management. The method comprises the steps that a channel state information prediction model based on deep learning replaces traditional feedback signals with user geographical location information, and multiple-input-multiple-output transmission is achieved; and a many-to-one matching model is designed and is used for realizing adaptive allocation of spectrum resource blocks among space-air-ground heterogeneous nodes, so that the matching stability is ensured, and the method has the characteristics of low complexity and rapid convergence. For cooperative transmission of multiple nodes and resource allocation among heterogeneous nodes, the spectrum efficiency of the system is improved through non-feedback channel state information prediction and a resource allocation mechanism. Compared with a single connection mode and polling-based resource scheduling in a traditional network, the flexible resource allocation algorithm disclosed by the invention remarkably improves the network capacity and improves the user communication quality.

The invention discloses an end-to-end intelligent direction of arrival estimation method based on subarray sampling, and belongs to the technical field of wireless communication. A sub-array sampling technology is adopted to replace traditional full-array sampling, and signal processing is completed by using fewer radio frequency links, so that the hardware cost is reduced; an end-to-end learning framework is adopted, so that a network can automatically capture features and rules according to a data set, and excessive dependence on a model is avoided; data features are preliminarily captured through a deep neural network, a pre-estimated value is given, then an iteration process of a maximum likelihood estimation method is converted into a deep expansion network, initialization is performed by using output of the deep neural network, and the effect of the method is improved while convergence is accelerated; a large number of data sets are generated through a sub-array sampling signal mathematical model, the network is trained, and the problem that overfitting is prone to occurring under a small data set is avoided. The method is suitable for the technical field of wireless communication, and realizes direction of arrival estimation with lower complexity and higher accuracy in a low-snapshot scene.

The invention discloses a low-complexity MSK coded modulation system phase synchronization receiving system and method, and relates to the technical field of continuous phase modulation. On one hand, an original message is coded and modulated by constructing a pre-coding mode, a receiving end realizes optimal sampling point extraction at the minimum intersymbol crosstalk moment after phase decoupling frequency conversion processing, MSK is converted into BPSK-like modulation, oversampling processing is not needed, modulation phase components of a receiving synchronization section can be offset, and the modulation efficiency of the receiving synchronization section is improved; therefore, accurate estimation of Doppler phase and frequency is realized, and the anti-Doppler capability is improved. On the other hand, coherent demodulation soft information is directly output after the real part of the signal subjected to Doppler frequency offset and phase offset compensation is obtained, the complexity of the demodulation algorithm is low, the performance of the demodulation algorithm is equivalent to that of BPSK, the demodulation algorithm is remarkably superior to that of an MSK optimal demodulation receiver in a low signal-to-noise ratio region, and the demodulation algorithm is particularly suitable for a bit interleaving coding modulation system combined with channel coding and has a wide application prospect. And reliable transmission under a low signal-to-noise ratio can be realized.

The invention discloses a source code vulnerability detection method based on multi-dimensional feature fusion, which comprises the following steps of: firstly representing a source code as a program dependency graph, and then slicing the program dependency graph to reduce complexity to obtain a slice dependency graph; then, the source code is converted into vector representation through naming standardization and a Doc2Vec technology, and semantic information is reserved; next, embedding learning is carried out on the slice image in the hyperbolic space and the Euclidean space respectively, vulnerability features under different perspectives are captured, and comprehensive and deep analysis of the source code is achieved by combining the hierarchical structure representation capability of the hyperbolic space and the local feature capture capability of the Euclidean space. And finally, designing a fusion strategy, effectively fusing the features extracted from the two spaces, and carrying out vulnerability classification prediction by a classification prediction module comprising a multi-layer perceptron. According to the method, comprehensive and deep analysis of the source code is realized, and potential vulnerabilities in the source code are identified and positioned more accurately.

The invention provides a signaling transmission method and device and a storage medium, relates to the technical field of communication, and is used for realizing efficient transmission of signaling in a low-complexity system. The method comprises the following steps: receiving a preamble sequence, wherein the preamble sequence comprises a first part and a second part; signaling is received based on the preamble sequence.

The invention relates to the technical field of point cloud compression coding, and discloses a point cloud projection video low-complexity coding optimization method based on depth structure prediction, which comprises the following steps of: 1, extracting an occupied video, a geometric video and an attribute video of a dynamic point cloud sequence under different code rates; step 2, extracting feature information of a near-layer frame to perform feature enhancement to obtain near-layer enhancement features # imgabs0 # corresponding to the occupancy graph, the attribute graph and the geometric graph; step 3, extracting feature information of a far-layer frame to perform feature enhancement to obtain near-layer enhancement features # imgabs0 # corresponding to the occupancy graph, the attribute graph and the geometric graph; 4, performing feature enhancement on a projection image block according to feature information of different types of frames, and realizing more accurate depth division structure prediction; and step 5, realizing low-complexity point cloud projection video coding acceleration by combining a depth division structure prediction result with an optimized coding algorithm.

The embodiment of the invention provides a model evaluation method and device, a storage medium and a program product. In the model evaluation method, after a reply result output by a target model for a target question is obtained, a question type corresponding to the target question can be obtained, and a target evaluation model matched with the question type in a plurality of evaluation models is called to evaluate the reply result. In the embodiment, in the multiple evaluation models, different evaluation models correspond to different question types, and different evaluation models have different parameter quantities. Based on this, a scheme for cooperatively evaluating the reply results output by the target model for different types of questions based on the evaluation models with different parameter quantities is realized, so that the evaluation model with low parameter magnitude can be conveniently adopted to evaluate the reply results of some questions with low complexity, the resource consumption is effectively reduced, and the evaluation efficiency is improved. And an evaluation model with relatively high parameter quantity can be adopted to evaluate reply results of some questions with relatively high complexity, so that the evaluation accuracy is improved.

The invention discloses a track foreign matter intrusion detection method based on an improved YOLOv8 model, and belongs to the field of foreign matter intrusion detection, which is an improvement of a deep learning algorithm taking YOLOv8 as a baseline model, mainly optimizes and enhances three aspects of backbone convolution, an attention mechanism and loss function calculation, and improves the detection accuracy on the premise of ensuring the detection speed. A lightweight detection model with lower complexity and higher detection precision is obtained, the requirements of real-time performance and accuracy of track foreign matter intrusion detection are met to a great extent, and a new foreign matter intrusion detection method is provided on the technical level.

The invention discloses a coding method and device, a decoding method and device, equipment and a storage medium, and belongs to the technical field of coding and decoding. In the encoding method, a data set is obtained, the data set comprises N pieces of data, and N is greater than or equal to 2. And if the occurrence frequency of the first data in the N pieces of data is greater than or equal to a reference threshold value, performing RLE on the first data. Wherein the reference threshold value is determined based on the N, the bits occupied by the first element and the bits occupied by the second element, the first element is determined based on the first data, and the second element is the continuous occurrence frequency of the first data. According to the method, the first data suitable for RLE is quickly and accurately determined from the N pieces of data on the basis of the reference threshold value with relatively low calculation complexity. Therefore, RLE can be carried out on the first data suitable for RLE in the N data, the RLE mode is reasonable, unnecessary calculation overhead and coding overhead are avoided, and the coding efficiency is improved.

The invention belongs to the technical field of artificial intelligence AI, and discloses an antenna design method and device, and the method comprises the steps: determining the design parameters of an antenna through multiple iterations; the ith iteration process is as follows: obtaining a plurality of vectors from the optimization space, wherein M elements in each vector are respectively used for representing M design parameters of the antenna; inputting the input distribution of the M design parameters and the plurality of vectors into an agent model of Bayesian optimization BO to obtain a plurality of groups of predicted values of the performance parameters of the antenna; the processing process of the proxy model comprises the step of calculating a covariance matrix of a plurality of random vectors, wherein the covariance between any two vectors is obtained by calculating a sample set obtained by carrying out input distribution sampling on the vectors. In the calculation process, an acceleration algorithm based on Nystrom approximation is adopted to reduce complexity, so that design parameters of the antenna are rapidly determined. The method can be suitable for input of design parameters of any distribution type, and the reasoning efficiency of the design parameter optimization process can be greatly improved.

This invention discloses a low-complexity decoding method for LDPC-Hadamard codes based on a prototype diagram, belonging to the field of digital signal processing. The implementation method is as follows: information bits are LDPC encoded, and then the constraint relationship of the check nodes of the LDPC code is encoded into Hadamard code to obtain the prototype diagram of the LDPC-Hadamard code; the encoded codewords are transmitted and received through a channel, and the PVN and HCN nodes are initialized with the received information; Hadamard decoding is performed using a symbol-by-symbol maximum a posteriori probability decoding algorithm, and DFHT approximation is used to reduce decoding complexity; the decoded information is transmitted to the PVN nodes; the updated HCN node information is summed with the information from the previous iteration, and the summation result is used to determine the decoding; a decoding decision is made on the PVN information, and decoding is performed. This invention combines LDPC encoding, Hadamard encoding, and Max-Log approximation calculation methods to reduce decoding complexity and enhance the stability and reliability of the communication system.

The invention discloses a random pre-coding multiple-input multiple-output orthogonal frequency division multiplexing wireless communication method and system, a transmitting end enables each symbol to fully utilize all sub-channels through random unitary transformation and random compression transformation, and at a receiving end, by means of a block diagonal channel matrix of MIMO-OFDM on a frequency domain, all sub-channels of the MIMO-OFDM are fully utilized, so that the random pre-coding multiple-input multiple-output orthogonal frequency division multiplexing wireless communication is realized. A cross-domain orthogonal approximate information transfer (CD-OAMP) detector is designed, which can perform signal detection with extremely low complexity, and can be optimal for replica maximum posteriori (MAP). The RP-MIMO-OFDM performance of the CD-OAMP detector exceeds the performance of MIMO-OFDM, the performance of MIMO-OTFS and the performance of MIMO-AFDM, the performance of RP-MIMO-OFDM is close to the performance of MIMO-IFDM, and the complexity is lower.

The invention provides a business process model instance construction method and device based on a data structure, and the method comprises the steps: carrying out the assembly according to a JSON structure and a business process drawn by a user, and generating a business process JSON data structure; performing data serialization on the business process JSON data structure to generate a database process draft basic information extension table; in response to a release process instruction input by a user, performing data deserialization according to the database process draft basic information extension table, generating business process series information and updating a business process series table; in response to a process starting instruction input by a user, determining a business process series information instance from the business process series table; according to the business process model architecture, the business process series information instances are spliced, and the business process model instance is generated, so that expansibility, maintainability and flexibility can be improved, front-end load work is reduced, complexity and error risks are reduced, and response speed is increased.

The invention discloses an interpretable model training method based on sensitivity analysis, and belongs to the technical field of specific calculation models, and the method comprises the steps: carrying out model training according to training set sample data to obtain a baseline model, evaluating the baseline model according to test set sample data, and recording a reference prediction index, the sample data comprising a plurality of variables; according to the training set sample data and the test set sample data of which any variable is removed, carrying out model retraining and evaluation to obtain a prediction index corresponding to the removed variable; and according to the prediction index and the reference prediction index, obtaining sensitivity of removing variables, and according to the sensitivity, obtaining a plurality of key variables for training of an interpretable model. According to the method, key variables are screened through sensitivity analysis, an interpretable model is constructed, a black box model is replaced, the influence of the variables on prediction is quantified, and the model stability is improved; the complexity and the over-fitting risk are reduced by eliminating redundant variables, and the consumption of computing resources is reduced.

The invention provides a programmable frequency control array radar waveform generation system and method based on an FPGA, and belongs to the technical field of radar systems and digital signal processing. In order to solve the technical problems of complex hardware system, inflexible parameter adjustment and poor multi-channel synchronism of the traditional frequency control array radar, the invention provides an integrated high-performance solution. The system adopts an FPGA (Field Programmable Gate Array) architecture of an integrated processing system (PS) and programmable logic (PL). The method comprises the following steps: a PS end receives waveform control parameters of an upper computer through a communication interface and transmits the waveform control parameters to a PL end; and a plurality of parallel direct digital frequency synthesizer (DDS) modules in the PL end synchronously generate multi-channel digital baseband signals with programmable frequency offset at high precision according to the parameters under the unified driving of the FPGA global clock network. And the digital signal is subjected to digital-to-analog conversion and radio frequency front-end processing to finally form a transmitting wave beam. According to the invention, flexible programmability of parameters is realized by utilizing a PS-PL collaborative architecture, the synchronization precision of multi-channel signals is remarkably improved through a global clock network and a parallel DDS module generated in the FPGA, and meanwhile, the system has the advantages of high integration level and low complexity.

The invention discloses a spacecraft dynamic test execution method based on telemetry data, and relates to the technical field of data adjustment. The method comprises the following steps: firstly, collecting telemetering data of each data source, testing and analyzing the telemetering data, and carrying out real-time reference alignment and data interpretation on the telemetering data of the plurality of data sources of the spacecraft so as to identify the validity and accuracy of the data; and finally, according to the interpretation result and the average time length of the telemetering control instruction, the acquisition strategy of telemetering data is optimized, the high efficiency and accuracy of the data acquisition process are ensured, and the accuracy and reliability of the dynamic test of the spacecraft are ensured. And the time sequence of the telemetering data is ensured by means of low-complexity time performance. Furthermore, telemetering data transmission of the tested software is used as a time sequence control point, and a time sequence control engine is used for performing time sequence synchronous control on the automatic test tool and the test target machine, so that a test result error caused by mismatching of a test time sequence is avoided.

A switched-element digital-to-analog converter (DAC) circuit for use, for example, in a successive-approximation register, SAR, analog-to-digital converter, ADC. The DAC circuit comprises a pool of unary circuit elements (410), each having a common nominal weighting value, and switching (420) and multiplexer (430) arrangements configured so that each unary circuit element (410) in the pool can be independently associated with any one of two or more bits of the switched-element DAC.