Abstract: Embodiments of the application provides a method for encoding. The method includes: receiving a to-be-encoded data block; encoding the data block at an aggregation level of 2L, where a formula used during the encoding is as follows: [ u . 2 ? ? L u . L ] ? [ G LN 0 G LN G LN ] = ? C . 2 ? ? L C . L ? ( I ) u . L = { u L u L - 1 … u 1 } , u . 2 ? ? L = ? { u 2 ? L u 2 ? L - 1 … u L + 1 } , ? c . L = { c L c L - 1 … c 1 } , c . 2 ? ? L = { c 2 ? L c 2 ? L - 1 … c L + 1 } , G LN = G N ? log 2 ( L ) ( II ) L=2n, and n is a natural number greater than or equal to 0; and outputting an encoded data block.

Abstract: The invention discloses a data transmission method and apparatus. The method determines a target CTU from multiple candidate CTUs according to a first partial identifier in a terminal device identifier, where the terminal device identifier includes the first partial identifier and a second partial identifier. The method sends an uplink data packet to a network device by using the target CTU, where the uplink data packet carries the second partial identifier. According to the data transmission method and apparatus disclosed, the first partial identifier in the terminal device identifier can be implicitly indicated to reduce signaling overheads of a system.

Abstract: A method for predicting a remaining useful life of a lithium battery based on a wavelet denoising and a relevance vector machine, relating to a method for estimating health condition and predicting remaining useful life of lithium battery, includes steps of: (1) obtaining health condition data of each of charge-discharge cycles of the lithium battery by measurement; (2) processing capacity data measured of the lithium battery with wavelet double denoising; (3) calculating a capacity threshold where the lithium battery fails; (4) referring to capacity data and charge-discharge cycle data of the lithium battery, applying a differential evolution algorithm for optimizing a width factor of the relevance vector machine; and (5) predicting the remaining useful life of the lithium battery with the relevance vector machine optimized by the differential evolution algorithm. The method is simple and effective, which can accurately predict remaining useful life of lithium battery.

Abstract: A Deep Belief Network (DBN) feature extraction-based analogue circuit fault diagnosis method comprises the following steps: a time-domain response signal of a tested analogue circuit is acquired, where the acquired time-domain response signal is an output voltage signal of the tested analogue circuit; DBN-based feature extraction is performed on the acquired voltage signal, wherein learning rates of restricted Boltzmann machines in a DBN are optimized and acquired by virtue of a quantum-behaved particle swarm optimization (QPSO); a support vector machine (SVM)-based fault diagnosis model is constructed, wherein a penalty factor and a width factor of an SVM are optimized and acquired by virtue of the QPSO; and feature data of test data are input into the SVM-based fault diagnosis model, and a fault diagnosis result is output, where the feature data of the test data is generated by performing the DBN-based feature extraction on the test data.

Abstract: A rate matching method for a polar code is provided, to improve performance. The method includes: encoding, based on an N*N encoding matrix of a polar code, a sequence including N first bits, to generate a mother code including N second bits, where the N first bits are in a one-to-one correspondence with N rows in the encoding matrix in sequence, and the N second bits are in a one-to-one correspondence with N columns in the encoding matrix in sequence; determining N?M to-be-punctured second bits from the N second bits, where at least one first bit in N?M first bits participating in encoding of the N?M second bits belongs to the first M first bits in the N first bits, and the N?M first bits are fixed bits; and puncturing the N?M second bits, to obtain a target polar code including M second bits.

Abstract: Embodiments of this application provide a method and an apparatus for constructing a sequence of polar codes. In an implementation, a tectonic sequence P? is read from a tectonic sequence P, where the length of the tectonic sequence P? is an encoding length M and the length of the tectonic sequence P is N. The tectonic sequence P? is demapped to a reliability ranking sequence Q? based on a rate matching rule, and K elements that have largest reliability values is read from the reliability ranking sequence Q?, to obtain an information bit sequence number set A.

Abstract: Embodiments of this application provide a signal transmission method, including: obtaining, by a communications device, a bit group from a to-be-sent bitstream, where the bit group includes at least two bits; modulating, by the communications device, the bit group based on a codebook to obtain at least two modulated symbols; mapping, by the communications device, each of the at least two modulated symbols to a corresponding timeslot in a frame; and sending, by the communications device, the mapped at least two modulated symbols, where different modulated symbols obtained by using a same bit group are mapped to different timeslots, and each timeslot includes a plurality of continuous modulated symbol mapping locations used to map modulated symbols obtained by using a plurality of bit groups of the to-be-sent bitstream.

Abstract: The invention discloses a data transmission method and apparatus. The method determines a target CTU from multiple candidate CTUs according to a first partial identifier in a terminal device identifier, where the terminal device identifier includes the first partial identifier and a second partial identifier. The method sends an uplink data packet to a network device by using the target CTU, where the uplink data packet carries the second partial identifier. According to the data transmission method and apparatus disclosed, the first partial identifier in the terminal device identifier can be implicitly indicated to reduce signaling overheads of a system.

Abstract: An analog circuit fault diagnosis method using a single testable node comprises the following steps: (1) obtaining prior sample data vectors under each fault mode; (2) computing a statistical average of the prior sample data vectors under each of the fault modes; (3) decomposing a signal by an orthogonal Haar wavelet filter set; (4) extracting the feature factor of the prior sample fault modes; (5) extracting a fault-mode-to-be-tested feature factor; (6) computing a correlation coefficient matrix and correlation metric parameters between the feature factor of the prior sample fault modes and the feature factor of the fault-mode-to-be-tested; and (7) detennining a fault mode according to a maximal correlation principle by comparing the correlation metric parameters.

Abstract: An analog circuit fault mode classification method comprises the following implementation steps: (1) collecting M groups of voltage signal sample vectors Vij to each of fault modes Fi of the analog circuit by using a data collection board; (2) sequentially extracting fault characteristic vectors VijF of the voltage signal sample vectors Vij by using subspace projection; (3) standardizing the extracted fault characteristic vectors VijF to obtain standardized fault characteristic vectors; (4) constructing a fault mode classifier based on a support vector machine, inputting the standardized fault characteristic vectors, performing learning and training on the classifier, and determining structure parameters of the classifier; and (5) completing determination of fault modes according to fault mode determination rules. The fault mode classifier of the present invention is simple in learning and training and reliable in mode classification accuracy.

Abstract: A method for predicting a remaining useful life of a lithium battery based on a wavelet denoising and a relevance vector machine, relating to a method for estimating health condition and predicting remaining useful life of lithium battery, includes steps of: (1) obtaining health condition data of each of charge-discharge cycles of the lithium battery by measurement; (2) processing capacity data measured of the lithium battery with wavelet double denoising; (3) calculating a capacity threshold where the lithium battery fails; (4) referring to capacity data and charge-discharge cycle data of the lithium battery, applying a differential evolution algorithm for optimizing a width factor of the relevance vector machine; and (5) predicting the remaining useful life of the lithium battery with the relevance vector machine optimized by the differential evolution algorithm. The method is simple and effective, which can accurately predict remaining useful life of lithium battery.