In this paper, the analysis and design method for the suboptimum Class- $\text{E}_{\mathrm {M}}/\text{F}_{3}$ inverter (i.e., only the zero-voltage switching (ZVS), the zero-voltage-derivative switching (ZVDS), the zero-current switching (ZCS) conditions are satisfied) with a duty ratio D =0.5 are presented. In the proposed design method, a new design parameter K called the slope of switch current (when the switch turns off) is introduced for defining the suboptimum degree and the distance from the optimum condition. By using the design method of the soft switching resonant inverter, the third harmonic filter circuit is innovatively introduced to reduce the current flowing through the parallel capacitor of the main circuit, and greatly reduce the peak switching voltage of the main circuit and auxiliary circuit. And the circuit waveforms and design equations can be derived in detail. Compared with the conventional Class- $\text{E}_{\mathrm {M}}$ inverter, the proposed inverter can offer the much lower peak switching voltage and higher efficiency. To verify the validity of the proposed method, a Class- $\text{E}_{\mathrm {M}}/\text{F}_{3}$ inverter operating at 1MHz is designed using the IRFZ24N transistor. The experimental results show that the output power of the new inverter is 15.138W, the efficiency reaches 96.4%, and the peak switching voltage of the main circuit and auxiliary circuit is reduced by 29.3% and 15.6%, respectively. The PSpice-simulation results and the experimental measurement results of the designed inverter are agreed with the analytical results, which verified the effectiveness of the proposed design method.
Abstract Maneuvering targets tracking technology has important research value in the fields of space target detection, civil aviation, ground traffic control and so on. To solve this problem, a new variable structure multi-model tracking algorithm based on fuzzy logic and strong tracking filter is proposed. The basic ideas of fuzzy logic inference and strong tracking filter are analyzed. An adaptive grid multi-model algorithm based on strong tracking filter and fuzzy interaction is established. The algorithm is also compared with generalized pseudo-Bayesian algorithm, interactive multi-model algorithm and traditional variable structure multi-model algorithm. The simulation results further prove the correctness and effectiveness of the proposed algorithm. Under the same simulation conditions, tracking performance of the proposed algorithm is significantly improved, and it has a higher cost-effectiveness ratio. Therefore, the proposed algorithm has a good application prospect in maneuvering target tracking.
For cyclic LDPC codes, we propose to use their automorphism groups to improve the iterative decoding performance. The basic idea is to construct nonequivalent parity-check matrices via column permutations. Three types of iterative decoders are devised to take advantage of the code's automorphism group. In this paper we focus on cyclic LDPC codes defined by a circulant parity-check matrix and consider two known subgroups of the automorphism group of a cyclic code. For the large class of idempotent-based cyclic LDPC codes in the literature, we show that the two subgroups only provide equivalent parity-check matrices and thus cannot be harnessed for iterative decoding. Towards exploiting the automorphism group of a code, we propose a new class of cyclic LDPC codes based on pseudo-cyclic MDS codes with two information symbols, for which nonequivalent parity-check matrices are obtained. Simulation results show that for our constructed codes of short lengths, the automorphism group can significantly enhance the iterative decoding performance.
To meet the requirement of high sampling rate and high resolution in a sampling system, a time-interleaved ADCs system is a good option. However there are several mismatches such as timing mismatch, gain mismatch and offset mismatch which make the performance of time-interleaved ADCs system deteriorate sharply. In the paper a new error detection method is proposed for estimating the error values. And a genius calibration algorithm is raised at the same time which is very simple in structure and easy to be implemented on FPGA with little resources. The scheme has been simulated in MATLAB with real data sampled by ADC. And the simulation results obviously indicate that the errors are eliminated effectively and the performance of ADCs system is greatly improved as well.
Abstract Atmospheric turbulence and pointing errors are two major factors affecting satellite-to-ground coherent optical communication links. This study considers the effects of power scintillation and phase jitter caused by atmospheric turbulence, as well as active mode compensation for wavefront phase distortion. Additionally, residual pointing errors due to platform micro-vibrations are taken into account, and a statistical channel model is derived under the combined effects of atmospheric turbulence and residual pointing errors during tracking and acquisition. Based on this model, a closed-form approximate expression for the average bit error rate (BER) of 16QAM satellite-to-ground coherent optical communication is derived using the Meijer-G function. The results show that the derived BER expression matches well with numerical integration results, confirming its accuracy and applicability under various link conditions. Through numerical simulations, the effects of residual pointing error variance, turbulence strength, satellite zenith angle, and transceiver antenna aperture on the performance of 16QAM satellite-to-ground coherent optical communication are evaluated. The findings offer valuable theoretical insights for the design and optimization of future satellite-to-ground 16QAM coherent optical communication systems.
Abstract Radar emitter individual identification is an important research topic in modern electronic intelligence and electronic support systems. Based on the analysis of the individual characteristics of the radar emitter, this paper proposes a radar emitter individual recognition algorithm based on the maximum entropy spectrum estimation feature of the pulse envelope. The method is characterized by the maximum entropy spectrum estimation of pulse envelope and combined with long short-term memory (LSTM) to complete the identification of radar emitter individuals. Compared with the traditional methods based on local features, the entropy sequence used in this paper can be regarded as a global estimation, so the features have better robustness. The simulation results show that when the transmitter transmits the same single-tone signal, the recognition accuracy is close to 82 % when the signal-to-noise ratio is 0 dB. When the signal-to-noise ratio is 5 dB, the recognition accuracy can reach 94 %, and at 10 dB, the recognition accuracy is close to 99 %. Under the condition that three radiation sources randomly send signals of nine different modulation types, the recognition accuracy reaches 80 % when the signal-to-noise ratio is 0 dB. When the signal-to-noise ratio is 5 dB, the recognition accuracy is improved to 90 %. When the signal-to-noise ratio is 10 dB, the recognition accuracy reaches 97 %. This conclusion proves that the signal type has little effect on the recognition accuracy, and the method has high accuracy and good robustness.
The signal in the receiver is mainly a combination of different modulation types due to the complex electromagnetic environment, which makes the modulation recognition of the mixed signal a hot topic in recent years. In response to the poor adaptability of existing mixed signals recognition methods, this paper proposes a new recognition method for mixed signals based on cyclic spectrum projection and deep neural network. Firstly, through theoretical derivation, we prove the feasibility of using cyclic spectrum for mixed communication signal identification. Then, we adopt grayscale projections on the two-dimensional cyclic spectrum as identifying representation. And a new nonlinear piecewise mapping and directed pseudo-clustering method are used to enhance the above-mentioned grayscale images, which reduces the impact of energy ratios and symbol rates on signal identification. Finally, we use deep neural networks to extract deep abstract modulation information to achieve effective recognition of mixed signals. Simulation results show that the proposed method is robust against noise. When signal-to-noise ratio is not less than 0 dB, the average recognition rate is greater than 95%. Furthermore, this method exhibits good robustness towards the changes in signal symbol rates and energy ratios between mixed signals.
Lung disease screening using Chest x-ray (CXR) radiographs can obviously decrease the incidence of lung cancer. Using computer-aided diagnosis system to assist doctors in lung disease screening can greatly improve the diagnosis efficiency. In this paper, a coarse feature reuse deep neural network for CXR lesion detection is proposed. Firstly, we design a coarse feature reuse (CFR) block that can reuse low-level semantic features and extract high-level semantic information, which is used to replace the max-pooling layer in the shallow part of the network to achieve better feature extraction. A novel backbone network - RRCNet, which combines RepVGG block and Resblock, is proposed. The RepVggblock is used for better feature extraction at shallow layers and the Resblock is used for better feature fusion at deep layers. Extensive experiments on VinDr-CXR dataset demonstrate that our RRCNet-based detection network outperformes other classic detectors on both mAP (17.67%) and inference speed (0.1426s).
Based jointly on idempotents and modular Golomb rulers, we construct a class of nonbinary cyclic low-density parity-check (LDPC) codes. The defining parity-check matrix is a sparse circulant, on which we put two constraints: 1) the characteristic polynomial is an idempotent, 2) the nonzero elements of the first row are located on a modular Golomb ruler. We show that the second constraint forms a necessary and sufficient condition for the Tanner graph to have no cycles of length 4. The minimum distance of the code is proved equal to the column weight of the parity-check matrix plus one. A search algorithm is presented, with which we obtain some high rate codes with large minimum distances. The issue of code equivalence is also discussed. Simulation results show that the obtained codes perform well under iterative decoding.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)