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Abstract The integration of electronic reconnaissance platforms and payloads in the electromagnetic and spatial domains of cognition is necessary for the successful execution of cognitive electronic reconnaissance missions. However, in partially observable environments, platforms and payloads interact in separate operational domains, making it a challenge for the model. The authors create a theoretical model of cognitive electronic reconnaissance, present an analysis method of model elements and derives adequate requirements for optimal radiation source sorting or recognition. Then, a 3D cognitive reconnaissance simulator ‘Scouter’ to simulate the dynamic interaction process of space electronic reconnaissance missions is developed and a deep reinforcement learning framework to optimise the electronic reconnaissance strategies of Unmanned Aerial Vehicle (UAV) is designed. For the first time, a method for judging reconnaissance area coverage using a behavioural angle is proposed. This method enables a deep reinforcement learning‐based UAV with better reconnaissance behaviours and to realise the stable targeting of the target by the receiving antenna beam during reconnaissance. Moreover, the fast convergence of UAV agent training is ensured through the normalisation of partially observable status and reward shaping. Verified by adequate numerical simulations, a trained UAV can complete a 100 reconnaissance cycles mission with a completion rate of 91.9%. Even after the reconnaissance cycles are prolonged to 600, the mission completion rate can still be maintained at 78.5%. The most satisfying aspect is that the UAV comprehends the idea of reconnaissance and behaves in a way that is typical of it, such as by reciprocating or circling the edge of the radar's detecting power.

Large-scale pre-training models have achieved great success in the field of natural language processing by using large-scale corpora and pre-training tasks.With the gradual development of large models, the continual learning ability of large models has become a new research focus.The continual learning technology of the Peng Cheng series large models, the exploration of practice and the still facing challenges were mainly introduced, including the Peng Cheng series continual learning technology through task expansion, data increment and knowledge reasoning, Peng Cheng PANGU multi-task continual learning and the practical exploration of the continual learning ability of the Peng Cheng TONGYAN open source large model, the vocabulary update, semantic mapping and knowledge conflicts that the large model faces in the process of continual learning.

Abstract Background Aberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identify a non-invasive DNA methylation-based screening approach using cell-free DNA (cfDNA) for early detection of hepatocellular carcinoma (HCC). Methods Differentially, DNA methylation blocks were determined by comparing methylation profiles of biopsy-proven HCC, liver cirrhosis, and normal tissue samples with high throughput DNA bisulfite sequencing. A multi-layer HCC screening model was subsequently constructed based on tissue-derived differentially methylated blocks (DMBs). This model was tested in a cohort consisting of 120 HCC, 92 liver cirrhotic, and 290 healthy plasma samples including 65 hepatitis B surface antigen-seropositive (HBsAg+) samples, independently validated in a cohort consisting of 67 HCC, 111 liver cirrhotic, and 242 healthy plasma samples including 56 HBsAg+ samples. Results Based on methylation profiling of tissue samples, 2321 DMBs were identified, which were subsequently used to construct a cfDNA-based HCC screening model, achieved a sensitivity of 86% and specificity of 98% in the training cohort and a sensitivity of 84% and specificity of 96% in the independent validation cohort. This model obtained a sensitivity of 76% in 37 early-stage HCC (Barcelona clinical liver cancer [BCLC] stage 0-A) patients. The screening model can effectively discriminate HCC patients from non-HCC controls, including liver cirrhotic patients, asymptomatic HBsAg+ and healthy individuals, achieving an AUC of 0.957(95% CI 0.939–0.975), whereas serum α-fetoprotein (AFP) only achieved an AUC of 0.803 (95% CI 0.758–0.847). Besides detecting patients with early-stage HCC from non-HCC controls, this model showed high capacity for distinguishing early-stage HCC from a high risk population (AUC=0.934; 95% CI 0.905–0.963), also significantly outperforming AFP. Furthermore, our model also showed superior performance in distinguishing HCC with normal AFP (< 20ng ml−1) from high risk population (AUC=0.93; 95% CI 0.892–0.969). Conclusions We have developed a sensitive blood-based non-invasive HCC screening model which can effectively distinguish early-stage HCC patients from high risk population and demonstrated its performance through an independent validation cohort. Trial registration The study was approved by the ethic committee of The Second Xiangya Hospital of Central South University (KYLL2018072) and Chongqing University Cancer Hospital (2019167). The study is registered at ClinicalTrials.gov(# NCT04383353 ).

The orthogonal frequency division multiplexing (OFDM) modulation technique has been used widely in visible light communications (VLC) systems to combat intersymbol interference. At the same time, the inherent drawback of OFDM with a high peak-to-average power ratio (PAPR) is brought into OFDM visible light communications (VLC-OFDM). Furthermore, considering the limited dynamic range characteristics of light-emitting diodes, the performance degradation caused by a high PAPR is more serious in VLC-OFDM. In this paper, we propose a partial transmit sequence (PTS) technique based on the combination of a genetic algorithm (GA) and a hill-climbing algorithm (GH-PTS) to solve the problem of high PAPR. GH-PTS is a modified PTS technique based on GA-PTS. Essentially, GH-PTS is a local optimization of GA-PTS. Simulation results show that the optimized technique is able to reduce PAPR more effectively without any loss of bit error rate performance than the GA-PTS technique in VLC-OFDM system. [ABSTRACT FROM AUTHOR]

We present a scheme based on precoding carrier allocation and independent component analysis (ICA) for indoor multiple-input-multiple-output (MIMO) visible light communication (VLC). In order to improve the reliability of the ICA algorithm for the mixed signal separation, at the sending end, frequency doubled-carriers are employed to module the parallel data which ensure that the modulated signals are independent of each other. The ICA algorithm is applied to separate the mixed signal at the receiving end directly without the requirement of the channel information. Simulation results show that this indoor MIMO VLC scheme can achieve excellent performance. When the signal-to-noise ratio is equal to 14 dB, the bit error ratio (BER) reaches the level of 10-5. So, the communication performance is superior to that of the commonly used schemes based on the channel estimation. [ABSTRACT FROM AUTHOR]