Graph conjoint attention (CAT) network is one of the best graph convolutional networks (GCNs) frameworks,
which uses a weighting mechanism to identify important neighbor nodes. However, this weighting mechanism is
learned based on static information, which means it is susceptible to noisy nodes and edges, resulting in significant
limitations. In this paper, a method is proposed to obtain context dynamically based on random walk, which allows
the context-based weighting mechanism to better avoid noise interference. Furthermore, the proposed context-based
weighting mechanism is combined with the node content-based weighting mechanism of the graph attention (GAT)
network to form a model based on a mixed weighting mechanism. The model is named as the context-based and
content-based graph convolutional network (CCGCN). CCGCN can better discover important neighbors, eliminate
noise edges, and learn node embedding by message passing. Experiments show that CCGCN achieves state-of-the-
art performance on node classification tasks in multiple datasets.

In the classification problem, deep kernel extreme learning machine (DKELM) has the characteristics of efficient
processing and superior performance, but its parameters optimization is difficult. To improve the classification
accuracy of DKELM, a DKELM algorithm optimized by the improved sparrow search algorithm (ISSA), named as
ISSA-DKELM, is proposed in this paper. Aiming at the parameter selection problem of DKELM, the DKELM
classifier is constructed by using the optimal parameters obtained by ISSA optimization. In order to make up for the
shortcomings of the basic sparrow search algorithm (SSA), the chaotic transformation is first applied to initialize the
sparrow position. Then, the position of the discoverer sparrow population is dynamically adjusted. A learning
operator in the teaching-learning-based algorithm is fused to improve the position update operation of the joiners.
Finally, the Gaussian mutation strategy is added in the later iteration of the algorithm to make the sparrow jump out
of local optimum. The experimental results show that the proposed DKELM classifier is feasible and effective, and
compared with other classification algorithms,the proposed DKELM algorithm aciheves better test accuracy.

The continuous phase modulation (CPM) technique is widely used in range telemetry due to its high spectral
efficiency and power efficiency. However, the demodulation performance of the traditional maximum likelihood
sequence detection (MLSD) algorithm significantly deteriorates in non-ideal synchronization or fading channels. To
address this issue, this work proposes a convolutional neural network (CNN) called the cascade parallel crossing
network (CPCNet) to enhance the robustness of CPM signals demodulation. The CPCNet model employs a multiple
parallel structure and feature fusion to extract richer features from CPM signals. This approach constructs feature
maps at different levels, resulting in a more comprehensive training of the model and improved demodulation
performance. Simulation results show that under Gaussian channel, the proposed CPCNet achieves the same bit
error rate (BER) performance as MLSD method when there is no timing error, but with 1/4 symbol period timing
error, the proposed method has 2 dB demodulation gain compared with CNN and convolutional long short-term
memory deep neural network (CLDNN). In addition, under Rayleigh channel, the BER of the proposed method is
reduced by 5% -87% compared to that of MLSD in the wide signal-to-noise ratio (SNR) region.

For the task of content retrieval, analysis and generation of film and television scene images in the field of
intelligent editing, fine-grained emotion recognition and prediction of images is of great significance. In this paper,
the fusion of traditional perceptual features, art features and multi-channel deep learning features are used to reflect
the emotion expression of different levels of the image. In addition, the integrated learning model with stacking
architecture based on linear regression coefficient and sentiment correlations, which is called the LS-stacking
model, is proposed according to the factor association between multi-dimensional emotions. The experimental
results prove that the mixed feature and LS-stacking model can predict well on the 16 emotion categories of the self-
built image dataset. This study improves the fine-grained recognition ability of image emotion by computers, which
helps to increase the intelligence and automation degree of visual retrieval and post-production system.

Mobile manipulators are used in a variety of fields because of their flexibility and maneuverability. The path
planning capability of the mobile manipulator is one of the important indicators to evaluate the performance of the
manipulator, but it is greatly challenged in the face of maps with narrow channel. To address the problem, an
improved hierarchical motion planner (IHMP) is proposed, which consists of a two-dimensional (2D) path planner
for the mobile base, and a three-dimensional (3D) trajectory planner for the on-board manipulator. Firstly, a
hybrid sampling strategy is proposed, which can reduce invalid nodes of the generated probabilistic roadmap.
Bridge test is used to locate the narrow channel areas, and a Gaussian sampler is deployed in these areas and the
boundaries. Meanwhile, a random sampler is deployed in the rest areas. Trajectory planner for on-board
manipulator is to generate a collision-free and safe trajectory in the narrow channel with collaboration of the 2D path
planner. The experimental results show that IHMP is effective for mobile manipulator motion planning in complex
static environments, especially in narrow channel.

In order to improve the accuracy of used car price prediction, a machine learning prediction model based on the
retention rate is proposed in this paper. Firstly, a random forest algorithm is used to filter the variables in the data.
Seven main characteristic variables that affect used car prices, such as new car price, service time, mileage and so
on, are filtered out. Then, the linear regression classification method is introduced to classify the test data into high
and low retention rate data. After that, the extreme gradient boosting ( XGBoost) regression model is built for the
two datasets respectively. The prediction results show that the comprehensive evaluation index of the proposed
model is 0. 548, which is significantly improved compared to 0. 488 of the original XGBoost model. Finally,
compared with other representative machine learning algorithms, this model shows certain advantages in terms of
mean absolute percentage error (MAPE), 5% accuracy rate and comprehensive evaluation index. As a result, the
retention rate-based machine learning model established in this paper has significant advantages in terms of the
accuracy of used car price prediction.

Lumped element lowpass filter (LPF) for ultra-high frequency (UHF) radio frequency (RF) front-end system is
presented based on multilayer liquid crystal polymer (LCP). The lumped element LPF can achieve miniaturization
and one transmission zero on the stopband by the 8-shaped inductor. The lumped element LPF is fabricated on a 4-
layer LCP substrate with a compact size of 9 mm × 14 mm × 0.193 mm. The measured cut off frequency of the
lumped element LPF is 0.5 GHz with insertion loss (IL) less than 0.37 dB. Both measured and simulated results
suggest that it is a possible candidate for the application of UHF RF front-end system.

In this paper, a W-band broadband vialess microstrip (MS)-to-MS vertical transition in multilayer liquid crystal
polymer (LCP) substrate is presented, which consists of two MS lines in the top layer, a common ground plane and
slotline resonators in the second layer, and a close-loop transmission-line in the third layer. To increase the
passband of the vialess vertical transition, an H-shaped slotline resonator is introduced, which greatly improves the
impedance performance of the slotline resonator, and the full-wave simulated results indicate that insertion loss
(IL) is less than 2 dB and return loss (RL) is better than 10 dB at W-band. To verify this design, the broadband
vertical transition is fabricated and measured. The measured results indicate that a broadband vertical transition
with RL better than 10 dB and IL less than 5.67 dB can be obtained in the frequency range from 70.00 GHz to
104.09 GHz. Due to the fabrication error in the preparation process, the measured results are deteriorated
compared to the simulated results, and the investigation indicates that the deviation is caused by the thickness error
of the LCP substrate.