In order to solve the problem of low detection rate of frequency hopping (FH) signal under low SNR, an FH signal detection technology based on deep neural network (vgg16 network) is proposed. Firstly, this paper introduces the principle of FH signal. Then, this paper presents the SSD deep neural network detection framework, and uses this framework to detect FH signal and estimate its parameters. Time-frequency distribution maps of FH signals are used as the input of deep neural network model to carry out tag learning and training, and then the trained model is used to realise FH signal detection. At the same time, aiming at the noise problem of time-frequency distribution map, the time-frequency graph correction method of K-means clustering algorithm is proposed. After modification, it is more tolerant to noise. The simulation results show that when the SNR is –4 dB, the detection rate of FH signal can reach more than 88%. Finally, the performance of parameter estimation of FH signal is analysed.

The modulated wideband converter (MWC) is a kind of sub-Nyquist sampling system which is developed from compressed sensing theory. It accomplishes highly accurate broadband sparse signal recovery by multichannel sub-Nyquist sampling sequences. However, when the number of sparse sub-bands becomes large, the amount of sampling channels increases proportionally. Besides, it is very hard to adjust the number of sampling channels when the sparsity changes, because its undersampling board is designed by a given sparsity. Such hardware cost and inconvenience are unacceptable in practical applications. This paper proposes a distributed modulated wideband converter (DMWC) scheme innovatively, which regards one sensor node as one sampling channel and combines MWC technology with a broadband cooperative spectrum sensing network perfectly. Being different from the MWC scheme, DMWC takes phase shift and transmission loss into account in the input terminal, which are unavoidable in practical application. Our scheme is not only able to recover the support of broadband sparse signals quickly and accurately, but also reduces the hardware cost of the single node drastically. Theoretical analysis and numerical simulations show that phase shift has no influence on the recovery of frequency support, but transmission loss degrades the recovery performance to a different extent. Nevertheless, we can increase the amount of cooperative nodes and select satisfactory nodes by a different transmission distance to improve the recovery performance. Furthermore, we can adjust the amount of cooperative nodes flexibly when the sparsity changes. It indicates DMWC is extremely effective in the broadband cooperative spectrum sensing network.

Searching multiple targets with swarm robots is a realistic and significant problem. The goal is to search the targets in the minimum time while avoiding collisions with other robots. In this paper, inspired by pedestrian behavior, swarm robotic pedestrian behavior (SRPB) was proposed. It considered many realistic constraints in the multi-target search problem, including limited communication range, limited working time, unknown sources, unknown extrema, the arbitrary initial location of robots, non-oriented search, and no central coordination. The performance of different cooperative strategies was evaluated in terms of average time to find the first, the half, and the last source, the number of located sources and the collision rate. Several experiments with different target signals, fixed initial location, arbitrary initial location, different population sizes, and the different number of targets were implemented. It was demonstrated by numerous experiments that SRPB had excellent stability, quick source seeking, a high number of located sources, and a low collision rate in various search strategies.

Several spectrum sensing systems based on sub‐Nyquist sampling have been extensively studied to deal with difficulties of traditional wideband spectrum sensing in cognitive radio (CR) networks. The modulated wideband converter (MWC) is an effective application and has drawn considerably more attention over the past few years. In this study, MWC with a nested array (NA) system is proposed to improve spectrum sensing performance, which is an array sensing system with sub‐Nyquist sampling. The simulations show that the connection of co‐array and MWC obtains the lower minimal system sampling rate than MWC. Second, the proposed system enables frequency and power spectrum estimation with sub‐Nyquist sampling for more sources than sensors. Finally, our alternative spectrum sensing system outperforms other conventional methods in terms of sensing accuracy and design complexity.

Aiming at the problem that single‐network hopping signals have not fully utilised its frequency domain sparse characteristic in the parameter estimation, this study proposes a parameter estimation of frequency hopping (FH) signal based on multi‐measurement vector sparse Bayesian learning (MSBL) in modulation wideband converter (MWC). Since the FH signal is sparse in the frequency domain, the authors apply the MSBL method to estimate its parameters. After the signal is sampled by the MWC, the MSBL algorithm is used to reconstruct its support set. Then the time–frequency ridge method is used to estimate the signal's hop duration, time‐hopping, and carrier frequency based on the time–frequency map. Simulation experiments show that under the condition of low signal‐to‐noise ratio, the parameter estimation performance in the case can be improved by up to 65% and anti‐noise performance can be improved up to 6 db compared with the existing method. The result is very close to the Nyquist full sampling and can greatly improve the accuracy of the FH signal parameter estimation in the MWC system and relieve the pressure of the hardware.

Sheet metal parts are widely used in the manufacturing industry. With the number of 3D models increasing, it is essential to retrieve existing resources. Effective similarity assessment would be useful in many downstream applications, such as design reuse and manufacture process revision. We propose a hierarchical representation and similarity assessment algorithm to retrieve 3D models of sheet metal parts. Based on single-side representation extracted from solid models, flat and bent faces are recognized and clustered into groups. The morphological relationship between these two groups is then described and an attributed graph is built to store the connection between flat groups. A hierarchical representation is derived by a simplification and assembly process. This structure supports similarity assessment in different levels of detail; thus, a multiple-resolution formula is introduced to measure the total similarity. The algorithms were implemented based on a neutral standard for the exchange of product model data (STEP) format and a prototype demonstrates the application in shape search and reuse in manufacturing industry.