Under the background of non-homogenous and dynamic time-varying clutter, the processing ability of the traditional constant false alarm rate (CFAR) detection algorithm is significantly reduced, as well as the detection performance. This paper proposes a CFAR detection algorithm based on clutter knowledge (CK-CFAR), as a new CFAR, to improve the detection performance adaptability of the radar in complex clutter background. With the acquired clutter prior knowledge, the algorithm can dynamically select parameters according to the change of background clutter and calculate the threshold. Compared with the detection algorithms such as CA-CFAR, GO-CFAR, SO-CFAR, and OS-CFAR, the simulation results show that CK-CFAR has excellent detection performance in the background of homogenous clutter and edge clutter. This algorithm can help radar adapt to the clutter with different distribution characteristics, effectively enhance radar detection in a complex environment. It is more in line with the development direction of the cognitive radar.

The traditional manual inspection is gradually replaced by the unmanned aerial vehicles (UAV) automatic inspection. However, due to the limited computational resources carried by the UAV, the existing deep learning-based algorithm needs a large amount of computational resources, which makes it impossible to realize the online detection. Moreover, there is no effective online detection system at present. To realize the high-precision online detection of electrical equipment, this paper proposes an SSD (Single Shot Multibox Detector) detection algorithm based on the improved Dual network for the images of insulators and spacers taken by UAVs. The proposed algorithm uses MnasNet and MobileNetv3 to form the Dual network to extract multi-level features, which overcomes the shortcoming of single convolutional network-based backbone for feature extraction. Then the features extracted from the two networks are fused together to obtain the features with high-level semantic information. Finally, the proposed algorithm is tested on the public dataset of the insulator and spacer. The experimental results show that the proposed algorithm can detect insulators and spacers efficiently. Compared with other methods, the proposed algorithm has the advantages of smaller model size and higher accuracy. The object detection accuracy of the proposed method is up to 95.1%.

Recently, many AI-aided layout design systems are developed to reduce tedious manual intervention based on deep learning. However, most methods focus on a specific generation task. This paper explores a challenging problem to obtain multiple layout design generation (LDG), which generates floor plan or urban plan from a boundary input under a unified framework. One of the main challenges of multiple LDG is to obtain reasonable topological structures of layout generation with irregular boundaries and layout elements for different types of design. This paper formulates the multiple LDG task as an image-to-image translation problem, and proposes a conditional generative adversarial network (GAN), called LDGAN, with adaptive modules. The framework of LDGAN is based on a generator-discriminator architecture, where the generator is integrated with conditional convolution constrained by the boundary input and the attention module with channel and spatial features. Qualitative and quantitative experiments were conducted on the SCUT-AutoALP and RPLAN datasets, and the comparison with the state-of-the-art methods illustrate the effectiveness and superiority of the proposed LDGAN.

Model-based sensorless control of permanent magnet synchronous motor (PMSM) is promising for high-speed operation to estimate motor state, which is the speed and the position of the rotor, via electric signals of the stator, beside the inevitable fact that estimation accuracy is degraded by electromagnet interference (EMI) from switching devices of the converter. In this paper, the simulation system based on Luenberger observer and phase-locked loop (PLL) has been established, analyzing impacts of EMI on motor state estimations theoretically, exploring influences of EMI with different cutoff frequency, rated speeds, frequencies and amplitudes. The results show that Luenberger observer and PLL have strong immunity, which enable PMSM can still operate stably even under certain degrees of interference. EMI produces sideband harmonics that enlarge pulsation errors of speed and position estimations. Additionally, estimation errors are positively correlated with cutoff frequency of low-pass filter and the amplitude of EMI, and negatively correlated with rated speed of the motor and the frequency of EMI.  When the frequency is too high, its effects on motor state estimations are negligible. This work contributes to the comprehensive understanding of how EMI affects motor state estimations, which further enhances practical application of sensorless PMSM.

Searchable symmetric encryption (SSE) enables clients to search encrypted data. Curtmola et al. (ACM CCS 2006) formalized a model and security notions of SSE and proposed two concrete constructions called SSE-1 and SSE-2. After the seminal work by Curtmola et al., SSE becomes an active area of encrypted search. In this paper, we focus on two unnoticed problems in the seminal paper by Curtmola et al. First, we show that SSE-2 does not appropriately implement Curtmola et al.'s construction idea for dummy addition. We refine SSE-2's (and its variants') dummy-adding procedure to keep the number of dummies sufficiently many but as small as possible. We then show how to extend it to the dynamic setting while keeping the dummy-adding procedure work well and implement our scheme to show its practical efficiency. Second, we point out that the SSE-1 can cause a search error when a searched keyword is not contained in any document file stored at a server and show how to fix it.