Recent advancements in environmental perception for autonomous vehicles have been driven by deep learning-based approaches. However, effective traffic target detection in complex environments remains a challenging task. This paper presents a novel dual-modal instance segmentation deep neural network (DM-ISDNN) by merging camera and LIDAR data, which can be used to deal with the problem of target detection in complex environments efficiently based on multi-sensor data fusion. Due to the sparseness of the LIDAR point cloud data, we propose a weight assignment function that assigns different weight coefficients to different feature pyramid convolutional layers for the LIDAR sub-network. We compare and analyze the adaptations of early-, middle-, and late-stage fusion architectures in depth. By comprehensively considering the detection accuracy and detection speed, the middle-stage fusion architecture with a weight assignment mechanism, with the best performance, is selected. This work has great significance for exploring the best feature fusion scheme of a multi-modal neural network. In addition, we apply a mask distribution function to improve the quality of the predicted mask. A dual-modal traffic object instance segmentation dataset is established using a 7481 camera and LIDAR data pairs from the KITTI dataset, with 79,118 manually annotated instance masks. To the best of our knowledge, there is no existing instance annotation for the KITTI dataset with such quality and volume. A novel dual-modal dataset, composed of 14,652 camera and LIDAR data pairs, is collected using our own developed autonomous vehicle under different environmental conditions in real driving scenarios, for which a total of 62,579 instance masks are obtained using semi-automatic annotation method. This dataset can be used to validate the detection performance under complex environmental conditions of instance segmentation networks. Experimental results on the dual-modal KITTI Benchmark demonstrate that DM-ISDNN using middle-stage data fusion and the weight assignment mechanism has better detection performance than single- and dual-modal networks with other data fusion strategies, which validates the robustness and effectiveness of the proposed method. Meanwhile, compared to the state-of-the-art instance segmentation networks, our method shows much better detection performance, in terms of AP and F1 score, on the dual-modal dataset collected under complex environmental conditions, which further validates the superiority of our method.

CubeSats provide a low-cost, convenient, and effective way of acquiring remote sensing data, and have great potential for remote sensing object detection. Although deep learning-based models have achieved excellent performance in object detection, they suffer from the problem of numerous parameters, making them difficult to deploy on CubeSats with limited memory and computational power. Existing approaches attempt to prune redundant parameters, but this inevitably causes a degradation in detection accuracy. In this paper, the novel Context-aware Dense Feature Distillation (CDFD) is proposed, guiding a small student network to integrate features extracted from multi-teacher networks to train a lightweight and superior detector for onboard remote sensing object detection. Specifically, a Contextual Feature Generation Module (CFGM) is designed to rebuild the non-local relationships between different pixels and transfer them from teacher to student, thus guiding students to extract rich contextual features to assist in remote sensing object detection. In addition, an Adaptive Dense Multi-teacher Distillation (ADMD) strategy is proposed, which performs adaptive weighted loss fusion of students with multiple well-trained teachers, guiding students to integrate the learning of helpful knowledge from multiple teachers. Extensive experiments were conducted on two large-scale remote sensing object detection datasets with various network structures; the results demonstrate that the trained lightweight network achieves auspicious performance. Our approach also shows good generality for existing state-of-the-art remote sensing object detectors. Furthermore, by experimenting on large general object datasets, we demonstrate that our approach is equally practical for general object detection distillation.

The paper is devoted to an emerging trend in control—a machine learning control. Despite the popularity of the idea of machine learning, there are various interpretations of this concept, and there is an urgent need for its strict mathematical formalization. An attempt to formalize the concept of machine learning is presented in this paper. The concepts of an unknown function, work area, training set are introduced, and a mathematical formulation of the machine learning problem is presented. Based on the presented formulation, the concept of machine learning control is considered. One of the problems of machine learning control is the general synthesis of control. It implies finding a control function that depends on the state of the object, which ensures the achievement of the control goal with the optimal value of the quality criterion from any initial state of some admissible region. Supervised and unsupervised approaches to solving a problem based on symbolic regression methods are considered. As a computational example, a problem of general synthesis of optimal control for a spacecraft landing on the surface of the Moon is considered as supervised machine learning control with a training set.

A fast and automated technique is crucial for bearing faults diagnosis during operation. To circumvent the intricacies of signal spectrum analysis, a diagnostic method named the pulse signal-to-noise ratio (PSNR) test is proposed by exploiting the time-domain sparsity of fault signals under a constant angular rate, which are modeled as periodic pulses with consistent duty cycle and power. The algorithm employs a statistic called pulse signal-to-noise ratio to both identify faults and determine their location. A simplified variant of the PSNR test, named pulse signal-to-noise amplitude ratio (PSNAR) test, is further proposed for near multiplication-free fast diagnosis. Data from Machinery Failure Prevention Technology (MFPT) and Case Western Reserve University (CWRU) were used to verify the algorithm.

In modern microwave personnel screening systems, a 2-D synthetic aperture is used to obtain a radar image with resolution limited by the diffraction of electromagnetic waves. Therefore, the antenna system of the screening system has large dimensions, a large number of switched channels, and a high cost, which limits a wider use of these systems, for example, at ground transport hubs or administrative buildings. In this article, we consider a different method of forming synthetic aperture, in which it is formed due to the relative movement of a subject near a stationary antenna system. Using the described experimental setup and imitation experiment with a mannequin, it was shown that the proposed method can significantly reduce the dimensions of a prospective screening system and the number of channels in the antenna system and at the same time obtain detailed radar images of hidden objects.

Determination of the concentration and size of the circulating immune complexes in the blood is an essential part of diagnostics of immune diseases. In this work, we suggest using the dynamic light scattering method to determine the sizes of circulating immune complexes in blood serum. By the dynamic light scattering spectrometer, we found that for healthy and sick donors, the size and concentration of circulating immune complexes differed significantly. The dynamics of formation of these complexes were also examined in this work. It was shown that the formation of immune complexes in the blood of healthy donors is faster than the same reactions in the blood serum of donors with diseases. The results can be used in the diagnostics of the immune status and detection of chronic inflammation. We can recommend the dynamic light scattering method for implementation in biomedical diagnostics.