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In this paper, the channel modeling for integrated sensing and communication for 6G systems are studied. The channel modeling approaches, requirements, and challenges for communication cases and sensing cases are summarized and discussed. In particular, a 3-D metallic test chart is designed as a reference for studying electromagnetic imaging quality. The deterministic channel modeling approaches, i.e., geometrical optics method, physical optics method, and integral equation method, are introduced to simulate the imaging channel of the designed 3-D test chart. And the electromagnetic inverse scattering method is adopted as an imaging tool. The test is experimentally measured by 140 GHz signal with horn antennas in a chamber. The modulation transfer function (MTF) is applied to assess the imaging quality numerically, and the simulated and measured images are compared, which suggests that the imaging by integral equation is in agreement very well with that of measurement for 3-D test chart, and the geometric optics and physical optics methods can only show the characteristics of 2-D optical imaging.

Single-crystal sapphire fiber sensors typically have a significant performance deterioration at high temperatures due to the corrosion of pollutants in the external environment. Thus, we propose and demonstrate a novel packaging method to protect them. Such an inert gas packaging method could effectively suppress the formation of microstructural features, such as loss spots, on the surface of sapphire fiber at high temperatures. Therefore, the high-temperature stability of sapphire fiber Bragg grating (SFBG) sensors is greatly enhanced, and they can work stably at 1600 °C for up to 20 hours. In addition, the high-temperature response of these inert-gas-packaged SFBG sensors was tested, and the experimental results showed that they are available for temperature sensing up to 1800 °C with a sensitivity of 49.6 pm/°C, Such inert-gas-packaged SFBG sensors are ideal candidates for high-temperature sensing in many areas such as the aerospace field and the petrochemical industry.

A massively parallel approach of the multilevel fast multipole algorithm (MLFMA) on Deep Computing Unit (DCU) clusters using the Heterogeneous Interface for Portability (HIP) programming model and the Message Passing Interface (MPI) is presented (DCU-MLFMA). The proposed MPI-based multi-DCU parallel implementation is capable of solving challenging problems involving billions of unknowns with a significant speedup, compared with the conventional CPU-based ternary parallelization approach of MLFMA (CPU-MLFMA). Numerical results, including a ship model with length about 6000 wavelengths modeled with 2.6 billion unknowns, are given to demonstrate the capability, as well as the efficiency of the presented method.

This paper presents a CPU/GPU asynchronous computing pattern based improved parallel multilevel fast multipole algorithm (MLFMA) for 3D electromagnetic scattering problems on GPU Cluster. In the presented parallel implementation, the matrix assembly process of the MLFMA is decomposed into CPU execution and GPU execution two parts. The former is performed on CPU using OpenMP multi-threading programming model, while the latter is performed on GPU with CUDA programming model. The execution time between the two parts is overlapped by using the feature of asynchronous execution between CPU and GPU. The performance of the proposed parallel implementation is investigated in terms of accuracy and efficiency. Numerical results show that, with the proposed parallel approach, over 10% speed-up can be attained, compared with the original parallel implementation.

6G is believed to go beyond communication and provide integrated sensing and computing capabilities for a vision of Connected Intelligence with everything connected, everything sensed, and everything intelligent. Integrated sensing and communication will play a vital role for the fusion of physical and cyber worlds. The exploration of higher frequency bands, larger bandwidth, and more advanced large antenna technologies is paving the way towards the goal. In particular, the study of THz opens the possibility to have high resolution sensing and imaging capability on a communication mobile device. In this paper, we take a step along this direction and justify such possibility by building a THz sensing prototype with millimeter level imaging resolution while considering the physical aperture constraint of typical mobile device.

With the rapid development of autonomous driving, real-time object detection on 360° images becomes more and more important. In this paper, we propose a panoramic virtual dataset for training object detectors on 360° images. The most important feature of our dataset includes (1) an auto-generated city scene is created for rendering 360° dataset. (2) annotation work for this dataset is automatic. In addition, we propose a modified YOLOv3 model called Pano-YOLO for real-time panoramic object detection. Compared with YOLOv3, mAP of Pano-YOLO drops 0.39%. While speed is 32.47% faster. Experiments are performed to show that models trained on our virtual dataset can be applied in real world. And Pano-YOLO is capable of real-time object detection task on high-resolution 360° panoramic images and videos.

In the deployments of cyber-physical systems, specifically predictive maintenance and Internet of Things applications, a staggering amount of data can be harvested, transmitted, and recorded. Although the collection of large data sets can be used for many solutions, its utilization is made difficult by the increased overhead on the transmission and limited processing capabilities of the underlying physical system. For such highly correlated and extensive data, this situation is usually described as data-rich, information-poor. We propose for the first time a novel one-stage method, called Information-Abstraction-Net (IA-Net), for the detection of abnormal events in multi-object anomaly detection scenarios by utilizing highly abstracted sensory information instead of the entire sampled data set to elevate the transmission and analysis needs of the system. We find that the computation complexity of IA-Net is reduced by half compared to competing solutions and the detection accuracy is increased by about 5-47%, as well.

Blockchain technology has gained growing popularity in recent years. While the technology works well for most applications, the vulnerability of the blockchain-based system is not well understood. The core part of a blockchain-based system is the consensus algorithm. It can be regarded as a protocol used by all network users to make decisions on the growth of the chain. Malicious users can exploit the vulnerabilities of the consensus algorithm to launch block withholding attacks to earn more profit by increasing their winning possibilities, which are significant threats to the security of blockchain-based applications. In this paper, we describe some scenarios of block withholding attacks and suggest effective mitigation methods accordingly. We propose two hybrid consensus algorithms against the block withholding attacks and evaluate the effectiveness of these algorithms, both analytically and through simulation. Simulation results from the SimBlock simulator verified the effectiveness of the proposed algorithms.

A compact dual-band bandpass filter (BPF) with controllable center frequencies (CFs) and bandwidths based on only one single substrate integrated waveguide (SIW) cavity has been presented. The proposed structure consists mainly of a pair of T-shaped slots which are etched on the top metal layer of the SIW cavity. The modes generated by the slots and the perturbed TE101 and TE102 modes in the cavity are used to achieve dual-frequency response. By changing the width and spacing of the two slots, the desired CFs and bandwidths of the passbands can be tuned independently. A prototype dual-band BPF operating at 3.49 and 9.27 GHz is designed and fabricated to validate the design approach. Measured results agree well with the simulated ones in this work.

This paper proposes a dual band dual-polarized base station antenna. The radiator of this antenna is a pair of modified crossed dipoles, which helps the antenna to operate at two frequency bands. A flat reflector instead of a shaped one is used to shape the radiation pattern of this antenna. The simulation and measurement results show that this antenna has good performance (VSWR 20 dB) at the 4G band and part of the 5G band. It also has good gains in the desired frequency bands.

In 6G, it is envisaged that the function of sensing and communication will coexist and be fully integrated in one system, sharing the same resources in time, frequency and space domains, as well as key elements including waveform, signal processing, hardware, etc. This paper discusses novel applications, key performance requirements, challenges and future research directions for Integrated Sensing and Communication (ISAC) design in 6G. First, four categories of ISAC use cases are described as new services in 6G together with their corresponding performance requirements on 6G design. In addition, it is demonstrated how the information obtained through sensing can significantly improve the performance of communication. Thereafter, the challenges in the design and evaluation of a practical ISAC system are discussed, including sensing and communication performance tradeoffs, hardware imperfections, as well as the investigation of an appropriate channel model. Finally, future research directions are presented to set the path for designing an efficient ISAC network.

In this paper, we consider the power line outage identification problem as a graph signal classification problem, where the signal at each vertex is given as a time series. We propose graph convolutional networks (GCNs) for the task of classifying signals supported on graphs. An important element of the GCN design is filter design. We consider filtering signals in either the vertex (spatial) domain, or the frequency (spectral) domain. Two basic architectures are proposed. In the spatial GCN architecture, the GCN uses a graph shift operator as the basic building block to incorporate the underlying graph structure into the convolution layer. The spatial filter directly utilizes the graph connectivity information. It defines the filter to be a polynomial in the graph shift operator to obtain the convolved features that aggregate neighborhood information of each node. In the spectral GCN architecture, a frequency filter is used instead. A graph Fourier transform operator first transforms the raw graph signal from the vertex domain to the frequency domain, and then a filter is defined using the graph's spectral parameters. The spectral GCN then uses the output from the graph Fourier transform to compute the convolved features. There are additional challenges to classify the time-evolving graph signal as the signal value at each vertex changes over time. The GCNs are designed to recognize different spatiotemporal patterns from high-dimensional data defined on a graph. The application of the proposed methods to power line outage identification shows that these GCN architectures can successfully classify abnormal signal patterns and identify the outage location.

We present in this paper a well-scaling parallel approach of the finite element-domain decomposition method (FEM-DDM) for solving large scale 3D scattering problems on many-core SW26010 processors. In this parallel approach, the whole solution domain is first decomposed in a two-level hierarchical way to minimize the inter process communication. Then the FEM matrix of each unit-level subdomain to be solved in the iterative solution of the interface problem from FEM-DDM, is solved via another sparse approximate inverse (SPAI) preconditioner accelerated iterative solver. This inner–outer iteration scheme is further accelerated by exploring many-core parallelization, with specially designed sparse matrix vector multiplications to improve the data access efficiency. The performance achievements are investigated in terms of accuracy, speed up, and scalability.

An efficient many-core parallel implementation of the multilevel fast multipole algorithm (MLFMA) for 3D electromagnetic scattering problems on SW26010 processor using the Athread programming model is presented. The performance achievements are investigated in terms of accuracy, speed up, and scalability. Numerical results demonstrate that our MLFMA implementation on SW26010 is capable for solving real-life problems with over one million unknowns with a remarkable speed-up(up to 8x).

Industrial IoT (IIoT) in conjunction with Ultra-Reliable Low-Latency Communications (URLLC) often struggles with data-rich, information-poor contexts. Blind Source Separation (BSS) is one of the key technologies which can obtain the desired high-value information from all of the observed raw sensory data. As shown by recent studies, BSS can be both fast enough for low-latency requirements and sufficiently accurate to be a reliable method in large IoT deployments. Nonetheless, the trade-off between signal context usage and data recovery accuracy often affects the separation quality of BSS. In this paper, we propose for the first time a novel dual path convolutional neural network model, called Y-Net, for high accuracy BSS. Specifically, the separation quality is improved by the parallel perception and joint combination of both high-and low-level features of input signals, which we demonstrated through extensive numerical evaluations. In particular, Y-Net improves the Source-to-Distortion Ratio by 2.70% to 35.32% for different target signals, while the model size is only slightly increased, compared to other current solutions.

In order to improve the utilization rate of library seats, this paper analyzes the problems existing in the traditional library seat management system and provide a novel intelligent management system of library seat. The system framework of intelligent management system is established firstly, and different system functions are designed using C/s software and Java language and VB language and XML technology. The experiment results show the new method can effectively improve the comprehensive utilization efficiency of the library seat management system.

Though there are many algorithms which devote to extracting the descriptors for the shapes, the correspondences between shapes established only by descriptor distance are not reliably. To address this issue, a new shape matching algorithm is proposed on the basis of the geometric consistency between shapes. Each shape pair is represented as a node in a graph, and the weight of each edge is computed while the descriptor R-histogram which represents the topological relationship between shapes is adopted. Then, the problem of descriptor matching can be formed as finding the principal cluster of the graph, which is solve by the Hungarian algorithm in this paper. Our proposed approach has been implemented and gives encouraging results under rotation, scaling, shearing and noise.

In this paper, an optical camera/inter-spacecraft measurement integrated navigation method to autonomously guide a formation of spacecraft for small body landing is proposed and the visual landmark recognition method is presented to improve the navigation performance. By introducing the relative range and line of sight (LOS) angle between spacecraft measured by radiometric and optical sensors, the states of two spacecraft are estimated, and the estimation performance degradation of velocity is reduced, which is caused by using LOS to the feature point on small body surface as the unique navigation information. The camera is used for state estimation of the spacecraft, and the inter-spacecraft measurement is introduced to improve the observability and accuracy of the onboard autonomous navigation system, which provides an effective way to guarantee the sufficient accuracy. The observability of the integrated navigation system has been proven based on the analysis of the observability matrix, which illustrates the improvement of observability of the navigation system. Furthermore, a high-accuracy center extraction method is used for crater landmark extraction to obtain accurate LOS measurement information, and the extraction error is less than 1 pixel. To verify the performance of the proposed navigation method, numerical simulations are set up. Simulation results illustrate that the novel navigation method proposed in this paper improves the observability and estimation accuracy of the navigation system.

As the robot gradually develops from indoor to outdoor, multi-sensor fusion is increasingly being applied to the field of robot perception. Among them, LiDAR and binocular sensors are most widely used. Robots use the complementary and redundant data they provide to perform environmental perception and implement certain functions of the robot, such as SLAM, autonomous obstacle avoidance and so on. However, the data obtained by each sensor are relative to its own coordinate system. Data must be converted to a unified coordinate system before data fusion. Therefore, the calibration of the transformation among sensors is the first problem to be solved in data fusion. Moreover, the accuracy of calibration is directly related to the effect of data fusion. Therefore the calibration between sensors is very important and necessary. In this paper, we propose a novel method to find accurate rigid-body transformation for the extrinsic calibration of a 3D LiDAR and a stereo cameras, using two Aruco calibration boards. Also the validity of the algorithm is demonstrated by some experiments.