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Point cloud processing plays an increasingly essential role in three-dimensional (3D) computer vision target, scene parsing, environmental perception, etc. Compared with using aligned point cloud data for classification and segmentation, the strictly rotation-invariant representations show enough robustness. Inspired by the great success of deep learning, we propose a novel neural network called Multi-head Attentional Point Cloud Classification and Segmentation Using Strictly Rotation-invariant Representations. Our research focuses on processing the point cloud rotated in any direction effectively and precisely. First of all, the strictly rotation-invariant point cloud representations are obtained through point projection. Then we apply a multi-head attentional convolution layer (MACL) using attention coding to develop the performance of point cloud feature extraction. Finally, our network assigns different responses and recognizes the overall geometry well through a key point descriptor, adding to the global feature. Our method can explore more in-depth information for accuracy enhancement with attention pooling and multi-layer perceptron (MLP) based on an advanced DenseNet. Our network enjoys 90.63% and 87.50% classification accuracy testing on ModelNet10 and ModelNet40, and 75.15% intersection over union metric (mIoU) evaluating on ShapeNet Part dataset, remaining under any rotation. Rotating experimental results indicate that our framework realizes better point cloud classification and segmentation performance than most state-of-the-art methods.

Beampattern synthesis is a key problem in many wireless applications. With the increasing scale of MIMO antenna array, it is highly desired to conduct beampattern synthesis on a sparse array to reduce the power and hardware cost. In this paper, we consider conducting beampattern synthesis and sparse array construction jointly. In the formulated problem, the beampattern synthesis is designed by minimizing the matching error to the beampattern template, and the Shannon entropy function is first introduced to impose the sparsity of the array. Then, for this nonconvex problem, an iterative method is proposed by leveraging on the alternating direction multiplier method (ADMM) and the majorization minimization (MM). Simulation results demonstrate that, compared with the benchmark, our approach achieves a good trade-off between array sparsity and beampattern matching error with less runtime.

In this paper, the problem of transmit beampattern synthesis for planar array, which deploys one-bit digital-to-analog converters (DACs), is investigated. By properly designing the emitted signal quantized by one-bit DACs, the synthesized transmit beampattern is able to approximate a desired beampattern closely. However, the formulated problem is highly nonconvex due to the quartic objective and the discrete binary constraint. To tackle this problem, we first reformulate it into quadratic objective with interval constraint. On this basis, the alternating direction multiplier method (ADMM) framework is used to solve the resulting quadratic program (QP) problem. Simulation results will demonstrate the effectiveness of our method.

In this paper, the problem of transmit beampattern synthesis (i.e., transmit beamforming) in multiple input multiple output (MIMO) radar which deploys one-bit digital-to-analog converts (DACs) is investigated. We aim to design appropriate transmit signal sequences, which are quantized by one-bit DACs, such that the amount of transmit energy can be focused into mainlobe region as much as possible, meanwhile, the leakage power of sidelobe region is minimized. It is shown that these requirements can be simultaneously fulfilled by minimizing the integrated sidelobe to mainlobe ratio (ISMR) of transmit beampattern with discrete binary constraints. According to this concept, we utilize the alternating direction multiplier method (ADMM) framework to solve the resulting nonconvex problem. Simulation results will demonstrate the effectiveness and improved performance of the proposed method.

Off-chain payment channel is a significant technology to improve transaction performance of blockchain by enabling two blockchain nodes to conduct micro-payments at high frequency without committing all of the transactions on the blockchain, ensuring the “instant finality” of transactions, reducing transaction costs and improving the scalability of the blockchain. Among different types of blockchains, the transaction performance of consortium blockchain is better than that of public blockchain, thus the application of state channel technology in consortium blockchain will further improve the transaction throughput. However, as far as we know, there seems to be seldom existing work in this aspect. A critical challenge is that the existing work applies the state channel technology only in the public blockchain system, and there are many differences between the consortium and public blockchain system in terms of architecture, application scenarios and privacy, etc., leading to certain technical thresholds for migration of the state channel technology. In this paper, we address this challenge by designing CBOP, an off-chain payment service that can interact with the consortium blockchain infrastructure of the Hyperledger Fabric. CBOP can carry out on-chain or off-chain transactions according to the demand of both parties, and provide strong security to protect against malicious attacks. It implements these benefits through two novel designs, dynamic partitioning algorithm for transactions and trusted security guarantee for off-chain payments. Experimental results show that CBOP can provide 300 % more transaction throughput than original design.

Software Defined Network (SDN) is an essential technology in the next-generation network, while the original SDN architecture with a single controller has a single point of failure and scalability issues. Nowadays, several multi-controller solutions are proposed to address the above issues. However, multiple controllers also bring malicious administrator threats and state consistency issues. Therefore, in this paper, we design a novel blockchain-based SDN architecture, named B-SDN, utilizing the characteristics of non-tamper and non-repudiation. We also re-construct the control layer of the SDN network with the combination of horizontal and vertical architecture. Finally, extensive experiments are conducted to evaluate the throughput, latency, memory usage, CPU usage, network traffic of the proposed architecture.

In this paper, a dual-band, circularly polarized (CP) and unidirectional radiative antenna is designed for ISM 2.45/5.8-GHz applications. Two crossed printed dipoles are employed to form the radiation elements of the antenna, which obtained the CP radiation by using a vacant-quarter ring. Each dipole arm is divided into two branches with unequal lengths, and a meander line is inserted into the longer branch to minish its size. A double layer artificial magnetic conductor (AMC) is designed as a reflector to achieve the unidirectional radiation, which exhibits a better performance than traditional single layer AMC. The simulation results show that the −10-dB impedance bandwidths are 2.2-2.6GHz (400MHz), 5.1-6.3GHz (1200MHz) and the 3-dB axial-ratio bandwidths are 2.31-2.56GHz (250MHz), 5.44-6GHz (560MHz) respectively, which absolutely cover the ISM 2.45/5.8-GHz frequency band.

For renewable energy sources such as photovoltaics and wind power gradually increase in the power system, the proportion of conventional synchronous generators has gradually decreased, and the system's primary frequency modulation capacity has weakened. The energy storage system has the advantage of fast active power response, which can effectively improve the dynamic frequency response characteristics of the system. According to its advantages, this paper proposes a fast frequency modulation method for energy storage systems. First, an equivalent model of the regional power grid where the energy storage system participates in primary frequency modulation is established. On this basis, amplitude-frequency characteristics are analyzed. Then, the PADE method is used to simplify the model, and the control parameters are calculated by simplifying the model. In addition, use the root-locus method to verify the control parameters. Finally, the effectiveness of the proposed fast frequency modulation control method for energy storage system is verified by the simulation results.

With the technological development of energy storage systems and their large-scale application in the power grid, it has become possible to use them as black-start power sources for the power grid. Compared with the traditional black-start recovery time, the black-start solution based on the energy storage system can achieve millisecond response, which is expected to greatly reduce the recovery time and reduce power loss after a power grid failure. By establishing a basic output model of the energy storage system and a 30-node power grid system model to configure the capacity of the energy storage system, and analyze the traditional grid black-start scheme and the scheme based on the energy storage system. Finally, the impact of different access points of the energy storage system in the power grid and the importance of the grid load on the black- start scheme is analyzed.

In this paper, the problem of transmit beampattern design in multiple-input multiple-output (MIMO) radar with one-bit digital-to-analog converts (DACs) is investigated. The one-bit waveform sequence can be properly designed by minimizing the integrated sidelobe to mainlobe ratio (ISMR) of the transmit beampattern. However, due to the minimum ISMR criterion and discrete constraint, the formulated optimization problem for such a design is nonconvex and thus difficult to tackle directly. To this end, we employ the semidefinite relaxation (SDR) technique to modify the original problem to its convex counterpart. More importantly, the dimension of resulting large-scale semidefinite programming (SDP) problem is greatly reduced via exploiting the special block-spare structure. Simulation results will demonstrate the effectiveness and improved performance of our method.

Blockchain is becoming increasingly popular, while the performance problems have confused its implementation, especially the limited size of the distributed network. In this paper, we propose Trust-PBFT, a combination of PeerTrust P2P trust calculation model and PBFT consensus algorithm. We first introduce PeerTrust to evaluate the trustworthy of the nodes that qualify as participants of PBFT, which replaces the original that all nodes participate in so that the size of the distributed network can be expanded arbitrarily. Besides, in order to simplify the structure of block storage, particularly the storage of the feedbacks of transaction used to calculate trust value of nodes, we design a tri-chain architecture to store accounts, transactions and feedbacks in three blockchains, respectively. Moreover, due to the introduction of PeerTrust, a weak benefit is to improve the fault tolerance performance efficiently. Finally, we design simulation experiments to evaluate the fault tolerance performance and scalability of Trust-PBFT.