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Millimeter-wave (mmWave) communication systems leverage the directional beamforming capabilities of antenna arrays equipped at the base stations (BS) to counteract the inherent high propagation path loss characteristic of mmWave channels. In downlink mmWave transmissions, i.e., from the BS to users, distinguishing users within the same beam direction poses a significant challenge. Additionally, digital baseband precoding techniques are limited in their ability to mitigate inter-user interference within identical beam directions, representing a fundamental constraint in mmWave downlink transmissions. This study introduces an innovative analog beamforming-based interference mitigation strategy for downlink transmissions in reconfigurable intelligent surface (RIS)-assisted hybrid analog–digital (HAD) mmWave systems. This is achieved through the joint design of analog beamformers and the corresponding coefficients at both the RIS and the BS. We first present derived closed-form approximation expressions for the achievable rate performance in the proposed scenario and establish a stringent upper bound on this performance in a large number of RIS elements regimes. The exclusive use of analog beamforming in the downlink phase allows our proposed transmission algorithm to function efficiently when equipped with low-resolution analog-to-digital/digital-to-analog converters (A/Ds) at the BS. The energy efficiency of the downlink transmission is evaluated through the deployment of six-bit A/Ds and six-bit pulse-amplitude modulation (PAM) signals across varying numbers of activated RIS elements. Numerical simulation results validate the effectiveness of our proposed algorithms in comparison to various benchmark schemes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel low-complexity hand gesture recognition framework via a multiple Frequency Modulation Continuous Wave (FMCW) radar-based sensing system. In this considered system, two radars are deployed distributively to acquire motion vectors from different observation perspectives. We first independently extract reflection points of the interested target from different radars by applying the proposed neighboring reflection points detection method after processing the traditional 2-dimensional Fast Fourier Transform (2D-FFT). The obtained sufficient corresponding information of detected reflection points, e.g., distances, velocities, and angle information, can be exploited to synthesize motion velocity vectors to achieve a high signal-to-noise ratio (SNR) performance, which does not require knowledge of the relative position of the two radars. Furthermore, we utilize a long short-term memory (LSTM) network as well as the synthesized motion velocity vectors to classify different gestures, which can achieve a significantly high accuracy of gesture recognition with a 1600-sample data set, e.g., 98.0 % . The experimental results also illustrate the robustness of the proposed gesture recognition systems, e.g., changing the environment background and adding new gesture performers. [ABSTRACT FROM AUTHOR]

Vessel trajectory prediction is an important step in route planning, which could help improve the efficiency of maritime transportation. In this article, a high-accuracy long-term trajectory prediction algorithm is proposed for oil tankers. The proposed algorithm extracts a set of waymark points that are representative of the key traveling patterns in an area of interest by applying DBSCAN clustering to historical AIS data. A novel path-finding algorithm is then developed to sequentially identify a subset of waymark points, from which the predicted trajectory to a fixed destination is produced. The proposed algorithm is tested using real data offered by the Danish Maritime Authority. Numerical results demonstrate that the proposed algorithm outperforms state-of-the-art vessel trajectory prediction algorithms and is able to make high-accuracy long-term trajectory predictions. [ABSTRACT FROM AUTHOR]

Compared to traditional rough casting grinding (RCG), the individualization of castings is very different, which makes it difficult to realize the automation of casting grinding. At this stage, the primary method is manual grinding. In this study, the regional casting grinding system based on feature points is adopted to achieve the personalized grinding of castings and improve the grinding efficiency and the automation level of the manufacturing process. After preprocessing the point cloud, the fast point feature histogram (FPFH) descriptor is used to describe the features of each region and construct the local template. The position of the local region is obtained by template matching. The random sample consensus (RANSAC) algorithm is used to calculate the plane and fit the point cloud to obtain the contact point trajectory of the grinding head. Then, according to different polishing methods, different polishing poses are generated. The simulation experimental results show that the system has good adaptability, and the consistency of finished products is good. [ABSTRACT FROM AUTHOR]

This paper proposes an effective strongest angles of arrival (AoAs) estimation algorithm for a hybrid millimeter wave (mmWave) communication system with 1-bit analog-to-digital/digital-to-analog converters (A/Ds) equipped at transceivers. The proposed algorithm aims to reduce the required number of estimation overheads, while maintaining the root mean square error (RMSE) of strongest AoA estimates at the base station. We obtain the quantization thresholds of A/Ds for different signal-to-noise ratios (SNRs) and numbers of antennas via numerical simulations, based on which, the strongest AoAs can be estimated with a small amount of overheads. The proposed algorithm is compared with conventional schemes including 1-bit FFT and 1-bit exhaustive search, as well as 1-bit Cramér-Rao lower bound. Simulation results verify the effectiveness of our proposed algorithm in terms of reducing estimation overheads while maintaining reasonable estimation performance in low SNRs. [ABSTRACT FROM AUTHOR]