EURASIP Journal on Advances in Signal Processing最新文献

Active noise control (ANC) is gaining ever-increasing attention owing to its powerful ability to attenuate low-frequency noise. The computational complexity of an ANC system may directly affect its computational efficiency, control performance, and hardware costs. Therefore, the focus of this paper is mainly on discussing the development of ANC systems with emphasis on reducing computational complexity. The ANC systems are classified into two groups of narrowband and broadband systems. The computational complexity analysis is provided to show the computational merit of each system with respect to the conventional ANC systems. In addition, numerical simulations are performed to evaluate the convergence speed and noise reduction performance of the considered systems. The results show that, in the narrowband ANC systems, the LFE-NANC, CFX-NANC and BFX-NANC systems enjoy better overall performance in terms of the computational complexity, convergence speed and steady-state error, and in the broadband ANC systems, the DF-BANC system has the lowest computational complexity but cannot effectively attenuate the broadband noise with high spectral dynamics, whereas the DS-BANC and MDS-BANC systems can. This study provides in-depth insight into current typical low-complexity ANC systems.

Automatic Dependent Surveillance-Broadcast (ADS-B) is a critical technology to transform aircraft navigation by improving safety and overall effectiveness in the aviation industry. However, overlapping of ADS-B signals is a large challenge, especially for space-based ADS-B systems. Existing traditional methods are not effective when dealing with cases that overlapped signals with small difference (such as power difference and carrier frequency difference) require to be separated. In order to generate an effective separation performance of the ADS-B signals by exploring its temporal relationship, Independently Convolutional Gated Recurrent Neural Unit (Ind-CGRU) is presented for encoder–decoder network construction. Experimental results on the dataset SR-ADSB demonstrate that the proposed Ind-CGRU achieves good performance.

Radar target detection with fewer echo pulses in non-Gaussian clutter background is a challenging problem. In this instance, the conventional detectors using coherent accumulation are not very satisfactory. In contrast, the matrix detector based on Riemannian manifolds has shown potential on this issue since the covariance matrix of radar echo data during one coherent processing interval(CPI) has a smooth manifold structure. The Affine Invariant (AI) Riemannian distance between the cell under test (CUT) and the reference cells has been used as a statistic to achieve improved detection performance. This paper uses the Bures–Wasserstein (BW) distance and Generalized Bures–Wasserstein (GBW) distance on Riemannian manifolds as test statistics of matrix detectors, and propose relevant target detection method. Maximizing the GBW distance is formulated as an optimization problem and is solved by the Riemannian trust-region (RTR) method to achieve enhanced discrimination for target detection. Our evaluation of simulated data and measured data show that the matrix detector based on GBW distance leads to a significant performance gain over existing methods.

This paper presents a target perception framework aimed at enhancing diver safety and facilitating underwater operations by extracting critical information from underwater scenes. The framework employs a layered processing approach, which encompasses water column imaging, constant false alarm rate detection, and local feature analysis. To simulate the diver's underwater environment, we conducted experiments with three distinct fields of view: fixed down-looking, fixed front-looking, and mobile side-looking perspectives. Our experimental findings demonstrate the framework's ability to accurately differentiate between false targets, stationary targets, and moving targets within the underwater scenes, as well as to capture the motion trajectories of dynamic targets. Furthermore, the application of 3D reconstruction techniques to underwater scene data enables the generation of approximate stereoscopic representations of divers and bubble groups.

This paper presents a target perception framework aimed at enhancing diver safety and facilitating underwater operations by extracting critical information from underwater scenes. The framework employs a layered processing approach, which encompasses water column imaging, constant false alarm rate detection, and local feature analysis. To simulate the diver's underwater environment, we conducted experiments with three distinct fields of view: fixed down-looking, fixed front-looking, and mobile side-looking perspectives. Our experimental findings demonstrate the framework's ability to accurately differentiate between false targets, stationary targets, and moving targets within the underwater scenes, as well as to capture the motion trajectories of dynamic targets. Furthermore, the application of 3D reconstruction techniques to underwater scene data enables the generation of approximate stereoscopic representations of divers and bubble groups.

Multi-Agent Systems are characterized by the presence of multiple independent agents and find diverse applications. In the context of smart cities, MAS is employed in traffic management to enhance operational efficiency, optimize resource utilization, and improve the quality of life for residents. This research paper focuses on the design of a multi-agent intelligent scheduling system, where passengers, vehicles, and carpooling platforms serve as intelligent agents. The primary objective of passengers is to identify suitable shared vehicles based on criteria such as waiting time, budget constraints, and willingness to carpool. Vehicles, on the other hand, organize their schedules based on passenger demands and designated routes. The carpooling platform takes into account resource allocation priority and optimization problems to ensure the efficient operation of the system. To address the issue of vehicle ordering, k-regret queries are utilized, while passenger preferences provide insight into determining loss factors. To safeguard privacy, differential privacy techniques and a random response mechanism are employed when dealing with multiple passenger queries. Furthermore, a direction-preserving insertion verification method is implemented to mitigate computational complexity. The effectiveness and efficiency of the proposed approach are validated through experimentation.

Multi-Agent Systems are characterized by the presence of multiple independent agents and find diverse applications. In the context of smart cities, MAS is employed in traffic management to enhance operational efficiency, optimize resource utilization, and improve the quality of life for residents. This research paper focuses on the design of a multi-agent intelligent scheduling system, where passengers, vehicles, and carpooling platforms serve as intelligent agents. The primary objective of passengers is to identify suitable shared vehicles based on criteria such as waiting time, budget constraints, and willingness to carpool. Vehicles, on the other hand, organize their schedules based on passenger demands and designated routes. The carpooling platform takes into account resource allocation priority and optimization problems to ensure the efficient operation of the system. To address the issue of vehicle ordering, k-regret queries are utilized, while passenger preferences provide insight into determining loss factors. To safeguard privacy, differential privacy techniques and a random response mechanism are employed when dealing with multiple passenger queries. Furthermore, a direction-preserving insertion verification method is implemented to mitigate computational complexity. The effectiveness and efficiency of the proposed approach are validated through experimentation.

In intelligent transportation systems, real-time video streaming via vehicle networks has been seen as a vital difficulty. The goal of this paper is to decrease the computational complexity of the versatile video coding (VVC) encoder for VANETs. In this paper, a low-complexity enhancement VVC encoder is designed for vehicular communication. First, a fast coding unit (CU) partitioning scheme based on CU texture features is proposed in VVC, which aims to decide the final type of CU partition by calculating CU texture complexity and gray-level co-occurrence matrix (GLCM). Second, to reduce the number of candidate prediction mode types in advance, a fast chroma intra-prediction mode optimization technique based on CU texture complexity aims to combine intra-prediction mode features. Moreover, the simulation outcomes demonstrate that the overall approach may substantially reduce encoding time, while the loss of coding efficiency is reasonably low. The encoding time can be reduced by up to 53.29% when compared to the VVC reference model, although the average BD rate is only raised by 1.26%. The suggested VVC encoder is also hardware-friendly and has a minimal level of complexity for video encoders used in connected vehicle applications.