With autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter wave (mmWave) radar ...and vision fusion is a mainstream solution for accurate obstacle detection. This article presents a detailed survey on mmWave radar and vision fusion based obstacle detection methods. First, we introduce the tasks, evaluation criteria, and datasets of object detection for autonomous driving. The process of mmWave radar and vision fusion is then divided into three parts: sensor deployment, sensor calibration, and sensor fusion, which are reviewed comprehensively. Specifically, we classify the fusion methods into data level, decision level, and feature level fusion methods. In addition, we introduce three-dimensional(3D) object detection, the fusion of lidar and vision in autonomous driving and multimodal information fusion, which are promising for the future. Finally, we summarize this article.
Automatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper, we propose a feature-based AMC framework for ...multiple overlapped sources. The framework first separates the overlapped sources via blind channel estimation and then conducts novel maximum-likelihood-based multicumulant classification (MLMC) for each of the sources. MLMC employs multiple cumulants of arbitrary orders and arbitrary lags as discriminating features and a maximum likelihood ratio test for decision making. Hence, MLMC maximizes the probability of correct classification under the condition that the selected cumulants are utilized. Moreover, both the case with perfect channel estimation and the practically more relevant case with blind channel estimations, called fast independent component analysis and natural gradient independent component analysis, are presented to facilitate the signal separation process. Extensive simulations are also conducted to verify the validity and the superiority of the proposed framework and the MLMC algorithm.
Joint radar and communication: A survey Feng, Zhiyong; Fang, Zixi; Wei, Zhiqing ...
China communications,
2020-Jan., 2020-1-00, 2020, Letnik:
17, Številka:
1
Journal Article
Recenzirano
Joint radar and communication (JRC) technology has become important for civil and military applications for decades. This paper introduces the concepts, characteristics and advantages of JRC ...technology, presenting the typical applications that have benefited from JRC technology currently and in the future. This paper explores the state-of-the-art of JRC in the levels of coexistence, cooperation, co-design and collaboration. Compared to previous surveys, this paper reviews the entire trends that drive the development of radar sensing and wireless communication using JRC. Specifically, we explore an open research issue on radar and communication operating with mutual benefits based on collaboration, which represents the fourth stage of JRC evolution. This paper provides useful perspectives for future researches of JRC technology.
For autonomous driving, it is important to detect obstacles in all scales accurately for safety consideration. In this paper, we propose a new spatial attention fusion (SAF) method for obstacle ...detection using mmWave radar and vision sensor, where the sparsity of radar points are considered in the proposed SAF. The proposed fusion method can be embedded in the feature-extraction stage, which leverages the features of mmWave radar and vision sensor effectively. Based on the SAF, an attention weight matrix is generated to fuse the vision features, which is different from the concatenation fusion and element-wise add fusion. Moreover, the proposed SAF can be trained by an end-to-end manner incorporated with the recent deep learning object detection framework. In addition, we build a generation model, which converts radar points to radar images for neural network training. Numerical results suggest that the newly developed fusion method achieves superior performance in public benchmarking. In addition, the source code will be released in the GitHub.
To support popular Internet of Things (IoT) applications such as virtual reality and mobile games, edge computing provides a front-end distributed computing archetype of centralized cloud computing ...with low latency and distributed data processing. However, it is challenging for multiple users to offload their computation tasks because they are competing for spectrum and computation as well as Radio Access Technologies (RAT) resources. In this paper, we investigate computation offloading mechanism of multiple selfish users with resource allocation in IoT edge computing networks by formulating it as a stochastic game. Each user is a learning agent observing its local network environment to learn optimal decisions on either local computing or edge computing with a goal of minimizing long term system cost by choosing its transmit power level, RAT and sub-channel without knowing any information of the other users. Since users' decisions are coupling at the gateway, we define the reward function of each user by considering the aggregated effect of other users. Therefore, a multi-agent reinforcement learning framework is developed to solve the game with the proposed Independent Learners based Multi-Agent Q-learning (IL-based MA-Q) algorithm. Simulations demonstrate that the proposed IL-based MA-Q algorithm is feasible to solve the formulated problem and is more energy efficient without extra cost on channel estimation at the centralized gateway. Finally, compared with the other three benchmark algorithms, it has better system cost performance and achieves distributed computation offloading.
Unmanned aerial vehicles (UAVs) can be deployed efficiently to provide high quality of service for Internet of Things (IoT). By using cooperative communication and relay technologies, a large swarm ...of UAVs can enlarge the effective coverage area of IoT services via multiple relay nodes. However, the low latency service requirement and the dynamic topology of UAV network bring in new challenges for the effective routing optimization among UAVs. In this paper, a layered UAV swarm network architecture is proposed and an optimal number of UAVs is analyzed. Furthermore, a low latency routing algorithm (LLRA) is designed based on the partial location information and the connectivity of the network architecture. Finally, the performance of the proposed LLRA is verified by numerical results, which can decrease the link average delay and improve the packet delivery ratio in contrast to traditional routing algorithms without layered architecture.
In this paper, the non-orthogonal multiple access (NOMA) technology is integrated into cognitive orthogonal frequency-division multiplexing (OFDM) systems, called cognitive OFDM-NOMA, to boost the ...system capacity. First, a capacity maximization problem is considered in half-duplex cognitive OFDM-NOMA systems with two accessible users on each subcarrier. Due to the intractability of the considered problem, we decompose it into three subproblems, i.e., the optimization of, respectively, sensing duration, user scheduling, and power allocation. By investigating and exploiting the characteristics of each subproblem, the optimal sensing duration adaptation, a matching-theory-based user scheduling, and the optimal power allocation are proposed correspondingly. An alternate iteration framework is further proposed to jointly optimize these three subproblems, with its convergence proved. Moreover, based on the non-cooperative game theory, a generalized power allocation algorithm is proposed and then used in the framework to accommodate half-duplex cognitive OFDM-NOMA systems with multiple users on each subcarrier. Finally, the proposed framework is extended to solve the capacity maximization problem in full-duplex cognitive OFDM-NOMA systems. Simulation results validate the superior performance of the proposed algorithms. For example, for the case of two accessible users, the proposed framework approaches the optimal solution with less than 1% capacity loss and 120 times lower complexity compared with exhaustive search.
Glutathione peroxidase (GPX) is one of the most important antioxidant enzymes for maintaining reactive oxygen species (ROS) homeostasis. Although studies on fungi have suggested many important ...physiological functions of GPX, few studies have examined the role of this enzyme in
Basidiomycetes
, particularly its functions in fruiting body developmental processes. In the present study, GPX-silenced (GPxi) strains were obtained by using RNA interference. The GPxi strains of
Hypsizygus marmoreus
showed defects in mycelial growth and fruiting body development. In addition, the results indicated essential roles of GPX in controlling ROS homeostasis by regulating intracellular H
2
O
2
levels, maintaining GSH/GSSG balance, and promoting antioxidant enzyme activity. Furthermore, lignocellulose enzyme activity levels were reduced and the mitochondrial phenotype and mitochondrial complex activity levels were changed in the
H. marmoreus
GPxi strains, possibly in response to impediments to mycelial growth and fruiting body development. These findings indicate that ROS homeostasis has a complex influence on growth, fruiting body development, GSH/GSSG balance, and carbon metabolism in
H. marmoreus
.
Key points
•
ROS balance, energy metabolism, fruiting development.
In face of the explosive surge of mobile data services, spectrum aggregation or carrier aggregation technology has been proposed to improve system throughput and spectrum efficiency (SE) by ...aggregating licensed and unlicensed spectrum bands. However, the system performances would be severely deteriorated by the channel access collision if the channel access and resource scheduling approaches are not coordinated among different networks in the same spectrum band. Therefore, in order to improve the system throughput and the SE, a fairness-based license-assisted access and resource scheduling scheme are designed for the coexisting systems, incorporating long term evolution-advanced and WiFi systems in the unlicensed band. The optimal sizes of the contention window in the proposed fairness-based channel access approach are obtained in terms of various density ratios between these two systems. Furthermore, a novel resource scheduling approach employing linear programming is proposed to maximize the utility function with the goal of improving the service experience of users and the SE with various spectrum qualities. The theoretical proofs and simulation results verify the enhanced performances of the proposed approaches in terms of key metrics, such as throughput, SE, delay, and packet loss ratio.
•Bar deterioration was significant especially in the upper reaches.•Channel bars tended to become less stable after the dam construction.•The most effective bar-forming discharge ranges and sediment ...load were determined.•The quantitative relationships between bars area and fluvial regimes were developed.
The evolution of channel bars in response to upstream damming has significant impacts on channel stability, navigation, and aquatic habitats. Here, the effects of the Three Gorges Dam (TGD) operation on downstream channel bars in the Yichang-Chenglingji Reach (YCR) were comprehensively analyzed using remote sensing images, cross-sectional profiles, and hydrological datasets. The morphodynamic adjustments of channel bars in the YCR were significantly different during the pre- and post-TGD periods (i.e., before and after the construction of the TGD). Specifically, the total area of channel bars did not exhibit any significant trend in the pre-TGD period, but displayed a significant reduction following the construction of the TGD, although the morphodynamic response of each sub-reach was different. The channel bars in the YCR were relatively stable in the pre-TGD period, but became more erodible in the post-TGD period. The length/width ratio (LWR) of the bars showed an overall increase trend during the whole period from 1992 to 2017, not changing before and after the dam construction. The water discharge that led to the greatest channel bars adjustment was 27,000–30,000 m3/s (corresponding to bankfull discharge) in the pre-TGD period and 15,000–18,000 m3/s (corresponding to the medium discharge that can submerge the surface of the bars) in the post-TGD period. In addition, the grain size of non-uniform sediment with the highest replenishment degree gradually reduced downstream; these finer sediments were the main sources of material for the channel bars in the YCR. Quantitative relationships between bars area, the most effective bar-forming discharge and suspended sediment load with the highest replenishment degree, were proposed based on the improved Delayed Response Model (DRM). Results indicate that geomorphic adjustments of channel bars in the YCR are closely related to the previous four-year flow and sediment regimes, implying a delayed response of the fluvial system to damming.
PDF
