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In this paper, we jointly design the power control and position dispatch for Multi-Unmanned Aerial Vehicle (UAV)-enabled communication in Device-to-Device (D2D) networks. Our objective is to maximize the total transmission rate of Downlink Users (DUs). Meanwhile, the Quality of Service (QoS) of all D2D users must be satisfied. We comprehensively considered the interference among D2D communications and downlink transmissions. The original problem is strongly non-convex, which requires high computational complexity for traditional optimization methods. And to make matters worse, the results are not necessarily globally optimal. In this paper, we propose a novel Graph Neural Networks (GNN) based approach that can map the considered system into a specific graph structure and achieve the optimal solution in a low complexity manner. Particularly, we first construct a GNN-based model for the proposed network, in which the transmission links and interference links are formulated as vertexes and edges, respectively. Then, by taking the channel state information and the coordinates of ground users as the inputs, as well as the location of UAVs and the transmission power of all transmitters as outputs, we obtain the mapping from inputs to outputs through training the parameters of GNN. Simulation results verified that the way to maximize the total transmission rate of DUs can be extracted effectively via the training on samples. Moreover, it also shows that the performance of proposed GNN-based method is better than that of traditional means.

Vascular orientation recognition (VOR) holds widespread applications in biomedical domains such as vascular image processing. Although many methods based on deep learning (DL) running on CPUs have been proposed and achieved appreciable results, they encounter a speed bottleneck. Therefore, an FPGA-based hardware design scheme is proposed in this paper to achieve high-speed VOR using small circuits. The design employs a streaming processing method for image thresholding and replaces static lookup tables (LUT) with the CORDIC algorithm to reduce resource consumption. Additionally, further compression of the angle mapping range is employed to achieve superior hardware resource optimization. Compared to purely software solutions, experimental results obtained with the simulated and actual vascular images demonstrate a 12.9x computational acceleration employing the proposed scheme. Moreover, the proposed scheme, in contrast to hardware implementations based on Lookup Table and Function Approximation, reduces LUT resource consumption by over 60%. Compared with two previous CORDIC algorithm studies, the proposed algorithm achieves a 14.6% and 20.5% reduction in LUT resource usage, thus showing enormous potential in real-time medical devices for tracking and computation of main vascular trajectory.

Based on the manned aerial vehicle (MAV) / unmanned aerial vehicle (UAV) intelligent cooperation platform, the search of multiple interfered signal sources with unknown locations and trajectory planning were studied.Considering the real-time and dynamic nature of the search process, a MAV/UAV intelligent collaborative target search and trajectory planning (MUICTSTP) algorithm based on multi-agent deep reinforcement learning (MADRL) was proposed.Each UAV made online decision on trajectory planning by sensing the received interference signal strength (RISS) values, and then transmitted the sensing information and decision-making actions to the MAV to obtain the global evaluation.The simulation results show that the proposed algorithm exhibits better performance in long-term RISS, collision, and other aspects compared to other algorithms, and the learning strategy is better.

This work investigates the potential of the aerial intelligent reflecting surface (AIRS) in secure communication, where an intelligent reflecting surface (IRS) carried by an unmanned aerial vehicle (UAV) is utilized to help the communication between the ground nodes. Specifically, we formulate the joint design of the AIRS’s deployment and the phase shift to maximize the secrecy rate. To solve the non-convex objective, we develop an alternating optimization (AO) approach, where the phase shift optimization is solved by the Riemannian manifold optimization (RMO) method, while the deployment optimization is handled by the successive convex approximation (SCA) technique. Furthermore, to reduce the computational complexity of the RMO method, an element-wise block coordinate descent (EBCD) based method is employed. Simulation results verify the effect of AIRS in improving the communication security, as well as the importance of designing the deployment and phase shift properly.

Abstract Background Anthocyanin, chlorophyll, and carotenoid pigments are widely distributed in plants, producing various colors. Ornamental kale (Brassica oleracea var. acephala DC) which has colorful inner leaves is an ideal plant to explore how these three pigments contribute to leaf color. The molecular mechanisms of the coloration in ornamental kale could provide reference for exploring the mechanisms of pigmentation in other plants. Results In this study, we sequenced the transcriptome and determined the pigment contents of an unusual cultivar of ornamental kale with three different types of leaf coloration: pink (C3), light pink (C2), and variegated pink–green (C1). A total of 23,965 differentially expressed genes were detected in pairwise comparisons among the three types of leaves. The results indicate that Bo9g058630 coding dihydroflavonol 4–reductase (DFR) and Bo3g019080 coding shikimate O–hydroxycinnamoyltransferase (HCT) acted in anthocyanin biosynthesis in pink leaves. Bo1g053420 coding pheophorbidase (PPD) and Bo3g012430 coding 15–cis–phytoene synthase (crtB) were identified as candidate genes for chlorophyll metabolism and carotenoid biosynthesis, respectively. The transcription factors TT8, MYBL2, GATA21, GLK2, and RR1 might participate in triggering the leaf color change in ornamental kale. Anthocyanin content was highest in C3 and lowest in C1. Chlorophyll and carotenoid contents were lowest in C2 and highest in C1. Conclusions Based on these findings, we suspected that the decrease in anthocyanin biosynthesis and the increase in chlorophyll and carotenoid biosynthesis might be the reason for the leaf changing from pink to variegate pink–green in this unusual cultivar. Our research provides insight into the molecular mechanisms of leaf coloration in ornamental kale, contributing to a theoretical foundation for breeding new varieties.

Leaf color is a crucial agronomic trait in ornamental kale. However, the molecular mechanism regulating leaf pigmentation patterns in green and white ornamental kale is not completely understood. To address this, we performed transcriptome and pigment content analyses of green and white kale leaf tissues. A total of 5,404 and 3,605 different expressed genes (DEGs) were identified in the green vs. white leaf and the green margin vs. white center samples. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis showed that 24 and 15 common DEGs in two pairwise comparisons were involved in chlorophyll metabolism and carotenoid biosynthesis, respectively. Seventeen genes related to chlorophyll biosynthesis were significantly upregulated in green leaf tissue, especially chlH and por. Of the 15 carotenoid biosynthesis genes, all except CYP707A and BG1 were lower expressed in white leaf tissue. Green leaf tissue exhibited higher levels of chlorophyll and carotenoids than white leaf tissue. In addition, the DEGs involved in photosystem and chlorophyll-binding proteins had higher expression in green leaf tissue. The PSBQ, LHCB1.3, LHCB2.4, and HSP70 may be key genes of photosynthesis and chloroplast formation. These results demonstrated that green and white coloration in ornamental kale leaves was caused by the combined effects of chlorophyll and carotenoid biosynthesis, chloroplast development, as well as photosynthesis. These findings enhance our understanding of the molecular mechanisms underlying leaf color development in ornamental kale.

Orthogonal multiple access (OMA) technologies have prevailed from the first-generation (1G) to the fourth-generation (4G) of modern mobile communications, primarily because of their low complexity. The key idea of OMA is to ensure that the communication resources allocated to different users are orthogonal in at least one radio resource dimension. As a result, the number of active users allowed to access the OMA system is strictly limited by the number of available orthogonal resources, which becomes less effective for supporting massive connectivity and achieving user fairness. In contrast to OMA, non-orthogonal multiple access (NOMA) simultaneously accommodates a multitude of users using the same radio resource block via superposition signaling and employs various transmit or receive signal processing techniques to mitigate the resulting interference. However, the success of NOMA technologies relies heavily on the implementation of advanced signal processing techniques for transceivers, which may introduce large processing delays and increase computational complexity. Thanks to recent progress in hardware and theory in signal processing and machine learning, high signal processing complexity has become more affordable and processing latency can be significantly reduced, which enables the development of NOMA. Thus, sophisticated signal processing algorithms for multi-user detection, scheduling, and interference management are indispensable for the successful implementation of NOMA in the next-generation wireless systems.

Traditional mobile edge computing (MEC) methods always assume that the wireless devices (WDs) can offload their data to the base stations (BSs) or the access points (APs) at any time, which are not practical due to the tension between a large number of the WDs and the limited spectrum resources. In this paper, a framework for MEC-enabled cognitive radio (CR) networks is proposed, which integrates three technologies: MEC, CR, and wireless power transfer (WPT). To obtain the spectrum for offloading, cooperative relaying is considered. Optimization problems are formulated to study the upper bound of the energy efficiency (EE) of the WD and to maximize the practical EE in both partial offloading and local computing scenarios, which are non-convex and intractable. In order to tackle these problems, a two-phase method is proposed. The transmit power, the time for energy harvesting (EH) and MEC, and the central processing unit (CPU) frequency of the WD are jointly optimized. Semi-closed-form solutions are obtained in partial offloading scenario by using fractional programming theory, Lagrangian dual decomposition, and successive pseudo-convex approximation (SPCA) methods. Closed-form solutions are obtained for local computing scenarios. The simulation results show the effects of the different parameters on the system performance.

The application of unmanned aerial vehicles (UAVs) is promising to improve the energy harvesting (EH) efficiency of simultaneous wireless and information and power transfer (SWIPT) system. However, the secure communications are challenging in UAV-assisted SWIPT systems due to the high probability of the existence of line-of-sight links between the UAV and eavesdroppers and the broadcast nature of SWIPT. In order to overcome it, a resource allocation problem is studied in an UAV-assisted SWIPT system, where the multiple eavesdroppers exist. The secrecy rate is maximized by jointly optimizing the trajectory and transmit power of the UAV. An alternative optimization algorithm is proposed to tackle the challenging non-convex problem. The simulation results demonstrate that our proposed resource allocation scheme outperforms other benchmark schemes in terms of the average secrecy rate. It is shown that our proposed algorithm is efficient to converge.

Energy-efficient computation is important in mobile edge computing (MEC) systems. However, the computation efficiency problem in the unmanned aerial vehicle (UAV)-enabled MEC systems has been rarely researched. In this paper, a UAV-enabled MEC system under partial computation offloading mode is investigated. The computation efficiency is maximized by jointly optimizing the offloading times, the central processing unit frequencies, the transmit powers of the user and the trajectory of the UAV. For the non-convex computation efficiency problem, a two-stage iterative algorithm is proposed. Moreover, we derive the closed-form expressions for the local computation frequency and the transmit power of the user, which facilitates our algorithm implementation. Simulation results demonstrate that the computation efficiency of our proposed joint optimization scheme is better than those of other benchmark schemes.

Cognitive radio and non-orthogonal multiple access are promising for alleviating the severe spectral scarcity problem encountered by the next generation wireless communication systems. In this paper, in order to improve energy efficiency and spectral efficiency, a non-orthogonal multiple access cognitive radio network with simultaneous wireless information and power transfer is studied under a practical non-linear energy harvesting model. A multi-objective resource optimization problem is formulated for maximizing the harvesting power of each energy harvesting receiver. This problem is non-convex and challenging to solve. A weighted Tchebycheff method is applied to solve the formulated problem. It is shown that the performance achieved under the non-linear energy harvesting model is better than that obtained under the linear energy harvesting model.

A framework for designing orthogonal chaotic phase-coded waveforms with space-time complementary coding is proposed for multiple-input multiple-output (MIMO) radar applications. The phase-coded waveform set to be transmitted is generated with an arbitrary family size and an arbitrary code length by using chaotic sequences. Due to the properties of chaos, this chaotic waveform set has many advantages in performance, such as anti-interference and low probability of intercept. However, it cannot be directly exploited due to the high range sidelobes, mutual interferences, and Doppler intolerance. In order to widely implement it in practice, we optimize the chaotic phase-coded waveform set from two aspects. First, the autocorrelation property of the waveform is improved by transmitting complementary chaotic phase-coded waveforms, and an adaptive clonal selection algorithm is utilized to optimize a pair of complementary chaotic phase-coded pulses. Second, the crosscorrelation among different waveforms is eliminated by implementing space-time coding into the complementary pulses. Moreover, to enhance the detection ability for moving targets in MIMO radars, a method of weighting different pulses by a null space vector is utilized at the receiver to compensate the interpulse Doppler phase shift and accumulate different pulses coherently. Simulation results demonstrate the efficiency of our proposed method.

Garbage enzyme (GE) is a vinegar or alcohol product derived from fermenting fresh kitchen waste, such as vegetable and fruit residues (peels, cuttings and bits), sugar (brown sugar, jaggery or molasses sugar) and water. Chinese honeylocust fruits (Gleditsia sinensis) have been used in China for at least 2000 years as a detergent. The aim of the study was to investigate the properties and application of Chinese honeylocust garbage enzyme (CHGE), which is produced when equal amounts of Chinese honeylocust fruits and fresh wastes are mixed. The results showed that CHGE had lesser microbial communities and lower surface tension than GE. CHGE also had higher viscosity, foam stability and emulsion stability than GE. Compared with GE, CHGE induced higher enzymatic amylase, cellulase, lipase and protease activities. CHGE had stronger detergency than GE and a 100× dilution of CHGE could significantly remove pesticide residues after a 30 min soaking treatment. The study showed that as a biological detergent, CHGE is safer and more environmentally friendly than GE and has remarkable washing and cleaning power. The preparation method of the detergent is simple: it can be prepared at home using fruit and vegetable waste, which is beneficial to the secondary utilization of waste and the reduction of pollution to the environment and damage to human health.

This paper studies joint power allocation and subcarrier pairing strategies for an orthogonal frequency division multiplexing-based amplify and forward relay system. The optimization target function is symbol error rate (SER) in the context of an additive white generalized Gaussian noise (AWGGN), which encompasses numerous noise types. For instance, the familiar additive white Gaussian noise (AWGN) is just a special case of AWGGN. At a given data rate, the SER performance is optimized under total and individual power constraints, respectively. Simulation results demonstrate superior performance gap through our adaptive resource allocation approach over equal power allocation as well as fixed subcarrier pairing strategies. The impacts of practical issues on the SER performance, such as relay location and unequal power budgets at a source and a relay, are further discussed.

Energy harvesting techniques are promising in next generation wireless communication systems. However, most of the existing works are based on an ideal linear energy harvesting model. In this paper, a multiple-input single-output cognitive radio network is studied under a practical non-linear energy harvesting model. In order to improve the security of both the primary network and the secondary network, a cooperative jamming scheme is proposed. A robust artificial noise aided beamforming design problem is formulated under the bounded channel state information error model. The formulated problem is non-convex and challenging to be solved. Using S-Procedure and the semidefinite relaxation method, a suboptimal beamforming can be obtained. Simulation results show that the performance achieved under the non-linear energy harvesting model may be better than that obtained under the linear energy harvesting model. It is also shown that the cooperation between the primary network and the secondary network can obtain a performance gain compared with that without this cooperation.

Wireless powered techniques have been recognized as promising techniques in future wireless communication systems, especially in cognitive radios (CRs) with energy-limit devices. However, most of the existing works focus on CRs with an ideal linear energy harvesting model. In this paper, a wireless powered wideband CR network is considered, and a practical non-linear energy harvesting model is adopted. To maximize the sum throughput of the secondary users, the energy harvesting time, channel allocation, and transmit power are jointly optimized. The closed-form expressions for the optimal transmit power and channel allocation are given. Simulation results show that there is a tradeoff between the harvesting energy and the sum throughput of the secondary users. It is also shown that the performance achieved under the non-linear energy harvesting model may equal to that achieved under the linear energy harvesting model.

Low-intensity (10 μmol m−2 s−1) white LED (light-emitting diode) light effectively delayed senescence and maintained the quality of postharvest pakchoi during storage at 20 °C. To investigate the mechanism of LED treatment in maintaining the quality of pakchoi, metabolite profiles reported previously were complemented by transcriptomic profiling to provide greater information. A total of 7761 differentially expressed genes (DEGs) were identified in response to the LED irradiation of pak-choi during postharvest storage. Several pathways were markedly induced by LED irradiation, with photosynthesis being the most notable. More specifically, porphyrin and chlorophyll metabolism and glucosinolate biosynthesis were significantly induced by LED irradiation, which is consistent with metabolomics reported previously. Additionally, chlorophyllide a, chlorophyll, as well as total glucosinolate content was positively induced by LED irradiation. Overall, LED irradiation delayed the senescence of postharvest pak-choi mainly by activating photosynthesis, inducting glucosinolate biosynthesis, and inhibiting the down-regulation of porphyrin and chlorophyll metabolism pathways. The present study provides new insights into the effect and the underlying mechanism of LED irradiation on delaying the senescence of pak-choi. LED irradiation represents a useful approach for extending the shelf life of pak-choi.

In an integrated radar-communication network, multiuser access techniques with minimal performance degradation and without range-Doppler ambiguities are required, especially in a dense user environment. In this paper, a multiuser access scheme with random subcarrier allocation mechanism is proposed for orthogonal frequency division multiplexing (OFDM) based integrated radar-communication networks. The expression of modulation Symbol-Domain method combined with sparse representation (SR) for range-Doppler estimation is introduced and a parallel reconstruction algorithm is employed. The radar target detection performance is improved with less spectrum occupation. Additionally, a Doppler frequency detector is exploited to decrease the computational complexity. Numerical simulations show that the proposed method outperforms the traditional modulation Symbol-Domain method under ideal and realistic nonideal scenarios.