Uniquely Decomposable Constellation Group-Based Sparse Vector Coding for Short Packet Communications

Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is proposed in this article.Additionally,in order to achieve an optimal BLER performance of UDCG-SVC,a problem to optimize the coding gain of UDCG-based superimposed constellation is formulated.Given the energy of rotation constellations in UDCG,this problem is solved by converting it into finding the maximized minimum Euclidean distance of the superimposed constellation.Simulation results demonstrate the validness of our derivation.We also find that the proposed UDCGSVC has better BLER performance compared to other SVC schemes,especially under the high order modulation scenarios.

NOMA-Aided Generalized Pre-Coded Quadrature Spatial Modulation for Downlink Communication Systems

Generalised pre-coding quadrature spatial modulation(GPQSM)is recently proposed to increase the spectral efficiency(SE)of GPSM,which extends the transmitted symbols into in-phase/quadrature domains.In this paper,a novel scheme named non-orthogonal multiple access(NOMA)-aided GPQSM(NOMA-GPQSM),which incorporates the GPQSM scheme into the multi-user communication networks with assist of NOMA,is proposed to further improve the SE and system performance.In NOMA-GPQSM,one base station(BS)is set to serve K users,where user 1 is closest to the BS,and user K is farthest from the BS.In addition,a low-complexity detection method is proposed to reduce the high detection complexity of the maximum-likelihood(ML)detection in successive interference cancellation(SIC)method for all users by NOMA-GPQSM.The theoretical analysis of the BER performance for all users is also derived.Simulation results show that near users achieve relatively good performance,and far users achieve acceptable performance by adjusting power factors for all users in NOMA-GPQSM.

Bidirectional multi-qubit quantum teleportation in noisy channel aided with weak measurement

Recently,bidirectional quantum teleportation has attracted a great deal of research attention.However,existing bidirectional teleportation schemes are normally discussed on the basis of perfect quantum environments.In this paper,we first put forward a bidirectional teleportation scheme to transport three-qubit Greenberger-Horne-Zeilinger（GHZ） states based on controled-not（CNOT） operation and single-qubit measurement.Then,we generalize it to the teleportation of multi-qubit GHZ states.Further,we discuss the influence of quantum noise on our scheme by the example of an amplitude damping channel,then we obtain the fidelity of the teleportation.Finally,we utilize the weak measurement and the corresponding reversing measurement to protect the quantum entanglement,which shows an effective enhancement of the teleportation fidelity.

Decoherence suppression for three-qubit W-like state using weak measurement and iteration method

Multi-qubit entanglement states are the key resources for various multipartite quantum communication tasks. For a class of generalized three-qubit quantum entanglement, W-like state, we demonstrate that the weak measurement and the reversal measurement are capable of suppressing the amplitude damping decoherence by reducing the initial damping factor into a smaller equivalent damping factor. Furthermore, we propose an iteration method in the weak measurement and the reversal measurement to enhance the success probability of the total measurements. Finally, we discuss how the number of the iterations influences the overall effect of decoherence suppression, and find that the ＂half iteration＂ method is a better option that has more practical value.

Proactive Content Delivery for Vehicles over Cellular Networks:the Fundamental Benefits of Computing and Caching

The emergence of self-driving technologies implies that a future vehicle will likely become an entertainment center that demands personalized multimedia contents with very high quality. The surge of vehicular content demand brings significant challenges for the fifth generation(5G) cellular communication network. To cope with the challenge of massive content delivery, previous studies suggested that the 5G mobile edge network should be designed to integrate communication, computing, and cache(3C) resources to enable advanced functionalities such as proactive content delivery and in-network caching. However, the fundamental benefits achievable by computing and caching in mobile communications networks are not yet properly understood. This paper proposes a novel theoretical framework to characterize the tradeoff among computing, cache, and communication resources required by the mobile edge network to fulfill the task of content delivery. Analytical and numerical results are obtained to characterize the 3C resource tradeoff curve. These results reveal key insights into the fundamental benefits of computing and caching in vehicular mobile content delivery networks.

Joint Dynamic Clustering and User Scheduling for Downlink Cloud Radio Access Network with Limited Feedback

In limited feedback-based CloudRAN(C-RAN) systems,the inter-cluster and intra-cluster interference together with the quantification error can seriously deteriorates the system spectral efficiency.We,in this paper,propose an efficient three-phase framework and corresponding algorithms for dealing with this problem.Firstly,a greedy scheduling algorithm based on the lower bound of the ergodic rate is performed for generating an elementary cluster in the first phase.And then the elementary cluster is divided into many small clusters according to the following proposed algorithms based on the short term instantaneous information in the second phase.In the end,based on the limited feedback two zero-forcing(ZF) precoding strategies are adopted for reducing the intra-cluster interference in the third phase.The provided Monte Carlo simulations show the effectiveness of our proposed algorithms in the respect of system spectral efficiency and average user rate.

Energy Optimization Oriented Three-Way Clustering Algorithm for Cloud Tasks

Cloud computing has developed as an important information technology paradigm which can provide on-demand services. Meanwhile,its energy consumption problem has attracted a grow-ing attention both from academic and industrial communities. In this paper,from the perspective of cloud tasks,the relationship between cloud tasks and cloud platform energy consumption is established and analyzed on the basis of the multidimensional attributes of cloud tasks. Furthermore,a three-way clustering algorithm of cloud tasks is proposed for saving energy. In the algorithm,f irst,t he cloud tasks are classified into three categories according to the content properties of the cloud tasks and resources respectively. Next,cloud tasks and cloud resources are clustered according to their computation characteristics（ e. g. computation-intensive,data-intensive）. Subsequently,greedy scheduling is performed. The simulation results showthat the proposed algorithm can significantly reduce the energy cost and improve resources utilization,compared with the general greedy scheduling algorithm.

Heuristic based data scheduling algorithm for OFDMA wireless network

A system model based on joint layer mechanism is formulated for optimal data scheduling over fixed point-to-point links in OFDMA ad-hoc wireless networks. A distributed scheduling algorithm （DSA） for system model optimization is proposed that combines the randomly chosen subcarrier according to the channel condition of local subcarriers with link power control to limit interference caused by the reuse of subcarrier among links. For the global fairness improvement of algorithms, a global power control scheduling algorithm （GPCSA） based on the proposed DSA is presented and dynamically allocates global power according to difference between average carrier-noise-ratio of selected local links and system link protection ratio. Simulation results demonstrate that the proposed algorithms achieve better efficiency and fairness compared with other existing algorithms.

Compensation for Radio-over-Fibre Uplink Based on Hybrid Neural Networks

The radio-over-fibre （ROF） uplink, which combines the merit of optical fibre with that of microwave technology, can supply the high capacity of communication. However, there are two major issues： nonlinear distortion of the optical link and the multipath dispersion of the wireless channel, affecting the performance of the system. We propose an equalizer based on hybrid neural networks. The compensation needs no estimation of the channel. The simulated result shows that the ROF uplink can be adequately compensated and the performance of the equalizer depends on the channel noise.

Chip Layout for Adaptive Line Enhancer Design using Adaptive Filtering Algorithms and Metrics Computation for Auscultation Signal Separation

Currently,the growth of micro and nano(very large scale integration-ultra large-scale integration)electronics technology has greatly impacted biomedical signal processing devices.These high-speed micro and nano technology devices are very reliable despite their capacity to operate at tremendous speed,and can be designed to consume less power in minimum response time,which is particularly useful in biomedical products.The rapid technological scaling of the metal-oxide-semi-conductor(MOS)devices aids in mapping multiple applications for a specific purpose on a single chip which motivates us to design a sophisticated,small and reliable application specific integrated circuit(ASIC)chip for future real time medical signal separation and processing(digital stetho-scopes and digital microelectromechanical systems(MEMS)microphone).In this paper,ASIC level implementation of the adaptive line enhancer design using adaptive filtering algorithms(least mean square(LMS)and normalized least mean square(NLMS))integrated design is used to separate the real-time auscultation sound signals effectively.Adaptive line enhancer(ALE)design is imple-mented in Verilog hardware description language(HDL)language to obtain both the network and adaptive algorithm in cadence Taiwan Semiconductor Manufacturing Company(TSMC)90 nm standard cell library environment for ASIC level implementation.Native compiled simulator(NC)sim and RC lab were used for functional verification and design constraints and the physical design is implemented in Encounter to obtain the Geometric Data Stream(GDS II).In this architecture,the area occupied is 0.08 mm,the total power consumed is 5.05 mW and the computation time of the proposed system is 0.82μs for LMS design and the area occupied is 0.14 mm,the total power consumed is 4.54 mW and the computation time of the proposed system is 0.03μs for NLMS design that will pave a better way in future electronic stethoscope design.

A COMPARISON OF TWO 2D CHANNEL ESTIMATORS FOR OFDM SYSTEM

The paper deals with two-dimensional (2D) channel estimation of Orthogonal Frequency Division Multiplexing (OFDM) system in slow fading wireless channel. We concentrate on two channel estimation schemes: Least Square (LS)+Weighted BiLinear (WBL) and LS+Linear Minimum Mean-Squared Error (LMMSE) where the first method is proposed in this paper. After theory analysis and simulation in Typical Urban (TU) channel, we find that LS+LMMSE achieves the optimal perform-ance by exploiting prior knowledge of channel whereas LS+WBL, without requiring channel knowl-edge and with only half of the computational amount of LS+LMMSE, approaches LS+LMMSE in Bit Error Ratio (BER) performance when the distance of two adjoining pilot symbols along frequency di-rection is sufficiently small. This makes LS+WBL very suitable for wideband wireless applications.

NONINVASIVE DETECTION OF BRAIN ACTIVITY VARIATION UNDER DIFFERENT DEPTH OF ANESTHESIA BY EEG COMPLEXITY

Objective To detect the change of brain activity under different depth of anesthesia (DOA) noninvasively. Methods The Lempel-Ziv complexity C(n) was used to analyze EEG and its four components (delta, theta, alpha, beta), which was recorded from SD rats under different DOA. The relationship between C(n) and DOA was studied. Results The C(n) of EEG will decrease while the depth of anesthesia increasing and vice versa. It can be used to detect the change of DOA sensitively. Compared with power spectrum, the change of C(n) is opposite to that of power spectrum. Only the C(n) of delta rhythm has obvious variations induced by the change of DOA, and the variations of delta is as similar as the EEG's. Conclusion The study shows that the desynchronized EEG is replaced by the synchronized EEG when rat goes into anesthesia state from awake, that is just the reason why complexity and power spectrum appear corresponding changes under different DOA. C(n) of delta rhythm dynamic change leads to the change of EEG, and the delta rhythm is the dominant rhythm during anesthesia for rats.

Integration of Communication and Navigation Technologies toward LEO-Enabled 6G Networks:A Survey

As the number of wireless applications and devices grows,higher standards for the quality of service and navigation performance of mobile networks are required.Numerous critical applications,including unmanned aerial vehicles,internet of things,digital twin,and military systems,require reliable communication and accurate navigation services.To meet these requirements,the development of the sixth generation(6G)network is necessary.6G networks provide seamless 3-dimensional coverage in space-air-ground-sea area,as well as deep coupling of communication,sensing,and computation.6G networks will be combined with low Earth orbit(LEO)satellites to construct a universal and intelligent integrated system of communication,sensing,and computing by utilizing the benefits of LEO satellites,such as miniaturization,modularity,large bandwidth,low latency,and wide area coverage.One of the critical construction tasks in this system is the integration of communication and navigation(ICAN),which can break the limitations of the global navigation satellite system,provide high-precision,robust navigation capability,and enable high-quality communication services.In this article,a comprehensive survey is presented for ICAN technologies toward LEO-enabled 6G networks(LEO-ICAN),including the framework design,system implementation,and key technologies.We also highlight the challenges and opportunities ahead faced by the LEO-ICAN system.Finally,the prospect development and future research trends are discussed,and a few ideas for practical and effective LEO-ICAN solutions are provided.This survey provides a reference for the theoretical design,technological innovation,and system implementation of LEO-ICAN,which is capable of coping with the demand of massive access and global seamless coverage in the upcoming 6G network era.

高铁隧道无线通信系统中的信道测量与建模综述

高铁的迅速发展为现有的高铁无线通信系统带来了一些新的挑战.一个准确描述隧道信道特性的信道模型,对高铁通信系统的设计和评估意义重大.由于隧道狭长的空间、隧道本身的边界性以及产生的波导效应等,高铁隧道中的信道特性不同于其他的高铁场景.此外,隧道信道的一些特性目前还没有被充分地研究.因此,考虑大尺度和小尺度衰落特性的准确的隧道信道模型是十分必要和重要的.本文全面地综述了已开展的隧道信道测量及采用不同方法建模的隧道信道模型,讨论了未来高铁隧道信道测量及建模的一些研究方向.

All-optical functions based on semiconductor ring lasers

Semiconductor ring laser （SRL） has been shown to possess robust bistability between its two possible directions, i.e., clockwise （cw） and counter-clockwise （ccw） lasing, routinely demonstrating directional extinction ratio （DER） of 〉 25 dB. In this paper, experimental schemes and results using the SRL as a universal photonic digital element to form all-optical logic, memory, and signal processing circuits are summarized. It is demonstrated that the SRL can be used for both combinatorial and sequential logic functions, and as all-optical regeneration devices. Furthermore, it is shown that a SRL logic circuit can be all-optically reconfigured to perform different all-optical logic functions.

Segmentation and measurement scheme for fish morphological features based on Mask R-CNN

The morphological features of fish,such as the body length,the body width,the caudal peduncle length,the caudal peduncle width,the pupil diameter,and the eye diameter are very important indicators in smart mariculture.Therefore,the accurate measurement of the morphological features is of great significance.However,the existing measurement methods mainly rely on manual measurement,which is operationally complex,low efficiency,and high subjectivity.To address these issues,this paper proposes a scheme for segmenting fish image and measuring fish morphological features indicators based on Mask R-CNN.Firstly,the fish body images are acquired by a home-made image acquisition device.Then,the fish images are preprocessed and labeled,and fed into the Mask R-CNN for training.Finally,the trained model is used to segment fish image,thus the morphological features indicators of the fish can be obtained.The experimental results demonstrate that the proposed scheme can segment the fish body in pure and complex backgrounds with remarkable performance.In pure background,the average relative errors(AREs)of all indicators measured all are less than 2.8%,and the AREs of body length and body width are less than 0.8%.In complex background,the AREs of all indicators are less than 3%,and the AREs of body length and body width is less than 1.8%.2020 China Agricultural University.Production and hosting by Elsevier B.V.on behalf of KeAi.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).1.Introduction With the advancing of its scientific and technological capabilities,China has made great achievements in the mariculture.The production accounts for more than 70%of the world’s overall mariculture output[1].The measurement of body length,body width and other morphological features of fish have wide application prospects in smart mariculture.Due to the difference in the quality and feeding ability of the Juvenile fish,the growth of the fish in the same pond is significantly different after a period of growth.Then,the fish needs to be classified.Grading can make fish grow better and improve feed utilization[2].The fish body length and body width are closely related to the weight of the fish.In the mariculture,the fishermen judge the growth of the fish by collecting the morphological feature of the fish,and use the information as an important reference for feeding,fishing and classification[3].At present,most of the measurement methods of fish body morphological features are manual,the operator usesmeasuring ruler to measure manually.It requires high technical level,and has high labor intensity and low efficiency.Furthermore,https://doi.

Simulation of electron behavior in PIG ion source for 9 MeV cyclotron

In this paper, we focus on a PIG source for producing intense H-ions inside a 9 MeV cyclotron. The properties of the PIG ion source were simulated for a variety of electric field distributions and magnetic field strengths using a CST particle studio. After analyzing the secondary electron emission（SEE） as a function of both magnetic and electric field strengths, we found that for the modeled PIG geometry, a magnetic field strength of 0.2 T provided the best results in terms of the number of secondary electrons. Furthermore, at 0.2 T, the number of secondary electrons proved to be greatest regardless of the cathode potential. Also, the modified PIG ion source with quartz insulation tubes was tested in a KIRAMS-13 cyclotron by varying the gas flow rate and arc current, respectively.The capacity of the designed ion source was also demonstrated by producing plasma inside the constructed 9 MeV cyclotron. As a result, the ion source is verified as being capable of producing an intense H-beam and high ion beam current for the desired 9 MeV cyclotron. The simulation results provide experimental constraints for optimizing the strength of the plasma and final ion beam current at a target inside a cyclotron.

Detection of Chondromalacia Patellae by Analysis of Intrinsic Mode Functions in Knee-Joint Vibration Signals

This paper presents the knee-joint vibration signal processing and pathological localization procedures using the empirical mode decomposition for patients with chondrom alacia patellae.The artifacts of baseline wander and random noise were identified in the decomposed monotonic trend and intrinsic mode functions (IMF) using the modeling method of probability density function and the confidence limit criterion.Then, the fluctuation parts in the signal were detected by the signal method turning for count. The results demonstrated that the quality of reconstructed signal can be greatly improved, with the removal of the baseline wander(adaptive trend) and the Gaussian distributed random noise. By detecting the turn signals in the artifact-free signal, the pathological segments related to chondrom alacia patellae can be effectively localized with the beginning and ending points of the span of turn signals.

Representation of 1/f signal with wavelet bases

The representation of 1/f signal with wavelet transformation is explored. It is shown that a class of 1/f signal can be represented via wavelet synthetic formula and that a statistically self-similar property of signals may be characterized by the correlation functions of wavelet coefficients in the wavelet domain.

Segmentation and density statistics of mariculture cages from remote sensing images using mask R-CNN

The normal growth of fishes is closely relevant to the density of mariculture. It is of greatsignificance to accurately calculate the breeding area of specific sea area from satelliteremote sensing images. However, there are no reports about cage segmentation and den-sity detection based on remote sensing images so far. And the accurate segmentation ofcages faces challenges from very large high-resolution images. Firstly, a new public mari-culture cage data set is built. Secondly, the training set is augmented via sample variationsto improve the robustness of the model. Then, for cage segmentation and density statistics,a new methodology based on Mask R-CNN is proposed. Using dividing and stitching tech-nologies, the entire remote sensing test images of the cage can be accurately segmented.Finally, using the trained model, the object detection features and segmentation character-istics can be obtained at the same time. Considering only the area within the target detec-tion frame, the proposed method can count the pixels in the segmented area, which canobtain accurate area and density while reducing time-consuming. Experimental resultsdemonstrate that, compared with traditional contour extraction method and U-Net basedscheme, the proposed scheme can significantly improve segmentation precision and mod-el’s robustness. The relative error of the actual area is only 1.3%.