Triad-displaced ULAs configuration for non-circular sources with larger continuous virtual aperture and enhanced degrees of freedom

Non-uniform linear array(NULA)configurations are well renowned due to their structural ability for providing increased degrees of freedom(DOF)and wider array aperture than uniform linear arrays(ULAs).These characteristics play a significant role in improving the direction-of-arrival(DOA)estimation accuracy.However,most of the existing NULA geometries are primarily applicable to circular sources(CSs),while they limitedly improve the DOF and continuous virtual aperture for noncircular sources(NCSs).Toward this purpose,we present a triaddisplaced ULAs(Tdis-ULAs)configuration for NCS.The TdisULAs structure generally consists of three ULAs,which are appropriately placed.The proposed antenna array approach fully exploits the non-circular characteristics of the sources.Given the same number of elements,the Tdis-ULAs design achieves more DOF and larger hole-free co-array aperture than its sparse array competitors.Advantageously,the number of uniform DOF,optimal distribution of elements among the ULAs,and precise element positions are uniquely determined by the closed-form expressions.Moreover,the proposed array also produces a filled resulting co-array.Numerical simulations are conducted to show the performance advantages of the proposed Tdis-ULAs configuration over its counterpart designs.

Gesture Recognition Based on Time-of-Flight Sensor and Residual Neural Network

With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors were widely applied due to their low cost. This paper explored the implementation of a human hand posture recognition system using ToF sensors and residual neural networks. Firstly, this paper reviewed the typical applications of human hand recognition. Secondly, this paper designed a hand gesture recognition system using a ToF sensor VL53L5. Subsequently, data preprocessing was conducted, followed by training the constructed residual neural network. Then, the recognition results were analyzed, indicating that gesture recognition based on the residual neural network achieved an accuracy of 98.5% in a 5-class classification scenario. Finally, the paper discussed existing issues and future research directions.

Research on the cultivation of innovative and creative thinking for science and technology talents in local engineering colleges and Universities

The direct target of engineering education is to cultivate engineers and industry leaders with the qualifi ed skills and strong competencies. To cultivate skilled technological talents in a local engineering college, the fi rst subject is to establish the discipline according to the major strategy of the local economic development requirements, so that the professional regional service function is relatively strong. Secondly, the individual work orientation and development direction are considered as well in the cultivated process. These aspects are required such as renewing education idea, strengthening the education of humanity quality, perfecting the system of continuing education, establishing the mechanism of lifelong learning.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Spatiotemporal characteristics and remote sensing estimation of colored dissolved organic matter around the Leizhou Peninsula

Colored dissolved organic matter(CDOM)is an important optically active substance in marine environment.Its biochemical conservatism makes it an important indicator to offshore pollution process.Monitoring the content,composition,and diffusion process of CDOM is a good approach to analyze the terrestrial input,optical properties,and ecological environment of offshore areas.The spatiotemporal characteristics of CDOM around the Leizhou Peninsula were analyzed based on field observation data collected in the autumn 2020 and spring 2021,and an empirical inversion model of the ag(355)(absorption coefficient at 355 nm)and spectral slope(Sg)(g stands for gelbstoff/gilvin,which is called CDOM)based on Sentinel-3A ocean and land color instrument(OLCI)images was constructed.The results show(1)the order of average ag(355)value around the Leizhou Peninsula was east coast(0.503/m)>Qiongzhou Strait(0.502/m)>west coast(0.365/m);(2)the best band combinations of CDOM inversion in spring and autumn were(B4+B11)/B3 and(B7-B1)/B6(B stands for the band of spectral images),and the final inversion results are close to the measured results,indicating that the model has good accuracy;(3)the S_(g)value of the CDOM absorption spectrum was fitted to the hyperbolicexponential model.The fitting accuracy of the model was higher than those of the exponential model and the hyperbolic model,and the best S_(g)inversion model was constructed by selecting Sg(275-295)and Sg(250-295)in spring and autumn;(4)the spatial distributions of ag(355)and S_(g)were inverted,and CDOM in the waters around the Leizhou Peninsula originated from terrestrial organic matter carried by coastal aquaculture zones and runoff in the northeast Zhanjiang and the northern Beibu Gulf.

A Review of Device-Free Indoor Positioning for Home-Based Care of the Aged:Techniques and Technologies

With the development of urbanization,the problem of neurological diseases brought about by population aging has gradually become a social problem of worldwide concern.Aging leads to gradual degeneration of the central nervous system,shrinkage of brain tissue,and decline in physical function in many elderlies,making them susceptible to neurological diseases such as Alzheimer’s disease(AD),stroke,Parkinson’s and major depressive disorder(MDD).Due to the influence of these neurological diseases,the elderly have troubles such as memory loss,inability to move,falling,and getting lost,which seriously affect their quality of life.Tracking and positioning of elderly with neurological diseases and keeping track of their location in real-time are necessary and crucial in order to detect and treat dangerous and unexpected situations in time.Considering that the elderly with neurological diseases forget to wear a positioning device or have mobility problems due to carrying a positioning device,device-free positioning as a passive positioning technology that detects device-free individuals is more suitable than traditional active positioning for the home-based care of the elderly with neurological diseases.This paper provides an extensive and in-depth survey of device-free indoor positioning technology for home-based care and an in-depth analysis of the main features of current positioning systems,as well as the techniques,technologies andmethods they employ,fromthe perspective of the needs of the elderly with neurological conditions.Moreover,evaluation criteria and possible solutions of positioning techniques for the home-based care of the elderly with neurological conditions are proposed.Finally,the opportunities and challenges for the development of indoor positioning technology in 6G mobile networks for home-based care of the elderly with neurological diseases are discussed.This review has provided comprehensive and effective tracking and positioning techniques,technologies and methods for the elderly,by which we can obtain the location information of the elderly in real-time and make home-based care more comfortable and safer for the elderly with neurological diseases.

Relationships between ionospheric parameters derived from ionosonde observations and characteristics of post-sunset GHz scintillation during high solar activities(2012−2013)at Sanya(18.3°N,109.6°E),China

In this study,we present characteristics of post-sunset GHz scintillation occurrence and their correlations with ionospheric parameters derived from ionosonde observations in high solar activity years(2012−2013)of solar cycle 24 at Sanya(18.3°N,109.6°E;dip lat.:12.8°N),China.The analyzed data include the F_(2)-layer’s critical frequency(foF_(2)),peak height(h_(m)F_(2)),and minimum virtual height(h’F),as well as the scale height around the F_(2)-layer peak(H_(m)),and virtual height(h’F_(5))and true height(hF_(5))measured at 5 MHz.We have investigated relationships between the equinoctial asymmetry of these scintillations and these ionospheric parameters.In addition,we calculate the growth rates of Rayleigh−Taylor instability on the basis of the ionosonde measurements and theoretical models,respectively.We find that the equinoctial asymmetry of scintillation onset time is associated with the scale length of the vertical electron density gradient(L),which has been shown to affect the growth of Rayleigh−Taylor instability at the bottom of the F-layer.The seasonal variations of foF_(2),H_(m)and scale length of vertical electron density gradient appear to cause the seasonal variations of scintillation occurrence;the equinoctial asymmetry of scintillation occurrence rate over low latitudes appears to be related to background electron density and vertical drifts in the F-layer around time of sunset.Further study is required to explain the observed correlational weakness in low latitudes between scintillation strength,represented by the daily maximum S4,and daily maximum values of foF_(2),h_(m)F_(2),h’F,H_(m),and also the drifts.

A Combined Reinforcement Learning and Model Predictive Control for Car-Following Maneuver of Autonomous Vehicles

Model predictive control is widely used in the design of autonomous driving algorithms.However,its parameters are sensitive to dynamically varying driving conditions,making it difficult to be implemented into practice.As a result,this study presents a self-learning algorithm based on reinforcement learning to tune a model predictive controller.Specifically,the proposed algorithm is used to extract features of dynamic traffic scenes and adjust the weight coefficients of the model predictive controller.In this method,a risk threshold model is proposed to classify the risk level of the scenes based on the scene features,and aid in the design of the reinforcement learning reward function and ultimately improve the adaptability of the model predictive controller to real-world scenarios.The proposed algorithm is compared to a pure model predictive controller in car-following case.According to the results,the proposed method enables autonomous vehicles to adjust the priority of performance indices reasonably in different scenarios according to risk variations,showing a good scenario adaptability with safety guaranteed.

Joint Design of ISAC Waveform Under PAPR Constraints

In this paper,we formulate the precoding problem of integrated sensing and communication(ISAC)waveform as a non-convex quadratically constrained quadratic programming(QCQP),in which the weighted sum of communication multi-user interference(MUI)and the gap between dual-use waveform and ideal radar waveform is minimized with peak-toaverage power ratio(PAPR)constraints.We propose an efficient algorithm based on alternating direction method of multipliers(ADMM),which is able to decouple multiple variables and provide a closed-form solution for each subproblem.In addition,to improve the sensing performance in both spatial and temporal domains,we propose a new criteria to design the ideal radar waveform,in which the beam pattern is made similar to the ideal one and the integrated sidelobe level of the ambiguity function in each target direction is minimized in the region of interest.The limited memory Broyden-Fletcher-Goldfarb-Shanno(LBFGS)algorithm is applied to the design of the ideal radar waveform which works as a reference in the design of the dual-function waveform.Numerical results indicate that the designed dual-function waveform is capable of offering good communication quality of service(QoS)and sensing performance.

Accelerated Primal-Dual Projection Neurodynamic Approach With Time Scaling for Linear and Set Constrained Convex Optimization Problems

The Nesterov accelerated dynamical approach serves as an essential tool for addressing convex optimization problems with accelerated convergence rates.Most previous studies in this field have primarily concentrated on unconstrained smooth con-vex optimization problems.In this paper,on the basis of primal-dual dynamical approach,Nesterov accelerated dynamical approach,projection operator and directional gradient,we present two accelerated primal-dual projection neurodynamic approaches with time scaling to address convex optimization problems with smooth and nonsmooth objective functions subject to linear and set constraints,which consist of a second-order ODE(ordinary differential equation)or differential conclusion system for the primal variables and a first-order ODE for the dual vari-ables.By satisfying specific conditions for time scaling,we demonstrate that the proposed approaches have a faster conver-gence rate.This only requires assuming convexity of the objective function.We validate the effectiveness of our proposed two accel-erated primal-dual projection neurodynamic approaches through numerical experiments.

Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

HCSP-Net:A Novel Model of Age-Related Macular Degeneration Classification Based on Color Fundus Photography

Age-related macular degeneration(AMD)ranks third among the most common causes of blindness.As the most conventional and direct method for identifying AMD,color fundus photography has become prominent owing to its consistency,ease of use,and good quality in extensive clinical practice.In this study,a convolutional neural network(CSPDarknet53)was combined with a transformer to construct a new hybrid model,HCSP-Net.This hybrid model was employed to tri-classify color fundus photography into the normal macula(NM),dry macular degeneration(DMD),and wet macular degeneration(WMD)based on clinical classification manifestations,thus identifying and resolving AMD as early as possible with color fundus photography.To further enhance the performance of this model,grouped convolution was introduced in this study without significantly increasing the number of parameters.HCSP-Net was validated using an independent test set.The average precision of HCSPNet in the diagnosis of AMD was 99.2%,the recall rate was 98.2%,the F1-Score was 98.7%,the PPV(positive predictive value)was 99.2%,and the NPV(negative predictive value)was 99.6%.Moreover,a knowledge distillation approach was also adopted to develop a lightweight student network(SCSP-Net).The experimental results revealed a noteworthy enhancement in the accuracy of SCSP-Net,rising from 94%to 97%,while remarkably reducing the parameter count to a quarter of HCSP-Net.This attribute positions SCSP-Net as a highly suitable candidate for the deployment of resource-constrained devices,which may provide ophthalmologists with an efficient tool for diagnosing AMD.

Air-Ground Collaborative Mobile Edge Computing:Architecture,Challenges,and Opportunities

By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.

Memristive feature and mechanism induced by laser-doping in defect-free 2D semiconductor materials

Memristors as non-volatile memory devices have gained numerous attentions owing to their advantages in storage,in-memory computing, synaptic applications, etc. In recent years, two-dimensional(2D) materials with moderate defects have been discovered to exist memristive feature. However, it is very difficult to obtain moderate defect degree in 2D materials, and studied on modulation means and mechanism becomes urgent and essential. In this work, we realized memristive feature with a bipolar switching and a configurable on/off ratio in a two-terminal MoS_(2) device(on/off ratio ~100), for the first time, from absent to present using laser-modulation to few-layer defect-free MoS_(2)(about 10 layers), and its retention time in both high resistance state and low resistance state can reach 2×10^(4) s. The mechanism of the laser-induced memristive feature has been cleared by dynamic Monte Carlo simulations and first-principles calculations. Furthermore, we verified the universality of the laser-modulation by investigating other 2D materials of TMDs. Our work will open a route to modulate and optimize the performance of 2D semiconductor memristive devices.

Exploration on the Order-orientated Cultivation of Engineering Master for Industry Oriented Demand

full-time Master of engineering education has developed rapidly in recent years, but in the talent cultivation, professional degree of master of engineering characteristics is not obvious, there are such problems as backward curriculum system, practice teaching is weak, tutor guidance effectiveness is not high, thesis and practice related to small. Colleges and universities need to further adjust and optimize the master of engineering training mode and promote the cooperation between colleges and enterprises, the order-form talent-training model as a handle, promote the depth of integration of production, study and research, and highlight the characteristics of talent cultivation.

A Survey of Gesture Recognition Using Frequency Modulated Continuous Wave Radar

With technology advances and human requirements increasing, human-computer interaction plays an important role in our daily lives. Among these interactions, gesture-based recognition offers a natural and intuitive user experience that does not require physical contact and is becoming increasingly prevalent across various fields. Gesture recognition systems based on Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar are receiving widespread attention due to their ability to operate without wearable sensors, their robustness to environmental factors, and the excellent penetrative ability of radar signals. This paper first reviews the current main gesture recognition applications. Subsequently, we introduce the system of gesture recognition based on FMCW radar and provide a general framework for gesture recognition, including gesture data acquisition, data preprocessing, and classification methods. We then discuss typical applications of gesture recognition systems and summarize the performance of these systems in terms of experimental environment, signal acquisition, signal processing, and classification methods. Specifically, we focus our study on four typical gesture recognition systems, including air-writing recognition, gesture command recognition, sign language recognition, and text input recognition. Finally, this paper addresses the challenges and unresolved problems in FMCW radar-based gesture recognition and provides insights into potential future research directions.

Influence of oxygen gas content on the structural and optical properties of ZnO thin films deposited by RF magnetron sputtering at room temperature

Zinc oxide （ZnO） thin films were deposited on sapphire （0001） substrates at room temperature by radiofrequency （RF） magnetron sputtering at oxygen gas contents of 0%, 25%, 50% and 75%, respectively. The influence of oxygen gas content on the structural and optical properties of ZnO thin films was studied by a surface profile measuring system, X-ray diffraction analysis, atomic force microscopy, and UV spectro- photometry. It is found that the size of ZnO crystalline grains increases first and then decreases with the increase of oxygen gas content, and the maximum grain size locates at the 25% oxygen gas content. The crystalline quality and average optical transmittance （〉90%） in the visi- ble-light region of the ZnO film prepared at an oxygen gas content of 25% are better than those of ZnO films at the other contents. The obtained results can be attributed to the resputtefing by energetic oxygen anions in the growing process.

Noise Reduction of Welding Defect Image Based on NSCT and Anisotropic Diffusion

In order to reduce noise effectively in the welding defect image and preserve the minutiae information, a noise reduction method of welding defect image based on nonsubsampled contourlet transform(NSCT) and anisotropic diffusion is proposed. Firstly, an X-ray welding defect image is decomposed by NSCT. Then total variation(TV) model and Catte_PM model are used for the obtained low-pass component and band-pass components, respectively. Finally, the denoised image is synthesized by inverse NSCT. Experimental results show that, compared with the hybrid method of wavelet threshold shrinkage with TV diffusion, the method combining NSCT with P_Laplace diffusion, and the method combining contourlet with TV model and adaptive contrast diffusion, the proposed method has a great improvement in the aspects of subjective visual effect, peak signal-to-noise ratio(PSNR) and mean-square error(MSE). Noise is suppressed more effectively and the minutiae information is preserved better in the image.

A Fully Distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic Algorithm in Smart Grid

A fully distributed microgrid system model is presented in this paper.In the user side,two types of load and plug-in electric vehicles are considered to schedule energy for more benefits.The charging and discharging states of the electric vehicles are represented by the zero-one variables with more flexibility.To solve the nonconvex optimization problem of the users,a novel neurodynamic algorithm which combines the neural network algorithm with the differential evolution algorithm is designed and its convergence speed is faster.A distributed algorithm with a new approach to deal with the inequality constraints is used to solve the convex optimization problem of the generators which can protect their privacy.Simulation results and comparative experiments show that the model and algorithms are effective.

Edge Detection of River in SAR Image Based on Contourlet Modulus Maxima and Improved Mathematical Morphology

To cope with the problems that edge detection operators are liable to make the detected edges too blurry for synthetic aperture radar(SAR)images,an edge detection method for detecting river in SAR images is proposed based on contourlet modulus maxima and improved mathematical morphology.The SAR image is firstly transformed to a contourlet domain.According to the directional information and gradient information of directional subband of contourlet transform,the modulus maximum and the improved mathematical morphology are used to detect high frequency and low frequency sub-image edges,respectively.Subsequently,the edges of river in SAR image are obtained after fusing the high frequency sub-image and the low frequency sub-image.Experimental results demonstrate that the proposed edge detection method can obtain more accurate edge location and reduce false edges,compared with the Canny method,the method based on wavelet and Canny,the method based on contourlet modulus maxima,and the method based on improved(ROEWA).The obtained river edges are complete and clear.