Environmental Information-Aided Electromagnetic Propagation Testbed for Maritime Communication

Maritime channel modeling can be affected by some key time-varying environmental factors.The ducting effect is one of the thorniest factors since it causes anomalous propagation enhancement and severe co-channel interference.Moreover,the atmospheric attenuation is much more severe in the ocean environment,resulting in shorter coverage distance and more link outage.In this paper,we propose an environmental information-aided electromagnetic propagation testbed.It is based on complex refractivity estimation and improved parabolic equation propagation model,taking into account both ducting effect and atmospheric attenuation.A large-scale temporal and spatial propagation measurement was conducted with meteorological acquisition.We consider practical path loss and ducting conditions to verify the testbed feasibility in these long-distance radio links.The simulation results are in good agreement with the measured data,which further reveal the basic temporal and spatial distribution of ducting effect at 3.5 GHz band.

Textile electronics for wearable applications

Textile electronics have become an indispensable part of wearable applications because of their large flexibility,light-weight,comfort and electronic functionality upon the merge of textiles and microelectronics.As a result,the fabrication of functional fibrous materials and the integration of textile electronic devices have attracted increasing interest in the wearable electronic community.Challenges are encountered in the development of textile electronics in a way that is electrically reliable and durable,without compromising on the deformability and comfort of a garment,including processing multiple materials with great mismatches in mechanical,thermal,and electrical properties and assembling various structures with the disparity in dimensional scales and surface roughness.Equal challenges lie in high-quality and cost-effective processes facilitated by high-level digital technology enabled design and manufacturing methods.This work reviews the manufacturing of textile-shaped electronics via the processing of functional fibrous materials from the perspective of hierarchical architectures,and discusses the heterogeneous integration of microelectronics into normal textiles upon the fabric circuit board and adapted electrical connections,broadly covering both conventional and advanced textile electronic production processes.We summarize the applications and obstacles of textile electronics explored so far in sensors,actuators,thermal management,energy fields,and displays.Finally,the main conclusions and outlook are provided while the remaining challenges of the fabrication and application of textile electronics are emphasized.

k-NN Based Bypass Entropy and Mutual Information Estimation for Incremental Remote-Sensing Image Compressibility Evaluation

Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.

MEC-Empowered Non-Terrestrial Network for 6G Wide-Area Time-Sensitive Internet of Things

In the upcoming sixth-generation(6G)era,the demand for constructing a wide-area time-sensitive Internet of Things(IoT)continues to increase.As conventional cellular technologies are difficult to directly use for wide-area time-sensitive IoT,it is beneficial to use non-terrestrial infrastructures,including satellites and unmanned aerial vehicles(UAVs).Thus,we can build a non-terrestrial network(NTN)using a cell-free architecture.Driven by the time-sensitive requirements and uneven distribution of IoT devices,the NTN must be empowered using mobile edge computing(MEC)while providing oasisoriented on-demand coverage for devices.Nevertheless,communication and MEC systems are coupled with each other under the influence of a complex propagation environment in the MEC-empowered NTN,which makes it difficult to coordinate the resources.In this study,we propose a process-oriented framework to design communication and MEC systems in a time-division manner.In this framework,large-scale channel state information(CSI)is used to characterize the complex propagation environment at an affordable cost,where a nonconvex latency minimization problem is formulated.Subsequently,the approximated problem is provided,and it can be decomposed into sub-problems.These sub-problems are then solved iteratively.The simulation results demonstrated the superiority of the proposed process-oriented scheme over other algorithms,implied that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of using resources,and confirmed that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT.

Independent component analysis to physical-layer network coding over wireless fading channels

A new combinational technology is proposed,which is feasible to apply physical-layer network coding（PNC） to wireless fading channels by employing the harmful interference strategically.The key step of PNC is that sources broadcast signals simultaneously without orthogonal scheduling.Naturally,the signals overlap in the free space at the receivers.Since the signals from different sources are mutual independent,rooted on this rational assumption,an enhanced joint diagonalization separation named altering row diagonalization（ARD） algorithm is exploited to separate these signals by maximizing the cost function measuring independence among them.This ARD PNC（APNC） methodology provides an innovative way to implement signal-level network coding at the presence of interference and without any priori information about channels in fading environments.In conclusions,the proposed APNC performs well with higher bandwidth utility and lower error rate.

MEASUREMENTS AND APPLICATIONS OF QoE FOR MULTIMEDIA COMMUNICATIONS

With the rapid development of smart devices and mobile networks,multimedia services will dominate most of data traffic in 4G/5G networks.Applications -such as conversational videos,online multimedia sharing,remote education,etc.have gained their popularity and will become more ubiquitous among customers.Tra-

Robustness Evaluation of Remote-Sensing Image Feature Detectors with TH Priori-Information Data Set

In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%.

Chlorine‑Rich Substitution Enabled 2D3D Hybrid Perovskites for High Efficiency and Stability in Sn‑Based Fiber‑Shaped Perovskite Solar Cells

Despite the impressive power conversion efficiency(PCE)beyond 25.5%,perovskite solar cells,especially the Sn-based variants,are poorly stable under normal operating conditions compared with the market-dominant silicon solar cells that can last for over 25 years.2D3D hybrid perovskite materials are one of the best options to overcome the instability chal-lenge without compromising efficiency.Indeed,a record performance of 1 year was reported in Pb-based 2D3D planar per-ovskite devices.However,the reaction between 2 and 3D perovskite molecules requires high temperatures(-300°C)and increased reaction time(-24 h)to achieve high-quality 2D3D hybrid perovskites.Herein,we base on the ability of chlorine to displace iodine from its ionic compounds in solutions to utilize chloride ions as catalysts for speeding up the reaction between iodine-based 2D and 3D perovskite molecules.The approach reduces the reaction time to-20 min and the reaction temperature to-100°C with the formation of high-quality 2D3D hybrid perovskites,free from pure 2D traces.Integrating the synthesized 2D3D hybrid perovskite material with 50%chlorine doping in a fiber-shaped solar cell architecture yielded the highest reported PCE of 11.96%in Sn-based fiber-shaped perovskite solar cells.The unencapsulated and encapsulated fiber-shaped solar cells could maintain 75%and 95.5%of their original PCE,respectively,after 3 months under room light and relative humidity of 35–40%,revealing the champion stability in Sn-based perovskite solar devices.The solar yarn also demonstrated constant energy output under changing light incident angles(0–180°).

织物电子器件及系统的发展现状、科学问题、核心技术和应用展望

织物电子学研究对象是具有产生、传输、调制或测量等电子功能的纤维或纤维集合体.织物电子系统通常由织物电子器件和微电子器件混合互补集成而成.它们作为可穿戴系统的一个重要分支,可为人类提供多种功能,如感知、驱动、自适应、交流、自我修复、记忆、学习等,而不影响系统本身的服用性.本文从织物电子器件和系统的定义出发,阐述了不同类别织物电子器件的发展现状,提出织物电子器件和系统的一些关键科学问题,如功能材料结构与性能的关系、织物电子器件和系统集成的多物理过程、评价系统及安全问题等.并结合在研发织物电子可穿戴系统及实施工程化产业化过程中的切身体会,介绍织物电子器件和系统方面的核心技术及其应用前景.重点对织物电子器件或系统在医疗健康、运动监测、智能防护及增强现实/虚拟现实4个方向的应用及其应用前景进行分析.

Fast Remote-Sensing Image Registration Using Priori Information and Robust Feature Extraction

In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information images,and use machine learning techniques to identify robust remote-sensing image features from state-of-the-art ScaleInvariant Feature Transform（SIFT） features. Next, we apply a hierarchical coarse-to-fine feature matching and image registration scheme on the basis of additional priori information, including a robust feature location map and platform imaging parameters. Numerical simulation results show that the proposed scheme increases position repetitiveness by 34%, and can speed up the overall image registration procedure by a factor of 7：47 while maintaining the accuracy of the image registration performance.

2D Materials Based Optoelectronic Memory: Convergence of Electronic Memory and Optical Sensor

Te continuous development of electron devices towards the trend of“More than Moore”requires functional diversifcation that can collect data(sensors)and store(memories)and process(computing units)information.Considering the large occupation proportion of image data in both data center and edge devices,a device integration with optical sensing and data storage and processing is highly demanded for future energy-efcient and miniaturized electronic system.Two-dimensional(2D)materials and their heterostructures have exhibited broadband photoresponse and high photoresponsivity in the confguration of optical sensors and showed fast switching speed,multi-bit data storage,and large ON/OFF ratio in memory devices.In addition,its ultrathin body thickness and transfer process at low temperature allow 2D materials to be heterogeneously integrated with other existing materials system.In this paper,we overview the state-of-the-art optoelectronic random-access memories(ORAMs)based on 2D materials,as well as ORAM synaptic devices and their applications in neural network and image processing.Te ORAM devices potentially enable direct storage/processing of sensory data from external environment.We also provide perspectives on possible directions of other neuromorphic sensor design(e.g.,auditory and olfactory)based on 2D materials towards the future smart electronic systems for artifcial intelligence.

Collaborative image compression and classification with multi-task learning for visual Internet of Things

Widespread deployment of the Internet of Things(Io T)has changed the way that network services are developed,deployed,and operated.Most onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io T.Typically,the sender would compress images,and then through the communication network,the receiver would decode images,and then analyze the images for applications.However,image compression and semantic inference are generally conducted separately,and thus,current compression algorithms cannot be transplanted for the use of semantic inference directly.A collaborative image compression and classification framework for visual Io T applications is proposed,which combines image compression with semantic inference by using multi-task learning.In particular,the multi-task Generative Adversarial Networks(GANs)are described,which include encoder,quantizer,generator,discriminator,and classifier to conduct simultaneously image compression and classification.The key to the proposed framework is the quantized latent representation used for compression and classification.GANs with perceptual quality can achieve low bitrate compression and reduce the amount of data transmitted.In addition,the design in which two tasks share the same feature can greatly reduce computing resources,which is especially applicable for environments with extremely limited resources.Using extensive experiments,the collaborative compression and classification framework is effective and useful for visual IoT applications.

DEFORMATION ANALYSIS AND FAILURE MODELLING OF WOVEN COMPOSITE PREFORM IN GENERAL BIAS EXTENSION

A series of bias extension tests was carried out on balanced plain woven composite preforms with various aspect ratios, disclosing that different aspect ratios may result in differ- ent initial failures. An energy method is adopted to quantify several deformation modes. By the competition of the required energies, the initial failure is predicted, showing a good accordance with the experimental observation. The results of the present research are valuable for the further understanding of the material＇s behaviour in bias extension test. It also provides an effective way for modelling the material＇s formability in other more complicated forming processes.

Effectively Lossless Subspace Appearance Model Compression Using Prior Information

Subspace appearance models are widely used in computer vision and image processing tasks to compactly represent the appearance variations of target objects. In order to ensure algorithm performance, they are typically stored in high-precision formats; this results in a large storage footprint, rendering redistribution costly and difficult. Since for most image and vision applications, pixel values are quantized to 8 bits by the acquisition apparatuses, we show that it is possible to construct a fixed-width, effectively Iossless representation of the bases vectors, in the sense that reconstructions from the original bases and from the quantized bases never deviate by more than half of the quantization step-size. In addition to directly applying this result to Iosslessly compress individual models, we also propose an algorithm to compress appearance models by utilizing prior information on the modeled objects in the form of prior appearance subspaces. Experiments conducted on the compression of person-specific face appearance models demonstrate the effectiveness of the proposed algorithms.

分子桥连石墨烯/银用于高性能导电浆料

印刷作为一种增材制备技术可以降低制备过程中的材料成本并减少对环境的污染.银导电浆料常用于各种电子印刷器件,如互联网、电感、天线等领域.然而贵金属银的高成本限制了其大规模应用.为了降低目前银导电浆料的成本,本文提出了一种分子桥连的石墨烯/银(MB-G/A)导电浆料,并应用于高导电性、低成本的纸基电子器件.石墨烯可以取代部分银纳米颗粒,降低成本的同时形成合适的导电网络,从而保证了良好的导电性能.采用巯基乙胺作为分子桥连剂,其一端通过叠氮反应锚定在石墨烯表面,另一端通过巯基官能团与银原子成键,实现石墨烯和银之间分子尺度的桥连,从而促进石墨烯/银界面的电荷传输.结果表明所制备的MB-G/A导电浆料的最大电导率可达2.0×10^(5)S m^(−1),且可成功应用于各种电子器件中.此外,最优的MB-G/A导电浆料的成本与纯银导电浆料相比减少了至少一半.该MB-G/A导电浆料在商业化电子器件领域具有良好的应用前景.