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Estimating coastal ground deformation caused by ocean tidal loading (OTL) through time-series interferometric synthetic aperture radar (InSAR) techniques is challenging but achievable. Based on the two-dimensional wavelet decomposition method to extract spatial long-wavelength interferometric phases, the OTL deformation, residual orbital error, and atmospheric delay error hidden in long-wavelength phases can be isolated by independent component analysis. The ground OTL deformation in time-series InSAR phases is then accurately estimated using tidal harmonic functions. Experiments conducted with Sentinel-1 measurements in coastal areas reveal the following findings. Leveraging the blind source separation method allows for the effective extraction of high spatial resolution and large-scale ground OTL deformation correlation signals from the time-series InSAR phase. The differences between parameter values of main constituents estimated by the modeled InSAR ground OTL deformation and the FES2014b global tidal model range from 1.1% to 5.8%. Additionally, the difference in their predicted OTL deformation values is within the range of 0.9–4.9 mm. Compared to the FES2014b model correction method, the proposed approach removes OTL deformation from the time-series InSAR and results in a 26.1% reduction in the residual standard deviation value of ground deformation, which leads to a significant enhancement in the accuracy of ground deformation acquired for coastal areas. This work provides a novel approach to the theoretical research on OTL deformations, contributing to the refinement of InSAR ground deformation monitoring accuracy in coastal areas.

Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorders.

The task of text summarization aims to provide highly condensed summaries of long textual information, with the ideal summary being both precise and concise. In recent years, there has been extensive research on the brevity of summaries, but these methods still have significant room for improvement in ROUGE scores, especially when the beam width is increased. We propose a new model called the DC (Dual-Level Contrastive Learning), which combines contrastive learning and data augmentation, and design a new scoring function during the training phase to enhance accuracy and conciseness. Ultimately, our framework achieves excellent ROUGE scores, ensuring concise and readable output even with increased beam width. Experimental results on the CNN/DailyMail (47.82 ROUGE-1, 0.017 VAR) and XSum (47.31 ROUGE-1, 0.0052 VAR) datasets demonstrate that our approach can significantly enhance the accuracy and conciseness of the summaries. Some metrics have exceeded those of the current state-of-the-art model BRIO, promoting the state-of-the-art performance to a higher level.

With the escalating requirements for maintenance of wind turbines, the deployment of Unmanned Aerial Vehicles (UAVs) for inspection tasks has become increasingly prevalent. However, wind turbine blades, which are thin and long, possess weak texture features that lead to target confusion when tracking specific parts of the dynamic blades. Additionally, wind turbine units, being large dynamic structures, often exceed the camera’s field of view (FOV) and exhibit unique motion characteristics. These factors make the visual tracking of specific components unstable due to the lack of global motion information. In order to address the aforementioned challenges and achieve consistent calibration of key components under the dynamic operating conditions of wind turbines, this study has adopted a strategy of integrating the Squeeze-and-Excitation Network (SEnet) into the backbone network of YOLOv5. Innovatively, two hyperparameters have been introduced into the existing loss function to adjust the weights of samples under conditions of data imbalance, thereby enhancing the performance of the detection model. In the application of the DeepSORT tracking algorithm, Long Short-Term Memory (LSTM) networks have been combined to predict the trajectory of the rotor blade’s central point, and an optimized Kalman filter has been employed to significantly improve the system’s adaptability and precision under various motion conditions. Empirical results from this study underscore the efficacy of the proposed method, demonstrating its capability to accurately differentiate individual blades as well as specific blade segments. Compared to the traditional YOLOv5, the enhanced YOLOv5-SE has demonstrated a 5.3% improvement in the Mean Average Precision (mAP_0.5) evaluation metric. Moreover, the improved DeepSORT algorithm has exhibited high efficiency in maintaining continuous and stable tracking of moving blades, adeptly handling scenarios where rotor blades frequently enter and exit the FOV. This advancement paves the way for the broader application of UAVs in wind turbine inspections, offering the potential for more efficient and accurate maintenance protocols.

Highways built in permafrost regions are susceptible to deformations and instability of the roadbed caused by climatic factors. Long-term deformation monitoring is essential to reveal the freeze-thaw-related deformations. When using Interferometric Synthetic Aperture Radar (InSAR) for permafrost highway monitoring, the majority of different physical phase components are usually considered as equally weighted, and the permafrost deformation-related components are mostly modeled with an empirical mathematical model. This may induce uncertainty and difficulties to remove the atmospheric delay and orbital error, which affects both the accuracy and efficiency of deformation estimation. To address these limitations, we propose an independent component analysis (ICA) based method for estimating the deformation of highways in permafrost regions (HPICA). In HPICA, the Fast ICA is utilized to separate the original InSAR unwrapped phases, and then the extracted freeze-thaw deformation-related components are modeled considering the climatic factors. The simulated experiments show that the spatial ICA can more accurately separate the deformation-related signals from the mixed signals than that of temporal ICA. The Maduo section of Gongyu Highway on the Tibetan Plateau was selected as a study area in the real-data experiment. The results showed the maximum cumulative settlement spanning January 2020 to January 2022 was up to –140.8 mm. A comparative analysis indicated that the modeling accuracy of HPICA is with significant improvement. Besides, HPICA could reveal the boundaries of different permafrost regions according to the nature of permafrost, thus assisting in spatial classification of different types of soil regions.

A chronic stroke affects hand mobility limiting the normal functioning of the finger joints. The voluntary tasks with a repetitive motion can identify the limitation in the range of motion (ROM) to enhance the hand functioning of stroke survivors. Therefore, we compared two voluntary tasks fast ball squeezing (FBS) and slow ball squeezing (SBS), and analyzed the variability of these tasks for finger pressure and ROM in chronic stroke survivors. An experimental study with 22 healthy and 39 chronic stroke participants was conducted. All of them wore an upper limb motion capture device (UMCD) to record the active (AROM) of finger joints during FBS and SBS tasks for 20 repetitions. The data were analyzed into the joint angle and pressure to compare these tasks and their variability. While comparing the FBS and SBS tasks, left-side pressure and both sides’ ROM attained the level of significance ( $p < 0.05$ ) except for right-side pressure in healthy controls. However, for stroke participants, right-side pressure and left-side ROM differed significantly. The finger pressure and ROM for the right vs. left side are changed significantly for both tasks in healthy control and stroke survivors except for SBS pressure in the stroke group ( $p$ = 0.119). The right FBS and SBS are more sensitive to changes in finger pressure and left SBS for ROM in healthy control while in stroke survivors, left FBS and SBS pressure and ROM are sensitive. The variability for pressure is higher and easy to detect as compared to ROM.

Repetitive Transcranial Magnetic Stimulation (rTMS) and transspinal electrical stimulation (tsES) have been proposed as a novel neurostimulation modality for individuals with incomplete spinal cord injury (iSCI). In this study, we integrated magnetic and electrical stimulators to provide neuromodulation therapy to individuals with incomplete spinal cord injury (iSCI). We designed a clinical trial comprising an 8-week treatment period and a 4-week treatment-free observation period. Cortical excitability, clinical features, inertial measurement unit and surface electromyography were assessed every 4 weeks. Twelve individuals with iSCI were recruited and randomly divided into a combined therapy group, a magnetic stimulation group, an electrical stimulation group, or a sham stimulation group. The magnetic and electric stimulations provided in this study were intermittent theta-burst stimulation (iTBS) and 2.5-mA direct current (DC) stimulation, respectively. Combined therapy, which involves iTBS and transspinal DC stimulation (tsDCS), was more effective than was iTBS alone or tsDCS alone in terms of increasing corticospinal excitability. In conclusion, the effectiveness of 8-week combined therapy in increasing corticospinal excitability faded 4 weeks after the cessation of treatment. According to the results, combination of iTBS rTMS and tsDCS treatment was more effective than was iTBS rTMS alone or tsDCS alone in enhancing corticospinal excitability. Although promising, the results of this study must be validated by studies with longer interventions and larger sample sizes.

Under the framework of switched system theory, the system states of three-phase voltage source converters (VSCs) can be regulated by subsystems switching according to the switching rule directly. However, because the subsystem selected for the next control cycle may same as that used in the present control cycle and the incorrect subsystem may be activated under system delay conditions, the unfixed switching frequency problem and the unstable subsystem switching problem can be found using the present switching control strategies. In this paper, a current switching predictive control (CSPC) method is proposed to the three-phase VSCs in the framework of switched system theory. The proposed method realizes the fixed switching frequency and avoids the unstable subsystem switching under system delay conditions, where advantages in the present methods, such as the accurate mathematical model, simple control structure, less control parameters, without vector modulation process are remained. The simulation and experiment results verify the effectiveness of the proposed methodology.

Transcranial temporal interference stimulation (tTIS) has been proposed as a new neuromodulation technology for non-invasive deep-brain stimulation (DBS). However, few studies have detailed the design method of a tTIS device and provided system validation. Thus, a detailed design and validation scheme of a novel tTIS device for animal brain stimulation are presented in this study. In the proposed tTIS device, a direct digital synthesizer (DDS) was used to generate a sine wave potential of different frequencies, which was converted to an adjustable sine wave current. A current transformer was used to produce electrical isolation of different channels, which eliminated the current crosstalk between channels and greatly increased the load capacity by amplifying the output voltage. Several in vitro experiments were first conducted to validate the tTIS device. Our results indicated that the error percentages of the stimulation currents were within ±2%. Current crosstalk between channels was almost completely eliminated. Then, in vivo electric field measurement shows that the 2-pole arrangement may provide better cortical targeting than the 4-pole mode. A pilot animal experiment was conducted in which evoked motion and electromyographic activation of the contralateral forelimb were observed, which indicated that the 2-pole tTIS had successfully activated the primary motor cortex in a rat. Motor activation induced by the 2-pole tTIS demonstrated the feasibility and safety potential when applying our tTIS device for neuromodulation.

Lidar sensors are commonly equipped on a mobile mapping system (MMS) to establish point clouds for HD map creation. However, the point clouds themselves do not contain object attributes. Therefore, human operators have to manually obtain objects’ position to assign attributes for further HD map conversion, inevitably resulting in time-consuming processes and significant labor costs. To solve the above problems, in this paper, we present an MMS equipped with non-survey grade Lidar, commercial grade camera, and entry level GNSS/INS, which incorporates ground control points (GCPs) with a Normal Distribution Transform Simultaneously Localization and Mapping (NDT SLAM) refinement and fluctuation adjustment to secure both absolute position accuracy and relative position accuracy of the reconstructed point cloud. Meanwhile, a deep neural network for image detection is employed to obtain the bounding box of traffic signs from each image frame. By applying the translation and rotation transformation between Lidar points and camera pixels, intersection of the detected object in the image and Lidar scan points can be found. By accumulating extracted Lidar points of the traffic sign in several detection frames, we can then obtain an accurate 3D geodetic coordinate of the traffic signs. Experimental results show that point clouds can be reconstructed with an average 3D RMSE of only 8.6cm, and center geodetic coordinates of traffic signs can be further extracted in sub-meter accuracy to significantly reduce labor work in HD map creation.

We put forward and provide the first illustration of a high capacity and high spectral efficient free space optical communication (FSOC) system using digital domain power division multiplexing orthogonal-frequency-division-multiplexed (DDPD-O-OFDM) signal utilizing 10-GHz class optical components. In the proof-of-concept demonstration, a data rate of 2 × 30.8 Gbit/s can be achieved for each polarization multiplexed wavelength channel, fulfilling the pre-forward-error-correction bit-error-ratio (pre-FEC BER = 3.8 × 10−3). By employing the DDPD-O-OFDM here, the FSOC per channel capacity can be enhanced not only allowing the spectral overlapping of different orthogonal subcarriers, as in the case of OFDM; but also allowing channel multiplexing in the power domain to increase the spectral efficiency. The principle of DDPD-O-OFDM is discussed; and BER and signal-to-noise ratio (SNR) of different DDPD channels are experimentally measured.

We propose to utilize a commercially available DIALux lighting design software for simulation and planning of machine learning (ML) based angle-of-arrival (AOA) visible light positioning (VLP) systems. Here, different ML models, for example, second order linear regression (LR), artificial neural-network (ANN), and convolutional neural-network (CNN) are employed. The proposed VLP simulator works well with different ML algorithms. The results show that the proposed scheme can acts as an effective indoor VLP planning and design tool. Besides, it may also alleviate the training data collection in ML based VLP systems.

In recent years, multibaseline phase unwrapping (MBPU) has been widely studied, but it is still suffering from the problem of low noise robustness. In view of this, two cluster correction methods for the cluster analysis-based MBPU algorithms are presented to improve the accuracy and effectiveness of the algorithm. In the first method, a suitable box centered on each pixel is selected and the cluster with the highest frequency in the box is taken as the corrected cluster to which the central pixel belongs; in the second method, all pixels are divided into core pixels and noncore pixels according to the predefined density, and then the cluster of the noncore pixels is corrected to the one that appears most frequently in the selected box centered on the noncore pixels. Experiments on both simulated and real datasets confirm the effectiveness of the proposed cluster correction methods.

Falls are the second leading cause of death from unintentional injuries in older adults. Although many systems have been used to detect falls, they are limited by the computational complexity of the algorithm. The images taken by the camera must be transmitted through a network to the back-end server for calculation. As the demand for Internet of Things increases, this architecture faces such problems as high bandwidth costs and server computing overload. Emerging methods reduce the workload of servers by transferring certain computing tasks from cloud servers to edge computing platforms. To this end, this study developed a fall detection system based on neuromorphic computing hardware, which streamlines and transplants the neural network model of the back-end computer to the edge computing platform. Through the neural network model with integer 8 bit precision deployed on the edge computing platform, the object photos obtained by the camera are converted into human motion features, and a support vector machine is then used for classification. After experimental evaluation, an accuracy of 96% was reached, the detection speed of the overall system was 11.5 frames per second, and the power consumption was 0.3 W. This system can monitor the fall events of older adults in real time and over a long period. All data were calculated on the edge computing platform. The system only reports fall events via Wi-Fi, thereby protecting the privacy of the user.

We propose and demonstrate a 100 Gbit/s silicon photonic (SiPh) coherent receiver (Rx) with advanced digital signal processing (DSP) for dual-polarization quadrature-phase-shift-keying (DP-QPSK) transmission. It employs integrated 2D polarization-splitting grating couplers (2D-GCs), modified hybrid mixers and high speed silicon-germanium (SiGe) photodiodes (PDs). The 2D-GC replaced the polarization beam splitters and rotators needed in traditional coherent Rx. Moreover, the SiGe PD has the bandwidth >50 GHz.

Compared with ground views and direct overhead views (for orbital satellites), aerial robotics allow for capturing videos from diverse viewpoints and scenes, thus, the content of aerial image is complex and changeable, and aerial video has complex inter-frame transforms stemming from the blend of camera motion, platform motion and jitter. In addition, poor quality and similar texture are common in long-distance and large-scale aerial video surveillance. All of these interferences make image registration of aerial video difficult. This article puts forward one image registration method suited to aerial video on the basis of the hypothesize-and-verify of RANSAC. The proposed accelerated RANSAC, named PSSC-RANSAC (Prior Sampling & Sample Check RANSAC), incorporates prior sampling, which comes from three levels of sample evaluation, including texture magnitude, spatial consistency and feature similarity, to generate more possibly correct samples in priority. Furthermore, prior information of sample, quality of sample subset and subset invariability are together used to check the sample subsets, and the incompatible arrangements of subsets are immediately ruled out in sample check stage, which speeds up the iteration further. Results of the experiment have proved the good performance of the presented PSSC-RANSAC at 90% contamination level. For typical image pairs, the number of iterations is reduced by at least 16.67% and evaluation computation is reduced by at least 11.01% compared with SVH-RANSAC, and the re-projection error is decreased by at least 4.44% and 6.31% compared with RANSAC and SVH-RANSAC, respectively. It can overcome the interferences, and is very suitable for image registration of aerial images.

The supply-demand matching (SDM) strategy is an important part of the transaction mechanism design of cloud manufacturing (CMfg) platforms, which has a significant impact on the evolution trend of cloud manufacturing ecosystems (CMEs). To help CMfg platform operators choose the appropriate SDM strategy, first, the evolution process of the CME was qualitatively analyzed, and the evolution process was divided into three stages: the germination period, the growth period and the stable period. Then, three types of market agent behavior models, service demanders (SDs), service providers (SPs) and platform operators (POs), were established, and a multiagent behavior simulation experiment was conducted. Finally, the evolution of CMEs with one-way and two-way SDM strategies for POs was compared and analyzed from three aspects: the overall utilization rate of SPs, the diversity of CMEs and the total output of CMEs. Simulation experiments show that, compared with the CME that adopts the one-way SDM strategy, the CME that adopts the two-way SDM strategy is approximately 33% faster to reach ecological balance, the overall utilization rate is approximately 98.7% higher, the diversity is approximately 6% higher, and the total output is approximately 91% higher. The two-way SDM strategy that comprehensively considers the respective preferences of SDs and SPs is more conducive to the healthy development of CMEs.

Monitoring surface deformation for highways built on soft clay subgrades is fundamental for understanding the dynamics of the settlement process and preventing the occurrence of safety accidents. Most of the traditional interferometric synthetic aperture radar (InSAR) deformation models for highway monitoring are based on a combination of one or several empirical functions, which ignores the underground mechanism of highway deformation. To overcome this limitation, we proposed an advanced InSAR approach that improved the InSAR deformation model and parameter estimation algorithm for soft clay road deformation monitoring. The improved InSAR deformation model is based on the Poisson curve, which considers the characteristics of the temporal physical deformation evolution of the soft clay. The improved algorithm for the unknown parameter estimation is based on a GARN algorithm, which can solve the parameters with better accuracy by combining the genetic algorithm and the regularized Newton iterative algorithm. To evaluate the better performance for the proposed approach and its feasibility for soft clay highway monitoring, both the simulation and the real data experiments on a soft clay highway in Foshan, China, are performed. The time-series deformation from January 1, 2015 to January 18, 2017, is retrieved, and the temporal deformational characteristics over this area are analyzed, which facilitates a greater understanding of the deformational evolution process of the soft clay highway. The results are verified in terms of the residual high-pass deformation and the final obtained vertical deformation compared with the external leveling measurements, which indicate the greater reliability of our method.

Recently, application scenario of crowdsourcing IoT has covered to e-healthcare service, smart home, smart city, internet of vehicles due to the proliferation of smart devices such as smart mobile devices, smart wearable device, smart medical devices and smart furniture, etc. Patient's data collected by the smart devices send to the various remote medical servers. A group of medical professionals remote access patient data stored at the medical server database. Smart home users want to remote real-time access information of smart devices at home. All these operations need via wireless remote communication, which is suffering from various kinds of threat and attacks. Hence, there are a large number of multi-factor remote authentication and key agreement schemes designed for the application of crowdsourcing IoT. However, in most existing related multi-factor schemes, all factors for identity authentication only act as a parameter for encrypting the local secret key. In this paper, we propose a new secure remote multi-factor authentication scheme that includes three factors: 1) user identity; 2) password; and 3) user biometrics, which are authenticated by the remote server, act as a part of the secret key and participate in the key agreement process. We choose the chaotic map since it has a smaller key size and lower computational overhead, and then achieve remote multi-factor authentication and key agreement by artfully employ it to zero-knowledge technology and the fuzzy extractor technology. Our scheme is more secure and robust since the user revealing nothing sensitive information, and the adversary cannot impersonate any user even if he gets the server's master key. We have done security proof for our proposed scheme using the Random-Or-Real(ROR) model, Burrows-Abadi-Needham (BAN) logic, and ProVerif 2.00 to show that the presented scheme is secure. Also, we give an additional security analysis for other various attacks. Finally, according to the test and simulation result, the proposed scheme is very suitable for the power-constrained smart devices, and in the next generation 5G communication environment, its applicability and usability will be greatly enhanced.

With mobile payments popular around the world, payers can conduct a payment anytime and anywhere. While providing great convenience, mobile payment also brings many payment security issues. This paper is the first comprehensive review of secure mobile payment. We classify the mobile payment into TPC (third-party payment company)-led mobile payment and Bank-led mobile payment, and based on this, summarize the system structure of mobile payment. Then we discuss the mobile payment security technology framework from Tokenization, PAN (bank card primary account number) binding, and Secure Payment Authentication, respectively. Besides, this paper introduces secure technologies(hardware and software) used in these procedures, discusses and analyzes the security issues that they have been encountered, summarise open issues, and proposes future development directions. In the end, we give the discussion and comparison of popular and representative mobile payment applications, including Alipay, Wechat Pay, Apple Pay, Samsung Pay, and Google Pay.

Mode-division-multiplexing (MDM) can increase the total on-chip transmission capacity. Silicon-on-insulator (SOI) based MDM multiplexer (MUX) and demultiplexer (DEMUX) using asymmetrical directional couplers (ADCs) are promising; however, they usually require long coupling lengths for mode conversion. Here, for the first time up to the authors' knowledge, we propose, simulate, fabricate and demonstrate a size reduced SOI based $4 \times 4$ MDM MUX and DEMUX using enhanced evanescent-wave coupling (EEC). Here, we illustrate by experiments and numerically analyses that by size reducing the ADC access coupling region, evanescent-wave coupling is enhanced. Significant coupling length reduction of ~80 % can be achieved, while similar MDM MUX and DEMUX performance can be observed. To experimentally evaluate the broadband and high-speed transmission operations of the proposed device, 48 wavelength channels each modulated by >60 Gbit/s orthogonal frequency division multiplexing (OFDM) signals are successfully mode multiplexed and demultiplexed, achieving a transmission capacity of 11.73 Tbit/s.

The coastal crustal deformation caused by ocean tidal loading (OTL) varies spatially and temporally, and this spatiotemporal variation in satellite-based interferometric synthetic aperture radar (InSAR) measurements needs to be determined. In this article, we propose a spatiotemporal modeling method to estimate the OTL displacements in InSAR measurements using the kinematic precise point positioning (PPP) solutions of a regional GPS network. We tested the method through an experiment using 25 Sentinel-1B images and long-term observations of 172 GPS reference sites from Southern California. The experimental results suggest that there are significant OTL and solid Earth tide effects in the differential InSAR interferogram, which is greater than 40 mm. We find that the spatial characteristics of OTL variations can be expressed as a high-order polynomial in the two variables of latitude and longitude, and the spatiotemporally modeled PPP tidal estimates of the high-density GPS sites can provide high precision OTL correction for all the pixels in the interferogram. In the last part of the study, we show that the spatial large-scale signals in the differential interferograms of Sentinel-1B data are mainly atmospheric delay, solid Earth tidal, and OTL effect, and demonstrate the importance of the tidal correction in the InSAR measurements.

Radio-over-fiber (ROF)-based mobile fronthaul network supporting the baseband unit and simple remote radio head is a promising network architecture for future wireless networks. In this paper, we propose and demonstrate a silicon photonics (SiPh)-based remote radio frontend (RRF) for the mm-wave ROF systems. The proposed SiPh-based RRF consisted of an integrated Mach-Zehnder modulator and a micro-ring modulator, producing a single-sideband 40 GHz millimeter-wave orthogonal-frequency-division-multiplexing ROF signal, which is robust against fiber chromatic dispersion in the mobile fronthaul transmission. The RRF is fabricated using SiPh platform and could be potentially low cost. High split-ratios can be potentially achieved. Data rate of 7.813 Gbit/s per wavelength channel is achieved in the experiment. Numerical analysis is also performed; there are good matches of the simulation and experimental results.

Traditional Chinese medicine (TCM) is effective in preventing and treating all manner of diseases, which has been incorporated into the latest global medical outline (Ver.2019) by World Health Organization (WHO). As one of the most important characteristics of TCM, syndrome differentiation (SD) provides curative effect assurance. SD is a high-dimensional complex function with symptoms/signs as input and syndrome type as output. Artificial neural network (ANN) provides an all-purpose data-driven solution to fit high-dimensional complex function, making ANN a promising approach for modeling intelligent SD (ISD) for TCM. In this paper, we chose chronic obstructive pulmonary disease (COPD) as an example for investigating ISD for TCM based on ANN. First, we built a full-group ANN model that combines ANN with full-group datasets composed of 18471 real clinical records. In addition, we built four extra models with ANN and four subgroup datasets. For comparison, we built another four models with four traditional machine-learning algorithms and the full-group datasets. We used accuracy and F1-scores to evaluate the models' performance. With an accuracy of 86.45% and an F1 score of 82.93%, the full-group ANN model outperformed the four comparison models built from traditional machine-learning algorithms, and however, the four subgroup models achieved a better performance than the full-group ANN model. We concluded that the ANN can potentially provide a way for ISD for TCM, and our subgroup modeling suggests ideas for further optimizing the ISD.

Identity-based mutual authentication and key agreement (ID-MAKA) between a mobile user and cloud service provider is necessary for accessing any cloud services. In recent years, there are a large number of ID-MAKA schemes had been proposed on mobile cloud computing services to make authentication and accessing process more usability, security, and scalability. In this paper, we propose a new ID-MAKA scheme for mobile cloud computing, which firstly achieves remote biometric-based authentication(remote servers authenticate user's biometrics), single sign-on (a single credential and single registration for accessing multiple servers) and center-less authentication(the registration center does not participate in the access procedure) in one scheme. In order to realize this target, we design a ZK-token based on ECC and cryptographic hash function, and then artfully employ it to introduce the fuzzy extractor technology and zero-knowledge technology into our scheme. Therefore, the user can access multiple cloud computing servers by registering only once in the registration center, and cloud computing servers can complete the biometric-based remote authentication and key agreement for the user without the registration center participating. In this way, our scheme greatly improves usability, scalability, and security compared to other existing solutions. We give a formal security proof for our scheme by using Real-Or-Random(RoR) model and Burrows-Abadi-Needham (BAN) logic to show that the present scheme is secure and security analysis for other known attacks. Finally, according to the experiment result, our scheme has lower computation and communication cost compared with most existing related schemes.

Silicon photonic (SiPh) technology applied for optical communication systems and network-on-chips have attracted many attentions. We propose and demonstrate the use of a 4 × 4 modedivision-multiplexing (MDM) - multiplexer (MUX) and demultiplexer (DEMUX) to provide 200-Gbit/s orthogonal-frequency-division-multiplexing (OFDM) per wavelength signal transmission. The MDM MUX and DEMUX are implemented by asymmetric directional couplers (ADCs), which are constructed by an access waveguide for the transmission of fundamental mode; and the bus waveguide for the transmission of higher order modes. When phase matching condition is satisfied between the access waveguide and bus waveguide, mode conversion can be achieved. Here, 47 wavelengths are successfully mode multiplexed and de-multiplexed achieving a total transmission capacity 9.4 Tbit/s. Numerically study and analysis of the SiPh MDM MUX/DEMUX are presented, and the experimental evaluations are performed.

Gait phase detection is an important procedure in the application of many lower limb auxiliary robots. The gait phase detection algorithms that utilize surface electromyography (sEMG) signals have been developed to overcome unnatural walking. However, traditional studies mostly consider precise gait walking events when the subject is focusing only on walking, and the accuracy of traditional algorithms is susceptible to more realistic gait scenarios, such as walking with cognitive tasks. In this paper, the gait phase detection is considered in more realistic and challenging scenarios, in which the subject is walking while performing cognitive tasks. A kernel linear discriminant analysis (LDA)-based nonlinear fusion model is proposed for gait recognition, which can effectively reduce the error caused by cognitive tasks, making it an ideal model for gait phase detection while cognitive tasks are performed in the process of walking. Furthermore, the Internet of Things (IoT) framework is incorporated to reduce the gait phase detection algorithm's process time by offloading the data from local sEMG sensors to the IoT server with powerful computation capability. The experiments have been conducted to validate our proposed algorithms, demonstrating that the boundaries between the stance period and swing period are more blurred when walking with cognitive tasks.

Data clustering is a well-known data analysis technique for organizing unlabeled datapoints into clusters on the basis of similarity measures. The real-world applications of data clustering include bioinformatics, vector quantization, data mining, geographical information systems, pattern recognition, image processing, and wireless sensors. The data in a cluster are similar (minimizing the intra-cluster distance) and differ from the data in other clusters (maximizing the inter-cluster distance). The cluster problem has been proven to be NP-hard, but can be solved using meta-heuristic algorithms, such as ant colony optimization, genetic algorithms, gravitational search algorithm (GSA), and particle swarm optimization (PSO). This paper proposes a memetic clustering algorithm with efficient search and fast convergence, respectively, based on PSO and GSA, called the memetic particle gravitation optimization (MPGO) algorithm. The two main mechanisms of MPGO are hybrid operation and diversity enhancement. The former involves the exchange of individuals from two subpopulations after a predefined number of function evaluations (FEs), whereas the latter involves an enhancement operator, which is similar to the crossover process of differential evolution, for enhancing the diversity of each system. Individuals from the PSO and GSA systems are selected for the exchange of solutions by using the roulette-wheel approach. The performance of the proposed algorithm was evaluated on 52 benchmark test functions, six UCI machine learning benchmarks, and image segmentation of six well-known images. A comparison with existing algorithms verified the superior performance of the proposed algorithm in terms of a fitness value, an accuracy rate, and a peak signal-to-noise ratio.

Precoding can effectively reduce the peak-to-average power ratio (PAPR) of the transmitted waveform but will usually degrade the bit error rate (BER) performance. In this paper, we investigate the PAPR-guaranteed BER minimization problem through precoding in uplink massive multiple input-multiple output (MIMO) systems. First, we formulate an optimization problem to minimize the BER via precoding design and derive the necessary condition of the optimal precoding matrix. Second, we discuss the BER minimization with PAPR constraint and propose a two-step distributed precoder to deal with this problem. Simulation results verify the effectiveness of the proposed precoding. Particularly, it is efficient for massive MIMO systems in terms of energy efficiency.

Recent advances in self-interference cancellation techniques have enabled in-band full-duplex (IBFD) transmission, which can double ergodic capacity and reduce end-to-end delay. However, how to solve the channel contention problem in IBFD radios with inter-node interference and asymmetric traffic is still a challenge. This paper presents pFD-MAC, a novel polling-based traffic-aware medium access control (MAC) protocol for centralized full-duplex wireless networks. To solve the channel contention problem and fully utilize the channel resources under full-duplex mode, we first design a novel polling-based transmission mechanism and make comprehensive investigations on the effect of polling profile in full-duplex communication. By characterizing the inter-node interference into a directed non-conflict graph, we study the polling profile generation problem in which our objective is to minimize the packet transmission time. The problem is then theoretically formulated and proved to be NP-hard, which means it cannot be solved in polynomial time. Thus, we develop a heuristic traffic-aware algorithm and apply it to work with the packet transmission procedure in parallel. Full-duplex communication opportunities are highly exploited by parallelly organizing per-node upstream/downstream traffic according to the generated polling profile. Moreover, to achieve fairness without sacrificing throughput, deficit round robin algorithm has been applied with respect to the access time considering concurrent transmission time. Simulation results reveal that our proposed protocol can achieve improved performance in terms of throughput and transmission delay while maintaining fairness, compared with two state-of-the-art centralized MAC protocols.

In this paper, we investigate and analyze a hybrid ambient backscatter (AmBack) scheme with harvest-then-transmit protocols. In this scheme, the whole transmission is divided into three phases (time slots): energy harvesting (EH), AmBack, and data transmission (DT). Depending on the type of the reflection coefficient (RC) for AmBack, two hybrid schemes are considered: variable RC (VRC) and fixed RC (FRC). In the VRC scheme, the RC changes with the channel state information, and the harvested energy in the first phase is totally used for DT. On the other hand, in the FRC scheme, the RC is fixed to be one, and the harvested energy in the first phase is in part used to cover the tag circuit operation in the second phase and in part for DT in the third phase. The resulting optimization problem is convex for the VRC scheme, but non-convex for the FRC scheme. However, by utilizing the maximum principle in convex optimization, we are able to derive closed-form expressions for optimal solutions for both schemes. In order to get more insights, we also analyze the upper bound performance of both schemes. Simulation results demonstrate that the AmBack/EH-AmBack scheme can always achieve the optimal performance.

The massive MIMO channel is characterized by non-stationarity and fast variation, thereby the channel state information obtained by traditional methods will be outdated and the system performance will be degraded. In this paper, we propose a channel prediction algorithm in massive MIMO environments. First, considering the channel characteristics, we propose a first-order Taylor expansion-based predictive channel modeling method. Then, a channel prediction algorithm consisting of the estimation stage and prediction stage is proposed and the interval of effective prediction (IEP) is derived. The performance of the proposed algorithm is testified by numerical simulations. It is shown that, within the IEP, a reliable channel prediction can be obtained with low computational complexity.