Search

The performance of high-speed intensity modulation direct detection (IM-DD) transmissions is severely degraded by the linear inter-symbol interference (ISI), due to both bandwidth-constrained optoelectrical components and the chromatic dispersion (CD). Meanwhile, nonlinear ISI due to intensity modulation and square-law detection can further deteriorate the IM-DD system performance significantly. Here, we first propose three metrics to quantify those impairments in IM-DD transmissions. Then, we compared both the BER performance and computational complexity of different DSP schemes, using a C-band PAM-4 experimental test-bed with an end-to-end 10-dB bandwidth of 17 GHz. Those DSP schemes include receiver-side feedforward equalization (FFE), receiver-side Volterra filter equalization (VFE), transmitter-side Tomlinson-Harashima precoding (THP) together with receiver-side FFE, transmitter-side THP together with receiver-side VFE, channel-shortening filter (CSF) together with maximum likelihood sequence estimation implemented with linear branch metrics (MLSE-LI-BM), and CSF together with MLSE implemented with nonlinear branch metrics (MLSE-NL-BM). Our experimental results indicate that, only the combination of CSF and MLSE-NL-BM enables a 112 Gbit/s transmission over 5 km fiber at the KP4-FEC threshold, but with an increased computational complexity.

The agricultural futures prices are generally considered difficult to forecast because the causes of fluctuations are incredibly complicated. We propose a text-based forecasting framework, which can effectively identify and quantify factors affecting agricultural futures based on massive online news headlines. A comprehensive list of influential factors can be formed using a text mining method called topic modeling. A new sentiment-analysis-based way is designed to quantify the factors such as the weather and policies that are important yet difficult to quantify. The proposed framework is empirically tested at forecasting soybean futures prices in the Chinese market. Testing was based on 9715 online news headlines from July 19, 2012 to July 9, 2018. The results show that the identified influential factors and sentiment-based variables are effective, and the proposed framework performs significantly better in medium-term and long-term forecasting than the benchmark model.

A novel bionic piezoelectric actuator based on the walrus motion to achieve high performance on large working stroke for micro/nano positioning systems is first proposed in this study. The structure of the proposed walrus type piezoelectric actuator is described, and its motion principle is presented in details. An experimental system is set up to verify its feasibility and explore its working performances. Experimental results indicate that the proposed walrus type piezoelectric actuator could realize large working stroke with only one driving unit and one coupled clamping unit; the maximum stepping displacement is ΔLmax = 19.5 μm in the case that the frequency f = 1 Hz and the voltage U = 120 V; the maximum speed Vmax = 2275.2 μm · s−1 when the frequency f = 900 Hz and the voltage U = 120 V; the maximum vertical load mmax = 350 g while the voltage U = 120 V and the frequency f = 1 Hz. This study shows the feasibility of mimicking the bionic motion of the real walrus animal to the design of piezoelectric actuators, which is hopeful for the real application of micro/nano positioning systems to achieve large working stroke and high performance.

In the field of automatic driving, identifying vehicles and pedestrians is the starting point of other automatic driving techniques. Using the information collected by the camera to detect traffic targets is particularly important. The main bottleneck of traffic object detection is due to the same category of targets, which may have different scales. For example, the pixel‐level of cars may range from 30 to 300 px, which will cause instability of positioning and classification. In this paper, a multi‐dimension feature pyramid is constructed in order to solve the multi‐scale problem. The feature pyramid is built by developing a U‐shaped module and using a cascade‐method. In order to verify the effectiveness of the U‐shaped module, we also designed a new one‐shot detector U‐FPNDet. The model first extracts the basic feature map by using the basic network and constructs the multi‐dimension feature pyramid. Next, a pyramid pooling module is used to get more context information from the scene. Finally, the detection network is run on each level of the pyramid to obtain the final result by NMS. By using this method, a state‐of‐the‐art performance is achieved on both detection and classification on commonly used benchmarks.

In this paper, we present a field-trial C-band 72Gbit/s optical on-off keying (OOK) system over 18.8km dispersion-uncompensated submarine optical cable in the South China Sea. Chromatic dispersion (CD) of 18.8km submarine optical cable causes four spectral nulls on the 36GHz bandwidth of 72Gbit/s OOK signal, which is the main obstacle for achieving an acceptable bit-error-rate (BER) performance. Decision feedback equalizer (DFE) is effective to compensate for the spectral nulls. However, DFE has a serious defect of burst-error propagation when the burst errors emerge due to the unstable submarine environment. Weighted DFE (WDFE) can be used to mitigate the burst-error propagation, but it cannot fully compensate for the spectral nulls because only a part of feedback symbols is directly decided. Fortunately, maximum likelihood sequence estimation (MLSE) can be added after the WDFE to simultaneously eliminate the resisting spectral distortions and implement optimal detection. Compared to the joint DFE and MLSE algorithm, the joint WDFE and MLSE algorithm can effectively suppress the burst-error propagation to obtain a maximum 2.9dB improvement of $\boldsymbol{Q}$ factor and eliminate the phenomenon of BER floor. In conclusion, the joint WDFE and MLSE algorithm can solve the burst-error propagation for the field-trial fiber-optic communications., Comment: Under review of Journal of Lightwave Techonlogy

Membrane separation has gained significant interest from the chemical industry due to their compactness, energy efficiency, and modularity. Recent trends in process intensification also emphasizes the importance of a more widespread adoption of membrane-based processes for significant cost savings and sustainable operation. We present a prototype software framework for the automated design, optimization, intensification, benchmarking, and technoeconomic analysis of various types of membrane systems, including gas separation, pervaporation, and vapor permeation, while covering wide ranges of their operations. The framework is based on a generic building block-based representation (Demirel, Li, and Hasan, Comput. Chem. Eng. 2017, 150, 2–38) that can be used for the automated process synthesis and screening of numerous designs for selecting optimal membrane modules, processes, and network configurations. We can also generate rank-ordered lists of optimal process flowsheets based on different objectives. We present several case studies to demonstrate the utility of the proposed approach and report substantially better solutions compared to the designs reported in the literature.

A portable Electrical Impedance Tomography (EIT) device has been developed for online thrombus detection in extracorporeal-circulation equipment. For the portable EIT device, circuits of multiplexer module are designed to expend channels, pipeline processing mechanism is introduced to increase processing speed, and data acquisition rate reaches 50 Frames Per Second (FPS) and the online tomography rate reaches 40 FPS with 8-electrode sensor. To test the characteristics of the proposed EIT device, a series of experiments are conducted. Firstly, small sensors with different size and plastic phantoms are used to test the static properties of the device. Testing results show that the quality of the reconstructed images is related to the diameter of sensor, and Image Correlation (IC) reaches 0.75 when diameter of sensor is 30 mm. Secondly, swine blood is used for making thrombus to verify the EIT device’s image reconstruction ability of thrombus in stationary conditions instead of human blood. Experiments show that the reconstructed image error is 8.07% when diameter of the sensor is 10 mm, and it reduces to 1.49% when diameter is 30 mm. Finally, experiments are conducted to measure the position of flowing thrombus. Euclidean distance between voltage data is used to judge the occurrence of thrombus. Experimental results indicate that thrombus is clearly distinguished from the background solution in fluids. As a conclusion, the proposed EIT device is of great significance for online thrombus detection in extracorporeal-circulation system.

The Internet of Vehicles (IoV) is a communication paradigm that connects the vehicles to the Internet for transferring information between the networks. One of the key challenges in IoV is the management of a massive amount of traffic generated from a large number of connected IoT-based vehicles. Network clustering strategies have been proposed to solve the challenges of traffic management in IoV networks. Traditional optimization approaches have been proposed to manage the resources of the network efficiently. However, the nature of next-generation IoV environment is highly dynamic, and the existing optimization technique cannot precisely formulate the dynamic characteristic of IoV networks. Reinforcement learning is a model-free technique where an agent learns from its environment for learning the optimal policies. We propose an experience-driven approach based on an Actor-Critic based Deep Reinforcement learning framework (AC-DRL) for efficiently selecting the cluster head (CH) for managing the resources of the network considering the noisy nature of IoV environment. The agent in the proposed AC-DRL can efficiently approximate and learn the state-action value function of the actor and action function of the critic for selecting the CH considering the dynamic condition of the network.The experimental results show an improvement of 28% and 15% respectively, in terms of satisfying the SLA requirement and 35% and 14% improvement in throughput compared to the static and DQN approaches.

The emerging technologies with IoT (Internet of Things) systems are elevated as a prototype and combination of the smart connectivity ecosystem. These ecosystems are appropriately connected in a smart healthcare system which are generating finest monitoring activities among the patients, well-organized diagnosis process, intensive support and care against the traditional healthcare operations. But facilitating these highly technological adaptations, the preserving personal information of the patients are on the risk with data leakage and privacy theft in the current revolution. Concerning secure protection and privacy theft of the patient’s information. We emphasized this paper on secure monitoring with the help of intelligently recorded summary’s keyframe extraction and applied two rounds lightweight cosine-transform encryption. This article includes firstly, a regimented process of keyframe extraction which is employed to retrieve meaningful frames of image through visual sensor with sending alert (quick notice) to authority. Secondly, employed two rounds of lightweight cosine-transform encryption operation of agreed (detected) keyframes to endure security and safety for the further any kinds of attacks from the adversary. The combined methodology corroborates highly usefulness with engendering appropriate results, little execution of encryption time (0.2277-0.2607), information entropy (7.9996), correlation coefficient (0.0010), robustness (NPCR 99.6383, UACI 33.3516), uniform histogram deviation (R 0.0359, G 0.0492, B 0.0582) and other well adopted secure ideology than any other keyframe or image encryption approaches. Furthermore, this incorporating method can effectively reduce vital communication cost, bandwidth issues, storage, data transmission cost and effective timely judicious analysis over the occurred activities and keep protection by using effective encryption methodology to remain attack free from any attacker or adversary, and provide confidentiality about patient’s privacy in the smart healthcare system.

On-time recovery and treatment of disease is always desirable. The use of Machine learning in health-care has grown very fast to diagnosis the different kinds of diseases in the past few years. In such a diagnosis, past and real-time data are playing very crucial role in using data mining techniques. Still, we are lacking in diagnosing the emotional mental disturbance accurately in the early stages. Thus,the initial diagnosis of depression expressively stances a great problem for both,researchers and clinical professionals. We have addressed the said problem in our proposed work using Pipeline Machine Learning technique where people based on emotional stages have been effectively classified into different groups in e-healthcare. To implement Hybrid classification, a well known machine learning multi-feature hybrid classifier is used by having the emotional stimulation in form of negative or positive people. In order to improve classification, an Ensemble Learning Algorithm is used which helps in choosing the more suitable features from the available genres-emotion data on online media. Additionally, Hold out validation method has been to split the dataset for training and testing of the predictive model. Further, performance evaluation measures have been applied to check the proposed system evaluation. This study is done on Genres-Tags MovieLens dataset. The experimental results show that applied ensemble method provides optimal classification performance by choosing the best subset of features. The said results proved the excellency of the proposed system which comes from the choosing most related features selected by the Integrated Learning algorithm. Additionally, suggested approach is used to accurately and effectively diagnose the depression in its early stage. It will help in recovery and treatment of depressed people. We conclude that use of the suggested method is highly suitable in all aspects of e-healthcare for depress stimulation.

Breast cancer is one the most critical disease and suffered many people around the world. The efficient and correct detection of breast cancer is still needed to ensure this medical issue although the researchers around the world are proposed different diagnostic methods for detection of this disease, however these existing methods still needed further improvement to correct and efficient detection of this disease. In this study, we proposed a new breast cancer identification method by using machine learning algorithms and clinical data. In the proposed method supervised (Relief algorithm) and unsupervised (Autoencoder, PCA algorithms) techniques have been used for related features selection from data set and then these selected features have been used for training and testing of classifier support vector machine for accurate and on time detection of breast cancer. Additionally, in the proposed approach k fold cross validation method has been used for model validation and best hyperparameters selection. The model performance evaluation metrics have been used for model performance evaluation. The BC data sets have been used for testing of the proposed method. The analysis of experimental results has been demonstrated that the features selected by Relief algorithm are more related for accurate detection of Breast cancer instead of features selected by Auotencoder and PCA algorithms. The proposed method has been attained high results in terms of accuracy on selected feature selected by Relief algorithm and achieved 99.91% accuracy. We have been employed McNemar’s statistical test for performance comparison of our different models. Further, the proposed method performance has been compared with baseline methods in the literature and the proposed method performance is high as compared to base line methods. Due to the high performance of the proposed method (Relief-Support vector machine) we highly recommended it for the diagnosis of breast cancer. In addition, the proposed method can be easily incorporated into the healthcare system for reliable diagnosis of Breast cancer.

Accurate and efficient three-dimensional (3D) streetscape reconstruction is the fundamental ability for an exploration vehicle to navigate safely and perform high-level tasks. Recently, remarkable ...

Process intensification (PI) is a design concept that offers innovative solutions for making a substantial improvement in terms of cost, energy efficiency, emission, environmental footprint, proces...

The accurate estimation of the clutter covariance matrix (CCM) is crucial for space-time adaptive processing (STAP). In this letter, a new intrinsic cyclic characteristic of CCM is found. Then, a novel STAP is proposed based on the cyclic characteristic. In the proposed method, the cyclic CCMs, i.e., the temporal cyclic CCM, the spatial cyclic CCM, and the spatial–temporal cyclic CCM, are first constructed based on the cyclic characteristic. Then, the cyclic CCMs are employed as the secondary data, and the more accurate CCM estimation is obtained by averaging the cyclic CCMs and the estimated CCM of the existing STAP methods. Compared with the existing methods, the proposed method has the following advantages: (1) the proposed method can be directly combined with the various existing STAP methods to improve their performance, (2) the output signal-to-clutter-plus-noise ratio (SCNR) of the proposed method is 2.055 dB higher than that of the traditional STAP methods reported in [7] – [9] , and (3) the output SCNR of the proposed method is 1.704 dB higher than that of the knowledge-aided STAP (KA-STAP) reported in [16] .

The current wave form of active boost PFC is simulated in time domain and frequency domain. With the voltage of 220 v, the inductance of 15 mH, the capacitance of 5040 μF, the load of 2500 W, the integrated power factor, fundamental power factor, and distortion are analyzed. The distortion is enhanced with the increase of frequency. Comparing high-order harmonic part under 10 mH and 15 Mh, it is demonstrated that the appropriate OFF phase angle changes with different requirement: the angle is 37 degree with distortion in least while the one is 34 degree with maximum integrated power factor. Topology and results are proposed. The results demonstrate that the power factor of real PFC circuit is up to 0.975.

Detection And Tracking of Moving Objects (DATMO) is essential and necessary for mobile mapping system to generate clean and accurate point clouds maps since dynamic targets in real-world scenarios will deteriorate the performance of whole system. In this research, a robust LiDAR-SLAM system is presented incorporated with a real-time dynamic objects removal module to improve the accuracy of 6 DOF pose estimation and precision of maps. The key idea of the proposed method is to efficiently cluster the sparse point clouds of moving objects and then track them independently so as to relieve their influence on the odometry and mapping results. In the back-end, in order to further refine the point clouds maps, a valid probabilistic map fusion method is performed based on the free-space theory. We have evaluated our system on the dataset collected from daily crowded environments full of moving objects, providing competitive results with the state-of-the-art system both on the pose estimation and point cloud mapping.

In this article, we propose and experimentally demonstrate a new discrete Fourier transform spread (DFT-S) spectrally efficient frequency division multiplexing (DFT-S SEFDM) system. DFT-S technique is adopted to reduce the peak-to-average-power-ratio (PAPR) of SEFDM signal in optical intensity modulation and direct detection (IM-DD) system for the first time. The existing detection algorithms for SEFDM signal, such as logarithmic-maximum-a-posterior (log-MAP) Viterbi and cascaded binary-phase-shift-keying iterative detection (CBID), are successfully modified to our DFT-S SEFDM system. Experimental results show that DFT-S SEFDM outperforms conventional SEFDM owing to its lower PAPR. In our test, to verify the benefits of our scheme, a net data rate of beyond 100 Gb/s can be achieved after 2-km standard single mode fiber (SSMF) transmission, and DFT-S SEFDM also can outperform DFT-S DMT owing to its tolerance to bandwidth limitation. We further verify the advantage of DFT-S SEFDM in IM-DD C-band system, and show its resistance to the impact of dispersion-induced power fading.

A wearable electrical impedance tomographic (wEIT) sensor with 8 electrodes is developed to realize gesture recognition with machine learning algorithms. To optimize the wEIT sensor, gesture recognition rates are compared by using a series of electrodes with different materials and shapes. To improve the gesture recognition rates, several Machine Learning algorithms are used to recognize three different gestures with the obtained voltage data. To clarify the gesture recognition mechanism, an electrical model of the electrode-skin contact impedance is established. Experimental results show that: rectangular copper electrodes realize the highest recognition rate; the existence of the electrode-skin contact impedance could improve the gesture recognition rate; Medium Gaussian SVM (Support Vector Machine) algorithm is the optimal algorithm with an average recognition rate of 95%.

This study had two main aims: (1) to provide a comprehensive review of terrestrial laser scanner (TLS) point cloud registration methods and a better understanding of their strengths and weaknesses; and (2) to provide a large-scale benchmark data set (Wuhan University TLS: Whu-TLS) to support the development of cutting-edge TLS point cloud registration methods, especially deep learning-based methods. In particular, we first conducted a thorough review of TLS point cloud registration methods in terms of pairwise coarse registration, pairwise fine registration, and multiview registration, as well as analyzing their strengths, weaknesses, and future research trends. We then reviewed the existing benchmark data sets (e.g., ETH Dataset and Robotic 3D Scanning Repository) for TLS point cloud registration and summarized their limitations. Finally, a new benchmark data set was assembled from 11 different environments (i.e., subway station, high-speed railway platform, mountain, forest, park, campus, residence, riverbank, heritage building, underground excavation, and tunnel environments) with variations in the point density, clutter, and occlusion. In addition, we summarized future research trends in this area, including auxiliary data-guided registration, deep learning-based registration, and multi-temporal point cloud registration.

The Internet of Things (IoT) has been evolving for more than a decade. Technological advancements have increased its popularity, but concerns and risks related to IoT are growing considerably along with the increased number of connected devices. In 2013, a new cryptography-based infrastructure called blockchain emerged with the potential to replace the existing cloud-based infrastructure of IoT through decentralization. In this article, we provide a taxonomy of the challenges in the current IoT infrastructure, and a literature survey with a taxonomy of the issues to expect in the future of the IoT after adopting blockchain as an infrastructure. The two architectures are compared based on their strengths and weaknesses. Then a brief survey of ongoing key research activities in blockchain is presented, which will have considerable impact on overcoming the challenges encountered in the applicability of blockchain in IoT. Finally, considering the challenges and issues in both infrastructures and the latest research activities, we propose a high-level hybrid IoT approach that uses the cloud, edge/fog, and blockchain together to avoid the limitations of each infrastructure.

The systemic importance of a bank is usually measured by its effect on the banking system, conditional on the insolvency of the bank and solvency of other banks. However, banks encounter different kinds of shocks simultaneously in reality. So that, the conditional results give biased estimates of banks’ systemic importance when potential risks are ignored. Researchers like Tarashev et al. proposed the Shapley value method to deal with risk interactions, but it suffers heavy computational costs. This paper proposes an ANOVA-like decomposition method to measure the systemic importance of banks in more complicated and realistic environments, which considers both interactions and individual effects of multiple shocks and provides a more exact estimation of systemic importance. It is found that the method proposed in this paper fits well in the network models. And meanwhile, a discussion between the method proposed in this paper and the Shapley value method is made based on the numerical example, which aims to demonstrate it’s the advantages. The Shapley value method requires 2 n subsystems, while the ANOVA-like decomposition method requires only n + 1 model runs. In the application part, the proposed method is adopted to measure the systemic importance of 16 Chinese listed banks. With low computational costs, the model outputs the individual effect, interaction, and total effect of each bank. The results confirm that interactions of different shocks play a significant role in the systemic importance of a bank; thus, the total effect considering interactions should be adopted.

The biological vision model is devoted to provide a novel technology approach by merging new cognitive visual features with inspired nerve cells cognitive intelligence cortex and try to relate with real worlds object recognition. To perceive an arbitrary natural scene from complex environment perception and sensing in robotic mobility and manipulation on unstructured random natural scene understanding is a challenging problem in the visual image processing. This paper has considered neural network (NN) which is nothing but the grid of “neurons like” nodes. Based on the NN technique,the authors have proposed a new scheme for the scene understanding and recognition. In addition, the significant intellectual visual features are also incorporated for scene expression; those are very crucial and provide cognitive intelligence to robot vision. Due to the dynamic nature of artificial neural network intelligence, we have adapted the attributes of the Gabor filter and Laplacian of Gaussian filter; those play the significant role in the robot visual perception. Through the study of perceptual ability of the natural scene image from complex environment for robot vision is enhanced with the integration of cognitive visual features and the scene expression.

Glioma is a kind of brain tumor that can arise at a distinct location along with dissimilar appearance and size. The high-grade glioma (HGG) is a serious kind of cancer when compare to low-graded glioma (LGG). The manual diagnosis process of these tumors is tiring and time consuming. Therefore, in clinical practices, MRI is useful to assess gliomas as it provides essential information of tumor regions. In this manuscript, an active deep learning-based feature selection approach is suggested to segment and recognize brain tumors. Contrast enhancement is made in the primary step and supplied to SbDL for saliency map construction, which later converts into binarized form by applying simple thresholding. In the classification phase, the Inception V3 pre-trained CNN model is employed for deep feature extraction. These features are simply concatenated along with dominant rotated LBP (DRLBP) for better texture analysis. Later, the concatenated vector is optimized through particle swarm optimization (PSO), so as to classify using softmax classifier. The experiments are conducted in two phases. At first, the segmentation approach SbDL is validated on BRATS2017 and BRATS2018 datasets. The achieved dice score for the BRAST2017 dataset is 83.73% for core tumor, 93.7% for the whole tumor and 79.94% for enhanced tumor. For BRATS2018 dataset, dice score obtained is 88.34% (core), 91.2% (whole) and 81.84% (enhanced). At the second, the classification strategy is applied on BRATS2013, 2014, 2017 and 2018 with an average accuracy of more than 92%. The overall results show that the presented method outperforms for both segmentation and classification of brain tumors.

Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.

Traffic vehicle behavior prediction is a necessary prerequisite for intelligent vehicle behavior decision and trajectory planning. The behaviors of vehicles are deeply interactive. In order to reasonably predict the future behavior of traffic vehicles, based on the Game theory, this paper designs the behavior prediction framework of traffic vehicles, and establishes the GMM(Gaussian Mixture Model)-HMM(Hidden Markov Model) behavior recognition model. Then, the revenue function is designed to model the driver's intent by calculating the vehicle's front running space, collision risk and comfort loss under each scenario. And the NGSIM dataset is used to train the parameters in the GMM-HMM model and those in the revenue function. Finally, two groups of experiments are designed to compare this method with the traditional method. The experimental results show that the proposed method can predict the future behavior of traffic vehicles earlier, and can also well reflect the interaction process of vehicle behavior, and has better robustness.

Accurate registration of light detection and ranging (LiDAR) point clouds and images is a prerequisite for integrating the spectral and geometrical information collected by low-cost unmanned aerial vehicle (UAV) systems. Most registration approaches take the directly georeferenced LiDAR point cloud as a rigid body, based on the assumption that the high-precision positioning and orientation system (POS) in the LiDAR system provides sufficient precision, and that the POS errors are negligible. However, due to the large errors of the low-precision POSs commonly used in the low-cost UAV LiDAR systems (ULSs), dramatic deformation may exist in the directly georeferenced ULS point cloud, resulting in non-rigid transformation between the images and the deformed ULS point cloud. As a result, registration may fail when using a rigid transformation between the images and the directly georeferenced LiDAR point clouds. To address this problem, we proposed NRLI-UAV, which is a non-rigid registration method for registration of sequential raw laser scans and images collected by low-cost UAV systems. NRLI-UAV is a two-step registration method that exploits trajectory correction and discrepancy minimization between the depths derived from structure from motion (SfM) and the raw laser scans to achieve LiDAR point cloud quality improvement. Firstly, the coarse registration procedure utilizes global navigation satellite system (GNSS) and inertial measurement unit (IMU)-aided SfM to obtain accurate image orientation and corrects the errors of the low-precision POS. Secondly, the fine registration procedure transforms the original 2D-3D registration to 3D-3D registration. This is performed by setting the oriented images as the reference, and iteratively minimizing the discrepancy between the depth maps derived from SfM and the raw laser scans, resulting in accurate registration between the images and the LiDAR point clouds. In addition, an improved LiDAR point cloud is generated in the mapping frame. Experiments were conducted with data collected by a low-cost UAV system in three challenging scenes to evaluate NRLI-UAV. The final registration errors of the images and the LiDAR point cloud are less than one pixel in image space and less than 0.13 m in object space. The LiDAR point cloud quality was also evaluated by plane fitting, and the results show that the LiDAR point cloud quality is improved by 8.8 times from 0.45 m (root-mean-square error [RMSE] of plane fitting) to 0.05 m (RMSE of plane fitting) using NRLI-UAV, demonstrating a high level of automation, robustness, and accuracy.

In the field of object detection, the research on the problem of detecting small face is the most extensive, but when there are objects with obvious scale differences in the image, the detection performance is not obvious, which is due to the scale invariance properties of the deep convolutional neural networks. Although in recent years, there have been some methods proposed to solve this problem such as FPN and SNIP, which is based on feature pyramid. However, they have not fundamentally solved the problem. A regional cascade multi-scale detection method has been proposed. First, a global detector and several local detectors have been trained, respectively. The global detector is trained by the original training set, while the local detector is trained by the sub-training set generated by the original training set. Second, the global detector can detect object roughly and the local detectors can produce more detailed results that improve the performance of global detector. Finally, to integrate the detection results of global detector and local detectors as the output, non-maximum suppression methods are used. The method can be carried in any depth model of object detection, has good scalability, and is more suitable for dense face detection.

In order to reduce the uncertainty of radar manoeuvring target tracking (RMTT) in cluttered background, a joint detection and tracking algorithm based on cognitive radar is proposed. First, a prism structure resolution cell of time-delay, Doppler and azimuth is designed. Then, an approximate expression of measurement error covariance including waveform and detection threshold parameters is given. Then, based on the idea of human brain perception-action cycle, a joint waveform and detection threshold adaptive tracking algorithm based on minimum information entropy criterion is proposed. Finally, a cognitive structure adaptive particle filter (CSAPF) algorithm, based on parallel structure of extended Kalman filter (EKF) and particle filter, are used with Probabilistic Data Association (PDA) algorithm for RMTT. During the process, CSAPF-PDA can always obtain the best tracking performance with the minimum number of particle samples, thus effectively taking into account the tracking accuracy and efficiency. The effectiveness of the proposed algorithm is verified by simulation experiments.

We experimentally demonstrate a beyond 100 Gb/s intensity modulation/direct detection (IM/DD) 16-QAM optical spectrally efficient frequency division multiplexing (SEFDM) transmission over 2-km standard single mode fiber (SSMF) with 20% signal bandwidth compression. Time-domain equalization (TDE), which outperforms frequency-domain equalization (FDE) in our system, is adopted for SEFDM system due to the inter-carrier interference (ICI) induced by the spectrum compression. A hybrid cascaded binary-phase-shift-keying iterative detection (CBID) together with fixed sphere decoder (FSD) is successfully utilized to eliminate ICI in such a high-speed interconnection system. Experiment results show that SEFDM outperforms conventional discrete multi-tone (DMT), occupying less frequency spectrum resource and achieving superior resistance to bandwidth limitation and dispersion-induced power fading in fiber links. We also show that CBID is a more effective scheme with 10% bandwidth compression, considering the receiver complexity, while CBID-FSD is proposed for further bandwidth compression.

With the ever-growing scale of social websites and online transactions, in past decade, Recommender System (RS) has become a crucial tool to overcome information overload, due to its powerful capability in information filtering and retrieval. Traditional rating prediction based RS could learn user’s preference according to the explicit feedback, however, such numerical user-item ratings are always unavailable in real life. By contrast, pairwise learning algorithms could directly optimize for ranking and provide personalized recommendation from implicit feedback, although suffering from such data sparsity and slow convergence problems. Motivated by these, in this article, a novel collaborative pairwise learning to rank method referred to as BPLR is proposed, which aims to improve the performance of personalized ranking from implicit feedback. To this end, BPLR tries to partition items into positive feedback, potential feedback and negative feedback, and takes account of the neighborhood relationship between users as well as the item similarity while deriving the potential candidates, moreover, a dynamic sampling strategy is designed to reduce the computational complexity and speed up model training. Empirical experiments over four real world datasets certificate the effectiveness and efficiency of BPLR, which could speed up convergence, and outperform state-of-the-art algorithms significantly in personalized top-N recommendation.

The inchworm type piezoelectric actuator is one novel actuator to ensure a large working stroke with high resolution which has attracted the continuous attentions from researchers all over the world. In this study, the motion principle of the inchworm type piezoelectric is discussed: the “walker” pattern, the “pusher” pattern and hybrid “walker-pusher” pattern. The classification (linear, rotary and multi-DOF) and development are introduced in details, some significant researches are illustrated. Finally, the future direction of inchworm type piezoelectric actuators is pointed out according the development of inchworm type piezoelectric actuators. This study shows the clear principle, design and future of inchworm type piezoelectric actuators which is meaningful for the development of piezoelectric actuators.

Polarization mode dispersion (PMD) is one of the fundamental properties of a standard single-mode fiber. It affects the propagating signals and degrades the performance of high-speed optical fiber communication systems. PMD also gives an effect on the nonlinear spectra or scattering data in nonlinear frequency division multiplexing (NFDM) systems. However, PMD is usually described in the linear frequency domain, and there are few investigations about the influence of PMD in the nonlinear frequency domain (NFD). An NFD-PMD model is needed to understand the impact of PMD in the NFD. In this work, using a linear approximation method, we first propose an NFD-PMD model and verify its effectiveness. With the guide of the NFD-PMD model, a blind NFD-PMD equalization scheme is designed. The simulation results indicate that the proposed NFD-PMD equalization scheme has better performance than the training sequence method based on linear frequency domain equalization.

Fast and early detection of infected patient is the most paramount step necessary to curb the spread of the COVID-19 disease. Radiographs have perhaps presented the fastest means of diagnosing COVID-19 in patients. The well-known standard for COVID-19 test requires a standard procedure and usually has low sensitivity. Previous studies have adopted various AI-based methods in detecting COVID-19 using both chest tomography and chest x-ray. In this study, the goal is to propose an enhanced convolutional neural multi-resolution wavelet network for COVID-19 pneumonia diagnosis. Our proposed model is a convolutional neural network integrated discrete wavelet transform of four level decomposition multiresolution analysis robust to handle few dataset which is very paramount due to the fast emergence of COVID-19. We evaluated our model based on three categories of public dataset of chest x-ray and chest tomography images. Our proposed model achieves 98.5% accuracy, 99.8% sensitivity, 98.2% specificity, and 99.6% AUC for multiple class categories with less training parameters. The results of this study show that our method achieves state-of-the-art result.

The outbreak of COVID-19 in more than 150 nations across the globe is severely impacting the health of people worldwide. A more reliable way to curb the spread of COVID-19 is early detection of infected patients for temporary isolation and care. Image base detection of COVID-19 presents the quickest way to diagnose patients. Few literatures have shown that chest radiograms of infected COVID-19 patients contain irregular characteristics. From related studies, we investigated the application of wavelet transform multi resolution analysis to detect COVID-19 patients using chest radiography. In this study, we proposed a wavelet based convolutional neural network to handle data scarcity in this era of COVID-19 fast emergence. We only considered four levels of wavelet transform decompositions. We utilized an open source dataset from National Institute Health containing several X-rays of pneumonia related diseases whereas the COVID-19 dataset is collected from Radiology Society North America. From the experimental results, our model achieved sensitivity greater than 90% while the specificity is above 90%. We also show the receiver operating characteristic and precision-recall curves of each decomposition level. Our results show that the performance of our proposed model is encouraging and outperformed previous state-of-the-art models.

This paper studies a variant of the Steiner tree problem in the Euclidean plane ℝ2: the minimum-number of a specific material for the full Steiner tree problem (MNFST, for short). This question is an extension of the Steiner tree problem and the full Steiner tree problem. It has a wide range of applications in real life. The MNFST has been shown to be NP-hard. In this paper, we propose two asymptotic polynomial-time approximation algorithms for this problem. These two algorithms satisfy OUT ≤ 2.428OPT + 1 and $OUT \leq 2.123sOPT + \frac{3}{2}$, respectively.

There is an increasing drug resistance of animal-derived pathogens, seriously posing a huge threat to the health of animals and humans. Traditional drug resistance testing methods are expensive, have low efficiency, and are time-consuming, making it difficult to evaluate overall drug resistance. To develop a better approach to detect drug resistance, a small sample of Escherichia coli resistance data from 2003 to 2014 in Chengdu, Sichuan Province was used, and multiple regression interpolation was applied to impute missing data based on the time series. Next, cluster analysis was used to classify anti-E. coli drugs. According to the classification results, a GM(1,1)-BP model was selected to analyze the changes in the drug resistance of E. coli, and a drug resistance prediction system was constructed based on the GM(1,1)-BP Neural Network model. The GM(1,1)-BP Neural Network model showed a good prediction effect using a small sample of drug resistance data, with a determination coefficient R2 of 0.7830 and an RMSE of only 0.0527. This model can be applied for the prediction of drug resistance trends of other animal-derived pathogenic bacteria, and provides the scientific and technical means for the effective assessment of bacterial resistance.

As China's science and technology industry enters a period of rapid development, data collection and information exchange, as important tools, have become more and more important in ensuring the stable operation of the Internet of Things. Facing various deficiencies in traditional logistics transportation and management, we must optimize the data exchange and information flow in the development process of the Internet of Things by applying WiFi technology, RFID technology and sensor network technology. This is conducive to China's continuous improvement of logistics and transportation, positioning efficiency and project quality. First, this article briefly explains the importance and necessity of electronic information technology. Secondly, the author analyzes the current situation of the problems in the electronic order generation process, and further proposes the strategy of promoting the optimization of electronic information technology in the transportation network and promoting the intelligent logistics and transportation management. This will help ensure the development quality and economic benefits of communication technology and satellite positioning technology.

Process intensification often requires innovative equipment and flowsheets to achieve significant improvement compared to traditional unit operations. Systematic methods are needed for automatic generation, screening, and discovery of promising intensification pathways at the conceptual design stage. In this contribution, we present a synopsis of the key elements of systematic process intensification (SPI), which include specification, representation, assessment, screening, and analysis. We conclude with a brief overview of future challenges and research opportunities in each of these areas.

Partial Discharge (PD) checking is a standout amongst the best technique for insulation condition evaluation of HV power system. PD releases inside a power transformer energize electromagnetic transients that can be identified utilizing sensors working in the ultra-high frequency band. Despite, on-line PD estimations are influenced by elevated amounts of electromagnetic interference that makes delicate PD identification exceptionally troublesome. In our proposed research present an approach which can reduce the noise level. Increase the quality of the PD signal. Here using an approximation logic which can reduce the complexity in terms of time and able to improve the quality. The proposed Double DWT able to get batter result as compares to existing PD techniques.

To promote trade facilitation and regional integration, several cross-border transport infrastructure (CBTI) projects have been implemented and are accompanied by high complexity, difficulty, and uncertainty. This presents a multi-dimensional challenge for risk assessment. However, the challenge is amplified by the presence of dual interdependent effects induced by risk factor cascading propagation and project interaction. The purpose of this study involves developing a fuzzy decision analysis method for risk assessment in CBTI projects by considering dual interdependent effects. First, a holistic risk assessment framework with five resolution phases is presented and includes preliminary preparation, information processing, interdependent effect analysis, risk aggregation, and risk rating. Subsequently, a fuzzy hybrid method is proposed based on extending and integrating the decision making trial and evaluation laboratory (DEMATEL), two-additive Choquet integral (TACI), and fuzzy reasoning . The method is used to clarify the risk profile of the CBTI project, and the risk sources and risk controllability are further diagnosed to support risk treatment. Finally, the effectiveness and superiority of the proposed method is demonstrated via a potential application. The results indicate that the proposed method that considers dual interdependent effects is advantageous in terms of ensuring risk assessment accuracy and risk treatment effectiveness in CBTI projects.

Precision positioning systems with large working stroke (millimeter or more) and micro/nano-scale positioning resolution are widely required in both scientific research and industries. For this kind of applications, piezoelectric materials based actuators show unique advantages and have been widely employed. To overcome the demerit of the limited working stroke for single piezoelectric element, various stepping motion principles have been proposed in the past years, and accordingly, stepping piezoelectric actuators with various structures have been designed and evaluated. This review is aimed to summarize the recent developments and achievements in stepping piezoelectric actuators with large working stroke. Especially, the emphasis is on three main types of stepping piezoelectric actuators, i.e., inchworm type, friction-inertia type, and parasitic type. The motion principles of these three types of piezoelectric actuators and the corresponding developments of various actuators are discussed respectively, followed by pointing out the existing problems in these three types of piezoelectric actuators and proposing some potential research directions in this topic. It is expected that this review is helpful for relevant researchers to understand stepping motion principles as well as piezoelectric actuators, and to successfully select and design stepping piezoelectric actuators for specific applications.

To integrate the multi-spectral imagery and laser scanning data for forest management, a low-cost multisensory UAV system, named Kylin Cloud, is introduced in this paper. The Kylin Cloud is composed several low-cost sensors (i.e., GNSS receiver, IMU, global shutter camera, multispectral camera, and laser scanner), providing the fusion of the imagery and laser scanning data for reliable forest inventory. Experiments were undertaken in a forest park in Wuhan. Results showed that the registration error of the multispectral Digital Orthophoto Map (DOM) and laser scanning data is about one pixel, demonstrating a high potential of the proposed low-cost system.

We propose an image encryption scheme based on pseudorandomly enhanced logistic map, random permutation and deoxyribonucleic acid coding in this paper. The scheme consists of two main phases: permutation and diffusion. Firstly, a random pixel permutation phase is carried out by a random permutation function to enhance the diffusion and statistical performance. Secondly, a DNA-based diffusion is performed via randomly selected DNA algebraic operations between the randomly permuted image matrix and a chaotic key matrix to produce the cipher image. Pseudorandomly enhanced logistic map is used to produce the key matrix while the direct logistic map is used to select DNA encoding/decoding rules and DNA algebraic operations. The chaos maps are driven by a SHA-256 hash value of the plain image. Experimental results based on several analyses including statistical, differential, key analyses, etc. show that the proposed scheme achieves robust encryption and provides sufficient resistance to various forms of attacks.

This paper proposes a novel linear stick-slip piezoelectric actuator with compact structure and a dual-functional flexure hinge mechanism, which can realize motion of high step efficiency, high speed and precision. The flexure hinge mechanism is used in the actuator for realizing parasitic motion and amplifying the displacement of the piezo stacks to enhance the motion performance of the actuator. The structure and the operating principle is described in detail. The pseudo-rigid-body method and the finite element analysis method are adopted in the design process. A series of experiments are carried out based on a manufactured prototype to prove the validity of the proposed actuator, and motion parameters of the prototype are obtained. The resolution of the prototype is 0.594 µm, the largest velocity is 0.742 mm/s, the largest load capacity is 3.6 kg and the largest step efficiency is 0.934. And a valid kinetic model is established for further investigation and design guide.

Risk measures play significant roles in determining the magnitude of risks. The traditional risk measures consider only the consequence $(C)$ and the probability $(P)$ and ignore the support of the knowledge behind to estimate $C$ and $P$ . Several researchers have suggested adding knowledge as a third dimension in the risk measures. However, the issues of how to embed the dimension of knowledge in the risk measures to output an explicit expression of the risk measure and how to measure the strength of knowledge remain unresolved. This paper proposes a new risk measure incorporating the dimension of knowledge, apart from $C$ and $P$ . It is shown that the proposed risk measure has the form of traditional risk measures when the risk assessor has full knowledge. In addition, a fuzzy multicriteria decision-making (MCDM) method is employed to assess the strength of knowledge. In the fuzzy MCDM method, an entropy optimization problem is solved to obtain fuzzy measures, which are critical for determining the score of the strength of knowledge. Finally, the proposed method is applied to a project risk assessment, showing the feasibility of the method.

We explore the use of two chaotic systems (Bernoulli shift map and Zizag map) coupled with deoxyribonucleic acid coding in an encryption scheme for medical images in this paper. The scheme consists of two main phases: Chaotic key generation and DNA diffusion. Firstly, the message digest algorithm 5 hash function is performed on the plain medical image and the hash value used in combination with the value of an input ASCII string to generate initial conditions and control parameters for two chaotic systems (Bernoulli shift map and Zigzag map). These chaotic systems are subsequently used to produce two separate key matrices. Secondly, a row-by-row diffusion operation between the plain image matrix and the two chaotic key matrices, using the DNA XOR algebraic operation is performed in an alternating pattern to produce the cipher image. The logistic map is used to select the DNA encoding and decoding rules for each row. Experimental results of statistical, differential and key analyses demonstrate that the proposed scheme is robust and provides resistance to various forms of attacks.

Risk matrices have been proven as useful risk management tools, especially in the cases where data are not sufficient. Current usage of risk matrices in both literature and practice is related to s...