Wireless distributed computing for cyclostationary feature detection

Recently, wireless distributed computing （WDC） concept has emerged promising manifolds improvements to current wireless technotogies. Despite the various expected benefits of this concept, significant drawbacks were addressed in the open literature. One of WDC key challenges is the impact of wireless channel quality on the load of distributed computations. Therefore, this research investigates the wireless channel impact on WDC performance when the tatter is applied to spectrum sensing in cognitive radio （CR） technology. However, a trade- off is found between accuracy and computational complexity in spectrum sensing approaches. Increasing these approaches accuracy is accompanied by an increase in computational complexity. This results in greater power consumption and processing time. A novel WDC scheme for cyclostationary feature detection spectrum sensing approach is proposed in this paper and thoroughly investigated. The benefits of the proposed scheme are firstly presented. Then, the impact of the wireless channel of the proposed scheme is addressed considering two scenarios. In the first scenario, workload matrices are distributed over the wireless channel

DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

Intrusion Detection System(IDS)in the cloud Computing(CC)environment has received paramount interest over the last few years.Among the latest approaches,Deep Learning(DL)-based IDS methods allow the discovery of attacks with the highest performance.In the CC environment,Distributed Denial of Service(DDoS)attacks are widespread.The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic,resulting in financial losses.Although various researchers have proposed many detection techniques,there are possible obstacles in terms of detection performance due to the use of insignificant traffic features.Therefore,in this paper,a hybrid deep learning mode based on hybridizing Convolutional Neural Network(CNN)with Long-Short-Term Memory(LSTM)is used due to its robustness and efficiency in detecting normal and attack traffic.Besides,the ensemble feature selection,mutualization aggregation between Particle Swarm Optimizer(PSO),Grey Wolf Optimizer(PSO),Krill Hird(KH),andWhale Optimization Algorithm(WOA),is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC.A benchmark dataset proposed by the Canadian Institute of Cybersecurity(CIC),called CICIDS 2017 is used to evaluate the proposed IDS.The results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 97.9%,98.3%,97.9%,98.1%,respectively.As a result,the proposed IDS achieves the requirements of getting high security,automatic,efficient,and self-decision detection of DDoS attacks.

Research on the big data feature mining technology based on the cloud computing

The cloud computing platform has the functions of efficiently allocating the dynamic resources, generating the dynamic computing and storage according to the user requests, and providing the good platform for the big data feature analysis and mining. The big data feature mining in the cloud computing environment is an effective method for the elficient application of the massive data in the information age. In the process of the big data mining, the method o f the big data feature mining based on the gradient sampling has the poor logicality. It only mines the big data features from a single-level perspective, which reduces the precision of the big data feature mining.

Syntactic Features of Computer English

In recent years there has been an apparent upsurge of interest in English in China. A whole new mass of people want to learnEnglish, not for the pleasure or prestige of mastering the language, but for the key role the language of English is playing in the world of tech-nology and commerce. As English became the accepted international language of technology and commerce, it created a new generation oflearners who knew specifically why they were learning a language. As a result, more and more people attach great importance to scien-tech-nical English. It is therefore how computer English, a branch of English for Science and Technology (EST), comes into being .This paper fo-cuses on the syntactic features of computer English.

Clinical and computed tomography features of adult abdominopelvic desmoplastic small round cell tumor

To investigate the clinical and computed tomography(CT)features of desmoplastic small round cell tumor(DSRCT),we retrospectively analyzed the clinical presentations,treatment and outcome,as well as CT manifestations of four cases of DSRCT confirmed by surgery and pathology.The CT manifestations of DSRCT were as follows:(1)multiple soft-tissue masses or diffuse peritoneal thickening in the abdomen and pelvis,with the dominant mass usually located in the pelvic cavity;(2)masses without an apparent organbased primary site;(3)mild to moderate homogeneous or heterogeneous enhancement in solid area on enhanced CT;and(4)secondary manifestations,such as ascites,hepatic metastases,lymphadenopathy,hydronephrosis and hydroureter.The prognosis and overall survival rates were generally poor.Commonly used treatment strategies including aggressive tumor resection,polychemotherapy,and radiotherapy,showed various therapeutic effects.CT of DSRCT shows characteristic features that are helpful in diagnosis.Early discovery and complete resection,coupled with postoperative adjuvant chemotherapy,are important for prognosis of DSRCT.Whole abdominopelvic rather than locoregional radiotherapy is more effective for unresectable DSRCT.

HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation

With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.

Evolutionary Computation Based Optimization of Image Zernike Moments Shape Feature Vector

The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.

Introducing the nth-Order Features Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (nth-FASAM-N): I. Mathematical Framework

This work presents the “nth-Order Feature Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (abbreviated as “nth-FASAM-N”), which will be shown to be the most efficient methodology for computing exact expressions of sensitivities, of any order, of model responses with respect to features of model parameters and, subsequently, with respect to the model’s uncertain parameters, boundaries, and internal interfaces. The unparalleled efficiency and accuracy of the nth-FASAM-N methodology stems from the maximal reduction of the number of adjoint computations (which are considered to be “large-scale” computations) for computing high-order sensitivities. When applying the nth-FASAM-N methodology to compute the second- and higher-order sensitivities, the number of large-scale computations is proportional to the number of “model features” as opposed to being proportional to the number of model parameters (which are considerably more than the number of features).When a model has no “feature” functions of parameters, but only comprises primary parameters, the nth-FASAM-N methodology becomes identical to the extant nth CASAM-N (“nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems”) methodology. Both the nth-FASAM-N and the nth-CASAM-N methodologies are formulated in linearly increasing higher-dimensional Hilbert spaces as opposed to exponentially increasing parameter-dimensional spaces thus overcoming the curse of dimensionality in sensitivity analysis of nonlinear systems. Both the nth-FASAM-N and the nth-CASAM-N are incomparably more efficient and more accurate than any other methods (statistical, finite differences, etc.) for computing exact expressions of response sensitivities of any order with respect to the model’s features and/or primary uncertain parameters, boundaries, and internal interfaces.

A NEW APPROACH TO THE DESIGN OF A SYSTEM FOR FEATURE EXTRACTION BASED ON HUMAN-COMPUTER COLLABORATIVE TACTIC

We are involved in an embarrassing situation that the limited capability of automated feature extraction in digital photogrammetric systems cannot satisfy the increasing needs for rapid acquisition of semantic information for applications. Facing this challenge, a new tactic, Human-Computer Collaborative (HCC) tactic, and a corresponding new method, Operator-Object Directed (OOD) method, are proposed for the design of a system for feature extraction from large scale aerial images. We hold that in almost all technical complex systems, full automation will be neither technically feasible nor socially acceptable. The system should be designed to optimize through the cooperative operation with two agents in the system: the hurtan and the computer.

Features of primary pancreatic lymphoma:A bi-institutional review with an emphasis on typical and atypical imaging features

BACKGROUND Primary pancreatic lymphoma(PPL)is a rare neoplasm.Being able to distinguish it from other pancreatic malignancies such as pancreatic ductal adenocarcinoma(PDAC)is important for appropriate management.Unlike PDAC,PPL is highly sensitive to chemotherapy and usually does not require surgery.Therefore,being able to identify PPL preoperatively will not only direct physicians towards the correct avenue of treatment,it will also avoid unnecessary surgical intervention.AIM To evaluate the typical and atypical multi-phasic computed tomography(CT)imaging features of PPL.METHODS A retrospective review was conducted of the clinical,radiological,and pathological records of all subjects with pathologically proven PPL who presented to our institutions between January 2000 and December 2020.Institutional review board approval was obtained for this investigation.The collected data were analyzed for subject demographics,clinical presentation,laboratory values,CT imaging features,and the treatment received.Presence of all CT imaging findings including size,site,morphology and imaging characteristics of PPL such as the presence or absence of nodal,vascular and ductal involvement in these subjects were recorded.Only those subjects who had a pre-treatment multiphasic CT of the abdomen were included in the study.RESULTS Twenty-nine cases of PPL were diagnosed between January 2000 and December 2020(mean age 66 years;13 males/16 females).All twenty-nine subjects were symptomatic but only 4 of the 29 subjects(14%)had B symptoms.Obstructive jaundice occurred in 24%of subjects.Elevated lactate dehydrogenase was seen in 81%of cases,whereas elevated cancer antigen 19-9 levels were present in only 10%of cases for which levels were recorded.The vast majority(90%)of tumors involved the pancreatic head and uncinate process.Mean tumor size was 7.8 cm(range,4.0-13.8 cm).PPL presented homogenous hypoenhancement on CT in 72%of cases.Small volume peripancreatic lymphadenopathy was seen in 28%of subjects.Tumors demonstrated encasement of superior mesenteric vessels in 69%of cases but vascular stenosis or occlusion only manifested in 5 out of the twentynine individuals(17%).Mild pancreatic duct dilatation was also infrequent and seen in only 17%of cases,whereas common bile duct(CBD)dilation was seen in 41%of subjects.Necrosis occurred in 10%of cases.Size did not impact the prevalence of pancreatic and CBD dilation,necrosis,or mesenteric root infiltration(P=0.525,P=0.294,P=0.543,and P=0.097,respectively).Pancreatic atrophy was not present in any of the subjects.CONCLUSION PPL is an uncommon diagnosis best made preoperatively to avoid unnecessary surgery and ensure adequate treatment.In addition to the typical CT findings of PPL,such as homogeneous hypoenhancement,absence of vascular stenosis and occlusion despite encasement,and peripancreatic lymphadenopathy,this study highlighted many less typical findings,including small volume necrosis and pancreatic and bile duct dilation.

Multi-core based parallel computing technique for content-based image retrieval

In this paper, we propose a parallel computing technique for content-based image retrieval （CBIR） system. This technique is mainly used for single node with multi-core processor, which is different from those based on cluster or network computing architecture. Due to its specific applications （such as medical image processing） and the harsh terms of hardware resource requirement, the CBIR system has been prevented from being widely used. With the increasing volume of the image database, the widespread use of multi-core processors, and the requirement of the retrieval accuracy and speed, we need to achieve a retrieval strategy which is based on multi-core processor to make the retrieval faster and more convenient than before. Experimental results demonstrate that this parallel architecture can significantly improve the performance of retrieval system. In addition, we also propose an efficient parallel technique with the combinations of the cluster and the multi-core techniques, which is supposed to gear to the new trend of the cloud computing.

Enhancing the robustness of object detection via 6G vehicular edge computing

Academic and industrial communities have been paying significant attention to the 6th Generation (6G) wireless communication systems after the commercial deployment of 5G cellular communications. Among the emerging technologies, Vehicular Edge Computing (VEC) can provide essential assurance for the robustness of Artificial Intelligence (AI) algorithms to be used in the 6G systems. Therefore, in this paper, a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed, taking the object detection task as an example. This strategy includes two stages: model stabilization and model adaptation. In the former, the state-of-the-art methods are appended to the model to improve its robustness. In the latter, two targeted compression methods are implemented, namely model parameter pruning and knowledge distillation, which result in a trade-off between model performance and runtime resources. Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals, where the introduced trade-off outperforms the other strategies available.

Feature Extraction and Diagnosis System Using Virtual Instrument Based on CI

Through investigating intelligent diagnosis method of Computational Intelligence (CI) and studying its application in fault feature extraction, a gear fault detection and Virtual Instrument Diagnostic System is developed by using the two hybrid programming method which combines both advantages of VC++ and MATLAB. The interface is designed by VC++ and the calculation of test data, signal processing and graphical display are completed by MATLAB. The pro-gram converted from M-file to VC++ is completed by interface software, and a various multi-functional gear fault di-agnosis software system is successfully obtained. The software system, which has many functions including the intro-duction of gear vibration signals, signal processing, graphical display, fault detection and diagnosis, monitoring and so on, especially, the ability of diagnosing gear faults. The method has an important application in the field of mechanical fault diagnosis.

A Hybrid Algorithm Based on PSO and GA for Feature Selection

One of the main problems of machine learning and data mining is to develop a basic model with a few features,to reduce the algorithms involved in classification’s computational complexity.In this paper,the collection of features has an essential importance in the classification process to be able minimize computational time,which decreases data size and increases the precision and effectiveness of specific machine learning activities.Due to its superiority to conventional optimization methods,several metaheuristics have been used to resolve FS issues.This is why hybrid metaheuristics help increase the search and convergence rate of the critical algorithms.A modern hybrid selection algorithm combining the two algorithms;the genetic algorithm(GA)and the Particle Swarm Optimization(PSO)to enhance search capabilities is developed in this paper.The efficacy of our proposed method is illustrated in a series of simulation phases,using the UCI learning array as a benchmark dataset.

Principal Face-based Recognition Approach for Machining Features of Aircraft Integral Panels

Feature recognition aims at extracting manufacturing features with geometrical information from solid model and is considered to be an efficient way of changing the interactive NC machining programming mode.Existing recognition methods have some disadvantages in practical applications.They can essentially handle prismatic components with regular shapes and are difficult to recognize the intersecting features and curved surfaces.Besides,the robustness of them is not strong enough.A new feature recognition approach is proposed based on the analysis of aircraft integral panels＇ geometry and machining characteristics.In this approach,the aircraft integral panel is divided into a number of local machining domains.The machining domains are extracted and recognized first by finding the principal face of machining domain and extracting the sides around the principal face.Then the machining domains are divided into various features in terms of the face type.The main sections of the proposed method are presented including the definition,classification and structure of machining domain,the relationship between machining domain and principal face loop,the rules of machining domains recognition,and the algorithm of machining feature recognition.In addition,a robotic feature recognition module is developed for aircraft integral panels and tested with several panels.Test results show that the strategy presented is robust and valid.Features extracted can be post processed and linked to various downstream applications.The approach is able to solve the difficulties in recognizing the aircraft integral panel＇s features and automatic obtaining the machining zone in NC programming,and can be used to further develop the automatic programming of NC machining.

Simulation on Dynamic Bending Features of Fabric Based on Fluid-Solid Interaction Technique

This paper is devoted to the two-dimensional nonlinear modeling of the fluid-solid interaction (FSI) between fabric and air flow, which is based on the Automatic Incremental Dynamic Nonlinear Analysis (AIDNA)-FSI program in order to study the dynamic bending features of fabrics in a specific air flow filed. The computational fluid dynamics (CFD) model for flow and the finite element model (FEM) for fabric was set up to constitute an FSI model in which the geometric nonlinear behavior and the dynamic stress-strain variation of the relatively soft fabric material were taken into account. Several FSI cases with different time-dependent wind load and the model frequency analysis for fabric were carried out. The dynamic response of fabric and the distribution of fluid variables were investigated. The results of numerical simulation and experiments fit quite well. Hence, this work contributes to the research of modeling the dynamic bending behavior of fabrics in air field.

Endoscopic classification and pathological features of primary intestinal lymphangiectasia

BACKGROUND The appearance of the intestinal mucosa during endoscopy varies among patients with primary intestinal lymphangiectasia(PIL).AIM To classify the endoscopic features of the intestinal mucosa in PIL under endoscopy,combine the patients’imaging and pathological characteristics of the patients,and explain their causes.METHODS We retrospectively analyzed the endoscopic images of 123 patients with PIL who were treated at the hospital between January 1,2007 and December 31,2018.We compared and analyzed all endoscopic images,classified them into four types according to the endoscopic features of the intestinal mucosa,and analyzed the post-lymphographic computed tomography(PLCT)and pathological characteristics of each type.RESULTS According to the endoscopic features of PIL in 123 patients observed during endoscopy,they were classified into four types:nodular-type,granular-type,vesicular-type,and edematous-type.PLCT showed diffuse thickening of the small intestinal wall,and no contrast agent was seen in the small intestinal wall and mesentery in the patients with nodular and granular types.Contrast agent was scattered in the small intestinal wall and mesentery in the patients with vesicular and edematous types.Analysis of the small intestinal mucosal pathology revealed that nodular-type and granulartype lymphangiectasia involved the small intestine mucosa in four layers,whereas ectasia of the vesicular-and edematous-type lymphatic vessels largely involved the lamina propria mucosae,submucosae,and muscular layers.CONCLUSION Endoscopic classification,combined with the patients’clinical manifestations and pathological examination results,is significant and very useful to clinicians when scoping patients with suspected PIL.

Feature Solution in the Process of Parameterizing Port Model

Aimed at attaining to an integrated and effective pattern to guide the port design process, this paper puts forward a new conception of feature solution, which is based on the parameterized feature modeling. With this solution, the overall port pre-design process can be conducted in a virtual pattern. Moreover, to evaluate the advantages of the new design pattern, an application of port system has been involved in this paper; and in the process of application a computational fluid dynamic analysis is concerned. An ideal effect of cleanness, high efficiency and high precision has been achieved.

A Single Image Derain Method Based on Residue Channel Decomposition in Edge Computing

The numerous photos captured by low-price Internet of Things(IoT)sensors are frequently affected by meteorological factors,especially rainfall.It causes varying sizes of white streaks on the image,destroying the image texture and ruining the performance of the outdoor computer vision system.Existing methods utilise training with pairs of images,which is difficult to cover all scenes and leads to domain gaps.In addition,the network structures adopt deep learning to map rain images to rain-free images,failing to use prior knowledge effectively.To solve these problems,we introduce a single image derain model in edge computing that combines prior knowledge of rain patterns with the learning capability of the neural network.Specifically,the algorithm first uses Residue Channel Prior to filter out the rainfall textural features then it uses the Feature Fusion Module to fuse the original image with the background feature information.This results in a pre-processed image which is fed into Half Instance Net(HINet)to recover a high-quality rain-free image with a clear and accurate structure,and the model does not rely on any rainfall assumptions.Experimental results on synthetic and real-world datasets show that the average peak signal-to-noise ratio of the model decreases by 0.37 dB on the synthetic dataset and increases by 0.43 dB on the real-world dataset,demonstrating that a combined model reduces the gap between synthetic data and natural rain scenes,improves the generalization ability of the derain network,and alleviates the overfitting problem.

Motion estimation based feature selection for visual SLAM

Feature selection is always an important issue in the visual SLAM （simultaneous location and mapping） literature. Considering that the location estimation can be improved by tracking features with larger value of visible time, a new feature selection method based on motion estimation is proposed. First, a k-step iteration algorithm is presented for visible time estimation using an affme motion model; then a delayed feature detection method is introduced for efficiently detecting features with the maximum visible time. As a means of validation for the proposed method, both simulation and real data experiments are carded out. Results show that the proposed method can improve both the estimation performance and the computational performance compared with the existing random feature selection method.