Rural power grid resources WebGIS technology research based on ArcIMS

Web GIS is a tendency of current geographical information system.Arcl MS is an excellent platform to construct Web GIS.This paper introduces the architecture,features and advantages of ArcI MS,which also builds a rural power resources information system based on ArcI MS.This system is a security assurance of reactive power optimization of distribution network,is an effective method of economic operation and is one of important measures to improve the voltage quality of distribution network.Applying ArcS DE to establish spatial database and transform the format of spatial data are key to realizing the system.

An Efficient Approach to Escalate the Speed of Training Convolution Neural Networks

Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation,instance segmentation, and many others. In image and video recognition applications, convolutional neural networks(CNNs) are widely employed. These networks provide better performance but at a higher cost of computation. With the advent of big data, the growing scale of datasets has made processing and model training a time-consuming operation, resulting in longer training times. Moreover, these large scale datasets contain redundant data points that have minimum impact on the final outcome of the model. To address these issues, an accelerated CNN system is proposed for speeding up training by eliminating the noncritical data points during training alongwith a model compression method. Furthermore, the identification of the critical input data is performed by aggregating the data points at two levels of granularity which are used for evaluating the impact on the model output.Extensive experiments are conducted using the proposed method on CIFAR-10 dataset on ResNet models giving a 40% reduction in number of FLOPs with a degradation of just 0.11% accuracy.

Deep Reinforcement Learning-Based Task Offloading and Service Migrating Policies in Service Caching-Assisted Mobile Edge Computing

Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

Intelligent Task Offloading and Collaborative Computation over D2D Communication

In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and collaborators.In the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading.First,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power.Second,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,respectively.Moreover,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.

Fast implementation of length-adaptive privacy amplification in quantum key distribution

Post-processing is indispensable in quantum key distribution （QKD）, which is aimed at sharing secret keys between two distant parties. It mainly consists of key reconciliation and privacy amplification, which is used for sharing the same keys and for distilling unconditional secret keys. In this paper, we focus on speeding up the privacy amplification process by choosing a simple multiplicative universal class of hash functions. By constructing an optimal multiplication algorithm based on four basic multiplication algorithms, we give a fast software implementation of length-adaptive privacy amplification. ＂Length-adaptive＂ indicates that the implementation of privacy amplification automatically adapts to different lengths of input blocks. When the lengths of the input blocks are 1 Mbit and 10 Mbit, the speed of privacy amplification can be as fast as 14.86 Mbps and 10.88 Mbps, respectively. Thus, it is practical for GHz or even higher repetition frequency QKD systems.

Digital Signal Processing Based Real Time Vehicular Detection System

Traffic monitoring is of major importance for enforcing traffic management policies.To accomplish this task,the detection of vehicle can be achieved by exploiting image analysis techniques.In this paper,a solution is presented to obtain various traffic parameters through vehicular video detection system(VVDS).VVDS exploits the algorithm based on virtual loops to detect moving vehicle in real time.This algorithm uses the background differencing method,and vehicles can be detected through luminance difference of pixels between background image and current image.Furthermore a novel technology named as spatio-temporal image sequences analysis is applied to background differencing to improve detection accuracy.Then a hardware implementation of a digital signal processing (DSP) based board is described in detail and the board can simultaneously process four-channel video from different cameras. The benefit of usage of DSP is that images of a roadway can be processed at frame rate due to DSP′s high performance.In the end,VVDS is tested on real-world scenes and experiment results show that the system is both fast and robust to the surveillance of transportation.

Human epidermal growth factor receptor 2 expression in mixed gastric carcinoma

AIM: To investigate human epidermal growth factor receptor 2(HER2) amplification and protein expression in mixed gastric carcinoma.METHODS: Fluorescence in situ hybridization and immunohistochemistry were used to detect HER2 amplification and protein expression in 277 cases of mixed gastric carcinoma. Protein staining intensity was rate as 1+, 2+, or 3+.RESULTS: Of the 277 cases, 114(41.2%) expressed HER2 protein. HER2 3+ staining was observed in 28/277(10.1%) cases, 2+ in 37/277(13.4%) cases, and 1+ in 49/277(17.7%) cases. A HER2 amplification rate of 17% was detected, of which 25/28(89.3%) were observed in the HER2 3+ staining group, 17/37(45.9%) in 2+, and 5/49(10.2%) in 1+. Of the 47 patients with HER2 amplification who received chemotherapy plus trastuzumab, 22 demonstrated median progression-free and overall survivals of 9.1 mo and 16.7 mo, respectively, which were significantly better than those achieved with chemotherapy alone(5.6 mo and 12.1 mo, respectively) in 19 previously treated patients(P s < 0.05). CONCLUSION: HER2 detection in mixed gastric carcinoma displays high heterogeneity. Relativelyquantitative parameters are needed for assessing the level of HER2 amplification and protein expression.

Safety of applying midazolam-ketamine-propofol sedation combination under the supervision of endoscopy nurse with patient-controlled analgesia pump in colonoscopy

AIM To compare the results of midazolam-ketaminepropofol sedation performed by an endoscopy nurse and anaesthetist during colonoscopy in terms of patient satisfaction and safety.METHODS American Statistical Association(ASA) Ⅰ-Ⅱ 60 patients who underwent colonoscopy under sedation were randomly divided into two groups: sedation under the supervision of an anaesthetist(SSA) and sedation under the supervision of an endoscopy nurse(SSEN). Both groups were initially administered 1 mg midazolam, 50 mg ketamine and 30-50 mg propofol. Continuation of sedation was performed by the anaesthetist in the SSAgroup and the nurse with a patient-controlled analgesia(PCA) pump in the SSEN group. The total propofol consumption, procedure duration, recovery times, pain using the visual analogue scale(VAS) and satisfaction score of the patients, and side effects were recorded. In addition, the patients were asked whether they remembered the procedure and whether they would prefer the same method in the case of re-endoscopy.RESULTS Total propofol consumption in the SSEN group was significantly higher(P < 0.05) than that in the SSA group. When the groups were compared in terms of VAS score, recovery time, patient satisfaction, recall of the procedure, re-preference for the same method in case of re-endoscopy, and side effects, there were no significant differences(P > 0.05) between the two groups. No long-term required intervention side effects were observed in either group.CONCLUSION Colonoscopy sedation in ASA Ⅰ-Ⅱ patients can be safely performed by an endoscopy nurse using PCA pump with the incidence of side effects and patient satisfaction levels similar to sedation under anaesthetist supervision.

SDN assisted Stackelberg Game model for LTE-WiFi offloading in 5G networks

The data traffic that is accumulated at the Macro Base Station(MBS)keeps on increasing as almost all the people start using mobile phones.The MBS cannot accommodate all user’s demands,and attempts to offload some users to the nearby small cells so that the user could get the expected service.For the MBS to offload data traffic to an Access Point(AP),it should offer an optimal economic incentive in a way its utility is maximized.Similarly,the APs should choose an optimal traffic to admit load for the price that it gets from MBS.To balance this tradeoff between the economic incentive and the admittance load to achieve optimal offloading,Software Defined Networking(SDN)assisted Stackelberg Game(SaSG)model is proposed.In this model,the MBS selects the users carefully to aggregate the service with AP,so that the user experiencing least service gets aggregated first.The MBS uses the Received Signal Strength Indicator(RSSI)value of the users as the main parameter for aggregating a particular user for a contract period with LTE and WiFi.Each player involved in the game tries to maximize their payoff utilities,and thus,while incorporating those utilities in real-time scenario,we obtain maximum throughput per user which experiences best data service without any lack in Quality of Experience(QoE).Thus,the proposed SaSG model proves better when compared with other game theory models,and hence an optimal data offloading is achieved.

Identification of emission sources of umbral flashes using phase congruency

The emission sources of umbral flashes （UFs） are believed to be closely related to running umbral and penumbral waves, and are concluded to be associated with umbral dots in the solar photosphere. Accurate identification of emission sources of UFs is crucial for investigating these physical phenomena and their inherent relationships. A relatively novel model of shape perception, namely phase congruency （PC）, uses phase information in the Fourier domain to identify the geometrical shape of the region of interest in different intensity levels, rather than intensity or gradient. Previous studies indicate that the model is suitable for identifying features with low contrast and low luminance. In the present paper, we applied the PC model to identify the emission sources of UFs and to locate their positions. For illustrating the high performance of our proposed method, two time sequences of Ca n H images derived from the Hinode/SOT on 2010 August 10 and 2013 August 20 were used. Furthermore, we also compared these results with the analysis results that are identified by the traditional/classical identification methods, including the gray-scale adjusted technique and the running difference technique. The result of our analysis demonstrates that our proposed method is more accurate and effective than the traditional identification methods when applied to identifying the emission sources of UFs and to locating their positions.

Boundedness for Maximal Bochner-Riesz Operator and Commutator on Generalized Morrey Space

In this paper,we have obtained the boundedness of maximal Bochner-Riesz operator on generalized Morrey space.Also,it is right for its commutator.

AN INTELLIGENT METHOD FOR REAL-TIME DETECTION OF DDOS ATTACK BASED ON FUZZY LOGIC

The paper puts forward a variance-time plots method based on slide-window mechanism tocalculate the Hurst parameter to detect Distribute Denial of Service(DDoS)attack in real time.Basedon fuzzy logic technology that can adjust itself dynamically under the fuzzy rules,an intelligent DDoSjudgment mechanism is designed.This new method calculates the Hurst parameter quickly and detectsDDoS attack in real time.Through comparing the detecting technologies based on statistics andfeature-packet respectively under different experiments,it is found that the new method can identifythe change of the Hurst parameter resulting from DDoS attack traffic with different intensities,andintelligently judge DDoS attack self-adaptively in real time.

Modulating Traffic Signal Phases to Realize Real-Time Traffic Control System

This paper proposes innovations to address challenges emanating from road traffic congestion. Improving economies create more car owners resulting in increased automobile manufacturing, increased vehicle population giving rise to higher emission of CO2 resulting in traffic congestion. Congested traffic has idling vehicles which emit higher CO2 and pollution. Besides, traffic congestion increases turnaround time, delivery time, commuting time and related logistical aspects. Commuting time negatively impacts working hours. Unless the traffic congestion is mitigated, the economy will take a beating creating a vicious ecology cycle. Building new roads, bridges or reconditioning of infrastructure is not always the best possible solutions. Efficient traffic management is a key to country’s economic growth. Various analytical models are employed to study, appreciate traffic congestion. The paper studies these models to infer that real time approach is the only solution. Several approaches are being worked on and few commercial systems too are available. These systems provide traffic information for course correction. However, it has latency and hence deviates from real time environment. Traffic congestion being highly dynamic in nature, it necessitates real time solution with real time inputs. It is proposed to integrate Real time traffic data with the traffic signal thus modulating the cycle timings at every junction. Deviation from static asymmetric cycle timing is implemented by assigning green phases based on density of vehicles. With minimalistic infrastructure and negligible incremental cost, the paper not only proposes to address traffic congestion but also paves the way for capturing traffic offenses, vehicle tracking and toll collection. The research is imminently realizable and makes a strong case for a PPP (Public Private Partnership) project.

Spatiotemporal interpolation of precipitation across Xinjiang, China using space-time CoKriging

In various environmental studies, geoscience variables not only have the characteristics of time and space, but also are influenced by other variables. Multivariate spatiotemporal variables can improve the accuracy of spatiotemporal estimation. Taking the monthly mean ground observation data of the period 1960–2013 precipitation in the Xinjiang Uygur Autonomous Region, China, the spatiotemporal distribution from January to December in 2013 was respectively estimated by space-time Kriging and space-time CoKriging. Modeling spatiotemporal direct variograms and a cross variogram was a key step in space-time CoKriging. Taking the monthly mean air relative humidity of the same site at the same time as the covariates, the spatiotemporal direct variograms and the spatiotemporal cross variogram of the monthly mean precipitation for the period 1960–2013 were modeled. The experimental results show that the space-time CoKriging reduces the mean square error by 31.46% compared with the space-time ordinary Kriging. The correlation coefficient between the estimated values and the observed values of the space-time CoKriging is 5.07% higher than the one of the space-time ordinary Kriging. Therefore, a space-time CoKriging interpolation with air humidity as a covariate improves the interpolation accuracy.

Indirect adaptive fuzzy control for a class of nonlinear discrete-time systems

An indirect adaptive fuzzy control scheme is developed for a class of nonlinear discrete-time systems. In this method, two fuzzy logic systems are used to approximate the unknown functions, and the parameters of membership functions in fuzzy logic systems are adjusted according to adaptive laws for the purpose of controlling the plant to track a reference trajectory. It is proved that the scheme can not only guarantee the boundedness of the input and output of the closed-loop system, but also make the tracking error converge to a small neighborhood of the origin. Simulation results indicate the effectiveness of this scheme.

EECDH to prevent MITM attack in cloud computing

Cloud computing has reached the peak of Gartner hype cycle,and now the focus of the whole telecom industry is the ability to scale data storage with minimal investment.But data privacy and communication issues will occur with the increment of the cloud data storage.The key privacy concern for scalability is caused by the dynamic membership allocation and multi-owner data sharing.This paper addresses the issues faced by multiple owners through a mutual authentication mechanism using the Enhanced Elliptic Curve Diffie-Hellman(EECDH)key exchange protocol along with the Elliptic Curve Digital Signature Algorithm(ECDSA).The proposed EECDH scheme is used to exchange the secured shared key among multiple owners and also to eliminate the Man-In-The-Middle(MITM)attacks with less computational complexity.By leveraging these algorithms,the integrity of data sharing among multiple owners is ensured.The EECDH improves the level of security only slightly increasing the time taken to encrypt and decrypt the data,and it is secured against the MITM attacks,which is experimented using the AVISPA tool.

EIAS:An Efficient Identity-Based Aggregate Signature Scheme for WSNs Against Coalition Attack

Wireless sensor networks(WSNs)are the major contributors to big data acquisition.The authenticity and integrity of the data are two most important basic requirements for various services based on big data.Data aggregation is a promising method to decrease operation cost for resource-constrained WSNs.However,the process of data acquisitions in WSNs are in open environments,data aggregation is vulnerable to more special security attacks with hiding feature and subjective fraudulence,such as coalition attack.Aimed to provide data authenticity and integrity protection for WSNs,an efficient and secure identity-based aggregate signature scheme(EIAS)is proposed in this paper.Rigorous security proof shows that our proposed scheme can be secure against all kinds of attacks.The performance comparisons shows EIAS has clear advantages in term of computation cost and communication cost when compared with similar data aggregation scheme for WSNs.

Hybrid Trust Based Reputation Mechanism for Discovering Malevolent Node in MANET

A self-contained connection of wireless links that functions without any infrastructure is known as Mobile Ad Hoc Network(MANET).A MANET’s nodes could engage actively and dynamically with one another.However,MAN-ETs,from the other side,are exposed to severe potential threats that are difficult to counter with present security methods.As a result,several safe communication protocols designed to enhance the secure interaction among MANET nodes.In this research,we offer a reputed optimal routing value among network nodes,secure computations,and misbehavior detection predicated on node’s trust levels with a Hybrid Trust based Reputation Mechanism(HTRM).In addition,the study designs a robust Public Key Infrastructure(PKI)system using the suggested trust evaluation method in terms of“key”generation,which is a crucial component of a PKI cryptosystem.We also concentrate on the solid node authenticating process that relies on pre-authentication.To ensure edge-to-edge security,we assess safe,trustworthy routes to secure computations and authenticate mobile nodes,incorporating uncertainty into the trust management solution.When compared to other protocols,our recommended approach performs better.Finally,we use simulations data and performance evaluation metrics to verify our suggested approach’s validity Our approach outperformed the competing systems in terms of overall end-to-end delay,packet delivery ratio,performance,power consumption,and key-computing time by 3.47%,3.152%,2.169%,and 3.527%,3.762%,significantly.